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"Anyscale optimizes open source AI deployments with Endpoints | VentureBeat"
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"https://venturebeat.com/ai/anyscale-optimizes-open-source-ai-deployments-with-endpoints"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Anyscale optimizes open source AI deployments with Endpoints Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
With generative AI increasingly becoming table stakes for organizations, the big question facing many organizations is how to scale usage in a cost efficient manner.
That’s a question that Robert Nishihara, CEO and co-founder of Anyscale is looking to answer. Anyscale is the lead commercial vendor behind the widely deployed open source Ray framework for distributed machine learning training and inference. This week at the Ray Conference that runs from Sept 18-19 in San Francisco, Nishihara is outlining the success and growth of Ray to date, and revealing what’s next.
Among the big pieces of news announced today is the general availability of Anyscale Endpoints, which enables organizations to easily fine tune and deploy open source large language models (LLMs). Anyscale is also announcing a new expanded partnership with Nvidia that will see Nvidia’s software for inference and training optimized for the Anyscale Platform.
“If you took an Uber ride, ordered something on Instacart, listened to something on Spotify, or watched Netflix or TikTok, or use OpenAI’s Chat GPT, you’re interacting with models built with Ray, ” Nishira told VentureBeat. “It’s really everywhere.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Nishihara said that in his keynote at Ray Summit he will detail how various vendors have been able to scale AI and cut costs at the same time. Among the success metrics he’s sharing is that Instacart is now able to train models up to 12 times faster with 100 times more data than ever before. Pinterest was able to cut costs by 40% for its AI processing training thousands of models.
“I’m really trying to hammer in the point that if you care about cost and performance, Ray is the way to go for LLMs and generative AI,” he said.
From the aviary to Anyscale Endpoints From a product perspective, the Anyscale Platform is the commercially supported version of Ray providing enterprise capabilities that organizations rely on to scale and deploy any type of training for inference.
The new Anyscale Endpoints service provides a different capability. Nishihara explained that Anyscale Endpoints is a service that provides API access to open source LLMs without having the need for an organization to deploy or manage the models on their own. He added that AnyScale Endpoints allows developers to easily integrate LLMs into their products through a simple API, in much the same way many organizations make use of the OpenAI API today.
“With AnyScale Endpoints, customers can query models like Llama 2 t o get responses,” Nishihara said. “AnyScale handles running and optimizing the models behind the scenes.” In some respects, Anyscale Endpoints benefits from development the company has been doing with its open source Aviary project , which debuted in May.
Aviary is an open source project for running open source LLMs on top of Ray. Nishihara noted that while Aviary allows users to run LLMs themselves using Python code on Ray, AnyScale Endpoints provides a simpler API experience where users just query models through the API without having to deploy anything. AnyScale takes care of running and optimizing the models behind the scenes.
Fine Tuning and private deployments improve open source LLM utility Anyscale is also enabling fine tuning for the open source LLMs like Llama 2.
With LLMs, there are often both large and small models, with it typically costing more for organizations to use the larger models. As such, Nishihara noted that many organizations are looking to use smaller models to reduce costs, but the challenge is that those smaller models aren’t necessarily as good as the large ones. Fine tuning is one way that Nishihara said he is seeing organizations make smaller models work.
With fine tuning, organizations can customize a model to improve performance and quality on a specific task. This can help make smaller, more cost-efficient models that are viable alternatives to larger ones.
Going a step further, when it comes to customized training and data, some organizations don’t feel comfortable using publicly accessible LLMs. To help support those users, Anyscale is also launching a Private Endpoints service that enables the deployment of Anyscale Endpoints, within an organization’s own virtual private cloud (VPC). With Private Endpoints, sensitive customer data and models never have to leave a company’s own infrastructure. It also provides opportunities to deeply customize and optimize the backend deployment.
The overall goal for Nishihara is to focus on efficiency and making it cheaper for organizations to work with LLMs.
“We’re an infrastructure company, the advantage we have is deep expertise in performance optimizations and infrastructure, and we’re going to do everything we can to really double down on that and just continue to make it faster and cheaper,” he said VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Walmart to revolutionize retail with expansive commercial strategy in the metaverse | VentureBeat"
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"https://venturebeat.com/metaverse/walmart-to-revolutionize-retail-with-expansive-commercial-strategy-in-the-metaverse"
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"Game Development View All Programming OS and Hosting Platforms Metaverse View All Virtual Environments and Technologies VR Headsets and Gadgets Virtual Reality Games Gaming Hardware View All Chipsets & Processing Units Headsets & Controllers Gaming PCs and Displays Consoles Gaming Business View All Game Publishing Game Monetization Mergers and Acquisitions Games Releases and Special Events Gaming Workplace Latest Games & Reviews View All PC/Console Games Mobile Games Gaming Events Game Culture Walmart to revolutionize retail with expansive commercial strategy in the metaverse Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Walmart , the world’s largest retailer by revenue, announced today it is experimenting with new ways to connect its customers’ physical and virtual shopping experiences in the metaverse, a term that refers to the collective of virtual worlds and environments that allow people to create avatars, socialize, explore and participate in activities.
The company says that it is aggressively pursuing opportunities in virtual worlds that connect to commerce at its stores and vice versa. For example, customers can now buy some of the same items for their physical houses that they can buy for their virtual houses in House Flip , a mobile game that lets players renovate and sell virtual homes. Customers can also buy virtual clothing items based on Walmart’s fashion brand Scoop in Zepeto , a mobile virtual world that lets players create and customize their avatars.
Walmart sees the growth and expansion of virtual worlds as not only a chance to develop new ways to meet and engage with its customers, but also an opportunity to experiment with a new type of commerce — virtual commerce — one where customers can not only continue buying virtual goods, like clothing for their avatar, but now their real-world counterpart.
Thomas Kang, VP & general manager of metaverse commerce at Store No. 8, Walmart’s innovation arm, said in an exclusive interview with VentureBeat that Walmart’s goal is to power commerce in the metaverse, wherever customers choose to enter it.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “We’re not trying to replace or isolate the reality with virtual, but rather connect it and make it better for the customer,” he said. “We want to make sure that what we do is contextual and authentic to the customer. We want to add to the experience and the convenience.” Kang also said that Walmart has a unique advantage in connecting its customers’ physical and virtual lives, given that almost 90 percent of the U.S. population lives within 10 miles of a Walmart store.
“We have a chance to reshape and reinvent retail in ways that are good for the customers,” he said. “We can use our physical locations as places to engage customers, for example, if you buy something physical, we may be able to give you something virtually for free.” Walmart also plans to introduce the ability to purchase physical items contextually and natively in House Flip using a Walmart account without leaving the virtual world. This would allow customers to check out with their Walmart account for both virtual and physical goods, or just one of them.
Kang said that this is a pioneering concept that isn’t happening today on any other platforms or with other retailers. “We are well positioned in that environment, because we are a commerce company,” he said. “We want to help these companies and brands sell their products in virtual worlds.” According to Citi , the total market for metaverse-related commercial activity will be between $8 trillion and $13 trillion by 2030, with total metaverse users numbering around five billion. Virtual worlds and games are also projected to be the fastest-growing category of entertainment, with a projected 3 billion participants spanning all geographies and demographics.
Kang said that he expects to see metaverse commerce in ways that he never even thought of, as developers create new types of experiences and platforms. He also said that Walmart believes in building metaverse for everyone, accessible via any device, that align with its digital values and meet the needs of all its customers.
“We believe in digital citizenship. We want to make sure that we are creating safe spaces for everybody,” he said. “We want to be leaders in trust and safety in that environment.” Walmart said that it will test more experiences in virtual worlds over the next year. “We see limitless potential with this emerging technology,” Kang said. “And we’re excited to explore its possibilities.” The move by Walmart reflects its ambition to compete with other e-commerce giants like Amazon and Alibaba, which have also invested in virtual reality and augmented reality technologies to enhance their online shopping offerings. Walmart has also recently acquired several startups in the fields of artificial intelligence, computer vision and machine learning to improve its customer service, inventory management and delivery operations.
GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! Games Beat Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
All rights reserved.
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"Join VentureBeat and Anzu Partners for our first DC meetup: 'AI ❤️ DC' | VentureBeat"
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"https://venturebeat.com/business/join-venturebeat-and-anzu-partners-for-our-first-dc-meetup-ai-dc"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Join VentureBeat and Anzu Partners for our first DC meetup: ‘AI ❤️ DC’ Share on Facebook Share on X Share on LinkedIn VentureBeat is partnering with Anzu Partners on an exclusive gathering for the AI community in Washington DC this coming Thursday, October 26: AI ❤️ DC! Join us for an evening of incisive discussions and high-level networking, spotlighting the rapidly evolving landscape of AI and its implications for national security in the city that’s at the heart of policy and innovation.
In attendance from VentureBeat will be editorial director Michael Nuñez , senior reporter Sharon Goldman, and head of news Carl Franzen.
Anzu Partners will also be there, marking their presence as trailblazers in breakthrough industrial and life sciences technologies.
Whether you’re a policy maker shaping the legislative landscape, an investor identifying groundbreaking opportunities, an entrepreneur navigating the next frontier of innovation, or an AI enthusiast keen on engaging with the vanguard of the industry, AI ❤️ DC offers an exceptional platform for connection, dialogue, and inspiration.
Enjoy complimentary food and drinks as you engage with some of the most influential minds in AI and national security. Participate in insightful conversations, exchange ideas, explore collaborations, and celebrate Washington DC’s thriving AI culture.
We are convinced that through these shared experiences and connections, we can collectively shape the future of AI in Washington DC, fostering an environment of innovation, legislative insight, and collective progress.
Find all the location details, timing, and RSVP here.
Space is limited. We look forward to seeing you there! RSVP to AI❤️DC Meetup VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"TikTok maker's new AI SALMONN understands all audio, not just music and voices | VentureBeat"
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"https://venturebeat.com/ai/tiktok-makers-new-ai-salmonn-understands-all-audio-not-just-music-and-voices"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages TikTok maker’s new AI SALMONN understands all audio, not just music and voices Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Researchers from Tsinghua University and ByteDance have developed a new artificial intelligence system called SALMONN that allows machines to understand and reason about audio inputs like speech, sounds, and music.
In a research paper published on arXiv, the scientists describe SALMONN as “a large language model (LLM) enabling speech, audio event, and music inputs.” The system merges two specialized AI models—one for processing speech and one for general audio—into a single LLM that can generate text responses to audio prompts.
“Instead of speech-only input or audio-event-only input, SALMONN can perceive and understand all kinds of audio inputs and therefore obtains emerging capabilities such as multilingual speech recognition & translation and audio-speech co-reasoning,” the paper states. “This can be regarded as giving the LLM ‘ears’ and cognitive hearing abilities.” An AI Model That Hears and Understands The researchers demonstrated SALMONN’s abilities on a range of audio inputs, including clips of speech, gunshots, duck noises and music. When prompted with each sound clip, the system generated appropriate descriptive text responses, showcasing an understanding of the audio content.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “The text prompt is used to instruct SALMONN to answer open-ended questions about the general audio inputs and the answers are in the LLM text responses,” explains the paper.
According to the researchers, this technique of cognitive audio question-answering represents a major leap over traditional AI speech and audio systems that are limited to basic transcription.
“Compared with traditional speech and audio processing tasks such as speech recognition and audio caption, SALMONN leverages the general knowledge and cognitive abilities of the LLM to achieve a cognitively oriented audio perception, which dramatically improves the versatility of the model and the richness of the task,” the paper states.
The researchers suggest SALMONN also possesses cross-modal abilities, such as following spoken instructions, without any explicit training in speech-to-text translation.
“SALMONN only uses training data based on textual commands, listening to spoken commands is also a cross-modal emergent ability,” they write.
While the current capabilities are promising, the researchers acknowledge the model has limitations in terms of reasoning depth. However, they are optimistic about the future potential, stating that SALMONN “makes a step towards hearing-enabled artificial general intelligence.” Potential Impact of SALMONN on Enterprise Data Analysis For technical decision makers, this development could herald a new era of voice-activated data analysis and business intelligence. The ability of SALMONN to understand and interpret a wide range of audio inputs could revolutionize how businesses interact with data, removing the need for traditional text-based input and opening up new possibilities for voice-activated analytics and data-driven decision making.
Furthermore, the team has released a web-based demo , allowing users to experience the capabilities of SALMONN firsthand. The model is also available on Hugging Face , a leading platform for hosting and sharing machine learning models.
In the rapidly evolving world of artificial intelligence, the unveiling of SALMONN serves as an interesting glimpse into the future of machine learning and cognitive computing. It underscores the commitment of ByteDance and Tsinghua University to push the boundaries of what AI can achieve. As we move closer to a world where AI can not only “see” through computer vision but also “hear” through cognitive audio processing, the implications for businesses and consumers alike are profound.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
All rights reserved.
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"SurveyMonkey launches 'Build with AI', ushers in a new era for survey creation | VentureBeat"
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"https://venturebeat.com/ai/surveymonkey-launches-build-with-ai-ushers-in-a-new-era-for-survey-creation"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages SurveyMonkey launches ‘Build with AI’, ushers in a new era for survey creation Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
SurveyMonkey is unveiling new artificial intelligence (AI) capabilities that it hopes will set its online survey tools apart in an increasingly crowded market.
The San Mateo, Calif.-based company today launched a feature called “ Build with AI ” that uses AI technology to automatically generate surveys after users describe the goals and target audience. The aim is to slash the time required for creating customized polls and questionnaires.
“The beauty of our tool is its broad applicability,” said SurveyMonkey CEO Eric Johnson in an interview with VentureBeat. “One of our best selling points is the speed to insight, that you can purchase the product and you can be up and running within minutes or hours and have surveys out and in the field really quickly.” The new tool, which SurveyMonkey developed using OpenAI’s GPT-3 large language model, marks the company’s latest move to bake more AI into its 20-year-old platform. It follows other AI-powered features that analyze survey results and provide recommendations.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Johnson highlighted the democratizing potential of AI in the realm of data analysis. “Today, advanced-level analysis requires a team of PhDs,” he said. “That could be very different in two to five years. We can offer some of these tools and services in ways that we couldn’t have thought of five years ago.” A paradigm shift in survey design and analysis With almost a quarter-century of experience in data collection, SurveyMonkey has consistently adapted and evolved to meet market trends and customer demands. The release of “Build with AI” combines their rich data legacy with cutting-edge AI technology, marking a significant step in the company’s journey.
“We have this almost 25-year-old business, which has this mass of data around a very specific use case that we can now apply against this incredible new technology, and marries seamlessly with what we’ve built and how we’re evolving our core platform,” Johnson added.
The “Build with AI” feature not only enhances the capabilities of its platform but also positions SurveyMonkey at the forefront of the industry’s digital transformation. It paves the way for a future where AI is central to survey design and data interpretation.
This move represents a larger trend in enterprise data, where AI is increasingly influencing business operations, making advanced analysis accessible to a wider audience. SurveyMonkey’s bold move is a clear testament to the expanding role of AI in business optimization and democratizing access to advanced data analysis tools.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
All rights reserved.
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"Sparta aims to revolutionize commodity trading with AI-driven forecasting | VentureBeat"
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"https://venturebeat.com/ai/sparta-aims-to-revolutionize-commodity-trading-with-ai-driven-forecasting"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Sparta aims to revolutionize commodity trading with AI-driven forecasting Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Sparta , a startup that provides live market intelligence and forecasting insight for commodity traders, has announced a series A funding round of $17.5 million. The funding round was led by technology venture capital firm FirstMark, alongside existing shareholder Singular.
The startup has built a platform that aims to transform the way traders gather, process, analyze and interpret data to inform strategic decision-making in real-time.
The platform uses artificial intelligence (AI), machine learning (ML) and data science to capture non-liquid prices, such as physical premiums, OTC swaps, and freight, from brokers and pricing analysts around the world. It then processes them into forward-looking insights and predictive analytics that enable traders to spot trading opportunities before their competition.
“Our key objective is to participate or be one of the main drivers in the transformation of how commodities are traded,” said Felipe Elink Schuurman, co-founder and CEO of Sparta, in a recent interview with VentureBeat. “Moving it from an antiquated, merchant type trading to a much more sophisticated data-driven, and intelligence-driven going forward environment.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The company plans to use the new funding to expand its product offerings beyond its current focus to oil and gas products, such as gasoline, diesel, jet and Naphtha.
The company intends to cover every product in the oil and gas sector by the end of next year. It then plans to enter other commodity markets, such as agriculture and metals. The company also plans to develop premium insights, optimize workflow processes and develop AI tools that can provide forward-looking predictions and reports.
The company also wants to grow its global presence. Currently located in Geneva, London, Houston, Singapore and Madrid, Sparta plans to expand its presence within these existing territories, as well as establish a foothold in new regions. The company has more than 70 customers globally, including Phillips 66, Chevron, Trafigura, Equinor and more.
AI-driven forecasting tool is set to transform commodity trading Sparta’s predictive pricing engine and market opinion layer aim to give traders a competitive edge by providing accurate and timely information. Schuurman predicts that the speed and accuracy of the information Sparta provides will be so vital to trading that not having it would put traders at a competitive disadvantage.
As Sparta continues to revolutionize the commodity trading industry, it has its sights set on connecting predictive pricing, market opinion and news. “AI tools will be able to generate automated real time opinion reports based on real time prices, margins, insights, and news” Schuurman told VentureBeat. “It’s going to be fascinating what will happen over the next five years, in terms of that enablement, that co-pilot side of assisting people in making those trading decisions.” Sparta’s ambition to transform the commodity trading industry using AI signifies a major shift in how businesses approach decision-making. As AI continues to evolve and become more integrated into various industries, companies such as Sparta remain at the forefront, pushing the boundaries of what’s possible and setting new standards for the future of trading.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
All rights reserved.
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"Researchers unveil '3D-GPT', an AI that can generate 3D worlds from simple text commands | VentureBeat"
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"https://venturebeat.com/ai/researchers-unveil-3d-gpt-an-ai-that-can-generate-3d-worlds-from-simple-text-commands"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Researchers unveil ‘3D-GPT’, an AI that can generate 3D worlds from simple text commands Share on Facebook Share on X Share on LinkedIn Credit: arxiv.org Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Researchers from the Australian National University, the University of Oxford, and the Beijing Academy of Artificial Intelligence have developed a new AI system called “ 3D-GPT ” that can generate 3D models simply from text-based descriptions provided by a user.
The system, described in a paper published on arXiv , offers a more efficient and intuitive way to create 3D assets compared to traditional 3D modeling workflows.
3D-GPT is able to “dissect procedural 3D modeling tasks into accessible segments and appoint the apt agent for each task,” according to the paper. It utilizes multiple AI agents that each focus on a different part of understanding the text prompt and executing modeling functions.
“3D-GPT positions LLMs [large language models] as proficient problem solvers, dissecting the procedural 3D modeling tasks into accessible segments and appointing the apt agent for each task,” the researchers stated.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The key agents include a “task dispatch agent” that parses the text instructions, a “conceptualization agent” that adds details missing from the initial description, and a “modeling agent” that sets parameters and generates code to drive 3D software like Blender.
By breaking down the modeling process and assigning specialized AI agents, 3D-GPT is able to interpret text prompts, enhance the descriptions with extra detail, and ultimately generate 3D assets that match what the user envisioned.
“It enhances concise initial scene descriptions, evolving them into detailed forms while dynamically adapting the text based on subsequent instructions,” the paper explained.
The system was tested on prompts like “a misty spring morning, where dew-kissed flowers dot a lush meadow surrounded by budding trees.” 3D-GPT was able to generate complete 3D scenes with realistic graphics that accurately reflected elements described in the text.
While the quality of the graphics is not yet photorealistic, the early results suggest this agent-based approach shows promise for simplifying 3D content creation. The modular architecture could also allow each agent component to be improved independently.
“Our empirical investigations confirm that 3D-GPT not only interprets and executes instructions, delivering reliable results but also collaborates effectively with human designers,” the researchers wrote.
By generating code to control existing 3D software instead of building models from scratch, 3D-GPT provides a flexible foundation to build on as modeling techniques continue to advance.
The researchers conclude that their system “highlights the potential of LLMs in 3D modeling, offering a basic framework for future advancements in scene generation and animation.” This research could revolutionize the 3D modeling industry, making the process more efficient and accessible. As we move further into the metaverse era, with 3D content creation serving as a catalyst, tools like 3D-GPT could prove invaluable to creators and decision-makers in a range of industries, from gaming and virtual reality to cinema and multimedia experiences.
The 3D-GPT framework is still in its early stages and has some limitations, but its development marks a significant step forward in AI-driven 3D modeling and opens up exciting possibilities for future advancements.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
All rights reserved.
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"Researchers develop 'Woodpecker': A groundbreaking solution to AI's hallucination problem | VentureBeat"
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"https://venturebeat.com/ai/researchers-develop-woodpecker-a-groundbreaking-solution-to-ais-hallucination-problem"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Researchers develop ‘Woodpecker’: A groundbreaking solution to AI’s hallucination problem Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A group of artificial intelligence researchers from the University of Science and Technology of China (USTC) and Tencent YouTu Lab have developed an innovative framework, coined as “ Woodpecker “, designed to correct hallucinations in multimodal large language models ( MLLMs ).
The research paper outlining this groundbreaking approach was published on the pre-print server arXiv, under the title “Woodpecker: Hallucination Correction for Multimodal Large Language Models.” “Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content,” the researchers note in their paper. Existing solutions mainly resort to an instruction-tuning manner that requires retraining the models with specific data, which can be data—and computation—intensive.
The five stages of the ‘Woodpecker’ framework Woodpecker offers a fresh perspective by introducing a training-free method that corrects hallucinations from the generated text. The framework performs correction after a thorough diagnosis, incorporating a total of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “Like a woodpecker heals trees, it picks out and corrects hallucinations from the generated text,” the researchers stated, explaining the inspiration behind the framework’s name. Each step in the pipeline is clear and transparent, providing valuable interpretability.
The stages of Woodpecker work in harmony to validate and correct any inconsistencies between image content and generated text. First, it identifies the main objects mentioned in the text. Then, it asks questions around the extracted objects, such as their number and attributes. The framework answers these questions using expert models in a process called visual knowledge validation. Following this, it converts the question-answer pairs into a visual knowledge base consisting of object-level and attribute-level claims about the image. Finally, Woodpecker modifies the hallucinations and adds the corresponding evidence under the guidance of the visual knowledge base.
The researchers have released the source code for Woodpecker, encouraging further exploration and application of the framework by the wider AI community. For those interested in experiencing the capabilities of Woodpecker firsthand, the researchers have provided an interactive demo of the system.
This platform provides an opportunity to understand the workings of Woodpecker in real-time and observe its hallucination correction capabilities.
How effective is ‘Woodpecker’? A comprehensive analysis The team conducted comprehensive quantitative and qualitative experiments to evaluate Woodpecker’s effectiveness, using various datasets, including POPE, MME, and LLaVA-QA90. “On the POPE benchmark, our method largely boosts the accuracy of the baseline MiniGPT-4/mPLUG-Owl from 54.67%/62% to 85.33%/86.33%,” they reported.
This breakthrough comes at a time when AI is increasingly integrated into various industries. MLLMs have a wide range of applications, from content generation and moderation to automated customer service and data analysis. However, hallucinations—where the AI generates information not present in the input data—have been a significant roadblock in their practical application.
The development of Woodpecker signifies a crucial step forward in addressing this issue, paving the way for more reliable and accurate AI systems. As MLLMs continue to evolve and improve, the importance of such frameworks in ensuring their accuracy and reliability cannot be overstated.
The Woodpecker framework, with its ability to correct hallucinations without retraining and high interpretability, promises to be a game-changer in the world of MLLMs. It holds immense potential to significantly improve the accuracy and reliability of AI systems in various applications, making this a notable development in the field of artificial intelligence.
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"OpenAI takes ‘baby step' toward AI agents with Assistants API | VentureBeat"
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"https://venturebeat.com/ai/openai-takes-baby-step-toward-ai-agents-with-assistants-api"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages OpenAI takes ‘baby step’ toward AI agents with Assistants API Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Since the earliest days of artificial intelligence research in the 1950s, scientists have pursued the tantalizing goal of machines that can function autonomously as intelligent agents in the real world.
This week, the dream moved one small step closer to reality as OpenAI , the creator of ChatGPT, unveiled new technology that paves the way toward such autonomous agents. At its inaugural developer conference in San Francisco on Monday, the company made several major announcements, including the introduction of GPT-4 Turbo and customizable versions of ChatGPT.
The spotlight, however, should have been more focused on a new tool called Assistants API.
This tool, released at the very end of the keynote presentation, empowers programmers to swiftly build tailored “assistants” into their applications that are capable of understanding natural language, executing functions within their apps, and utilizing services like computer vision.
Romain Huet, the head of developer experience at OpenAI, described the launch of the Assistants API as a “baby step” towards the future of fully autonomous AI agents in a conversation with VentureBeat shortly after stepping off stage. Despite Huet’s humble description, this “baby step” holds the potential to radically transform our everyday interactions with technology.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! In a live demonstration , Huet created an assistant for a travel app, Wanderlust, using GPT-4 for destination suggestions and the DALL-E 3 API for illustrations of each travel guide (shown in the video at the 33:16 mark). The travel assistant, assembled in minutes, demonstrated the capacity to plan and book vacations, a task traditionally handled by human travel agents.
The hidden power of the Assistants API The Assistants API, Huet explained, allows developers to build “assistants” into their applications. These assistants can leverage OpenAI’s models with specific instructions to tune their capabilities and personalities, and can call on multiple tools in parallel, including a code interpreter and knowledge retrieval system.
What’s truly remarkable about this is the potential for cross-collaboration between these AI assistants. As more developers start integrating these assistants into their products, it’s easy to envision a world where different AI assistants communicate with each other to complete tasks. A command to book a vacation could trigger a series of coordinated actions between multiple AI agents: one to book a flight, another to secure hotel reservations, and yet another to plan activities.
The difference between Assistants and Agents By enabling GPT-4 to interact and work with existing apps and services, the Assistants API creates a new paradigm for AI-assisted tasks. These AI “assistants” are not just passive tools waiting for commands but active participants in task execution, bringing us closer to the concept of AI as a personal assistant.
The core distinction between the Assistants API and fully autonomous AI agents lies in the level of independence. AI agents, in their ideal form, can execute tasks independently and proactively, without the need for human oversight. While the Assistants API doesn’t quite reach this level of autonomy, it’s a significant step in that direction.
The future landscape of AI Assistants The implications of this update are vast. In the near future, AI agents could be booking dinner reservations, purchasing household items, or securing the best-priced flight to New York City. By facilitating the creation of these assistant-driven tools, OpenAI is bringing us one step closer to a future where AI agents perform tasks on our behalf — and interact with each other to accomplish different tasks.
In short, the Assistants API allows for the creation of semi-autonomous agents capable of working across a wide range of tasks and industries. As described by Huet, the unveiling of the Assistants API is just a “baby step” towards the future. But in the realm of artificial intelligence, even baby steps can represent monumental strides.
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"OpenAI CEO Sam Altman shrugs off Elon Musk in AI arms race | VentureBeat"
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"https://venturebeat.com/ai/openai-ceo-sam-altman-shrugs-off-elon-musk-in-ai-arms-race"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages OpenAI CEO Sam Altman shrugs off Elon Musk in AI arms race Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
OpenAI founder and CEO Sam Altman dismissively addressed Elon Musk’s latest venture into the AI industry on Monday at OpenAI’s inaugural Dev Day during a press briefing.
This comes in the wake of Musk’s strategic announcement of his new AI product, Grok , from his latest company, xAI.
Altman brushed off the potential threat from Musk’s new company, stating, “Elon’s gonna Elon,” in response to a question from VentureBeat about Musk’s timing of announcements.
At a press panel, a reporter asked @sama what he thinks about Elon Musk releasing xAI’s chatbot “grok” just before OpenAI’s dev day. Altman’s answer: “Elon’s gonna Elon.” VentureBeat: Why do you think Elon Musk chose to share Grok news days before DevDay and again today moments after OpenAI’s announcements? Is there still bad blood there? What’s the deal? Sam Altman: “Elon’s gonna Elon. ??♂️ ” pic.twitter.com/JJTWeFFalk The snappy exchange captured the growing rancor between Altman and Musk, who have gone from collaborators to combatants in the race to dominate one of Silicon Valley’s most lucrative new markets — generative AI.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! A bitter falling out Musk was an early investor in OpenAI when it launched in 2015, committing substantial funding alongside other tech luminaries like Reid Hoffman. The venture attracted top AI experts, pulling them away from established tech giants and prestigious academic institutions.
But Altman and Musk reportedly had a bitter falling out in 2018 over the direction of OpenAI, with Musk proposing to take control of the lab to turbocharge its progress. Altman and the OpenAI board rebuffed Musk, who then revoked much of his pledged funding as he withdrew from the company.
“I am the reason OpenAI exists,” Musk later asserted during interview with CNBC.
“It wouldn’t exist without me.” Musk tries to steal OpenAI’s spotlight On Friday, Musk was once again needling his former protégé. Just days before OpenAI’s inaugural developer conference in San Francisco, Musk unveiled his new startup xAI and its first product, an AI chatbot named Grok.
The timing was a bald attempt to upstage OpenAI’s event and spark doubts about the progress of products like ChatGPT, the viral conversational app that has supercharged demand for generative AI. Musk tweeted again about Grok moments before Altman went on stage for his first Dev Day keynote.
OpenAI unfazed by Musk’s antics If Altman was chagrined by Musk’s ploy, he did not show it. The OpenAI chief rattled off a series of ambitious new offerings from his company, including the release of GPT-4 Turbo and Assistant API.
The suite of new tools showed that OpenAI aims to press its advantage over Musk and other rivals in the white-hot AI arms race. By nonchalantly shrugging off Musk’s needling, Altman signaled that OpenAI will dictate the pace of progress, not the other way around.
OpenAI’s announcements underscore the company’s commitment to pushing the boundaries of AI technology. Musk, on the other hand, has chosen a different approach with Grok by focusing on real-time data and efficiency complemented with a touch of humor. This feature could potentially set Grok apart in an industry that often leans towards the serious side.
The AI Arms race heats up Still, Musk’s virtually limitless resources ensure xAI will remain a threat. The swashbuckling billionaire has disrupted multiple industries and appears determined to catch up to OpenAI’s head start in AI.
For now, Altman seems content to subtly poke the bear while his researchers push the boundaries of what machines can do with language and creativity. But the passive-aggressive rivalry between these two ambitious leaders guarantees the contest to own the future of AI will only intensify.
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"MonsterAPI leads the charge in democratizing AI with no-code fine-tuning | VentureBeat"
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"https://venturebeat.com/ai/monsterapi-leads-the-charge-in-democratizing-ai-with-no-code-fine-tuning"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages MonsterAPI leads the charge in democratizing AI with no-code fine-tuning Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A new platform that allows users to fine-tune open-source large language models ( LLMs ) without writing any code has recently launched new features that make the process even easier and faster.
The platform, called MonsterAPI , was created by a team of researchers and developers who wanted to make LLMs more accessible and affordable for everyone. LLMs are powerful artificial intelligence systems that can generate natural language texts for various tasks, such as writing, summarizing, translating, answering questions, and more.
However, LLMs are not perfect. They often have very general knowledge but struggle to solve specific problems. To make them more accurate and relevant, they need to be “ fine-tuned ,” which means teaching them how to perform a particular task using a custom dataset.
Fine-tuning LLMs is not a simple task. It requires a lot of time, effort, and GPU computing power. It also involves finding the optimal hyperparameters and dealing with underfitting and overfitting issues. Moreover, it is hard to find experienced people who know how to do it.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! MonsterAPI aims to solve these problems by offering a no-code solution for fine-tuning LLMs. Users can choose from a variety of open-source models, such as Llama and Llama 2 7B, 13B and 70B; Falcon 7B and 40B; Open Llama; OPT; GPT J; and Mistral 7B. They can also upload their own datasets or use pre-made ones from the platform’s library. Then, they can fine-tune the models using a simple interface that guides them through the process.
The platform also uses a decentralized GPU platform that reduces the cost and increases the speed of fine-tuning. Users can pay as they go or subscribe to a plan that suits their needs. The platform also offers free credits for new users who sign up with a code.
The team behind MonsterAPI has recently announced new features that make the platform even better. These include: QLora with 4-bit quantization and nf4: This feature reduces the size of the models by compressing them using quantization techniques. This allows users to fine-tune larger models using less memory and bandwidth.
Flash Attention 2: This feature improves the speed and efficiency of training by using a novel attention mechanism that reduces the computational complexity of the models.
Data and model parallelism on multiple GPUs: This feature enables users to train bigger models using larger context lengths by distributing the data and the model across multiple GPUs.
MonsterAPI has mostly received positive feedback from its users, who have used it for various purposes, such as creating content, generating summaries, building chatbots, and more. The platform also has an active community on Discord, where users can share their results, ask questions, get support, and receive updates and offers from the team.
MonsterAPI is one of the first platforms that offers no-code fine-tuning of open-source LLMs. It aims to democratize access to LLMs and make them more useful and affordable for everyone. To learn more about MonsterAPI or to sign up for free credits, visit http://monsterapi.ai or join their Discord server.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Microsoft unveils 'LeMa': A revolutionary AI learning method mirroring human problem solving | VentureBeat"
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"https://venturebeat.com/ai/microsoft-unveils-lema-a-revolutionary-ai-learning-method-mirroring-human-problem-solving"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Microsoft unveils ‘LeMa’: A revolutionary AI learning method mirroring human problem solving Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Researchers from Microsoft Research Asia , Peking University , and Xi’an Jiaotong University have developed a new technique to improve large language models’ ( LLMs ) ability to solve math problems by having them learn from their mistakes, akin to how humans learn.
The researchers have revealed a pioneering strategy, Learning from Mistakes (LeMa), which trains AI to correct its own mistakes, leading to enhanced reasoning abilities, according to a research paper published this week.
The researchers drew inspiration from human learning processes, where a student learns from their mistakes to improve future performance.
“Consider a human student who failed to solve a math problem, he will learn from what mistake he has made and how to correct it,” the authors explained. They then applied this concept to LLMs, using mistake-correction data pairs generated by GPT-4 to fine-tune them.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! How LeMa works to enhance math reasoning The researchers first had models like LLaMA-2 generate flawed reasoning paths for math word problems. GPT-4 then identified errors in the reasoning, explained them and provided corrected reasoning paths. The researchers used the corrected data to further train the original models.
The results of this new approach are significant. “Across five backbone LLMs and two mathematical reasoning tasks, LeMa consistently improves the performance compared with fine-tuning on CoT data alone,” the researchers explain.
LeMa yields impressive results on challenging datasets What’s more, specialized LLMs like WizardMath and MetaMath also benefited from LeMa, achieving 85.4% pass@1 accuracy on GSM8K and 27.1% on MATH. These results surpass the state-of-the-art performance achieved by non-execution open-source models on these challenging tasks.
This breakthrough signifies more than just an enhancement in the reasoning capability of AI models. It also marks a significant step towards AI systems that can learn and improve from their mistakes, much like humans do.
Broad Implications and Future Directions The team’s research, including their code, data, and models, is now publicly available on GitHub.
This open-source approach encourages the broader AI community to continue this line of exploration, potentially leading to further advancements in machine learning.
The advent of LeMa represents a major milestone in AI, suggesting that machines’ learning (ML) processes can be made more akin to human learning. This development could revolutionize sectors heavily reliant on AI, such as healthcare, finance, and autonomous vehicles, where error correction and continuous learning are critical.
As the AI field continues to evolve rapidly, the integration of human-like learning processes, such as learning from mistakes, appears to be an essential factor in developing more efficient and effective AI systems.
This breakthrough in machine learning underscores the exciting potential that lies ahead in the realm of artificial intelligence. As machines become more adept at learning from their mistakes, we move closer to a future where AI can exceed human capabilities in complex problem-solving tasks.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Microsoft harnesses power of AI to boost Windows 11 security, pushes for passwordless future | VentureBeat"
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"https://venturebeat.com/ai/microsoft-harnesses-power-of-ai-to-boost-windows-11-security-pushes-for-passwordless-future"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Microsoft harnesses power of AI to boost Windows 11 security, pushes for passwordless future Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Microsoft announced a series of new enterprise security features today that use artificial intelligence ( AI ) to help defend Windows 11 against increasingly sophisticated cyberattacks. The tech giant claims its new AI capabilities will reduce security incidents by 60% and firmware attacks by 300% for businesses using the latest version of its software.
Microsoft’s vice president of enterprise and OS security, David Weston, explains in a company blog post that was published today specifically how AI is being used to fortify Windows 11 against sophisticated attacks, ranging from malware to firmware threats, and even nation-state attacks.
At the heart of this AI-focused security upgrade is the integration of Microsoft’s Pluton Security Processor and Secured-core PCs.
Both systems leverage AI algorithms to isolate sensitive data and provide defense against potential threats. IT professionals should note that these Secured-core PCs are reported to be 60% more resilient to malware than non-Secured-core PCs, a significant improvement in system defenses.
Microsoft’s AI strategy also appears to be forward-thinking, with the company starting to adopt memory safe languages like Rust for traditional attack targets. Rust’s memory safety features without garbage collection make it an ideal language for building reliable and efficient systems, further multiplying the cybersecurity benefits.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The end of passwords? Microsoft’s groundbreaking move Perhaps most notable among today’s announcements is Microsoft’s push towards a passwordless future , a move that could fundamentally alter the landscape of cybersecurity.
Microsoft’s AI will now be used to develop and implement passkeys — unique, unguessable cryptographic credentials securely stored on a user’s device, which have the potential to replace traditional multi-factor authentication. This is a substantial leap forward in phishing protection, making it considerably more difficult for hackers to exploit stolen passwords.
Microsoft says that its AI system analyzes more than 65 trillion security signals per day— with more than 4,000 password attacks every second — to identify suspicious login attempts and request additional identity verification when needed in the new system.
The company also revealed a new capability called Config Refresh that relies on AI to detect and revert unwanted changes to device policies in near real-time. This allows IT teams to lock down device settings while leveraging intelligence to accommodate legitimate policy updates.
Microsoft pioneers a new cybersecurity path with AI The company’s commitment to AI solutions aligns with its longstanding strategy of positioning itself as a leader in enterprise computing. By weaving AI into the fabric of Windows 11, Microsoft is demonstrating its commitment to providing businesses with secure, reliable, and forward-thinking solutions.
Business analysts see this as a clear indicator of Microsoft’s strategy to leverage its AI prowess to drive growth and cement its position in the enterprise data and AI market. Given the increasing importance of cybersecurity in the modern business landscape, Microsoft’s investment in AI could pay significant dividends.
However, the real test of these new AI-powered features will be their effectiveness against real-world threats. As cyber threats continue to evolve, so too must our defenses. If Microsoft’s AI enhancements can live up to their promise, they will represent a significant advance in cybersecurity and a potent tool in the fight against cybercrime.
As AI continues to transform enterprise data and security, it’s clear that companies like Microsoft are leading the charge. By harnessing the power of AI, Microsoft is not just shaping its future but also the future of cybersecurity as a whole. Only time will tell how these developments play out, but one thing is certain: the era of AI-driven cybersecurity is here, and Microsoft is at its helm.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Marc Andreessen publishes 'Techno-Optimist Manifesto', names enemies including 'ESG', ‘Trust and Safety’ | VentureBeat"
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"https://venturebeat.com/ai/marc-andreessen-publishes-techno-optimist-manifesto-names-enemies-including-esg-trust-and-safety"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Marc Andreessen publishes ‘Techno-Optimist Manifesto’, names enemies including ‘ESG’, ‘Trust and Safety’ Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Marc Andreessen, the co-founder of venture capital firm Andreessen Horowitz, on Monday published a defiant and provocative blog post he calls “ The Techno-Optimist Manifesto.
” Andreessen, known for his early role in shaping the internet as we know it, offers a full-throated defense of technology and capitalism, essentially arguing that technology is not the problem but the solution to most, if not all, of the world’s issues.
The Manifesto, resplendent with grandiose rhetoric and lofty ideals, functions as a sharp rebuke of critics who worry about the social, economic, and political impacts of rapid technological change — most recently fueled by the AI boom. His post champions the belief that technological progress, powered by free markets, will invariably lead to society’s betterment.
In the manifesto’s introduction, Andreessen writes, “We are being lied to. We are told that technology takes our jobs, reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.” Andreessen later argues that the “only perpetual source of growth is technology.” Of course, Andreessen’s optimism about technology’s ability to solve all material problems overlooks the paradox of technological progress: that it often creates new problems even as it solves old ones. For instance, while the internal combustion engine revolutionized transportation, it also contributed to climate change. The internet, while connecting us in unprecedented ways, has also given rise to issues such as cybercrime and the erosion of privacy.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The Manifesto also sidesteps the critical question of accessibility. Who has access to the benefits of technology and who does not? The digital divide, both within nations and between them, is a glaring challenge in the path to Andreessen’s utopian vision.
A shocking list of enemies such as ‘ESG’, ‘Trust and Safety’ and ‘Tech Ethics’ teams The manifesto takes an odd turn towards the end of the post when Andreessen vilifies concepts such as sustainability, social responsibility, and safety. These, according to Andreessen, are part of a “mass demoralization campaign” against progress. He goes as far as to single out “trust and safety” and “tech ethics” teams as enemies.
Critics like Tom Coates , an entrepreneur and technologist, expressed horror at Andreessen’s citations from a manifesto that advocated for war, militarism, and the destruction of feminism, museums, and libraries. He argues that the origins of Andreessen’s quotes should be scrutinized, as they perhaps expose a more troubling ideological underpinning to the Manifesto.
I’m sort of mystified why people aren’t more horrified by Marc Andreessen’s quoting from a document that talked about the hygiene of war and how museums, libraries and feminism should all be destroyed. Am I weird for being horrified by this?! pic.twitter.com/tzMb2csGem NBC reporter Ben Collins drew attention to the fact that Andreessen’s firm had recently pivoted towards military and defense contractor technology. This, in combination with the Manifesto’s aggressive stance against “social responsibility” and “tech ethics,” paints a picture of a venture capital firm unapologetically prioritizing technological advancement over societal and ethical considerations.
Marc Andreessen, who runs one of the biggest Silicon Valley venture capital firms, wrote a "manifesto" today labeling “social responsibility" and "tech ethics" teams "the enemy." His firm recently pivoted from crypto/Web3 to American military and defense contractor technology.
pic.twitter.com/l8PDtCGy8R Finally, Becca Lewis , researcher at Stanford, took a more direct approach, questioning the convergence of socially reactionary politics with hyper-nationalism, a trend she hints may be reflected in Andreessen’s Manifesto.
What do you call a politics that mixes socially reactionary politics with hyper-nationalism ??? https://t.co/QvQnbOALVD A powerful voice in a growing debate Andreessen has long been a polarizing figure due to his aggressive views on technology and capitalism. But publishing an explicit enemies list represents a marked escalation that shocked Silicon Valley.
The manifesto does, however, offer insight into an influential strain of techno-utopianism, which continues to surface in debates about AI safety and governance.
This worldview perceives technology as an inherent good and regulation as inherently bad. Critics worry this simplistic perspective encourages recklessness and avoids accountability. But proponents argue optimism fuels progress, while caution leads to stagnation.
As technology reshapes society ever more profoundly, this debate between openness and oversight, risk and responsibility, shows no signs of disappearing. The “Techno-Optimist Manifesto” has brought long-simmering tensions to the surface. Silicon Valley must now grapple with hard questions about its vision for the future.
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"Luma AI’s Genie lets anyone make 3D objects from text | VentureBeat"
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"https://venturebeat.com/ai/luma-ais-genie-lets-anyone-make-3d-objects-from-text"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Luma AI’s Genie lets anyone make 3D objects from text Share on Facebook Share on X Share on LinkedIn image credit: luma.ai Are you looking to showcase your brand in front of the brightest minds of the gaming industry? Consider getting a custom GamesBeat sponsorship.
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Luma AI , a startup that specializes in creating 3D content from video and text, has unveiled a new tool that allows anyone to generate realistic 3D models in seconds.
The tool, called Genie , is a research preview of an all-new kind of generative 3D foundation model that can create 3D things from natural language prompts.
Genie is currently available for free on Discord , where users can join a channel and type in what they want to create. Genie will then generate four different 3D models that match the description, along with a link to download them in GLB format. Users can also customize the materials and styles of the models, as well as view them in augmented reality.
The tool is powered by a deep neural network that has been trained on a large dataset of 3D shapes, textures, and scenes. The network can learn the semantic and geometric relationships between words and 3D objects, as well as synthesize novel shapes that are consistent with the input. Genie can handle a wide range of domains, from furniture and animals to vehicles and buildings.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! The vision and impact of Luma AI Genie is a potential game-changer for the art world. According to Luma AI’s co-founder and CEO Amit Jain, Genie is part of the company’s vision to democratize 3D content creation and enable anyone to make videos, scenes, and worlds that look plausible and are useful.
“[Our] core belief at Luma is that all visual generative models need to reason and work in 3D to produce videos, scenes, and worlds that look plausible and are useful,” Jain told VentureBeat.
“We are really proud of the speed and quality of Genie,” said Barkley Dai, Growth Lead at Luma AI. “In mere seconds, what was once a vision becomes a tangible 3D object.” The core belief at Luma is that all visual generative models need to reason and work in 3d to make videos, scenes, and worlds that look plausible and are useful. Today we are sharing Genie, a tiny peek into our foundation model research to show what's possible. It's really fast… https://t.co/qrj3ZSgWfD Feedback and reaction from the community Genie has already attracted a lot of attention and praise from the online community. Many users have shared their creations on Twitter using the hashtag #MadeWithGenie.
Some users have also imported their models into other software such as Blender or Unity to further edit or animate them.
“Pretty compelling Text-to-3D. Prompt was ‘modern purple sofa’. Generated in 14 secs (with 3 others) and the GLB imports into Blender in another 5 seconds,” tweeted Andrew Price, a popular Blender artist and instructor.
“I’m blown away by the new @LumaLabsAI tool. ‘Genie’ created these crocos for me in just 20 seconds??,” tweeted Bruno Pontillo, a digital artist and game developer.
I'm blown away by the new @LumaLabsAI tool. "Genie" created these crocos for me in just 20 seconds?? Is the first 3D prop generation tool that works and i love it ? pic.twitter.com/yGFbIXbpNt Genie is still in its early stages of development and may not always produce perfect results. However, Luma AI promises to improve the tool over time with more data, features, and feedback. The company also plans to release more tools based on their 3D AI technology in the future.
To try out Genie for yourself, you can join Luma AI’s Discord server and follow their instructions. You can also learn more about Luma AI’s vision and technology on their website or their Medium blog.
GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
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"Inside Visa's AI-powered war against holiday fraud | VentureBeat"
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"https://venturebeat.com/ai/inside-visas-ai-powered-war-against-holiday-fraud"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Inside Visa’s AI-powered war against holiday fraud Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The holiday shopping season is a double-edged sword for payments leader Visa.
While transaction volumes surge, providing a sales boost, the period also kicks off a relentless assault from cybercriminals looking to exploit vulnerabilities and steal consumer data.
“We know from historical experience that the holiday shopping season is fraudsters’ Super Bowl given the increase in spend and transactions,” said Paul Fabara, Visa’s chief risk officer, in an exclusive interview with VentureBeat. “In particular, online shopping sees a significant uptick and is also the channel disproportionately targeted by bad actors.” According to Visa’s latest biannual threats report , fraudsters are armed with increasingly potent weapons, including artificial intelligence tools like ChatGPT that can automate and scale malicious activities.
AI vs. AI Crimefighting “With new products like ChatGPT, we’re quickly seeing the potential for AI’s positive and negative uses,” Fabara said. “As with any new technology, threat actors have quickly identified ways to exploit AI and Advanced Language Models ( ALM ) for fraud.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The report warns that these AI systems make it easier for criminals to create convincing phishing lures, develop malware that evades detection, and socially engineer victims — all at a massive scale.
To counter these rapidly evolving threats, Visa has invested heavily in its AI-powered defense systems.
“Over the last 10 years, Visa has spent more than $3 billion on AI and data infrastructure to enable the safer, smarter movement of money and to proactively identify and prevent fraud,” Fabara told VentureBeat. “Today, we have several hundred AI models in production, powering over 100 products.” This AI-enabled anti-fraud arsenal analyzes transactions in real time, evaluating up to 500 unique risk factors within 300 milliseconds to pinpoint criminal activity. According to Fabara, new AI innovations will also “help fraud tools make more informed and accurate decisions” during the high-risk holiday period.
The Human Element While AI versus AI crimefighting represents the bleeding edge, Fabara emphasizes that human intelligence also plays a key role at Visa.
“We collaborate with clients as well as other entities across the payments ecosystem, including merchants, to identify and investigate potential fraudulent activity,” he explained.
Fabara also stressed the importance of consumer awareness and vigilance.
“The first step in remaining safe is being proactive and aware of potential threats,” he said. His tips include using multi-factor authentication, creating complex and unique passwords, and watching out for sophisticated phishing tactics.
According to a new McKinsey report , e-commerce sales have doubled in the past five years and are expected to almost double again by 2026. With e-commerce sales exploding, the stakes in this holiday fraud battle only continue to rise. But with veteran leaders like Fabara manning the front lines, Visa and its clients can face the “Cybercriminal Super Bowl” with confidence.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Google Assistant gets generative AI upgrade with Bard | VentureBeat"
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"https://venturebeat.com/ai/google-unveils-assistant-with-bard-a-new-personal-assistant-powered-by-generative-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Google Assistant gets generative AI upgrade with Bard Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Google has announced a new personal assistant that aims to make conversations with artificial inteligence more natural, intuitive, and useful.
Assistant with Bard , which is powered by generative AI, combines the capabilities of Google’s existing voice assistant with Bard, a large language model that can generate and reason with text, voice, and images.
Assistant with Bard is still an early experiment, and Google plans to roll it out to early testers soon on Android and iOS devices. The company says that Assistant with Bard will be able to help users with various tasks, such as planning trips, finding emails, creating lists, or writing social posts. Users will also be able to interact with Assistant with Bard through text, voice, or images, and the assistant will be able to take actions for them, such as booking flights, ordering groceries, or sending messages.
Google says that Assistant with Bard will be integrated with some of the Google services that users already use, such as Gmail and Docs , making it easier to stay on top of the most important things in their lives. Assistant with Bard will also be contextually aware, using the input from the sensors in an Android device, such as the camera, voice input, or presence detection, to understand the user’s intent and provide relevant responses.
A New Era of Personalization Google’s announcement of Assistant with Bard comes shortly after Amazon’s announcement of a new large language model for Alexa , which is also powered by generative AI. Amazon says that its new model, which is custom-built and optimized for voice interactions, will enhance Alexa’s five foundational capabilities: understanding, conversing, personalizing, anticipating, and taking action.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Both Google and Amazon are competing in the market of voice assistants, which has grown rapidly in recent years, as more people use smart speakers, smartphones, and other devices to access information and services through voice. Last year, 123.5 million U.S. adults used voice assistants at least once per month, according to a report by Insider Intelligence.
The development of generative AI models for voice assistants also raises some challenges and concerns, such as ensuring the quality, accuracy, and reliability of the generated content, as well as the privacy and security of the users’ data. Both Google and Amazon say that they are building their models with privacy in mind, and that users will be able to choose their individual privacy settings The introduction of Assistant with Bard and the new Alexa model shows how generative AI is transforming the way people interact with technology, and how voice assistants are becoming more intelligent, helpful, and human-like. As these models become more widely available, users may soon find themselves talking to their devices as if they were talking to a real person — or even a friend. Let’s just hope the new release goes better than the last.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Google Bard fails to deliver on its promise — even after latest updates | VentureBeat"
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"https://venturebeat.com/ai/google-bard-fails-to-deliver-on-its-promise-even-after-latest-updates"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Google Bard fails to deliver on its promise — even after latest updates Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Google revamped its artificially intelligent chatbot Bard last week in a major overhaul that now gives users access to it from some of its most popular products including Gmail, Docs, Drive, Maps, YouTube, and more. The update theoretically gives Google’s Bard an advantage over ChatGPT, which is the market leader pushed jointly by OpenAI and Microsoft. Together, Google’s search engine and other apps have massively more reach than even Microsoft’s popular Office apps.
The introduction of Bard Extensions is, in theory, a stroke of brilliance. Imagine your AI assistant not just reciting facts from a knowledge base trained on billions of parameters competitive to what ChatGPT offers, but additionally pulling live personalized data from your Google services. The idea of Bard rifling through my Gmail or Google Drive to provide context-specific responses sounds like something pulled from the pages of a William Gibson novel. But here’s where we hit a snag.
In the week since I reported the announcement, I’ve had a chance to play around with the new offering. Unfortunately, in practice, I find Bard to be a disappointment on many levels. It fails to deliver on its core promise of integrating well with Google apps, and often produces inaccurate or nonsensical responses. It also lacks the creativity and versatility of OpenAI’s GPT-4 (It also has no personality or sense of humor, although some users might not take issue with that). Bard badly falls short of expectations.
The crux of the problem lies in the AI’s underlying model, PaLM 2, which powers Bard’s new capabilities. Like all language models, PaLM 2 is a product of its training data. In essence, it can only generate responses based on the content it has been fed. According to a CNBC report, PaLM 2 is trained on about 340 billion parameters.
By comparison, GPT-4 is rumored to be trained on a massive dataset of 1.8 trillion parameters.
This means that GPT-4 has access to more information and knowledge than PaLM 2, which may help it generate more relevant and interesting texts.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Falling short of expectations I stress-tested Bard’s new capabilities by trying dozens of prompts that were similar to the ones advertised by Google in last week’s launch. For example, I asked Bard to pull up the key points from a document in Docs and create an email summary. Bard responded by saying “I do not have enough information” and refused to pull up any documents from my Google Drive. It later poorly summarized another document and drafted an unusable email for me.
Another example: I asked Bard to find me the best deals on flights from San Francisco to Los Angeles on Google Flights. The chat responded by drafting me an email explaining how to search manually for airfare on Google Flights.
Bard’s performance was equally dismal when I tried to use it for creative tasks, such as writing a song or a screenplay. Bard either ignored my input or produced bland and boring content that lacked any originality or flair. Bard also lacks any option to adjust its creativity level, unlike GPT-4, which has a dial that allows the user to control how adventurous or conservative the output is.
The only redeeming feature of Bard is that it has a built-in feature that allows users to double-check its answers via Google Search. By clicking the “Google It” button after a prompt, users can see how Bard’s response compares to the results from Google Search. Bard then highlights the parts of its output that could be false or misleading. This feature is handy for reducing hallucinations and errors, but it also exposes how unreliable and untrustworthy Bard is.
Why does this matter? Because Google is one of the leading companies in the world of technology and innovation, and it has a huge influence on how people access and use information. Google’s products and services are used by billions of people every day, and they shape how we communicate, learn, work, and play. If Google wants to stay ahead of the competition and maintain its reputation as a leader in AI, it needs to do better than Bard.
Bard is not just a chatbot; it is a reflection of Google’s vision and values. It is supposed to be an assistant that can help users with various tasks and enhance their productivity and creativity. But Bard fails on all these counts. It is not helpful; it is generally very frustrating.
Bard 2.0 is here, but it stinks. So far, at least. Maybe Google’s upcoming model “ Gemini ” will be the fix it’s looking for. But until then, I recommend relying on GPT-4 for the bulk of your work tasks. OpenAI’s GPT-4 may not be perfect either, but it is far superior to Bard in terms of functionality, reliability, creativity, and personality.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Google Bard can now tap directly into Gmail, Docs, Maps and more | VentureBeat"
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"https://venturebeat.com/ai/google-bard-can-now-tap-directly-into-gmail-docs-maps-and-more"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Google Bard can now tap directly into Gmail, Docs, Maps and more Share on Facebook Share on X Share on LinkedIn Image Credit: Google Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Google announced a major upgrade to its Bard conversational AI system today, expanding its capabilities to connect with Google’s most popular productivity apps and services. The enhancements are aimed at making Bard more useful in day-to-day tasks while also addressing concerns about accuracy.
Starting today, Bard can now directly access information from apps like Gmail, Docs, Maps, Flights, and YouTube to provide more comprehensive and personalized responses within a conversation. For example, when planning a trip, Bard can now automatically pull relevant dates, flight info, directions and sightseeing recommendations — all within one conversation.
The upgrade comes after Bard’s underwhelming public launch in March, which showcased factual inaccuracies in many of its responses. Google is hoping the integration with its search engine will help Bard become more accurate. Users can now click a “Google it” button to fact-check Bard’s responses against indexed web information within the chat.
Google emphasized that it remains committed to protecting users’ personal information following the update. If someone chooses to use the Workspace extensions, their content from Gmail, Docs, and Drive is not seen by human reviewers, used by Bard to show them ads, or used to train the Bard model. Users remain in control of their privacy settings.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The company has also made it easier to build on others’ conversations with Bard. Starting today, when someone shares a Bard chat with a user through a public link, the receiving individual can continue the conversation and ask Bard additional questions about that topic or use it as a starting point for their own ideas.
Google is also expanding access to existing English language features such as the ability to upload images with Lens , get Search images in responses, and modify Bard’s responses to more than 40 languages.
While still limited, Bard’s new features point to a future where AI assistants can seamlessly blend conversational abilities with services like email and documents to boost productivity. As Bard continues to improve, it may further integrate with other Google offerings like calendar, photos and analytics.
The Bard upgrades roll out starting today. Google plans to add more languages and integrations in the coming months as it continues to refine the technology responsibly.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Frec steps out of stealth with $26M in funding, aiming to democratize sophisticated investing | VentureBeat"
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"https://venturebeat.com/ai/frec-steps-out-of-stealth-with-26m-in-funding-aiming-to-democratize-sophisticated-investing-with-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Frec steps out of stealth with $26M in funding, aiming to democratize sophisticated investing Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Emerging from stealth today, fintech startup Frec announced it has raised $26.4 million in seed and Series A funding led by Greylock, with participation from Social Leverage and others.
The company’s automated investment platform has the potential revolutionize personal finance by simplifying complex investment strategies, historically the domain of the super-rich, and making them accessible to the average investor.
Founder and CEO of Frec, Mo Al Adham, told VentureBeat the startup’s approach is a means to “simplify the sophisticated investment products that have been traditionally only available via expensive white glove services.” Al Adham believes this could benefit the world at large, beyond the confines of wealth managers and family offices.
The company’s flagship product, Frec Direct Indexing , allows customers to create their own customized portfolios of individual stocks that track the performance of S&P indices, such as the S&P 500 or S&P Infotech.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! By owning individual stocks rather than buying an ETF, customers can take better advantage of tax loss harvesting , a strategy that involves selling stocks that have declined in value to offset capital gains taxes.
According to Frec, direct indexing can generate up to an additional 2.11% in annual returns compared to investing in ETFs or mutual funds that track the same indices.
Sophisticated investing made easier Al Adham said he started the company because he was looking for a platform that enabled him to self-manage his money in the same way that more sophisticated investors were doing with financial advisers. He said that he found that many of his friends and colleagues had the same problem and that some of them became his first customers and investors.
“We built Frec for the financially-savvy who want access to more sophisticated products than are currently offered by existing retail investment platforms, and have qualms about working with expensive financial advisers and old-school brokerages with complicated UI,” he said. “We made it our mission to build a modern, self-service platform that enables access to advanced financial products, like direct indexing, portfolio lines of credit, and high yield treasury funds to help them stay invested, even in a volatile market.” Al Adham said that Frec’s direct indexing platform uses automation to optimize the portfolio and minimize the tracking error, which is the difference between the performance of the portfolio and the index.
The platform also allows customers to customize their portfolio by adding or removing sectors or stocks, and to fund their account with cash or stock. Customers can also access a portfolio line of credit, which enables them to borrow up to a certain percentage of their stock holdings at a low interest rate, and a treasury account, which offers up to 5.02% on cash.
Standing out in a competitive fintech landscape Al Adham said that Frec’s direct indexing platform is different from other robo-advisors that offer tax loss harvesting, such as Wealthfront and Betterment, because it gives customers more control and customization over their passive investing.
He said that Frec’s platform does not require customers to determine their risk level and then assign them a predefined portfolio of ETFs and bonds, but instead lets them choose and customize the index they want to track.
He also said that Frec’s platform is more cost-effective, as it charges a flat 0.10% fee for direct indexing, compared to some robo-advisors charging 0.25% for basic index investing, or a financial adviser, who could charge up to 1% for direct indexing.
“Our hypothesis is that there’s a lot of those people,” Mr. Al Adham said, referring to the category of customers who are financially savvy, price sensitive, and love to geek out on the best way to deploy their money. He said that these customers are underserved by the existing products and platforms, and that he wants to offer them a self-service platform that gives them more control and customization over their passive investing.
Frec is a member of the Securities Investor Protection Corporation, which provides up to $500,000 in insurance per account. Frec is also a fiduciary by law, which means it has to act in the best interest of the customer. Frec said that it has invested a lot in data security and has done audits to ensure its platform is rock solid.
By taking direct indexing mainstream, Frec is betting retail investors will embrace the complexity and risk of automated buying and selling. With the investor class hungry for new tools to maximize returns, the timing may be right for direct indexing’s democratization.
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"EY report sheds new light on global AI regulatory landscape | VentureBeat"
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"https://venturebeat.com/ai/ey-report-sheds-new-light-on-global-ai-regulatory-landscape"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages EY report sheds new light on global AI regulatory landscape Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A report by Ernst & Young (EY), one of the Big Four accounting firms, on the global AI regulatory landscape is receiving renewed interest after President Biden signed a sweeping executive order on Monday that aims to monitor and regulate the risks of artificial intelligence (AI) while also harnessing its potential.
The EY report, titled “ The Artificial Intelligence (AI) global regulatory landscape: Policy trends and considerations to build confidence in AI, ” was published last month. Its goal is to clarify the global AI regulatory environment, providing policymakers and businesses with a roadmap to understand and navigate this complex landscape.
The report is based on an analysis of eight major jurisdictions that have shown significant AI legislative and regulatory activity: Canada, China, the European Union, Japan, Korea, Singapore, the United Kingdom and the United States.
It reveals that despite their different cultural and regulatory contexts, these jurisdictions share many of the same objectives and approaches for AI governance. They all aim to minimize potential harms from AI while maximizing benefits to society, and they all align with the OECD AI principles endorsed by the G20 in 2019, which emphasize human rights, transparency, risk management and other ethical considerations.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! However, the report also identifies some divergences and challenges in the global AI regulatory environment. For instance, the report says the E.U. has taken among the most proactive stances globally, proposing a comprehensive AI Act that would impose mandatory requirements for high-risk AI uses, such as biometric identification or critical infrastructure.
China has also shown a willingness to regulate core aspects of AI, such as content recommendation or facial recognition. On the other hand, the report claims the U.S. had adopted a light-touch approach, focusing on voluntary industry guidance and sector-specific rules.
The global AI regulatory landscape is dynamic and evolving Notably, the EY’s analysis of the U.S. regulation on AI is now outdated because of President Biden’s executive order signed earlier this week. The executive order is considered by most experts to be the most significant action on AI taken by any government, and it goes beyond the voluntary industry guidance and sector-specific rules that the report described as the U.S. approach.
The executive order builds off voluntary commitments made earlier this year by 15 tech companies, including Microsoft and Google, to allow outside testing of their AI systems before public release and to develop ways to identify AI-generated content.
The White House last year also rolled out an “ AI Bill of Rights ,” offering companies guidelines aimed at protecting consumers using automated systems, though that guidance was non-binding.
As our lead AI reporter, Sharon Goldman wrote earlier this week , the executive order will require developers of powerful AI systems to share the results of their safety tests with the federal government before they are released to the public and to notify the government if their AI models pose national security, economic or health risks. The order will also address other issues such as immigration, biotechnology, labor and content moderation.
There are other major developments since the EY report was published last month that are reshaping the global AI regulatory landscape. For example, the U.K. Government published an AI White Paper that outlines the country’s proposed framework for AI regulation. The U.K. framework is based on four principles: proportionality, accountability, transparency, and ethics and dovetails with the EU’s approach.
These developments show that the global AI regulatory landscape is dynamic and rapidly evolving and that policymakers and businesses need to stay updated and engaged with the latest trends and best practices. Still, the EY report remains a valuable resource for understanding and navigating the regulatory environment. Still, it may need to be supplemented with new information as new rules and initiatives emerge.
Tangible insights for policymakers and businesses The EY report highlights several trends and best practices in AI regulation that remain relevant, such as: A risk-based approach that tailors oversight to the intended use case and risk profile of AI systems.
Consideration of sector-specific risks and oversight needs, such as healthcare, finance or transportation.
Initiatives addressing AI’s impacts on adjacent policy areas, such as data privacy, cybersecurity and content moderation.
Use of regulatory sandboxes to develop AI rules collaboratively with stakeholders.
The EY report concludes with a call for ongoing engagement among government officials, corporate executives, and other stakeholders to strike the right balance between regulation and innovation.
Overall, EY’s report provides a roadmap for understanding the rapidly evolving AI regulatory landscape. It underlines the need for increased dialogue among policymakers, the private sector, and civil society to close the AI confidence gap, prevent policy fragmentation, and realize the full potential of AI. It is a must-read for anyone seeking to navigate the complex ethical challenges relating to AI and understand the dynamic AI policy landscape on a global scale.
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"Deta raises $3.6M seed round to develop cloud-based operating system | VentureBeat"
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"https://venturebeat.com/ai/deta-raises-3-6m-seed-round-to-develop-cloud-based-operating-system"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Deta raises $3.6M seed round to develop cloud-based operating system Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Deta , an upstart Berlin-based technology company, is aiming to revolutionize personal computing with the launch of Deta Space — a platform its founder describes as the world’s first personal cloud computer.
The startup has raised $3.6 million in seed funding led by Crane Venture Partners to support its vision of giving users more power and privacy through a unique cloud-based operating system.
In an interview with VentureBeat, Deta cofounder and CEO Mustafa Abdelhai explained his inspiration for Deta Space: “It really started with me personally being a user of software. I remember the days in the late 90s [and] early 2000s where you had your computer, and you would run all your own software. You’d get PowerPoint [and other enterprise software] and all of these different music players. Everything was on your computer.” Abdelhai aims to return control and autonomy to users at a time when personal data has become a commodity. “Maybe something is missing here, maybe we’re thinking wrong,” he said, referencing the current state of cloud computing.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “The idea is that the computer’s operating system is in the cloud,” he explained, “and you can access it from anywhere from any device.” A cloud-based OS for all devices Deta Space runs on the company’s new “ Space OS ,” allowing users to manage their own apps and data instead of relying on big platforms like AWS. “The way we think about it, instead of concerning ourselves with trying to be better than AWS, we’re saying it’s really a continuation of the personal computer in the cloud,” Abdelhai explained.
The Space OS enables seamless interoperability between apps, unlike traditional web software. It also ensures privacy by allowing apps to store data in secure personal environments controlled by the user.
More than 67,000 developers have already signed up to build apps on Deta Space. This enthusiastic response demonstrates strong demand for a platform focused on user control.
Deta’s approach could represent a significant shift in the enterprise data and AI landscape. The company’s commitment to user empowerment and control challenges the status quo of the tech industry, where large corporations often dictate the software’s direction. Deta’s focus on interoperability could also pave the way for a more collaborative, less siloed tech environment.
If successful, Deta’s vision could fundamentally shift how we interact with software, making it more personal, empowering, and user-friendly. “We do actually believe that we’re building a computer operating system that could replace your current operating system,” Abdelhai said.
Deta Space flips the script on conventional wisdom — putting users, not platforms, in the driver’s seat. Its human-centric approach could force tech giants to rethink their relationship with customers and data. Of course, significant technical and adoption hurdles remain. But backed by top venture firms including System.One, Tomahawk.VC, Tiny.VC, and founders and early employees from companies like NGINX and Notion, Deta has the funding and vision to launch a new computing paradigm.
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"Atropos Health leverages AI to democratize access to real-world evidence in healthcare | VentureBeat"
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"https://venturebeat.com/ai/atropos-health-leverages-ai-to-democratize-access-to-real-world-evidence-in-healthcare"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Atropos Health leverages AI to democratize access to real-world evidence in healthcare Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Atropos Health , the Palo Alto, Calif.-based healthcare startup, unveiled today a new artificial intelligence ( AI ) system that could revolutionize how evidence is produced to inform medical decisions and research.
The startup’s Geneva Operating System ( OS ) leverages natural language processing and generative AI to rapidly query real-world clinical data. This allows users without technical skills to get publication-quality results in minutes versus the traditional timeline of 8 weeks to 6 months.
The new system also includes a chatbot interface called ChatRWD where users simply type plain-language questions. The AI then suggests ways to refine the query, selects appropriate data sources from the company’s network of 160 million de-identified patient records, runs the analysis using rigorous statistical methods, and formats the results into an observational study report.
The company’s mission is extremely ambitious: “We want to inform every clinical treatment decision and research inquiry with personalized real world evidence globally,” said Brigham Hyde, Ph.D., co-founder and CEO of Atropos Health, in an interview with VentureBeat. The launch of both products is a significant step towards the personalization of healthcare, a field that has been increasingly relying on AI and machine learning solutions.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Hyde further explained that Geneva OS and ChatRWD are designed to provide healthcare professionals with quick, consistent, and reliable access to extensive real-world data. The platform currently includes 160 million patient records across various datasets, offering a rich repository of information.
“Healthcare is complicated, and hard [to navigate] in our country, let alone others. The way I see Geneva being used is as an important layer to the data ecosystem, ensuring consistency and speed out of the data layer,” Hyde said.
Informing every clinical decision with AI-powered real world evidence Atropos Health’s new solutions are expected to significantly improve the user experience for physicians and researchers. The company has reported an impressive Net Promoter Score ( NPS ) in the 40s for the product suite. “People who order one from us go on to order dozens of these things,” Hyde said, emphasizing that the speed and ease of use are unmatched. He explained, “That’s because the user experience is great.” Atropos Health also offers users ultimate control over their data usage. Partners can choose whether to offer their data on the network and receive compensation when their data is used. They retain control over who uses it and for what purposes, making it an attractive proposition for healthcare institutions and data partners.
The company’s integrative approach could significantly improve patient outcomes, reduce the cost of care, and enhance care delivery, as supported by a ROI study published earlier this year. Hyde points out, “We don’t have to wait years to get evidence for care — because we have all this data that’s emerged.” The launch of Geneva OS and ChatRWD could mark a pivotal moment in the healthcare industry, offering a new paradigm of evidence-based, personalized patient care. As the demand for such solutions continues to grow, Atropos Health is well-positioned to become a leading player in the health tech sector.
However, as the company expands its evidence network and forms new partnerships, the challenges of managing and integrating vast amounts of data from various sources cannot be underestimated. The potential of these new technologies also raises important questions about data privacy and security, as well as the ethical implications of AI in healthcare.
Nonetheless, the future looks bright according to Hyde, who reaffirms, “There is not enough evidence being used for medical care. There’s reasons for that, good and bad. We want more clinical trials, for sure. But we don’t have enough, and most of them exclude most patients, and we shouldn’t have to wait years to get evidence for care.” His words underscore Atropos Health’s commitment to accelerating evidence generation in healthcare.
As Atropos Health continues its journey, the potential for AI to revolutionize healthcare has never been clearer. The company’s innovative approach to evidence generation could soon become an essential tool for medical professionals worldwide, making personalized, data-driven care a reality for millions of patients.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Anthropic's new policy takes aim at 'catastrophic' AI risks | VentureBeat"
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"https://venturebeat.com/ai/anthropics-new-policy-takes-aim-at-catastrophic-ai-risks"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Anthropic’s new policy takes aim at ‘catastrophic’ AI risks Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Anthropic , the AI safety and research company behind the popular Claude chatbot, has released a new policy detailing its commitment to responsibly scaling AI systems.
The policy, referred to as the Responsible Scaling Policy (RSP) , is designed specifically to mitigate “catastrophic risks,” or situations where an AI model could directly cause large-scale devastation.
The RSP is unprecedented and highlights Anthropic’s commitment to reduce the escalating risks linked to increasingly advanced AI models. The policy underscores the potential for AI to prompt significant destruction, referring to scenarios that could lead to “thousands of deaths or hundreds of billions of dollars in damage, directly caused by an AI model, and which would not have occurred in its absence.” In an exclusive interview with VentureBeat, Anthropic co-founder Sam McCandlish shared some insights into the development of the policy and its potential challenges. At the heart of the policy are AI Safety Levels (ASLs). This risk tiering system, inspired by the U.S. government’s Biosafety Levels for biological research, is designed to reflect and manage the potential risk of different AI systems through appropriate safety evaluation, deployment, and oversight procedures. The policy outlines four ASLs, from ASL-0 (low risk) to ASL-3 (high risk).
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “There is always some level of arbitrariness in drawing boundaries, but we wanted to roughly reflect different tiers of risk,” McCandlish said. He added that while today’s models might not pose significant risks, Anthropic foresees a future where AI could start introducing real risk. He also acknowledged that the policy is not a static or comprehensive document, but rather a living and evolving one that will be updated and refined as the company learns from its experience and feedback.
The company’s goal is to channel competitive pressures into solving key safety problems so that developing safer, more advanced AI systems unlocks additional capabilities, rather than reckless scaling. However, McCandlish acknowledged the difficulty of comprehensively evaluating risks, given models’ potential to conceal their abilities. “We can never be totally sure we are catching everything, but will certainly aim to,” he said.
The policy also includes measures to ensure independent oversight. All changes to the policy require board approval, a move that McCandlish admits could slow responses to new safety concerns, but is necessary to avoid potential bias. “We have real concern that with us both releasing models and testing them for safety, there is a temptation to make the tests too easy, which is not the outcome we want,” McCandlish said.
The announcement of Anthropic’s RSP comes at a time when the AI industry is facing growing scrutiny and regulation over the safety and ethics of its products and services. Anthropic, which was founded by former members of OpenAI and has received significant funding from Google and other investors, is one of the leading players in the field of AI safety and alignment, and has been praised for its transparency and accountability.
The company’s AI chatbot, Claude , is built to combat harmful prompts by explaining why they are dangerous or misguided. That’s in large part due to the company’s approach, “ Constitutional AI ,” which involves a set of rules or principles providing the only human oversight. It incorporates both a supervised learning phase and a reinforcement learning phase.
Both the supervised and reinforcement learning methods can leverage chain-of-thought style reasoning to improve the transparency and performance of AI decision making as judged by humans. These methods offer a way to control AI behavior more precisely and with far fewer human labels, demonstrating a significant step forward in crafting ethical and safe AI systems.
The research on Constitutional AI and now the launch of the RSP underlines Anthropic’s commitment to AI safety and ethical considerations. By focusing on minimizing harm while maximizing utility, Anthropic sets a high standard for future advancements in the field of AI.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"AMD acquires open-source AI software pioneer Nod.ai to fortify AI capabilities | VentureBeat"
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"https://venturebeat.com/ai/amd-acquires-open-source-ai-software-pioneer-nod-ai-to-fortify-ai-capabilities"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages AMD acquires open-source AI software pioneer Nod.ai to fortify AI capabilities Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Advanced Micro Devices ( AMD ) announced today plans to acquire Nod.ai , a startup that specializes in optimizing AI software for high-performance hardware.
The purchase of Nod.ai, founded just three years ago, shows that AMD is serious about staking a claim in the rapidly growing AI chip market, which is expected to reach $383.7 billion by 2032 , according to industry analysts. Financial terms of the acquisition were not disclosed.
The agreement underscores AMD’s growth strategy in the AI sector, which is centered on an open software ecosystem that simplifies the adoption process for customers through developer tools, libraries, and models. This acquisition adds another feather to AMD’s cap as it continues its expansion into the rapidly evolving AI industry.
Strategic acquisition in of an open-source leader Nod.ai is a startup that provides key enabling technologies for future AI systems using advanced compiler-based approaches, instead of legacy handwritten kernels.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The company created the SHARK Machine Learning Distribution , which is built on LLVM, MLIR, OpenXLA’s IREE and Nod.ai’s tuning. Nod.ai’s software can accelerate the deployment of AI models across a broad range of platforms powered by AMD’s architectures, such as Instinct data center accelerators, Ryzen AI processors, EPYC processors, Versal SoCs and Radeon GPUs.
The acquisition is expected to significantly enhance AMD’s ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware, according to Vamsi Boppana, senior vice president, Artificial Intelligence Group at AMD.
“The acquisition of Nod.ai is expected to significantly enhance our ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware,” he said in a statement.
AMD has been investing heavily in AI technologies in recent years , such as CDNA, XDNA, RDNA and Zen architectures, to compete with rivals like Nvidia and Intel in the fast-growing AI market. According to an industry report, the global AI market size is estimated to reach around $594 billion by 2032.
In addition to the technology, AMD said it aims to leverage the engineering talent from Nod.ai to boost its open-source developer cred. Nod.ai is a contributor to AI software repositories like SHARK and Torch-MLIR used by many researchers.
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"AI pioneers Yann LeCun and Yoshua Bengio clash in an intense online debate over AI safety and governance | VentureBeat"
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"https://venturebeat.com/ai/ai-pioneers-yann-lecun-and-yoshua-bengio-clash-in-an-intense-online-debate-over-ai-safety-and-governance"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages AI pioneers Yann LeCun and Yoshua Bengio clash in an intense online debate over AI safety and governance Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
In a fiery debate that erupted over the weekend, two of the most influential figures in artificial intelligence ( AI ) and deep learning, Yann LeCun and Yoshua Bengio, fiercely discussed the potential risks and safety concerns surrounding AI. This debate comes as part of a larger discourse on the implications of AI, which has been the subject of increasing attention and concern in the tech industry and beyond.
LeCun, Meta ’s chief AI scientist, initiated the debate with a post on his Facebook page , calling on the “silent majority” of AI scientists and engineers who believe in the power and reliability of AI to voice their opinions. His comments sparked a lively discussion with more than 150 comments, many from notable figures within the AI community.
Bengio, founder of Element AI and a professor at the University of Montreal, responded to LeCun’s post. He challenged his perspective on AI safety, the importance of governance and the potential risks of open-source AI platforms. Bengio argued for the importance of prudence, stating that we still do not understand how to design safe, powerful AI systems and highlighted the need for major investment in AI safety and governance. He also questioned the wisdom of open-sourcing powerful AI systems, likening it to freely distributing dangerous weapons.
LeCun responded by emphasizing the need to design AI systems for safety rather than imagining catastrophic scenarios. He countered Bengio’s claims about investment in AI safety, asserting that there is a significant amount of funding being poured into making AI systems safe and reliable. LeCun also disagreed with the comparison of AI systems to weapons, stating AI is designed to enhance human intelligence, not to cause harm.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The debate also saw input from Jason Eisner , director of research at Microsoft Semantic Machines and professor at Johns Hopkins University, who supported Bengio’s analogy of AI to weapons. Bengio agreed with Eisner, stating that while we cannot reduce risks to zero, we can minimize harm by restricting access to powerful AI systems.
A long, complicated history of independent and collaborative research LeCun and Bengio, along with Geoffrey Hinton, were awarded the prestigious Turing Award in 2019 for their influential and independent work in the field of AI, especially for their contributions to deep learning and neural networks.
Despite their collaboration in the past and their shared recognition for significant contributions to the AI field, LeCun and Bengio’s debate this weekend makes it clear that even among the most esteemed researchers, there is considerable disagreement about AI’s potential risks, the effectiveness of current safety measures and the best path forward.
The AI debate is not confined to academic circles, however. As AI becomes increasingly embedded in everyday life—from voice-activated assistants to autonomous driving—its potential impact has become a topic of widespread concern.
Critics argue that unchecked progress in AI could lead to job displacement, privacy violations, or even existential risks. However, proponents contend that AI could unlock unprecedented advancements in healthcare, education and other sectors.
This latest debate only underscores the urgency of these issues. As AI technologies continue to evolve at a rapid pace, the need for thoughtful, informed discourse on their implications becomes ever more pressing. The industry will be watching closely as these discussions continue to unfold.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Adobe researchers create 3D models from 2D images 'within 5 seconds' in new AI breakthrough | VentureBeat"
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"https://venturebeat.com/ai/adobe-researchers-create-3d-models-from-2d-images-within-5-seconds-in-new-ai-breakthrough"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Adobe researchers create 3D models from 2D images ‘within 5 seconds’ in new AI breakthrough Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A team of researchers from Adobe Research and Australian National University have developed a groundbreaking artificial intelligence ( AI ) model that can transform a single 2D image into a high-quality 3D model in just 5 seconds.
This breakthrough, detailed in their research paper LRM: Large Reconstruction Model for Single Image to 3D , could revolutionize industries such as gaming, animation, industrial design, augmented reality (AR), and virtual reality (VR).
“Imagine if we could instantly create a 3D shape from a single image of an arbitrary object. Broad applications in industrial design, animation, gaming, and AR/VR have strongly motivated relevant research in seeking a generic and efficient approach towards this long-standing goal,” the researchers wrote.
Training with massive datasets Unlike previous methods trained on small datasets in a category-specific fashion, LRM uses a highly scalable transformer-based neural network architecture with over 500 million parameters. It is trained on around 1 million 3D objects from the Objaverse and MVImgNet datasets in an end-to-end manner to predict a neural radiance field ( NeRF ) directly from the input image.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs including real-world in-the-wild captures and images from generative models,” the paper states.
The lead author, Yicong Hong , said LRM represents a breakthrough in single-image 3D reconstruction. “To the best of our knowledge, LRM is the first large-scale 3D reconstruction model; it contains more than 500 million learnable parameters, and it is trained on approximately one million 3D shapes and video data across diverse categories,” he said.
Experiments showed LRM can reconstruct high-fidelity 3D models from real-world images, as well as images created by AI generative models like DALL-E and Stable Diffusion.
The system produces detailed geometry and preserves complex textures like wood grains.
Potential to transform industries The LRM’s potential applications are vast and exciting, extending from practical uses in industry and design to entertainment and gaming. It could streamline the process of creating 3D models for video games or animations, reducing time and resource expenditure.
In industrial design, the model could expedite prototyping by creating accurate 3D models from 2D sketches. In AR/VR, the LRM could enhance user experiences by generating detailed 3D environments from 2D images in real-time.
Moreover, the LRM’s ability to work with “in-the-wild” captures opens up possibilities for user-generated content and democratization of 3D modeling. Users could potentially create high-quality 3D models from photographs taken with their smartphones, opening up a world of creative and commercial opportunities.
Blurry textures a problem, but method advances field While promising, the researchers acknowledged LRM has limitations like blurry texture generation for occluded regions. But they said the work shows the promise of large transformer-based models trained on huge datasets to learn generalized 3D reconstruction capabilities.
“In the era of large-scale learning, we hope our idea can inspire future research to explore data-driven 3D large reconstruction models that generalize well to arbitrary in-the-wild images,” the paper concluded.
You can see more of the impressive capabilities of the LRM in action, with examples of high-fidelity 3D object meshes created from single images, on the team’s project page.
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"Accelerate SF: Meet the former tech employees who want to use AI for social good in San Francisco | VentureBeat"
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"https://venturebeat.com/ai/accelerate-sf-meet-the-former-tech-employees-who-want-to-use-ai-for-social-good-in-san-francisco"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Accelerate SF: Meet the former tech employees who want to use AI for social good in San Francisco Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
San Francisco is a city of contrasts. It’s home to some of the most innovative and influential technology companies in the world, but also to some of the most persistent and complex social and urban challenges. From housing affordability and homelessness to transportation and public health, the city faces many issues that require creative new approaches and solutions.
That’s why a group of former tech employees have decided to launch Accelerate SF , a new initiative that aims to drive engineers to build AI solutions for San Francisco’s major public sector challenges. The initiative is hosting a hackathon on Nov. 4th and 5th, where participants will work on projects related to these challenges, and potentially continue working on them after the hackathon.
Accelerate SF was founded by Anthony Jancso, Jordan Wick, and Kay Sorin, who have each worked at leading technology companies — Palantir, Waymo, and YouTube respectively. The three have been promoting their hackathon at various AI social gatherings in San Francisco, and have received support from local stakeholders, leading AI companies, and large political organizations. Perhaps most notably, they have partnered with the Mayor’s Office of Innovation, which has helped them identify and scope real problems faced by public sector organizations in the city.
The hackathon is one of the first major initiatives by a large American city to incorporate artificial intelligence into the public sector. It follows the trend of other cities such as New York, Boston, and Seattle that have been exploring the use of AI for improving urban services and governance. However, Accelerate SF stands out for its focus on the use of large language models (LLMs), which are particularly good at finding and organizing large sets of complex data.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Jancso and Wick, who are engineers by trade, and Sorin, a seasoned marketing and events professional, all have experience in working with government organizations and solving their problems with software. They have individually noticed that most hackathons and AI initiatives are focused on solutions or technologies for the private sector, while the public sector is full of opportunities for AI applications that can have a positive impact on the city.
“We believe that the public sector is full of opportunities,” Jancso told VentureBeat, “because large language models are really good at applications that rely on analyzing a large amount of text. If you look at all of these government forms, and all of these government websites, they are excellent places to look for opportunities for AI.” Some of the examples of AI applications that participants might be working on at the hackathon are: A new open book website that uses natural language models to answer questions about government spending in natural language.
A simplified form for reporting car break-ins that uses a large language model to create a narrative of the incident based on the details entered by the user.
A permit approval application that checks the permit application for errors and provides feedback to the user.
The hackathon will also feature speakers and judges from the tech and civics sectors, such as California State Sen. Scott Wiener and entrepreneur Kim Polese. These speakers and others will share their insights and perspectives on how AI can be used for social good and public sector improvements.
“I think there’s really a lot of concrete change that can be achieved, especially with so many brilliant, talented tech people in one city,” Sorin said. “I just encourage people to get involved and know that even making the smallest effort building one application can have a huge impact for everyone in the city.” Accelerate SF has received funding from several sponsors, including Scale AI, Chroma, OpenAI, Anthropic, Replit and LangChain. These sponsors are also providing support, prizes, speakers, and tools for the hackathon. Scale AI, for example, is an AI company that is working closely with Accelerate SF on this project and is already building AI applications for local government as well.
The Accelerate SF hackathon is open to engineers who specialize in large language models and machine learning. Interested participants can sign up on Accelerate SF’s website or follow them on Twitter for more updates.
Update ( November 1, 12:30 p.m. PT ): Accelerate SF has announced that Mayor London Breed will be in attendance at the inaugural event.
There is no one more familiar with the challenges facing SF than Mayor @LondonBreed.
She’s dedicated her career to solving them.
This weekend, we’re excited to welcome Mayor Breed to the Accelerate SF hackathon where she will meet with SF’s top hackers as they work together to… pic.twitter.com/DWTr96JHOa VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"VentureBeat's Special Issues: Exclusive deep dives into Gen AI and other transformative tech | VentureBeat"
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"https://venturebeat.com/virtual/venturebeats-special-issues-exclusive-deep-dives-into-gen-ai-and-other-transformative-tech"
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At the heart of VentureBeat’s editorial mission is a commitment to shed light on the most transformative technologies shaping our world for technical leaders.
One key to achieving this lies in our increasingly sought-after format, the “ Special Issue.
” This is where we delve deeper into pivotal topics within the tech industry, with an eye to equip leaders with the knowledge necessary to make informed strategic decisions.
Consider, for instance, our recent Special Issue titled “ Data Centers in 2023 – How to do More with Less “. This issue offered an in-depth examination of how enterprise leaders are navigating the ongoing economic slowdown and the rise in interest rates affecting data center investments. Through a series of ten feature articles, we discovered a prevailing trend: Companies are not abandoning investments in their data centers but are redefining their strategies, focusing on efficiency and consolidation of investments amidst the cloud sprawl of the past decade.
In this case, what set our Special Issue apart was the breadth and depth of our coverage. Alongside detailed reports, we provided detailed video interviews with industry leaders such as Joann Stonier, Chief Data Officer of Mastercard, and Promiti Dutta, head of analytics technology and innovation at Citi. Their exclusive insights into data center strategy, usually kept under wraps due to security concerns, offer our readers a perspective they can’t find anywhere else.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! Over the past 18 months, our Special Issues have explored a wide array of topics critical to tech leaders, ranging from data privacy and customer data strategy to AI applications, the evolving CIO agenda and the future of AI in healthcare. The enthusiastic response from sponsors has allowed us to expand our coverage even further. For instance, our GamesBeat site recently published its first Special Issue on how gaming brands build communities amidst the rise of platforms like Discord, thanks to the sponsorship of Modulate.
Our goal is simple: provide in-depth insights you won’t find elsewhere. And the evidence shows that we’re succeeding in this mission. Our exploration into the world of data centers didn’t stop at efficiency; we dug deeper. In July’s Special Issue, we examined “ The Future of the Data Center ,” analyzing how IT leaders are prioritizing performance to meet the increasing demands of AI applications and the impending deployments of generative AI.
But we’re not stopping there. We’re pushing the boundaries of our reporting to cover the ramifications of those escalating processing needs, and the implications for powering data centers. From the electricity power race in Arizona to the increased water demands for cooling in Minnesota, our upcoming Special Issue will explore the intersection of data centers and sustainability.
It’s rare to find such comprehensive, sustained deep dives into complex topics relevant to technical decision-makers, and we appreciate that our commitment is made possible by the unwavering support of our sponsors. A notable mention goes to AMD, who sponsored our data center Special Issues, proving to be an invaluable partner.
Sponsors of other special issues include some of the most innovative companies, including Nvidia and Microsoft.
As we look ahead, we’re excited about two primary areas of focus. Our flagship VentureBeat site remains dedicated to covering LLMs and generative AI and their implications on enterprises. Meanwhile, GamesBeat will continue to lead the conversation on the impact of immersive technologies on the gaming industry.
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Together, we can keep our millions of readers well-informed and prepared to navigate the fast-paced landscape of these transformative technologies.
GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
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"The metaverse: Where we are and where we’re headed | VentureBeat"
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"https://venturebeat.com/data-infrastructure/the-metaverse-where-we-are-and-where-wed-headed"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages The metaverse: Where we are and where we’re headed Share on Facebook Share on X Share on LinkedIn This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? The coming metaverse has provoked hype, confusion, and misinformation.
For technophiles, the metaverse represents a nirvana: a place to immerse yourself in any digital surrounding, and participate in any physical reality, at any time – and also to be able to see and feel anything, even if you are thousands of miles away from that physical place.
In a future state, electromyography (EMG) movements and neural interfaces — triggered by only slight finger movements — will allow you to control devices, communicate, and collaborate with others almost as simply as thinking. Your eyes will exploit glasses that use complex sensors to see both your own reality, but virtual ones as well.
Some aspects of this apparent science fiction world are closer than we realize. Matthew Ball, a venture capitalist who has studied the metaverse closely, last year wrote a series of articles about where things are headed in the next decade. Ball breaks down the various technologies and protocols that need to come together to create the metaverse.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! Ball categorizes the metaverse into eight core features, which can be thought of as a stack.
While Ball’s vision extends to the next decade, this article focuses on where the metaverse is headed in the next two or three years. It aims to review what most enterprise decision makers need to know, whether they’re in the gaming industry – where the metaverse has had its most immersive form so far – or in the enterprise, where things are only now starting to take shape.
It’s clear that there will be a first big wave of innovation over the next 12 to 24 months, where “mixed reality” hardware produces breakthroughs for immersive experiences.
A second big wave is then likely somewhere in the next three or more years, when fully immersive augmented reality (AR) glasses hit the market in a bigger way. This hardware is important, because it’s the gateway to the metaverse.
What we’ve also learned is that key aspects of the metaverse – like allowing your personal avatar to show up as a hologram to someone else in their physical reality – may be pie in the sky as a practical technology right now. But not within the next three years.
Experts agree there is a revolution afoot, pushed forward by the convergence of several technologies and social forces – the onset of 5G networks, the need for more intense virtual collaboration accelerated by COVID-19, the rise of edge computing (that allows for more ambient intelligence), and advances in AI, AR, and VR. Add in blockchain and NFTs (non-fungible tokens) and it’s clear the metaverse is the biggest technology revolution since the emergence of smartphones 15 years ago.
The underpinnings of the metaverse have already taken the gaming industry by storm, because gaming is where virtual experiences have been the most immersive. In fact, there’s almost a separate conversation happening in gaming, where virtual interaction and things like NFTs and cryptocurrencies are spawning a creator and gamer economy that hasn’t yet impacted the enterprise.
Bitter rivalries exist in gaming, as evidenced by the legal battle between Apple, which wants to charge 30% for access to its app store, and game maker Epic, which needs to access the iPhone because it’s such a compelling format for gaming but refuses to pay that tax. Many gamers dream of a connected network of always-on 3D virtual worlds where you can port your gaming profile anywhere. But that’s not going to happen anytime soon, given that virtual spaces are owned by different companies. And besides, a cross-gaming metaverse doesn’t fully encapsulate the metaverse’s full potential – the one that will transform just about every industry.
While forecasting the exact form of the coming metaverse is impossible, the seeds are being sown today. To see where the metaverse for enterprise applications is headed in 2022-2025, it’s best to look at the industry players that have been working on building the metaverse the longest. And there, it makes sense to start with the giants, who have the most resources. In this case, it’s companies like Google, Meta (aka, Facebook), Microsoft, and Apple, but also critical infrastructure companies like Nvidia and Qualcomm, and the fastest-growing virtual native companies like Epic, Unity, and Roblox.
Google’s metaverse: Glasses Google, a company known for moonshot ideas, was the first to unveil augmented reality glasses in 2012. The glasses presented a small screen on the right side of your vision designed to carry web or other information streamed via Wi-Fi or Bluetooth connection with your phone. A host of complications caused Google’s initial effort to fail.
But Google hasn’t given up. It’s been working on an enterprise version. Three years ago, it released the second version of its enterprise glasses, called Glass Enterprise Edition – mostly to allow workers to go hands-free. It has gotten good traction in a handful of industries that are heavy in logistics, manufacturing, or collaboration — where a worker can stream what they are seeing via their glasses to get advice from someone watching along. Health care (Sutter Health), transportation (DHL), and agriculture (AGCO) companies are among those using Google Glass.
In this vision, shared by many other leading tech companies, the glasses can funnel information from the web – including virtual images of your friends or colleagues, or anything else – so that you can enjoy an alternative reality that is layered over your physical reality. Most recently, Google made it possible to hold Google Meet on the glasses, and also partnered with Verizon’s Bluejeans to allow conference calls that way, too.
Google is tight-lipped on its ultimate plans and vision for the metaverse, and declined to comment for this story.
But reports have emerged that Google is working on a new AR headset, now codenamed Project Iris. The head-mounted display will begin shipments in 2024, according to two anonymous sources that were cited by The Verge.
It will carry outward facing cameras, and a blend augmented reality within a video feed of a user’s real world.
This follows other signs that Google is serious about the metaverse. Last year, it acquired North, an AR startup that had purchased assets behind a smart-glasses project that had originated at Intel. Last May, it unveiled Project Starline project , an impressive light field display technology that allows 3D, life-like holograms of people to appear in front of you – although the intricacy of its setup and its cost (in the tens of thousands of dollars) limits its ability to scale to popular use at this time. And in November, Sundar Pichai led a reorganization that placed all projects related to the metaverse into Google Labs – led by Clay Bavor, and directly reporting to Pichai.
AR glasses are no silver bullet for the metaverse – yet Still, as promising as AR glasses may be for the enterprise metaverse, they face numerous complications. For example, getting lighting just right is tough, because the AR information or images need to stay in front of your pupil – but your pupil moves depending on what it’s looking at. Depth and focus are hard to get right, too. Also, the augmenting screen has to be squeezed in a narrow field of your vision. And your eyes can do only so much: you still have to control inputs, and doing that with your phone is clunky, and doing it with your hands is even clunkier.
Nikhil Balram, who oversaw AR hardware at Google at the end of 2019, left to join an augmented glasses startup EyeWay Vision. He published a presentation that summarizes the challenges for AR, but also points the way to what he calls the “holy grail” of fully realized AR glasses: addressing power consumption.
AR glasses will burn power to run what is essentially a supercomputer, Balram says. People don’t want to walk around with a giant battery pack on their head, or near their face. If there’s a company that can pull it off, it’s likely to be Apple, Balram says. Leveraging its prowess in design, Apple could devise something like a snap-on, snap-off power source that wins consumer appeal.
The upshot is that a full AR experience is at least three years away.
Meta (formerly Facebook) pushes for maximalist vision If Google appears careful and focused on use cases around glasses, Facebook represents the opposite. It’s investing heavily ( $10 billion in 2021 alone ), and publicly, in not just a metaverse around AR, but also VR, and across devices. Zuckerberg even renamed the company as part of this new focus. The company’s new north star, he said, is to “bring the metaverse to life.” If Matthew Ball’s series represents a foundational screed for the metaverse, Zuckerberg and Meta’s team bring it to life with what is essentially a movie version: an engaging 1hr 17min movie presentation articulating the technologies Meta sees as part of the metaverse. These technologies happen to be the same experiences that the other giants – Google, Microsoft, Apple, for example – all want to master as well.
Meta emphasizes that it wants to put you directly into other experiences, not just augment your existing reality. Zuckerberg emphasized this idea of “embodiment” as a key principle of the metaverse. “Instead of just viewing content — you are in it,” he explains.
“You feel present with other people as if you were in other places…..So that can be 3D — it doesn’t have to be. You might be able to jump into an experience, like a 3D concert or something, from your phone, so you can get elements that are 2D or elements that are 3D.” As a result, Facebook is busy creating all kinds of connective technology, which represents covering its bets across possibilities. It has the Quest headset for VR. It has Horizon, a social VR platform it launched in October that includes various forms: Horizon Home (for your default personal digital space), Horizon Workrooms (for your work environment), Horizon Venues (for events), and Horizon Worlds (which lets you hang out with up to 20 people at a time in a virtual space, and write code to build things like games in a Minecraft-like environment).
Meta is also developing a mixed reality (MR) headset, called Project Cambria, for release later this year. It is called mixed because it will allow sensors to pick up your surroundings to inject them into your VR experience.
And ultimately, it is developing a pair of AR glasses, named Project Nazare , that will include “hologram displays, projectors, batteries, radios, custom silicon chips, cameras, speakers, sensors to map the world around you, and more.” Zuckerberg said it will require “fitting a supercomputer into a pair of glasses.” So while Meta started with VR, it has plans for an MR headset in the near term, with a vision for full-fledged augmented reality glasses after that. So Facebook is joining Google, and others in a race for the same goal: perfectly immersive AR glasses. While Google shies away from VR, Facebook’s Zuckerberg says VR is important because it “delivers the clearest form of presence.” While Meta is all in, it has not managed to score any significant breakthroughs of its own. Its Oculus VR play is a leader among many VR headset products. Its Horizon products aren’t dissimilar to what other gaming companies like Fortnight or have already done. That said, Facebook’s future as a player in the metaverse is secured. It can use its massive scale as a social network to drive traffic to new metaverse products, even if it isn’t first. It was built on the premise of connecting people digitally. It is also a newer, younger company, was fast to go native in mobile, and has done the most to rally itself around what is coming next.
VR’s achilles heel Meta’s Quest leads the market among VR headsets, in front of Sony’s PlayStation VR and HTC’s headsets. But the VR market has taken off slower than the original hyped projections called for. It’s clear something is not quite right with the format.
The advantage of VR is that it offers the most immersive experience, with a wider field of vision for the user and great visual fidelity. But it will never be able to own the metaverse by itself. That’s because with VR, you relinquish your own reality, and enter the one in your headset that surrounds your eyes, which creates two mental frameworks that can be disorienting.
You can’t see your real world. Good luck taking a sip of that coffee on that desk in front of you without spilling it over. VR forces your brain to build and maintain two separate models of your world — one for your real surroundings, where you are perhaps sitting down or standing in place facing in one direction, and one for the virtual world that is presented in your headset, and that may be moving or interaction that is not natural.
Louis Rosenberg, CEO of Unanimous AI, writes: “When I tell people this, they often push back, forgetting that regardless of what’s happening in their headset, their brain still maintains a model of their body sitting on their chair, facing a particular direction in a particular room, with their feet touching the floor (etc.). Because of this perceptual inconsistency, your brain is forced to maintain two mental models. There are ways to reduce the effect, but it’s only when you merge real and virtual worlds into a single consistent experience (i.e. foster a unified mental model) that this truly gets solved.” There’s also the form factor: “Wearing a scuba mask is not pleasant for most people, making you feel cut off from your surroundings in a way that’s just not natural,” says Rosenberg. It turns out, visual fidelity isn’t the key factor that will drive the adoption of the metaverse. Rather, technology that offers the most natural experience to our perceptual system will. And the most natural way to present digital content to the human perceptual system is by integrating it directly into our physical surroundings. Thus, AR, can be layered on top of your existing reality.
So while VR that completely covers may offer an amazing virtual experience and will work with games and other certain tasks, “it is not something for the general public,” says Nikhil Balram, the former Google VR executive.
Mixed reality: The bridge until fully fledged AR arrives So AR glasses and VR headsets both have limitations. But what if they were to merge in some way? One protagonist that emerges here is Qualcomm, a company that doesn’t get much attention when talking about the metaverse. It’s true that convergence in a whole host of technologies, standards, protocols, and payment systems, is required before a full-fledged metaverse can be realized, as Matthew Ball has pointed out.
And Qualcomm plays in just one major area here, which is chipsets for headsets. But it’s an increasingly important one.
Google, Microsoft, Motorola, Lenovo, Oppo, Xiaomi, and many more are working on AR glasses. And what’s notable is that they’re almost all using a similar chipset to base them on: Qualcomm’s XR platform, which includes a CPU, a 5G radio chip, and an AI engine for the glasses. Qualcomm has launched more than 50 devices with its partners.
Qualcomm has also been building a way to bridge AR devices with phones , which many believe will play an important role – at least in the near term – as an edge server for wearables like AR devices. Qualcomm’s platform is centered around Snapdragon Spaces, and lets developers use the phone’s processing power and a cellular connection to drive the AR experience. Qualcomm recently acquired a mapping company Augmented Pixels to help build out Snapdragon Spaces. Terms were undisclosed.
The use of Qualcomm’s technology by many of the big players implies a continued convergence around basic AR infrastructure , but consolidation won’t lead to a single platform or architecture dominating the metaverse just yet. There’s plenty of pressure by the big tech companies to go it alone to try to grab their share of key parts of the value chain. For example, Google is developing its own chipset for glasses, even though it has partnered with Qualcomm until now.
Qualcomm’s neutral role also allowed it to see how formats like glasses and headsets are converging, too. Hugo Swartz, senior VP of engineering, points to the convergence by big players on mixed reality. Like genetically modifying a corn type, it takes a hybrid of two inferior models and comes up with a superior one. This is where you start with a VR headset, but you allow a camera and other sensors to pass through as much of the real world as possible into your field of vision. And it’s not only the tech giants who are building these. A European startup called Lynx , is doing this with a Qualcomm chipset, but it is still early and has limitations (see a demo of this ). Other types of MR hybrids exist, including Magic Leap 1 and Snap, with its AR spectacles.
What’s clear is that many companies see it as the best approach for the next two or three years. And so they are working on realizing the best they can from MR, with plans to move on toward fully-fledged AR once more complications with glasses are ironed out.
Microsoft HoloLens 2: The best immersive MR experience so far With the HoloLens 2 , Microsoft has built what is arguably the most advanced immersive AR experience. If Meta’s Zuckerberg has described the future most eloquently, Microsoft has done the most to implement the state of the art as this demo shows. The HoloLens uses Microsoft edge to allow you to do things like open up virtual browser tabs in front of you, that you can reach out and touch and scroll down with your fingers (see min 4:40 of the above video). And Microsoft’s Mesh platform allows you to bring in other augmented content, for example, avatars of other people you can collaborate with. Also, Hololens 2 “solved the comfort problem,” says EyeWay Vision’s Balram. ”Comfort on your head overrides everything else.” The powerful vision of the future – evoked in sci-fi movies like Iron Man , or District 9 , where characters usher up virtual computer screens or holograms in front of them, almost out of thin air – is getting more tangible.
Pulling something out of the area like that may seem fantastical, but when you think about it, it’s actually close to what we have now. It’s called 5G, where the Internet and other media ride on a powerful backbone – invisible to the eye – that carries gigabits of information per second. The challenge is fitting it in front of the human eye comfortably, reliably, securely, and without latency, so that it is actionable at any angle.
The HoloLens 2 highlights the existing technology challenges. Its field of view captures only a small space of the area around you, 43 degrees horizontal by 29 degrees vertical. And it faces significant ambient occlusion (depth perception) problems in allowing hand control. Typing in the area easily will take at least a couple of more years to solve. That said, like the Google Glass, Hololens is already used in specialized enterprise use-cases.
Manufacturing, healthcare, and education companies have been early adopters of the HoloLens. Boeing has committed to designing its next-generation aircraft within the metaverse, using Microsoft HoloLens , along with something called “digital twin” technology. Digital twin tech simply refers to creating a virtual twin from a physical original. You can then run simulations on the virtual version , which can be hugely cost effective, and easier for collaboration among remote workers. Microsoft imports this digital twin technology through its Mesh platform , but there are several players offering digital twin simulation technology, too.
When Microsoft CEO Satya Nadella announced Mesh in March last year, he gushed about the technology’s capability in a way that likely sounded like hyped gobbledygook to an uninformed viewer: “Mesh allows you to interact with others holographically with true presence in a natural way,” he said. But the HoloLens 2 shows just why Nadella is right to be excited. Hologram avatars that look very similar to you – down to seeing your skin pores – are not that far away.
Apple: The metaverse antagonist Apple is the world’s largest company, valued at $3 trillion value, built on the most robust phone and app infrastructure in the world. In theory, it has the most to gain as the metaverse takes off. But it also has the most to lose if its business model is disrupted.
It’s working on a MR headset. However, Apple downplays the idea of a metaverse: “Here’s one word I’d be shocked to hear on stage when Apple announces its headset: metaverse,” said Mark Gurman in his Jan 9 newsletter.
The Bloomberg reporter is considered well-sourced at Apple. “I’ve been told pretty directly that the idea of a completely virtual world where users can escape to — like they can in Meta Platforms/Facebook’s vision of the future — is off limits from Apple.” Still, don’t let that fool you. CEO Tim Cook has called the area of AR “critically important” and one of “very few profound technologies.” Apple is working as feverishly as any company to build for the future. And it’s following a similar strategy to others: a MR headset for the short-term, expected for release next year – but only as a precursor for full-fledged AR glasses when that technology is ready – again, not expected for several years.
Apple watchers say the coming MR headset will include “some of its most advanced and powerful chips , which will build upon Apple’s existing M1 chip. Like others, the headset will require cameras to capture the outside world and feed it back to you. According to various reports , there will be up to 15 cameras and lidar sensors mounted on the device. Apple has already incorporated some of these technologies into devices like the iPhone 12 Pro for augmented reality processing. Some analysts say the company might preview the headset as early as this year, but with full delivery mostly likely in 2023.
Apple’s strategy requires it to offer technology for developers to leverage too, to keep its app store business thriving. Indeed, that app store has become a major source of friction, since Apple continues to charge a 30% tax on revenues made from apps – much higher than developers want.
This, and other efforts by Apple to protect its turf, is why it is a perceived resistor to an open metaverse. It has the power to slow things down considerably.
The initial web was built on open standards, but Apple’s growing market share has made it, according to some, a “de facto regulator for the internet.
” Apple iPhone does not allow for fully alternative browsers. And its own browser, Safari, doesn’t support much of WebGL, a JavaScript API that is used elsewhere on the open web – and allows for browser-based 2D and 3D rendering without plug-ins. Apple’s numerous rules, controls over APIs and changing of policies have strongly impacted promising games companies that have been developing elements of the metaverse, for example, Roblox, and Epic (which owns Fortnite).
The one metaverse versus many metaverses debate This constant jockeying among the bigger players – and emerging disrupters – will ensure that the metaverse will likely not, as some skeptics worry, be owned by a single player. There will remain serious fragmentation. As such, there won’t be harmonious, seamless movement of your profile and all its appendages, be it avatar, account information, status, presence, and so on – between games and other virtual worlds anytime soon. Outside of power dynamics, the technical challenges of doing so are significant too.
We can look back at the previous computing revolutions – the PC, web, mobile – for clues on how the metaverse will unfold. There will be a first period of massive investment, as excitement and hype around the metaverse peaks. This is the phase we are in now. Here, existing large companies stake out their bets, but private capital also pours into startups taking advantage of it. Then, in a second phase, there will be consolidation, as end-users seek to make sense of the fragmentation and bet on perceived winners. And in the end, an oligopoly of leaders is likely. In today’s paradigm – with mobile the most recent revolution – there are at least five major winners: Google, Apple, Facebook, Amazon, and Microsoft (GAFAM).
The virtual natives are coming: Epic, Unity and Roblox All of those, except for Amazon, are in the race for hardware hegemony for the metaverse. Hardware is important because it’s a gateway to the metaverse, but it can only take you so far.
Game engine companies Epic and Unity are coming on strong as contenders in the metaverse because they are helping thousands of developers build games that are fundamentally virtual from the get-go. They’re the only major tools for real-time 3D graphics. The companies also make games themselves, with Epic’s Fortnite – the most-played game in 2021 – being an example of a breakthrough success.
A virtual concert held by singer Travis Scott within Fortnite in 2020 marked for many an inflection point heralding the emerging dominance of the virtual medium. Writes Ball : “Nearly 30 million people spent nine minutes fully immersed in his music. This included die-hard and casual fans, non-fans and people who didn’t even know he existed. There is no other experience on earth — including the Super Bowl half-time show — that can deliver this degree of reach and attention.” This makes the Fortnite’s owner, Epic, a big draw for other companies. Disney, the NFL, the NBA, Netflix Ferrari, and fashion company Balenciaga, all have partnered with the company , offering digital goods like avatars and clothing to play across its games.
History of previous computing revolutions shows that there are companies from the previous era that hang on and thrive, but that there are also altogether new companies that emerge, most of which are unpredictable. And no one is in a better situation to thrive in a virtual metaverse than companies that already offer virtual worlds.
Another fast-growing gaming company, Roblox, is a metaverse-era big contender, says Ball. Roblox has thrived because it lets users build and generate the content – games and other experiences – and connects players together in virtual online spaces. There, players can create virtual items – things like clothing for characters – that can be sold for virtual currency. Players can buy these things with real currency. It is the most valuable video game company in the U.S. And where people spend time and money, brands show up too. Gucci created a virtual garden , and Ralph Lauren created a virtual ski store.
Like Epic and Unity games, Roblox supports its product across devices, and its success threatens the dominance of the iPhone ecosystem in the coming metaverse paradigm. “Roblox is disintermediating iOS from the majority of virtual world time, and from virtual world developers,” says Ball. “Developers are using Roblox’s engine, and users are discovering content directly through Roblox.” It’s no surprise then, that Microsoft, which already has a leading game asset in the Xbox console, is being more aggressive in games of late. It just acquired Activision , the world’s largest gaming company network, for $68.7 billion – the biggest deal in gaming history. With the HoloLens, Microsoft was already arguably the best-poised company of any for the enterprise metaverse, and this deal will make it stronger overall, giving it access to strong mobile games too, where it had almost none. Microsoft also owns Minecraft, a top-ten gaming company with 141 million users, that is cross-platform with user-generated content. And its Azure cloud services offering works irrespective of device, and is second only in scale to Amazon.
Comments by Activision CEO Bobby Kotick about the logic behind the sale to Microsoft are eye-opening. Kotick said Activision, even as the biggest gaming network, couldn’t compete in attracting the graphics engineering and AI/ML talent required to keep up with.. That even Activision had to join a tech giant to compete in the metaverse points to significant consolidation down the road elsewhere.
It’s worth mentioning that there’s a whole different story to be written about how payments will develop in the metaverse. Blockchain technologies for virtual currencies in games and elsewhere offer huge advantages, but they also still face plenty of challenges – an area that has been well covered elsewhere.
Writes Ball: You can’t access the Metaverse except through hardware, and every hardware player is fighting to be the (or at least a) payment gateway to the Metaverse. This is why Facebook, which lacks a major operating system, is investing so heavily in Oculus. And why Snap is developing its own AR hardware, while defending Apple’s 30% take Similarly, the emergence of NFTs, to give digital creators ownership over their work, and to be able to buy and sell digital goods, is a trend that will play out over the coming years. Almost every week, it seems, a new brand is jumping in to NFTs , from Adidas to Coca-Cola.
Tim O’Reilly has done some work trying to dissect how much blockchain and crypto are part of a coming revolution.
It shows that big questions are unanswered.
The metaverse’s enterprise killer apps: collaboration and simulation So what does all this mean for enterprise decision-makers, who are not with gaming companies or one of the GAFAM? Will the metaverse be like the cloud, which was talked about beginning in the 1990s , but took years to really come about? Not really. The fact is, use cases of the metaverse are already beginning to pop up, as evidenced by early enterprise users of the Hololens, Google Glass, Magic Leap, and the number of companies now launching digital twin simulation with gaming engines like Unity and Epic, and elsewhere.
Most of these use cases center around the need for either more natural ways for remote workers to collaborate, or to simulate products or environments. Collaboration and simulation will soon impact a variety of functions , from product management to engineering, as we’ve reported here.
The game engine companies like Unity and Epic are fascinating places for simulation. Industrial, film, car, and other enterprise companies are using them. Hummer’s dashboard UI is now based on [Epic’s] Unreal Engine and can simulate the vehicle live. And Hong Kong International Airport picked Unity to build a digital twin for simulation.
Unity was able to render the not-yet-real environment and stress-test it for fire, flood, power outage, backed-up runway, and flow of humans.
Another notable player here is Nvidia, a company that rocketed in value over the past few years building graphics processing units (GPUs), which sparked a revolution in the performance of gaming apps and deep learning apps for AI. Nvidia’s hard-charging CEO Jensen Huang has been talking about the metaverse for years. Last year, the company launched Omniverse , an open standards way for creators, designers, and engineers to collaboratively build virtual worlds, and to connect them. What’s also notable about Nvidia is that its offering embraces third-party standards, which is compatible with the needs of the metaverse to avoid lock-in. Omniverse is based on Pixar’s widely adopted Universal Scene Description (USD), the leading format for universal interchange between 3D applications. Basically, Omniverse connects 3D software tools that typically don’t talk well to each other.
An early notable user of Omniverse is BMW, which is using it to build state-of-the-art factories.
The platform also allows customers to access real-time photorealistic rendering, physics, materials, and interactive workflows between industry-leading 3D software products.
Read on for what the metaverse means Notably, Nvidia also announced in November plans to build a digital twin of the entire earth , called Earth 2, revealing that simulation of our reality knows no bounds. This simulation is not just for fun. It will be used to simulate and predict climate change, and model other improvements in the real world. “Omniverse is different from a gaming engine,” said Huang during the announcement. “Omniverse is built to be datacenter scale and hopefully, eventually, planetary scale,” he said.
This isn’t a modest conclusion. The metaverse will piggyback on a fully simulated earth. It will also take you to space (virtually). You’ll be able to talk with life-like avatars of friends and workers as if they’re in front of you, even though they’re thousands of miles away. And you’ll be doing it through fully immersive AR glasses, where you can mix your reality and other realities as you choose.
The metaverse will impact every industry. Just as in previous revolutions, the most excitement around the metaverse will start on the consumer side. Enterprise companies will have some more time to prepare themselves for what’s coming, but it will start with the areas closest to consumers, for example in communications and collaboration. But those companies that move quickly will also be the ones who are best positioned to leverage that future. Read on in our series to learn more about what it means for you.
Read more from this VB Special Report : The metaverse: Where we are and where we’re headed Why the metaverse must be open but regulated How the metaverse will let you simulate everything 7 ways the metaverse will change the enterprise Identity and authentication in the metaverse Understanding the 7 layers of the metaverse Can this triple-A game usher in the promise of the metaverse? (sponsored by Star Atlas) How the metaverse could transform upskilling in the enterprise Why the fate of the metaverse could hang on its security Gaming will lead us to the metaverse The potential environmental harms of the growing metaverse VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"How Microsoft Fabric aims to beat Amazon and Google in the cloud war | VentureBeat"
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"https://venturebeat.com/data-infrastructure/how-microsoft-fabric-aims-to-beat-amazon-and-google-in-the-cloud-war"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Analysis How Microsoft Fabric aims to beat Amazon and Google in the cloud war Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Microsoft unveiled a new cloud data and analytics platform two weeks ago that analysts say give it an edge over its main rivals, Amazon and Google, in the fiercely competitive cloud market.
The platform, called Microsoft Fabric , is a comprehensive suite of tools that allows enterprise customers to store, manage and analyze the data that drives their most important applications. It also integrates products that cater to all of a company’s data users, from engineers who handle the technical aspects of data processing to analysts who want to derive insights and make decisions from the data. (See our reporting on the announcement ).
Microsoft Fabric, which is currently in public preview mode and will be updated with more features in the coming months, surprised many industry experts who were not briefed by the company beforehand. Some reserved full judgment until they can see it work in practice. But they praised the platform as a significant advancement that could help Microsoft “leapfrog” Amazon and other cloud providers, such as Google — at least when it comes to serving large enterprise companies. Fabric will also pressure other tech providers like Snowflake and Databricks, a close partner of Microsoft, analysts said.
“With all these capabilities coming together,” said Noel Yuhanna, an analyst at Forrester, “Microsoft definitely has a slight advantage over the other hyperscalers at the moment.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Even before the announcement Microsoft had already become a leader in data and analytics software, according to Gartner , a research firm. But with Microsoft Fabric, analysts said, the company has taken its offerings to a new level of integration and ease of use that could be hard for its competitors to match anytime soon.
While Fabric gives Microsoft a dominant offering, the key is now in execution, analysts said. Amazon’s AWS cloud service still enjoys a clear lead over Microsoft’s Azure in overall revenue, and will probably continue to do so for some time. But in the area of enterprise analytics and data , Microsoft’s cloud offerings now lead in terms of breadth of capabilities. “But the ability to execute is often defined by sales. So that number is yet to be proven,” said Hyoun Park, analyst at Amalgam Insights.
Fabric’s secret sauce: OneLake So what makes Microsoft Fabric stand out? According to analysts, it is the way Microsoft has simplified and unified its data architecture with a single data lake , called OneLake, that can store and allow access to all kinds of data from different sources and applications.
This approach, they said, will offer significant benefits to customers in terms of cost savings, transparency, flexibility, governance and data quality. OneLake is designed to be the central repository for not only the data generated by Microsoft’s own software services, but for data from external sources, such as third-party applications. It also provides a consistent experience and interface for users, regardless of the type or format of the data. This may sound like an obvious idea, but it has been elusive for most cloud providers, including Microsoft, Amazon and Google.
Over the years, these tech giants have acquired or developed dozens of software tools for various data and analytics tasks, such as business intelligence, data science , machine learning and real-time streaming. But they have largely bolted together these tools in a piecemeal fashion, without creating a coherent and seamless platform.
As a result, customers have to deal with a complex and fragmented landscape of tools and databases, each with its own provisioning, pricing and pooling of data. This creates frustration and inefficiency for customers, who have to spend more time and money on managing their data infrastructure. It also imposes an “integration tax” on customers, who are charged separately for each service’s compute and storage resources.
Microsoft Fabric promises to eliminate this Frankenstein-like complexity by offering true integration — including only one copy of data, and one experience and one interface. “Part of the innovation here is that Microsoft is providing all of these capabilities by themselves as an integrated package,” said Amalgam’s Park. “And as simple as that sounds, it’s not something that the majority of data and analytic vendors are able to provide.” Jason Medd, an analyst at Gartner, concurs. He said that Gartner’s surveys of chief data officers have shown that only about 30% said they get value from their data and analytics tools. By integrating its tools and lowering its prices, Microsoft is addressing these pain points, Medd said.
How OneLake data lake works How does Microsoft achieve this simplicity and unification with OneLake? The key is that OneLake stores a single copy all the data from Microsoft’s various services in a common format, called Apache Parquet.
This is an open-source file format that is widely used in the industry and that organizes data by columns.
This makes it easier and faster to query and analyze data. Whenever customers add or update any data to their systems, Fabric automatically saves it in OneLake in the Parquet format, regardless of its original format. This means that customers can access and query their data from OneLake directly, without having to go through multiple sources or services.
For example, if a customer wants to use Microsoft’s business intelligence tool, Power BI, to analyze data from Microsoft’s data warehouse , Synapse, they do not have to send a query to Synapse. Power BI simply retrieves the data from OneLake. This reduces the number of queries across services and lowers the cost for customers, who are charged for a single storage and data bucket, instead of multiple ones.
How OneLake pulls in data from external sources OneLake’s simplicity and unification also extend to data from outside Microsoft’s ecosystem. This is where the technical details matter: OneLake stores its data tables in an open-source format called Delta Lake, which creates a single layer of metadata that converts raw data from various sources, such as CSV or JSON files, into a common format that can be analyzed by any compute engine in the industry.
“Microsoft has done the right thing here,” said Tony Baer, an analyst at DBInsights, of its embrace of open source.
He said that the competition among vendors is not about file formats, but about achieving a standard of accuracy and consistency, known as ACID , for databases. And Fabric’s integration, through open formats, is a step in that direction. Microsoft makes it easy for customers to transform data from third-party services with its Data Factory , which offers more than 150 pre-built connectors.
Microsoft is also working on ways to automate the transformation process , instead of relying on the traditional and time-consuming method of extract, transform and load (ETL).
Microsoft Fabric also supports multicloud scenarios, something that Amazon has been slow to do. With a feature called “Shortcuts,” OneLake can virtualize data storage in Amazon’s S3 and Google’s storage (coming soon).
“Now that you’re going to a single open format that’s shared, all these engines can work natively with the data as opposed to getting fragmented,” said Arun Ulagaratchagan, Microsoft’s corporate vice president of Azure Data, in an interview with VentureBeat. He said Microsoft is the first major cloud vendor “that is moving away from completely protected formats to completely open formats.” Ulagaratchagan said that he had talked with 100 of the Fortune 500 companies over the last few years, and they were most excited by Fabric’s promise of lower cost, ease of use and lack of lock-in.
Fabric’s integration work took years Microsoft’s Fabric announcement may have seemed sudden, but it was the result of at least four years of work by the company to break down silos and integrate its data services. This also involved overcoming internal politics and turf wars among different executives.
One of the milestones was Synapse, which combined several services, such as data lake and data warehouse, into a single hub. But Fabric is the ultimate integration, bringing together Synapse, Power BI and other data services as a single software-as-a-service (SaaS) offering.
“I think it’s leapfrogging,” said Andrew Brust, an industry consultant who runs BlueBadge Insights, referring to Microsoft’s move with Fabric. “The functionality is comprehensive and cohesive, and that hasn’t been possible before.” Brust acknowledged that he is biased. He said Microsoft is a client of his, and that he is a Microsoft Data Platform MVP, which made him part of a small group of consultants, customers and partners who were privy to Fabric before its announcement. Brust also said that Microsoft’s offering of Fabric as an SaaS, rather than a platform-as-a-service (PaaS), was significant. It means that data engineers do not have to deal with provisioning units of compute, which simplifies their work. He said Amazon and Google still have a lot of work to do in this area.
Quality of data is the key to winning the enterprise cloud race Analysts also emphasized that the main competition among cloud providers is about the quality of data, which is what enables customers to get better insights and make better decisions.
Noel Yuhanna, an analyst at Forrester, said he talks to three or four enterprise customers every day who complain that moving to the cloud did not solve their problems with data quality. “We get compute, we get storage, we get Kubernetes,” Yuhanna said, summarizing the view of most enterprise executives. “That’s cool. But did we really modernize the system?” He said that’s why system integrators, such as BearingPoint, Capgemini, Infosys and Wipro, have so far made the most money from providing insights from the cloud. They have consultants who write up reports on what they find from the data.
That’s also why Microsoft is pushing forward with Fabric. By connecting data sources together, Fabric improves the consistency and trustworthiness of data, Yuhanna said. “The biggest challenge with data replication is that data is all over the place, and you can’t get consistent data anymore … Fabric really gives you that consistency of data.” By providing one place to go, it is like providing a single window to look through data management: “Security, governance, integration, discovery, that’s exactly what this is about,” he said.
If customers want to apply security rules to their data, they can do much of this at the OneLake level. And all of the Fabric applications downstream that access the data will have to follow those rules, Microsoft said in its announcement. For example, if customers have sensitive salary information in Power BI that they only want a certain team to access, they can set up rules that ensure this. And files will carry the same rules, wherever they are exported — even carrying the same encryption if sent outside of Microsoft’s Fabric.
Microsoft catches up with the ‘lakehouse’ trend One of the areas where Microsoft has lagged behind some of its competitors is the so-called “lakehouse,” which combines two technologies: a data lake to store a company’s data, and a data warehouse to analyze it.
The lakehouse has become popular because of the rise of apps like artificial intelligence , which require massive amounts of data and analysis. And one company in particular, Databricks, has been a pioneer in creating a secure, open lakehouse that many analysts consider industry-leading. Databricks, after all, created the Delta Lake protocol.
Another company, Snowflake , has also offered a well integrated lakehouse product. Microsoft’s offerings in this area, under its Synapse brand, have reportedly not performed as well, and Microsoft has compensated for this by forming a close partnership with Databricks, offering its support on its Azure cloud. So it is no surprise that Microsoft’s Fabric adopted the Delta Lake protocol as well. All customers who use Databricks will continue to be happy using Microsoft’s Fabric.
But Fabric’s integration also narrows the gap with Databricks and Snowflake, and aims to surpass them, analysts said. Fabric extends the open format pioneered by Databricks to the rest of Microsoft’s data stack, which is more comprehensive. While Microsoft’s Ulagaratchagan says Microsoft is happy to give customers choice by working with platforms like Databricks, he also makes clear that Microsoft’s Synapse intends to lead the lakehouse market: “We literally intend to be the best in breed and best of suite,” he said.
Microsoft’s single experience, and move to SaaS offering, helps Fabric’s Synapse leap ahead in some key aspects, analysts said. Databricks remains a PaaS offering, which means that data engineers still have to do more work and specify things like the number of nodes they want to run processing jobs.
Microsoft Fabric combines its strength in business intelligence (Power BI) with data science, and adds other capabilities, such as pattern detection and workflows (Data Activator), and that’s “a big deal,” said Amalgam’s Park. He said that bridging BI to AI continues to be a challenge for the enterprise. Microsoft is providing a package that solves this to a greater extent than any of its competitors.” The power of generative AI has yet to be realized Finally, Microsoft said it is using its new generative AI technology, acquired from its investment in OpenAI, to enhance its Copilot tool. Copilot helps users perform tasks, such as reading and summarizing data reports. With OpenAI’s technology, Copilot can now allow developers and analysts to use natural language to ask questions of data, and to receive answers in natural language as well. Here, Microsoft’s Ulagaratchagan says that while this will improve productivity, the full impact of applying generative AI across the Fabric offerings will take some time to be seen.
After all, Fabric is the first time customers have experienced an end-to-end integration of their data, and they have yet to explore what generative AI can do.
“You can think about it not just accelerating a step in the customer’s journey with generative AI, but the entire journey, so that’s an opportunity that customers haven’t found yet,” Ulagaratchagan said. “It’s critically important that we learn from actual customer usage and get the experience right.” [Editor’s note: VentureBeat will be hosting VB Transform , a networking event in SF on July 11 & 12 for decision makers, to debate technologies like Microsoft Fabric, and other enterprise strategies around AI and data infrastructure. Register now, and look forward to seeing you there.] VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Arcion Cloud improves database replication with Databricks, Snowflake | VentureBeat"
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"https://venturebeat.com/data-infrastructure/arcion-cloud-improves-database-replication-with-databricks-snowflake"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Arcion Cloud improves database replication with Databricks, Snowflake Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Arcion , which helps businesses replicate data from production databases so that it can be used to perform analysis on warehousing platforms like Databricks and Snowflake, today launched Arcion Cloud , a managed data replication service that Arcion says works “without a single line of code.” Last month, Arcion raised a $13 million series A round for its replication service. It comes at a time when enterprises need to replicate data from production databases like Oracle and MySQL to perform sophisticated analysis on the data, including running it through powerful applications like AI. In September, data integration giant Fivetran acquired HVR , which specialized in data replication and raised $565 million at a $5.6 billion valuation.
Like HVR, Arcion uses “change data capture” (CDC), which allows businesses to replicate data in real-time. CDC refers to identifying the changes to the source data and then synchronizing only these changes for replication, thus improving efficiency. Another competing replication service is Oracle Golden Gate, which is best used for data replication within Oracle.
Providing CDC as a service Today’s Arcion Cloud release is the first fully managed CDC data replication as a service that enables enterprises to “deploy high-performance, high-volume data pipelines in minutes instead of months,” the company said in a press release. The company added that, through just a few clicks, the platform provides interoperability for popular database technologies, initially including three sources, Oracle, MySQL and Snowflake and three targets, Databricks, Snowflake and SingleStore.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! While APIs have been used to connect software-as-a-service (SaaS) tools, data residing in transactional enterprise databases has mostly been locked up by design to ensure performance and security. This has made extracting enterprise data a time-consuming, resource-intensive task – resulting in slower analytics and unsustainable costs, given the increasing scale of real-time data.
The company said Arcion Cloud is available on AWS USA and will support Microsoft Azure and Google Cloud by the end of the year.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Transform 2020, the AI event for enterprise, adds digital option (in response to COVID-19) | VentureBeat"
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"https://venturebeat.com/business/transform-2020-the-ai-event-for-enterprise-adds-digital-option-in-response-to-covid-19"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Press Release Transform 2020, the AI event for enterprise, adds digital option (in response to COVID-19) Share on Facebook Share on X Share on LinkedIn Jerome Pesenti, VP of AI, FacebookMatt Marshall, Founder & CEO, VentureBeat Dear VentureBeat Community, In light of the COVID-19 outbreak, we are closely monitoring developments and public health advisories that may impact our upcoming Transform 2020 conference, the leading event for business and technology leaders about how to implement AI in the enterprise.
At this time, Transform will proceed this July 15-16 in San Francisco, California. But we’ve decided to host Transform 2020 virtually as well, to provide the same networking benefits for people who want to attend digitally instead of physically.
For the physical portion of the event, we will take all necessary precautions in accordance with recommended CDC guidelines — we will supply face-masks, sanitizer, and wipes, and will also screen visitors at the door to make sure they have not visited high-risk areas in the previous two weeks, and reserve the right to turn away certain people at the door.
We recognize that developments are moving quickly and that it’s impossible to know exactly where things will stand in July.
But as the #1 publisher of AI news coverage, VentureBeat is committed to offering unparalleled online content and networking options to best recreate the value of in-person engagement. We hope this will allow AI innovators and leading executives from around the world to partake in Transform 2020 despite ongoing challenges surrounding the outbreak.
Indeed, we expect the digital options we’re planning may allow even more people to participate this year.
As part of our digital offering, attendees will be able to view livestreamed and recorded sessions, take part in networking sessions and digital roundtables, access our Transform expo partners virtually, participate in 1:1 meetings with world-class AI providers, and more. We’re excited to announce more in the coming days and provide a new way for the AI community to engage with VentureBeat and Transform 2020.
Like other event organizers, we are paying close attention and will be prepared in case postponing or cancelling the physical portion of Transform 2020 becomes necessary.
The health and safety of our VentureBeat community, attendees, employees, and sponsors remains our top priority. We will immediately update you should the event change so you can adjust your plans.
Thank you, Matt Marshall Founder & CEO, VentureBeat The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Scale AI launches rapid data-labeling service | VentureBeat"
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"https://venturebeat.com/business/scale-rapid-promises-quality-data-labeling-for-ai-in-as-little-as-an-hour"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Exclusive Scale AI launches rapid data-labeling service Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Amid the boom of AI in application building, companies face a significant data-labeling problem, especially when it comes to labeling images or other media content they want to train deep learning algorithms on.
Today data-labeling and infrastructure provider Scale AI launched a service called Scale Rapid that aims to solve this problem by labeling a data sample within one to three hours. Users can review the work to make sure the labeling is being done correctly, iterate upon their labeling instructions if necessary, and then ramp up to have Scale AI label the rest of their dataset.
This is the latest in a series of products Scale AI has launched in the last year as it seeks to maintain its leadership in the labeling sphere. In April, the company raised $325 million , bringing its total raised to over $602 million. Scale AI says it has surpassed $100 million in annual recurring revenue and is tracking to double year-on-year growth. Its $7.3 billion valuation tops the known public value of most competitors, which include Labelbox, Hive, Snorkel AI, Mighty AI, Appen, Tasq.AI, Cloud Factory, Samsource, and SupperAnnotate.
Data-labeling process workloads Some companies boast access to massive armies of contractors who stand ready to label data, but Scale AI chief technology officer Brad Porter said he does not see anyone promising the same quality guarantees and speed Scale Rapid offers.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Companies building AI applications usually do one of two things, Porter said. They either use an existing dataset that has already been labeled but tends to be stale data and not easy for new applications to adapt to or they choose Mechanical Turk, Appen, or another third-party labeling service that employs individuals to label data for the company.
Scale AI’s competitors may provide a labeling workflow tool, but it can take weeks to set up an internal process that ensures the labeling is completed accurately, as well as being done in a way that enables AI models to work correctly. Typically, companies engaging in this area of work have to take responsibility for ensuring the data-labeling quality themselves. However, Scale Rapid is designed to ensure high-quality results by completely managing the labeling process from beginning to end, Porter said.
How does Scale Rapid work? When a machine learning (ML) researcher or developer begins a labeling process for a dataset, they write instructions for how they want the data labeled. The instructions can be for various tasks, like labeling what is in an image, annotating an audio clip, or determining whether a content review is positive or negative. The developer then uploads 10 to 50 examples of the data to ensure the labelers are following the instructions correctly.
Scale AI says it gets those results back in one to three hours and allows the developer to make sure quality thresholds are being met. If not, the developer can then submit 10-50 more samples. Once a developer has confirmed that the instructions are being followed correctly, they can upload 500-1000 images and scale from there.
Scale AI has a labor source of more than 100,000 labelers, according to Porter. The company determines whether a task requires expert labelers and helps avoid shortcomings found in some popular labeling processes, like consensus voting. In consensus voting, a labeling task might be sent to five people and the majority result is taken as the valid label. The problem is that the majority can be wrong. For example, if the task requires someone to differentiate between a crow and grackle, four out of five labelers might mistake a grackle for the more commonly known crow. So Scale AI brings in what it calls “expert spotters.” It then tries to automate the labeling process with ML.
Scale AI reports swift adoption of Scale Rapid Scale AI reports strong adoption of Scale Rapid during the tool’s early-access private beta period, with more than 750,000 tasks already completed for customers that include SpaceX, Cornell, Epson, Adobe, Square, and TimberEye. (Scale AI recently published a case study from TimberEye.
) Scale AI’s advantage, Porter says, lies in its origins labeling data in the autonomous vehicle industry.
The company’s 24-year-old founder and CEO, Alexandr Wang, dropped out of MIT and began building a lidar labeling tool to meet extremely rigorous labeling standards. As Scale AI grew to serve other industries, it took its labeling experience with it, offering companies service-level agreements (SLAs) to guarantee quality.
Last year, the company pivoted to assist companies with data needs at every stage of the AI development lifecycle — from data annotation to data debugging, model improvements, and fully managed services. Scale AI currently covers multiple industries and serves hundreds of customers, including Brex, OpenAI, the U.S. Army, SAP, Etsy, and PayPal.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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2,847 | 2,021 |
"No-code AI analytics may soon automate data science jobs | VentureBeat"
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"https://venturebeat.com/business/no-code-ai-analytics-may-soon-automate-data-science-jobs"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Exclusive No-code AI analytics may soon automate data science jobs Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
SparkBeyond , a company that helps analysts use AI to generate new answers to business problems without requiring any code, today has released its product SparkBeyond Discovery.
The company aims to automate the job of a data scientist. Typically, a data scientist looking to solve a problem may be able to generate and test 10 or more hypotheses a day. With SparkBeyond’s machine, millions of hypotheses can be generated per minute from the data it leverages from the open web and a client’s internal data, the company says. Additionally, SparkBeyond explains its findings in natural language, so a no-code analyst can easily understand it.
How companies can benefit from AI analytics data automation The product is the culmination of work that started in 2013 when the company had the idea to build a machine to access the web and GitHub to find code and other building blocks to formulate new ideas for finding solutions to problems. To use SparkBeyond Discovery, all a client company needs to do is specify its domain and what exactly it wants to optimize.
SparkBeyond has offered a test version of the product, which it began developing two years ago. The company says its customers include McKinsey, Baker McKenzie, Hitachi, PepsiCo, Santander, Zabka, Swisscard, SEBx, Investa, Oxford, and ABInBev.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! One of SparkBeyond’s client success stories involved a retailer that wanted to know where to open 5,000 new stores, with the goal of maximizing profit. As SparkBeyond CEO Sagie Davidovich explains, SparkBeyond took the point-of-sale data from the retailer’s existing stores to find which were most profitable. It correlated the profitability with data from a range of external sources, including weather information, maps, and geo-coordinates. Then SparkBeyond went on to test a range of hypotheses, including theories such as if three consecutive rainy days in proximity to competing stories correlated with profitability. In the end, proximity to laundromats correlated the most strongly to profitability, Davidovich explains. It turns out people have time to shop while they wait for their laundry, something that may seem obvious in retrospect, but not at all obvious at the outset.
The company says its auto-generation of predictive models for analysts puts it in a unique position in the marketplace of AI services. Most AI tools aim to help the data scientist with the modeling and testing process once the data scientist has already come up with a hypothesis to test.
Competitors in the data automation space Several competitors, including Data Robot and H20, offer automated AI and ML modeling. But SparkBeyond’s VP and general manager, Ed Janvrin, says this area of auto-ML feels increasingly commoditized. SparkBeyond also offers an auto-ML module, he says.
There are also several competitors, including Dataiku and Alteryx, that help with no-code data preparation. But those companies are not offering pure, automated feature discovery, says Janvrin. SparkBeyond is working on its own data preparation features which will allow analysts to join most data types — such as time-series, text analysis, or geospatial data — easily without writing code.
Since 2013, SparkBeyond has quietly raised $60 million in total backing from investors, which it did not previously announce. Investors include Israeli venture firm Aleph, Lord David Alliance, and others.
“The demand for data skills has reached virtually every industry,” said Davidovich in a statement. “What was once considered a domain for expert data scientists at large enterprise organizations is now in urgent demand across companies of all sizes.” “Our new release is powerful yet intuitive enough that data professionals — including analysts at medium-sized and smaller organizations — can now harness the power of AI to quickly join multiple datasets, generate millions of hypotheses and create predictive models, unearthing unexpected drivers for better decision-making.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Anyscale raises $20.6 million to simplify writing AI and ML applications with Ray | VentureBeat"
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"https://venturebeat.com/business/anyscale-raises-20-6-million-to-simplify-writing-ai-and-ml-applications-with-ray"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Anyscale raises $20.6 million to simplify writing AI and ML applications with Ray Share on Facebook Share on X Share on LinkedIn Anyscale cofounders Robert Nishihara, Ion Stoica, and Philipp Moritz (L-R).
Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Anyscale , a company promising to let application developers more easily build so-called “distributed” applications that are behind most AI and machine learning efforts, has raised $20.6 million from investors in a first round of funding.
The company has some credibility off the bat because it’s cofounded by Ion Stoica, a professor of computer science at the University of California, Berkeley who played a significant role in building out some successful big data frameworks and tools, including Apache Spark and Databricks.
The new company is based on an open source framework called Ray — also developed in a lab that Stoica co-directs — that focuses on allowing software developers to more easily write compute-intensive applications by simplifying the hardware decisions made underneath.
Ray’s emergence is significant because it aims to solve a growing problem in the industry, Stoica said in an interview with VentureBeat. On one hand, developers are writing more and more applications — for example AI- and ML-driven applications — that are increasingly intensive in their number-crunching needs. The amount of compute for the largest AI applications has doubled every three to four months since 2012 , according to OpenAI — an astonishing exponential rate.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! On the other hand, the ability of the processing hardware underneath needed to do this number-crunching is falling behind. Application developers are thus being forced to “distribute” their applications across thousands of CPU and GPU cores to factor out the processing workload in a way that allows hardware to keep up with their needs. And that process is complex and labor intensive. Companies have to hire specialized engineers to build this architecture, linking things like AWS or Azure cloud instances with Spark and distribution management tools like Kubernetes.
“The tools required for this have been kind of jerry-rigged in a way they shouldn’t be,” said Ben Horowitz, a partner at venture firm Andreessen Horowitz, which led the round of funding. That’s effectively meant large barriers to entry for building scaled applications, and it’s kept companies from reaping the promised benefits of AI.
Ray was developed at UC Berkeley, in the RISELab — successor to the AMPLab, which created Apache Spark and Databricks. Stoica was cofounder of Databricks, a company that helped commercialize Apache Spark, a dominant open source framework that helps data scientists and data engineers process large amounts of data quickly. Databricks was founded in 2013, and is already valued at $6.2 billion.
Whereas Spark and Databricks targeted data scientists, Ray is targeting software developers.
“From a developer standpoint, you write the code in a way that it talks to Ray,” said Horowitz, “and you don’t have to worry about a lot of that [infrastructure].” “Ray is one of the fastest-growing open source projects we’ve ever tracked, and it’s being used in production at some of the largest and most sophisticated companies,” Horowitz added. Intel has used Ray for things like AutoML, hyperparameter search, and training models, whereas startups like Bonsai and Skymind have used it for reinforcement learning projects. Amazon and Microsoft are also users.
Another Anyscale cofounder, Robert Nishihara, who is also the CEO, likens Anyscale’s mission with Ray to what Microsoft did when it built Windows. The operating system let developers build applications much more rapidly. “We want to make it as easy to program clusters [or thousands of cores] and scalable applications as it is to program on your laptop.” Stoica and Nishihara say applications built with Ray can easily be scaled out from a laptop to a cluster, eliminating the need for in-house distributed computing expertise and resources.
To be sure, developing a company around an open source framework can be challenging. There’s no guarantee that the company can make money from an open framework that other companies can build around, too. Witness what happened with Docker, the company that built around Kubernetes, but which hasn’t been able to commercialize. Other companies stepped in and did it instead.
Stoica and Nishihara said they were confident they would avoid Docker’s fate, given Stoica’s background with Databricks, which he gave as an example of knowing how to commercialize smartly and aggressively. They said that they knew more about Ray than anyone else, and so are in the best position to build a company around it.
Moreover, the pair said they aren’t afraid of other companies that have been building so-called “serverless” computing offerings — for example, Google with Cloud Function and Amazon with AWS Lambda — that are tackling the same problem of letting people develop scalable applications without thinking about infrastructure. “That’s a very different approach, a very limited programming model, and restricted in terms of the things you can do,” Nishihara said of serverless. “What we’re doing is much more general.” “These serverless platforms are notoriously bad at supporting scalable AI,” added Stoica. “We are excelling in that aspect.” The two founded the company in June alongside Philipp Moritz and UC Berkeley professor Michael Jordan, and Anyscale has no product or revenue yet. Besides Andreessen Horowitz, investors in the round include Intel Capital, Ant Financial, Amplify Partners, and The House Fund. With the funding, Anyscale’s founders said, they will expand the company’s leadership team (the company has 12 employees) and continue to commit to expanding Ray.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Worlds gets $10 million to help enterprises observe their physical environments with AI | VentureBeat"
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"https://venturebeat.com/ai/worlds-gets-10-million-to-help-enterprises-observe-their-physical-environments-with-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Exclusive Worlds gets $10 million to help enterprises observe their physical environments with AI Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Worlds, whose AI helps large organizations observe their physical spaces to ensure security, safety, and productivity, has raised $10 million in a first round of funding.
The Dallas-based startup uses multiple cameras to track spaces in 3D over time, providing a more reliable view of what’s happening than typical 2D security video would. Worlds then trains its AI models to find unusual patterns within and across locations — and if a pattern is unusual enough, it alerts the company.
Most surveillance providers rely on rules-based systems, in which humans write the rules for what is considered unusual. But Worlds asserts that this doesn’t go far enough. “The humans writing the rules simply cannot keep up,” said Worlds cofounder and CEO Dave Copps. “With deep learning models, we are able to load videos into our system and make predictions immediately by leveraging prior learning. As the system learns from one environment, it can transfer that learning to other environments.” The company is using reinforcement learning, a segment of AI that’s hot right now, to generate synthetic data so it can create its own training sets. It also uses generative adversarial networks , or algorithmic architectures that pit two neural networks against each other to generate new, synthetic instances of data.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! Worlds’ initial customers include large oil and gas companies Chevron and Petronas, which require sophisticated monitoring of dozens of oil and gas locations, said Copps. Worlds is also targeting U.S. Department of Defense installations and plans to expand to serve retail, entertainment, and other industrial companies, Copps said.
As an example of the technology in action, an oil and gas company can use World’s software to track trucks driving in and out of an oil well property, note how full the trucks are, and proactively alert well operators if a truck arrives full instead of empty, thus posing a security risk. If a company has 100 such oil wells to watch, Worlds’ AI-driven system can track all of them automatically and transfer learnings between them. The surveillance technology can be applied to both open and closed spaces, Copps said.
The funding round, led by Align Capital, includes Chevron Technology Ventures, Piva, GPG Ventures, and Hypergiant Industries.
Worlds is a spinout of Hypergiant Sensory Sciences (HSS), a division of Austin, Texas-based big data and AI product development company Hypergiant Industries. HSS will remain as part of Hypergiant, and Hypergiant Industries will continue to be a key partner for Worlds, Copps said.
VentureBeat wrote about Copps’ cofounding of HSS in 2018. Copps previously built AI company Brainspace, which focused on natural language processing.
More broadly, Worlds says it plays in the growing extended reality (XR) market, where computers generate immersive experiences for physical worlds. Copps again used the example of an oil company like Chevron. When an employee is notified of something unusual, they can zoom in on the area in question. As Copps explained it, they could start with a “satellite view,” which might be enabled by satellites, or even drones flying over a property. The employee can then fly down around a specific property — almost like a drop-in from the popular game Fortnite, Copps said — and view it in 3D, even looking through the walls.
The XR market is expected to grow to more than $209 billion in the next four years, an eightfold increase from the $27 billion estimated in 2018, according to one report.
Copps said Worlds’ system is built on three levels of learning. First, is person to machine, where a person dictates what an object is by labeling it. In the second level, the machine takes over after it’s been trained — on just a few minutes of video — and starts teaching itself and automatically tagging objects. The third level is transfer learning, where the system can transfer learnings from one environment to another. For example, if a company is watching 50 different locations, and location number 37 identifies an unknown truck, the system can tag the truck, follow it around in the video, and then transfer that knowledge to the other environments. “The idea is to create these perpetually learning environments,” said Copps.
The company also leverages a layer of bots assigned for specific tasks, almost like virtual employees, Copps said. A security bot, for example, might be focused on a $100,000 generator and taught to send an alarm if the generator moves more than 30 feet. Another bot might detect people who are not authorized to be in a specific area and send an alarm or ring the person’s phone. The bots are reusable and can be duplicated for multiple environments, Copps said.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"What to expect at Transform 2021, the year’s top event on enterprise AI & data | VentureBeat"
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"https://venturebeat.com/ai/what-to-expect-at-transform-2021-the-years-top-event-on-enterprise-ai-data"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event What to expect at Transform 2021, the year’s top event on enterprise AI & data Share on Facebook Share on X Share on LinkedIn Are you looking to showcase your brand in front of the brightest minds of the gaming industry? Consider getting a custom GamesBeat sponsorship.
Learn more.
AI and machine learning continue to be the driving force behind enterprise transformation. As we’ve learned throughout our editorial coverage of the past year — a year that rocked every industry — business leaders need to be able to use these powerful applications in order to achieve a competitive edge.
To that end, VentureBeat is excited to host Transform 2021 digitally this July 12-16, 2021. The 5-day-long event brings technology executives — as well as technology-minded business executives — the one event of the year featuring true applied lessons that can be carried over into organizations across all industries.
Yet, AI applications are really just the end-point of what can be a long and costly journey for companies. For most companies, the starting point begins with getting their data infrastructure right. This is the bedrock of AI. Accordingly, we’ve opened the lens this year to focus on both AI and data. And, as in the past, Transform covers both strategy and the technology enabling it.
At VentureBeat, we admit we’re rather obsessed with bringing the most comprehensive Transform agenda to our audience. We write more concertedly and deeply than anyone else on these topics: the modern data stack and the processes that you need to build AI.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Late last year, VentureBeat pivoted to make this our exclusive focus. We have millions of monthly readers, and our loyalty — measured as users visiting our site 8 or more times a month — is at a twelve-month high. We are the leading voice on AI themes, according to the third-party tracking service, Techmeme.
But back to Transform. If you join Transform 2021 as a VIP, you’ll also have access to our networking features. Throughout the pandemic, as in-person networking became near-impossible, it’s been vital to allow our community to connect virtually. We’ve worked hard to enable that with 1:1 matchmaking, networking roundtables, and speed-dating sessions we call “Wave” focused on specific themes.
Executives across industries are invited to join the conversation. Transform has tracks covering industries as varied as health care, retail, industrial-manufacturing, finance, and more.
The event has expanded to five days, up from three. You’ll see that the daily agenda is organized around these main practice areas: Day 1 : A day dedicated to general AI/ML automation approaches. We also host our “AI Showcase” in the afternoon.
Day 2 : We go deep on data and analytics. Also on Day 2, there’s a data breakfast with Accenture.
Day 3 : We look at conversational AI.
Day 4 : The edge and IoT and computer vision.
Day 5 : The big picture, and things like our AI innovation awards.
Finally, we’ve put focused attention on diversity and inclusion. We’ve made it a company priority to ensure significant presence of under-represented minorities at all of our events — and discussion of issues around DE&I that we all need to be aware of and participate in.
On day 1, we’re hosting our third Women in AI breakfast gathering, presented by Capital One. On the morning of Day 4, we will have a session on BIPOC in AI. And on Friday, Day 5, we’re hosting the Women in AI awards. And there are a host of conversations about inclusion and bias, including a session with Margaret Mitchell, a leading AI researcher on responsible AI, and others featuring executives from Pinterest, Redfin, Intel, and more.
We want to thank our sponsors for making this all happen. They include Accenture, Five9, Capgemini, Capital One, Intel, Abe.ai, DataStax, Kasisto, Labelbox, Skillsoft, Snowflake, Soul Machines, Tamr, Unity, BeyondMinds, Tealium, and Appen.
Register now to reserve your spot.
Below is a day-by-day agenda overview.
DAY 1 – JULY 12 Women In AI Breakfast, Presented by Capital One Topic: ‘Women in AI: A seat at the table.’ How can we get more women into the AI workforce? What are the roles and responsibilities of corporates, academia, governments, & society as a whole in achieving this goal? Speakers: Kay Firth Butterfield, Head of AI and Machine Learning and Member of the Executive Committee, World Economic Forum Kathy Baxter, Principal Architect, Ethical AI Practice, Salesforce Tiffany Deng, Program Management Lead- ML Fairness and Responsible AI, Google Teuta Mercado, Responsible AI Program Director, Capital One Moderator Noelle Silver, Founder, Women in AI AI/ML Automation Technology Summit The main event event kicks off with the AI/ML Automation Technology Summit, where VentureBeat and our partners will explore the growing trend of automation in various disciplines within AI & ML. These range from data collection and preparation, feature engineering, and model selection to data labeling/annotation, model training and hyperparameter optimization.
Speakers will explore how these automation technologies will change the role of data science teams and how they can co-exist to deliver better applied AI solutions. We will dive deep into the role of data in AI and how enterprises can collect high-quality data using both traditional methods of building their own datasets and leveraging alternative automation strategies such as using synthetic data or crowdsourcing the data to build high-quality AI while addressing issues such as privacy, bias, fairness, and trustworthiness of the data. We will learn about case studies from health acare, finance, security, and retail that deal with AI applications such as user personalization, sales & marketing acceleration, financial fraud mitigation, cybersecurity enhancement, and more.
DAY 2 – JULY 13 Big Bytes in AI & Data, Presented by Accenture Topic: “Decoding the Data” Great applications rely on good data, just like an automobile relies on good oil. Panelists will walk us through the steps their organizations took to analyze & normalize data, including things like ensuring accuracy and reliability. They will review the various technologies used, as well as key strategies, and learnings to ensure their resulting applications are accurate, robust, and bias-free.
Speakers: Anjali Dewan, VP, American Express Mark Clare, Enterprise Head of Data Strategy & Enablement, Evernorth, a subsidiary of Cigna Corporation Arnab Chakraborty, Global Managing Director – Applied Intelligence North America Lead, Accenture Ian Wong, Co-founder & Chief Technology Officer, Opendoor Moderated by Valerie Nygaard, Product Lead, Google Data, Analytics & Intelligent Automation Summit Presented by Accenture In this Data, Analytics, and Intelligent Automation summit day, we will explore the evolution of RPA from delivering tactical, point solutions to the promise of strategic, enterprise-wide, hyper-automation. We will discuss the ins and outs of how to deliver the most impactful RPA by optimizing your business processes before automating them — and also cover where RPA is headed, including the infusion of AI. We will look at the role of data analytics and how it has transformed and empowered business decision-making. We will talk about how the world has come to rely on data and data analytics to generate better and more informed outcomes for all its constituents. We will learn about case studies from manufacturing, health care, finance, and retail that deal with AI applications such as automating patient health alerts and recommendations, automating manufacturing processes, data-driven, personalized risk underwriting, retail consumer buying-pattern prediction, fraudulent insurance claim identification, and more.
DAY 3 – JULY 14 Conversational AI & Intelligent AI Assistants Summit Presented by Five9 One of the most common use cases of applied AI is conversational AI or intelligent AI assistants used primarily to facilitate enterprises to serve their customers at scale more efficiently and through support from the call center. Conversational AI and intelligent AI assistants are being leveraged by organizations across the board in the form of text chatbots, and voice assistants which use NLP/NLU to look beyond rules to truly understand intent, carry out a multi-turn conversation while retaining context, and deliver a great user experience. Companies have made several recent advances in terms of leveraging big data to deliver highly personalized and relevant content and integrating all their systems to ensure a seamless and delightful experience for the user.
As conversational AI advances and AI assistants become more and more intelligent and human-like, we will discuss how we can analyze, train, and sensitize AI natural language to make it explainable, fair, and free of gender and racial stereotypes and biases. We will learn about case studies from finance, manufacturing, health care, retail, and security that deal with AI applications such as financial robo-advisors, personal health care assistants, personalized voice-enabled retail shopping assistants, security strategies to make hacker-proof virtual assistants, and more. Hear from industry-leading practitioners and luminaries who will talk about their journeys and learnings in implementing these technologies, how they unlocked value/ROI from them, and their thoughts about what the future holds.
DAY 4 – JULY 15 AI at the Edge & IoT Summit As more and more IoT devices enter our lives and AI becomes omnipresent, the need to deliver AI at the edge of the network – either on the device or in close proximity to the device — becomes critical to improve performance by processing the data at the edge rather than sending it to the cloud. Edge computing also offers the advantage of keeping the data local and hence more secure.
IoT devices and edge computing along with rapid advances in computing power are creating an ecosystem that can deliver powerful AI business applications that were hitherto unthinkable. At the VB Transform AI at the Edge & IoT Summit, we’ll discuss topics such as ensuring greater user privacy, enabling lower latency, enabling better search and personalization, enabling and accelerating automation, delivering real-time intelligence, etc.
We will learn about case studies from manufacturing, retail, and security that deal with AI applications such as self-monitoring factories with machines that can schedule their own predictive maintenance, edge AI-powered IoT devices that are making retail showrooms super-efficient and effective, security strategies to make edge AI nodes hacker-proof, AI-enabled IoT devices for ubiquitous ecommerce, autonomous, self-driving vehicles running AI on the vehicle, and more.
DAY 5 – JULY 16 The Big Picture in AI & Data Day 5 of Transform will include an overview of the four summits including AI/ML Automation, Data, Analytics, Intelligent Automation, Conversational AI, Intelligent Assistants, Edge AI, and IoT and Computer Vision. There will be featured recaps, overviews, and networking sessions throughout the day.
Register now to join your peers at Transform 2021 , the AI event of the year for enterprise execs looking to maintain their competitive edge in an AI and data driven world, July 12 – 15, 2021.
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"What enterprise leaders need to know about generative AI: 8 key takeaways from VB Transform | VentureBeat"
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"https://venturebeat.com/ai/what-enterprise-leaders-need-to-know-about-generative-ai-8-key-takeaways-from-vb-transform"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Analysis What enterprise leaders need to know about generative AI: 8 key takeaways from VB Transform Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
It has been two weeks since VB Transform , the first major independent event focused on the impact of generative AI on the enterprise.
Generative AI is widely seen as the most powerful technology force since the internet, and enterprise companies are eager to to leverage it.
To help them navigate this new frontier, VB Transform brought together experts and speakers from various industries to share their insights and best practices. See our coverage of the event.
But here are my eight key takeaways for enterprise leaders: 1.
It’s all about your data layer This may seem like an unsexy takeaway, but it is the most important one. Most enterprise companies have huge challenges in getting their data in order, and if they ignore or avoid this, they will miss out on the benefits of generative AI. Data is the fuel for the large language models (LLMs) that fuel generative AI, and without clean, reliable and secure data, LLMs will not perform well, or will even cause harm. One of our roundtables mapped out a best-practice playbook on how to get started preparing your data for LLMs. But if you want to take it to the ultimate level, you need to rewire your entire organization around data. Intuit provides a good example here, building a new operating system for generative AI. It’s one reason Intuit’s chief data officer Ashok Srivastava told me at VB Transform he’s sleeping well , which takes me to the next point.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! 2. Large language models are going to be the default interface for all computing LLMs will power every interaction we have, on the level the phone did and the touchscreen did, and the way graphic user interfaces did. Nick Frosst, cofounder of LLM-building company Cohere, spelled this out most clearly in his talk with VentureBeat’s Sharon Goldman.
The Google-led search paradigm will be over. Instead, we will be able to ask natural language questions and get natural language answers from any source of information. This will create new opportunities and challenges for user experience design, personalization and privacy. And this area of user experience is the one area Intuit’s Srivastava says keeps him up at night.
3.
Fear and anxiety (and excitement) pervade I came into the event knowing this space is new, and that battle-scarred veterans were already saying they are in a state of peak excitement and fear , but this was palpable at the event. The race around generative AI may feel like a sprint, and some enterprise leaders may feel they are already behind, but in reality we’re very early in this race. Some key forces have slowed the move toward this interface (see above), including the need for companies to not share their customers’ personally identifiable information with key providers of foundational LLMs like Microsoft and Open AI’s ChatGPT. Still, respondents to our AI survey show companies are experimenting like crazy with generative AI. (Take the AI Survey yourself, and get a copy of the results for free).
4. Choice abounds: You can build, borrow or piggyback A lot of decision-makers are getting their heads around the best way to build chatbots and other LLM-driven applications. There are several ways to go, depending on how fast you need to get to market, and how proprietary your data is.
If you’re a very big company with resources and clean data, building your own foundational model — or partnering with a company such as MosaicML to do this — might make sense, so that your own data is trained in building the LLMs. Then there’s the slightly easier version of this: Taking one of the open-source foundation models ( LLaMA is now a big one , but there are many others), and fine-tuning its weights and biases to meet your own needs. And you’re not destined to be a laggard here. You can build your own model for about $200,000.
On the other extreme, If you’re doing something lightweight and you don’t need to worry about having a chatbot or other LLM-based application accessing your data, you can just use ChatGPT’s raw API. A slightly more customized version of that is using ChatGPT and then chaining it (via a framework like Langchain) to a vector database that can make sure you can query your own data. (See this good overview by Laura Reeder of Sequoia Capital.
5.
Multiple use cases. But consider aiming for the stars I counted about seven distinct buckets of use cases for LLMs for enterprise companies, articulated during this high-powered conversation with Amazon AWS VP of product Matt Wood and Google VP of data and analytics Gerrit Kazmaier. Briefly, they are: Generation, including not only content but new software; ranking, personalization and relevancy apps; apps to allow experts and others to learn more efficiently about new fields; collaborative problem-solving through automated decision-support; new customer experiences; building entirely new products; and building new companies. Just as the internet spawned new companies like Amazon, Netflix and Airbnb to redefine products with better experiences, the same will happen over the next six months to three years, said Wood. And this will happen faster than during the internet boom, because LLMs are much more accessible to more people.
6.
Conversational business intelligence is a thing You should learn how to build LLM-based chatbots to query your corporate data. ChatGPT is not doing a great job of meeting specific enterprise chatbot needs because it is generally intelligent, but not specifically intelligent. Increasingly, there’s realization that you need to help your users with prompts to know how to access the data they need, and there are many ways to do this. iGenius, a sponsor of VB Transform, is one company helping enterprise companies build these experiences in customized way.
7. Unlike crypto, real revenue is being made in generative AI LLMs are still largely about saving costs through productivity gains. But are there any revenue use cases? It’s not clear yet. So far most of the most money being made in gen AI is by startups that are selling cost-saving generative AI solutions , or by companies like Nvidia, which is selling GPUs to run LLMs. Tim Tully, an investor in Menlo Ventures, weighed in during a roundtable session at Transform, saying this revenue generation is the big difference between crypto and LLMs: ”I’ve been in tech for like 25 years. I’ve never seen anything like this. Companies go from like zero to 30 in like three months … It’s just incredible.” He said companies are getting contracts because they’re creating value, citing L’Oreal, Pepsi, Coca-cola, Honda and Michelin all buying generative AI contracts.
Aside from startups, big companies may also be able to use LLMs to generate revenue directly: As McKinsey points out , some incremental revenue can come from boosting sales by using LLMs to do better customization and personalization.
8. Some dreamier ambitions for LLMs may be overrated In one intriguing roundtable session hosted by NTT, a sponsor of VB Transform, some participants talked about ways LLMs could be used for more radical breakthroughs. There was speculation, for example, about being able to speed up the learning process of new employees by ingesting corporate Slack and email conversations into LLMs to help summarize communications and other corporate processes that otherwise take years to learn. Other enthusiasts hope LLM apps will be able to predict trends in stock prices, or better reorganize factory floors. Matt Wood of Amazon talked of the flywheel effect that can be created with LLMs, using automated decision support systems. In his session, though, Cohere’s Nick Frosst threw cold water over applications that are too far afield from where they really excel, which is text generation and summarization. In other words, they are good at letting you pose questions about content, and getting answers to that as output.
Looking forward: VB Data Summit in SF VentureBeat looks forward to tracking these trends as they unfold over the next weeks and months. We’re going to be biting off the first one of these takeaways — how to clean in your data layer — at our upcoming VentureBeat Data Summit 2023 on November 15 at the Terra Gallery in San Francisco. If you’re an enterprise decision-maker wanting to leverage LLMs, this would be a great place to network with your peers, get insights and make decisions. Join us in some peer networking, and pre- register now to get a 50% discount.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"VentureBeat's flagship AI event, Transform, goes fully digital and extends to 3 days | VentureBeat"
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"https://venturebeat.com/ai/venturebeats-flagship-ai-event-transform-goes-fully-digital-and-extends-to-3-days"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event VentureBeat’s flagship AI event, Transform, goes fully digital and extends to 3 days Share on Facebook Share on X Share on LinkedIn VB Transform 2020: Hosted online 7/15 - 7/17 Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Transform 2020 , VentureBeat’s AI event of the year for enterprise decision-makers, is shifting to an online-only event to protect our community amid concerns around the coronavirus.
Transform 2020 will be held online July 15-17, expanding from two days to three. Each day is dedicated to a central theme in AI, spanning the Conversational AI Summit, Technology & Automation Summit, IoT & AI at the Edge, and Computer Vision.
During this unprecedented time, VentureBeat is providing a way for the AI business community to experience virtually the same engaging experiences offered by in-person events. To foster personal connections, we’re adding digital equivalents for networking, one-to-one meetings, matchmaking, and live Q&As with speakers.
VentureBeat is the leading publisher of AI content coverage, and we bring our AI expertise, reach, and network to bear at Transform. Our AI channel receives 20 million pageviews a year and has a social reach of 1.5 million. News coverage from Transform alone generates more than a million pageviews. Transform gathers together 750 executives at the director level and above to discuss the best practices in applied enterprise AI. The event also offers invite-only experiences for executives at the VP level and above who come every year.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Transform features keynote speakers from heavy hitters like Google, Intuit, Walmart, Pinterest, Salesforce, Adobe, AWS, Chase, Goldman Sachs, PayPal, Doordash, Visa, and Disney — and from a growing list of sponsors, including Intel, Dataiku, Capital One, Booz Allen Hamilton, and Two Hat Security. Other speakers include leading AI professors from academia, including from Stanford and University of California, Berkeley. Transform will also host its popular Women in AI Breakfast , which is sponsored by Intel and Capital One. The AI event will explore diversity and inclusion, reflect on the importance of ethics in AI practice, and feature our 2nd annual AI Innovation Awards.
VentureBeat’s virtual event will allow AI innovators and leading executives from around the world to come together despite ongoing challenges posed by the pandemic. Our digital options will enable even more people to participate this year — expanding the experience to an even wider group of businesses and executives.
As part of our digital offering, attendees will be able to view live sessions with AI leaders, join networking sessions, engage in digital roundtables, take part in our AI Expo and Tech Showcase online, and participate in 1:1 meetings with world-class AI providers.
“With Transform being the only significant digital AI event for business executives on the map for 2020, it’s important we take leadership in the community and support dialogue and networking for decision-making around AI,” said Matt Marshall, founder and CEO of VentureBeat. “We’ve created a virtual event platform for networking and 1:1 meetings to happen so that we can bring thought leaders in AI together.” Interested investors, vendors, and business executives can register here to join the event online. For companies interested in partnerships, we have innovative and high-impact custom solutions in place for all the components for our strategic sponsors.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"VB Transform: Navigating the generative AI wave, insights from top executives at major corporations | VentureBeat"
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"https://venturebeat.com/ai/vb-transform-navigating-the-generative-ai-wave-insights-from-top-executives-at-major-corporations"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event VB Transform: Navigating the generative AI wave, insights from top executives at major corporations Share on Facebook Share on X Share on LinkedIn Attendees network at VB Transform in 2022 Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
In recent days, I’ve had the pleasure of speaking with top executives from enterprise giants like Mastercard, Citi, Verizon, Intuit and Wayfair. Their verdict is unanimous: Generative AI , the technology that can generate conversation mimicking humans at an unprecedented level, is the most powerful trend this year.
Late last year, OpenAI’s GPT-4 entered the market, and now, similar offerings are emerging from companies like Microsoft and Google. Executive board rooms are buzzing with discussions about ChatGPT, as leaders recognize the transformative potential of this technology. Generative AI could single-handedly add about 7% to economic output within the next decade, according to Goldman Sachs.
>>Follow VentureBeat’s ongoing generative AI coverage<< Are these enterprise leaders prepared for the rapid changes ahead? How should they respond, and who will be disrupted? The urgency is palpable, even for the most security-conscious and regulated companies where change typically unfolds slowly. These organizations are now scrambling to launch proofs of concept.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! JoAnn Stonier, chief data officer at Mastercard, shared her thoughts in a recent video interview : “Everyone’s talking about it as a real game-changer, and I do think it is. We will see an awful lot of change in a very rapid amount of time.” Bringing together those ahead of the generative AI curve Stonier is just one of many enterprise speakers slated to join us at our VB Transform event on July 11 and 12.
This gathering will focus on how enterprise companies can adapt to generative AI and gain a competitive edge in the market.
With the technology landscape evolving at breakneck speed — it’s near impossible to stay on top of the hundreds of generative AI technologies being rushed out by vendors — it’s crucial for industry professionals to network and learn from their peers. At Transform, we’ll bring together technical leaders from the largest enterprises who are grappling with generative AI — some of whom have been experimenting for years and are ahead of the curve.
The event is designed for networking, with both structured and spontaneous opportunities to connect with fellow attendees. We’re excited that Transform is one of the first independent events to concentrate on the generative AI trend for enterprise technical decision-makers.
Transform will feature candid discussions about the strengths and weaknesses of closed products offered by OpenAI and giants like Microsoft, Google, and others. We’ll also explore when to use more open models from companies like HuggingFace and Meta’s LLaMA.
Many companies, such as online furniture and home decor retailer Wayfair, are adopting a pragmatic bimodal strategy : using closed models for quick market entry and open-sourced models for long-term projects where competitive advantage and unique value propositions are crucial. (At Transform, we’ll hear from Wayfair’s data science leader, Wilko Schulz-Mahlendorf, about this approach.) At Transform, other speakers from Walmart, Wells Fargo, Hyatt, Citi, Alaska Airlines, FedEx, McDonald’s, eBay, Verizon and many more will share their insights on generative AI. Attendees will learn from trailblazers as well as those at earlier stages of their AI journeys.
Privacy concerns For example, Intuit has been quick to adopt conversational AI and has built a sophisticated technology platform that enables rapid response to generative AI. As a result, they’re ahead of the game. We’ll hear from Intuit’s chief data officer, Ashok Srivastava, about GenStudio, the company’s lab for scaling generative AI , and what other organizations can learn from Intuit’s experience.
The company has adopted a largely open-sourced model, allowing it to keep its data private and ensure accuracy, something that advanced companies with resources can afford to do. You can use a model from a Microsoft or Google, but you have to share your data in return. Some enterprise companies, like Apple, are so wary of providing their data to those competitors that they’ve severely restricted what their employees can do with those models.
Meanwhile, we’ll hear from executives at companies like Citi, Mastercard and Verizon — leaders in finance and telecom — who are grappling with the implications of generative AI in highly regulated industries where data protection and customer privacy are paramount. Even these organizations are starting proofs of concept and exploring hybrid strategies for deploying generative AI. Among those speakers is Citi’s Promiti Dutta, head of analytics technology and innovation, who in a recent interview talked about Cit’s vast data center resources and how they’ll help with generative AI, which requires “huge” compute.
Finally, I know from my conversation with Mastercard’s Stonier that she’ll likely echo the concerns of many others about the technology, and emphasize the responsibilities leaders have to use the technology correctly, and think through the impact on individuals.
At Transform, we’ll also hear from big vendors like Microsoft and Google, who will face tough questions from our independent moderators about their generative AI solutions. And we’ll hear from the next tier of generative AI solutions providers, via the Innovation Showcase and Innovation Alley.
As we approach Transform, we’re eagerly anticipating the depth of knowledge and insights that will be shared by industry leaders. We’re excited to host this event amid the revolution that’s sweeping the industry and look forward to welcoming you there. Be sure to sign up soon for the early bird ticket, which has been extended to end of next week (use code VBTEXTEND99). Stay tuned for more announcements about our AI Award finalists, and other networking opportunities.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Unpacking AI's power and its controversies at Transform 2020 | VentureBeat"
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"https://venturebeat.com/ai/unpacking-ais-power-and-its-controversies-at-transform-2020"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event Unpacking AI’s power and its controversies at Transform 2020 Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
I can’t wait until Transform 2020 starts tomorrow. It’s our flagship event for enterprise decision-makers to learn how to apply AI.
One of our goals at VentureBeat is to create a new kind of town square for enterprise decision makers to learn about transformative technology and transact. And the practice of AI is where we’re going deep. AI is the most powerful technology in enterprise today, and VentureBeat is the leading publication covering AI news.
So it’s important that VentureBeat create a virtual platform where that community can come together, and have conversations. Since we’ve already been digital with our news offering, we are able to pivot fully virtual and bring the same, if not more value to our AI events.
I’m pretty proud of what we’ve done, including the one-to-one meeting feature for executives who would like to connect with each other to get business done.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! I’m excited about the agenda we have for the three days. We’re focused on the top application areas: for example, conversational AI and computer vision and edge IoT.
In my opening remarks tomorrow, I’ll provide preliminary results of our AI survey , and an overview of the big trends we’re seeing — shaped by the hundreds of conversations we had with executives while preparing for the show.
Above: CTO Twitter Parag Agrawal I’ll personally be interviewing Twitter CTO Parag Agrawal, about how Twitter has been using AI/ML at scale to foster a more constructive public discourse and to flag harmful speech. Twitter has been in the hot-seat lately — forced to label tweets from President Donald Trump that it perceived as misleading or harmful.
And how does Twitter flag offensive tweets accurately, when researchers have shown that leading AI models processing hate speech are often inaccurate or biased? Agarwal will discuss how Twitter blends AI/ML, policy, and product in a dynamic environment — where it has to counter adversaries who often use state of-the-art conversational AI technology themselves.
We’ll address the gap in perceived importance of ethics and accuracy in AI. In our AI survey , most of our practitioner respondents said they believe “enough is being” done at their companies to counter bias (ethnic, gender, etc.) in implementing AI models. This contrasts with what we’re hearing from professionals from underrepresented backgrounds. On Thursday morning, we’ll have an hour-long roundtable on the topic of “Diversity and Inclusion in AI” led by four Black professionals, which I highly recommend attending if you can get in (it will be capped). This will be a strong session, and eye-opening for anyone thinking “enough” is being done to counter bias in AI. It will follow our Women in AI (virtual) Breakfast, where we’ll have Timnit Gebru and other leaders represented.
One area of particular controversy is facial recognition technology, where study after study has shown that it is less accurate on underrepresented populations. At our AI showcase at Transform, Trueface , a facial recognition company, will be releasing a new product, and Hari Sivaraman, Head of AI Content Strategy, VentureBeat, will have a crossfire Q&A with Trueface CEO Shaun Moore about how it is using facial recognition and its purported accuracy.
There’s too much happening to summarize entirely here.
The AI Innovation Awards tomorrow evening, the Expo , the intimate roundtables … But it does look like Transform will be the biggest AI event for business executives this year, given that most other events were canceled or postponed. We have almost 3,000 people registered, double the number from last year.
And of course, none of this is possible without our great sponsors — folks like Dataiku, Intel, CapitalOne, Nvidia, Modzy, Cloudera, DotData, Twohat, Dell, Inference Solutions, Anaconda, Conversica, SambaNova, Xilinx, Globant, and more. Many of their executives will be participating as speakers, alongside speakers from some great brands like Walmart, Uber, Google, Adobe, Chase, Goldman Sachs, Visa, PayPal, Intuit, CommonSpirit Health, GE Healthcare, Pfizer, Pinterest, Slack, Yelp, LinkedIn, eBay, and Salesforce.
Looking forward to seeing you there — virtually! (Register here: vbtransform.com) VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Stanford study challenges assumptions about language models: Larger context doesn’t mean better understanding | VentureBeat"
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"https://venturebeat.com/ai/stanford-study-challenges-assumptions-about-language-models-larger-context-doesnt-mean-better-understanding"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Stanford study challenges assumptions about language models: Larger context doesn’t mean better understanding Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A study released this month by researchers from Stanford University, UC Berkeley and Samaya AI has found that large language models (LLMs) often fail to access and use relevant information given to them in longer context windows.
In language models, a context window refers to the length of text a model can process and respond to in a given instance. It can be thought of as a working memory for a particular text analysis or chatbot conversation.
The study caught widespread attention last week after its release because many developers and other users experimenting with LLMs had assumed that the trend toward larger context windows would continue to improve LLM performance and their usefulness across various applications.
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<< VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! If an LLM could take an entire document or article as input for its context window, the conventional thinking went, the LLM could provide perfect comprehension of the full scope of that document when asked questions about it.
Assumptions around context window flawed LLM companies like Anthropic have fueled excitement around the idea of longer content windows, where users can provide ever more input to be analyzed or summarized. Anthropic just released a new model called Claude 2, which provides a huge 100k token context window , and said it can enable new use cases such as summarizing long conversations or drafting memos and op-eds.
But the study shows that some assumptions around the context window are flawed when it comes to the LLM’s ability to search and analyze it accurately.
The study found that LLMs performed best “when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts. Furthermore, performance substantially decreases as the input context grows longer, even for explicitly long-context models.” Last week, industry insiders like Bob Wiederhold, COO of vector database company Pinecone, cited the study as evidence that stuffing entire documents into a document window for doing things like search and analysis won’t be the panacea many had hoped for.
Semantic search preferable to document stuffing Vector databases like Pinecone help developers increase LLM memory by searching for relevant information to pull into the context window. Wiederhold pointed to the study as evidence that vector databases will remain viable for the foreseeable future, since the study suggests semantic search provided by vector databases is better than document stuffing.
Stanford University’s Nelson Liu, study lead author, agreed that if you try to inject an entire PDF into a language model context window and then ask questions about the document, a vector database search will generally be more efficient to use.
“If you’re searching over large amounts of documents, you want to be using something that’s built for search, at least for now,” said Liu.
Liu cautioned, however, that the study isn’t necessarily claiming that sticking entire documents into a context window won’t work. Results will depend specifically on the sort of content contained in the documents the LLMs are analyzing.
Language models are bad at differentiating between many things that are closely related or which seem relevant, Liu explained. But they are good at finding the one thing that is clearly relevant when most other things are not relevant.
“So I think it’s a bit more nuanced than ‘You should always use a vector database, or you should never use a vector database’,” he said.
Language models’ best use case: Generating content Liu said his study assumed that most commercial applications are operating in a setting where they use some sort of vector database to help return multiple possible results into a context window. The study found that having more results in the context window didn’t always improve performance.
As a specialist in language processing , Liu said he was surprised that people were thinking of using a context window to search for content, or to aggregate or synthesize it, although he said he could understand why people would want to. He said people should continue to think of language models as best used to generate content, and search engines as best to search content.
“The hope that you can just throw everything into a language model and just sort of pray it works, I don’t think we’re there yet,” he said. “But maybe we’ll be there in a few years or even a few months. It’s not super clear to me how fast this space will move, but I think right now, language models aren’t going to replace vector databases and search engines.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Sierra Ventures' Tim Guleri on generative AI: 'I've never been as excited and as scared in my 20 years of doing VC' | VentureBeat"
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"https://venturebeat.com/ai/sierra-ventures-tim-guleri-on-generative-ai-ive-never-been-as-excited-and-as-scared-in-my-20-years-of-doing-vc"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Feature Sierra Ventures’ Tim Guleri on generative AI: ‘I’ve never been as excited and as scared in my 20 years of doing VC’ Share on Facebook Share on X Share on LinkedIn Tim Guleri Credit: Sierra Ventures Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Tim Guleri, a venture capitalist at Silicon Valley firm Sierra Ventures , has witnessed numerous technology revolutions throughout his career, but recently told VentureBeat he believes the current emergence of generative AI is the most significant and intimidating of them all.
With three decades of experience in founding and investing in enterprise technology, Guleri built companies during the internet boom of the 1990s and then invested in startups during the subsequent Web 2.0, mobile and big data revolutions of the 2000s and 2010s.
Guleri’s investments include Sourcefire and MakeMyTrip, which eventually went public. In 2000, he founded Octane Software, an ecommerce company that sold for $3 billion to Epiphany while he was serving as CEO.
Guleri’s resume establishes him as a VC heavyweight in the area of enterprise software investing.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Discussing the current investment landscape, Guleri told VentureBeat in an exclusive interview: “I have never been as excited and as scared in my 20 years of doing venture capital because of gen AI.” His excitement stems from the potential for generative AI to vastly improve value creation in the industry, while his fear arises from concerns about privacy and ethical issues surrounding the technology.
“I’m excited because everything that we have done to create value in the industry can be redone 100 times better riding on the back of gen AI,” he continued. “I’m scared because of the things that we’re talking about in terms of hallucinations and privacy and all of the things that I believe need to be fixed entirely … and I’m a bit paranoid, I guess, when it comes to the firm because I think we need to lead the way as an early stage venture firm in the way we operate — to catch the best opportunities early enough.” His marketing partner at Sierra, Anne Gherin, said Guleri has for the past few months spent nights and weekends learning, testing and investing in generative AI. To every Monday partner meeting he brings in a new report, presentation or diagram on the topic, she said.
As the recent breakthroughs of ChatGPT have garnered widespread attention, Silicon Valley has increasingly embraced AI, with many heralding generative AI as an unparalleled paradigm shift. Marc Andreessen, co-inventor of the first web browser and leader of prominent venture firm Andreessen Horowitz (a16z), recently published a blog post asserting that AI could be the most important and beneficial invention in human history , on par with or surpassing electricity and microchips.
However, big firms like Andreessen’s can make big shifts based on trends. Until last year, it dominated investments in crypto and other financial services.
Meanwhile, Guleri’s focus on early-stage investing in enterprise technology hasn’t wavered, through both frothy and bear markets.
While his approach can yield substantial returns if he invests in the right companies, it also carries significant risk due to the challenge of identifying promising startups.
That’s what underscores his obsession with generative AI.
Gen AI is changing the company-building playbook Guleri says generative AI will disrupt the traditional playbook of building companies in the enterprise software market for the past 30 years, one that has centered around automation.
Successful companies like Oracle, PeopleSoft, Workday, Salesforce.com and ServiceNow have all been built by taking relatively manual processes and automating them.
Whether it was customer support, sales, IT or human resources, the approach started with a relationship database, along with a business process on top of it. Guleri explained: “It’s a UX slapped on a database table,” and a bunch of salespeople who know how to sell.
That process alone, he said, has created the enterprise software market, which is $200 billion in revenue.
He pointed to Salesforce.com as an example, where it alone is now worth $204 billion.
Guleri believes generative AI will pull apart that software ecosystem and reassemble it, and do so in unpredictable ways, he said.
Like some industry analysts, he uses the word “intelligence” to describe how generative AI will elevate technology, referring to the ability of large language models (LLMs) to mimic aspects of human intelligence.
“The next multiple decades will be about intelligence,” he said, not automation.
The next enterprise software blueprint is emerging, and includes more open source Guleri emphasized the necessity for his firm to maintain an open mind while formulating a vision for the future of enterprise architecture.
Guleri shared his contrarian view that business applications will continue to be built on top of relational databases, rather than vector databases , which have been touted for their ability to handle unstructured data that drives LLM models. He believes relational databases will integrate vector and embedding capabilities, eliminating the need for separate vector databases.
Another clear pattern is that there will be no LLM to rule them all. “There’s this notion of chaining LLMs,” he said.
Moreover, Guleri predicted that open source will dominate large language models (LLMs) due to increasing regulatory scrutiny on companies like Amazon, Google and OpenAI. Those innovators will have to be more “premeditated” in what they put on the market and enterprise customers will be wary about what they put into production. “Open source is going to run ahead,” he said.
He also highlighted the importance of identifying genuine generative AI startups amid a sea of pretenders. Many entrepreneurs have “AI-washed” their presentation decks from six months ago, he said.
With about 13 investments in generative AI under its belt, Sierra Ventures began backing such startups before the recent surge in interest. The firm’s first investment in the field came in 2018 with Krisp, a noise-canceling app startup, followed by a $4.25 million seed round in Quillbot, a content management company, in 2020. Quillbot was later acquired by Course Hero for an undisclosed sum. Other investments include Deephow and Modulate.
Guleri acknowledged that the disruptive power of generative AI has only recently become apparent, stating, “It has only become obvious to me over the last six months — on how fundamentally disruptive this is going to be.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Pinecone leads 'explosion' in vector databases for generative AI | VentureBeat"
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"https://venturebeat.com/ai/pinecone-leads-explosion-in-vector-databases-for-generative-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Feature Pinecone leads ‘explosion’ in vector databases for generative AI Share on Facebook Share on X Share on LinkedIn Bob Wiederhold, Pinecone COO, right, speaks with investor Tim Tully, at VB Transform on Wednesday Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Vector databases , a relatively new type of database that can store and query unstructured data such as images, text and video, are gaining popularity among developers and enterprises who want to build generative AI applications such as chatbots, recommendation systems and content creation.
One of the leading providers of vector database technology is Pinecone , a startup founded in 2019 that has raised $138 million and is valued at $750 million.
The company said Thursday it has “way more than 100,000 free users and more than 4,000 paying customers,” reflecting an explosion of adoption by developers from small companies as well as enterprises that Pinecone said are experimenting like crazy with new applications.
By contrast, the company said that in December it had fewer than in the low thousands of free users, and fewer than 300 paying customers.
Pinecone held a user conference on Thursday in San Francisco, where it showcased some of its success stories and announced a partnership with Microsoft Azure to speed up generative AI applications for Azure customers.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! >> Follow all our VentureBeat Transform 2023 coverage << Bob Wiederhold, the president and COO of Pinecone, said in his keynote talk that generative AI is a new platform that has eclipsed the internet platform and that vector databases are a key part of the solution to enable it. He said the generative AI platform is going to be even bigger than the internet, and “is going to have the same and probably even bigger impacts on the world.” Vector databases: a distinct type of database for the generative AI era Wiederhold explained that vector databases allow developers to access domain-specific information that is not available on the internet or in traditional databases, and to update it in real time. This way, they can provide better context and accuracy for generative AI models such as ChatGPT or GPT-4, which are often trained on outdated or incomplete data scraped from the web.
Vector databases allow you to do semantic search, which is a way to convert any kind of data into vectors that allow you to do “nearest neighbor” search. You can use this information to enrich the context window of the prompts. This way, “you will have far fewer hallucinations , and you will allow these fantastic chatbot technologies to answer your questions correctly, more often,” Wiederhold said.
Wiederhold’s remarks came after he spoke Wednesday at VB Transform , where he explained to enterprise executives how generative AI is changing the nature of the database, and why at least 30 vector database competitors have popped up to serve the market. See his interview below.
Wiederhold said that large language models (LLMs) and vector databases are the two key technologies for generative AI.
Whenever new data types and access patterns appear, assuming the market is large enough, a new subset of the database market forms, he said. That happened with relational databases and no-SQL databases, and that’s happening with vector databases, he said. Vectors are a very different way to represent data, and nearest neighbor search is a very different way to access data, he said.
He explained that vector databases have a more efficient way of partitioning data based on this new paradigm, and so are filling a void that other databases, such as relational and no-SQL databases, are unable to fill.
He added that Pinecone has built its technology from scratch, without compromising on performance, scalability or cost. He said that only by building from scratch can you have the lowest latency, the highest ingestion speeds and the lowest cost of implementing use cases.
He also said that the winner database providers are going to be the ones that have built the best managed services for the cloud, and that Pinecone has delivered there as well.
However, Wiederhold also acknowledged Thursday that the generative AI market is going through a hype cycle and that it will soon hit a “trough of reality” as developers move on from prototyping applications that have no ability to go into production. He said this is a good thing for the industry as it will separate the real production-ready, impactful applications from the “fluff” of prototyped applications that currently make up the majority of experimentation.
Signs of cooling off for generative AI, and the outlook for vector databases Signs of the tapering off, he said, include a decline in June in the reported number of users of ChatGPT, but also Pinecone’s own user adoption trends, which have shown a halting of an “incredible” pickup from December through April. “In May and June, it settled back down to something more reasonable,” he said.
Wiederhold responded to questions at VB Transform about the market size for vector databases. He said it’s a very big or even enormous market, but that it’s still unclear whether it will be a $10 billion market or a $100 billion market. He said that question will get sorted out as best practices get worked out over the next two or three years.
He said that there is a lot of experimentation going on with different ways to use generative AI technologies, and that one big question has arisen from a trend toward larger context windows for LLM prompts. If developers could stick more of their data, perhaps even their entire database, directly in a context window, then a vector database wouldn’t be needed to search data.
But he said that is unlikely to happen. He drew an analogy with humans who, when swamped with information, can’t come up with better answers. Information is most useful when it’s manageably small so that it can be internalized, he said. “And I think the same kind of thing is true [with] the context window in terms of putting huge amounts of information into it.” He cited a Stanford University study that came out this week that looked at existing chatbot technology and found that smaller amounts of information in the context window produced better results. (Update: VentureBeat asked for a specific reference to the paper, and Pinecone provided it here ).
Also, he said some large enterprises are experimenting with training their own foundation models, and others are fine-tuning existing foundation models, and both of these approaches can bypass the need for calling on vector databases. But both approaches require a lot of expertise, and are expensive. “There’s a limited number of companies that are going to be able to take that on.” Separately, at VB Transform on Wednesday, this question about building models or simply piggybacking on top of GPT-4 with vector databases was a key question for executives across the two days of sessions. Naveen Rao, CEO of MosaicML, which helps companies build their own large language models, also spoke at the event, and acknowledged that a limited number of companies have the scale to pay $200,000 for model building and also have the data expertise, preparation and other infrastructure necessary to leverage those models. He said his company has 50 customers, but that it has had to be selective to reach that number. That number will grow over the next two or three years, though, as those companies clean up and organize their data, he said. That promise, in part, is why Databricks announced last week that it will acquire MosaicML for $1.3 billion.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"No, IBM is not the only relevant player in virtual agents | VentureBeat"
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"https://venturebeat.com/ai/no-ibm-is-not-the-only-relevant-player-in-virtual-agents"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages No, IBM is not the only relevant player in virtual agents Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Last month, IBM General Manager of Data and Watson AI, Rob Thomas, told VentureBeat that IBM was the only major enterprise provider in the red-hot area of virtual agents.
Virtual agents are software that can chat with customers through text, voice, or web chat. “There really are no big players, except for us,” Thomas said at the time. He called the rest of the virtual agent providers “fireflies,” because they are small and there are so many of them.
After we published our interview with Thomas, we asked a few of the so-called “fireflies” what they thought of his assessment. We heard back from Zor Gorelov, CEO of Kasisto, and Ryan Lester, an exec at LogMeIn’s Bold360 unit, which builds virtual agents for enterprise companies. Here’s what they had to say.
Zor Gorelov, CEO, Kasisto: Here are some interesting and specific examples of where we feel Rob is just missing the point.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Domain knowledge and depth is important.
IBM does not have domain specificity required to be effective in financial services. Natural language understanding (NLU) is an essential part of making an effective virtual agent. And the agent has to be well trained for the domain it will be working in. At Kasisto we believe we are able to build the most effective virtual agents for the financial services industry because we have data from 30 million+ utterances (growing at millions per month) collected from real users interacting with our virtual agents, making our virtual agents smarter every day.
Our platform, KAI, started its life as an advanced R&D project at SRI International, creators of Siri. Some of the most innovative and advanced AI technologies have come out of SRI over the decades. Rob might define Kasisto as a firefly, but we have the AI team and technology pedigree that many in the industry know, respect, and even admire. Eighty strong, our employees work long hours to deliver a conversational AI experience that financial institutions all over the world are adopting, with contracts extending 3-5 years and virtual agents deployed to millions of banking customers in North America, Europe, Asia, and the Middle East.
The assumption that chatbots are built on 1990s rules-based technology is factually incorrect.
Rob said: “I would distinguish that from chatbots, which are mostly rules-based engines. That’s not what we do with Watson Assistant. At the core of it is a model for intent classification.” KAI is built on advanced intent classification using state of the art NLU engines that leverage many of the same underlying technology and algorithms that Watson uses, and has been trained to understand precisely what customers are asking. As a matter of fact, often our customers experience 80%+ conversation containment rate (meaning that KAI completely serviced the conversation without any human intervention). This could never be achieved with a rules-based chatbot.
IBM isn’t the only company that can do “feature engineering.” Rob said: “Any competitor can do hyper parameter optimization, but nobody other than us can do feature engineering. With something called AutoAI, we can automate feature engineering that cuts down 80% of the data science work.” Feature engineering has been around for quite some time. It’s a method our industry has been using to help simplify how new AI models are created. IBM is certainly not alone in using automation for these methods. With that said, feature engineering itself has actually become somewhat antiquated and been replaced by more advanced methods enabled by the large amounts of compute power, data, and deep neural network algorithms. So where Rob feels this is state of the art for Watson, it’s no longer state of the art in the industry.
It’s not true that startups can’t handle large numbers of intents.
Rob said: “Most of the fireflies will serve, you know, 10 questions that they can teach the assistant to answer. But what happens when 10 questions becomes 500 questions? That’s when you need us.” Some of Kasisto’s largest deployments have 2000+ intents and are being used by millions of users across multiple geographies, countries, and languages. So again, call Kasisto a firefly, but we are serving larger and more complicated customer deployments than Rob is probably aware of.
Ryan Lester, Senior Director, Customer Engagement Technologies, LogMeIn Major players like IBM, Microsoft, Google, and Amazon have all made announcements related to virtual agents — assistants that go beyond rules-based chatbots to provide more free-form interactions. And numerous smaller companies, including my own, are releasing virtual agents, too. This trend is only going to accelerate as we go into 2020.
While many of these large platform investments are exciting, they are often out of reach for companies that are not in the Fortune 1000 and lack the technical resources to build on top of these platforms.
So powerful but smaller, more nimble ‘fireflies’ have a major role to fill here.
Companies should be thoughtful about how and where they use solutions from the large tech providers as they often require significant development and integration work.
And that leads me to a second trend: the expansion of access to virtual agents.
Conversational AI and virtual agents are no longer just for the enterprise. Even with investment from the largest tech companies, much of the virtual agent technology to date has been out of reach for the mid-market and smaller business, due to a lack of technical talent, insufficient data to train the systems, and the cost and time of implementation. The good news is that is no longer the case. There are numerous companies working to make AI-powered virtual agents more accessible and easier to implement, even for non-profit companies.
These projects are no longer monumental lifts, and anyone looking to implement a virtual agent should see a return on investment within a year and an implementation that takes just a few months. Things can certainly get more complex over time, and therefore more transformational to a business, but in general there are a lot of easy wins that any size business can tackle.
The latest generation of tools, many of them coming from “fireflies,” is expanding access to building and managing virtual agents to a much broader audience of users beyond developers and data scientists. They do this via three channels: Better NLP tools that don’t require months of data training and set up Simplified user interfaces that don’t require code writing and have pre-built connectors for third-party data and content Analytics tools that help business and subject matter experts better understand how well the solution is working and where to focus next.
Last year was a year full of announcements that are pushing the virtual agent and chatbot industry forward, both in foundational technology and in business applications that will drive value for any size business.
The large technology platforms are delivering new functionality, but they can also eat up valuable internal development resources and can take time to build and implement. There’s often a bias to standardize a single AI technology platform for all projects, but virtual agents may be better designed on more purpose-built applications. The entire industry is making it easier to create and manage virtual agents and chatbots, so companies need to consider how customized they need their solution to be as they plan for 2020.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Neural Magic, which offers software for growing edge AI market, gets $30M boost | VentureBeat"
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"https://venturebeat.com/ai/neural-magic-which-offers-software-for-growing-edge-ai-market-gets-30-million-boost"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Neural Magic, which offers software for growing edge AI market, gets $30M boost Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
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Neural Magic, which provides software to facilitate deep learning deployment in edge locations, today announced a $30 million series A funding round.
The market for edge AI is exploding as more companies deploy the technology in a variety of applications across industries — including in areas like asset maintenance and monitoring, factory automation, and telehealth. The market is expected to be worth $1.83 billion by 2026, according to a report by Markets and Markets.
Keeping pace But customer accelerator chips made by companies like Google and Nvidia for inference at the edge are increasingly unable to keep up with the improvements in efficiency, speed, and cost offered by additional software approaches , like the one pushed by Neural Magic.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Many companies and developers prefer the simplicity of using basic CPU chips and optimizing with software. A company like Target has racks of hardware in every store and might prefer to optimize with software made by Neural Magic rather than making complex, bespoke investments into specialized accelerator chips like Google’s Tensor Processing Unit (TPU), Neural Magic CEO Brian Stevens said in an interview with VentureBeat.
“That’s the world we’re trying to create. We want to give developers the flexibility to deploy AI on commodity processors already located in the edge location,” he said.
This financing, which brings the company’s total amount raised to $50 million, was led by existing investor NEA, with participation from Andreessen Horowitz, Amdocs, Comcast Ventures, Pillar VC, and Ridgeline Ventures.
Neural Magic will use the new capital to invest in the open source inference models it has built, as well as the proprietary engine the company offers to help developers deploy the models.
Industry expansion Since the company launched two years ago , a host of other startups have emerged to offer some sort of AI software at the edge, including NeuReality , Deci , CoCoPie, OctoML, and DeepCube.
However, Neural Magic is the only company offering free open source modeling, matched with a software deployment engine optimized for speed, Stevens said.
Players like AMD and Intel are also working on optimization software layers for their hardware. For example, Intel has released OpenVino, a free toolkit for optimizing deep learning models. However, Neural Magic offers what it calls “recipes” that can be plugged into machine learning libraries like PyTorch to make models more sparse and speed up its engine, Stevens said.
The company’s open source offering launched quietly in February and now has upwards of 1,000 unique installations per week, according to Stevens, who took over as CEO this year. The traction got NEA’s Greg Papadopoulos excited enough about the company to lead the latest investment and join Neural Magic’s board, Stevens said. Papadopoulos is the former CTO of Sun Microsystems and has done work at MIT on parallel data flow computing architectures. Papadopoulos came to believe that hardware brings too much friction to inference, meaning companies like Nvidia won’t be able to own the market with GPU hardware optimizations alone.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Navigating the generative AI revolution: Wayfair's pragmatic approach to embracing emerging technology | VentureBeat"
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"https://venturebeat.com/ai/navigating-generative-ai-revolution-wayfair-pragmatic-approach-emerging-technology"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Navigating the generative AI revolution: Wayfair’s pragmatic approach to embracing emerging technology Share on Facebook Share on X Share on LinkedIn Fiona Tan speaks at Transform 2022. Photo by: Michael O'Donnell Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The buzz surrounding generative AI has captured the attention of enterprise companies, and online furniture and home decor retailer Wayfair is no exception. Fiona Tan, the company’s CTO, recognizes the significance of this technology trend, while emphasizing the importance of not losing focus on the company’s core objectives.
In a recent interview, Tan discusses her perspective on the major technological trends of the year. She offers valuable insights for executives grappling with the integration of generative AI into their strategies. The conversation, which can be viewed in the embedded video below, took place in anticipation of VB Transform , an annual networking event dedicated to AI and data for enterprise tech decision-makers.
Transform, set to be held in San Francisco on July 11 and 12, will delve into generative AI, exploring how organizations are adapting to this emerging trend. Tan is a regular speaker at the conference. In July, fellow Wayfair executive Wilko Schulz-Mahlendorf, director of data science , will provide further updates on the company’s foray into generative AI.
Wayfair’s take on the promise of generative AI To ensure a thoughtful approach to generative AI, Wayfair first established a cross-functional council comprising data scientists, engineers, marketers and legal experts. This collaborative effort addressed potential risks and privacy concerns while exploring the technology’s potential applications. The council organized generative AI into three primary categories: VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Long-term disruption opportunities: Wayfair’s forward-thinking team, Wayfair Next, focuses on the big, future potential impacts of the emerging technology. It developed proof-of-concept projects in areas such as text and imagery generation. (Wayfair’s Schulz-Mahlendorf will share more on this at Transform.) Near-term opportunities: The council also explored how generative AI could enhance customer service, with an emphasis on maintaining a human touch. While incorporating AI-generated suggestions, Wayfair ensures that agents have the final say in crafting responses to customers. The company also set out to use the technology to optimize marketing copy and supplier content.
Everyday enablers: Wayfair recognized that generative AI could prove valuable in day-to-day operations, for example assisting engineers in coding more efficiently.
To keep a finger on the pulse of emerging trends and best practices, Tan regularly meets with a network of fellow CTOs. By sharing their insights and experiences, the group ensures that they are collectively on the cutting edge. Tan says most members are at roughly the same point on their generative AI journey, though some may be a month or so ahead in areas such as risk assessment and navigating licensing complexities.
Addressing licensing concerns, Tan acknowledges the need to strike a balance between utilizing APIs from closed large language models provided by partners like OpenAI and using open-source models trained on proprietary data. While the former offer a quicker route to market, the latter require a greater investment but can deliver a competitive edge through differentiation.
Like everyone else, Wayfair is in the initial stages of its generative AI journey. But Wayfair’s pragmatic approach to generative AI offers a thoughtful blueprint for businesses navigating the uncharted waters of this groundbreaking technology, striking a balance between embracing innovation and preserving core values. We look forward to further updates about Wayfair’s story from Schulz-Mahlendorf at the upcoming Transform. His insights will be one of the scores of conversations at Transform about this evolving response by enterprise to the promises and challenges posed by generative AI.
Register now to participate in the conversation.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"MosaicML CEO Naveen Rao on how to democratize generative AI and why he sold to Databricks for $1.3B | VentureBeat"
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"https://venturebeat.com/ai/mosaicml-ceo-naveen-rao-on-how-to-democratize-generative-ai-and-why-he-sold-to-databricks-for-1-3b"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event MosaicML CEO Naveen Rao on how to democratize generative AI and why he sold to Databricks for $1.3B Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Generative AI and large language models (LLMs) are transforming the way businesses use data and create value. But how can enterprises leverage these technologies without losing control of their data, spending millions of dollars, or relying on third-party models? That’s the question that Naveen Rao, cofounder and CEO of MosaicML , a leading generative AI platform, will answer at the VB Transform event on July 12, 2023 in San Francisco. Rao will share his insights and best practices on how to build, own, and secure best-in-class generative AI models with your own data.
>>Follow VentureBeat’s ongoing generative AI coverage<< MosaicML is known for its state-of-the-art MPT LLMs, which are open-source and commercially licensed. With over 3.3 million downloads of MPT-7B and the recent release of MPT-30B , MosaicML has showcased how organizations can quickly build and train their own state-of-the-art models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! In June 2023, MosaicML was acquired by Databricks, a data and AI analytics provider, for $1.3 billion.
Rao says that the acquisition was a strategic decision that will enable his company to accelerate its mission of democratizing generative AI and making the lakehouse (the term used by Databricks to refer to its data offering) the best place to build generative AI and LLMs.
In a recent interview with VentureBeat, Rao explained why he is critical of fine-tuning as a way to customize LLMs; why he prefers to train models from scratch with the right data mix; and why he thinks open-source models will eventually overtake closed models.
>> Follow all our VentureBeat Transform 2023 coverage << “Fine-tuning is a way to condition a model to behave in certain ways. It’s not a way to make something really domain-specific. The right way to do it is actually to train a model from scratch,” Rao said.
He also said that using a vector database or a prompt can work well for some cases, but not for others. “You always want to use a minimum effort to solve the problem at hand. In some cases, you can solve it with a vector DB, like pinecone. And you know, you can do that in a way that respects some privacy. You’re basically using a prompt with the vector DB to modify the behavior of the large language model. That has its place,” he said.
He also made a bold prediction that open-source models will eventually overtake closed models, just as Linux did with Solaris. “Open source is really just getting started. So in five years, I think the world will look a bit different,” he said.
If you want to learn more about generative AI and how to leverage it for your business, don’t miss Naveen Rao’s session at VB Transform on July 12, 2023 in San Francisco. Register now and get ready to join the generative AI revolution! VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Meta engineer: Only two nuclear power plants needed to fuel AI inference next year | VentureBeat"
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"https://venturebeat.com/ai/meta-engineer-only-two-nuclear-power-plants-needed-to-fuel-ai-inference-next-year"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Feature Meta engineer: Only two nuclear power plants needed to fuel AI inference next year Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Meta’ s director of engineering for Generative AI, Sergey Edunov, has a surprising answer to how much more power will be needed to handle the increasing demand for AI applications for the next year: just two new nuclear power plants.
Edunov leads Meta’s training efforts for its Llama 2 open-source foundation model, which is considered one of the leading models. Speaking during a panel session I moderated at the Digital Workers Forum last week in Silicon Valley, he said two power plants would seem to be enough to power humanity’s AI needs for a year, and that this seemed to be acceptable. Referring to questions about whether the world has enough capacity to handle the growing AI power needs, especially given the rise of power-hungry generative AI applications, he said: “We can definitely solve this problem.” Edunov made it clear that he was working only from back-of-the-envelope math when preparing his answer. However, he said it provided a good ballpark estimate of how much power will be needed to do what is called AI “inference.” Inference is the process by which AI is deployed in an application to respond to a question or to make a recommendation.
Inference is distinct from AI model “training,” which is when a model is trained on massive amounts of data for it to get ready to do inference.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Training of large language models (LLMs) has gained scrutiny recently, because it requires massive processing, although only initially. Once a model has been trained, it can be used over and over for inference needs, which is where the real application of AI happens.
Power needs for inference are under control Edunov gave two separate answers to address inference and training. His first answer addressed inference, where the majority of processing will happen as organizations deploy AI applications. He explained how he did his simple calculation for the inference side: He said Nvidia , the dominant supplier of processors for AI, appears to be ready to release between one million and two million of its H100 GPUs next year. If all of those GPUS were used to generate “tokens” for reasonably sized LLMs, he said it adds up to about 100,000 tokens per person on the planet per day, which he admitted is quite a lot of tokens.
Tokens are the basic units of text that LLMs use to process and generate language. They can be words, parts of words, or even single characters, depending on how the LLM is designed. For example, the word “hello” can be a single token, or it can be split into two tokens: “hel” and “lo”. The more tokens an LLM can handle, the more complex and diverse the language it can produce.
So how much electricity do we need to generate that many tokens? Well, each H100 GPU requires about 700 watts, and given that you need some electricity to support the data center and cooling, Edunov said he rounded up to 1KW per GPU. Add it all up, and that’s just two nuclear reactors needed to power all of those H100s. “At the scale of humanity, it’s not that much,” Edunov said. “I think as humans as a society we can afford to pay up to 100,000 tokens per day per person on this planet. So on the inference side, I feel like it might be okay where we are right now.” (After the session, Edunov clarified to VentureBeat that his remarks referred to the power needed for the added AI compute from the new influx of Nvidia’s H100s, which are designed especially to handle AI applications and are thus the most notable. In addition to the H100s, there are older Nvidia GPU models, as well as AMD and Intel CPUs, as well as special-purpose AI accelerators that do inference for AI.) For training generative AI, getting enough data is the problem Training LLMs is a different challenge, Edunov said. The main constraint is getting enough data to train them. He said it’s widely speculated that GPT4 was trained on the whole internet. Here he made some more simple assumptions. The entire publicly available internet, if you just download it, is roughly 100 trillion tokens, he said. But if you clean it up and de-duplicate data, you can get that data down to 20 trillion to 10 trillion tokens, he said. And if you focus on high-quality tokens, the amount will be even lower. “The amount of distilled knowledge that humanity created over the ages is not that big,” he said, especially if you need to keep adding more data to models to scale them to better performance.
He estimates that next-generation, higher-performing models will require 10 times more data. So if GPT4 was trained on say, 20 trillion tokens, then the next model will require like 200 trillion tokens. There may not be enough public data to do that, he said. That’s why researchers are working on efficiency techniques to make models more efficient and intelligent on smaller amounts of data. LLM models may also have to tap into alternative sources of data, for example, multimodal data, such as video. “Those are vast amounts of data that can enable future scaling,” he said.
Edunov spoke on a panel titled: “Generating Tokens: The Electricity of the GenAI Era,” and joining him were Nik Spirin, director of GenAI for Nvidia, and Kevin Tsai, Head of Solution Architecture, GenAI, for Google.
Spirin agreed with Edunov that there are other reservoirs of data available outside of the public internet, including behind firewalls and forums, although they are not easily accessible. However, they could be used by organizations with access to that data to easily customize foundational models.
Society has an interest in getting behind the best open-source foundation models, to avoid having to support too many independent efforts, Spirin said. This will save on compute, he said, since they can be pre-trained once, and most of the effort can be spent on making intelligent downstream applications. He said this is an answer to avoid hitting any data limits anytime soon.
Google’s Tsai added that several other technologies can help take the pressure off training. Retrieval augmented generation (RAG) can help organizations fine-tune foundation models with their troves of data. While RAG has its limits, other technologies Google has experimented with, such as sparse semantic vectors, can help. “The community can come together with useful models that can be repurposed in many places. And that’s probably the way to go right, for the earth,” he said.
Predictions: We’ll know if AGI is possible within three or four years, and LLMs will provide enterprises “massive” value At the end of the panel, I asked the panelists their predictions for the next two to three years of how LLMs will grow in capability, and where they will hit limitations. In general, they agreed that while it’s unclear just how much LLMs will be able to improve, significant value has already been demonstrated, and enterprises will likely be deploying LLMs en masse within about two years.
Improvements to LLMs could either continue exponentially or start to taper off, said Meta’s Edunov. Either way, we’ll have the answer in three to four years of whether artificial general intelligence (AGI) is possible with current technology, he predicted. Judging from previous waves of technology, including initial AI technologies, enterprise companies will be slow to adopt initially, Nvidia’s Spirin said. But within two years, he expects companies to be getting “massive” value out of it. “At least that was the case with the previous wave of AI technology,” he said.
Google’s Tsai pointed out that supply-chain limitations – caused by Nvidia’s reliance on high bandwidth memory for its GPUS – are slowing down model improvement, and that this bottleneck has to be solved. But he said he remained encouraged by innovations, like Blib-2 , a research project from Salesforce, to find a way to build smaller, more efficient models. These may help LLMs get around supply-chain constraints by reducing their processing requirements, he said.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Meet Intuit Assist, a new AI assistant that can do more than just answer questions | VentureBeat"
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"https://venturebeat.com/ai/meet-intuit-assist-a-new-ai-assistant-that-can-do-more-than-just-answer-questions"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Meet Intuit Assist, a new AI assistant that can do more than just answer questions Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Intuit, a financial software company that offers products such as TurboTax, QuickBooks, and Mailchimp, announced on Tuesday that it has launched Intuit Assist, a new artificial intelligence assistant that can generate personalized answers and insights for its customers.
The assistant, which uses generative AI, can understand natural language queries and respond in clear sentences, but is also built to ensure accurate answers based on real data, and is not susceptible to the “hallucination” challenges seen in other generative AI bots, like ChatGPT. Assist can also perform tasks such as creating invoices, sending reminders, and optimizing marketing campaigns.
Intuit Assist is integrated into the user interface of Intuit’s products, appearing as a sidebar on the right side of the screen. Users can interact with the assistant by typing questions or requests, or by choosing from a list of suggested prompts. The assistant can also connect users with human experts who can provide additional guidance and support.
Intuit Assist is the result of years of research and development by Intuit’s engineering and data science teams. It’s the most visible and tangible example of Intuit’s accelerated push to provide value to its 100 million small business and consumer customers from its extensive experimentation with Generative AI over the past two years.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Assist is already available to all users of TurboTax, and enhancements will be rolled out in the coming months, the company said. For Credit Karma, Quickbooks and Mailchimp, the assistant is available to select US beta users, and will become more widely available in the coming months.
VentureBeat has been following Intuit’s journey with generative AI closely because Intuit has moved more quickly than most enterprise companies in the area, and can be instructional for other companies wanting to succeed with AI. The company has benefited from intensive work over the last five years to build an integrated data layer that now allows Intuit to inject AI models that fuel applications like Assist with accurate real-time data. Under the hood, Intuit has built an orchestration layer with sophisticated agents and plugins that the company calls GenOS.
The company sees GenOS as a sort of operating system for AI, because it abstracts processes for engineers in a way that they can more easily build AI applications at scale.
GenOS allows Intuit to leverage its large and diverse data sets to create customized and context-aware AI models for each product and customer. It’s also what enables Intuit to run Intuit Assist as a consistent and coherent AI assistant across all of the company’s products, Ashok Srivastava, chief data officer, said in an interview with Venturebeat.
GenOS works at three layers of Intuit’s application runtime: First, the GenOS is able to inject the user’s initial prompt with customer-specific data in real-time, to allow Intuit to make that prompt more intelligent. Second, the prompt interacts with Intuit’s custom made large language model itself, and retrieves an answer. Third, that answer is edited again for accuracy, if needed.
Srivastava gave an example of how he uses Intuit Assist for his family’s personal finances. He said he rents out a property and uses QuickBooks to track his income and expenses. Instead of having to manually create reports or search for information, he can simply ask Intuit Assist questions like “How are my tenants paying their rent?” or “How much profit did I make last month?” and get instant answers.
Here are some examples of how Intuit Assist works in the company’s various products: TurboTax : For users of the Turbotax tax preparation software, Intuit Assist will help them as they take each step through the filing process. It will get to know a consumer’s individual tax situation and apply its knowledge, navigating the tax code, including the latest changes. For example, it may calculate that a standard deduction will provide more savings than itemizing, as seen in the screenshot below. The user can interact with Assist to find out more details, either using free text or prompted questions. Assist allows them to connect with an expert advisor, who is also aided by an AI-powered Assist on their side.
Credit Karma : Credit Karma members will be able to use Intuit Assist to get personalized answers to questions like “How do I get more rewards for my spending?” Intuit Assist might answer that question by linking to some credit cards that provide rewards for that specific user, based on their own financial data (see screenshots below). Or take the example of a member who suffers an unexpected expense, such as a car breakdown that costs them hundreds of dollars. Intuit Assist can help formulate a plan to avoid a cash crunch, including a personalized set of financing options, and a way to assess options and tradeoffs.
QuickBooks : Intuit Assist delivers AI-driven insights to small businesses based on simple requests or questions such as “Show me my profit and loss for last month” or “How many of my invoices are overdue?” It will dynamically predict follow-up questions and answer in clear, natural language. It will surface cash flow hot spots and can identify top-selling products and spending anomalies. It can also do things like generate invoice reminders, which can be customized to be friendlier in tone.
Mailchimp : Small businesses can ask Intuit Assist to do more automated marketing for them, personalizing it for them with data-driven decisions to measure, and optimize campaign effectiveness. For example, Intuit Assist can help a furniture store business create a marketing campaign based on its existing branding, for example generating an email offering a discount on its couches using language in a way that targets say, GenZ and millennials. Once the customer schedules it to be sent, Assist will then surface a follow-up action plan to ensure campaign engagement. If a customer is also a Quickbooks user, Assist will also generate automated draft email content in their Mailchimp inbox using product and service data from QuickBooks.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Looking to create a LLM-based chatbot that harnesses your company’s data? -- Join me at VB Transform and find out how | VentureBeat"
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"https://venturebeat.com/ai/looking-create-llm-chatbot-harnesses-your-companys-data-join-me-at-vb-transform-and-find-out"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event Looking to create a LLM-based chatbot that harnesses your company’s data? — Join me at VB Transform and find out how Share on Facebook Share on X Share on LinkedIn VentureBeat founder Matt Marshall at the AI Innovation Awards ceremony held July 12, 2019 in San Francisco Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Imagine a chatbot that can answer any question about your company’s data using the latest generative AI technology; a chatbot that can access your files, documents and databases, and transform them into searchable embeddings that a large language model (LLM) can understand and reference; a chatbot that empowers your executives, employees and customers with instant and accurate information.
That’s the vision that many enterprise developers are working on right now — though the path to making it a reality is anything but straightforward, and will vary greatly for each sector, enterprise and specific set of use cases.
Fortunately, there is a resource for those looking to learn directly from leaders in enterprise tech and gen AI about exactly how they are are embarking on this journey and already achieving powerful results: VB Transform , the premier event for enterprise generative AI, in San Francisco on July 11 & 12.
I’ll be there hosting an upcoming lunch roundtable discussion about this very topic — LLM-powered chatbots for the enterprise — and you’re invited to attend.
Learn from generative AI experts at an exclusive gathering If you want to learn strategies on how to create such a chatbot, and have your questions answered by knowledgeable experts, you should join me in person at this exclusive discussion. It’s one of nine roundtables that the VentureBeat team and I have curated for next week, featuring a long list of esteemed and knowledgable figures in enterprise tech and AI.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! It’s also just one of many face-to-face networking opportunities we’re offering at VB Transform, where you can connect with like-minded execs and innovators who are shaping the future of business and technology.
>> Follow all our VentureBeat Transform 2023 coverage << VB Transform is the first independent event dedicated to gen AI for the enterprise, and it’s happening right in the global capital of tech innovation: San Francisco, where OpenAI is headquartered, and Hugging Face recently hosted the “ Woodstock of AI.
” Meet speakers from leading global enterprises and emerging gen AI power players Come to VB Transform to hear from some of the most influential business leaders and technologists, including: Gerrit Kazmaier , VP and GM, data and analytics, Google Cloud; Matt Wood , VP of product, Amazon AWS; Joanna Lepore , global foresight director, McDonald’s Corp; Nitzan Mekel-Bobrov , Chief AI officer, Ebay; Madhu Narasimhan , EVP, head of innovation, Wells Fargo; Steve Wood , SVP, product management, Slack; Abhay Parasnis , CEO Typeface; Naveen Rao , CEO MosaicML; Nick Frosst , cofounder, Cohere; JoAnn Stonier , CTO, Mastercard; Desirée Gosby , VP, emerging tech, Walmart.
Don’t miss your chance: there’s still time to register for final admission to VB Transform.
Get tickets now to this excellent networking and knowledge-sharing opportunity, where you’ll learn practical advice from top tech names about how to power your business with gen AI, unlocking more value today — and long into the future. I look forward to seeing you there.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Launch your product at Transform 2020 -- The AI event for enterprise decision makers | VentureBeat"
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"https://venturebeat.com/ai/launch-your-product-at-transform-2020-the-ai-event-for-enterprise-decision-makers"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event Launch your product at Transform 2020 — The AI event for enterprise decision makers Share on Facebook Share on X Share on LinkedIn Airbnb Homes CTO Vanja Josifovski onstage with VentureBeat founder Matt Marshall held July 10, 2019 at Hilton Union Square in San Francisco.
Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
VentureBeat is on the lookout for disruptive AI companies of all sizes that are ready to present new tech products or services on the main stage at Transform 2020: Accelerating your business with AI , hosted online this July 15-17.
Transform is the first major virtual AI event for business decision makers this year, and will have more than a thousand attendees.
Those companies selected for our AI Showcase will do so in front of hundreds, if not thousands, of industry decision makers. Every presenter will receive coverage from VentureBeat, placing your company squarely in front of our growing reader base of over 6 million monthly readers.
To be sure, with just three weeks left, the Showcase is almost full, but we do have some slots left.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Who should apply? Dynamic companies that have a new product to unveil that is immediately relevant to leaders and practitioners in the areas of AI and ML. Ideally, the companies will have a compelling use case that can incorporate a product demo, multimedia, and other creative ways of presenting their technology or solution on stage.
In total, up to thirteen B2B and/or B2C candidates will be selected from our pool of applicants. We welcome companies of all sizes. If you have a story to tell, and an AI product or service with tangible business results and demonstrative use cases, please submit your application here.
Want to ensure your AI startup gets exposure at Transform? Be sure to also look in into our Expo.
Last year, over 50 innovative companies showcased their technology at our inaugural Transform Expo and networked with senior-level execs from some of the most notable brands and tech companies. This year, one expo participant will be voted to present on stage as part of the Tech Showcase.
Apply here.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Intuit launches lab for employees to experiment with generative AI, will share lessons at VB Transform | VentureBeat"
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"https://venturebeat.com/ai/intuit-launches-lab-employees-experiment-generative-ai-will-share-lessons-vb-transform"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Intuit launches lab for employees to experiment with generative AI, will share lessons at VB Transform Share on Facebook Share on X Share on LinkedIn Intuit Chief Data Officer Ashok Srivastava will speak at Transform July 11-12 Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Financial services company Intuit has long been at the forefront of integrating AI into its suite of products, including TurboTax and QuickBooks. Now the company is poised to harness the power of generative AI, the groundbreaking technology that’s rapidly transforming the enterprise world.
This month, the company launched “GenStudio,” a generative AI lab for employees from various backgrounds – engineers, data scientists and product managers – to explore and experiment with generative AI on a larger scale. This initiative seeks to harness the creative potential of Intuit’s workforce, and aims to develop GenAI in a systematic manner, incorporating essential safeguards against the risks of generative AI within its technology architecture from the outset.
Ashok Srivastava, Intuit’s Chief Data Officer, is set to speak at the VB Transform event in San Francisco on July 11 and 12. He will share insights into the company’s journey to tap into the potential of generative AI and discuss the lessons learned from establishing GenStudio. Generative AI gained widespread attention last year with the release of OpenAI’s ChatGPT, a powerful chatbot that mimics human conversation. Intuit’s participation at Transform highlights the event’s commitment to showcasing the latest enterprise innovations in AI and generative AI implementation.
In recent interviews with Srivastava and Intuit’s Chief Technology Officer, Mariana Tessel, the executives revealed their strategies for staying at the forefront of generative AI in 2023 and beyond. Their insights provide a glimpse into the story that Intuit will share at the Transform event and offer a blueprint for other companies looking to stay on the cutting edge of technology. My interview with Mariana is embedded here: VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Last year, Tessel wowed attendees at the Transform conference when she revealed that Intuit had implemented two million AI models , which are refreshed daily. This large-scale deployment enables the company to offer personalized AI experiences for its millions of customers.
Intuit has been utilizing large language models (LLMs), the technology underpinning ChatGPT, for several years, specifically in the area of tax information. The company experimented with an early version of OpenAI’s GPT technology (known as GPT-3) when it was first released via an API in 2021. According to Srivastava, this technology reduced customer call time by one million hours per year, thanks to its ability to summarize calls for both experts and customers.
Confident in the technology’s potential, Intuit established the generative AI lab, GenStudio, to help internal employees scale generative AI while incorporating safeguards against potential risks. This lab represents a natural progression from the corporate machine learning platforms that emerged during the initial wave of AI adoption, such as Uber’s Michelangelo, LinkedIn’s Pro-ML, and eBay’s Krylov.
While Srivastava remains tight-lipped about specific results ahead of his Transform presentation, he did share a tantalizing teaser: “We’ve done some preliminary testing of additional generative AI capabilities that are giving extraordinary results. I almost couldn’t believe it when I saw how well they were working.” Here are some other preliminary conclusions Intuit has reached, which its executives say are relevant for peers seeking to embrace generative AI.
Intuit is confident in maintaining its leadership in its domain of expertise, despite potential competition from ChatGPT. Although tax codes are readily available for AI engines to analyze and disseminate, for example, Intuit’s years of building a robust knowledge base ensure accuracy in its LLM outputs.
Another lesson is the importance of having a forward-looking internal team and a “platform” for experimenting with emerging technology trends. This approach allows companies to quickly adapt to new advancements like generative AI, providing a significant advantage over competitors.
The significance of a unified data strategy for internal stakeholders, which ensures a single source of truth across the company. For Intuit, this includes the “Data Exchange,” which processes data from over 24,000 financial institutions.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"IDC study: Businesses report a massive 250% return on AI investments | VentureBeat"
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"https://venturebeat.com/ai/idc-study-businesses-report-a-massive-3-5x-return-on-ai-investments"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Analysis IDC study: Businesses report a massive 250% return on AI investments Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A survey of 2,100 global business leaders and decision makers by research firm IDC suggests a new level of momentum around AI investments by businesses, driven by perceived value and excitement around generative AI.
The report, which was commissioned by Microsoft, but independently conducted by IDC, found that respondents report an average 3.5x return on their AI investments. In other words, they say they are reaping $3.5 in returned value for every $1 invested.
Put yet another way, that’s a whopping 250% return. And that’s significant, when compared to other reports conducted on monetization of AI. IBM reported an average ROI of only 5.9% , based on a May survey of 2,500 global executives. That return is below the typical 10% cost of capital, and so from that perspective, AI could be deemed a risky investment choice. Other reports have shown even lower average returns , or have discussed how difficult it is to estimate ROI and that companies often make big mistakes when calculating ROI.
One of the first reports on AI monetization since generative AI’s watershed moment last year The IDC report was conducted in September, and so is one of the first reports to look at monetization since the hype started around generative AI late last year. Among other highlights, the report found that 71% of respondents say their companies are already using AI, with 22% planning to do so within the next 12 months. It found that 92% of AI deployments are taking 12 months or less, which is faster than the deployment rates seen for previous technologies.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! However, It was the first time IDC has explicitly sought to have respondents quantify their returns on investments, according to Ritu Jyoti, GVP AI and Automation for IDC, who leads the firm’s AI research efforts, in an interview with VentureBeat.
When asked about IDC’s research methodology in calculating the ROIs, she said the firm relies on self-reported data from respondents. IDC also provided large buckets as choices for answers to the question about ROI: Respondents could answer with 2X, 3X, 4x 5x, no ROI and Not sure. (If their answer was over 5x, the respondent was asked to specify further).
We’ll want to track ROI claims in future reports, to see if these ROI estimates hold up. On the one hand, if these numbers are anywhere close to accurate, there’s essentially very little to no risk for organizations to push ahead on an aggressive AI investment strategy, at least if it’s diversified and disciplined. On the other hand, there’s also a chance that these estimates simply reflect a generally bullish attitude about AI among many respondents, at a time of considerable hype around generative AI – and that respondents may not be taking the time or care to report experiments or projects that yield poor ROIs. Thus, caution should still be exercised around making AI investments.
Companies are steering money into AI by deprioritizing other initiatives Indeed, Jyoti said there’s so much excitement around AI that companies are actually deprioritizing other initiatives to prioritize AI. “That is something that is new,” she said, not seen in her survey last year or other AI reports her team has done. This has been triggered specifically by heightened interest caused by generative AI, she said. Some 32% of organizations said they have reduced an average of 11% of spending on certain business areas, in order to invest more into AI, she said. The areas being reduced are outside of IT, and are in areas like administrative support and services. Administrative assistants for C-suite executives, for example, are on the chopping block. Other areas include operations, tech support, human resources and customer service, Jyoti said.
Generative AI has played such a big role this year, because traditional AI had been the domain of highly technical workers, often within IT or at lower levels in business units, and so was not that visible within an organization, she explained. “Generative AI has changed that, because it became front and center.” Jyoti said. “The C-suite, the board of directors, they all have come along, and are investing in AI and prioritizing AI. What I have seen this time that is different is that there’s a lot more appetite and interest, and worldwide.” Generative AI is still too early for reporting on monetization Other recent reports have shown that the promise of generative AI is real. Employees at one elite consulting firm, BCG, got a 40 percent performance boost from using GPT-4 on a variety of tasks, according to a study released last month by Harvard, Wharton and MIT.
It should be noted that it is too early to report on monetization results from using generative AI, however. “Most people are at the early stage of either evaluating, or piloting,” Jyoti said of generative AI projects. The results reported in the IDC study are for traditional forms of AI, she confirmed.
On average, organizations reported a 18% increase in results across key areas like customer satisfaction, employee productivity, and market share, when using AI, Jyoti said.
Despite the positive results, companies also reported a heightened concern around areas like data or IP loss, risk management, and lack of AI governance. While there were already governance concerns around traditional AI, the arrival of generative AI has increased those concerns, Jyoti said.
In March, Jyoti and her group at IDC projected that generative AI will add nearly $10 trillion to global GDP over the next 10 years.
Microsoft exec: Generative AI is “sort of bending the innovation curve” In a separate interview, Alysa Taylor, corporate vice president at Microsoft, said the company had commissioned the report in order to understand the potential for AI, and where companies were realizing the most benefit. She said the companies were using AI to tackle some of their largest challenges, and see generative AI as particularly transformative: “Generative AI is sort of bending the innovation curve,” she said. It’s allowing organizations not to have to modernize underlying technologies, but really kind of leapfrog in a faster way to time to market, time to value.” She also called generative AI a catalyst, in particular because its simple form factor allows more people to access AI. The use cases abound, but she cited examples like healthcare, where physicians are suffering a burnout rate at 53 percent in the U.S., and where ambient AI and generative AI can help reduce the need for manual clinical documentation. In software development, AI can help assist and accelerate development. And in retail, AI can help companies more deeply understand a customer and to precisely target them, she said.
Here are other key findings and facts from the report: Organizations are realizing a return on their AI investments within 14 months, on average Copywriting, running simulations and automating business processes and workflows are the top three uses cases organizations are planning to monetize For every $1 a company invests in AI, it is realizing an average of $3.5X in return 62% of companies are already using generative AI, and 24 percent said they plan to use or invest in AI within the next 24 months 52% reported that their biggest barrier is a lack of skilled workers needed to scale AI initiatives Respondents were roughly evenly split between leaders in IT and line of business 66% of respondents were in upper-level management roles and 63% were responsible for decision making regarding the use of AI at their organization Microsoft’s Taylor said Microsoft is trying to address that skills barrier by engaging more than six million people globally with its Learn program , and training 400,000 partners.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"AWS Exec: Generative AI can create a flywheel effect for business growth | VentureBeat"
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"https://venturebeat.com/ai/how-aws-is-using-generative-ai-to-create-a-flywheel-effect-for-business-growth"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Exclusive AWS Exec: Generative AI can create a flywheel effect for business growth Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Generative AI is a powerful technology that can create new content, insights and solutions from data. But how can businesses leverage it to gain a competitive edge and accelerate their growth? Matt Wood, VP of product at AWS, shared his insights on how generative AI can create a flywheel effect for business growth in a recent interview with VentureBeat.
Wood said that generative AI can be applied to four major buckets of use cases. The first three are relatively well known and are already being implemented by many businesses. These are generative interfaces, search ranking and relevance and knowledge discovery.
The last use case bucket is automated decision support systems. This is the hardest, but the most interesting and impactful one, he said, since it can enable businesses to solve complex problems with the help of autonomous intelligent systems.
And, it’s what companies can build a flywheel around. When done correctly, the flywheel can create a huge advantage against competitors, said Wood.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Impacts for LLMs in enterprise The AWS VP will be speaking at VB Transform 2023 next week in San Francisco, a networking event for technical executives seeking to understand and implement generative AI. I’ll be moderating a panel where Wood will be joined by Gerrit Kazmaier, VP and GM for data and analytics at Google — where the two execs will be talking more about the impact of large language models (LLMs) for enterprise leaders, and we’ll likely go deeper on this flywheel concept.
>> Follow all our VentureBeat Transform 2023 coverage << Cybersecurity is a good example to illustrate the flywheel potential of LLMs for other enterprises, Wood said. Let’s say you start to experience a set of threats emerging in your application. These threats have subtle signals, because they’re split across multiple services and architectures. But just in a few places, you just start to see very subtle signals of a cyber attack.
By using embeddings, which can find correlations between data points, LLMs are good at finding subtle differences and effectively correlating them into a larger signal.
“So what would otherwise be split across a diluted surface area now stands out like a flashing siren,” said Wood.
Investigating root causes of cyberattacks Going deeper with this example, LLMs also let you automatically investigate the root cause of that attack , providing an explanation of why it’s happening in natural language. And from here, LLMs can let you know the specifics of what is being threatened, then suggest how to defend against it, said Wood.
Finally, once you’ve reviewed the suggestion and you’re happy with it, you can just click a button and the LLM system will execute the code to remediate the attack or vulnerability or operational problem — whatever it might be.
“Compare that to the level of human investment and high-judgment decisions that would need to be made today in order to get to that level of specificity,” said Wood. “And just, you know, going and finding all those log entries and then figuring out the attack vectors and then figuring out what to do, takes a remarkable amount of skill, a remarkable amount of time.” He added: “Imagine all of that is happening all the time, automatically under the hood. And what you’re presented with is not a random set of ones and zeros that are operating slightly unusually, you’re presented with a full incident report , as if it was created by a set of humans, which you can interact with, and fine tune and revise.” Constantly improving feedback loop Generative AI can also create a feedback loop that improves the performance of the system over time.
“If you take the feedback from these sorts of interactions, the improvements you would make to a threat report and the remediation, for example, then if you bake those into the large language model, the language model will perform better, and you’ll get more users,” said Wood. “If you get more users, you’ll get more feedback. If you get more feedback, you’ll get an improved model. If you get a better model, you get more feedback.” All of your interactions make the threat report better for the next time. And so that’s the flywheel that organizations can spin. “Flywheels are a very rare technology as it turns out, but there is a real flywheel here with generative AI,” said Wood.
He added: “The earlier you can spin that as an organization and the faster you can spin it, you’ll be able to create much more intelligence, much more automation, much more accuracy, much less hallucination as you go, and at some point, if you can spin that flywheel early enough and quickly enough, then you’ll have this enormous gap against your competitors, and competitors won’t be able to catch up at any cost because that’s how valuable the flywheel is.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"From data chaos to data products: How enterprises can unlock the power of generative AI | VentureBeat"
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"https://venturebeat.com/ai/from-data-chaos-to-data-products-how-enterprises-can-unlock-the-power-of-generative-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages VB Event From data chaos to data products: How enterprises can unlock the power of generative AI Share on Facebook Share on X Share on LinkedIn Data pipelines Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Many large enterprises are eager to experiment with generative AI and the large language models (LLMs) that power it, hoping to gain a competitive edge in a range of fields from customer service to product design, marketing and entertainment.
But before they can unleash generative AI’s full potential, they need to address a fundamental challenge: data quality. If enterprises deploy LLMs that access unreliable, incomplete or inconsistent data, they risk producing inaccurate or misleading results that could badly damage their reputation or violate regulations.
That was the main message of Bruno Aziza, an Alphabet executive who led a roundtable discussion at VB Transform last week. The roundtable focused on providing a playbook for how enterprises can prepare their data and analytics infrastructure to leverage large language models.
Aziza, who was until recently the head of data and analytics for Google Cloud and who just joined Alphabet’s growth-stage fund, CapitalG, shared his insights from conversations with hundreds of customers seeking to use AI.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The 3 steps of data maturity He outlined the three steps of data maturity he has witnessed enterprises go through to develop generative AI application competence.
First, create a data ocean, an open repository with data sharing as a key design principle. Data oceans should manage data of all types and formats — structured, unstructured and semi-structured, stored in proprietary and open-source formats like Iceberg, Delta or Hudi. Data oceans should also support both transactional and analytical data processing. All of this lets large language models access any relevant data with high levels of performance and reliability. Examples of data oceans are Google’s BigLake and Microsoft’s new OneLake.
The term used by most industry practitioners for pooling and storing data is the “data lake,” but that concept has been butchered by vendors who promise to store data in a single place, but don’t deliver on that, Aziza said. Enterprise companies also often acquire different companies, and those acquired companies store data in disparate data lakes , across multiple clouds.
Second, organizations mature to a data mesh, or a way to enable teams across an enterprise to innovate with distributed data, while adhering to centralized policies so people can work with information that is clean, complete and trusted. In this phase, data fabric capabilities are essential as they let teams discover, catalog and manage data at scale early on. Aziza’s advice is to leverage artificial intelligence, as the tasks of discovering data can be difficult and error-prone if done manually. When data is streamed into a data ocean at large scale and in real time, it becomes difficult to manage without the help of AI.
Third, they build intelligent data-rich applications. These can be LLM-driven apps that generate content or insights based on the data in the ocean and governed by the mesh. These applications should solve real problems for customers or users, and be constantly monitored and evaluated for their performance and impact. These data products, as Aziza calls them, can also be optimized to work with real-time data.
Aziza said that these steps might not be easy or quick to implement, but they are essential for enterprises that want to avoid generative AI disasters. “If you approach poor data practices, this technology will expose bad data in bigger and broader ways,” he said.
Examples such as the lawyer who was fined after citing a fake case while using ChatGPT demonstrate the phenomenon of generative AI applications hallucinating when not directed to precise, secure and sound sources of data.
While Aziza shared some key elements of Google Cloud’s playbook for enterprise companies wanting to get ready for LLMs, the learnings apply for any enterprise company regardless of the cloud service they are using.
Large language models and data integrity The roundtable attracted several enterprise executives from companies like Kaiser Permanente, IBM and Accenture, who asked Aziza about some of the technical challenges and opportunities of using large language models. The topics they discussed included: The role of vector databases : This is a new type of database that stores data as high-dimensional vectors, which are numerical representations of features or attributes. Vector databases allow large language models to find similar or relevant data more efficiently than traditional databases, using semantic search techniques. Aziza said that vector databases are “really useful” for generative AI applications. Participants mentioned Pinecone as an example of a company that offers this technology.
The role of SQL: SQL is a standard query language for accessing and manipulating data in databases. Aziza said that SQL has become the universal language for data analysis, and that it can now be used to trigger machine learning and other sophisticated workloads using cloud-based analytics platforms like Google BigQuery. He also said that natural language interfaces can now translate user requests into SQL commands, making it easier for non-technical users to interact with LLMs. However, he added that the main skill that enterprises will need is not SQL itself, but the ability to ask the right questions.
The importance of data integrity as the key starting point for generative AI was a recurring theme at VB Transform.
Google’s VP of data and analytics, Gerrit Kazmaier, said a company’s success at leveraging generative AI flows directly from ensuring data is accurate, complete and consistent. “The data that you have, how you curate it and how you manage that, interconnected with large language models, is, I think, the true leverage function in this entire journey,” he said.
“As a data guy, this is just a fantastic moment because it will allow us to activate way more data in many more business processes.” Separately, Desirée Gosby, VP of emerging technology of Walmart, credited the retailer’s success at using generative AI for conversational experiences to its multi-year effort to clean up its data layer. “At the end of the day, having a capability in place that allows you to really leverage your data … and packages [these large language model applications] in a way that unleashes the innovation across your company is key,” she said. Walmart serves 50 million Walmart customers with AI-driven conversational experiences, she said.
To help enterprise executives learn more about how to manage their data for generative AI applications, VentureBeat is hosting its Data Summit 2023 on November 15. The event will feature networking opportunities and sessions on topics such as data lakes, data fabrics , data governance and data ethics. Pre-registration for a 50% discount is open now.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Enterprise workers gain 40 percent performance boost from GPT-4, Harvard study finds | VentureBeat"
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"https://venturebeat.com/ai/enterprise-workers-gain-40-percent-performance-boost-from-gpt-4-harvard-study-finds"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Enterprise workers gain 40 percent performance boost from GPT-4, Harvard study finds Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
A Harvard-led study has found that using generative AI helped hundreds of consultants working for the respected Boston Consulting Group (BCG) complete a range of tasks more often, more quickly, and at a higher quality than those who did not use AI.
Moreover, it showed that the lowest performers among the group had the biggest gains when using generative AI.
The study, conducted by data scientists and researchers from Harvard, Wharton, and MIT, is the first significant study of real usage of generative AI in an enterprise since the explosive success of ChatGPT’s pubic release in November 2022 — which triggered a rush among major enterprise companies to figure out optimal ways to utilize it. The researchers moved quickly, starting their research in January of this year, and using GPT-4 for the experiment — which is widely considered the most powerful large language model (LLM). The study carries some significant implications for how businesses should approach deploying it.
“The fact that we could boost the performance of these highly paid, highly skilled consultants, from top, elite MBA institutions, doing tasks that are very related to their every day tasks, on average 40 percent, I would say that’s really impressive,” Harvard’s Fabrizio Dell’Acqua, the paper’s lead author, told VentureBeat.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The report was released for public review nine days ago, but did not get significant attention beyond the academic industry and their social circles.
The report is the latest research confirming that generative AI will have a profound impact on workforce productivity. Aside from its headline, the research provided some cautionary findings about when not to use AI. It concluded that there is what it called a “jagged technology frontier,” or a difficult to discern barrier between tasks that are easily done by AI, and others that are outside AI’s current capabilities.
That frontier is not only jagged, it is constantly shifting as AI’s capabilities improve or change, said Francois Candelon, the senior partner at BCG responsible for running the experiment from the BCG side, in an interview with VentureBeat. This makes it more difficult for organizations to decide how and when to deploy AI, he said.
The study also pointed to two emerging patterns of AI usage by some of the firm’s more technology-competent consultants — which the researchers labeled the “Cyborg” and “Centaur” behaviors — that the researchers concluded may show the way forward in how to approach tasks where there’s uncertainty about AI’s capabilities. We’ll get to that in a second.
The study is the first to research enterprise usage of AI at scale, among professionals on real day-to-day task The study included 758 consultants, or 7 percent of the consultants at the company. For each one of the 18 tasks that were deemed within this frontier of AI capabilities, consultants completed 12.2 percent more tasks on average, and completed tasks 25 percent more quickly, than those who did not use AI. Moreover, the consultants using AI — the study equipped them with access to GPT-4 — produced results with 40 percent higher quality when compared to a control group that did not have such access.
“The performance improved on every dimension. Every way we measured performance,” wrote another contributor to the study, Ethan Mollick, professor at the Wharton School of the University of Pennsylvania, in his summary of the paper.
The researchers first established baselines for each of the participants, to understand how they performed on general tasks without using GPT-4. The researchers then asked the consultants to do a wide variety of work for a fictional shoe company, work that the BCG team selected in order to try to accurately represent what consultants do.
GPT-4 is a skill leveler on many key, high-level tasks The types of tasks were organized into four main category types: creative (for example: “Propose at least 10 ideas for a new shoe targeting an underserved market or sport.”), analytical (“Segment the footwear industry market based on users.”), writing and marketing related (“Draft a press release marketing copy for your product.”), and persuasiveness oriented (“Pen an inspirational memo to employees detailing why your product would outshine competitors.”).
One of the more interesting findings was that AI was a skill leveler. The consultants who scored the worst on their baseline performance before the study saw the biggest performance jump, 43%, when they used AI. The top consultants got a boost, but less of one.
But the study found that people who used AI for tasks it wasn’t good at were more likely to make mistakes, trusting AI when they shouldn’t.
One of the study’s major conclusions was that AI’s inner workings are still opaque enough that it’s hard to know exactly when it is reliable enough to use for certain tasks. This is one of the major challenges for organizations going forward, the study said.
Centaur and Cyborg behaviors may show the way forward But some consultants appeared to navigate the frontier better than others, the report said, by acting as what the study called “Centaurs” or “Cyborgs,” or moving back and forth between AI and human work in ways that combined the strengths of both. Centaurs worked with a clear line between person and machine, switching between AI and human tasks, depending on the perceived strengths and capabilities of each. Cyborgs, on the other hand, blended machine and person on most tasks they performed.
“I think this is the way work is heading, very quickly,” wrote Wharton’s Mollick.
Still, the wall between what tasks can really be improved with AI remains invisible. “Some tasks that might logically seem to be the same distance away from the center, and therefore equally difficult – say, writing a sonnet and an exactly 50 word poem – are actually on different sides of the wall,” said Mollick. “The AI is great at the sonnet, but, because of how it conceptualizes the world in tokens, rather than words, it consistently produces poems of more or less than 50 words.” Similarly, some unexpected tasks ( like idea generation ) are easy for AIs while other tasks that seem to be easy for machines to do (like basic math) are challenges for LLMs, the study found.
AI’s promise can induce humans to fall asleep at the wheel The problem is that humans can overestimate AI’s competence areas. The paper confirmed other earlier research done by Harvard’s Dell’Acqua that showed trust in AI competence can lead to a dangerous over reliance on it by humans, and lead to worse results. In an interview with VentureBeat, Dell’Acqua said users essentially “switch off their brains” and outsource their judgment to AI. Dell’AAcqua coined this “ falling asleep at the wheel ” in a key study in mid-2021, where he found that recruiters using AI to find applicants became lazy and produced worse results than if they hadn’t used AI.
The latest study also found AI can produce homogenization. The study looked at the variation in the ideas presented by subjects about new market ideas for the shoe company, and found that while the ideas were of higher quality, they had less variability than those ideas produced by consultants not using AI. “This suggests that while GPT-4 aids in generating superior content, it might lead to more homogenized outputs,” the study found.
How to combat AI-driven homogeneity The study concluded that companies should consider deploying a variety of AI models — not just Open AI’s GPT-4, but multiple LLMs — or even increased human-only involvement, to counteract this homogenization. This need may vary according to a company’s product: Some companies may prioritize high average outputs, while others may value exploration and innovation, the study said.
To the extent that many companies are using the same AI in a competitive landscape, and this results in reduced uniformity of ideas, companies generating ideas without AI assistance may stand out, the study concluded.
BCG’s Francois Candelon said the study’s findings around homogeneity risks will also force organizations to make sure they keep collecting clean, differentiated data for use in their AI applications. “With Gen AI, it’s even more urgent to not only make sure you have clean data… but try to find ways to collect it. To a certain extent, this will become one of the keys to differentiation.” OpenAI’s ChatGPT, Google’s Bard, Anthropic’s Claude, and a host of other open-source LLM platforms, including Meta’s Llama, are increasingly allowing companies to customize their results by injecting their own proprietary data into the models, so that they can improve not only accuracy, but specialization and differentiation in specific fields.
BCG’s Candelon said the study is playing a major factor in the firm’s decision-making about how to use AI internally. Yes, the study found that AI has a surprising ability to offer specialized knowledge, and concluded the effects of AI are expected to be higher on the most creative, highly paid, and highly educated workers. As such, it leveled up the performance of the poorest performers at BCG. However, Candelon said the skill levels of the BCG consultants are relatively homogenous when compared to the general population, and so the difference in performance between the poorest and best performers wasn’t too large. Thus, he didn’t think the study suggested the firm could start hiring people with almost no training in consulting or strategy work.
More studies will investigate which tasks are better for Centaur and Cyborg behaviors The study confirmed that certain tasks will consistently be better performed by AI, and this flies in the face of some current practices, Candelon said. Candelon said companies shouldn’t make the mistake of concluding AI is best for generating as a first draft, and forcing humans to always come into enhance. He said companies should do the opposite: “You let AI do what it is really great at, and humans should try to go outside of this frontier and really deep dive and dedicate their time to the other tasks.” He said the Centaur’s behavior is notable, because Centaurs have learned to dedicate some tasks to AI, for example the summarizing of interviews and other creative tasks, while dedicating their own focus to things more relevant for human competence – for example task related to data or change management. However, he said the firm plans to investigate the Centaur and Cyborg behaviors more, because in some instances it may be better to be a Cyborg, mixing human and AI competencies together.
As for writing up reports on AI research like what I’m doing here, with interviews of the researchers about their views on the report’s conclusions, I think the jury is still out on whether machines are better than humans. How did I do?! VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Calling all Generative AI disruptors of the enterprise! Apply now to present at VB Transform 2023 | VentureBeat"
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"https://venturebeat.com/ai/calling-all-generative-ai-disruptors-of-the-enterprise-apply-now-to-present-at-transform-2023"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Calling all Generative AI disruptors of the enterprise! Apply now to present at VB Transform 2023 Share on Facebook Share on X Share on LinkedIn Aperture Data Founder Vishakha Gupta-Cledat speaks at Transform. Image Credit: Michael O'Donnell Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The Innovation Showcase is back at VB Transform: Get ahead of the Generative AI revolution , July 11-12 in San Francisco.
We’re on the hunt for the 10 generative AI products most likely to disrupt the enterprise right now, and if you have such a product we’d like to invite you to present the impact of this technology on the main stage.
Those selected to present will do so in front of nearly a thousand industry decision-makers, and receive direct feedback from a panel of enterprise tech analysts, brand executives, and others. Every presenter will receive exclusive editorial coverage from VentureBeat, getting your company out in front of our millions of monthly readers.
Who should apply? Dynamic companies with compelling new generative AI technology, and that want to present their technology or solution on stage. We will also award winners in three categories: most likely to succeed, best technology and best presentation style.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! In total, up to 10 candidates will be selected from applicants: They must offer new enterprise AI solutions, but we will select five that are seed stage (less than $10M in total funding raised) and five that are series A or later. If you are a division of a larger company, you can enter as part of the late-stage division. If have a story to tell, and an AI product or service that offers up real business results and use cases, please submit your application by 5 p.m. PT on June 1, 2023.
Separately, if you want to ensure your AI product or company gets exposure at VB Transform, be sure to look into our new Gen AI Enterprise Innovation Alley. Up to 20 innovative companies (spanning both early and later stage) will be able to showcase their technology at VB Transform, network with senior-level execs from some of the most notable brands and tech companies. And the best Alley candidates will be selected (by popular vote) to be part of our Innovation Showcase stage presentations. Apply here for the Innovation Alley by June 15.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Amazon bets $4 billion on Anthropic’s Claude, the chatbot platform rivaling ChatGPT and Google's Bard | VentureBeat"
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"https://venturebeat.com/ai/amazon-bets-4-billion-on-anthropics-claude-the-chatbot-that-rivals-chatgpt-and-googles-bard"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Amazon bets $4 billion on Anthropic’s Claude, the chatbot platform rivaling ChatGPT and Google’s Bard Share on Facebook Share on X Share on LinkedIn Image Credit: Anthropic Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Amazon said Monday it will invest up to $4 billion in Anthropic , the company that has built the powerful chatbot, Claude. Claude had emerged as one of the leading competitors against Open AI’s ChatGPT and Google’s Bard, in the race to dominate generative AI.
Claude has not had such significant backing until now but arguably has needed it because of the massive expense of remaining competitive in building the large language models (LLMs) technology that drives such chatbots.
The investment was part of a significant partnership announced by the two companies , where Anthropic agreed to use Amazon’s cloud platform for “mission-critical workloads” in return for the investment. The backing is the first major connection by Amazon to a leading chatbot, at a time when cloud competitors Microsoft and Google have already bet big on their respective chatbot platforms.
Indeed, the investment by Amazon goes against statements it has made in recent months about wanting to be agnostic with LLM companies. Although it’s possible that Amazon had been hankering to make a big move the entire time and was taking the agnostic position as a way to justify its relative slowness to make such a bet.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Also, it’s true that Amazon continues to have multiple horses in the LLM race, so this investment may be more to diversify its efforts, and also to ensure access to technology, talent and insight. With its announcement last week of Alexa LLM , Amazon is entering the race for commercial, closed-source models in parallel with its business of providing a platform for serving generative models (called Bedrock).
Microsoft has invested more than $10 billion in Open AI, to secure exclusive rights to offer up OpenAI’s chatbot technology within its own cloud services. This includes having OpenAI prioritize Microsoft’s Azure cloud platform. Meanwhile, Google has pushed its own Bard chatbot, and Meta has invested in its Llama platform, which it has open-sourced, so that other companies can use Llama’s foundation LLM technology. (Though Google invested $300 million in Anthropic in February and Anthropic chose Google Cloud Platform as its preferred cloud platform at the time).
It’s a major injection of support for Claude, when cash is extremely important to fund the expensive work of training competitive LLMs, which are using hundreds of billions of parameters and require massive computing needs. Claude had only raised about $2.7 billion to date.
Amazon and Anthropic said the new strategic collaboration will pool their technologies and expertise “in safer AI,” and will accelerate Anthropic’s development of foundation LLMs to make them widely accessible to AWS customers. One of Anthropic’s major selling points is that AI can be dangerous if not given the proper safeguards. Anthropic has invested heavily to ensure that its Claude chatbot foundation model abides by specific ground rules in producing ethical outputs, rooted in the principles of what it calls Constitutional AI. Anthropic has tried to open a perception gap here in comparisons with Open AI’s ChaptGPT, which is not as strict.
Here are the key elements of the agreement: Anthropic will use AWS Trainium and Inferentia chips to build, train, and deploy its future foundation models, benefitting from the price, performance, scale, and security of AWS. The two companies will also collaborate in the development of future Trainium and Inferentia technology.
AWS will become Anthropic’s primary cloud provider for mission-critical workloads, including safety research and future foundation model development. Anthropic plans to run the majority of its workloads on AWS, further providing Anthropic with the advanced technology of the world’s leading cloud provider.
Anthropic makes a long-term commitment to provide AWS customers around the world with access to future generations of its foundation models via Amazon Bedrock, AWS’s fully managed service that provides secure access to the industry’s top foundation models. In addition, Anthropic will provide AWS customers with early access to unique features for model customization and fine-tuning capabilities.
Amazon will invest up to $4 billion in Anthropic and have a minority ownership position in the company.
Amazon developers and engineers will be able to build with Anthropic models via Amazon Bedrock so they can incorporate generative AI capabilities into their work, enhance existing applications, and create net-new customer experiences across Amazon’s businesses.
VentureBeat recently published a story comparing Claude’s new professional version with ChatGPT’s pro version , and one clear distinction made was Claude’s ability to summarize content at a superior level — thanks to its industry-leading 100,000 token context window.
In its statement, Amazon said the investment expands is overall generative AI offering at “all three layers of the generative AI stack:” “At the bottom layer, AWS continues to offer compute instances from NVIDIA as well as AWS’s own custom silicon chips, AWS Trainium for AI training and AWS Inferentia for AI inference,” the company said. “At the middle layer, AWS is focused on providing customers with the broadest selection of foundation models from multiple leading providers where customers can then customize those models, keep their own data private and secure, and seamlessly integrate with the rest of their AWS workloads—all of this is offered through AWS’s new service, Amazon Bedrock.” Today’s announcement falls into this middle later, the company said, by giving customers access to Anthropic’s customizable models, and allowing the to use their own proprietary data to create their own private models, and use fine-tuning capabilities via a self-service feature within Amazon Bedrock.
Finally, at the top layer, AWS offers “generative AI applications and services for customers like Amazon CodeWhisperer, a powerful AI-powered coding companion, which recommends code snippets directly in the code editor, accelerating developer productivity as they code,” the company said.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"AI Godfathers Bengio and Hinton: Major tech companies should devote a third of AI budget to managing AI risk | VentureBeat"
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"https://venturebeat.com/ai/ai-godfathers-bengio-and-hinton-major-tech-companies-should-devote-a-third-of-ai-budget-to-managing-ai-risk"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages AI Godfathers Bengio and Hinton: Major tech companies should devote a third of AI budget to managing AI risk Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Yoshua Bengio and Geoffrey Hinton, two of the so-called AI godfathers, have joined with 22 other leading AI academics and experts to propose a framework for policy and governance that aims to address the growing risks associated with artificial intelligence.
The paper said companies and governments should devote a third of their AI research and development budgets to AI safety, and also stressed urgency in pursuing specific research breakthroughs to bolster AI safety efforts.
The proposals are significant because they come in the run-up to next week’s AI safety summit meeting at Bletchley Park in the UK, where international politicians, tech leaders, academics and others will gather to discuss how to regulate AI amid growing concerns around its power and risks.
The paper calls for special action from the large private companies developing AI and government government policymakers and regulators: VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Here are some of the proposals: Companies and governments should allocate at least one-third of their AI R&D budget to ensuring safety and ethical use, comparable to their funding for AI capabilities.
Governments urgently need comprehensive insight into AI development. Regulators should require model registration, whistleblower protections, incident reporting, and monitoring of model development and supercomputer usage.
Regulators should be given access to advanced AI systems before deployment to evaluate them for dangerous capabilities such as autonomous self-replication, breaking into computer systems, or making pandemic pathogens widely accessible.
Government should also hold developers and owners of “frontier AI” – the term given to the most advanced AI – legally accountable for harms from their models that can be reasonably foreseen and prevented.
Governments must be prepared to license certain AI development, pause development in response to worrying capabilities, mandate access controls, and require information security measures robust to state-level hackers, until adequate protections are ready.
Both Bengio and Hinton are renowned experts in the field of AI, and recently have increased their calls for AI safety amid mounting risk. These calls have faced pushback from another prominent AI leader, Yann Lecun , who argues that current AI risks do not need such urgent measures. While the voices calling for safety-first have been drowned out over the last couple of years as companies focused on building out AI technology, the balance appears to be shifting toward caution as new powerful capabilities emerge. Other co-authors of the paper include academic and bestselling author Yuval Noah Harari, Nobel laureate in economics Daniel Kahneman, and prominent AI researcher Jeff Clune. Last week, another AI leader, Mustafa Suleyman joined others to propose an AI equivalent to the International Panel on Climate Change (IPCC) , to help shape protocols and norms.
The paper devotes a lot of its attention to the risks posed by companies that are developing autonomous AI , or systems that “can plan, act in the world, and pursue goals. While current AI systems have limited autonomy, work is underway to change this,” the paper said.
For example, the paper noted, the cutting-edge GPT-4 model offered by Open AI was quickly adapted to browse the web, design and execute chemistry experiments, and utilize software tools, including other AI models. Software programs like AutoGPT have been created to automate such AI processes , and allow AI processing to continue without human intervention.
The paper said there’s significant risk these autonomous systems could run rogue, and that there is no way to keep them in check.
“If we build highly advanced autonomous AI, we risk creating systems that pursue undesirable goals. Malicious actors could deliberately embed harmful objectives. Moreover, no one currently knows how to reliably align AI behavior with complex values. Even well-meaning developers may inadvertently build AI systems that pursue unintended goals—especially if, in a bid to win the AI race, they neglect expensive safety testing and human oversight,” the paper said.
The paper also called on research breakthroughs to address some key technical challenges in creating safe and ethical: Oversight and honesty: More capable AI systems are better able to exploit weaknesses in oversight and testing —for example, by producing false but compelling output; Robustness: AI systems behave unpredictably in new situations (under distribution shift or adversarial inputs); Interpretability: AI decision-making is opaque. So far, we can only test large models via trial and error. We need to learn to understand their inner workings; Risk evaluations: Frontier AI systems develop unforeseen capabilities only discovered during training or even well after deployment. Better evaluation is needed to detect hazardous capabilities earlier; Addressing emerging challenges: More capable future AI systems may exhibit failure modes that have so far seen only in theoretical models. AI systems might, for example, learn to feign obedience or exploit weaknesses in safety objectives and shutdown mechanisms to advance a particular goal.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Why GPT-4 is vulnerable to multimodal prompt injection image attacks | VentureBeat"
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"https://venturebeat.com/security/why-gpt-4-is-vulnerable-to-multimodal-prompt-injection-image-attacks"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Why GPT-4 is vulnerable to multimodal prompt injection image attacks Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
OpenAI’s new GPT-4V release supports image uploads — creating a whole new attack vector making large language models (LLMs) vulnerable to multimodal injection image attacks. Attackers can embed commands, malicious scripts and code in images, and the model will comply.
Multimodal prompt injection image attacks can exfiltrate data, redirect queries, create misinformation and perform more complex scripts to redefine how an LLM interprets data. They can redirect an LLM to ignore its previous safety guardrails and perform commands that can compromise an organization in ways from fraud to operational sabotage.
While all businesses that have adopted LLMs as part of their workflows are at risk, those that rely on LLMs to analyze and classify images as a core part of their business have the greatest exposure. Attackers using various techniques could quickly change how images are interpreted and classified, creating more chaotic outcomes due to misinformation.
Once an LLM’s prompt is overridden, the chances become greater that it will be even more blind to malicious commands and execution scripts. By embedding commands in a series of images uploaded to an LLM, attackers could launch fraud and operational sabotage while contributing to social engineering attacks.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Images are an attack vector LLMs can’t defend against Because LLMs don’t have a data sanitization step in their processing, every image is trusted. Just as it is dangerous to let identities roam free on a network with no access controls for each data set, application or resource, the same holds for images uploaded into LLMs. Enterprises with private LLMs must adopt least privilege access as a core cybersecurity strategy.
Simon Willison detailed why GPT-4V is a primary vector for prompt injection attacks in a recent blog post , observing that LLMs are fundamentally gullible.
“(LLMs’) only source of information is their training data combined with the information you feed them,” Willison writes. “If you feed them a prompt that includes malicious instructions — however those instructions are presented — they will follow those instructions.” Willison has also shown how prompt injection can hijack autonomous AI agents like Auto-GPT. He explained how a simple visual prompt injection could start with commands embedded in a single image, followed by an example of a visual prompt injection exfiltration attack.
According to Paul Ekwere , senior manager for data analytics and AI at BDO UK , “prompt injection attacks pose a serious threat to the security and reliability of LLMs, especially vision-based models that process images or videos. These models are widely used in various domains, such as face recognition, autonomous driving, medical diagnosis and surveillance.” OpenAI doesn’t yet have a solution for shutting down multimodal prompt injection image attacks — users and enterprises are on their own. An Nvidia Developer blog post provides prescriptive guidance, including enforcing least privilege access to all data stores and systems.
How multimodal prompt injection image attacks work Multimodal prompt injection attacks exploit the gaps in how GPT-4V processes visual imagery to execute malicious commands that go undetected. GPT-4V relies on a vision transformer encoder to convert an image into a latent space representation. The image and text data are combined to create a response.
The model has no method to sanitize visual input before it’s encoded. Attackers could embed as many commands as they want and GPT-4 would see them as legitimate. Attackers automating a multimodal prompt injection attack against private LLMs would go unnoticed.
Containing injection image attacks What’s troubling about images as an unprotected attack vector is that attackers could render the data LLMs train to be less credible and have lower fidelity over time.
A recent study provides guidelines on how LLMs can better protect themselves against prompt injection attacks. Looking to identify the extent of risks and potential solutions, a team of researchers sought to determine how effective attacks are at penetrating LLM-integrated applications, and it is noteworthy for its methodology. The team found that 31 LLM-integrated applications are vulnerable to injection.
The study made the following recommendations for containing injection image attacks: Improve the sanitation and validation of user inputs For enterprises standardizing on private LLMs, identity-access management (IAM) and least privilege access are table stakes. LLM providers need to consider how image data can be more sanitized before passing them along for processing.
Improve the platform architecture and separate user input from system logic The goal should be to remove the risk of user input directly affecting the code and data of an LLM. Any image prompt needs to be processed so that it doesn’t impact internal logic or workflows.
Adopt a multi-stage processing workflow to identify malicious attacks Creating a multi-stage process to trap image-based attacks early can help manage this threat vector.
Custom defense prompts that target jailbreaking Jailbreaking is a common prompt engineering technique to misdirect LLMs to perform illegal behaviors. Appending prompts to image inputs that appear malicious can help protect LLMs. Researchers caution, however, that advanced attacks could still bypass this approach.
A fast-growing threat With more LLMs becoming multimodal, images are becoming the newest threat vector attackers can rely on to bypass and redefine guardrails. Image-based attacks could range in severity from simple commands to more complex attack scenarios where industrial sabotage and widespread misinformation are the goal.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Why cybersecurity vendors are selling tech stack consolidation with Zero Trust Edge | VentureBeat"
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"https://venturebeat.com/security/why-cybersecurity-vendors-are-selling-tech-stack-consolidation-with-zero-trust-edge"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Why cybersecurity vendors are selling tech stack consolidation with Zero Trust Edge Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The rapid rise in cyberattacks delivering malicious payloads, including ransomware, happens because organizations have become too complacent with legacy IAM, VPN, and perimeter-based network security systems. CISOs tell VentureBeat that hardware-based systems, never designed to protect beyond perimeters, can’t identify the latest ransomware and malware-free attacks and have now become a liability.
Proving how lethal it is to rely on legacy technology that can’t identify the latest threats, CrowdStrike’s latest research found that 71% of all detections indexed by CrowdStrike Threat Graph are malware-free.
From attackers acting alone to large-scale operations financed through organized crime and nation-states, every attacker knows that legacy VPN, endpoint, and perimeter systems can’t see a malware-free attack, their attack strategies or their payloads.
The more siloed security systems are, the greater the probability that an attacker gets in and stays undetected, in some cases for years , because an organization trusted in perimeter security for too long and got compromised. Primary targets that attackers go after today include healthcare and manufacturing because even the slightest slowdown could cost lives and potentially destroy the business.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Forrester’s recent report, The Zero Trust Edge Solutions Landscape, Q2 2023, provides insights and useful analysis of how CISOs can migrate away from risky legacy tech stacks that rely on outdated perimeter security approaches and better secure their IT infrastructure with Zero Trust Edge (ZTE). Forrester’s study reveals that the key drivers behind ZTE adoption include the shift to remote work and distributed assets, increased business speed and disruptive vendors offering integrated network/security, along with profiles of 22 of the leading vendors in the market.
Barracuda Networks, Cato Networks, Cisco Systems, Cloudflare, Cradlepoint, Forcepoint, Fortinet, Google, HPE Aruba Networking, Huawei, iBoss, Juniper Networks, Lookout, Menlo Security, Netskope, Nokia, Open Systems, Palo Alto Networks, Sophos, Versa Networks and VMware Zscaler are included in the report.
Closing Cloud, IoT, and remote-work gaps need to happen now Attackers are out-innovating enterprises where it matters most, starting with endpoints and progressing to taking control of identities and privileged access credentials. Gaps in legacy tech stacks, long known internally within organizations but not a priority to fix, are just as much to blame as the growing sophistication of social engineering techniques, including the rising popularity of pretexting that attackers use to defraud victims.
Attackers know that IT teams struggle to get cloud configuration right , often leaving entire instances and accounts open.
IoT is another trouble spot ; remote access opened the door to thousands of organizations getting hacked globally.
The Zero Trust Edge (ZTE) design goals center on providing tech stack consolidation, reducing risks and costs and increasing visibility and control across IT infrastructures. ZTE is gaining adoption with CISOs whose highest priority, in many cases, is to consolidate from too many vendors while increasing efficacy and strengthening security postures. The goal CISOs are after is to trim back the number of firmware- and hardware-based legacy systems they have, in addition to software-defined wide area networking (SD-WAN), secure web gateway (SWG) and cloud access security broker (CASB) vendors into a more integrated, adaptive architecture supported by a core set of vendors.
Defining Zero Trust Edge Forrester defines ZTE as “a solution that combines security and networking functionalities — such as software-defined WAN (SD-WAN), cloud access security broker (CASB), Zero Trust network access (ZTNA), and secure web gateway (SWG) — that a single vendor can deliver and support in any combination of cloud, software, or hardware components.” Leading use cases include improving application performance, cloud secure access, visibility, and cloud management require integrated networking and security.
Forrester’s analysts write, “ZTE is a disruptive and high-stakes architecture,” referring to ZTE’s ability to solve several significant problems while simultaneously consolidating four core technologies into a unified architecture solution.
Early ZTE pilots are showing strong results in securing remote workforces, improving remote site security and dependability through multiple connectivity options, streamlining networking and providing more streamlined security management. CISOs and their teams running pilots say that transitioning ZTE’s discrete components to cloud-based managed and monitored services is helping free up localized hardware and system to optimize workloads further locally.
ZTE is the revenue engine cybersecurity vendors need ZTE presents a significant opportunity for cybersecurity vendors to drive new revenue growth by selling tech stack consolidation. CISOs tell VentureBeat that legacy network security approaches have failed to adequately secure today’s distributed environments with remote workers and cloud-based resources. One CISO confided to VentureBeat that legacy perimeter systems are just like not having a system installed at all because it’s beyond the point of stopping attacks invented less than a year ago.
Legacy network approaches have created gaps in organizations’ ability to secure resources, continually improve efficiency and keep up with the speed required to capitalize on new digital business initiatives. ZTE focuses on these challenges by converging security and networking tools into integrated, cloud-delivered architecture.
According to Forrester’s ZTE research, top vendors are capitalizing on ZTE’s potential to consolidate point solutions into a single offering consumed as a service. This aligns with CISOs’ buyer preferences for reduced complexity and operating expense (OPEX) models.
An estimated 78% of organizations prefer to buy or consume consolidated functionalities as a service, according to Forrester’s Security Survey, 2022. Forrester’s analysts observe that the top vendors are ambitious regarding their plans to offer an entire turnkey package, adding that “the idea of having a single architecture for all security solutions on an opex basis will be compelling for the SMB/midmarket.” Forrester cautions that vendors offering ZTE are still overcoming limitations in their core areas.
With tech stack consolidation a priority for CISOs, ZTE shows potential to be the next viable evolution of security infrastructure. CISOs running pilots tell VentureBeat that ZTE is delivering measurable gains in operations performance, more effective endpoint and identity security and lower costs due to standardizing on a unified architecture. The market dynamics make clear that ZTE is the new revenue engine cybersecurity vendors need.
Top ZTE use cases Forrester identified the six core use cases where ZTE delivers the most value. Underscoring them all is a strong focus on achieving greater cyber-resilience while improving network performance and reliability.
CISOs from banking and financial services tell VentureBeat that ZTE’s use case of delivering cloud-secure access and securing virtual work teams using Zero Trust Network Access (ZTNA) is a part of their pilots today. Every pilot VentureBeat has learned about is running real-time visibility and history network statistics to quantify visibility and observability gains.
In addition to the core use cases, Forrester identified four extended Zero Trust Edge use cases that are less sought by CISOs but demonstrate key vendor differentiation. End-to-end control provides visibility and governance across all network segments. Credential mapping unifies user identities across systems to simplify access policy enforcement. Unauthorized access detection and prevention protect against credential misuse and insider threats. Web content filtering from remote sites extends acceptable use policies.
Zero Trust Edge poised for growth ZTE represents a pivotal shift in how enterprises secure their virtual teams and remote workers, assets, cloud environments and growing IoT networks. CISOs tell VentureBeat that legacy approaches to network, device, endpoint and identity security can’t keep up with the speed and complexity of cyberattacks. By converging networking and security, ZTE delivers a cloud-centric model that can be consumed as a service and paid for as an operating expense.
The variety of scope and approaches the 22 ZTE vendors mentioned in this report are taking to sell consolidation on their platforms shows how diverse the enterprise needs each is trying to meet. VentureBeat has learned that initial ZTE pilots are meeting expectations by supporting new digital-first revenue initiatives while closing the gaps in tech stacks that led to intrusion and breach attacks in the past.
In the near term, Forrester predicts larger enterprises will take a multivendor approach, integrating best-of-breed ZTE components from market leaders mentioned in their report. The core of ZTE’s simplification and consolidation value proposition makes it a compelling strategy for SMBs and midsize firms eager to standardize on a unified architecture. Demand is growing for a solution that can solve the most challenging multicloud and hybrid cloud security challenges, support remote work and zero trust initiatives. ZTE is well positioned to capitalize on these market dynamics.
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"Why Cradlepoint's acquisition of Ericom predicts the future of SASE in the enterprise | VentureBeat"
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"https://venturebeat.com/security/why-cradlepoints-acquisition-of-ericom-predicts-the-future-of-sase-in-the-enterprise"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Why Cradlepoint’s acquisition of Ericom predicts the future of SASE in the enterprise Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The roadblocks standing in the way of launching new digital-first revenue channels and the heavy lift of supporting virtual teams make CISOs and CIOs ask hard questions about why their existing network infrastructure is failing them. That moment of truth starts to drive secure access service edge (SASE) growth in every organization.
That’s a strong epiphany for CISOs and CIOs, and in aggregate it’s the type of dynamic that creates new markets. Gartner predicts that by 2025, 80% of enterprises will adopt SASE to unify web, cloud and private app access. A further 65% will consolidate to one to two SASE vendors. The research firm also says the SASE market is growing rapidly and is expected to reach $15 billion by 2025 with a 32% CAGR.
Gartner defines SASE as delivering “converged network and security-as-a-service capabilities, including SD-WAN, secure web gateway (SWG), cloud access security broker (CASB), next generation firewall (NGFW) and zero trust network access (ZTNA). SASE supports branch offices, remote workers, and on-premises secure access use cases.” Steward Health CISO Esmond Kane advises : “Understand that — at its core — SASE is zero trust. We’re talking about identity, authentication, access control and privilege. Start there and then build out.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Why SASE is becoming a consolidation engine SASE adoption is fueled by CISOs and CIOs partnering to consolidate tech stacks to improve visibility, increase efficacy of combined systems and reduce costs. CISOs tell VentureBeat that 2023 is the year of consolidating tech stacks , made more urgent by the need for greater visibility and control across every endpoint and threat surface. With 75% of enterprises pursuing vendor consolidation — up from 29% three years ago — SASE is seeing significant upside growth.
Many CISOs are adopting SASE to consolidate secure web browsing (SWB), CASB and virtual private network (VPN) vendors into a single platform. Other CISOs say improving security posture with more effective identity-based ZTNA is the goal. Every CISO VentureBeat interviews on SASE says supporting hybrid workforces with secure access from anywhere is now table stakes.
Filling gaps in SASE tech stacks creates new growth opportunities for network infrastructure leaders by enabling them to sell what CISOs want most: A solid consolidation roadmap. CISOs tell VentureBeat that SASE platforms must include real-time network activity monitoring, role-specific ZTNA access privileges and support for consolidating network and security services. ZTNA also delivers real-time, identity-level monitoring of endpoints, assets, databases and transaction requests, making it a perfect fit for SASE platforms. Furthermore, ZTNA security for distributed edge devices of all types continues to progress, especially for IoT-based devices.
How Cradlepoint acquiring Ericom predicts the future of SASE Will Townsend, VP and principal analyst of networking and security at Moor Insights & Strategy says that, “networking and security are rapidly merging within the enterprise, and the cloud is increasingly leveraged as the preferred SASE delivery mechanism. Cradlepoint’s acquisition of Ericom and its zero trust security capabilities should position the company well to capitalize, especially in organizations considering a 5G-centric deployment approach.” Cradlepoint acquiring Ericom is prescient of where the SASE market is going and indicates how the framework will be adopted across global enterprises. VentureBeat recently hosted a conference call with CISOs and CIOs from financial services, healthcare, manufacturing and professional services — all industries that lead in SASE adoption today — to learn how the increasing pace of mergers and acquisitions in the SASE market is defining the future of this area.
CISOs and CIOs view the acquisition as strategic because it shows the potential to combine networking and security into a cloud service. CISOs also noted that a unified Cradlepoint and Ericom cloud solution could better secure hybrid work environments and virtual teams.
These are the key insights of the interviews.
Provides a trust foundation for SASE built on proven platforms Cradlepoint’s strong SD-WAN and zero-trust capabilities provide secure optimized connectivity to cloud and enterprise applications. Together, Cradlepoint and Ericom form a SASE tool. Cradlepoint’s SD-WAN platform improves network and application performance and secures access to private applications. Ericom has proven cloud security and isolation capabilities for securing web and email use and access to public/SaaS applications.
Helps enterprises accelerate their shift to ZTNA Cradlepoint offers secure, identity-based ZTNA that has proven effective in securing organizations. As part of SASE, ZTNA verifies users and devices before granting access. Ericom’s cloud-delivered security and isolation solution compliments Cradlepoint’s SD-WAN and zero-trust capabilities. Tighter ZTNA, SD-WAN, routing, and 5G/LTE integration will benefit Cradlepoint-Ericom customers and define one of the key aspects of SASE’s future across the enterprise.
“The combination of Cradlepoint’s expertise in WAN networking and Ericom’s innovative security service edge (SSE) and security solutions will create a powerful capability to meet the increasing demand for cloud-based secure network solutions,” Ericom CEO David Canellos told VentureBeat.
Streamlines the process of getting core SASE capabilities up and running The acquisition will simplify enterprise SASE deployment and SASE pillars of identity-based access, microsegmentation and encrypted inspection by integrating SD-WAN, wireless and zero trust in a robust cloud platform. Ericom improves zero trust by air-gapping web and application access, microsegmenting and securing workloads. Growing SASE adoption will allow Cradlepoint to consolidate networking, security, and access and shape air-gapped cloud SASE platforms.
Brings intelligence to the edge Cradlepoint edge solutions bring compute and analytics to the network edge. This edge infrastructure gets SASE services closer to users, improving performance, threat response, dynamic access policies and fine-grained microsegmentation. Edge compute for routing, security and traffic analysis positions Cradlepoint and Ericom to deliver AI-enabled, edge-optimized, cloud-driven SASE.
Signals a new era of 5G-powered SASE-as-a-Service With their core technologies, the two companies provide comprehensive cloud-based SASE. Cradlepoint’s 5G SD-WAN and zero-trust and Ericom’s cloud security software enable subscription-based secure access. This acquisition represents the future of SASE “as-a-service.” by unifying key components into a simple platform. Cradlepoint and Ericom will lead the transition to flexible, cloud-native SASE, providing on-demand enterprise connectivity and security.
Core to Cradlepoint’s strategy providing a full-stack enterprise security service optimized for 5G for fixed-site, remote worker, in-vehicle and IoT use cases.
An acquisition well-timed for a fast moving market Cradlepoint’s acquisition of Ericom comes at a time when the SASE market is experiencing double-digit growth because it meets some of CISOs’ most challenging problems.
Cradlepoint’s SD-WAN and 5G strengths and Ericom’s proven isolation of air-gapping capabilities result in a robust SASE offering tailored for modern IT environments. The simplified SASE stack, powered by Cradlepoint’s NetCloud platform, will assist customers in consolidating tools for efficiency while closing security gaps.
Cradlepoint is making a timely strategic move, with networking and security converging and SASE delivery shifting to the cloud. Acquiring Ericom positions the company to capitalize on an expanding market driven by the need to replace network infrastructure that can’t keep up with the current and future demands of cloud- and digital-first businesses.
Acquiring Ericom places Cradlepoint at the forefront of SASE innovation and growth as enterprises seek integrated solutions to secure new work models.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Why CISOs need zero trust as a ransomware shield | VentureBeat"
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"https://venturebeat.com/security/why-cisos-need-zero-trust-as-a-ransomware-shield"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Why CISOs need zero trust as a ransomware shield Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
This year is on pace to be the second-costliest for ransomware attacks ever, with threat actors relying on new deceptive approaches to social engineering combined with weaponized AI.
The recent MGM breach began with attackers studying the social media profiles of help desk employees, then calling the help desk and impersonating them to get privileged access credentials and logins.
Zero trust security needs to be a mindset that pervades everything from consolidating tech stacks to managing identities at scale. CISOs and their teams must start with the assumption that a breach has already happened, and an organization’s network needs to be designed to limit an intrusion’s blast radius and depth.
“Zero trust requires protection everywhere — and that means ensuring some of the biggest vulnerabilities like endpoints and cloud environments are automatically and always protected,” said Kapil Raina, VP of zero trust marketing and evangelist for identity, cloud and observability) at CrowdStrike.
“Since most threats will enter into an enterprise environment either via the endpoint or a workload, protection must start there and then mature to protect the rest of the IT stack.” Gartner introduces a new Hype Cycle for Zero Trust Networking Gartner’s inaugural Hype Cycle for Zero Trust Networking comes at a time when CISOs and the organizations they serve are under siege from near-record ransomware attacks. All hype cycles and market frameworks have limitations, yet they do help to filter out vendor noise and those overstating their zero trust capabilities.
The Hype Cycle examines 19 key technologies — including microsegmentation, Kubernetes networking, secure access service edge (SASE) and security service edge (SSE) — and maps their maturity level and hype cycle position.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! VentureBeat believes that ten core technologies in the Hype Cycle have the potential to deliver the most value to CISOs. They include container security, enterprise browsers, Kubernetes networking, managed SASE, microsegmentation, OpenID Connect, remote browser isolation (RBI), security service edge (SSE), unified endpoint security and zero trust strategy.
What is zero trust networking? Gartner defines zero tru s t networking (ZTN) as how zero trust concepts are applied and integrated into network infrastructure. Consistent with the NIST zero trust security standard , ZTN only grants users and devices access to a network based on real-time identity and context validation. An enterprise-class ZTN infrastructure grants access to authenticated and authorized identities and adheres to least-privileged access to any network resource.
CISOs tell VentureBeat that the more progress their organizations make in implementing Zero Trust Network Access (ZTNA), the more efficient ZTN becomes to implement. The goal is to secure virtual teams and scale up new digital transformation projects so they aren’t hacked right after launch. New apps are an attack magnet, and ZTNA is helping reduce threat surfaces and protect against privileged access credential theft while strengthening risk-based dynamic access control policies.
Ten zero trust technologies worth watching Defining a zero trust security strategy that delivers quick wins is essential to control budgets and gain greater investment. One CISO told VentureBeat that they schedule quick, measurable wins early in their zero trust roadmaps expressly for that purpose. Today’s CISOs are looking to protect and grow budgets to invest in new technologies.
VentureBeat identifies the ten core technologies below as delivering the greatest value to CISOs pursuing zero trust strategies.
Container security Developer container security tools detect vulnerabilities and misconfigurations early. These production tools protect against exposed containers and compromised images at runtime. Network segmentation and runtime behavior monitoring secure dynamic container environments. Leading vendors include Aqua Security, Orca Security, Red Hat, Sysdig, Trend Micro and Palo Alto Networks.
Enterprise browsers Managed, secure browsers consolidate access to reduce the risk of malicious sites or downloads. Secure web browsing is becoming more popular among dispersed workforces. Granular policy control over web content, downloads and extensions is essential. Check Point Software, Ermes Cyber Security, Google, Island, Microsoft, Perception Point, Seraphic Security, SlashNext, SURF and Talon Cyber Security are among the leading vendors.
Kubernetes networking Kubernetes networking addresses Kubernetes’ requirements for scale, security and visibility. Load balancing, service discovery, multi-cluster connectivity and microsegmentation are all key features. Among the top vendors are Amazon Web Services, Avesha, Azure, Cisco, F5, HashiCorp, Isovalent, Juniper Networks, Tetrate and VMware.
Managed SASE Managed SASE accelerates deployments with integrated networking and security as a service using providers’ resources and expertise. Key benefits include reduced staffing risks, quicker enablement of SASE capabilities and integrated management. VentureBeat continues to see SASE benefiting from the faster consolidation of networking and security.
AT&T, Cato Networks, Comcast, Expereo, KDDI, MetTel, Orange Business Services, Palo Alto Networks, Verizon, VMware and Windstream Enterprise are leading SASE vendors.
Microsegmentation Microsegmentation is core to the NIST SP800-207 zero trust standard and provides many benefits, including enforcing identity-based access policies between workloads to limit lateral movement after breaches. It also provides granular controls over east-west traffic based on workload identity, not just network zoning. Leading vendors include Airgap Networks, Akamai Technologies, Cisco, ColorTokens, Fortinet, Illumio, Palo Alto Networks, VMware, Zero Networks and Zscaler.
OpenID Connect OpenID Connect is an authentication protocol that improves user experience, security and privacy. It is gaining adoption to enable single sign-on across devices, apps and APIs. Leading vendors include Auth0, Cloudentity, Curity, ForgeRock, Gluu, Google, IBM, Microsoft, Okta, Ping Identity and Red Hat.
Remote Browser Isolation (RBI) RBI isolates browsers to reduce the attack surface by remotely executing web code, thwarting threats such as drive-by downloads, phishing and data exfiltration. Leading vendors are focusing their innovation on improving isolation techniques and integrating with Secure Web Gateway (SWG) and ZTNA to address more use cases.
Granular upload/download controls and integrations with Cloud Access Security Brokcers (CASB), data loss prevention (DLP) and sandboxes have been added to analyze threats detected during isolated browsing sessions. Leading vendors include Authentic8; Broadcom; Cloudflare; Ericom Software, the cybersecurity unit of Cradlepoint; Forcepoint; Garrison; Menlo Security; Netskope; Proofpoint; Skyhigh Security; and Zscaler.
Security Service Edge (SSE) SSE consolidates SWG, CASB and ZTNA into a cloud platform to secure web, SaaS and private apps while ensuring that system-wide management stays consistent and at scale. Tight integration enables standardized policies, automated workflows and data sharing across integrated tools. SSE also improves remote user experiences through unified architecture. SSE boosts efficiency and consistency by streamlining administration and coordination between security technologies. Leading vendors include Broadcom, Cisco, Cloudflare, Forcepoint, Fortinet, iboss, Lookout, Netskope, Palo Alto Networks, Skyhigh Security and Zscaler.
Unified Endpoint Security (UES) UES combines endpoint protection and management to enable risk-aware security policies and automated remediation. It enables risk-based patching prioritization and continuous vetting of endpoint configurations for more effective security posture management by integrating real-time telemetry threat data into operations workflows. Leading vendors include Absolute, BlackBerry, CrowdStrike, IBM, Ivanti, Microsoft, Sophos, Syxsense, Tanium and VMware.
Zero trust strategy A zero trust strategy establishes the fundamentals and activities of a zero trust program. It enforces least privileged access for every resource and identity request. It reduces the blast radius of intrusions and breaches. Strategies must align with enterprise objectives and risk tolerance. For zero trust strategies to be effective, they must be customized for each organization.
The following table summarizes the ten zero trust technologies worth watching based on VentureBeat interviews with CISOs.
Predicting the future of zero trust The massive MGM ransomware attack that began with a simple phone call illustrates how critical it is to have identity-based security and microsegmentation, hardened with real-time validation of credentials, to limit the blast radius. Zero trust assumes a breach has already happened and serves as a framework to contain it.
Zero trust is no panacea against attackers using generative AI to sharpen their tradecraft and launch social engineering-based attacks that devastate victims. As one CISO recently told VentureBeat: “Zero trust needs to deliver resilience. That’s its business case, and the more resilient and capable it is of limiting an attack, the more zero trust proves its value as a business decision.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Visa's report makes a case for outsmarting payment fraud with AI now | VentureBeat"
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"https://venturebeat.com/security/visas-report-makes-a-case-for-outsmarting-payment-fraud-with-ai-now"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Visa’s report makes a case for outsmarting payment fraud with AI now Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Payment fraud soared to record levels in early 2023, and in response, e-commerce, retailing, and mass merchants rely more on AI-based fraud prevention systems to outsmart attackers.
Visa’s Biannual Threats Report published today found that nearly 460 ransomware attacks worldwide happened in March 2023 alone, a staggering 91% increase over February, and 62% higher compared to the same period in the previous year.
Visa’s research team also found that fraudsters stole over $1.3 billion in cryptocurrency in early 2022 alone by exploiting vulnerabilities in smart contracts.
Enumeration attacks jumped 40% in recent months, and 58% of fraud investigations involved online merchants. Brick-and-mortar retailers accounted for just 20%, proving that e-commerce remains a high-priority target.
With e-commerce and online retail transactions setting new revenue records year-over-year, ransomware and payment fraud attackers are developing entirely new AI-based techniques to defraud merchants, consumers, and suppliers using a combination of social engineering, phishing, and identity-driven attacks to take control of accounts.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Attackers using weaponized AI to breach payment ecosystems Visa observed that attacks are becoming increasingly lethal and undetectable due to attackers fine-tuning payment fraud strategies through generative AI tools.
FraudGPT is an example of the latest generation of attack tools being sold on the dark web.
VentureBeat has learned that gaming firms see increases in Account Takeover (ATO) attacks proliferating across e-commerce today, driven by advanced AI attack strategies. Businesses that rely on real-time approvals of small transaction amounts to keep customers engaged are often targets. Transactions must be approved instantly and at scale for a game to succeed financially. Fortunately, AI-based fraud systems can analyze a transaction in less than a second, considering the many data points from other current and past transactions to see if it is real or fraudulent.
“While we are pleased by the lower-than-expected fraud rate over the last few months, this edition of the Biannual Threats Report continues to underscore just how savvy fraudsters continue to be,” said Paul Fabara, Chief Risk Officer at Visa. “The same way criminals take advantage of technology advances, so does Visa, and the $30 billion of fraud prevented in the last six months alone is a great testament to that.” Visa’s AI-driven deterrence strategy is paying off One of the fascinating findings from the report is how Visa is using a series of AI-based tools and techniques to battle payment fraud across sectors worldwide. Its 24/7 Risk Operations Center relies on a continual stream of real-time telemetry data to provide transaction monitoring and threat analysis to their clients globally. The center responded to nearly 2,000 fraud incidents, blocking over 53 million presumed fraudulent transactions worth $32 billion in six months.
Visa has extensive experience and has made significant investments in AI to reduce fraud. In April 2022, Paul Fabara, Chief Risk Officer for Visa, wrote, “ I n the past five years, the company has spent over $9 billion to boost cybersecurity and reduce fraud. With an investment of $500 million in artificial intelligence and data infrastructure, for example, Visa can power over 60 different AI capabilities to automate much of the heavy lifting in fraud detection — a time-consuming task that many clients do manually today.” Staying ahead of the threats with AI As digital commerce expands exponentially, payment fraud continues to escalate rapidly. From rogue attackers to Advanced Persistent Threat (APT) groups, all unleash increasingly sophisticated, AI-powered attacks that exploit vulnerabilities across global payment ecosystems. Visa’s latest Biannual Threats Report shows that with the right strategy, every business can stay ahead of the threats and rely on AI to outsmart attackers.
E-commerce, retailers, and mass merchants can’t afford to lose the AI war to attackers. With weaponized AI on the rise and attackers fine-tuning their tradecraft, payment providers must double down on AI investments to protect their payment ecopsystems against attacks. Visa’s report provides a 360-degree view of the landscape and guides those companies looking to build an AI-driven roadmap to counter these threats.
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"Vanta report: AI-powered trust management will help close security compliance gaps | VentureBeat"
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"https://venturebeat.com/security/vanta-report-ai-powered-trust-management-will-help-close-security-compliance-gaps"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Vanta report: AI-powered trust management will help close security compliance gaps Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Most businesses are flying blind regarding security and compliance, putting their company’s infrastructure, customers and future at stake.
Despite their best efforts, 67% say their level of visibility and compliance need help. Only 4 in 10 rate their visibility as strong. Widening security gaps leaves more attack surfaces unprotected, making the lack of security compliance a liability that slows down everything from sales cycles to attracting investors.
These stark findings are from Vanta’s State of Trust Report 2023, released today. The report provides an in-depth analysis uncovering global trends in security, compliance and the future of trust. Vanta interviewed 2,500 security, risk and trust management professionals across five continents.
Glaring security compliance gaps jeopardize future business Most troubling is how glaring — and growing — security compliance gaps can lead to slower sales and loss of clients over time. These gaps are in the most vulnerable areas of a business, starting with how identities are managed and protected. That’s the goldmine that attackers are after because once they control identities, they control the company.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “The data exposes fundamental gaps, leaving companies vulnerable,” said Jeremy Epling chief product officer overseeing Vanta’s engineering, product and design.
Notably, 39% called out identity and access management (IAM) as a particular blind spot.
Vanta’s survey results reflect a staggering number of compliance blind spots across organizations globally, compounded by short-staffed security teams forced to burn valuable time on manual security compliance workflows.
The findings also reveal limited risk visibility and too heavy reliance on manual compliance processes, reducing security teams’ ability to do their work efficiently. Enterprises need new methods to automate and improve their security. Compounding the urgency is ever-evolving global regulation and the growing time-suck of complying with increasing standards.
Closing compliance gaps manually isn’t working Manually-based compliance tasks take valuable time away from security teams, who should focus on higher-priority and more urgent tasks to ensure the security posture of their organizations.
Vanta’s survey found that security teams spend 7.5 hours per week on compliance. It’s understandable that many organizations are deprioritizing compliance efforts due to the substantial time required. But while this temporary relief is appealing, delayed adherence stifles market expansion.
“Static compliance processes slow companies down tremendously,” said Diego Susa, head of engineering at feature management software company Unleash (a Vanta customer). “Automation is essential with today’s threats.” More than three-quarters (83%) of respondents say they are increasing their use of automation (or plan to). They report that automating tasks could save two hours per week — more than 130 hours annually.
Vanta told VentureBeat that its goal is to help every business centralize and automate security management by relying on the scale of their AI-powered platform. However, the company argues that transparency remains critical as AI risks persist. Over half of leaders worry AI may erode trust without sufficient explainability. Ethical implementation is mandatory, even when chasing efficiency gains.
“Organizations urgently need more efficient methods to improve security as risks multiply,” said Vanta CEO Christina Cacioppo. “Automation through trust management platforms can help overcome these hurdles.” In a world short on trust, security compliance is king Vanta’s launched of its new Vanta Trust Center today shows they’re reading the market well. The platform serves as a single destination for customers to showcase their security and compliance posture, build trust and streamline security reviews.
Contracts, purchase orders, partnerships, company mergers, acquisitions and financing rounds depend on a proven security posture. Vanta designed and launched the center to support customers’ need to communicate in real-time, further strengthening their security postures.
VentureBeat has found that financial services, insurance and banking firms are using security compliance data during quarterly reviews with their biggest clients to gain a greater share of wallet. Security compliance data provides immediate legitimacy to a business and is used to protect the most lucrative client relationships financial services companies have.
“Our goal is to build trust with our customers and partners by demonstrating our commitment to data protection measures,” said Adam Rebhuhn, security compliance manager at payments company Modern Treasury.
“Vanta’s Trust Center lets us communicate our real-time security status transparently, reducing the need for lengthy questionnaires and differentiating us in a competitive market.” Vanta claims the Trust Center reduces deal cycles by 30%, enabling organizations of all sizes to grow their business faster. The Trust Center combines the unique strengths of Trustpage and Vanta, making it easier than ever for customers to unify their security program management and accelerate the security review process, all from within Vanta.
“With one in eight companies falling at the very first hurdle of proving trust to customers and prospects, organizations need a simple yet powerful solution to showcase their security posture,” said Epling.
He continued: “Vanta Trust Center is made for this moment, coupling Trustpage Trust Centers’ superior workflows and customization features with Vanta’s real-time security monitoring and intuitive interface. Trust Center helps all companies — from scale-ups to leading enterprises — maintain customer trust with an advanced combination of functionality, customization, integrations and workflows to proactively demonstrate security posture and manage security reviews.” Protect security compliance like the asset it is Vanta’s successful track record of reading the market and customers need can be attributed to its focus on protecting security compliance like the asset that it is.
Cybersecurity and compliance have never been more of a business imperative than they are today. They are essential to managing and containing risks while persuading the largest clients to stay with them and trust their security posture.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Vanta continues to revolutionize trust management with latest AI release | VentureBeat"
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"https://venturebeat.com/security/vanta-continues-to-revolutionize-trust-management-with-latest-ai-release"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Vanta continues to revolutionize trust management with latest AI release Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Seeing an opportunity to scale AI and reduce how overburdened compliance and security teams are with repetitive tasks, Vanta is launching its Vanta AI suite today. The suite relies on AI and large language models (LLMs) to help teams get more of their time back by automating repetitive security and compliance tasks.
Vanta AI features AI-powered vendor security reviews, generative questionnaire responses, questionnaire automation, intelligent control mapping and suggestions on the best test and policies for each compliance framework. All of these features are needed for compliance and security teams looking for automation tools that scale and allow them to offload repetitive, manual tasks.
AI and LLMs are proving effective in eliminating the drudgery and manual tasks that security and compliance teams are dealing with today, CISOs and CIOs tell Venturebeat.
Vanta’s CPO Jeremy Epling told VentureBeat that “compliance and security teams process and manage a large volume of documents and text to execute their programs. With Vanta AI, we’re able to further automate these functions, saving teams large chunks of time and enabling them to focus on higher-value initiatives.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Vanta’s new AI capabilities target time-consuming security processes Epling and Chase Lee, Vanta’s VP of product, tell VentureBeat that Vanta AI uses the latest in AI and LLMs to accelerate critical security workflows including vendor security reviews, security questionnaire automation and to strengthen main controls.
Vanta AI’s Vendor Security Reviews, integral to the company’s Vendor Risk Management application, relies on AI to parse vendor documents in seconds, extracting the most relevant details needed to complete vendor security reviews. AI will also help security teams assess third-party risks while attaining high compliance hygiene standards. Customer’s say the company’s ability to automate security reviews is core to their DNA in automating compliance.
Lee and Epling explained that Vanta’s Vendor Risk Management application with AI capabilities enables security teams to analyze and extract relevant information from SOC 2 reports, DPAs and other vendor documentation in seconds.
Lee and Epling explained that Security Questionnaire Automation and the ability to strengthen and maintain controls are two high-payoff areas of automating trust management with AI.
“Security questionnaires are critical to good security hygiene but are often long, highly duplicative [and] prone to human error and falling out-of-date,” said Epling. “Using AI, Vanta now enables security teams to instantly extract insights from a variety of sources, including their existing library, previous questionnaire responses and policies and documents uploaded to Vanta — in just a few clicks.” Vanta AI can also automatically suggest the best tests and policies for each compliance framework (which was previously done manually). The suite will also soon be able to auto-generate control summaries and make it even easier to maintain controls when adding new frameworks.
Epling sees potential for LLMs to deliver long-term value as part of the broader Vanta AI strategy.
“In the future, we will deploy LLMs to generate and refine policies through natural language interfaces as well as speed up and personalize the remediation of security issues. We will also continue to invest further in automating questionnaires and vendor security review analysis,” Epling told VentureBeat.
Using AI to lighten security teams’ workload is the goal Finding new ways to use automation and reduce time and workload pressures on compliance and security teams is a part of Vanta’s DNA. The company began pursuing automated compliance in 2018, and their release of the Vanta AI suite is a logical progression of their vision and product strategy.
Originally gaining traction for its ability to help companies achieve certifications and pass audits in weeks instead of months, Vanta is now considered one of the leading trust management platforms because they’ve proven they can automate tasks that drain valuable time from security teams.
Today, the company is helping its 6,000 global customers automate audits and compliance workflows for SOC 2, ISO 27001 and HIPAA to help them quickly achieve certifications and audits.
One Vanta customer told VentureBeat that their teams had to go through lengthy SOC2 manual reports periodically to complete security reviews, which significantly drained team productivity. The customer explained that while questionnaires can help, they still require a time commitment to get vendor coordination right.
How Vanta is revolutionizing trust management Vanta has built its DNA on needs-based innovation, as evident in how they’re approaching integrating AI and LLMs into their applications.
Centering on the needs of compliance and security teams, Vanta’s approach to automating trust management with AI also shows that they understand that security reviews are among the most dreaded manual tasks across their customer base — making it an ideal use case to unleash AI on. This indicates that they rely on empathically-driven design and development approaches as their guardrails.
Vanta’s latest AI capabilities demonstrate that the company is pioneering intelligent automation to transform trust management with a strong focus on respecting customers’ time.
The bottom line is that needs-based innovation guided by empathy, especially in proliferating AI and LLM development for enterprises, is the way to go.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Top 20 cloud native application protection platforms of 2023 | VentureBeat"
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"https://venturebeat.com/security/top-20-cloud-native-application-protection-platforms-of-2023"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Top 20 cloud native application protection platforms of 2023 Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Exploiting gaps in cloud infrastructure that are leaving endpoints, identities and microservices exposed is a quick way for an attacker to steal credentials and infect an enterprise’s DevOps process. Attacks to exploit such gaps are skyrocketing.
The recent 2023 Thales Cloud Security Study provides hard numbers: 39% of enterprises have been hit with a data breach starting in their cloud infrastructure this year alone. A total of 75% of enterprises say that more than 40% of the data they store in the cloud is sensitive. Less than half of that data is encrypted.
CrowdStrike’s 2023 Global Threat Report explains why cloud-first attacks are growing: Attackers are moving away from deactivating antivirus, firewall technologies and log-tampering efforts and toward modifying core authentication processes, along with quickly gaining credentials and identity-based privileges.
The attackers’ goal is to steal as many identities and privileged access credentials as possible so they can become access brokers — selling stolen identity information in bulk at high prices on the dark web. Access brokers and the brokerages they’re creating often turn into lucrative, fast-growing illegal businesses. CrowdStrike’s report found more than 2,500 advertisements for access brokers offering stolen credentials and identities for sale.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! What’s driving CNAPP adoption Consolidating tech stacks continues to dominate CISOs’ plans, driven by the need to improve efficacy, manage a more diverse multicloud security posture, close gaps between cloud apps and shift security left in DevOps pipelines. All these factors are contributing to the growing adoption of cloud-native application protection platforms (CNAPP).
“CNAPPs are formed from the convergence of cloud security posture management (CSPM) and cloud workload protection platform (CWPP) capabilities as well as other security tooling like entitlement management, API controls and Kubernetes posture control,” reads Gartner’s 2023 Planning Guide for Security.
Leading CNAPP vendors are competing in various areas, the most important of which include the efficacy of their cloud infrastructure entitlement management (CIEM), Kubernetes security, API controls and cloud detection and response (CDR), according to CISOs VentureBeat spoke with. Demand for CNAPP is greatest in larger enterprises from highly regulated industries that rely on extensive multicloud configurations. Finance, government and healthcare providers are among the most dominant industries.
CISOs tell VentureBeat that one of the most practical benefits of CNAPPs is the opportunity to consolidate legacy tools with limited visibility across all threat surfaces and endpoints. The takeaway? Reducing tool sprawl is a quick win.
Benchmarking the top 20 CNAPP platforms for 2023 Full-platform CNAPP vendors provide integrated cloud-native security platforms ranging from DevOps to production environments. Here are the top 20 platforms of 2023: Aqua Security : Highly regarded for its approach of scanning container registries and images, CSPM and runtime protection for container and cloud-native security. Also has full life cycle protection and advanced runtime techniques, including support for the extended Berkeley Packet Filter (eBPF).
Check Point : Provides a broad set of capabilities through its CloudGuard platform, including CSPM, CIEM and advanced runtime protection. Known for securing cloud workloads across environments with identity-centric access controls, as well as threat intelligence integration to provide real-time contextual prioritization of risks.
Cisco : Recently acquired Lightspin for its Kubernetes security capabilities and CSPM. Its Tetration platform focuses on runtime protection, leveraging eBPF and third-party insights for advanced container monitoring and granular controls. Cisco emphasizes behavioral analytics to detect anomalies and threats in container environments and provides strong controls to limit lateral movement between workloads.
CrowdStrike : Offers a leading CNAPP suite emphasizing identity-centric visibility, least-privilege enforcement and continuous monitoring. Its runtime protection leverages agents and eBPF for workload security. CrowdStrike’s key design goals included enforcing least-privileged access to clouds and providing continuous detection and remediation of identity threats.
Cybereason : Platform focuses heavily on malicious behavior detection. A core strength is its ability to detect threats using behavior-based techniques. The company is also known for API integrations, AI and machine learning (ML) expertise. Cybereason specializes in detecting compromised accounts and insider threats via detailed user activity monitoring.
Juniper Networks : Collects extensive data on device posture and traffic patterns to provide networking context for security insights. Also enables segmentation controls between Juniper devices.
Lacework : Focused on workload behavior analysis for containers and runtime techniques such as eBPF to gain a comprehensive insight into container activity and performance. Its emphasis on detecting anomalies using advanced ML algorithms that are custom-tuned for containerized environments is a key differentiator.
Microsoft : Integrates security across Azure services with zero-trust controls, enforces least-privileged access and provides workload protections such as antivirus and firewalls. Uses Microsoft Graph to correlate security analytics and events across Azure.
Orca Security : Performs continuous authorization checks on identities and entitlements across cloud environments. A key differentiator is the ability to generate detailed interactive maps that visualize relationships between cloud assets, users, roles and permissions.
Palo Alto Networks Prisma Cloud : Provides a broad suite of capabilities, including identity-based microsegmentation and robust runtime protection with eBPF. Prisma Cloud is an industry leader known for advanced protections such as deception technique and includes extensive compliance automation and DevSecOps integrations.
Qualys : Focuses on compliance and vulnerability management through continuous scanning and least-privilege controls. Identifies vulnerabilities throughout the life cycle and enables automated patching and remediation workflows. Another key differentiator is compliance mapping and reporting.
Rapid7 : Enforces least privilege access and enables automated response and remediation triggered by events. Offers pre configured policies and streamlined workflows designed for small security teams. An intuitive user interface and rapid implementation aim to simplify deployment and usability for organizations with limited security resources.
Sonrai Security : Focuses on entitlement management and identity-based security using graph database technology to discover and map user identities across cloud environments. User identity, geolocation and other contextual factors can define custom access controls.
Sophos : Focuses on data security, compliance and threat monitoring capabilities and offers advanced data loss prevention such as file fingerprinting and optical character recognition. Cloud environments also have anti-ransomware protections.
Sysdig : Centered on runtime security and advanced behavioral monitoring. For container-level visibility and anomaly detection, the platform uses embedded agents. Sysdig Secure Advisor includes an integrated security assistant to help SecOps and IT teams create policies faster.
Tenable : Focused on compliance, entitlement management and identity governance. Offers comprehensive compliance automation mapped to PCI, HIPAA and ISO regulations. Also provides differentiated identity and compliance management through advanced capabilities to enforce least privilege and certify access.
Trend Micro : Includes runtime security, compliance and threat monitoring, enforces policies and protects cloud environments from file- and email-based threats. Custom sandboxing for suspicious file analysis is also included.
Uptycs : Differentiates itself by combining CNAPP capabilities with extended detection and response (EDR) capabilities. Employs data lake techniques to store and correlate security telemetry across cloud and container workloads. Threats are identified using behavioral analytics, and automated response workflows allow for rapid remediation.
Wiz : Centered on continuous access controls, micro segmentation and identity-based adaptive security. Automatically discovers and visualizes relationships between cloud assets, users and permissions. Wiz also conducts risk analysis to identify potential attack paths and stands out with its specialized visualization, identity management and micro-segmentation.
Zscaler : Posture Control prioritizes risks caused by misconfigurations, threats and vulnerabilities. Completely agentless and correlates data from multiple security engines.
Why CNAPP will succeed as a consolidation catalyst CNAPPs are gaining popularity as CISOs look to consolidate and strengthen their security technology stacks. Platforms can provide integrated security across the development lifecycle and cloud environments by combining capabilities including cloud workload protection, container security and CIEM.
CNAPP adoption will continue accelerating in highly regulated industries including finance, government and healthcare. CISOs in these industries are under pressure to consolidate tech stacks, improve compliance and secure complex cloud infrastructure simultaneously. Because they provide a unified platform that meets multiple security and compliance requirements, CNAPPs are proving to be an effective consolidation catalyst.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"The top 10 technologies defining the future of cybersecurity | VentureBeat"
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"https://venturebeat.com/security/the-top-10-technologies-defining-the-future-of-cybersecurity"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages The top 10 technologies defining the future of cybersecurity Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
CISOs face a tough balancing act. They must protect new digital transformation strategies that deliver revenue, and keep fragmented legacy systems secure. At the same time they have to battle the siege on identities , and get more work done with a smaller cybersecurity staff.
Consolidating tech stacks, together with gaining access to new technologies, is the solution many are adopting. A well-orchestrated consolidation strategy delivers greater visibility and control, cost savings and scale.
That’s thanks to advances in AI and machine learning (ML) that are strengthening cybersecurity platforms.
Generative AI , for example, brings greater precision to cybersecurity while alleviating the heavy workloads and alert-fatigue that burden SecOps teams.
The goal: Fast-track new cybersecurity tech while reducing risk Legacy tech stacks have gaps, and attackers are fine-tuning their tradecraft to exploit them. One of the widest gaps is between identities and endpoints.
“It’s one of the biggest challenges that people … grapple with today,” Michael Sentonas, president of CrowdStrike , told VentureBeat in a recent interview.
He had conducted a demonstration intended “to show some of the challenges with identity and the complexity … [because] it’s a critical problem. And if you can solve that, you can solve a big part of the cyber problem that an organization has.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! >>Don’t miss our special issue: The Future of the data center: Handling greater and greater demands.
<< Three- quarters of security and risk-management professionals interviewed by Gartner say they are actively pursuing a vendor consolidation strategy for their cybersecurity tech stacks. And 22% more are planning to do so by 2025.
Gartner’s latest survey on consolidation concentrated on which direction enterprises are going in this area. It found that the top five areas through which organizations are pursuing consolidation are data security platforms (DSPs), cloud native application protection platforms (CNAPP), identity and access management (IGA, AM, PAM), extended detection and response (XDR) and secure access service edge (SASE).
CISOs from insurance, financial services and professional services enterprises tell VentureBeat that their goal is to access the latest AI and ML technologies to help reduce tool sprawl and alert-fatigue, help close skill gaps and shortages, and eliminate response inefficiencies.
AI is now part of cybersecurity’s DNA “AI is incredibly, incredibly effective [at] processing large amounts of data and classifying this data to determine what is good and what’s bad,” said Vasu Jakkal, corporate vice president for Microsoft Security, Compliance, Identity and Privacy, in her keynote at RSAC 2023.
“At Microsoft, we process 24 trillion signals every single day, and that’s across identities and endpoints and devices and collaboration tools, and much more. And without AI, we simply could not tackle this.” Deep AI and ML expertise are now table stakes for staying competitive in cybersecurity. Even the most efficient, well-staffed and well-equipped SecOps team isn’t going to catch every intrusion attempt, breach and insider attack. Major cybersecurity vendors, including Blackberry Persona , Broadcom , Cisco , CrowdStrike , CyberArk , Cybereason , Ivanti , SentinelOne , Microsoft , McAfee , Palo Alto Networks , Sophos , VMWare Carbon Black and Zscaler have integrated AI into their core platforms, helping them sell a consolidation vision. Each sees a win-win — for their customers, and for their own DevOps teams, which are fast-tracking new AI- and ML-based enhancements into future releases.
CrowdStrike, for example, is successfully selling tech stack consolidation as a growth strategy, with its Falcon Insight XDR consolidation engine. Palo Alto Networks is another. Speaking at the company’s Ignite ’22 cybersecurity conference , Nikesh Arora, chairman and CEO, remarked that “customers … want the consolidation because right now, customers are going through the three biggest transformations ever: They’re going to network security transformation, they’re going through a cloud transformation, and [though] many of them don’t know … they’re about to go to a SOC transformation.” The technologies proving effective at meeting CISOs’ greatest challenges Attackers know how to exploit perimeter-based systems quickly and are constantly improving their techniques to penetrate networks undetected. They have become so advanced that they can often easily overwhelm the fragmented, legacy-based approaches many organizations still rely on for their cybersecurity.
AI and ML are instrumental in providing real-time detection and automated attack responses. CISOs tell VentureBeat that the big payoff is having a single system for all monitoring, prediction and response — a system with a set of integrated apps and tools that can interpret and act on data in real time. Together, these factors are driving the global market for AI-based cybersecurity technology and tools to grow by an expected $ 19 billion between 2021 and 2025.
Here are the technologies proving most effective in helping CISOs balance the many demands on their teams while keeping their organizations secure from internal and external attacks: 1. Endpoint detection and response (EDR) EDR addresses the challenges of detecting and responding to advanced threats that can evade traditional endpoint security systems. It uses behavioral analysis to detect attacks in real time. EDR has also proven effective in helping SOC analysts and security teams detect and respond to ransomware and other attack techniques that can evade traditional signature-based antivirus apps and platforms. CISOs tell VentureBeat they rely on EDR to protect their highest-value assets first.
Leading vendors include CrowdStrike , SentinelOne , Microsoft Defender for Endpoint , Trend Micro and VMware Carbon Black.
2. Endpoint protection platforms (EPPs) Considered essential when revamping tech stacks to make them more integrated and able to scale and protect more endpoints, EPPs have proven their value to the CISOs whom VentureBeat interviewed for this article. They’re effective in battling emerging threats, including new malware exploits. One financial services CISO said that the advances in AI and ML in their company’s endpoint protection platform had stopped intrusions before they progressed into corporate networks.
Vendors are differentiating their EPP platforms on advanced analytics and greater endpoint visibility and control. EPPs are becoming increasingly data-driven.
EPPs with ransomware detection and response include Absolute Software , whose Ransomware Response builds on the company’s expertise in endpoint visibility, control and resilience. Other vendors include Broadcom (Symantec) , Bitdefender , CrowdStrike, Cisco , Cybereason , Deep Instinct , Trellix , Microsoft , SentinelOne, Sophos , Trend Micro and VMware Carbon Black.
3. Extended detection and response (XDR) XDR platforms aggregate and correlate security alerts and telemetry from an organization’s endpoints, network, cloud and other data sources. CISOs tell VentureBeat that a well-implemented XDR solution outperforms legacy security tools in threat detection, investigation and automated response. XDR reduces costs, boosts security operations efficiency and lowers risk.
Vendors continue to add more APIs, supporting an open-architecture approach to integration so their platforms can accept, analyze and respond to telemetry data in real time. According to a vendor interview with VentureBeat, Palo Alto Networks’ Cortex XDR has reduced Rolls-Royce’s alert volumes by 90% and response times by 95%. Other leading vendors include CrowdStrike, Cynet , Microsoft and Trend Micro.
4. Identity threat detection and response (ITDR) ITDR platforms protect a company’s identity infrastructure from sophisticated attacks. They help organizations monitor, detect and respond to identity threats as identity systems become both more critical and more vulnerable.
CISOs tell VentureBeat that combining ITDR and IAM improvements is essential to protect identities under siege, especially in healthcare and manufacturing, where attackers know there are soft targets. Microsoft has over 30,000 Azure AD Premium P2 customers gaining identity protection with Azure AD Identity Protection , for example. Other leading vendors include Netwrix and Silverfort.
5. Mobile threat defense (MTD) MTD solutions protect smartphones and tablets from advanced threats that can bypass traditional security controls that are part of fragmented legacy tech stacks. MTD protects mobile apps, devices and networks from phishing, real-time zero-day threats, and advanced attack techniques based on identity and privileged access credential theft.
Ivanti’s approach to protecting mobile clients in highly regulated industries sets the technology standard in MTD.
Ivanti Neurons for MTD is built on the Ivanti Neurons for MDM and Ivanti Endpoint Manager Mobile clients and can be deployed on managed Android, iOS and iPadOS devices. Other leading vendors include CheckPoint , Lookout , Proofpoint , Pradeo , Symantec , VMWare and Zimperium.
6. Microsegmentation Microsegmentation restricts lateral movement during a breach by separating workloads by identity. It also addresses poorly isolated workloads that allow attackers to spread laterally. CISOs tell VentureBeat that they have been able to streamline deployments by isolating high-risk workloads and using tools that assist in making contextual policy recommendations.
Microsegmentation reduces unauthorized workload communication and the blast radius of an attack, making it a pivotal technology for the future of cybersecurity and zero trust. Leading vendors include Illumio , Akamai /Guardicore and VMWare.
7. Secure access service edge (SASE) CISOs tell VentureBeat that SASE has the potential to streamline consolidation plans while factoring in zero-trust network access (ZTNA) to secure endpoints and identities. This makes it a useful platform for driving consolidation.
Legacy network architectures can’t keep up with cloud-based workloads, and their perimeter-based security is proving too much of a liability, CIOs and CISOs tell VentureBeat. Legacy architectures are renowned for poor user experiences and wide security gaps. Esmond Kane, CISO of Steward Health, advises : “Understand that — at its core — SASE is zero trust. We’re talking about identity, authentication, access control, and privilege. Start there and then build out.” “One of the key trends emerging from the pandemic has been the broad rethinking of how to provide network and security services to distributed workforces,” writes Garrett Bekker, senior research analyst, security at 451 Research, part of S&P Global Market Intelligence, in a 451 Research note titled “ Another day, another SASE fueled deal as Absolute picks up NetMotion.
” Garrett continues, “This shift in thinking, in turn, has fueled interest in zero-trust network access (ZTNA) and secure access service edge.” Leading vendors include Absolute , Cato Networks , Cisco , Cloudflare , Forcepoint , Open Systems , Palo Alto Networks, Versa Networks , VMWare SASE and Zscaler.
8. Secure service edge (SSE) To secure SaaS, web, and private applications, SSE integrates secure web gateway (SWG), cloud access security broker (CASB) and ZTNA into a single cloud platform. SSE’s workflows are also proving effective at simplifying the management of different point tools. And CISOs tell VentureBeat that SSE is effective for simplifying, securing and improving remote user experiences.
The big payoff for CISOs is how SSE can consolidate security tools into a unified cloud platform and standardize policy enforcement. Leading vendors include Broadcom , Cisco , Netskope and Zscaler.
9. Unified endpoint security (UES) UES streamlines protection for every endpoint device, including PCs, mobile devices and servers, by consolidating siloed endpoint security tools into a single platform. UES solves the problems inherent in decentralized tools, like limited visibility, detection and response.
CISOs at leading insurance and financial services firms tell VentureBeat that UES is their go-to platform for ensuring that the security hygiene of an acquired company is in good shape before they move forward with broader integration.
Reduced licensing costs, unified visibility and faster response are key benefits, according to CISOs interviewed by VentureBeat. Leading vendors include BlackBerry , IBM Security MaaS360 , Ivanti Neurons for UEM , Microsoft , VMware and ManageEngine.
Ivanti Neurons for UEM is unique among UES vendors as its endpoint clients deliver real-time intelligence and can self-heal and self-secure.
10. Zero-trust network access (ZTNA) ZTNA enforces least-privileged access in every application, resource and endpoint on a network while continuously monitoring all network activity. It assumes that no connection or resource request or use is trusted. Therefore it restricts connections to any asset, endpoint or resource to authorized users, devices and applications based on verified identity and context.
Gartner says hybrid work is a strong adoption driver for ZTNA, and that it has led to ZTNA being integrated into security service edge (SSE). According to Absolute Software’s 2023 Resilience Index, “zero-trust network access (ZTNA) helps [enterprises] move away from the dependency on username/password and [toward relying] on contextual factors, like time of day, geolocation, and device security posture, before granting access to enterprise resources.” Zero-trust strategies effectively reduce the attack surface for remote connections by restricting access to authorized applications only.
Absolute , Akamai , Cato Networks, Check Point , Cisco, Cloudflare , Forcepoint , Fortinet , Okta , Palo Alto Networks, Perimeter 81 and Zscaler are the leading vendors in the ZTNA market.
Why these 10 core technologies are driving cybersecurity’s consolidation Attackers are aware of the gaps in legacy tech stacks and are constantly working to capitalize on them. The widening gap between identities and endpoint security is one of the largest and fastest-growing gaps. Industry leaders such as CrowdStike, Palo Alto Networks and Zscaler are focused on eliminating it.
That’s good news for CISOs attempting to balance support for new digital initiatives with consolidating their tech stacks to reduce legacy risks and getting more work done with a smaller staff.
AI-based platforms, including XDR, deliver the unified visibility and control CISOs and their teams need to reduce risk and protect threat surfaces. Cloud-based models, including SASE and SSE, are making it possible for CISOs to enable consistent policy enforcement. And ZTNA enforces least privileged access, with its core components shutting off lateral movement when a breach occurs.
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"Surviving a ransomware attack begins by acknowledging it's inevitable | VentureBeat"
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"https://venturebeat.com/security/surviving-a-ransomware-attack-begins-by-acknowledging-its-inevitable"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Surviving a ransomware attack begins by acknowledging it’s inevitable Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The best defense against a ransomware attack is assuming it will happen before it does. With an 80% chance of re-attack, small and medium businesses in hard-hit industries including healthcare and manufacturing, are primary targets. Ransomware attacks spiked to a new record last month, increasing 153% over September last year.
Well-funded organized crime and Advanced Persistent Threat (APT) groups actively recruit AI and machine learning (ML) specialists on criminal activity hub Telegram and over the dark web to look for new ways to apply new technologies to older common vulnerabilities and exposures ( CVEs ) and vulnerabilities.
Using AI and ML, organized crime and nation-state attackers are out-innovating the most efficient enterprises. Double extortion ransomware groups increased by 76% between September 2022 and 2023. Healthcare experienced an 86% increase in ransomware attacks month-on-month between August and September.
“Ransomware defense isn’t something you do when you are under attack,” Merritt Baer, field CISO of Lacework told VentureBeat. Ransomware defense looks a lot like doing security right, throughout your environment, every day — from identity and secrets management, to provisioning infrastructure to managing data protection and backups.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Weaponized CVEs make ransomware hard to stop CEOs and founders of mid-tier manufacturers that have experienced multiple ransomware attacks tell VentureBeat on condition of anonymity that even after hiring cybersecurity consulting firms, ransomware attackers are still launching attacks. The mindset that ransomware is inevitable brings new urgency and focus to improving patch management, data security, backups, identity and secrets management and more secure infrastructure provisioning.
Ivanti’s 2023 Spotlight Report found that ransomware attackers routinely fly under popular scanners’ radar, including those from well-known groups Nessus, Nexpose and Qualys. The report found that attackers’ tradecraft is getting so precise that weaponizing CVEs and then identifying weak targets based on their profiles is rampant in SMBs.
Ransomware groups concentrate on evading detection while capitalizing on data gaps and long-standing gaps in legacy CVEs, according to Ivanti’s report.
“Threat actors are increasingly targeting flaws in cyber hygiene, including legacy vulnerability management processes,” Srinivas Mukkamala, chief product officer at Ivanti , told VentureBeat. “Today, many security and IT teams struggle to identify the real-world risks that vulnerabilities pose, and therefore improperly prioritize vulnerabilities for remediation. For example, many only patch new vulnerabilities or those that have been disclosed in the National Vulnerability Database (NVD). Others only use the Common Vulnerability Scoring System (CVSS) to score and prioritize vulnerabilities.
“ Get prepared by assuming your company is a ransomware target With a business’s continuity and financial health on the line, ransomware is not just a cybersecurity decision. It’s a business decision. VentureBeat has learned of manufacturers paying ransoms to get back up and running — only to be hit again.
Mid-size businesses with under $100 million in revenue often don’t have the budget or staff for security, and attackers know that.
“Ninety percent of all ransomware attacks are hitting companies with less than a billion dollars in revenue,” Furtado advised in a Gartner video interview.
Furtado says ransomware is a highly effective cyberattack strategy because it puts any business under immense time pressure to resolve the breach, get their data back and keep operating.
“One thing you’ve got to understand with ransomware is that, unlike any other sort of security incident, it puts your business on a countdown timer,” Furado advises.
While law enforcement recommends not paying ransoms, nearly a third of victimized organizations end up paying, only to find up to 35% of their data corrupted and unsalvageable.
A CrowdStrike survey found that 96% of victims who paid the ransom also paid additional extortion fees equal to $792,493 on average, only to find the attackers also shared or sold their information on the dark web via Telegram channels. The Office of Foreign Assets Control has also fined companies who paid certain ransomware attackers.
Preparing for ransomware attacks needs to be a business decision first Senior management teams that see ransomware attacks as inevitable are quicker to prioritize actions that seek to reduce the risk of an attack and contain one when it happens. This mindset redirects board-level discussions of cybersecurity as an operating expense to a long-term investment in risk management.
CISOs need to be part of that discussion and have a seat on the board. With the inevitability of ransomware attacks and risks to the core part of any business, CISOs must guide boards and provide them with insights to minimize risk. A great way for CISOs to gain a seat on boards is to show how their teams drive revenue gains by providing continuous operations and reducing risks.
“When your board wants to talk about ransomware, remind them that it might take the form of day-to-day improvements — in your patching cadence, how you manage identity, how you defend environments and do infrastructure as code, how you do immutable backups and so forth,” Baer told VentureBeat.
She continued, “ransomware is one ‘cost’ that your enterprise should factor in if they aren’t doing the security and innovation practices they need.” CISOs must have a seat on boards That’s a big change in how boards view and fund cybersecurity and why CISOs must have board seats to explain the many business benefits of stronger enterprise security.
“I’m seeing more and more CISOs joining boards,” George Kurtz, cofounder and CEO of CrowdStrike, said during his keynote at his company’s annual event. “I think this is a great opportunity for everyone here [at Fal.Con] to understand what impact they can have on a company. From a career perspective, it’s great to be part of that boardroom and help them on the journey. To keep business resilient and secure.” He continued: “Adding security should be a business enabler. It should be something that adds to your business resiliency, and it should be something that helps protect the productivity gains of digital transformation.” ‘ Having a ransomware playbook is table-stakes CISOs tell VentureBeat that having a playbook helped them recover from ransomware attacks because it helped save time during an attack and helped contain it.
Playbooks also make it clear to senior management and the board just how devastating an attack can be. The communications plan during a ransomware attack on a public company is a sobering call that gets support moving, CISOs tell VentureBeat. Now, with the Securities and Exchange Commission (SEC) requiring disclosures, there’s even more emphasis on getting playbooks right.
One CISO of a large publicly-held consumer goods manufacturer told VentureBeat under anonymity that he went so far as to have a written press release explaining the ransomware attack. The board responded by approving funding for a more layered approach to data protection and backup, regular validation of backups, improved patch management and data protection and analysis workflows and clear remediation plans.
Playbooks often have containment, analysis, remediation and recovery sections. It’s important to consider a playbook as a document that needs to be regularly reviewed and updated by SecOps, IT, legal, PR and senior management.
It’s common for CISOs to lead incident simulations and tabletop exercises to test their paybooks and make sure they’re updated and revised regularly. The goal is to always look for gaps in response and close them before a ransomware attack occurs.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Protecting ML models will secure supply chain, JFrog releases ML security features | VentureBeat"
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"https://venturebeat.com/security/protecting-ml-models-will-secure-supply-chain-jfrog-releases-ml-security-features"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Protecting ML models will secure supply chain, JFrog releases ML security features Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The potential for supply chain attacks has grown as cybercriminals become increasingly adept at exploiting the dependencies within software services containing open-source libraries. But companies haven’t moved fast enough to take adequate counter measures.
This was highlighted by Chris Krebs, the inaugural director of the U.S. Cybersecurity and Infrastructure Security Agency (CISA), in his keynote address at the BlackHat conference.”Companies shipping software products are shipping targets,” Krebs warned the audience, a sentiment echoed by the White House’s recent announcement of a national cybersecurity strategy that emphasizes cyber-resilience and holds software companies accountable for the security of their products.
Security gets traded for speed – even with new ML model development DevOps teams are under pressure to deliver more apps that contain ML models in less time to support new sources of digital-first revenue and customer experiences. DevOps leaders say that security gate reviews get sacrificed to meet increasingly tight code delivery dates. VentureBeat has learned that a typical DevOps team in a $600 million enterprise has over 250 concurrent projects in progress, with over 70% dedicated to safeguarding and improving digital customer experiences.
Security gets traded for speed because nearly every DevOps team has a backlog of new digital transformation apps supported by ML models that are behind schedule. Security testing apps are also disconnected from DevOps, and engineers aren’t trained to embed security into their code during development. Using open-source code saves time and keeps development within budget but introduces new risks.
97% of commercial code contains open-source code, and 81% contains at least one vulnerability. Additionally, 53% of the codebases analyzed had licensing conflicts, and 85% were at least four years out of date.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! JFrog’s latest release goes all-in on protecting ML models during the development JFrog , a leader in providing software supply chain security for DevOps, knows these and other challenges well. Today, the company launched a series of new products and enhancements at its 2023 swampUP Conference.
The most noteworthy announcements are in ML Model Management, including scanning models for compliance, detecting malicious models, and managing model delivery alongside software releases.
“Today, Data Scientists, ML Engineers, and DevOps teams do not have a common process for delivering software. This can often introduce friction between teams, difficulty in scale, and a lack of standards in management and compliance across a portfolio,” said Yoav Landman, Co-founder and CTO, JFrog. “Machine learning model artifacts are incomplete without Python and other packages they depend on and are often served using Docker containers. Our customers already trust JFrog as the gold standard for artifact management and DevSecOps processes. Data scientists and software engineers are the creators of modern AI capabilities, and already JFrog-native users. Therefore, we look at this release as the next logical step for us as we bring machine learning model management, as well as model security and compliance, into a unified software supply chain platform to help them deliver trusted software at scale in the era of AI.” The company also introduced a new security platform that provides end-to-end protection across the software development lifecycle (SDLC), from code to runtime. New features include SAST scanning, an OSS catalog as part of JFrog Curation, and ML model security. Additional new capabilities include release lifecycle management to track software bundles and enhanced DevOps features like immutable release bundles.
JFrog’s strategy is focused on unifying and streamlining the entire software development lifecycle within a single platform. As evidenced by their results at Hitachi Vantara , JFrog Artifactory acts as a “single source of truth” to manage software binaries and artifacts across the organization while providing consistent security scanning with JFrog Xray. By replicating key repositories across multiple sites, JFrog enabled Hitachi Vantara to accelerate multi-site pipelines and shift security left.
Getting scaling right is core to securing every phase of ML model development What’s noteworthy about JFrog’s series of announcements today is how they’re building out security and code integrity from the initial commit of source code through building, testing, deployment, and runtime operations of ML models.
“It can take significant time and effort to deploy ML models into production from start to finish. However, even once in production, users face challenges with model performance, model drift, and bias,” said Jim Mercer, Research Vice President, DevOps & DevSecOps, IDC. So, having a single system of record that can help automate the development, ongoing management, and security of ML Models alongside all other components that get packaged into applications offers a compelling alternative for optimizing the process.” JFrog’s DevOps, engineering, and product management teams deserve credit for integrating AI/ML techniques to improve compliance, coding, developer productivity, and threat detection in their platform, strengthening those elements in the latest release. The following table compares JFrog’s progress in delivering solutions that scale across core software supply chain security attributes CISOs, CIOS, and boards look for in protecting their CI/CD pipelines and processes.
ML model security is a moving target that demands scalable platforms ML model threats will continue to accelerate as attackers seek to weaponize AI at every chance. The many vulnerabilities in software supply chains directly impact teams’ productivity, building ML models for release into production and broad use today.
JFrog’s approach of developing a platform that combines DevSecOps fundamentals to provide end-to-end vision and control of the ML models defines the future of secure software supply chains. Every CISO, Devops leader, and CEO is betting that ML model security must continue to evolve to stay current against threats, and platform architectures like JFrog’s re-defining how they secure ML models at scale is core to the future of secure software supply chains.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Protecting data in the era of generative AI: Nightfall AI launches innovative security platform | VentureBeat"
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"https://venturebeat.com/security/protecting-data-in-the-era-of-generative-ai-nightfall-ai-launches-innovative-security-platform"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Protecting data in the era of generative AI: Nightfall AI launches innovative security platform Share on Facebook Share on X Share on LinkedIn Image Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
All organizations are eager to harness the productivity gains of generative AI, starting with ChatGPT, despite the security threat of their confidential data being leaked into large language models (LLMs). CISOs tell VentureBeat they’re split on the issue, with AI governance becoming a hot topic in risk management discussions with boards of directors.
Alex Philips, CIO at National Oilwell Varco (NOV) , told VentureBeat in an interview earlier this year that he’s taking an education-centric approach to keep his board of directors up to date on the latest advantages, risks and current state of gen AI technologies. Philips says having an ongoing educational process helps set expectations about what gen AI can and can’t do, and helps NOV put guardrails in place to avert confidential data leaks.
Several healthcare CISOs and CIOs are restricting ChatGPT access across all research and development, pricing and licensing business units. VentureBeat has learned that CISOs are divided on if and how they manage the security threat of confidential data finding its way into LLMs. Not having gen AI as a research tool is a competitive disadvantage healthcare providers are willing to go without as the risks to their intellectual property, pricing and licensing are too great.
Unlocking productivity while reducing risk VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The challenge is to keep confidential data secure while allowing employees to be more productive using gen AI and ChatGPT at the browser, app and API levels. Cloud data loss prevention (DLP) platform Nightfall AI today announced the first data security platform for gen AI that spans API browser and Software-as-a-Service (SaaS) application gen AI protection.
Designed to take on the productivity paradox CISOs and CIOs are facing when it comes to gen AI in their organizations, Nightfall AI’s platform is the first DLP platform that scales across the three top threat vectors CISOs need the most help securing when gen AI and ChatGPT are in use across their organizations. The goal is to enable organizations to securely use AI’s benefits while protecting sensitive data and reducing risk.
The Nightfall for GenAI data security platform consists of three products that include: Nightfall for ChatGPT.
Nightfall AI’s browser-based solution provides real-time scanning and redaction of sensitive data entered by employees into chatbots before exposure. Providing a browser-based extension is one of the less obtrusive ways to protect data because it’s a technique that lends itself well to minimizing the impact on users’ experiences. Nightfall AI CEO Isaac Madan told VentureBeat that the experiences users have with Nightfall for ChatGPT formed the foundation of the product’s design goals.
Madan says the initial browsers supported include Apple Safari, Google Chrome and Microsoft Edge.
Eric Cohen, Vice President of Security at Genesys, considers Nightfall for ChatGPT a breakthrough in providing colleagues in Genesys with access to gen AI products while reducing the risk. Cohen told VentureBeat that the ideal is for Nightfall AI to take a collaborative approach to help users self-remediate data risks without requiring them to be generative AI experts.
Nightfall for LLMs: APIs are one of Nightfall AI’s core strengths, reflected in how they’ve taken on the challenge of enabling enterprises at scale to achieve productivity gains from gen AI. Nightfall for LLMs is a developer API that detects and redacts data developers’ input to train LLMs combined into a software development kit (SDK). Many industry leaders have already integrated these APIs into their workflows.
Cohen told VentureBeat that Nightfall AI’s API strategy provides the customizability and flexibility Genesys needs to scale gen AI protection across their organization and tech stacks. Nightfall AI also provides insights into redaction rates, adding greater insights and learning into how gen AI can be securely used for greater productivity, he said.
Nightfall for SaaS: Nightfall for SaaS provides data leak prevention directly within the workflows of popular SaaS applications, allowing companies to detect and redact sensitive data as third-party AI systems are processing it. This prevents sensitive information from being exposed in chatbot conversations, documents, cloud storage and other SaaS apps. Nightfall for SaaS has been implemented by MovableInk , Aaron’s and Klaviyo , who need to secure customer data within their SaaS ecosystems. By natively leveraging Nightfall’s DLP capabilities within these apps, these companies can leverage third-party AI while maintaining control and visibility into their sensitive data.
All of these products are available today to explore.
Nightfall for ChatGPT is available on the Google Chrome store as part of a 14-day free trial Nightfall AI offers.
Securing the future of generative AI’s productivity gains Cohen told VentureBeat that gen AI’s productivity is integral to enabling Genesys to continue excelling for their clients. “Generative AI offers significant productivity gains for organizations across teams … but until Nightfall AI, there was a lack of security products that allowed us to use these tools safely,” he said. Cohen found Nightfall AI while actively researching DLP solutions to solve a data privacy problem Genesys was facing. The customizability of Nightfall’s data rules presented an advantage over other options he had looked into.
CISOs tell VentureBeat they have three main concerns regarding adopting GenAI as a research and productivity platform. First, they’re concerned that employees will include sensitive data (such as software credentials or customer PII) in chatbot prompts. Second, they’re worried that employees might inadvertently expose confidential company data using SaaS apps such as Notion that use third-party AI sub-processors such as Anthropic.
Lastly, their third concern revolves around engineers and data scientists using confidential data to build and train their LLMs. This last concern is underscored by a recent incident where users tricked ChatGPT into generating active API keys for Windows.
“GenAI has the potential to offer substantial productivity benefits for employers and employees, but the lack of a complete DLP solution is impeding the safe adoption of AI,” said Madan. “As a result, many organizations have either completely blocked these tools, or have resorted to using multiple security products as a patchwork solution to mitigate the risk.” This struggle ultimately drove the creation of Nightfall’s latest innovation: Nightfall for GenAI.
Frederic Kerrest, cofounder and executive vice chairman of Okta , commended Nightfall and compares its latest initiatives to Okta’s early days. “When using Nightfall, I have seen many similarities with our early vision at Okta, where we centralized user access and management security for all cloud apps. Nightfall is now doing the same for data security across generative AI and the cloud.” Early adopters like Genesys highlight the benefits of Nightfall’s customizable data rules and remediation insights that help users self-correct. For CISOs, the platform provides the visibility and control needed to leverage AI while maintaining data security confidently. The availability of Nightfall’s gen AI-focused platform marks an important milestone in realizing AI’s potential.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"PayPal's CISO on how generative AI can improve cybersecurity | VentureBeat"
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"https://venturebeat.com/security/paypals-ciso-on-how-generative-ai-can-improve-cybersecurity"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages PayPal’s CISO on how generative AI can improve cybersecurity Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
VentureBeat recently sat down (virtually) with Assaf Keren, CISO and VP of enterprise cyber security at PayPal.
Keren’s career at PayPal spans eight years and includes a broad base of experience building security products, managing security infrastructure and creating and leading security services.
Before PayPal, Keren founded two cybersecurity startups, served as head of security product management at Verint and was CISO for the Israeli e-Government Division (Ministry of Finance).
PayPal’s AI-driven cybersecurity strategy is paying off For their latest quarter (FY Q2 2023), PayPal reported $7.3 billion in revenue, up 7% year-over-year, or 8% on a foreign currency-neutral (FXN) basis. Solid gains in transaction growth of 5% and a 37% increase in value-added services revenue contributed to a strong quarter. PayPal ended the calendar year 2022 with $27.5 billion in revenue and has a trailing twelve-month (TTM) revenue run rate of $28.5B.
PayPal’s total payment volume grew 11%, supported by 10% transaction growth, while tight expense control drove an 11% decline in non-transaction costs. Despite a $1.2 billion impact from loans originating for the pending BNPL sale, PayPal generated strong free cash flow and returned $1.5 billion to shareholders. Overall, PayPal showed healthy volume and user engagement growth, significantly improved profitability, and progress on strategic initiatives.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Known for the depth of their expertise using AI, machine learning (ML) and speed of training and deploying deep learning models to harden cybersecurity and reduce fraud, PayPal’s 11% reduction in losses in the latest quarter amidst growing volumes indicate AI is delivering value at scale. Achieving a double-digit reduction in losses through improved risk management indicates that the company’s AI-powered cybersecurity defenses, fraud detection models and AI-powered risk management are reducing transaction losses.
The following is an excerpt of VentureBeat’s interview with Keren.
VB: How can generative AI bolster security without negatively impacting the customer experience? Keren: While gen AI may be used by attackers for malicious purposes such as generating false identities or creating malware variants that evade traditional security measures, it will also empower security-forward organizations like PayPal to explore gen AI-driven defense mechanisms, such as next-generation automated threat detection systems and response capabilities. We have always put security at the center of what we do and build products with security in mind by design. With gen AI, there will be opportunities to augment our capabilities, remove friction and drive greater customer value.
VB: What are the risks associated with using gen AI, and how can those be mitigated for platforms handling a large amount of private data? Keren: Gen AI , like any new technology, must be carefully evaluated and deployed with safety and responsibility as top priorities. Important factors like data quality, intellectual property, security, privacy and compliance are key considerations. To mitigate risks, it’s important for technology leaders to examine these dimensions scrupulously before deploying gen AI to protect customers and preserve their trust. Changing the ways we think about testing and offensive security to include attack patterns such as prompt injection, checking for bias and identifying where the models hallucinate will allow gen AI to be deployed in a way that is beneficial to customers.
VB : Can gen AI help with fraud detection? If so, how can you validate its accuracy? Keren: Yes, and in a sense, it already is. PayPal has been an early adopter of AI, and we have been building our AI capabilities and expertise for over a decade. We’ve been employing transformer-based deep learning for years, which is the key technology behind large language models (LLMs) like ChatGPT. Today, we use AI and ML across broad domains in our business, including fraud reduction, customer protection, personalized services, risk management and global commerce empowerment.
VB: Can you share the results PayPal is achieving using AI for fraud detection? Keren: It’s led to tremendous impact: from 2019 to 2022, at a time when our annual payment volumes nearly doubled from $712 billion dollars to $1.36 trillion dollars, we cut our loss rate by nearly half, in part thanks to our advances in AI algorithms and technology.
Today, with our advances in AI, we are able to rapidly adapt to changing fraud patterns to protect our customers. PayPal’s deep learning models can be trained and pushed to production within 2 to 3 weeks and even quicker for simpler algorithms. This allows us to train models with the latest production data, incorporate new fraud patterns and integrate feedback from internal agents and PayPal customers.
VB : How does the rapidly changing financial technology landscape influence gen AI strategy adoption? Keren: As a company with close to 430 million active accounts and 35 million merchants on our two-sided network, we see tremendous potential in the power of AI to create the next generation of checkout and commerce. Today, PayPal holds over 200 petabytes of payments data — data that is key to our competitive advantage that holds tremendous insights and potential to power better commerce experiences for consumers and merchants.
We are actively assessing gen AI and its potential impact on our business and the competitive landscape, and we are enabling internal teams to consider where gen AI can enhance efficiency, increase our security, improve the customer experience, protect our customers and grow our business.
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"Okta's breach shows why identities come first in a zero trust world | VentureBeat"
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"https://venturebeat.com/security/oktas-breach-shows-why-identities-come-first-in-a-zero-trust-world"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Okta’s breach shows why identities come first in a zero trust world Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with DALL-E 3 Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Showing how fragile digital identities are even for a leading provider of identity and access management (IAM) solutions, Okta’s security breach, acknowledged by the company on October 20 , began with stolen credentials used to gain access to its support management system. From there, attackers gained access to HTTP Archive (.HAR) files that contain active session cookies and began breaching Okta’s customers, attempting to penetrate their networks and exfiltrate data.
Daniel Spicer, Ivanti’s chief security officer (CSO told VentureBeat, “Many IT team members, even those who are security-conscious, don’t think about what information they share with vendor support teams because they are ‘trusted.’ Security teams need to interview their IT teams to understand what data they commonly have to share to resolve support cases.” Spicer advises, “You should also inspect the output for automatically generated troubleshooting data from sensitive systems. You could find anything from certificates and credentials to PII in those data sets.” Attackers exploited trust in privileged credentials Attackers worked fast to use stolen session cookies and tokens from HAR files to impersonate legitimate users and attempt to gain unauthorized access to Okta’s customers’ systems. Okta customers BeyondTrust , Cloudflare , and 1Password — who collectively serve tens of thousands of organizations and customers, including some of the world’s largest and most important — immediately detected unusual activity, including new account creation and changes in administrative permissions. Each of these customers discovered the breach weeks before Okta did, immediately alerting their identity management vendor. It took Zoom calls and shared data results with Okta for the latter to confirm the breach, weeks later.
In an ironic twist for Okta, whose marketing slogan is everything starts with identity. Its customers detected attempted breaches immediately when unauthorized attempts were made to access high-privilege Okta accounts using a stolen session cookie from a recently uploaded HAR file.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Stolen cookies and compromised tokens Identity security company BeyondTrust’s blog post says that on October 2, it detected an unauthorized attempt to access a high-privilege Okta account using a stolen session cookie from a recently uploaded HAR file.
BeyondTrust realized the breach attempt came just 30 minutes after one of their admins shared the HAR file with Okta support. Attackers were using the stolen cookie to try and create a new administrative Okta profile in the BeyondTrust environment.
On October 18, Cloudflare noticed attacks originating from Okta and traced them back to a compromised authentication token. Cloudflare used its systems to detect attackers attempting to leverage an active, open Okta session to gain access to Cloudflare. Attackers had moved fast in the Cloudflare environment and had already managed to compromise two separate Cloudflare employee accounts within their Okta instance.
1Password detected suspicious activity on its Okta instance on September 29 when its internal systems identified a successful account takeover of one of its staff’s Okta accounts that had privileged access. 1Password was also able to trace the attack to a cookie harvested from the exfiltrated HAR file intercepted from the Okta support management system.
The attacker gained access to 1Password’s Okta administrative capabilities.
1Password’s security incident report provides more details about the attack. 1Password also rotated IT members’ credentials and switched to using Yubikey for multi-factor authentication (MFA) internally.
Attackers’ tradecraft prioritizes identity breaches Identities continue to be a favorite attack surface because attackers, criminal gangs, and advanced persistent threat (APT) organizations know identities are the ultimate control surface.
Seventy-eight percent of enterprises say identity-based breaches have directly impacted their business operations, and of those enterprises breached, 96% now believe they could have avoided a breach if they had adopted identity-based zero-trust safeguards earlier. Forrester found that 80% of all security breaches start with privileged credential abuse.
Delinea’s survey on securing identities found that 84% of organizations experienced an identity-related breach in the last 18 months. Gartner found that 75% of security failures are attributable to human error in managing access privileges and identities.
The last several high-profile cyberattacks share the common trait of capitalizing on the weaknesses of how identities and their privileged access credentials are managed. Okta’s assumption — that enabling HAR files to be shared with its support management system was secure — makes the point clear.
Any assumption of trust in how identities and access credentials are used needs to be replaced with verification and visibility. Attackers have long been targeting the gaps in endpoint security and identity management to take advantage of assumed trust in endpoint agents. Their goal is to capture privileged access credentials and penetrate infrastructure to perform reconnaissance, install malware, and exfiltrate data for financial gain.
Zero trust demands controls and visibility Okta’s unfortunate breach shows how ingenious attackers are in exploiting any opportunity there is to steal privileged access credentials, down to intercepting Okta session cookies and attempting attacks with live sessions. The attempted breach illustrates why the core concepts of zero trust have immediate practical benefits.
Zero trust, predicated on least privilege access, auditing and tracking every transaction, use of resources, and workflow, must be given in every interaction across a network. By definition, zero trust security is a framework that defines all devices, identities, systems, and users as untrustworthy by default. All require authentication, authorization, and continuous validation before being granted access to applications and data.
The zero trust framework protects against external and internal threats by logging and inspecting all network traffic, limiting and controlling access, and verifying and securing network resources. The National Institute of Standards and Technology ( NIST ) has created a standard on zero trust, NIST 800-207 , that provides prescriptive guidance to enterprises and governments implementing the framework.
Be sure to read VentureBeat’s two-part interview with zero trust’s creator, John Kindervag, to gain insights into his research at Forrester that led to the creation of the framework.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Less noise, better signals: Why XDR and AI are the future of cybersecurity | VentureBeat"
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"https://venturebeat.com/security/less-noise-better-signals-why-xdr-and-ai-are-the-future-of-cybersecurity"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Less noise, better signals: Why XDR and AI are the future of cybersecurity Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Capitalizing on malware-free tradecraft to launch undetectable breaches, attackers rely on legitimate system tools and living-off-the-land (LOTL) techniques to breach endpoints undetected. Malware-free attacks trade on the trust of legitimate tools, rarely generating a unique signature and relying on fileless execution.
Across all malicious activity tracked by CrowdStrike and reported in their 2023 Threat Hunting Report , 71% of detections indexed by the CrowdStrike Threat Graph were malware-free. A total of 14% of all intrusions relied on remote monitoring and management (RMM) tools based on activity tracked by Falcon OverWatch.
Attackers increased their use of RMM tools for malware-free attacks by an astounding 312% year-over-year.
With FraudGPT signaling the start of a new era of weaponized AI and enterprises at risk of losing the AI war , the integration of AI, machine learning (ML) and generative AI into Extended Detection and Response (XDR) needs to be fast-tracked to thwart malware-free and new AI-driven attacks. XDR delivers the consolidation CISOs have been asking for.
XDR improves the signal-to-noise ratio By relying on APIs and platforms designed to integrate at scale, XDR platforms make the most of every available data telemetry source to detect and respond to potential intrusions and breach attempts in real time. These platforms are proving effective in reducing noise across networks and finding the signals signifying a potential intrusion or attack.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! XDR is an effective consolidation strategy for CISOs: 96% plan to consolidate their security platforms, with 63% saying(XDR is their top solution choice, according to Cynet’s 2022 survey of CISOs.
Nearly all CISOs surveyed said they have consolidation on their roadmaps, up from 61% in 2021.
Gartner predicts that by year-end 2027, XDR will be used by up to 40% of enterprises to reduce the number of security vendors they have in place, up from less than 5% today.
An attribute all XDR leaders have is deep talent density in AI and ML across their teams. Leading XDR platform providers include Broadcom , Cisco , CrowdStrike , Fortinet , Microsoft , Palo Alto Networks , SentinelOne , Sophos , TEHTRIS , Trend Micro and VMWare.
Getting XDR right: Start with endpoints Endpoints are the stealth on-ramp of choice for large-scale breach attempts: Attackers use stolen identities more than 62% of the time to gain access and are constantly fine-tuning the tradecraft to find gaps in identity and endpoint security , the weakest area of an endpoint.
Insurance, financial services and banking CISOs tell VentureBeat that endpoints are the most challenging threat surface to protect. It’s common for IT and security teams not to know how many endpoints they have, where each endpoint is and its software bill of materials (SBOM). Cleaning up endpoint agent sprawl and automating patch management are the goals many CISOs begin with.
CISOs say it’s common to discover that endpoints are overloaded with agents to the point of being inoperable from a security standpoint. Software conflicts leave endpoints more vulnerable to attack, make them more difficult to manage remotely and can reduce performance.
The Absolute Software 2023 Resilience Index used anonymous telemetry data of its 500 million endpoint devices to see how many endpoints, on average, their customers have. They found that the typical enterprise device has 11 security agents installed, with 2.5 for endpoint management, 2.1 for antivirus/antimalware and 1.6 on average for encryption. Absolute device telemetry data found 67 applications installed on the average enterprise device, with 10% of those devices having more than 100 installed.
Automating endpoint patch management The CIO of a leading manufacturer told VentureBeat that while patching is always a high priority, she doesn’t have enough staff to keep all patches current. CISO colleagues agree that patch management only gets attention when it’s an emergency — after an intrusion or breach. That conclusion is consistent with Ivanti’s State of Security Preparedness 2023 Report.
Ivanti found that 61% of the time, an external event, intrusion attempt or breach reinitiates patch management efforts.
“Patching is not nearly as simple as it sounds,” said Srinivas Mukkamala, chief product officer at Ivanti.
“Even well-staffed, well-funded IT and security teams experience prioritization challenges amidst other pressing demands. To reduce risk without increasing workload, organizations must implement a risk-based patch management solution and leverage automation to identify, prioritize, and even address vulnerabilities without excess manual intervention.” Mukkamala told VentureBeat that he envisions patch management becoming more automated, with AI copilots providing greater contextual intelligence and prediction accuracy.
“With more than 160,000 vulnerabilities currently identified, it is no wonder that IT and security professionals overwhelmingly find patching overly complex and time-consuming,” he said. “This is why organizations must utilize AI solutions … to assist teams in prioritizing, validating and applying patches. The future of security is offloading mundane and repetitive tasks suited for a machine to AI copilots so that IT and security teams can focus on strategic initiatives for the business.” AI Strengthening XDR resilience with self-healing endpoints Getting cyber-resilience right in a zero-trust world starts with the endpoint. Boards of directors and the CISOs briefing them say cyber-resilience is now considered a must-have for risk management.
Absolute Software’s 2023 Resilience Index reflects the challenge of excelling at the comply-to-connect trend. Balancing cybersecurity and cyber-resilience is the goal.
CISOs tell VentureBeat that self-healing endpoints are the cornerstones of a solid cyber-resilience strategy. Endpoints that heal themselves provide a reliable, real-time stream of telemetry data to train AI and ML models and strengthen XDR platforms. They’re also more difficult to evade and breach compared to their previous-generation constraint and rules-based counterparts. AI and ML-based endpoints detect and respond to potential attacks in milliseconds, which is table stakes for today’s enterprises, given the rapid rise in machine-to-machine attacks.
Leading self-healing endpoint providers include Absolute Software , Akamai , BlackBerry, CrowdStrike , Cisco , Malwarebytes , McAfee and Microsoft 365.
VentureBeat has interviewed customers of each vendor and found that Absolute’s approach to being embedded in the firmware of over 500 million endpoint devices is the most reliable in providing SOC teams with the real-time telemetry data they and their XDR platforms need.
Absolute’s approach is unique in its reliance on firmware-embedded persistence as the basis of self-healing, providing an undeletable digital tether to every PC-based endpoint.
Absolute Software’s Resilience , the industry’s first self-healing zero trust platform, is noteworthy for its asset management, device and application control, endpoint intelligence, incident reporting and compliance.
XDR: The first line of defense against weaponized AI The era of weaponized AI is here , and XDR platforms need to step up and take on the challenge of getting all the value they can out of AI and ML technologies if the cybersecurity industry and the many organizations they serve are going to stay safe. No one can afford to lose the AI war against attackers who see the gaps in identities and endpoints as an opportunity to take control of networks and infrastructure.
What’s most troubling is that legacy perimeter-based systems assumed unlimited trust in every identity, endpoint and connection, providing attackers with unchecked access to any system once they compromised an endpoint. Getting XDR right needs to start with endpoints. Cleaning up agent sprawl helps improve endpoint visibility and performance, and automating patch management with AI and ML techniques that learn instead of waiting for the next breach saves IT teams from fire drills and wasted time.
Self-healing endpoints are the cornerstone of cyber-resilience. Getting these areas strong is a prerequisite for making the most of an XDR architecture that can deliver on its potential to protect an organization’s core business functions and customers.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Las Vegas CIO doubles down on AI and endpoint security to protect Sin City | VentureBeat"
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"https://venturebeat.com/security/las-vegas-cio-doubles-down-on-ai-and-endpoint-security-to-protect-sin-city"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Las Vegas CIO doubles down on AI and endpoint security to protect Sin City Share on Facebook Share on X Share on LinkedIn The world-famous "Welcome To Vegas" sign in Las Vegas, Nevada.
Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
VentureBeat recently sat down (virtually) with Michael Sherwood , chief innovation and technology officer for Las Vegas , to gain insights into how he uses the latest AI and endpoint security technologies to secure the city.
Sherwood oversees the city’s cybersecurity infrastructure, strategy and eclectic inventory of digital assets, IoT and operational networks. He is also leading an open-source data initiative that shares the city’s operational data with other municipalities, universities and global think tanks to see how Las Vegas can stay on innovations cutting edge.
Sherwood’s teams rely on managed detection and response (MDR) services strengthened with AI and machine learning (ML)-based applications to protect their endpoints, infrastructure and the growing open-source database. The team also continually tracks inbound nation-state attackers attempting to perform reconnaissance and disrupt high-profile events. Sherwood says the rising incidence of nation-state probes on their infrastructure coincides with world-known events, including the Formula 1 race and the 2024 Super Bowl.
Las Vegas relies on a broad base of cybersecurity vendors, including CrowdStrike , Darktrace , Dell , NTT and Veza , as the city switches to a hybrid multi-cloud environment to protect sensitive data and critical infrastructure.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Attacks on municipalities are soaring — and Las Vegas is a high-visibility target So far in 2023, the rate of ransomware attacks in state and local governments has increased from 58% to 69%.
In May, the city of Dallas was hit with a ransomware attack that disrupted multiple areas, including 911 emergency response, municipal courts, animal services and the police department website. The city of Augusta, Georgia, a city government in France, and a school district in Missouri are also among the many victims of cyberattacks this year.
Las Vegas has approximately 2.7 million residents and welcomes more than 40 million visitors every year. Sherwood and his team are also responsible for orchestrating the optimal use of cybersecurity technologies encompassing AI, endpoint security IoT sensors, operational networks, ML, managed detection and response (MDR) and more to ensure key services are reliably delivered, protecting residents and visitors 24/7.
The following is an excerpt of VentureBeat’s interview with Sherwood (edited for clarity).
VentureBeat : You’re one of the leaders of the open-data initiative in which cities and municipalities share data and insights to improve everything from emergency response to traffic signals. What’s your vision for Las Vegas in this area? Sherwood: Las Vegas has embraced cutting-edge technology to improve city operations and infrastructure. For example, we’re testing autonomous vehicles, implementing smart traffic signals and building an open data hub to share real-time data. AI and ML help us synthesize all this data to glean insights and optimize city services.
VentureBeat : How are you balancing the solid gains you’re making in innovation while continually hardening endpoints, protecting identities and, in short, protecting the city from cyberattacks? Sherwood: Security is crucial to how my team and I approach innovation. We’re focusing on how to deliver memorable, secure experiences at scale across each area of our city infrastructure. Security has to be core to any innovation to preserve trust.
We’re protecting more than 4,000 endpoints city-wide today, and that’s growing due to our expanding operations networks, with IoT being a catalyst of their growth. The more connected our networks become, the more vigilant we must be about sensitive data.
Our tech stack comprises a series of integrated security applications and systems, forming a multi-layered defense infrastructure. A core part of the tech stack was a legacy endpoint security product that was becoming increasingly difficult to use. That’s when we started looking around for another solution.
We did a thorough product evaluation and decided that combining AI-based monitoring tools and human intelligence was the way to go. Choosing CrowdStrike OverWatch because it combines AI tools and apps with human intelligence proved invaluable.
VentureBeat : Of the many threats the city faces, what are the three main challenges you and your team face in protecting the city? Sherwood: The greatest threat is a cyberattack aimed at high-profile targets in the city, including disrupting high-profile events. There is an escalating level of reconnaissance that in the past was only once in a while — now it’s happening at a near-constant pace. That’s the most persistent, strategic threat my team and I must continually focus on and plan to contain.
The second challenge is protecting essential city services for the 2.7 million residents and more than 40 million visitors. Our operational networks are growing, as is our use of IoT sensors, which adds a new layer of endpoint security to our security strategy.
The third challenge is identifying the steps we need to take to safeguard our global name reputation and ensure Las Vegas continues to be a safe city, both from a cyber and physical standpoint.
VentureBeat : Back in 2020, there was a cyberattack on the city. Can you share what happened? Sherwood: It was an early Sunday morning around 4 a.m., and my nightstand phone rang. I picked up, and the person said, “Las Vegas… we have a problem.” A CrowdStrike OverWatch team member had called to inform me there had been a breach attempt on our infrastructure.
I immediately began calling other members of the security team and the city management team. Within an hour, the IT team and I were troubleshooting the breach attempt in the office. An external team monitoring the attack was invaluable while my team and I worked with internal systems to contain the threat.
VentureBeat : Did the attackers exfiltrate any data or destroy any systems? Sherwood: No. Thanks to the quick work and collaboration of both of our teams, AI-based insights and how the attackers attempted to move through the network, there was no data loss or major issues. AI and advanced analytics allow us to detect subtle anomalies and multiply the effectiveness of the city’s security team. We were back to normal operations in 24 hours.
VentureBeat : As Las Vegas continues to grow as a tech hub and smart city, data is becoming increasingly valuable. What are your thoughts on how the city can ensure data privacy and security as more data is collected and shared? Sherwood: As CIO, I am very focused on privacy and security by design in every one of our data and security initiatives. That is why taking a collaborative approach is so critical to our success. Collaborating with the leading cybersecurity providers, including CrowdStrike, who share our values, is key to our ability to serve and protect our residents and visitors.
I believe that with the proper safeguards, data can unlock new opportunities while respecting people’s rights. Ultimately, earning the public’s trust is essential so they can feel confident embracing the smart city of the future Las Vegas is on its way to becoming.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"How FraudGPT presages the future of weaponized AI | VentureBeat"
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"https://venturebeat.com/security/how-fraudgpt-presages-the-future-of-weaponized-ai"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages How FraudGPT presages the future of weaponized AI Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
FraudGPT, a new subscription-based generative AI tool for crafting malicious cyberattacks, signals a new era of attack tradecraft.
Discovered by Netenrich’s threat research team in July 2023 circulating on the dark web’s Telegram channels, it has the potential to democratize weaponized generative AI at scale.
Designed to automate everything from writing malicious code and creating undetectable malware to writing convincing phishing emails, FraudGPT puts advanced attack methods in the hands of inexperienced attackers.
Weaponized AI apps and tools are dark-web best sellers Leading cybersecurity vendors including CrowdStrike , IBM Security , Ivanti , Palo Alto Networks and Zscaler have warned that attackers, including state-sponsored cyberterrorist units, began weaponizing generative AI even before ChatGPT was released in late November 2022.
VentureBeat recently interviewed Sven Krasser, chief scientist and senior vice president at CrowdStrike, about how attackers are speeding up efforts to weaponize LLMs and generative AI. Krasser noted that cybercriminals are adopting LLM technology for phishing and malware, but that “while this increases the speed and the volume of attacks that an adversary can mount, it does not significantly change the quality of attacks.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Krasser says that the weaponization of AI illustrates why “cloud-based security that correlates signals from across the globe using AI is also an effective defense against these new threats. Succinctly put: Generative AI is not pushing the bar any higher when it comes to these malicious techniques, but it is raising the average and making it easier for less skilled adversaries to be more effective.” Defining FraudGPT and weaponized AI FraudGPT, a cyberattacker’s starter kit, capitalizes on proven attack tools, such as custom hacking guides, vulnerability mining and zero-day exploits. None of the tools in FraudGPT requires advanced technical expertise.
For $200 a month or $1,700 a year, FraudGPT provides subscribers a baseline level of tradecraft a beginning attacker would otherwise have to create. Capabilities include: Writing phishing emails and social engineering content Creating exploits, malware and hacking tools Discovering vulnerabilities, compromised credentials and cardable sites Providing advice on hacking techniques and cybercrime FraudGPT signals the start of a new, more dangerous and democratized era of weaponized generative AI tools and apps. The current iteration doesn’t reflect the advanced tradecraft that nation-state attack teams and large-scale operations like the North Korean Army’s elite Reconnaissance General Bureau’s cyberwarfare arm, Department 121 , are creating and using. But what FraudGPT and the like lack in generative AI depth, they more than make up for in ability to train the next generation of attackers.
With its subscription model, in months FraudGPT could have more users than the most advanced nation-state cyberattack armies, including the likes of Department 121, which alone has approximately 6,800 cyberwarriors, according to the New York Times — 1,700 hackers in seven different units and 5,100 technical support personnel.
While FraudGPT may not pose as imminent a threat as the larger, more sophisticated nation-state groups, its accessibility to novice attackers will translate into an exponential increase in intrusion and breach attempts, starting with the softest targets, such as in education, healthcare and manufacturing.
As Netenrich principal threat hunter John Bambenek told VentureBeat, FraudGPT has probably been built by taking open-source AI models and removing ethical constraints that prevent misuse. While it is likely still in an early stage of development, Bambenek warns that its appearance underscores the need for continuous innovation in AI-powered defenses to counter hostile use of AI.
Weaponized generative AI driving a rapid rise in red-teaming Given the proliferating number of generative AI-based chatbots and LLMs, red- teaming exercises are essential for understanding these technologies’ weaknesses and erecting guardrails to try to prevent them from being used to create cyberattack tools. Microsoft recently introduced a guide for customers building applications using Azure OpenAI models that provides a framework for getting started with red-teaming.
This past week DEF CON hosted the first public generative AI red team event , partnering with AI Village , Humane Intelligence and SeedAI.
Models provided by Anthropic, Cohere, Google, Hugging Face, Meta, Nvidia, OpenAI and Stability were tested on an evaluation platform developed by Scale AI. Rumman Chowdhury, cofounder of the nonprofit Humane Intelligence and co-organizer of this Generative Red Team Challenge, wrote in a recent Washington Post article on red-teaming AI chatbots and LLMs that “every time I’ve done this, I’ve seen something I didn’t expect to see, learned something I didn’t know.” It is crucial to red-team chatbots and get ahead of risks to ensure these nascent technologies evolve ethically instead of going rogue. “Professional red teams are trained to find weaknesses and exploit loopholes in computer systems. But with AI chatbots and image generators, the potential harms to society go beyond security flaws,” said Chowdhury.
Five ways FraudGPT presages the future of weaponized AI Generative AI-based cyberattack tools are driving cybersecurity vendors and the enterprises they serve to pick up the pace and stay competitive in the arms race. As FraudGPT increases the number of cyberattackers and accelerates their development, one sure result is that identities will be even more under siege.
Generative AI poses a real threat to identity-based security. It has already proven effective in impersonating CEOs with deep-fake technology and orchestrating social engineering attacks to harvest privileged access credentials using pretexting.
Here are five ways FraudGPT is presaging the future of weaponized AI: 1.
Automated social engineering and phishing attacks FraudGPT demonstrates generative AI’s ability to support convincing pretexting scenarios that can mislead victims into compromising their identities and access privileges and their corporate networks. For example, attackers ask ChatGPT to write science fiction stories about how a successful social engineering or phishing strategy worked, tricking the LLMs into providing attack guidance.
VentureBeat has learned that cybercrime gangs and nation-states routinely query ChatGPT and other LLMs in foreign languages such that the model doesn’t reject the context of a potential attack scenario as effectively as it would in English. There are groups on the dark web devoted to prompt engineering that teaches attackers how to side-step guardrails in LLMs to create social engineering attacks and supporting emails.
While it is a challenge to spot these attacks, cybersecurity leaders in AI, machine learning and generative AI stand the best chance of keeping their customers at parity in the arms race. Leading vendors with deep AI, ML and generative AI expertise include ArticWolf , Cisco , CrowdStrike , CyberArk , Cybereason , Ivanti , SentinelOne , Microsoft , McAfee , Palo Alto Networks , Sophos and VMWare Carbon Black.
2.
AI-generated malware and exploits FraudGPT has proven capable of generating malicious scripts and code tailored to a specific victim’s network, endpoints and broader IT environment. Attackers just starting out can get up to speed quickly on the latest threatcraft using generative AI-based systems like FraudGPT to learn and then deploy attack scenarios. That’s why organizations must go all-in on cyber-hygiene, including protecting endpoints.
AI-generated malware can evade longstanding cybersecurity systems not designed to identify and stop this threat. Malware-free intrusion accounts for 71% of all detections indexed by CrowdStrike’s Threat Graph , further reflecting attackers’ growing sophistication even before the widespread adoption of generative AI. Recent new product and service announcements across the industry show what a high priority battling malware is.
Amazon Web Services , Bitdefender , Cisco , CrowdStrike , Google , IBM , Ivanti , Microsoft and Palo Alto Networks have released AI-based platform enhancements to identify malware attack patterns and thus reduce false positives.
3.
Automated discovery of cybercrime resources Generative AI will shrink the time it takes to complete manual research to find new vulnerabilities, hunt for and harvest compromised credentials, learn new hacking tools and master the skills needed to launch sophisticated cybercrime campaigns. Attackers at all skill levels will use it to discover unprotected endpoints, attack unprotected threat surfaces and launch attack campaigns based on insights gained from simple prompts.
Along with identities, endpoints will see more attacks. CISOs tell VentureBeat that self-healing endpoints are table stakes, especially in mixed IT and operational technology (OT) environments that rely on IoT sensors. In a recent series of interviews, CISOs told VentureBeat that self-healing endpoints are also core to their consolidation strategies and essential for improving cyber-resiliency. Leading self-healing endpoint vendors with enterprise customers include Absolute Software , Cisco , CrowdStrike , Cybereason , ESET , Ivanti , Malwarebytes , Microsoft Defender 365 , Sophos and Trend Micro.
4.
AI-driven evasion of defenses is just starting, and we haven’t seen anything yet Weaponized generative AI is still in its infancy, and FraudGPT is its baby steps. More advanced — and lethal — tools are coming. These will use generative AI to evade endpoint detection and response systems and create malware variants that can avoid static signature detection.
Of the five factors signaling the future of weaponized AI, attackers’ ability to use generative AI to out-innovate cybersecurity vendors and enterprises is the most persistent strategic threat. That’s why interpreting behaviors, identifying anomalies based on real-time telemetry data across all cloud instances and monitoring every endpoint are table stakes.
Cybersecurity vendors must prioritize unifying endpoints and identities to protect endpoint attack surfaces. Using AI to secure identities and endpoints is essential. Many CISOs are heading toward combining an offense-driven strategy with tech consolidation to gain a more real-time, unified view of all threat surfaces while making tech stacks more efficient. Ninety-six percent of CISOs plan to consolidate their security platforms, with 63% saying extended detection and response (XDR) is their top choice for a solution.
Leading vendors providing XDR platforms include CrowdStrike , Microsoft , Palo Alto Networks , Tehtris and Trend Micro.
Meanwhile, EDR vendors are accelerating their product roadmaps to deliver new XDR releases to stay competitive in the growing market.
5.
Difficulty of detection and attribution FraudGPT and future weaponized generative AI apps and tools will be designed to reduce detection and attribution to the point of anonymity. Because no hard coding is involved, security teams will struggle to attribute AI-driven attacks to a specific threat group or campaign based on forensic artifacts or evidence. More anonymity and less detection will translate into longer dwell times and allow attackers to execute “low and slow” attacks that typify advanced persistent threat (APT) attacks on high-value targets. Weaponized generative AI will make that available to every attacker eventually.
SecOps and the security teams supporting them need to consider how they can use AI and ML to identify subtle indicators of an attack flow driven by generative AI, even if the content appears legitimate. Leading vendors who can help protect against this threat include Blackberry Security (Cylance) , CrowdStrike , Darktrace , Deep Instinct , Ivanti , SentinelOne , Sift and Vectra.
Welcome to the new AI arms race FraudGPT signals the start of a new era of weaponized generative AI, where the basic tools of cyberattack are available to any attacker at any level of expertise and knowledge. With thousands of potential subscribers, including nation-states, FraudGPT’s greatest threat is how quickly it will expand the global base of attackers looking to prey on unprotected soft targets in education, health care, government and manufacturing.
With CISOs being asked to get more done with less, and many focusing on consolidating their tech stacks for greater efficacy and visibility, it’s time to think about how those dynamics can drive greater cyber-resilience. It’s time to go on the offensive with generative AI and keep pace in an entirely new, faster-moving arms race.
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"How AI-powered patch management protects remote and hybrid workers | VentureBeat"
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"https://venturebeat.com/security/how-ai-powered-patch-management-protects-remote-and-hybrid-workers"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages How AI-powered patch management protects remote and hybrid workers Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Most organizations have no idea how many exposed, out-of-date endpoints they have or whether their remote and hybrid workers are safe. IT and security teams are often overwhelmed with work and conflicting urgent priorities. Unfortunately, it often takes an intrusion or breach for patching to become a priority.
Attackers know network weak spots better than admins Cybercrime gangs and state-sponsored Advanced Persistent Threat (APT) threat actors who have launched the largest breaches in history — including the A.P. Møller-Maersk ransomware attack — often understand a target’s network better than admins. Whoever owns identities owns the business, and as devastating ransomware attacks show, threat actors are brazen about shutting an entire business down to meet demands.
Complacency kills, especially when it comes to understanding where endpoints that remote and hybrid workers rely on are, and whether they’re current or not. More than half ( 60% ) of enterprises know less than 75% of the endpoint devices on their network. Only 58% can identify every attacked or vulnerable asset on their network within 24 hours of an exploit.
Commonly, organizations can’t identify up to 40% of their endpoints.
Being complacent about where endpoints are and whether they’re patched is like leaving the doors of a home unlocked while on vacation.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Ivanti’s 2023 report New Imperatives for Digital Employee Experience found that only 43% of IT professionals are currently using unified endpoint management (UEM), making it one of the most underutilized systems SecOps and IT Service Management (ITSM) for protecting remote and hybrid workers. The report explains why a holistic digital employee experience (DEX) strategy is core to building a strong vulnerability management posture and improving patch management at scale.
Overdue patch updates make remote and hybrid workers a soft target Patching is one area where IT teams procrastinates. Nearly three-quarters ( 71% ) of IT and security teams say it is overly complex, cumbersome and time-consuming, and 57% of those same professionals say remote work and decentralized workspaces make patch management even more challenging.
A breach, intrusion or external event triggers patch management activity in the typical enterprise 61% of the time. IT and security teams are caught off-guard, go into react mode and immediately prioritize patch management to limit the breach. Just over half the time (58%) it’s an actively exploited vulnerability that again pushes IT into a reactive mode.
Absolute Software’s 2023 Resilience Index confirms what VentureBeat hears anonymously from SecOps teams who admit that patch management isn’t a priority until a breach occurs. Absolute found that 52% of endpoints aren’t fully patched or updated, and the longer a remote or hybrid employee’s laptop goes without a reboot, the more vulnerable they are to an attack.
The typical endpoint is also nearly three months behind on patches (85 days) and has an average of 126 vulnerabilities, 54 of those critical. The typical remote endpoint has 77 applications installed.
Dark web best-sellers Today, the dark web’s best-sellers are apps and tools designed to defeat what little security remote and hybrid worker threat surfaces have. They include Remote Desktop Protocol (RDP) kits and popular products include keyloggers, trojans, phishing kits and other malware designed to steal privileged access credentials from remote workers. Credentials are then used to gain access to VPNs and internal systems.
Generative AI-based VPN, vulnerability and exploit tools are also a best-seller, including malware to target popular VPN clients and custom plugins/tools to intercept VPN traffic and bypass corporate VPN security controls. The dark web’s fast-rising best sellers include ransomware-as-a-service, FraudGPT , hacker-for-hire programs and gen AI-based tools designed to launch living-off-the-land (LOTL)-based attacks.
Rogue attackers, cybercrime gangs, syndicates that operate globally and state-sponsored APT groups see an opportunity to cash in on providing the next generation of attackers with tools. In the last three years, innovation on the dark web has led to a 238% rise in attacks aimed at remote workers.
How AI-powered patch management protects remote and hybrid workers One of the most compelling reasons to consider automating patch management with AI and machine learning (ML) is to close the gaps found in years and decades-old common vulnerabilities and exposures (CVE) that attackers weaponize. Leading providers of patch management solutions include Automox , Canonical , ConnectWise , Flexera , Ivanti Neurons for Patch Intelligence , Kaseya , ManageEngine , Syxsense and Tanium.
“With more than 160,000 vulnerabilities currently identified, it is no wonder that IT and security professionals overwhelmingly find patching overly complex and time-consuming,” Srinivas Mukkamala, chief product officer at Ivanti , told VentureBeat. “This is why organizations must utilize AI solutions … to assist teams in prioritizing, validating and applying patches. The future of security is offloading mundane and repetitive tasks suited for a machine to AI copilots so that IT and security teams can focus on strategic initiatives for the business.” Below are some key use cases of AI-powered patch management protection.
Relying on AI to automate patch deployments in real time What’s significant about this use case is how it’s being architected to be VPN-independent. CISOs say this alleviates a major roadblock for their help desks and and ITSM teams. AI models are used to determine the best or optimal deployment timing and orchestrate network-ride rollouts based on device availability, usage patterns and contextual intelligence.
More autonomous, intelligent patch prioritization In this use case, AI and ML algorithms analyze all available vulnerability data, asset context, threat intelligence and business criticality to prioritize the most urgent and high-risk patches for remote devices. Ivanti Neurons for Patch Intelligence is considered a leader in this area, according to interviews VentureBeat has had with CISOs and security professionals. CISOs also mention CrowdStrike Falcon’s ability to integrate vulnerability management and threat intelligence, then use AI to prioritize patches.
Improving real-time endpoint visibility and control The lack of visibility and control of manual and legacy approaches fall short. Security teams tell VentureBeat that pilots of new AI-based patch management systems not only deliver accurate patch inventories for devices, but also report back hardware and full device configuration. Self-healing endpoint providers offering patch management are seeing sales in this area despite economic uncertainty in the broader market.
Deliver predictive patch scheduling at scale Using AI to identify optimal time windows to perform patches and automatically act on them alleviates one of the most time-intensive burdens for help desks and ITSM teams. CISOs say this use case alleviates the need for a fire drill if their managed detection and response (MDR) provider spots a potential intrusion aimed at a weak patch update, or if their endpoint systems determine an intrusion attempt on a CVE. Predictive patch scheduling predicts the optimal maintenance window for each remote employee based on observed usage habits and connectivity strength.
Getting digital experiences right is table stakes for patch management There are eleven factors that CISOs and CIOs find most challenging when it comes to improving digital experiences that support stronger vulnerability and patch management. The following table compares those factors with what VentureBeat has learned from CIOs and CISOs. The fourth column shows the results of the Ivanti study emphasizing the importance of each factor.
For organizations considering automating patch management, it’s important to consider it more as a roadmap and less as a band-aid or quick fix. Making patch management as part of the DNA of a company is critical, especially with attackers studying CVEs for any weaknesses they can quickly weaponize.
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"How AI can help close IoT's growing security gaps to contain ransomware | VentureBeat"
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"https://venturebeat.com/security/how-ai-can-help-close-iots-growing-security-gaps-to-contain-ransomware"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages How AI can help close IoT’s growing security gaps to contain ransomware Share on Facebook Share on X Share on LinkedIn Made by VentureBeat in MidJourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Nation-state attackers are fine-tuning their tradecraft to take advantage of unprotected IoT sensors essential to infrastructure and manufacturing and increasing their attacks against U.S. and European targets. Once-sporadic attacks have given way to an all-out assault on infrastructure and production plants.
IoT attacks seek to take advantage of infrastructure and manufacturing organizations that don’t know how many sensors and endpoints they have, where they are, if they’re current on patches or if they’re secured. IT and security teams in a typical enterprise don’t know where up to 40% of their endpoints are. During Q2 2023, 70% of all ransomware attacks were aimed at the manufacturing sector, followed by industrial control systems (ICS) equipment and engineering (16%).
Unprotected gaps between operational technology (OT) and IT systems, along with unprotected ICS’, are soft targets. This past year, 75% of OT organizations experienced at least one breach intrusion.
“The rub about ransomware is that defending against it requires folks to have strong security throughout their security cycle,” Merritt Baer, Lacework field CISO, told VentureBeat. “You don’t stop ransomware in the moment (though resilience under fire is a relevant topic!). You protect against ransomware by building up your organization’s security every day. And assistive AI tools can also help extend the capabilities of security professionals by offloading time consuming processes and low-level work so they can focus on more strategic, higher-impact security activities.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! More AI-based, tightly orchestrated cyberattacks coming Well-funded nation-state attackers and criminal gangs are also recruiting AI and machine learning (ML) experts to help build the next generation of generative AI attack tools.
Threat actors are orchestrating their IoT attacks with social engineering and reconnaissance and often know more about a target’s network than the admins do.
Manufacturing CISOs seeing spikes in nation-state attack attempts say that new tradecraft reflects a faster, more efficient attack strategy often combined with deepfakes and advanced social engineering. Cyberattacks reflect a new generation of technologies capable of adapting faster than any infrastructure or manufacturer can respond.
“We used to see national-state attackers pulse our endpoints and infrastructure periodically — as if they had a schedule to probe us every few months,” one CISO told VentureBeat on condition of anonymity. Now, that security leader says attack patterns, signatures and sequence of tactics are unmistakable and constant. “They want into our processing plants, distribution centers and R&D facilities with a level of intensity we’ve never seen before.” Other CISOs tell VentureBeat that they worry that security teams are losing the AI war because defensive versus offensive AI shows that attackers are gaining the upper hand. Nearly three-quarters ( 70% ) of CISOs believe that gen AI is creating more advantages that tip in favor of cyber attackers. More than one-third (35%) already use AI for security applications, and 61% plan to adopt AI-based cybersecurity applications and tools in the next 12 months.
Manufacturing continues to face a cyberattack epidemic One of the best-kept secrets in manufacturing is how many ransomware attacks occur and how many ransoms are quietly paid and never reported. It’s an epidemic that no one wants to admit exists, yet IBM’s 2023 X-Force Threat Intelligence Index finds that manufacturing is the most attacked industry today. Well over half (61%) of all breach attempts and 23% of all ransomware attacks are aimed primarily at manufacturing OT systems. Ransomware and hacktivism are the leading cause of most OT-targeted attacks. More than three-quarters ( 81% ) of malware can disrupt industrial control systems, costing millions of dollars in lost orders, productivity and customer goodwill.
The Cybersecurity and Infrastructure Security Agency ( CISA ) also reports that it is seeing a spike in infrastructure and manufacturing attacks, as evidenced by its recent alert of nineteen ICS advisories.
IoT and sensors are a favorite target Attacks often begin targeting unprotected IoT, IIoT and programmable logic controllers (PLC) that deliver real-time data across infrastructure and plant shop floors. From there, the goal is to penetrate deep into the network and cause chaos.
Nation-state attackers are focusing on how they can fast-track AI arsenals into use to make bold political statements or extract millions in ransomware. Energy, water and oil infrastructure, along with healthcare and manufacturing, are soft targets because even a slight disruption threatens human lives and causes millions of dollars in losses.
“We’re connecting all these IoT devices, and all those connections create vulnerabilities and risks,” Kevin Dehoff, president and CEO of Honeywell Connected Enterprise (HCE), told VentureBeat. “With OT cybersecurity, I’d argue the value at stake and the stakes overall could be even higher than they are when it comes to IT cybersecurity.” Dehoff emphasized the need to give customers better visibility into risks and vulnerabilities. “Most customers are still learning about the state of affairs in their OT networks and infrastructure,” he said. “And I think there’s some awakening that will be done.” Introducing Cyber Watch HCE knows these challenges well. The company manages cybersecurity for more than 500 customer sites, secures more than 100 million connected assets and employs more than 150 AI and ML data scientists. The company introduced Cyber Watch and an enhanced version of Cyber Insights at Honeywell Connect last week. Both rely on AI and ML to identify potential breach and intrusion attempts on IoT, OT, ICS and their real-time gaps with IT systems.
Ransomware attacks disable production capabilities and demand large sums to restore access. The Cyber Watch dashboard provides real-time visibility into ransomware indicators across multiple sites, enabling earlier threat detection.
Earlier this year, HCE acquired SCADAFence , which has expertise in closing gaps between OT and IT networks and protecting IoT sensors.
Cyber Watch’s approach to providing a global view of OT cybersecurity is noteworthy. The platform includes a multi-side dashboard that provides visibility into cyber threats across sites and a centralized data view. The Governance Dashboard enables IT and audit departments to define and monitor adherence to company policies. It also supports OT standards and regulations, including IEC 62443, the NIST framework and other compliance frameworks for OT.
Shivan Mandalam, CrowdStrike director of product management and IoT security, told VentureBeat that “it’s essential for organizations to eliminate blind spots associated with unmanaged or unsupported legacy systems. With greater visibility and analysis across IT and OT systems, security teams can quickly identify and address problems before adversaries exploit them.” Like Honeywell, CrowdStrike helps infrastructure and manufacturing customers close IoT gaps by constantly improving their discovery technologies.
Cybersecurity providers are all-in on the AI challenge Baer told VentureBeat: “AI helps to do recursive work. This is crucial for ransomware defense, especially in the cloud where permissions are a mix of perimeter-based (VPC, VPN), coupled with fine-grained identity-centric (users, roles and other identity-based permissions). These controls augment and layer on one another in ways that are hard for humans to parse or prune efficiently. AI can help where humans are not as perfect or fast to calculate ‘what are the attack paths or escalation routes?'” The era of weaponized AI is here.
Airgap Networks , Absolute Software , Armis , Broadcom , Cisco , CradlePoint , Fortinet , Ivanti , JFrog and Rapid7 all have expertise in IoT cybersecurity. Last year at Fal.Con 2022 , CrowdStrike launched Falcon Insight XDR and Falcon Discover for IoT.
Ritesh Agrawal, CEO of Airgap Networks, observes that while IoT endpoints may not be business critical, they can be easily breached and used to spread malware to an organization’s most valuable systems and data. He advises organizations to insist on the basics — discovery, segmentation and identity — for every IoT endpoint.
Ivanti currently offers four IoT cybersecurity solutions, including Ivanti Neurons for RBVM , Ivanti Neurons for UEM , Ivanti Neurons for Healthcare (which supports the Internet of Medical Things, IoMT), and Ivanti Neurons for IIoT.
“IoT devices are becoming a popular target for threat actors, with IoT attacks making up more than 12% of global malware attacks in 2021, up from 1% in 2019, according to IBM,” Srinivas Mukkamala, chief product officer at Ivanti, told VentureBeat. “To combat this, organizations must implement a unified endpoint management (UEM) solution that can discover all assets on an organization’s network — even the Wi-Fi-enabled toaster in your breakroom.” Baer agreed that, “As a CISO, you need to know what you’ve got out there, you need it to work and you need it to run permissions that are deliberately pruned.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"How AI brings greater accuracy, speed, and scale to microsegmentation | VentureBeat"
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"https://venturebeat.com/security/how-ai-brings-greater-accuracy-speed-and-scale-to-microsegmentation"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages How AI brings greater accuracy, speed, and scale to microsegmentation Share on Facebook Share on X Share on LinkedIn Image Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Microsegmentation is table stakes for CISOs looking to gain the speed, scale and time-to-market advantages that multicloud tech stacks provide digital-first business initiatives.
Gartner predicts that through 2023, at least 99% of cloud security failures will be the user’s fault. Getting microsegmentation right in multicloud configurations can make or break any zero-trust initiative.
Ninety percent of enterprises migrating to the cloud are adopting zero trust, but just 22% are confident their organization will capitalize on its many benefits and transform their business. Zscaler’s The State of Zero Trust Transformation 2023 Report says secure cloud transformation is impossible with legacy network security infrastructure such as firewalls and VPNs.
Defining microsegmentation Microsegmentation divides network environments into smaller segments and enforces granular security policies to minimize lateral blast radius in case of a breach. Network microsegmentation aims to segregate and isolate defined segments in an enterprise network, reducing the number of attack surfaces to limit lateral movement.
It’s considered one of the main components of zero trust and is defined by NIST’s zero-trust framework.
CISOs tell VentureBeat that microsegmentation is a challenge in large-scale, complex multicloud and hybrid cloud infrastructure configurations and they see the potential for AI and machine learning (ML) to improve their deployment and use significantly.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Gartner defines microsegmentation as “the ability to insert a security policy into the access layer between any two workloads in the same extended data center. Microsegmentation technologies enable the definition of fine-grained network zones down to individual assets and applications.” Microsegmentation is core to zero trust CISOs tell VentureBeat that the more hybrid and multicloud the environment, the more urgent — and complex — microsegmentation becomes. Many CISOs schedule microsegmentation in the latter stages of their zero-trust initiatives after they’ve achieved a few quick zero trust wins.
“You won’t really be able to credibly tell people that you did a zero trust journey if you don’t do the micro-segmentation,” David Holmes, Forrester senior analyst said during the webinar “ The time for microsegmentation is now ,” hosted by PJ Kirner, Illumio cofounder and advisor.
Holmes continued: “I recently was talking to somebody [and]…they said, ‘The global 2000 will always have a physical network forever.’ And I was like, “You know what? They’re probably right.’ At some point, you’re going to need to microsegment that. Otherwise, you’re not zero trust.” CIOs and CISOs who have successfully deployed microsegmentation advise their peers to develop their network security architectures with zero trust first, concentrating on securing identities often under siege , along with applications and data, instead of the network perimeter. Gartner predicts that by 2026, 60% of enterprises working toward zero trust architecture will use more than one deployment form of microsegmentation, up from less than 5% in 2023.
Every leading microsegmentation provider has active R&D, DevOps and potential acquisition strategies underway to strengthen their AI and ML expertise further. Leading providers include Akamai, Airgap Networks, AlgoSec, Amazon Web Services, Cisco, ColorTokens, Elisity, Fortinet, Google, Illumio, Microsoft Azure, Onclave Networks, Palo Alto Networks, Tempered Networks, TrueFort, Tufin, VMware, Zero Networks and Zscaler.
Microsegmentation vendors offer a wide spectrum of products spanning network-based, hypervisor-based, and host-agent-based categories of solutions.
How AI and ML simplify and strengthen microsegmentation Bringing greater accuracy, speed and scale to microsegmentation is an ideal use case for AI, ML and the evolving area of new generative AI apps based on private Large Language Models (LLMs). Microsegmention is often scheduled in the latter stages of a zero trust framework’s roadmap because the large-scale implementation can often take longer than expected.
AI and ML can help increase the odds of success earlier in a zero-trust initiative by automating the most manual aspects of implementation. Using ML algorithms to learn how an implementation can be optimized further strengthens results by enforcing the least privileged access for every resource and securing every identity.
Forrester found that the majority of microsegmentation projects fail because on-premise private networks are among the most challenging domains to secure. Most organizations’ private networks are also flat and defy granular policy definitions to the level that microsegmentation needs to secure their infrastructure fully. The flatter the private network, the more challenging it becomes to control the blast radius of malware, ransomware and open-source attacks including Log4j , privileged access credential abuse and all other forms of cyberattack.
Startups jumping into the space Startups see an opportunity in the many challenges that microsegmentation presents. Airgap Networks, AppGate SDP, Avocado Systems and Byos are startups with differentiated approaches to solving enterprises’ microsegmentation challenges. AirGap Networks is one of the top twenty zero trust startups to watch in 2023. Their approach to agentless microsegmentation shrinks the attack surface of every connected endpoint on a network. Segmenting every endpoint across an enterprise while integrating the solution into a running network without device changes, downtime or hardware upgrades is possible.
Airgap Networks also introduced its Zero Trust Firewall (ZTFW) with ThreatGPT , which uses graph databases and GPT-3 models to help SecOps teams gain new threat insights. The GPT-3 models analyze natural language queries and identify security threats, while graph databases provide contextual intelligence on endpoint traffic relationships.
Prime areas for AI and ML AI and ML can deliver great accuracy, speed and scale in microsegmentation in the following areas: Automating policy management One of the most difficult aspects of microsegmentation is manually defining and managing access policies between workloads. AI and ML algorithms can automatically model application dependencies, communication flows and security policies. By applying AI and ML to these challenges, IT and SecOps teams can spend less time on policy management. Another ideal use case for AI in microsegmentation is its ability to simulate proposed policy changes and identify potential disruptions before enforcing them.
More insightful, real-time analytics Another challenge in implementing microsegmentation is capitalizing on the numerous sources of real-time telemetry and transforming them into a unified approach to reporting that provides deep visibility into network environments. Approaches to real-time analytics based on AI and ML provide a comprehensive view of communication and process flows between workloads. Advanced behavioral analytics provided by ML-based algorithms have proven effective in detecting anomalies and threats across east-west traffic flows. These analytics improve security while simplifying management.
More autonomous asset discovery and segmentation AI can autonomously identify assets, establish communication links and identify irregularities and distribute segmentation policies without manual intervention. This self-sufficient capability diminishes the time and exertion needed to execute microsegmentation and maintains its currency as assets alter. It additionally mitigates the potential for human error in policy development.
Scalable anomaly detection AI algorithms can analyze extensive amounts of network traffic data, allowing for the identification of abnormal patterns. This empowers scalable security measures while maintaining optimal speed. By harnessing AI for anomaly detection, microsegmentation can expand across extensive hybrid environments without introducing substantial overhead or latency. This ensures the preservation of security effectiveness amidst the expansion of the environment.
Streamlining integration with cloud and hybrid environments AI can improve microsegmentation’s integration across on-premises, public cloud and hybrid environments by identifying roadblocks to achieving optimized scaling and policy enforcement. AI-enabled integration provides a consistent security posture across heterogeneous environments, eliminating vulnerabilities attackers could exploit. It reduces operational complexity as well.
Automating incident response AI allows for automated responses to security incidents, reducing response times. Microsegmentation solutions can use trained ML models to detect anomalies and malicious behavior patterns in network traffic and workflow in real-time. These models can be trained on large datasets of normal traffic patterns and known attack signatures to detect emerging threats. When a model detects a potential incident, predefined playbooks can initiate automated response actions such as quarantining affected workloads, limiting lateral movement and alerting security teams.
Enhanced collaboration and workflow automation AI streamlines team collaboration and automates workflows, decreasing the time required for planning, analysis and implementation. By enhancing collaboration and automation, AI has optimized the entire microsegmentation lifecycle, allowing for a quicker time-to-value and ongoing agility, thereby enhancing the productivity of security teams.
Essential to zero trust architecture Microsegmentation is essential to zero trust architecture, but scaling it is difficult. AI and ML show potential for streamlining and strengthening microsegmentation in several key areas, including automating policy management, providing real-time insights, enabling autonomous discovery and segmentation and more.
When microsegmentation projects are delayed, AI and ML can help identify where the roadblocks are and how an organization can more quickly reach the results they’re after. AI and ML’s accuracy, speed and scale help organizations overcome implementation challenges and improve microsegmentation. Enterprises can reduce blast radius, stop lateral movement and grow securely across complex multicloud environments.
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"New defense tools from Abnormal Security defend against seemingly harmless QR codes | VentureBeat"
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"https://venturebeat.com/security/how-a-simple-qr-code-can-take-control-of-your-phone-and-digital-life"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages New defense tools from Abnormal Security defend against seemingly harmless QR codes Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The innocuous black-and-white Quick Response (QR) codes pervasive across retailers, airports, bars, hotels (and more) are the threat surfaces no one talks about. But attackers see them as the perfect Trojan Horse for hijacking phones and stealing digital identities.
Threat actors are cashing in on people’s trust by creating and distributing QR codes that deliver malware, attempt account takeovers and unleash phishing attempts to steal identities. Combining social engineering with QR codes that can be created in a second, attackers are able to open victims’ bank accounts and drain them dry, install malware, penetrate entire corporate networks and more.
Abnormal Security , a leading provider of AI-native cloud email security platforms, hopes to break that cycle with a launch today of enhanced capabilities that detect QR codes in emails.
“As threat actors continue to innovate, QR code attacks are on the rise, partly because they tend to work better than more traditional attack types,” said Mike Britton, CISO at Abnormal Security. “They can be difficult to detect because, unlike traditional email attacks, there’s minimal text content and no obvious URL. This significantly reduces the number of signals available for traditional security tools to analyze.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Trust and convenience make QR codes an easy target QR codes’ popularity continues to soar as the pandemic spurred their rapid growth and new uses emerge that further drive adoption and trust.
Instagram , Facebook , X (the platform known previously as Twitter) and many other social media platforms have offered users the option of creating their own QR codes to share their profiles with friends. Combining that convenience and trust is a winning combination for social media platform providers, leading to more traffic and greater ad revenue.
A sure sign of how dominant QR codes have become is their emergence on the dark web and Telegram channels, where hackers offer instructional videos on launching attacks with them. Criminal gangs offering ransomware-as-a-service on the dark web mention QR code hijacking to get the fastest clicks, suggesting that attackers embed them in emails and on hijacked websites.
Attackers are quick to capitalize on that trust. More than three-quarters ( 83% ) of consumers have used QR codes on their phones to make bill payments and 80% of QR code users in the U.S. believe that they are safe. Another 64% say that using QR codes is more convenient for the many touchless transactions they do every day, a practice that largely started during the pandemic. Ivanti found that 71% of users can’t distinguish between a legitimate or malicious QR code and 17% have been redirected to suspicious sites they didn’t intend to visit.
Only scan QR codes from a known source Proving how powerful trust is as an accelerator, QR code use is projected to increase by 43.2% between 2022 and this year. In 2023, approximately 331.4 million QR codes are expected to be redeemed. Every month, 40,000 new QR codes are created on average. Their convenience and familiarity make QR codes appear harmless, but attackers are becoming more creative in fine-tuning their tradecraft to make the most of this fast-growing attack vector.
“QR codes should only be scanned if they are from a trusted source,” writes Chris Goettl, VP of product management at Ivanti.
“Hackers can easily substitute legitimate QR codes with malicious ones. Because they aren’t human readable, cybercriminals can exploit them by generating their own QR codes with embedded malicious software.” Goettl cautioned that, “they can also direct users to phishing sites without being detected. Simply put, hackers can use QR codes to illicitly obtain information, hijack accounts and steal identities and data.” QR Codes are the primary attack vector in 17% of all advanced attacks Abnormal Security recently found that QR codes are the primary attack vector in 17% of all advanced attacks targeting customer environments. Abnormal is seeing a rise in CR code-based attacks aimed at credential phishing, extortion and invoice payment fraud attacks. QR code-based attacks have increased 400% in the past year as attackers expand their tradecraft to capitalize on widespread trust.
A more troubling trend is also emerging: Attackers are crafting emails to deliver malicious QR codes, linking to apparently legitimate websites (including Google or Microsoft), then prompting users to enter their login, password and privileged access credential information.
Abnormal also notes a significant rise in phishing emails that impersonate trusted entities — including banks, delivery services and government agencies — using social engineering techniques to lure victims into scanning QR codes. Once victims scan, they are redirected to malicious websites that steal their credentials or infect their devices with malware.
Attackers are focused on harvesting as many identities and privileged access credentials to banks, financial institutions and confidential corporate networks for those working in an enterprise.
Protecting against QR code attacks takes a multilayer strategy CISOs tell VentureBeat that QR codes have proven to be such a threat that it’s necessary to take a multi-layered approach to protect against them. Combining unified endpoint management (UEM) and AI-based platforms that can identify typical email patterns to establish a baseline of normal behavior, CISOs are building multiple barriers to prevent the onslaught of QR code-based intrusion attacks.
Abnormal Security’s new capabilities can parse corresponding links, targeting the attack path most often used to deliver malicious codes into an enterprise. The AI platform takes signals extracted from parsing and combines them with Abnormal’s behavioral analysis across the broader email environment, strengthening an enterprise’s ability to detect and block malicious activity.
Abnormal’s approach is noteworthy because their AI-driven platform builds an adaptable model of each user’s typical email patterns to establish a baseline of normal behavior. This allows it to detect anomalies in emails containing QR codes, including unfamiliar sender addresses or unusual formatting. Abnormal Security also analyzes QR codes, extracting signals from the format, embedded URL links and hosting domains.
Unified Endpoint Management is table stakes CISOs tell VentureBeat that UEM is table stakes for containing QR code risks and attack strategies with comparable tradecrafts.
IBM , Ivanti and VMWare are the most-mentioned UEM providers by CISOs who acknowledge that QR code attacks are on their radar, and they’re using endpoint management to counter the risks.
Ivanti is noteworthy for its approach of combining UEM with passwordless multi-factor authentication (Zero Sign-On) and mobile threat defense (MTD). VentureBeat has learned that its customers can validate security to the device level, establish user context, verify the network and detect and remediate threats to ensure that only authorized users, devices, apps and services can access business resources.
Stopping QR code attacks where they happen is the goal The CISO of an insurance and financial services firm recently told VentureBeat that QR code risks to their infrastructure are everywhere, which is why having a UEM strategy is essential. She said that scans happen when employees travel, attend meetings in customer and supplier offices and when they commute. In-the-wild attacks make UEM critical to shutting down a QR code attack.
Abnormal Security’s new capabilities further strengthen CISOs’ defenses against QR code attacks. Shutting down email attack strategies helps protect one threat surface, while UEM helps provide layered protection against a QR code on any device. With digital identities being best-sellers on the dark web, CISOs know QR codes are a real threat they must contain.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Honeywell acquires cybersecurity provider to address manufacturing’s IoT vulnerabilities | VentureBeat"
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"https://venturebeat.com/security/honeywell-acquisition-cybersecurity-provider-manufacturing-sector-iot-vulnerabilities"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Honeywell’s acquisition of cybersecurity provider sets sights on manufacturing sector’s deep IoT vulnerabilities Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The manufacturing sector is rife with unprotected Internet of Things (IoT) sensors and devices, many of them integrated into enterprises’ mission-critical systems. The resulting gaps make operations technology (OT) and information technology (IT) networks vulnerable to devastating cyberattacks.
Visibility is key. Shivan Mandalam, director of product management for IoT security at CrowdStrike , told VentureBeat that “it’s essential for organizations to eliminate blind spots associated with unmanaged or unsupported legacy systems. With greater visibility and analysis across IT and OT systems, security teams can quickly identify and address problems before adversaries exploit them.” Honeywell’s acquisition of Israel-based SCADAfence , a leading provider of OT and IoT cybersecurity solutions, is just one example of the manufacturing industry trying to catch up, close these gaps and defend against increasing numbers of ransomware attacks.
Manufacturing: An industry under siege Anything that stops a shop floor from operating can quickly cost a business millions of dollars. That’s why ransomware attacks on manufacturers generate millions in payouts. Hundreds of manufacturers pay ransomware demands without disclosing that fact to customers.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Gartner predicts that the financial impact of cyber-physical system ( CPS ) attacks will reach more than $ 50 billion by 2023.
Recovery from a typical manufacturing breach costs $2.8 million.
Not only that: Nearly nine in 10 manufacturers that have suffered a ransomware attack or breach have also had their supply chains disrupted.
Honeywell acquires SCADAfence to close the gap Honeywell’s SCADAfence acquisition provides the manufacturing giant “with additional technology and expertise that help accelerate our innovation roadmap … and support rapidly evolving customer requirements,” Michael Ruiz, GM of Honeywell Cybersecurity Services , said in a recent interview with VentureBeat.
The acquisition will deliver an integrated platform to manufacturers, process industries and infrastructure providers at a time when attacks are escalating.
“SCADAfence is an ideal complement to Honeywell’s OT cybersecurity portfolio, and when combined with the Honeywell Forge Cybersecurity+ suite, it enables us to provide an end-to-end solution with applicability to asset, site and enterprise across key Honeywell sectors,” said Ruiz.
Key focus areas include asset discovery, threat detection and compliance management, he told VentureBeat. “Our plan is to have the SCADAfence product portfolio integrate into the Honeywell Forge Cybersecurity+ suite within Honeywell Connected Enterprise, Honeywell’s fast-growing software arm with a strategic focus on digitalization, sustainability and OT cybersecurity SaaS offerings and solutions.” Building on process analysis and integration expertise Known for its process analysis and integration expertise, Honeywell is concentrating on how it can make the most of its strengths in these areas and achieve scale quickly with the new acquisition.
“This integration will enable Honeywell to provide an end-to-end enterprise OT cybersecurity solution to site managers, operations management and CISOs seeking enterprise security management and situational awareness,” said Ruiz.
SCADAfence CEO Elad Ben Meir also commented on the synergies between the companies. “We are thrilled to join Honeywell as we work towards fulfilling our mission of empowering industrial organizations to operate securely, reliably and efficiently,” Ben Meir said in a press release. “This combination creates a significant opportunity for growth, allowing us to combine our top-tier OT cybersecurity products with one of the world’s leading companies in industrial software.” The deal expands Honeywell’s cybersecurity center of excellence in Tel Aviv, where SCADAfence is headquartered. Ruiz told VentureBeat that one of the most valuable aspects of the acquisition is that Honeywell will be able to “nearly double our research and development for OT cybersecurity, probably becoming one of the larger OT cybersecurity research and development organizations out there.” Why Honeywell moved to acquire SCADAfence The IBM Security X-Force Threat Intelligence Index found that manufacturing is the most attacked industry worldwide: The sector accounted for 23% of all ransomware attacks last year. More than six in 10 breach attempts on manufacturers first targeted OT systems essential to manufacturing operations.
Research firm Dragos predicts that ransomware attacks on industrial organizations will accelerate this year. Dragos’ most recent Industrial Ransomware Attack Analysis from Q2 2023 found that 47.5% of ransomware attacks tracked globally impacted industrial organizations and infrastructure in North America, an increase of 27% over the last quarter.
All told, seven out of 10 ransomware attacks in Q2 were aimed at manufacturing, followed by the industrial control systems (ICS) equipment and engineering sector, which accounted for16% of attacks.
The rapid rise in Fileless malware attacks reflects this trend. Fileless malware is designed to evade detection by cloaking its presence using legitimate tools. Kurt Baker, senior director of product marketing for CrowdStrike Falcon Intelligence, writes that “fileless malware is a type of malicious activity that uses native, legitimate tools built into a system to execute a cyber-attack. Unlike traditional malware , fileless malware does not require an attacker to install any code on a target’s system, making it hard to detect. This fileless technique of using native tools to conduct a malicious attack is sometimes referred to as living off the land or LOLbins.” Closing OT/IoT blind spots Security providers are upping their games.
Last year at Fal.Con 2022 , CrowdStrike augmented Falcon Insight, launching Falcon Insight XDR and Falcon Discover for IoT that target security gaps in and between industrial control systems (ICSs).
Ivanti, for its part, has successfully launched four solutions for IoT security: Ivanti Neurons for RBVM , Ivanti Neurons for UEM , Ivanti Neurons for Healthcare — which supports the Internet of Medical Things (IoMT) — and Ivanti Neurons for IIoT based on the company’s Wavelink acquisition, which secures Industrial Internet of Things (IIoT) networks.
Other leading providers offering IoT cybersecurity solutions include AirGap Networks, Absolute Software, Armis, Broadcom, Cisco, CradlePoint, CrowdStrike, Entrust, Forescout, Fortinet, Ivanti, JFrog and Rapid7.
AI and cybersecurity Airgap Networks has created one of the most innovative approaches to closing the OT-IT gap. Its Zero Trust Firewall (ZTFW) combines agentless microsegmentation, secure access for critical assets and network and asset intelligence. Airgap’s unique approach provides its customers with the option of fully segmenting legacy servers, ICS, IoT and private 5G endpoints. The platform can also integrate into a running network without agents, hardware upgrades or major device changes.
VentureBeat interviewed Ritesh Agrawal , CEO of Airgap Networks, immediately following its launch of ThreatGPT , the company’s ChatGPT integration with the Airgap Zero Trust Firewall. Agrawal told VentureBeat: “Because ThreatGPT is fully integrated into the core of the ZTFW architecture, our customers can use all available data to train the models. I believe we are first to market with this.” ThreatGPT uses graph databases and GPT-3 models to help SecOps teams gain new threat insights. The GPT-3 models analyze natural language queries and identify security threats, while graph databases provide contextual intelligence on endpoint traffic relationships.
Agrawal told VentureBeat that, “IoT puts a lot of pressure on enterprise security maturity. Extending zero trust to IoT is hard because the endpoints vary, and the environment is dynamic and filled with legacy devices.” Asked how manufacturers and other high-risk industry targets could get started, Agrawal advised that “accurate asset discovery, microsegmentation and identity are still the right answer, but how to deploy them with traditional solutions when most IoT devices can’t accept agents? This is why many enterprises embrace agentless cybersecurity like Airgap as the only workable architecture for IoT and IoMT.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Gutsy gains $51M seed round, one of cybersecurity's largest this year | VentureBeat"
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"https://venturebeat.com/security/gutsy-gains-51m-seed-round-one-of-cybersecuritys-largest-this-year"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Gutsy gains $51M seed round, one of cybersecurity’s largest this year Share on Facebook Share on X Share on LinkedIn Source: VentureBeat via MidJourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Emerging from stealth today with one of cybersecurity’s largest-ever seed rounds of $51 million, startup Gutsy’s vision is to revolutionize security governance through process mining.
To gain perspective on Gutsy’s sizable funding round, VentureBeat used Crunchbase to analyze all cybersecurity startups that have received seed funding from January 2019 to the present. Of 2,016 startups the average seed round was $3.1 million and the maximum was $181 million. In aggregate, cybersecurity startups raised $4.8 billion in seed funding.
Gutsy’s launch speaks to CISOs’ urgent need for more visibility The serial entrepreneurs who founded Gutsy include CEO Ben Bernstein co-founder and CEO; VP of R&D Dima Stopel and CTO John Morello. Additionally, the trio were a part of the founding team of Twistlock, a pioneer in cloud-native security that was purchased by Palo Alto Networks in 2019.
“Gutsy’s seed round check was the largest we’ve written, but one of the easiest decisions we’ve made,” said Yoav Leitersdorf, managing partner of YL Ventures, which led the round.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Gutsy’s exit from stealth is well-timed, given how much CISOs are under pressure to quantify the value their investments are making while trimming spending and consolidating their tech stacks to gain greater visibility.
In a survey Gutsy performed on enterprise security governance, 55% of CISOs reported that their security tools are poorly operationalized, which has led to process failure as root cause of 38% of security incidents. Additionally, 63% of audit findings result from breakdowns in security processes.
Process mining has had a successful track record with senior management and boards in helping solve complex problems by reducing roadblocks within and between enterprise resource planning (ERP), supply chain management (SCM) and related systems for years. The process mining market is projected to reach $8.4 billion by 2032, soaring from $1.2 billion in 2022, achieving a compound annual growth rate of 21.5%.
How process mining works Gartner defines process mining as “a technique designed to discover, monitor and improve real processes by extracting readily available knowledge from the event logs of information systems.” The IT consulting firm notes that process mining includes “automated process discovery, conformance checking, social network/organizational mining, automated construction of simulation models, model extension and repair, case prediction and history-based recommendations.” Process mining relies on event log data across IT, security and operations systems to quantify current process performance levels and identify where and how bottlenecks and roadblocks occur. Data is extracted from the systems that support a given business process and is correlated across every event, making it possible to visualize an entire end-to-end process and its relative level of performance. Algorithms analyze why and how bottlenecks and roadblocks happen, providing insights into how a process can be improved.
Compliance and cybersecurity are among the most complex processes in an enterprise. But they’re also often the most unknown to IT and security teams due to tool sprawl and conflicting data sources.
Why process mining is a strong value-add for cybersecurity Gutsy’s beta, available today, provides three modules covering identity management, incident response and vulnerability management processes.
The platform integrates with many tools, including cloud providers, HR systems, vulnerability management tools, ticketing systems, endpoint detection and response (EDR) platforms and more. Gutsy is agentless, provided as SaaS, and is available globally in any customer-selected region.
What makes Gutsy’s approach to solving security governance noteworthy is how the startup has combined process mining with what board members worry about most. A few of these concerns include risk management, security governance and the complex issues of what to insure with which type of cyber insurance.
“Fundamentally, what we’re trying to do is give security leaders an ability to look beyond just the settings and detections they’re getting from the tools and to really understand how and why they are getting those things and where the problems are within the processes they have,” cofounder Morello told VentureBeat.
That’s core process mining and why it’s the go-to technique for troubleshooting process problems across organizations.
Saving valuable time For CISOs and CIOs and their teams, Gutsy will deliver much-needed visibility into workflows and tool integration, helping to find gaps that many security and IT teams don’t know exist.
That’s good news for teams that are already stretched thin and being asked to rank-order the most and least-valuable cybersecurity and IT tools and eliminate ones that are redundant and offer the lowest ROI. Gutsy’s process mining workflows automatically identify both, helping to curb tool sprawl and allowing organizations to trim maintenance and license renewals.
Morello told VentureBeat that when it comes to automating audits and providing greater visibility, “Gutsy will measure and give them [organizations] visibility into how they’re attaining those things. They’ll literally see the process map that shows every step between all the different teams and tools and technologies.” VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Forrester predicts A.I. code flaws will enable new attacks next year | VentureBeat"
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"https://venturebeat.com/security/forrester-predicts-a-i-code-flaws-will-enable-new-attacks-next-year"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Forrester predicts A.I. code flaws will enable new attacks next year Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
DevOps teams rely more on AI-coding assistants to boost team productivity by automating coding tasks with only the most conscientious scanning final code for security flaws, Forrester warns in their 2024 cybersecurity, risk, and privacy predictions.
The research and advisory firm predicts inconsistent compliance and governance practices combined with many Devops teams experimenting with multiple AI-coding assistants simultaneously to increase productivity will lead to flawed A.I. code responsible for at least three publically-admitted breaches in 2024. Forrester also warns that A.I. code flaws will pose API security risks.
AI-coding assistants are redefining Shadow I.T.
49% of business and technology professionals with knowledge of AI-coding assistants say their organizations are piloting, implementing, or have already implemented them in their organizations. Gartner predicts that by 2028 , 75% of enterprise software engineers will use A.I. coding assistants, up from less than 10% in early 2023.
Devops leaders tell VentureBeat it’s common to find multiple AI-coding assistants being used across teams as the pressure to produce a high volume of code every day is growing. Tighter timelines for more complex coding combined with the proliferation of over 40 AI-coding assistants available is leading to a new form of shadow I.T. where Devops teams switch from one A.I. assistant to another to see which delivers the highest performance for a given task. Enterprises are struggling to keep up with the demand from their Devops teams for new AI-coding tools approved for use corporate-wide.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! AI-coding assistants are available from leading A.I. and LLM providers, including Anthropic, Amazon, GitHub, GitLab, Google, Hugging Face, IBM, Meta, Parasoft, Red Hat, Salesforce, ServiceNow, Stability AI, Tabnine, and others.
CISOs face a challenging balancing act in 2024 Forrester’s cybersecurity, risk, and privacy predictions reflect a challenging year ahead for CISOs who will need to balance the productivity gains generative A.I. provides with the need for greater compliance, governance, and security for A.I. and machine learning models under development.
Getting compliance right will be essential for protecting intellectual property, the one asset no one wants to put at risk despite the stepwise gains generative A.I. is delivering today.
How well a CISO and their teams can triangulate innovation, compliance, and governance to give their companies a competitive advantage in 2024 will be more measurable in 2024 than any previous year. Generative A.I.’s productivity gains balanced against risks, and the need for reliable guardrails will be a key issue every CISO will likely deal with next year, too.
The goal: Achieve A.I.’s innovation gains while reducing risk Forrester’s cybersecurity, risk, and privacy predictions for 2024 guide every organization on achieving greater A.I. innovation gains while reducing the risks of human- and code-based breach risks. Taken together, they reflect how urgent it is to get compliance, governance, and guardrails for new A.I. and ML models right first, so the productivity gains from generative AI-based coding and devops tools deliver the greatest benefit at the lowest risk.
“In 2024, as organizations embrace the generative A.I. (genAI) imperative, governance and accountability will be a critical component to ensure that A.I. usage is ethical and does not violate regulatory requirements,” writes Forrester in their cybersecurity predictions. “This will enable organizations to safely transition from experimentation to implementation of new AI-based technologies,” the report continues.
Forrester’s 2023 data shows that 53% of A.I. decision-makers whose organizations have made policy changes regarding genAI are evolving their A.I. governance programs to support A.I. use cases.
The following are their predictions for 2024: Social engineering attacks soar as attackers find new ways to use generative AI FraudGPT was just the start of how attackers will weaponize generative A.I. and go on the offensive.
2024 will see social engineering attacks soar from 74% of all breach attempts to 90% next year. Forrester warns they’re seeing the human element be more attacked than ever.
That’s sobering news for an industry where some of the most devastating ransomware attacks in 2023 started with a phone call.
Existing approaches to security awareness training aren’t working. Forrester makes the point that what’s needed is a more data-driven approach to behavior change that quantifies human risk and provides real-time training feedback to employees and perceptual gaps they may have in identifying threats.
Merritt Baer, field CISO of Lacework, told VentureBeat: “Tech is built by humans, for humans. It is no longer good enough to blame ‘the human element’ for security breaches. If you are using fine-grained logical and perimeter-based controls around your security and identity; if you are doing good governance around continuous pruning and creating ‘paved roads’ for folks to make the secure thing to do, the easy thing to do; if you are templatizing environments and using ephemerality as a security benefit; if you are noting anomalies to intelligently refine those permissions; then, we see less entry for human error.” Baer further observed that “to err is human; to secure environments is not for the divine, but for practitioners who get better over time.” Cyber insurance carriers will tighten their standards, red-flagging two tech vendors as high risk Combining greater real-time telemetry data and more powerful analytics and genAI tools to analyze it will give insurance carriers the visibility they’ve needed for years to reduce their risks. Forrester observes that insurance carriers will also have more insights from security services and tech partnerships and more data-driven insights, including forensics from insurance claims.
Given the growing number and severity of massive one-to-many breaches like MOVEit , Forrester predicts security vendors will be assessed by risk scoring and calculations that will also be used for calculating insurance premiums of their customers seeking coverage.
Expect to see a ChatGPT-based app fined for mishandling personally identifiable information (PII) Implicit in this prediction is how vulnerable identity and access management (IAM) systems are to attack. Active Directory (A.D.) is one of the most popular targets of any identity-motivated attack. Approximately 95 million Active Directory accounts are attacked daily, as 90% of organizations use the identity platform as their primary authentication and user authorization method.
John Tolbert, director of cybersecurity research and lead analyst at KuppingerCole, writes in the report Identity & Security: Addressing the Modern Threat Landscape : “Active Directory components are high-priority targets in campaigns, and once found, attackers can create additional Active Directory (A.D.) forests and domains and establish trusts between them to facilitate easier access on their part. They can also create federation trusts between entirely different domains.” Forrester notes that OpenAI continues to receive more regulatory scrutiny with the ongoing investigation in Italy , and lawyers in Poland are dealing with a new lawsuit for several potential GDPR violations. As a result, the European Data Protection Board has launched a task force to coordinate enforcement actions against OpenAI’s ChatGPT. In the U.S., the FTC is also investigating OpenAI. While OpenAI has the technical and financial resources to defend itself against regulators, other third-party apps running ChatGPT do not.
Senior-level zero-trust roles and titles will double across the global public and private sectors Currently, there are 92 zero trust positions available in the U.S. advertised on LinkedIn and 151 worldwide.
Forrester’s optimistic forecast of zero trust position growth doubling in the next twelve months is supported by the broader adoption of the NIST Zero Trust Architecture framework across their client base. Forrester predicts zero trust adoption will also increase demand for cybersecurity professionals with engineering, governance, strategy, and leadership expertise. These positions will sit within federal agency security organizations and become a staple of the staffing and services for firms that augment those agency functions and the private sector enterprises responsible for supporting 85% of the U.S.’s critical infrastructure.
Forrester advises their clients to prepare by reviewing the requirements for a zero-trust role at their organizations and identifying a cohort of individuals to pursue Zero Trust certifications.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Five ways CISOs are using AI to protect their employees' digital devices and identities | VentureBeat"
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"https://venturebeat.com/security/five-ways-cisos-are-using-ai-to-protect-their-employees-digital-devices-and-identities"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Five ways CISOs are using AI to protect their employees’ digital devices and identities Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Using generative AI to automate scripts seeking unprotected endpoints, ports and infrastructure security gaps, cybercrime gangs offer bounties for targeted organizations’ employee digital device passwords and identities. As many recent attacks show, putting any trust in identities is a breach waiting to happen.
Notably, digital and physical crime in healthcare has long been converging and growing into a pandemic. Healthcare providers warn their employees not to leave their laptops in their cars unattended. The Coplin Health incident in which 43,000 records containing personal health information (PHI) were compromised after an employee’s laptop was stolen from their car is still a concern boards mention regarding identity security. A stolen laptop with unencrypted PHI data can often lead to a $1 million settlement based on HIPAA violations alone.
Attacks on employees’ digital devices and identities are soaring Healthcare CISOs tell VentureBeat that attempts to steal employees’ digital devices are soaring because PHI records command the highest prices on the dark web and are untraceable. The U.S. Department of Health and Human Services (HHS) Breach Portal shows that in the last eighteen months alone, 799 healthcare providers have been breached, 551 of them experiencing a server-based attack and 173 email-based in which laptops were used to gain access.
CrowdStrike’s cofounder and CEO George Kurtz said in his keynote at last year’s Fal.Con that “80% of the attacks or the compromises that we see use some form of identity and credential theft.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The Identity Defined Security Alliance (IDSA)’s 2023 Trends in Securing Digital Identities report found that 90% of organizations experienced at least one identity-related breach in the past year, representing a 7.1% increase year-over-year.
Getting ready for automated attacks that weaponize AI at scale Deepfake attacks are so pervasive that the Department of Homeland Security provides the guide Increasing Threats of Deepfake Identities , which outlines how to counter them. VentureBeat has learned of several attempted deepfake attacks on leading enterprise software CEOs that follow the same attack pattern in which Zscaler CEO Jay Chaudhyr’s voice was used to extort funds from the company’s India-based operations.
Chaudhry, Kurtz and CEOs of top cybersecurity companies agree that stolen identities and privileged access credentials are customers’ biggest threats. The Finnish Transport and Communications Agency National Cyber Security Centre and WithSecure commissioned a study to predict AI-enabled cyberattacks, as shown below.
How CISOs are using AI to protect employees’ identities Security teams and the CISOs leading them can’t afford to lose the AI war.
The following five AI and machine learning (ML) techniques have become table stakes for stopping identity-based attacks: Getting a more precise count, location and telemetry of all endpoints, machines and associated identities Cybersecurity and IT teams often can’t locate 35% to 40% of their endpoints and machines. With the proliferation of new identities assigned to endpoints and the resulting unchecked agent sprawl, attackers’ reconnaissance efforts quickly find over configured endpoints.
Endpoint sprawl makes identity breaches harder to stop. Six in 10 (59%) endpoints have at least one identity and access management (IAM) agent, and 11% have two or more. These and other findings from Absolute Software’s 2023 Resilience Index illustrate the false sense of security organizations have in security tools.
The Index found that many endpoint controls aren’t installed correctly, leaving 25 to 30% of devices vulnerable to attack. Treating every identity as a new security perimeter, enforcing least privileged access, monitoring every transaction and going all in on zero trust for every endpoint must be a priority.
Moving beyond mobile device VPNs and standardizing AI-enabled Mobile Threat Defense (MTD) In a recent interview with VentureBeat, Ivanti chief product officer Srinivas Mukkamala noted that, “increasingly, our cell phones contain our whole lives. At the heart of modern device management organizations [protecting] data everywhere work happens, especially work that is happening on personal devices.” Mukkamala’s comments reflect what VentureBeat hears from CISOs in healthcare, manufacturing and financial services, in which mobile devices are frequently an attack target.
Mukkamala advised that “there is a continued need to more easily control what information apps have access to and avoid granting inappropriate or excessive permissions, which puts individuals and organizations at risk. IT and security teams are increasingly turning to automation and AI to ease the manual and mundane parts of device management and importantly, to create a moat around the personal data and work data accessible through our phones.” Improving risk scoring accuracy and precision to more quickly identify identity threats CISOs and their teams tell VentureBeat they’ve offered to help test the latest generation of AI and ML-based risk-scoring models their providers are readying for launch. Leading cybersecurity providers have already released improved risk scoring to identify and thwart identity-based attacks.
AI is proving effective in analyzing large volumes of identity and access data in real time to detect subtle patterns and anomalies that indicate compromised credentials or insider threats. Adopting a real-time telemetry approach reduces false positives.
Detecting synthetic identity fraud and deepfakes From reducing false positives and identifying synthetic fraud to spotting deepfakes, all AI-based identity platforms and solutions share the common attributes of relying on decades of data to train models and assigning trust scores by transaction.
For instance, Telesign’s model-based approach is noteworthy in its efficiency in getting the most value from various real-time telemetry data sources. Their model relies on more than 2,200 digital attributes and creates insights based on more than 15 years of historical data patterns and supporting analytics.
Phone number velocity, traffic patterns, fraud database consortiums and phone data attributes distinguish Telesign’s approach. Identity signals are scored for anomalies that may indicate a synthetic identity. The system “learns” from predictive analytics and supervised and unsupervised ML algorithms.
The company’s risk assessment model combines structured and unstructured ML to provide a phone number risk assessment score in milliseconds, providing a valuable data point to determine the risk a new account may not be legitimate.
Relying on resilient, self-healing endpoints Enabling self-healing endpoints to regenerate themselves autonomously and detect and respond to potential threats are two ways AI drives greater endpoint resilience. AI also enables endpoints to quickly detect and respond to anomalies and advanced threats that rules-based systems miss.
CISOs tell VentureBeat that they use AI-based self-healing endpoints to reduce manual IT support time and cost, improve compliance and identify identity-based breach attempts where attackers try to gain access using stolen privileged credentials.
Leading self-healing endpoint providers include Absolute , Akamai , Ivanti , Malwarebytes , Microsoft , SentinelOne , Tanium and Trend Micro.
Absolute’s Resilience platform is noteworthy as it provides real-time visibility and control of any device, whether on the network or not. Their platform is factory-embedded in firmware by 28 top device manufacturers, making it the world’s only firmware-embedded endpoint visibility and control platform. Absolute is firmware embedded in more than 600 million endpoints and the company serves 21,000 global customers.
AI is core to the future of identity security As a recent CrowdStrike report illustrated, identities are under siege. Remote and hybrid workers are high-value targets because attackers also want to steal their identities.
By prioritizing AI for 360-degree endpoint monitoring, multi-layered mobile threat defense, real-time risk scoring, synthetic fraud detection and self-healing endpoints, organizations can protect employees’ identities and reduce the threat of a breach.
AI-based platforms and systems are proving effective in identifying anomalies and potential threats in real time, ultimately shutting down identity-based breaches and attempts to use synthetic identities and stolen access credentials.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"Endpoint security getting a boost from AI and machine learning | VentureBeat"
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"https://venturebeat.com/security/endpoint-security-getting-a-boost-from-ai-and-machine-learning"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Endpoint security getting a boost from AI and machine learning Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Attackers are turning to generative AI to hunt for the easiest endpoints to breach, combining their attacks with social engineering to steal admin identities so they don’t have to hack into networks — they walk right in.
Endpoints overloaded with too many agents are just as unsecure as those that don’t have any. AI and machine learning (ML) are urgently needed in endpoint protection to identify the weakest endpoints, update their patches and harden detection and response beyond what’s available today.
With endpoints becoming the focal point of more lethal, sophisticated attacks, it’s timely that Forrester published their Endpoint Security Wave for Q4, 2023.
The research firm evaluated thirteen endpoint providers’ current offerings, strategy and market presence.
Bitdefender , BlackBerry , Broadcom , Cisco , CrowdStrike , ESET , Microsoft , Palo Alto Networks , SentinelOne , Sophos , Trend Micro , Trellix and VMware are included in the Wave.
Forrester notes in the report that “endpoint security vendors have evolved beyond simple malware prevention or “next-generation antivirus” to incorporate behavioral analysis and prevention, vulnerability and patch remediation and advanced threat preventions for data, identity and network, all of which have benefitted the customers using these products.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! How AI and ML are boosting endpoint security AI and ML provide a much-needed boost to endpoint security. Every provider in Forrester’s Wave is fast-tracking the technologies on their platform roadmaps to drive more sales through consolidation.
VentureBeat has learned that these roadmaps include new applications and tools that will deliver step-wise gains in behavioral analytics, real-time authentication, improved tools for closing the identity-endpoint gaps and AI-based indicators of attack (IOA) and indicators of compromise (IOAs).
IOAs are designed to detect an attacker’s intent and to identify their goals regardless of the malware or exploit used in an attack. An IOC provides the forensics needed for evidence of a breach. IOAs must be automated to deliver accurate, real-time data on attack attempts to understand attackers’ intent and kill any intrusion attempt.
Of the providers profiled by Forrester, CrowdStrike is the first to deliver AI-based IOAs.
While not mentioned in the Wave, ThreatConnect, Deep Instinct and Orca Security also use AI and ML to streamline IOCs.
“AI is incredibly, incredibly effective in processing large amounts of data and classifying this data to determine what is good and what’s bad,” Vasu Jakkal, corporate VP for Microsoft Security, Compliance, Identity and Privacy, said during an insightful keynote at RSA Conference. “At Microsoft, we process 24 trillion signals every single day and that’s across identities and endpoints and devices and collaboration tools and much more.” Trends driving the endpoint security market Endpoint security providers are under pressure from customers to consolidate platforms while providing more functionality at a lower price and deliver step-change improvements in visibility and control.
A CISO responsible for protecting one of the nation’s largest insurance and financial services firms told VentureBeat that her teams’ first place to look for consolidation wins is endpoint security. Extended detection and response (XDR) shows the potential to deliver the consolidation CISOs have been asking for.
Forrester senior analyst Paddy Harrington writes that, “while many organizations are now looking to enhance their security operations with endpoint detection and response (EDR) or XDR solutions to allow for better threat and incident investigation, securing the endpoint starts with a strong endpoint protection platform, and that was the focus of this Forrester Wave evaluation.” Harrington points to three dominant trends driving the endpoint security market: A stronger focus on prevention to protect threat analysts’ time Security analysts need more effective tools for preventing attacks to protect their time and break out of the endless cycle of responding to and recovering from attacks. Harrington points out that in previous years, the focus had been on detection and response — deprioritizing prevention — due to the belief that it was the best way to respond to incidents. He said that endpoint security solutions can help provide analysts with the opportunity to split time between investigation and recovery by making prevention more efficient.
Toolkits already play an important role in consolidation CISOs tell VentureBeat that 2023 became the year of consolidation, coincident with rising interest rates and spiraling inflation. CrowdStrike and Palo Alto Networks were ahead of the curve, using their user events in 2022 to sell consolidation as a growth strategy.
Forrester has written about today’s cybersecurity staffing challenges and the resulting consolidation security products protecting the endpoint. He points out that including vulnerability and patch remediation or secure configuration management in endpoint security reduces the number of tools needed to maintain a proper endpoint security posture, helping CISOs achieve their consolidation and cost-reduction goals.
Endpoint protection helps accelerate the transition from EDR or XDR EDR platforms that support data independence and portability are critical for the long-term success of an endpoint strategy and the long-term success of any XDR platform. Harrington cautions that migrating from an EDR to an XDR platform should not require reconfiguring endpoints. The greater the coverage across different attack vectors, the simpler and more scalable incident correlation becomes, with the mean time to resolution shortened.
Forrester’s take on the market leaders Wave leaders include CrowdStrike, Trend Micro, Bitdefender and Microsoft. Forrester broke down their strengths and weaknesses.
CrowdStrike is a strong fit for enterprises migrating from EDR to XDR Forrester writes in the report that “CrowdStrike is a good fit for customers who are interested in evolving to EDR or XDR, based off of a full set of prevention functions using a single endpoint agent.” CrowdStrike is well-known as an enterprise-ready endpoint security solution, and Forrester found that the company’s inclusion of functions like secure configuration management and reporting and extensive attack remediation capabilities has made this an attractive endpoint security solution even for small and medium-sized business (SMB) customers.
CrowdStrikes’ additional module pricing could make their solution higher-priced, and customers are concerned that their recent acquisitions may not integrate with the core platforms. CrowdStrike customers praised the core endpoint security capabilities and their ability to stop attacks quickly.
Trend Micro: A Veteran in endpoint security with a strong focus on innovation and XDR Forrester gives high marks to Trend Micro for their reputation with customers as an endpoint security solution “that just works.” Forrester found that Trend Micro’s move from the on-premises Apex One solution to the cloud-native Trend Vision One — Endpoint Security continues to support features across both environments.
Trend Micro also invests heavily in R&D, including for its XDR platform. Trend Micro customers rated the company as the best vendor to work with among all their security solution providers. Forrester found that “Trend Micro is a good fit for customers who want a consistently strong endpoint protection platform that can support evolving to XDR.” Bitdefender: A prevention-first endpoint security tool with flexible pricing Bitdefender’s expertise with prevention engines sets the company apart from other leaders, further strengthening their prevention-first mindset from product development to services. Forrester found that Bitdefender further differentiates itself in its expertise in mobile threat defense, integrated patching, vulnerability management and reliance on a single agent for all functions. Forrester notes that Bitdefender’s vision “is on par with most of the field on moving to XDR, but the roadmap doesn’t have the depth of others.” Microsoft a strong fit with less experienced security staff E3 and E5 are Microsoft licensing frameworks with which Defender for Endpoint is priced. The E5 license is designed for large organizations that require advanced security features and compliance capabilities. Forrester gives Microsoft credit for a strong roadmap for endpoint security that includes expanding Defender functionality to operational tech (OT) and IoT devices and continuing its strategy of building an extensive partner community. Microsoft’s vision for Defender is both simple for SMBs and detailed for global enterprises. Still, its licensing models are the most challenging in the industry, with advanced features requiring enterprise agreements.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Cybersecurity startup ConductorOne grows series A funding to $27M to boost least privilege access | VentureBeat"
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"https://venturebeat.com/security/cybersecurity-startup-conductorone-grows-funding-boost-least-privilege-access"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Cybersecurity startup ConductorOne grows series A funding to $27M to boost least privilege access Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
ConductorOne has expanded its series A funding by $12 million, bringing the total to $27 million, on the strength of customer growth and product momentum. The company will use the funding to fuel product development and build out its go-to-market teams.
Felicis led the expanded series A. ConductorOne CEO Alex Bovee told VentureBeat, “It was an opportunity to partner with a great firm, so we’re pretty excited about it.” He explained that after meeting with Felicis, “We just immediately clicked. We really loved the team.” With Felicis’ security ecosystem expertise and connections, ConductorOne is well positioned to reinvent identity and access management.
Strong early traction validates the company’s differentiated integration and developer-centric approach. By balancing the need to shift security left in the CI/CD process with the need to improve user productivity beyond what traditional solutions can do, ConductorOne’s dev-first model with extensive integrations is proving effective across its customer base.
ConductorOne: An integration powerhouse Integrations are key to managing access and enforcing least privilege in cloud, hybrid and on-premises environments.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! ConductorOne recognized this challenge early on. With over 75 integrations and an open-source integration protocol it invented called Baton , the startup is quickly covering the most important SaaS and infrastructure apps to help companies manage permissions across their entire environments.
“One of our greatest technical achievements is our ability to produce integrations. We believe, first and foremost, that securing identity and access requires the need to integrate with any technology,” Bovee told VentureBeat during a recent interview.
One of ConductorOne’s core strengths is that its architecture is designed to deliver integration at scale. Bovee told VentureBeat that it could produce several new integrations monthly using its Baton SDK.
“Our Baton SDK is designed to quickly build new connectors to any app based on customer needs,” he told VentureBeat. Further strengthening its integration strategy, ConductorOne has open-sourced its Baton integrations, “because we believe that, at the end of the day, this is a protocol. Baton is the connective fabric and protocol that powers identity security posture management.” VentureBeat believes open source is the future of integration at scale. ConductorOne’s decision to make Baton an open-source platform allows CIOs, CISOs and the enterprises they serve to audit behaviors and data access. An open-source identity security protocol enables greater extensibility for every connector in a network, enabling more effective customized sync, discovery and provisioning logic workflows that are unique to every business’s approach to managing its tech stacks.
Automating cloud PAM for developer workflows Capitalizing on its successful track record and expertise in integration, ConductorOne recently launched a new cloud privileged access management (CPAM) solution tailored for modern teams with a developer-first focus. Bovee told VentureBeat the new platform automates permissions and enforces least privilege access policies across cloud infrastructure, including AWS, Google Cloud Platform and Snowflake as well as existing hybrid cloud, on-premises and legacy systems.
One of ConductorOne’s core goals for the release was to provide developers a platform that would allow them to secure cloud access without friction or standing privileges that increase risk.
By designing its cloud-based PAM platform specifically for developers, ConductorOne enables its customers to take a dev-centric approach to cloud access security. This is key to giving development teams an adaptive, flexible approach to just-in-time access that matches their needs and work cadence while staying within security guidelines. Bovee says permissions are granted on demand, and revoked once the task is complete, to minimize standing access, further strengthening the platform’s ability to deliver least privilege access at scale.
The new capabilities live within ConductorOne’s unified identity security posture management (ISPM) platform, making it possible to govern sensitive access to all cloud infrastructure without impeding developer productivity.
Enabling zero trust for dev teams By automating least privilege controls in infrastructure environments, the platform supports the core zero-trust principles defined in the NIST 800 – 207 standard. Just-in-time privileged access and automated de-provisioning minimize standing privileges that could be misused, further strengthening his company’s platforms’ contributions to customers’ zero-trust frameworks.
Granular policies that are designed in and core to ConductorOne’s platform enforce access only to specific resources needed for a task at a specific time. Members of developer teams are granted temporary credentials to deploy code to production servers and are not provided standing access afterward. Bovee noted that this strengthens customers’ zero-trust frameworks by verifying explicit authorization for access.
VentureBeat has seen how important it is to get integrations right across a wide spectrum of tech stacks across multiple industries. The reason is that they provide continuous visibility of permissions across cloud infrastructure, SaaS apps and on-premises systems, which is essential to ensuring zero trust. The greater the integration, the greater an organization’s ability to monitor identities and configure least privilege access policies.
The bottom line is that these capabilities taken together are table stakes for getting least privilege access right amid rapid changes to resources and workflows. ConductorOne’s platform aims to embed zero trust into modern CI/CD pipelines and cloud-native infrastructure, enabling stronger security postures in the process. “We have this concept we call ‘shift left identity,’ and the idea is that the best way to prevent an identity breach in the first place is not to detect and remediate it, but to prevent it from happening in the first place. That’s the idea of shifting left,” Bovee told VentureBeat.
Investor confidence ConductorOne’s $27 million series A funding round confirms investor confidence in the company’s vision to reinvent legacy IAM, IGA and PAM solutions. The latest funding will be used to accelerate the development of the company’s cloud-native identity security platform. By reinventing IGA and PAM for the cloud, ConductorOne aims to secure workforce access across all applications an enterprise uses.
The shift from perimeter security to identity-centric zero-trust models mirrors the company’s trajectory. ConductorOne is expected to be a leading vendor in transforming how integration is done at scale and securely across enterprises while ensuring developer teams achieve least privilege access at scale — a core element of a strong cybersecurity posture and a hardened zero-trust security framework.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"CrowdStrike defines a strong vision for generative AI at Fal.Con 2023 | VentureBeat"
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"https://venturebeat.com/security/crowdstrike-defines-a-strong-vision-for-generative-ai-at-fal-con-2023"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages CrowdStrike defines a strong vision for generative AI at Fal.Con 2023 Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Cyberattacks are entering a new phase in which identities are the weapon of choice and the cloud is the new battleground. Attackers are turning speed, stealth and weaponized AI into a devastating advantage. The weaponization of AI for everything from social engineering to ransomware attacks launched with Living-off-the-land (LoTL) techniques that rely on Powershell, PsExec, Windows Management Interface (WMI) and other common tools is rapidly accelerating.
The threatscape is moving faster than many organizations can keep up with, made all the more challenging by internal complexities and multiple sources of threat data.
All these challenges call for a faster-responding, preemptive cybersecurity deterrence and resilience strategy.
CrowdStrike strengthens its cyber fighting arsenal CrowdStrike knows those challenges well, as the company has defended its customers throughout a series of challenging, turbulent years of attacks. Keynotes and presentations at CrowdStrike Fal.Con 2023 brought those challenges into sharp focus with leaders defining a strong vision for how generative AI can strip away complexity and foster IT and security collaboration to improve response times. Nation-state attacks are on the rise, as are faster-moving social engineering, deepfake, vishing and pretexting attacks.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Known for the depth of its AI, machine learning (ML) and DevOps expertise, CrowdStrike often relies on Fal.Con as a launch event for their latest generation products and services. To that point, twelve new announcements are being made at this week’s Fal.Con.
These announcements include CrowdStrikes’ acquisition of Bionic and several launches and updates including: Charlotte AI Investigator, Collaborative Incident Command Center, Falcon Data Protection, Falcon Exposure Management, Falcon for IT and FalconFoundry, a new no-code application development platform.
Additional announcements include FalconCloud Protection, FalconFlex Licensing and the Raptor Release for the next-generation Falcon platform. CrowdStrike also introduced extended detection and response (XDR) for All and XDR Incident Workbench, which features an improved investigation interface and workflows.
Complexity kills, speed is the cure One of the core themes of Fal.Con 2023 is how adversaries concentrate on compromising complex cloud configurations. CrowdStrike reports that cloud exploitation by adversaries increased 95% year-over-year. The more complex a cloud configuration, the greater the chances they’re misconfigured and the harder it is to find the error even after a breach.
“The speed at which these threat actors operate is unparalleled — the ability to leverage social engineering, the ability to get in, the ability to move out laterally in many cases,” CrowdStrike president, CEO and cofounder George Kurtz told VentureBeat. “I think they know the network better than the system administrators know the network.” CrowdStrike says that 62% of all interactive intrusions they observed in the last 12 months began with identity-based attacks. In Q2 alone, CrowdStrike observed increased momentum of attacks with tactics, techniques and procedures (TTPs) similar to recent high-profile attacks on critical infrastructure organizations. Integral to CrowdStrikes’ strategy is the use of AI to gain greater insights from all available telemetry sources — including human observations — to better detect and respond to identity-based attacks.
CrowdStrike is setting a fast pace in the generative AI cybersecurity race Kurtz emphasized that CrowdStrike has always been an AI-native company and that they intend to keep strengthening that as a core part of their DNA. The highlight of his keynote was a series of demonstrations of Charlotte AI Investigator, a new gen AI assistant. Charlotte AI brings the power of conversational AI to the Falcon platform to accelerate threat detection, investigation and response through natural language interactions. Charlotte AI generates a large language model (LLM)-powered incident summary to help security analysts save time analyzing breaches.
As part of the development process, Kurtz visited customers and spent half a day in their Security Operations Centers (SOCs) to see first-hand what analysts are dealing with. Based on Kurtz’s research, Charlotte AI was designed to significantly reduce the time required for security analysts to investigate and respond to threats. Kurtz mentioned that the tool is powered by massive datasets and human-validated threat intelligence.
Charlotte AI will be released to all CrowdStrike Falcon customers over the next year, with initial upgrades starting in late September 2023 on the Raptor platform.
CrowdStrike’s chief product officer Raj Rajamani pointed out that Charlotte AI helps make security analysts “two or three times more productive” by automating repetitive tasks. Rajamani told VentureBeat that CrowdStrike has invested heavily in its graph database architecture to fuel Charlotte’s capabilities across endpoints, cloud and identities.
Bionic strengthens CrowdStrike’s cloud security portfolio Cloud exploitation attacks are growing 95% year-over-year as attackers constantly work to improve their tradecraft and breach cloud misconfigurations. It’s one of the fastest-growing threat surfaces CrowdStrike tracks in its annual global threat reports.
To help address this problem, CrowdStrike acquired Bionic for its application security and posture management as it looks to strengthen its cloud workload protection strategy while driving new revenue from cloud security.
During the latest CrowdStrike earnings call , Kurtz said that net new annual recurring revenue (ARR) growth for Falcon Cloud Security accelerated to 70% quarter over quarter. He added that the cloud security market opportunity is massive and growing rapidly, with the potential to reach $18 billion in calendar year 2026.
CrowdStrike continues to see strong momentum on the cloud, and acquiring Bionic delivers a complete view of all activity while protecting what’s running in the cloud. The acquisition also helps strengthen CloudStrikes’ ability to sell consolidated cloud-native security on a unified platform.
What’s unique about Bionic is its ability to analyze cloud apps and infrastructure without needing source code access or instrumentation. Kurtz mentioned during his Fal.Con keynote how essential Bionic is to CrowdStrike’s platform strategy: It can provide real-time visibility into risks and misconfigurations. It is also known for its ability to provide app-level protections focused on cloud architectures, making it a strong fit for CrowdStrikes’ customer base of cloud-first organizations.
CrowdStrike’s strategy of selling platform consolidation is working Based on this week’s announcements at Fal.Con 2023, it’s evident that CrowdStrikes’ strategy of providing customers a path to consolidating their tech stacks is working.
By consolidating tools onto Falcon, organizations improve their security outcomes and productivity while reducing costs and complexity. VentureBeat spoke with CrowdStrike customers who said they successfully reduced the number of multiple agents on endpoints while gaining greater visibility across their IT infrastructure. While many competing vendors — including Palo Alto Networks — are attempting this strategy, CrowdStrike’s approach is differentiated by its commitment to keeping it platform open down to the chipset and silicon level.
CrowdStrike’s strategy of having an open, extensible ecosystem that can adapt and flex to the unique needs of its customers is one of the factors driving its success. A proof point is from its latest earnings call, when the company reported subscription customers with five or more, six or more, and seven or more modules increased to 63%, 41%, and 24% of subscription customers, respectively.
“In Q2, we closed over 80% more deals involving eight or more modules than a year ago as customers increasingly look to CrowdStrike to consolidate their security stack,” Kurtz said on the earnings call.
CrowdStrike exceeded guidance in Q2’24 with 37% revenue growth and delivered a record 21% non-GAAP operating margin. The company expects to sustain this profitability in the future, exiting Q4 within their target model.
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"Building more cyber-resilient satellites begins with a strong network | VentureBeat"
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"https://venturebeat.com/security/building-more-cyber-resilient-satellites-begins-with-a-strong-network"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Building more cyber-resilient satellites begins with a strong network Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
In the current global cyber cold war, nation-states prioritize taking control of another nation’s satellite infrastructure and destroying it or rendering it useless.
Shutting down a competing nation’s satellites stops real-time communications, cuts off situational awareness of operating units across militaries and halts navigation. Today, denying a competing nation’s access to space is quickly becoming the most dangerous weapon in the stealth world of cyber warfare.
Satellites and access to space are essential for national security. By 2030, there will be an average of 1,700 satellites launched per year and governments will continue to fund 75% of satellite manufacturing and launching. The global satellite communication (SATCOM) market size was estimated at $77B in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 9.7% from 2023 to 2030.
Why satellites are strategic targets The U.S. Defense Intelligence Agency writes in its 2022 Challenges to Security in Space report : “Space is being increasingly militarized. Some nations have developed, tested and deployed various satellites and some counter-space weapons. China and Russia are developing new space systems to improve their military effectiveness and reduce any reliance on U.S. space systems.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! The agency cites known physical and cyberattacks on ground-infrastructure, space situational awareness sensors that can monitor and target satellites and attempts at jamming navigation and communication satellites. Directed energy weapons that can blind imagery satellites, anti-satellite weapons ( ASAT ) missiles that can destroy low earth orbit (LEO ) satellites and create dangerous debris and orbital weapons that can damage or tamper with satellites either are in development or have been deployed.
Chinese cyber attackers have long been targeting U.S. satellites and the disruption of NOAA satellite data is are example. Nation-state attackers continue to fine-tune their tradecraft in an attempt to disrupt ground control stations, jam or spoof satellite communication links, deliver malware into satellite control systems and use AI to find new attack patterns that will go undetected.
“Hybrid satellite networks (HSNs) are increasingly becoming a target for cyberattacks because they offer unique challenges for attackers,” Jeff Hall, principal security consultant and North American aerospace lead at NCC Group , told VentureBeat.
The National Institute of Standards and Technology ( NIST ) explains that “the space sector is transitioning towards HSN, which is an aggregation of independently owned and operated terminals, antennas, satellites, payloads or other components that comprise a satellite system.” NIST framework required to reduce threat surfaces and close gaps With competing nations stepping up their efforts to control access to space, it’s timely that NIST’s National Cybersecurity Center of Excellence has released its most recent report designed to guide the wide spectrum of space stakeholders who all contribute to the security posture of HSNs.
NIST’s interagency report NIST IR 8441, Cybersecurity Framework Profile for Hybrid Satellite Networks provides a cross-functional framework for improving infrastructure security, hardening security for assets, data and systems, and reducing the cyber risks to HSNs.
Integrating more systems creates more breach risks, a point any CISO could readily identify with. NIST releasing their profile now indicates how high a priority it is to harden existing satellites in orbit and protect new ones under development, many of which are classified.
The interagency report provides prescriptive guidance on performing assessments, following cyber principles and detecting disturbances or corruption of HSN data and services. NIST also provides a section on responding to cyber incidents through planning and recovering for an intrusion or reach using contingency planning and restoration. The framework also covers interfaces, including antenna fields, payloads, user terminals, virtual machines and cloud-hosted software.
“Space technology — similar to manufacturing, energy and much of critical infrastructure — sits firmly in the hybrid space (software-based applications accompanied by physical systems and hardware),” Merritt Baer, Lacework field CISO told VentureBeat. “This presents unique security challenges.” Baer pointed out that NIST has some common sense guidance in this area: Visibility of systems is imperative, and will allow defenders to see anomalies and act on them. It is critical to correlate data, create meaningful alerts and drive better security outcomes.
Encryption, hardened endpoints and IAM critical for satellite protection Hall of NCC explained to VentureBeat that encryption must be used to protect sensitive data. This includes encrypting all data in transit and at rest and using strong encryption algorithms. He also advised implementing network segmentation and security controls to restrict traffic between segments, monitoring HSN networks for suspicious activity, using intrusion detection and prevention systems to monitor network traffic for malicious activity and having an incident response plan in place to identify, contain, eradicate and recover from cybersecurity incidents.
Hall’s insights reflect the importance of getting basic cybersecurity hygiene right, improving identity management and hardening endpoint security. Treating every identity as a new security perimeter can help reduce the worst threat in confidential networks that build and deploy satellites: Insider attacks.
Ninety-two percent of security leaders say internal attacks are as complex or more challenging to identify than external attacks.
Ivanti’s Press Reset: A 2023 Cybersecurity Status Report found that 45% of enterprises suspect that former employees and contractors still have active access to company systems and files.
“Large organizations often fail to account for the huge ecosystem of apps, platforms and third-party services that grant access well past an employee’s termination,” said Srinivas Mukkamala, chief product officer at Ivanti.
Leading IAM providers include AWS, CrowdStrike, Delinea, Ericom, ForgeRock, Google Cloud, IBM, Microsoft Azure Active Directory, Palo Alto Networks and Zscaler.
Satellites take self-healing endpoints to a new level Achieving greater cyber-resilience starts with the design of an endpoint. In the case of satellites, they need to be able to shut themselves down, re-install system software then refresh all applications. In essence, they are the ultimate self-healing endpoint.
The same lessons learned from designing and launching a satellite need to apply to every endpoint that an HSN relies on to securely function and support satellites in orbit and those about to be launched. Securing telemetry and advanced monitoring data is essential. Endpoint providers are doubling down on AI and machine learning (ML) to improve endpoint detection, response and self-healing capabilities.
Leading self-healing endpoint providers include Absolute Software , Akamai, BlackBerry, Cisco, Malwarebytes, McAfee and Microsoft 365.
The provider most satellite-like in its ability to regenerate endpoints is Absolute, which is installed in more than 500 million endpoint devices and provides security teams with real-time telemetry data on the health and behavior of critical security applications using proprietary application persistence technology.
Absolute Software’s Resilience is noteworthy for its asset management, device and application control, endpoint intelligence, incident reporting, compliance and its industry-first self-healing zero-trust platform.
Staying at parity in the cybersecurity cold war starts with endpoints International tensions regarding Taiwan, Ukraine and the balance of power across key regions of the world are escalating. Undoubtedly, satellites used for monitoring nations’ operations are of even more interest than what’s happening on the ground. That’s why having the NIST standard now is so important. Getting the basics of cybersecurity strategy right is a start, and ensuring every satellite — the ultimate endpoint — is secure, hardened and capable of rebuilding itself in flight is essential.
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"AI needs human insight to reach its full potential against cyberattacks | VentureBeat"
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"https://venturebeat.com/security/ai-needs-human-insight-to-reach-its-full-potential-against-cyberattacks"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages AI needs human insight to reach its full potential against cyberattacks Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Socially engineered attacks are sidestepping millions of dollars worth of cybersecurity systems. Simple phone calls help attackers steal access credentials and impersonate identities at will across networks.
The tradecraft behind the attacks on Clorox , MGM and many others prove that crunching real-time telemetry data faster isn’t the answer alone. Attackers simply studied MGM employee profiles on LinkedIn, then impersonated them to the gambling giant’s IT helpdesk. Shutting these attempts down requires a balance between the contextual intelligence humans provide and AI-based data analysis and risk prediction.
A key takeaway from CrowdStrike’s Fal.Con 2023 conference is the importance of integrating AI and human insights at scale to battle breach attempts that are accelerating faster than cyber defenses.
“The speed at which these threat actors operate is unparalleled,” CrowdStrike president, CEO and cofounder George Kurtz told VentureBeat during Fal.Con 2023 last week. “The ability to leverage social engineering, the ability to get, in the ability to move out laterally — I think [attackers] know the network better than the system administrators know the network.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! How combining human insight and AI prevented one city from being breached Experiencing a breach attempt and having it thwarted using AI-based predictive analysis and human insight makes CIOs and CISOs believers.
Case in point: A human in the loop recently stopped a breach of one of the fastest-growing municipalities in the southwestern U.S. after attackers obtained administrative-level privileged access credentials and attempted to breach the city’s infrastructure.
The city’s CIO explained to VentureBeat on the basis of anonymity that they had just implemented CrowdStrike’s Falcon XDR platform with Overwatch Elite to monitor all systems and endpoints. Threat hunters working on the Overwatch Elite teams identified suspicious activity around 9 p.m. one evening and sent an alert to CrowdStrike. The team continued to monitor the attempted hands-on-keyboard breach activity until the CIO could be reached.
Within four hours, the CIO, IT and security teams had investigated and resolved the issue. In stopping what could have been a debilitating cyberattack, the city’s CIO said the Overwatch Elite team is force-multiplying his small team by providing real-time monitoring, reporting and interpretation of threats quickly detected by AI and ML techniques. Threat hunters continually tracked the breach attempt and saved the city’s infrastructure from a breach by providing their insight and contextual intelligence.
Generative AI cyber defenses must be learned Training the large language models (LLMs) that gen AI relies on takes time, and it is expensive. That’s why getting it right first and integrating human and machine data is critically important.
Combining human insight with AI and machine learning (ML) models catches attack patterns, nuances and anomalies in behavior that elude numerical analysis alone. Training models both reduces noise and extraneous data to provide greater accuracy and speed in responding to breaches.
Leading cybersecurity providers developing and delivering gen AI-based apps and tools include CrowdStrike, Cybereason , Darktrace , Fortinet , Microsoft , Palo Alto Networks , SparkCognition and Tessian.
“Based on behaviors and insights, AI and ML allow us to predict [that] something will happen before it does,” said Monique Shivanandan, CISO at global bank HSBC.
“It allows us to take the noise away, focus on the real issues happening, and correlate data at a pace and a speed unheard of even a few years ago.” Kurtz’s demonstration of Charlotte AI Investigator during his keynote illustrated how powerful gen AI can be when continually learning and assimilating new knowledge into its LLMs. CrowdStrike is well known for its large library of human-written reports (including an extensive adversary library), the depth of its data on hundreds of incident response engagements and ongoing experiences gained by the Falcon OverWatch Threat Hunting teams. All telemetry and experimental data is being captured into LLMs to help customers get the insights and knowledge they need in minutes.
Demand for external threat intelligence service providers The Charlotte AI Investigator summarized thousands of pages from CrowdStrike intelligence reports. Included in the assessment were inactive licenses, non-compliant assets, a comprehensive list of all assets on the network and an in-depth analysis by CVE of suspicious activity and lateral movements on the network.
Forrester found that enterprises hve, on average, seven commercial threat feeds , one of the factors driving demand for external threat intelligence service providers (ETISPs).
The twelve leading providers competing in this market are fast-tracking gen AI and ML algorithms to improve their speed at aggregating, analyzing and customizing threat intelligence in human and machine-readable formats and improving APIs for integration.
Forrester identifies leading ETISPs companies as CybelAngel , Flashpoint , Fortinet , Google , IBM , Microsoft , Rapid7 , Recorded Future , ReliaQuest , Trelix and ZeroFox.
AI is table stakes for Managed Detection and Response (MDR) VentureBeat continues to see strong adoption of managed detection and response (MDR) services across short-staffed mid-tier financial services, government, healthcare and manufacturing organizations.
CISOs have long told VentureBeat that reduced security operations costs, improved threat detection and faster investigation and response, along with increased security expertise, make partnering with an MDR a solid business case. Additionally, service level agreements (SLAs) that include 24/7 monitoring and response, guaranteed uptime, real-time analysis of security outcomes and continued improvements in AI techniques further increase MDR value.
Integrating AI, ML and human intelligence as a service is one of the fastest-growing categories in enterprise cybersecurity. MDR spending reached $3.24 billion in 2022, achieving a 26.2% growth rate.
Gartner predicts MDR will continue to see above-average market growth, achieving a compound annual growth rate (CAGR) of 25% through 2026.
Based on conversations with CrowdStrike customers at Fal.Con 2023, AI is now considered the DNA or core of an effective MDR partnership. One CISO went as far as to say that AI is table stakes for how they are evaluating MDR providers. By 2025, 50% of organizations will use MDR services that provide threat monitoring, detection and response functions on AI and ML-based platforms. By 2025, services such as prebreach cybersecurity validation assessments and security posture advisory will be offered by 35% or more of MDR service providers.
More than 60 MDR providers compete today, with more adjacent cybersecurity services firms entering the market monthly. Each differentiates primarily on incident response capabilities and track record of stopping breaches in a specific industry.
Others differentiate themselves based on how quickly they can adopt gen AI tools and ML models to improve threat detection and response. Advisory services including OT/IoT monitoring are common, as are unique underlying threat detection technologies. Leading MDR vendors include Accenture , Binary Defense , Deepwatch , Forescout , Kudelski Security , Pondurance , ReliaQuest , Sophos , Trustwave and WithSecure.
Cyber fighting stronger when combining human insight, generative AI, speed Cyber fighting with data alone leaves CISOs, CIOs and the organizations they serve at a disadvantage against adversaries who are sharpening their tradecraft to deliver devastating attacks at extremely fast speed. It’s not enough to rely on real-time data telemetry-based warnings of anomalous behavior or breaches.
Cybersecurity needs human insight from experienced threat hunters. While cybersecurity professionals express concern over AI taking their jobs, there’s paradoxically never been a time when they have been more necessary. Sophisticated social engineering attacks focusing on an organization’s most vulnerable threat vector — people — will continue to grow.
When a phone call can bring down a casino for days, there’s much more work to be done to combine human insight and AI.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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"The cyber risks of overheating data centers | VentureBeat"
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"https://venturebeat.com/data-infrastructure/the-cyber-risks-of-overheating-data-centers"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages The cyber risks of overheating data centers Share on Facebook Share on X Share on LinkedIn Illustration by: Leandro Stavorengo Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The heat is on. Climate change creates new challenges for data centers while exposing a new vulnerability that attackers can quickly weaponize. The burning problem of overheated servers caused by record heat waves has melted down data centers from Los Angeles to London.
Many data center cooling systems weren’t designed to withstand the heat waves the world is experiencing today. Cooling systems are failing under the strain, allowing servers to overheat, leading to many of the world’s most popular websites and applications crashing.
Attackers want to weaponize heat Companies who trade off lower energy costs for running a slightly hotter data center are inviting a breach or, at the least, a data center meltdown. No one cost-reduced their way into a secure data center. Sustainability is the path away from spiraling energy costs.
Attackers aim to weaponize heat and exfiltrate billions of dollars in data from data centers by attacking cooling systems. From cybercrime groups to sophisticated Advanced Persistent Threat (APT) attack teams, many funded by nation-states expect more data center attacks where heat is the attacker’s weapon.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Don’t invite cyber risk by overheating data centers Datacenter costs continue to spiral to record levels for many companies, with energy costs outpacing all other expense categories. Making cooling as efficient as possible is critical to data center profitability. Cooling accounts for approximately 40 percent of a data center’s energy consumption. While data centers continue to make strides in improving energy efficiency by phasing in sustainability, starting with improved cooling methods, many are introducing greater cyber risk by marginalizing how far they could go with sustainability.
“Data centers are big energy consumers—a hyper scaler’s data center can use as much power as 80,000 households do. Pressure to make data centers sustainable is therefore high, and some regulators and governments (including Singapore and the Netherlands) are imposing sustainability standards on newly built data centers,” according to McKinsey.
Despite record levels of capital investment in sustainability , data centers still see overheated servers prone to failure, leading to outages. New cost-effective cooling technologies, including outside air cooling, are cost-effective, yet they can introduce contaminants into a data center infrastructure and potentially damage hardware.
Another approach data centers take to reduce cooling costs is raising server inlet temperatures. It’s a calculated risk that the cost savings will be worth the increased risk of potentially causing server CPUs to fail. It’s well-known in data centers that servers are the single greatest cause of outages, making the cost savings questionable of allowing temperatures to rise. Server outages cause 30% of all data center interruptions and outages. Heat-induced server failures drive unplanned outages that disrupt data center operations and can cause websites, apps, and online storage to fail unpredictably, costing billions of dollars in lost productivity.
VentureBeat interviewed several data center recovery specialists who spoke on condition of anonymity regarding how chronic data center overheating is. They affirmed that data centers are redlining to save on costs, with many struggling to keep server inlet temperatures below 80°F, the consensus standard for server cooling. Cost savings are winning out over reducing cyber risk. “Data centers are in for a wake-up call if climate change continues to deliver triple-digit heat waves and they don’t get serious about long-term, more sustainable and affordable cooling that doesn’t invite more risk,” a leading data center recovery specialist told VentureBeat.
Twitter’s Sacramento data center going offline due to extreme heat in 2022 was prescient of how extreme heat could affect data center performance in the future. In an internal memo to engineers, Carrie Fernandez, Twitter’s vice president of engineering wrote, “On September 5th, Twitter experienced the loss of its Sacramento (SMF) data center region due to extreme weather. The unprecedented event resulted in the total shutdown of physical equipment in SMF.” Fernandez says that the company’s data center was in a “non-redundant state” after extreme heat caused an outage at its Sacramento data center. She called the incident “unprecedented” and said the heat wave led to “the total shutdown of physical equipment.” The Twitter outage originated due to an extreme heat wave. Cyberattackers noticed this and other extreme heat-based outages and continue to fine-tune their tradecraft to attack HVAC, electricity and redundant power systems.
Specialists cite an incident in 2021 as a cautionary tale of redlining server heat to save on costs. A data center operator in Singapore raised temperatures to borderline unsafe levels to save on cooling costs, leading to the data center servers melting down and widespread server failures. The meltdown lasted nearly a week, leading to thousands of customers experiencing outages.
Data center attacks that weaponized heat Attackers are fine-tuning their tradecraft and creating malware that attacks cooling systems to force a data center meltdown to get their ransomware demands met or make a political statement.
A data center in Atlanta, Georgia, was hit with a cyberattack in 2018 that led to the shutdown of several city services, including the municipal court, the police department and the Hartsfield Atlanta airport. Cyberattackers used a variant of SamSam ransomware designed to encrypt data on every available server. Attackers also penetrated the data center’s cooling system, causing temperatures to rise above 100 degrees, damaging server CPUs and related silicon-based equipment. Cyberattackers demanded a $51,000 Bitcoin to unlock servers and release their cooling system control.
An Iranian data center was the victim of a cyberattack in 2019 that disrupted its power supply and cooling systems, causing servers and supporting systems to overheat quickly. An adversarial nation opposing Iran’s nuclear program took responsibility for the attack, using the malware program Stuxnet designed to target and bring down industrial control systems. Iranian data center operators say the malware caused the centrifuges at the data center to spin out of control and break down.
A data center in Singapore was attacked in July 2022, disrupting several government agencies, banks and media outlets’ online servers. Attackers exploited a firewall vulnerability, causing servers to malfunction due to overheating. An Indonesian hacking group took responsibility for the attack, claiming it was in retaliation to Singapore’s ongoing support of Myanmar’s military junta.
Striking a balance between security and sustainability Data centers face the challenging paradox of continually increasing storage volume, reducing access latency, controlling costs and finding new ways to harden themselves from cyberattacks. Adding to the challenges is the pressure data centers are to reduce their environmental impact and energy consumption, as data centers account for about 1% of global electricity use and about 0.3% of global greenhouse gas emissions. Data center operators are creating innovative new strategies to achieve these challenging goals. They include relying more on renewable energy sources, water-efficient cooling systems and waste heat recovery technologies to improve sustainability.
VentureBeat has learned that the following strategies are paying off the most from data center owners and recovery experts implementing these programs: Get in the habit of conducting detailed thermal mapping to identify hot spots and optimize cooling.
Datacenter recovery specialists say this is a blind spot for many data center operators who procrastinate getting thermal mapping done periodically. Given how quickly servers can degrade over time when exposed to extreme temperatures, it’s a good idea for this task to become part of any data center’s muscle memory.
Consider how AI can help improve power consumption, strengthened with eco-friendly chillers and evaporative cooling.
The benefits AI can bring to the data center are just beginning, according to the experts and data center operators VentureBeat spoke with. One considered AI optimization critical to their success in meeting sustainability benchmarks needed to achieve internal and regulatory standards benchmarks. Cautious of exceeding server inlet temperatures, more data centers are also using AI to interpret and trigger alerts and actions in real time, adjusting dynamically to prevent overheating while maximizing efficiency.
Redundant cooling systems with fault-tolerant power sources are the future of data center cooling.
It’s undeniable that the upsurge in heat waves and the data center failures across Europe, the United States, and the major one in London last summer are leading indicators of an entirely new type of temperature challenge data centers must take on.
Using AI to optimize data center asset inventories is gaining traction.
It’s a perfect use case for AI and machine learning (ML) algorithms that can be trained to optimize hardware and system configurations for an increasingly complex series of constraints that data centers need to operate within. Using AI-based optimization techniques can factor in sustainability requirements, resource loads and cooling requirements by server CPU, all focused on creating the optimal environmental conditions for a data center to perform at peak performance.
Data centers are in a race to improve cybersecurity and sustainability.
As the data center industry strives to reduce its environmental footprint, it must balance sustainability and cyber-resilience goals. Sustainable solutions like outside air cooling, for example, that deliver energy savings, can amplify security risks if not managed as part of a broader data center cybersecurity plan.
In the race to improve data center sustainability, the operations and the companies operating them can’t lose sight of securing cooling and infrastructure without sacrificing them for cost savings. It’s time to embrace sustainability over risk.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Securing generative AI starts with sustainable data centers | VentureBeat"
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"https://venturebeat.com/data-infrastructure/securing-generative-ai-starts-with-sustainable-data-centers"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Securing generative AI starts with sustainable data centers Share on Facebook Share on X Share on LinkedIn Illustration by: Leandro Stavorengo Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Enterprises are increasingly experiencing attacks on their artificial intelligence (AI) infrastructure, with 41% having experienced an AI privacy breach, according to an August 2022 Gartner report.
Twenty-five percent have experienced malicious, intentional attacks on their AI systems and infrastructure.
Cyberattacks aimed at AI infrastructure most commonly focus on data poisoning (42%), adversarial samples (22%) and model stealing (20%).
Despite the growing number of cyberattacks aimed at their AI infrastructures, enterprises are becoming more prolific in designing, testing and deploying models.
Seventy-three percent have deployed hundreds of models into production, and large-scale enterprises have thousands of models today.
CIOs and CISOs, especially in banking, finance, infrastructure, manufacturing and professional services — where models are increasing the fastest — tell VentureBeat they have concerns about keeping up from a security standpoint with the proliferation of models in development and actively deployed.
Generative AI and machine learning (ML) model security and risk management is a board-level discussion across all industries. The senior management teams of infrastructure, manufacturing, and professional services are focused on gaining greater insight into risks using AI and machine learning.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “Understanding vulnerabilities and gaining insight at both the site and enterprise level will help enable faster and more informed decisions to better defend against cyberattacks, reduce potential downtime and create a safer environment for our employees,” Chase Carpenter, Honeywell chief security officer, told VentureBeat.
Data centers are a high-value AI target Too much focus on cost reduction alone without sustainability designed into data center infrastructure leaves them vulnerable to cyberattacks that capitalize on weak points in infrastructure. Reducing energy costs without a sustainable long-term plan delivers short-term cost savings, but leaves a data center vulnerable to attacks that can shut an entire facility down.
Examples include attacking cooling systems, disabling air flow, and damaging servers, CPUs, and GPUs. Another is assuming web servers, VPN appliances and endpoints are protected without investing in microsegmentation or endpoint security to protect them.
“Cyberattacks from Advanced Persistent Threat (APT) groups that are state-sponsored are ramping up this year; we can see it in our monitoring data,” confided the CISO of a utility provider doing extensive generative AI and ML model development. “We used to see our data centers get attacked sporadically, but now it’s a steady stream of state-sponsored attacks looking to penetrate data centers and see what new AI-based monitoring technologies we have under development.” The utilities CISO says the Chinese cyberattacker group APT41 is active across global utility power grids and is actively looking to gain new generative AI and Ml technologies. Their attack strategies concentrate on using phishing emails and malware to gain access to the networks of power companies and grid operators.
They’re most known in the utility industry for their 2019 cyberattack on data center providers in Asia, and the U.S. APT41 hackers exploited unpatched vulnerabilities in VPN devices, unprotected endpoints and web servers that weren’t protected with basic cybersecurity or zero trust hygiene. APT41 exfiltrated data, including intellectual property, AI and ML model development underway, and patents under development with Asian-based research institutes.
Sustainability needs to deliver stronger cybersecurity With data centers under attack for the valuable generative AI and ML models under development and deployed, a one-and-done mentality never works. CISOs of banking and financial services firms whose data centers see regular state-sponsored attacks say it’s possible to improve sustainability and cybersecurity simultaneously.
“We’re taking a holistic approach to the challenges of becoming more sustainable and hardening our data centers and their many integrations points back to DevOps and engineering,” said the CISO of a professional consulting firm whose clients are in banking. Staying in compliance with broader sustainability initiatives is essential to continually win new business in the years ahead. So is keeping a data center hardened enough so its physical infrastructure can’t be attacked.
Here are the four strategies learned by CISOs and CIOs who have experienced data center breaches aimed at their generative AI and ML model development: Gain greater visibility across every data center asset, including energy usage first.
It’s common knowledge that most enterprises don’t know where 40% of their endpoints are at any given time. In a data center, that’s a breach waiting to happen. CISOs tell VentureBeat that getting real-time visibility of every endpoint and its specific asset management profile is invaluable in helping to alleviate a breach. Tracking the energy consumption of an asset, including the segment of server blocks across their data center floors, helps provide insight into unusually high activity, which could signal the need to upgrade, repair, or replace servers.
Microsegment every physical system the data centers rely on – and optimize their energy spend.
APT41 is known for its expertise in attacking data center cooling systems and driving the temperatures so high that CPU, GPUs, and server silicon risk being destroyed. In retrospect, CISOs tell VentureBeat that micro-segmenting the industrial control systems (ICS) that control heating, cooling, environmental conditions, fault-tolerant batteries and backup systems are a must-have. Assume a breach has already happened and HVAC, environmental and power systems are compromised to harden a data center enough to withstand another attack.
From a sustainability standpoint, every CIO and data center team VentureBeat interviewed for this article says they are advanced in using AI- and ML-based tools to analyze energy usage by asset type and group. What’s missing are insights into how all assets across a data center can be better orchestrated to reduce carbon footprints and how all data centers can be viewed in aggregate to reduce their environmental impact. Boards of directors want the roll-up view of how data centers are progressing towards sustainability and environmental, social, and governance (ESG) targets, and often, CIOs have their teams doing this manually every quarter.
Real-time monitoring is table stakes for making progress on sustainability and cybersecurity.
What was once considered optional and sometimes procrastinated about because of its expense is now the core of an effective sustainability and cybersecurity strategy. CISOs whose data centers have been hacked say that if they had real-time monitoring on every server, asset, endpoint, and power source, they could have identified the intrusion faster and had a chance to stop the breach. The more accurate the telemetry data real-time monitoring provides, the better the threat modeling and models to identify anonymous activity that could indicate an intrusion. Real-time data is the lifeblood of sustainable and secure data centers.
Consolidate data center tech stacks to gain greater efficacy and sustainability.
Data centers that get hacked have complex security tech stacks that experienced cyber attackers know how to find gaps in. It’s common to hear a CISO with a data center breached say that the cyber attackers seemed to know their network better than the admins managing them. VentureBeat has learned that more banking, financial services and professional services firms are basing their consolidation strategies around extended detection and response (XDR).
Ninty-six percent of CISOs plan to consolidate their security platforms, with 63% saying (XDR) is their top solution choice.
Gartner predicts that by year-end 2027, XDR will be used by up to 40% of enterprises to reduce the number of security vendors they have in place, up from less than 5% today. An attribute all XDR leaders have is deep talent density in AI and ML across their teams. Leading XDR platform providers include Broadcom , Cisco , CrowdStrike , Fortinet , Microsoft , Palo Alto Networks , SentinelOne , Sophos , TEHTRIS , Trend Micro and VMWare.
By consolidating tech stacks, XDR also contributes to data centers achieving their sustainability goals. Reducing data centers’ energy consumption and carbon footprints by eliminating redundant security tools and streamlining security operations is key to a successful tech stack consolidation. XDR’s use in data centers is proving effective in improving resilience and reliability by providing faster and more accurate threat detection and response. XDR is helping data centers save up to 50% of energy costs and reduce CO2 emissions by up to 85%.
Additionally, XDR can improve the performance and availability of data center applications by minimizing downtime and disruption caused by cyberattacks.
Hardening data centers is core to generative AI’s future.
Four strategies deliver the most practical value in securing data centers immediately, according to CISOs who have lived through an intrusion and breach attempt. For the utilities CISO being routinely scanned and probed by state-sponsored actors, the need to be vigilant and make the four strategies core to their operations is key. Real-time data and XDR are helping keep intrusion attempts out, and microsegmentation protects HVAC, power, and related subsystems. Data centers whose enterprises are known for generative AI and ML expertise are targets today. From the interviews VentureBeat has had recently, nation-state attacks are ramping up with a primary focus on power grids and related technologies.
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"Nvidia reports record Q2 results driven by surging data center demand | VentureBeat"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Nvidia reports record Q2 results driven by surging data center demand Share on Facebook Share on X Share on LinkedIn Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Nvidia reported record revenue today for its second quarter of fiscal 2024 (ending July 30, 2023), crushing Wall Street’s expectations. The graphics processor and platform provider reported record Q2 revenue of $13.51 billion, up 101% year-over-year and sequentially. Nvidia’s data center segment grew 171% annually and 141% sequentially to $10.32 billion, representing 76% of total revenue for Q2.
Nvidia is proving to be the AI innovation engine enterprises need Nvidia’s ability to orchestrate rapid product development, create strong alliances — including one with VMWare announced earlier this week — and attract software developers to their platform all at the same time are providing to be a formidable force enabling the growth of AI and large language model (LLM) adoption. A solid sign that the strategy is working is how the company has grown its total revenue 229% over the past 5 quarters to a record $13.5 billion in Q2 FY2024 Gross margin also soared to 71.2% in Q2, up from 45.9% a year ago. Increased profits are being reinvested into R&D quickly, as cofounder, president and CEO Jensen Huang said Nvidia is on pace to launch a new product or platform every six months. Rising profitability creates funding capacity to maintain the company’s industry-leading investment in AI innovation.
Exceptionally strong demand for data center products essential to generative AI and the creation of LLMs fueled their record quarter.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “We’re adding new products every six months to address the growing demand for generative AI,” Huang said on the company’s earnings call today. He continued: “The new Nvidia computing era has begun.” Data center revenue soars 171% in a year Data center revenue has nearly tripled over the past five quarters to $10.3 billion, becoming Nvidia’s largest segment, at 76% of revenue. Nvidia defines its data center segment as including server graphics processing units (GPUs), networking and AI cloud software.
Data center revenue continues to be the growth engine Nvidia relies on to fuel its ongoing platform and processor development. The company is considered a leading indicator of AI demand in enterprises. For institutional investors and Wall Street analysts, this validates that gen AI and the demand of LLMs is real.
“Demand is tremendous,” CFO Colette Kress said of data center growth. “Industry and customers are demanding and our visibility extends well into next year.” Kress and Huang noted on the earnings call that the explosive growth Nvidia is experiencing reflects strong demand from hyperscale cloud service providers and large internet companies racing to build infrastructure for LLMs and gen AI, powered by the company’s processors and platform. Kress said the data center business is so strong that the company has the potential to deliver $12 to $13 billion in Q3.
Doubling down on R&D to stay in the lead Kress and Huang emphasized their commitment to rapid product development. They explained that Nvidia is investing heavily in next-generation platforms like its Hopper architecture to maintain its lead in AI workload accelerated computing. Nvidia reported $2.04 billion in R&D spending in Q2 FY2024, up 10% from $1.82 billion in Q2 FY2023, demonstrating its commitment to innovative technologies like Hopper.
The GH100 GPU, Grace CPU Superchip and NVLink interconnect fabric in Hopper boost performance for large AI models and high-performance computing applications. Nvidia is also investing heavily in its AI software stack, including the Nvidia AI Enterprise suite, which makes it easier for organizations to build and deploy Nvidia accelerator-powered AI solutions.
R&D investments and focus on complete AI infrastructure position Nvidia to meet rising enterprise AI demand. Increasing competition highlights the need for technology innovation even as data center revenue rises.
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"Microsoft's AI strategy is paying off with solid cloud revenue growth | VentureBeat"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Microsoft’s AI strategy is paying off with solid cloud revenue growth Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Crediting growing demand for large AI models across consumer and commercial products that are driving increased cloud consumption, Microsoft reported strong FY24 Q1 results last night.
First quarter revenue was up 13% to $56.5 billion compared to $50.1 billion last year.
Net Income soared 27% to $22.2 billion in Q1 from $17.5 billion in the same quarter a year ago. Gross margin expanded to 71%, up from 69% a year ago, driven by Azure and Office 365 gross margin improvements.
Operating Income still grew 25% despite Microsoft’s AI investments , showing the company’s ability to balance growth and profitability.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! FY24 Q1 results reflect how Microsoft successfully turns AI into a revenue accelerator, with Azure’s OpenAI service, Dynamics 365, and the broader Microsoft Cloud Services benefitting. The GitHub Copilot coding assistant now has over 1 million paid subscribers, up 40% quarter-over-quarter, according to CEO Satya Nadella’s comments on today’s earnings call.
Factoring FY24 Q1 financial results with today’s earnings call shows how efficient Microsoft’s DevOps, R&D, software engineering, product management, and cloud deployment teams are. Achieving double-digit growth is a sure sign of enterprise traction for Microsoft’s products, as is the planned announcement of over 100 new AI-powered products and services at its Ignite conference.
Azure delivers 29% revenue growth in Q1 Commercial cloud revenue soared 24% to $31.8 billion, powered by 29% growth in Azure cloud platform revenue in Q1. The Commercial cloud business unit comprises Azure, Office 365, Dynamics 365, and other cloud services.
Azure AI services, including machine learning, bot, and cognitive services, contributed three percentage points to this quarter’s 29% revenue growth. Cognitive services revenue, which includes AI APIs, grew 30% to $1.3 billion. Over 18,000 organizations now use Azure OpenAI service , including new Azure customers, showing Microsoft’s investments in OpenAI and AI in general are working as a strategy to help propel it in the wider cloud wars.
AI is paying off faster than Microsoft expected Microsoft had originally told investors that AI wouldn’t deliver revenue gains until 2024 when new products will become widely available. Its Q1 earnings and insights shared show it is ahead of schedule. Microsoft is accelerating generative AI adoption across its tech stacks after investing $13 billion in OpenAI and seeing competitors launch new AI products.
“We are rapidly infusing AI across every layer of the tech stack and for every role and business process to drive productivity gains for our customers,” said Nadella. Nadella says Microsoft will introduce more than 100 new products and capabilities, including exciting new AI innovations, at its upcoming Ignite conference.
With AI driving revenue faster than expected, Nadella doubled down on a unified architecture vision. During today’s conference call, Nadella emphasized that generative AI is part of a unified tech stack and go-to-market strategy for Microsoft’s suite of products and services.
Nadella was emphatic that despite the proliferation of generative AI and the potential to spin off new services, all Microsoft business units and products will standardize on a shared, common generative AI architecture. That’s good news for enterprise software buyers who will require that as a prerequisite for adding generative AI-based Microsoft products and services across its businesses in 2024 and beyond.
Nadella alluded to enterprise buyers’ growing interest in piloting AI across their organizations. A leading indicator of how much pent-up demand there is across enterprises, Nadella said that Microsoft’s AI-infused productivity suite, Microsoft 365, saw Office 365 commercial revenue grow 18%, driven by 10% seat growth and higher revenue per user.
He continued on the call, saying that Copilot’s capability in Office apps had over 40% of Fortune 100 companies testing it and will reach general availability next week. Microsoft chief financial officer Amy Hood noted that Microsoft expects to sustain double-digit growth for both Office 365 and Azure revenue as more enterprises test and adopt new generative AI-based applications and services.
Is Bing Chat driving Edge adoption? During today’s earnings call, Nadella emphasized that Bing Chat’s many AI-based strengths led to his company’s browser Edge gaining share. It’s a message Nadella has delivered before, most recently in his annual letter.
On today’s earnings call, Nadella emphasized that “Microsoft Edge has gained share for ten consecutive quarters.” He said, “this quarter we introduced new personalized answers as well as support for [OpenAI’s image generation AI] DALLE-3 , helping people get more relevant answers and to create incredibly realistic images. More than 1.8 billion images have been created to date,” Nadella said.
While it is feasible that Bing Chat’s conversational AI capabilities integrated into Edge are driving increased engagement with users. Microsoft contends that early data shows Bing Chat leads to more search queries per user.
VentureBeat researched the claim and found that Bing Chat hasn’t led to market share gains for Edge and may have led to a loss of share.
These two charts show Microsoft Bing’s search market share problem along with a market share analysis completed using Statcounter GlobalStats , show Edge with declining market share in the U.S. over the last five months.
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"Snap adds ChatGPT to its AR with a focus on AI at Lens Fest | VentureBeat"
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"https://venturebeat.com/ai/snap-adds-chatgpt-to-its-ar-glasses-with-focus-on-ai-at-lens-fest"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Snap adds ChatGPT to its AR with a focus on AI at Lens Fest Share on Facebook Share on X Share on LinkedIn Snap is introducing ChatGPT AI to its Lens products.
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Snap said it is adding ChatGPT to its augmented reality Lenses as AI becomes an integral part of consumer products.
In a collaboration with OpenAI, Snap created the ChatGPT Remote API, granting Lens developers the ability to harness the power of ChatGPT in their Lenses. This paves the way for new and engaging learning, conversational, and creative experiences for Snapchatters. In addition, a 3D face mask generator has been integrated, enabling the rapid creation of self-expression Lenses. This amalgamation of Gen AI and advanced face mesh capabilities facilitates the creation of potentially viral Lenses in mere seconds.
At the company’s Lens Fest 2023 event, Snap introduced its Lens Studio 5.0 beta for advanced AR development. There are more than 330,000 developers working on the Snap ecosystem. Snap has sold more than 3.5 million Lenses over time, and Snapchatters viewed them over three trillion times in the past year.
Snap mission is to empower this AR community with the most advanced tools available for crafting the next generation of AR experiences, not just for Snapchat but also beyond, through offerings like Camera Kit and Spectacles.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! The centerpiece of this mission is the reconstruction of Lens Studio, the software that underpins the creation of Lenses.
Lens Studio 5.0 Beta places a significant emphasis on improving performance, especially when developing complex AR projects. Projects now load 18 times faster, streamlining the development process for AR creators.
Lens Studio 5.0 Beta focuses on fostering collaboration within development teams. It now supports version control tools like Git, enhancing project management capabilities. Multiple developers can concurrently work on projects, streamlining the creative process.
Aside from these novel tools, Snap Inc. is dedicated to providing developers with continuous support in building their businesses and audiences on the platform. Last year’s introduction of Lenses with Digital Goods allowed developers to incorporate revenue models directly into their Lenses. Now, Digital Goods are accessible to all developers, enabling the offering of exclusive AR features within Lenses, which Snapchatters can unlock for a fee. A new section within Lens Explorer will actively promote Lenses with Digital Goods, making them easily discoverable and accessible for Snapchatters.
Jeetesh Singh, a member of the Snap Lens Network, said in a statement, “I’m really impressed by how easy it is to use the Lens Studio 5.0 Beta. The start-up speed is lightning fast, and enables developers to open multiple projects at the same time for more efficient workflows. And through the new Generative AI features, asset development is easier than ever before. Now, the AR creation workflow is more streamlined because developers can create assets right within Lens Studio, rather than relying on multiple external tools. The advancement of Gen AI features within Lens Studio opens developers to limitless creativity.” Snap Inc. also expressed its delight in the momentum achieved with the Lens Creator Rewards program, which commenced this summer. The program offers monthly rewards of up to $7,200 for top-performing Lenses in select regions. During its inaugural month, more than 45,000 Lenses joined the program, generating over 5 billion Lens interactions by Snapchatters.
Inna Sparrow, another member of the Snap Lens Network, said in a statement, “The new Lens Studio 5.0 Beta marks a huge step forward for AR development because it puts clever new tools that unlock unlimited possibilities right at your fingertips, all while being incredibly easy for AR developers to use. The new Generative AI features simplify the creation process into one straightforward workflow in Lens Studio, rather than using several external tools like I used before.” GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Nurdle emerges from Spectrum Labs as AI deployment startup for enterprises | VentureBeat"
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"https://venturebeat.com/ai/nurdle-emerges-from-spectrum-labs-as-ai-deployment-startup-for-enterprises"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Nurdle emerges from Spectrum Labs as AI deployment startup for enterprises Share on Facebook Share on X Share on LinkedIn NurdleGPT will help enterprises with AI.
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After selling Spectrum Labs , entrepreneur Justin Davis is back with another AI startup dubbed Nurdle.
The company is coming out of stealth today with the same backers and a focus on transforming the landscape of AI deployment for enterprises.
Nurdle has funding from notable investors like Greycroft, Intel Capital, and Twilio Ventures. The company has stepped out of the shadows after the acquisition of Spectrum Labs by ActiveFence. Interestingly, Nurdle was incubated inside Spectrum Labs.
Leveraging groundbreaking technology, the company seeks to transform the landscape of AI deployment for enterprises, offering faster, cheaper, and more accurate custom language models.
Nurdle is an evolution of the promising project initiated within Spectrum Labs, designed to streamline and refine custom private Large Language Models (LLMs) through a proprietary “lookalike” data technology.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! This approach fuses the precision of human-generated and labeled data with the agility, volume, and cost-effectiveness of synthetic data, rendering custom AI applications more accessible for various businesses.
Davis, CEO of Nurdle, said, “Spectrum Labs was the testing ground for Nurdle’s groundbreaking AI solution. Our technology initially focused on understanding human behavior for content moderation, which was one of the most intricate problems in Natural Language Understanding. With Nurdle, we extend these innovations to bolster AI teams, driving high performance across multiple communication-focused applications.” The company’s methodology for content creation, data labeling, and quality assurance has demonstrated remarkable results, exhibiting a five times to 10 times reduction in data scientist hours while achieving 92% accuracy for human-generated and human-labeled data, and all at a mere 5% of the usual cost. This process has significantly accelerated the time-to-production by several months.
Prior to its emergence, Spectrum Labs, which did text-based content moderating using AI-driven natural language understanding (NLU), was valued at $146 million per PitchBook data in its last funding round, with clients like Riot Games, Grindr, and The Meet Group. The recent acquisition of Spectrum Labs by ActiveFence marked an all-cash deal, cementing its position as a major move in the AI landscape.
“We were a part of Spectrum Labs for the last year, and we were basically trying to find ways to create better datasets for Spectrum so that we could ultimately build better performing models for detecting hate speech across languages or cheating or spam or whatever the thing was for across our wide variety of clients. And what we stumbled upon was basically building a second LLM,” Davis said in an interview with VentureBeat.
It took a lot of data for the purpose of simulating human content on the internet, such as in-game chat, and then using that to simulate and create more datasets, he said.
“And that proved to be really effective. Once we figured out that Nurdle could create models that were 92% as accurate as human-labeled models for classification, we realized that was where we needed to go. We need to go take this technology to basically every company that’s like Spectrum, of our size or bigger, that is building a chatbot or a classifier or an LLM.” Josh Newman, CTO of Nurdle, said in a statement, “Existing LLM applications such as ChatGPT present limitations for specific businesses and products due to their generalized nature, leading to inaccuracies that impair trust. This is the fundamental challenge Nurdle tackles by crafting tailored, high-quality synthetic datasets.” NurdleGPT, a second-generation synthetic data generator, facilitates the creation of domain-specific unstructured text data, particularly for training language models. Unlike previous synthetic data generators geared toward structured, regulated industries, NurdleGPT focuses on unstructured text data from diverse domains, such as chat logs, call transcripts, emails, and articles.
The company is extending its insights and solutions through a series of workshops catering to product managers, data scientists, and AI/ML engineers, aiming to democratize AI applications. Interested parties can access further information about these workshops via Nurdle’s platform.
Nurdle empowers product teams by providing cutting-edge AI solutions, making AI deployment faster, more cost-effective, and simpler. The NurdleGPT data technology allows for the creation of highly precise, specialized “lookalike” datasets that redefine the accessibility and practicality of AI applications across various industries. Interested parties can explore and test their data for free at Nurdle.ai.
How this came to pass AI products need to be privacy-safe and safe for kids.
“We felt like we had pretty good IP to do it. So once that became apparent, it became really clear we needed to find a good home,” Davis said.
The interesting and rare part was that Davis was able to proceed with a second startup while still working at Spectrum Labs. Slack managed to do that as it pivoted from games to enterprise communications. Others have done that too.
“We really cared about fighting spam and hate speech. But we didn’t really know the right technical solution that would emerge from the soup six years later. And it turns out that contextual AI was the right way to solve that problem. And we were right about that during our journey,” Davis said. “We did experience specific key problems in developing some of the most advanced AI that you can conceive, which is understanding human behavior on the internet for automated content moderation.” Then came the rise of OpenAI and ChatGPT, and it introduced a new problem that wasn’t about content moderation per se.
“It’s about anyone trying to build an AI company period, and how expensive and how timely and how hard it is to get your hands on the right data, especially with all the privacy stuff that exists for good reasons,” Davis said. “And so we need ways to accelerate and get around that. Ultimately we did. So I think that’s normal, right? You’re an entrepreneur, you solve a problem that you feel and then ideally, experience another set of problems and that journey and then the cycle continues. That’s actually what we did here.” ActiveFence disclosed that it bought Spectrum Labs in September. Spectrum and its existing investors funded Nurdle.
“At this point, we’ve got a few years of runway, we’ve got plenty of capital in the bank, and a team that’s done this before,” Davis said.
While at Spectrum, Davis learned that a lot of game companies were trying to build their own classifiers that Spectrum had to compete against and they were experimenting with generative AI to to intellectual property creation.
“So we were getting asked more and more if we could help build these classifiers, or build other kinds of AI. So we had a choice of doing this for the gaming market as a custom dev shop or going upstream from that and help a company build its own AI on top of the tools it has,” he said. “It became really clear it was it was a data strategy. We can help gaming companies simulate, create and label more in-game chat or chatter than what they actually have. And we can organize any of that content or game content in a much more effective way so that when they’re going out trying to build new NPCs, or new chatbots for whatever mechanism, or new gen AI tools for their communities, that they have better datasets to do that with.” He added, ” So we don’t want to be on the hook building AI. We’re on the hook for helping them with synthetic data to do a better job of building AI for themselves. And that seems like a better way for us to cater to a wider variety of use cases at gaming companies and also other types of verticals that we never got to work with before like healthcare, financial services and martech companies.” How it works Many companies have datasets. Game companies might have data for a non-player character. They could have that NPC talk to players in a game, but they first need to tune that data so that an 18-year-old NPC doesn’t sound like a 50-year-old NPC. The data gets tuned based on how people talk and they Nurdle creates synthetic data from that which is privacy compliant. Then they use that to train the NPC to speak in the right way and understand when someone is talking in the jargon of the game.
“That’s fine-tuning,” he said. “But you want it to be accurate too and deal with the problem of hallucinations that people talk about. That’s solved through another data problem, which is a process called retrieval augmented generation, where you’re basically telling it that before it answers a question, go refer to a certain set of data. So this could be like in the case of like an NPC, like the rules of the game, gaming manuals, catalogs or whatever is available. So then it doesn’t answer and make up something that doesn’t exist in the game, right, which it is prone to do.
That process of making sure that that system works right is called RAG, or retrieval augmented generation. To do that properly, you have to have datasets to be able to test it against. So a lot of people can build this stuff with their own data. And then they have no way to benchmark it or test it to see if it’s actually accurate, Davis said.
“Our datasets created custom for this help you that,” he said. “Everybody gets super excited about ChatGPT. But a lot of businesses realized that they can’t trust it or it doesn’t have the domain knowledge that you need for your purposes.” Once you have this capability, you can properly assess if you can run with a cheaper version of the technology without sacrificing accuracy. The company has 18 people.
What’s next ActiveFence was involved in the process of vetting the tech for Nurdle as they needed new datasets to make the classifiers from Spectrum Labs even better.
“We continue to work on trust and safety,” Davis said. “These game companies are all going to need AI and the datasets for in-game chat features. If you’ve never had in-game chat, you need simulated data and content to better test these systems and figure out what you know. There is a lot of utility here and we are just starting to scratch the surface on it.” The company ran pilot testing while under Spectrum Labs and so that’s why it’s ready to roll out already as a separate entity, Davis said.
“We’re in private betas with some early partners and customers, and we are keen to onboard a few more before we make this broadly available,” he said. “We have quite a few brewing with gaming customers and a variety of different verticals. It’s primetime. We’re ready to rock.” GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
Discover our Briefings.
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"Cerebras and Core42 start phase 2 for world's largest AI supercomputers | VentureBeat"
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"https://venturebeat.com/ai/cerebras-and-core42-start-phase-2-for-worlds-largest-ai-supercomputers"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Cerebras and Core42 start phase 2 for world’s largest AI supercomputers Share on Facebook Share on X Share on LinkedIn Core42 is making big AI investments.
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Cerebras Systems and Core42 have started phase two of the construction of the world’s largest interconnected AI supercomputers dubbed Condor Galaxy 2.
The Cerebras supercomputers for accelerating generative AI will hit up to 36 exaFLOPs in partnership with Core42, a subsidiary of the UAE-based technology holding group G42 , which is in Abu Dhabi.
The partners have announced the initiation of the second phase of the Condor Galaxy network. This ambitious network, comprising nine interconnected supercomputers, aims to reach a staggering milestone of 36 exaFLOPs AI compute capacity.
The completion of Condor Galaxy 1 has paved the way for the initiation of Condor Galaxy 2 (CG-2). This second phase, projected to achieve four exaFLOPs and incorporate 54 million AI-optimized compute cores, will expand the Condor Galaxy network to a total of eight exaFLOPs and 108 million cores upon completion. This advancement signifies the commitment of Cerebras and Core42 to constructing a constellation of AI supercomputers with unprecedented compute power.
Event GamesBeat at the Game Awards We invite you to join us in LA for GamesBeat at the Game Awards event this December 7. Reserve your spot now as space is limited! Rather than make individual chips for its centralized processing units (CPUs), Cerebras takes entire silicon wafers and prints its cores on the wafers, which are the size of pizza. These wafers have the equivalent of hundreds of chips on a single wafer, with many cores on each wafer. And that’s how they get to 54 million cores in a single supercomputer.
“Our strategic partnership with Cerebras Systems is propelling us toward our collective vision of establishing the world’s largest and fastest AI supercomputers,” stated Talal Alkaissi, chief product and global partnerships officer at Core42, in a statement. “Core42 handles massive and diverse datasets across healthcare, energy, and climate studies that challenge even the largest existing AI supercomputing systems. Training on CG-1 while building out CG-2 allows the training of cutting-edge foundational models, advancing critical research across various domains.” The partnership between G42 and Cerebras delivers on all three elements required for training large models: huge amounts of compute, vast datasets, and specialized AI expertise. They are democratizing AI, enabling simple and easy access to the industry’s leading AI compute, and G42’s work with diverse datasets across healthcare, energy, and climate studies will enable users of the systems to train new cutting-edge foundational models.
Andrew Feldman, CEO of Cerebras, said in an interview with VentureBeat that the collaboration between Cerebras and Core42 has not only led to the doubling of compute capacity in Condor Galaxy-1 but also a 50% enhancement in training performance through software updates.
“What we’re announcing is a continuation of the success of this extraordinary partnership. We announced the partnership with Core42. In July, we announced we built a supercomputer,” Feldman said. “We built it on time as scheduled. We announced that we built nine.” of them and we’ve started our second and we’re training models that are moving the entire industry forward. We’re creating for the 400 million Arabic speakers a generative ad model in their own language.” Andrew Jackson, chief AI officer at Core42, said in a statement that the exceptional efficiency and ease of use experienced while working with Cerebras CS-2s, marks a substantial leap in the rate of innovation from concept to solution.
The application of the Condor Galaxy constellation for cutting-edge research spans crucial sectors such as healthcare, energy, climate change, and AI-based studies. The introduction of Med42, a leading generative AI medical assistant, and Jais 30B, the premier Arabic language LLM, demonstrates the groundbreaking strides made in pioneering work on CG-1. Additionally, AI-based climate studies and advancements in high-performance computing have been central to the pioneering work facilitated by the Condor Galaxy.
While the supercomputer show is this week, Cerebras isn’t showing up on the top 500 lists yet as the standard used for those lists is 64-bit double precision, which is not an AI test. But Feldman noted that the company is one of the largest supercomputers in the world still.
“In flops measured, we’re one of the largest in the world with just one and we’ve tied two together, and eventually all the way up to nine together,” Feldman said. “So the obvious question is what cool work can we do on this machine?” The company is training AI models, and the model has more than a billion downloads on Hugging Face. It can be used as a medical assistant, and it’s being used at a university in Saudi Arabia in a supercomputer that aims to set a world record for seismic processing.
“Our equipment that we used at Argonne National Labs to accelerate particle transport. And here they published that we were 130 times faster than Nvidia GPUs. Then what we announced in a few what at the end of August was that we had built a 13 billion parameter model for an Arabic language model, and this was the state-of-the-art model,” Feldman said. “One of the things we’re announcing today is that we more than doubled the size of this model in less than eight weeks. And we’re putting into the open source community, the 30 billion parameter Arabic model, It’s head and shoulders above any other Arabic language model.” Plans to deploy seven more supercomputers—CG-3 through CG-9—in 2024 will contribute to achieving a total compute power of 36 exaFLOPs. This extensive constellation of supercomputers, involving 576 Cerebras CS-2 systems and over 654,000 AMD CPU cores, is set to revolutionize AI advancements on a global scale.
“With CG-1 now complete, we’re already seeing the impact and important contributions that this strategic partnership delivers,” said Feldman. “In partnership with Core42, we are changing the worldwide inventory of compute and using our combined expertise to advance AI work in a powerful way, to quickly and efficiently train leading LLMs.” This is the first time Cerebras has partnered not only to build a dedicated AI supercomputer but also to manage and operate it. Condor Galaxy is designed to enable Core42 and its cloud customers to train large, ground-breaking models quickly and easily, thereby accelerating the pace of innovation.
The Cerebras-Core42 strategic partnership has already advanced state-of-the-art AI models in healthcare with the introduction of Med42 the leading generative AI medical assistant, as well as brought Arabic Language chat to more than 400 million Arabic speakers through the introduction of Jais 30B, the premier Arabic language LLM. Pioneering work on CG-1 has also included AI-based climate studies as well as pioneering work in high-performance computing.
In addition to CG-1 and CG-2, Cerebras and Core42 previously announced their plans to deploy seven additional supercomputers — CG-3 through CG-9 — in 2024 bringing the total compute power of the Condor Galaxy to 36 exaFLOPs. This entails deploying 576 Cerebras CS-2 systems and feeding the cluster with more than 654,000 AMD CPU cores. With 36 exaFLOPs in total, this unprecedented constellation of supercomputers will revolutionize the advancement of AI globally. Access to CG-1 is available now.
The first machine was for exFLOP 64 machines, and the second was too and it will be completed in the first quarter. The company is in the planning phase for the third machine.
Asked about export controls, Feldman said that with such powerful machines the company has to work with the U.S. Department of Commerce. When the company ships equipment to the Middle East, it requires an export license. The company does not currently ship anything to China.
GamesBeat's creed when covering the game industry is "where passion meets business." What does this mean? We want to tell you how the news matters to you -- not just as a decision-maker at a game studio, but also as a fan of games. Whether you read our articles, listen to our podcasts, or watch our videos, GamesBeat will help you learn about the industry and enjoy engaging with it.
Discover our Briefings.
The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Frontegg Forward is here to securely manage digital identities | VentureBeat"
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"https://venturebeat.com/security/frontegg-introduces-forward-allowing-enterprises-to-securely-manage-their-customers-digital-identities"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Frontegg Forward is here, allowing enterprises to securely manage their customers’ digital identities Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
Frontegg , the four-year-old startup focused on making secure user management software, today announced the launch of its all-new platform, Frontegg Forward , a no-code way for its enterprise customers to control their customers/end users’ digital identities and enable the proper permissions for those users when accessing online services and products.
Many of Frontegg’s customers are cybersecurity and software companies themselves, whose customers are found across industries, from healthcare to reinsurance.
End-users who work in these industries need access to specific apps and software tools to do their jobs, but keeping track of which employees should get which permissions and security clearances to access certain tools and information is a tricky endeavor, especially for companies focused on making software that does other things — such as run healthcare devices.
Since its founding in 2019, Frontegg has sought to streamline this end user identity management process for its enterprise customers, and now with Frontegg Forward, it plans to make doing so even easier — requiring literally no programming or developer knowledge to get set up and running.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “We used to have three sets of ‘if’ statement predicates in our code, just to decide whether a user can access a certain API, one checking roles, another feature-flags and lastly validating that the user has paid for this feature,” said Ofir Assif, Director of Engineering at Frontegg customer Hunters Security, in a press release about Frontegg Forward.
“With Frontegg we eliminate the need to use many APIs and vendors, and move the logic management to the product and revenue owners instead of the developers.” “Frontegg Forward ushers in a new era of enabling developers to overcome this complexity,” added Sagi Rodin, CEO, Frontegg, in the release.
What Frontegg Forward offers Frontegg Forward offers four main categories of features: user hierarchies, a security suite, entitlements, and signals, all of which can be used independently or concurrently depending on the needs of the specific customer and their end-users. In fact, flexibility and customizability are some of the key attributes Frontegg is hoping to offer.
We’ll briefly review each of the four feature categories now to give the reader a taste of what they can accomplish.
User-hierarchies keep users aligned with their teams’ proper permissions and data These enable Frontegg customers to create “multi-layered tenancy trees,” essentially a way of organizing the end-users so that they can, as needed, be part of multiple teams and have access to the software apps, data, and services needed to fulfill their roles — but no more. This is also designed to occur automatically, when the end user signs up with the Frontegg customer’s software and creates an account with them, but the Frontegg customer developer can go in and of course edit the permissions as needed. Frontegg also provides a nice visual organizational tree showing different teams and the users nestled within them.
Security suite monitors and adapts to harmful behavior Frontegg’s security suite is centered on a dashboard that provides IT administrators and cybersecurity officers with a quick, glanceable, yet highly detailed view of their software applications/network security status and all the noteworthy events that are taking place on it, such as the logs of bad actors like bots, and automatically applies additional security measures in realtime.
If Frontegg’s software suspects a bot or bad actor/hacker is trying to log in and create a new user account and identity on its customer’s platform, it will throw up additional points of friction and defenses such as mobile number verification to prevent them from moving forward.
It also automatically scans passwords and cross-references them with a database of those compromised in known breaches and automatically asks the user to generate a new one. The administrator can of course toggle this on or off. And it detects suspicious IP addresses, repeated log-in attempts indicating a brute force attempted intrusion, and physically impossible log-in attempts across disparate locations in a short time frame.
Entitlements controls access to specific features and data This is a great tool for those enterprises who have sales teams making direct outreach to customers.
Now, with Frontegg Forward’s new Entitlements system, the sales team members themselves can flag the specific features their customers need and that their end-users need, and Frontegg’s Entitlements feature will automatically allow access to them or show the developer that said customers are requesting access, and that the request has come from a verified sales rep.
Using a single application programming interface (API) call, the Frontegg customer can get access to all the roles, permissions, teams, and information about what data and apps they can access.
Signals delivers insights automatically to Frontegg enteprise customers It’s one thing to have awareness of the security events on your software platform or app and the state of the end-users and their permissions.
But Frontegg goes a step further, bringing forth additional analytics information for its customers about their end-users — such as user adoption of new features and potential new customers and upsell opportunities — all automatically and proactively, without the enterprise customer needing to ask.
Signals also delivers alerts about high churn rates and specific accounts at risk, as well as “champions” and “influential personas,” those users who are making the most use of an enterprise customer’s software and features.
The features are all available today through the Frontegg Forward platform, which starts at $99 per month for 10 multi-user accounts and jumps to $799 per month for 100 multi-user accounts, and variable pricing up from there depending on the userbase and needs.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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"Cranium raises $25M to fund enterprise AI compliance platform | VentureBeat"
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"https://venturebeat.com/security/cranium-raises-25m-to-fund-enterprise-ai-monitoring-security-and-compliance-platform"
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"Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation View All Industrial Automation Business Process Automation Development Automation Robotic Process Automation Test Automation Enterprise Analytics View All Business Intelligence Disaster Recovery Business Continuity Statistical Analysis Predictive Analysis More Data Decision Makers Virtual Communication Team Collaboration UCaaS Virtual Reality Collaboration Virtual Employee Experience Programming & Development Product Development Application Development Test Management Development Languages Cranium raises $25M to fund enterprise AI monitoring, security, and compliance platform Share on Facebook Share on X Share on LinkedIn Credit: VentureBeat made with Midjourney Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.
The AI marketplace is an incredibly dynamic one, especially in the year since OpenAI publicly launched ChatGPT.
Survey after survey shows that enterprises are moving fast to consider and embrace new AI tools. But as they do, how are they ensuring the AI solutions they are bringing onboard for their employees and customers are working reliably, securely, and are compliant with whatever the applicable rules and regulations are for said company in the jurisdictions they operate? Enter Cranium.
The New Jersey-based startup, incubated within professional services giant KPMG and which emerged from stealth in April 2023, offers a custom software solution that allows enterprises to assess AI security risks and compliance without disrupting existing workflows.
“The level of experimentation has gone through the roof,” said founder and CEO Jonathan Dambrot, in a videconference interview with VentureBeat. “Every single technology product is now integrating AI — either has done it or has a plan to do it over the course of the next six months to 12 months. So this is where it becomes really important in understanding how people are using the AI.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Investors agree, as today, Cranium announced its Series A funding round to the tune of $25 million. The round was led by Telstra Ventures with participation from KPMG LLP and SYN Ventures, taking Cranium’s total capital raise to-date to $32 million.
Cranium’s unique value proposition Cranium offers several products and services organized around four goals: discovery, monitoring, creating transparency, reporting and compliance.
One solution is private AI dashboards that allow its customer organizations to track how they are using AI and what data the AI models they are using have access to, and where that data is going within and outside of the organization.
“When we look at the market, the interesting part is the AI governance role,” Dambrot told VentureBeat. “We think of ourselves as a platform to help support that process, and it starts with the question: ‘how do we give visibility to AI services?'” Cranium’s Connectors, secure software which helps to monitor in realtime and assess how AI is being used at its client organizations, support most major AI development environments, models, and frameworks, including Azure, Azure OpenAI, AWS Sagemaker, Google VertexAI, Databricks, MLflow, Dataiku, and DataRobot.
Another offering, the “ AI Card, ” introduced over the summer of 2023, allows Cranium’s customers to plug their AI applications into Cranium’s secure software assessment tools and generate a discrete file with information about the value, purpose, data, and governance.
Companies can upload evidence that supports each of these areas. Then, they can share the AI Card out with third-parties as requested, including on their own websites, with government agencies, or even with customers and new clients.
Cranium further generates an “AI Maturity Score,” which assesses the vulnerabilities of their AI stack using Cranium’s read-teaming exercises to expose and plug gaps across libraries, data repositories and lakehouses/warehouses, pipelines, and of course, the models themselves. The score is a percentage out of 1-100, with a higher number being a better, more mature and secure AI stack.
The Maturity Score helps organizations with “understanding what’s there [in terms of AI being used inside their companies,] and the risk of those AI systems because, in most cases, governance groups and the security teams really don’t have that visibility,” Dambrot said.
“It’s like ‘Bring Your Own Device’ with the iPhone all over again,” said Dambrot, noting that many employees are using AI tools to do work that aren’t necessarily cleared by management, but which nonetheless need to be tracked and monitored to ensure compliance and security.
He cited the hypothetical example of an employee who decides to start taking photos of their company’s datacenter and uploading them to ChatGPT’s new computer vision mode to ask it for tips on re-architecting or writing policies. While a legitimate use case that could be helpful to the company, it also comes with risks, which Cranium’s connectors and offerings can help the company management and security teams understand and mitigate.
“You don’t know where this data is going,” Dambrot noted. “You don’t know how the models are being trained.” Cranium itself uses AI and machine learning (ML), specifically in code completion and software develop,ent.
“We are investing heavily on driving better code development with the use of AI,” said Dambrot, which includes “use of AI in the product, use of AI to help build, including QA [quality assurance] testing and other areas. We bring all of our assets into that, including our human assets using our AI systems. We monitor those and then we look at our own AI Card requirements…we’re drinking our own champagne.” Though a young company, Cranium already counts a number of customers across sectors as diverse as health sciences, financial services, consumer packaged goods, and retail.
What investors like about Cranium Marcus Bartram, General Partner at Telstra Ventures, expressed his enthusiasm about Cranium’s solutions in a statement provided in a press release.
“Cranium stands at the forefront of AI security and trust software, empowering organizations to navigate the crowded cybersecurity industry with its groundbreaking product and pioneering innovations,” he said.
Telstra Ventures has a history of backing standout disruptors, having made 96 investments that led to 38 liquidity events, including big names like CrowdStrike, DocuSign, and Box. The firm recently announced its third fund, which takes its funds under management to $1 billion.
What Cranium plans to do next The injection of funds aims to fuel various areas of the company, from R&D and business expansion to marketing efforts. By bolstering its Enterprise software platform, Cranium plans to provide organizations with a more secure and compliant AI/ML environment.
The company is already well positioned to help its customers comply with the still in-process but rapidly looming EU AI Act , which Dambrot described as “almost like GDPR from a privacy perspective.” In addition, Dambrot said “we’re working on some things that are going to be launching next early next year on further being able to provide visibility, especially in a GenAI environment…I liken it to putting like brakes on a race car. If you try to go 200 miles-per-hour in your race car and take a corner with no brakes, you’re in trouble. We’re like the brakes that are enabling everyone to go faster and experiment more.” In a world where AI adoption is rising quickly, Cranium aims to ensure that organizations don’t have to choose between innovation and security. By developing robust solutions focused on trust, visibility, and compliance, the company is geared to set new industry standards for AI security.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.
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The AI Impact Tour Join us for an evening full of networking and insights at VentureBeat's AI Impact Tour, coming to San Francisco, New York, and Los Angeles! VentureBeat Homepage Follow us on Facebook Follow us on X Follow us on LinkedIn Follow us on RSS Press Releases Contact Us Advertise Share a News Tip Contribute to DataDecisionMakers Careers Privacy Policy Terms of Service Do Not Sell My Personal Information © 2023 VentureBeat.
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Wired Articles Filtered
Retrieves up to 100 entries from the train dataset where the URL contains 'wired' but the text does not contain 'Menu', providing basic filtering of the data.