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"DocuSign invests $15 million in AI contract discovery startup Seal Software | VentureBeat"
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"https://venturebeat.com/2019/03/28/docusign-invests-15-million-in-ai-contract-discovery-startup-seal-software"
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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 DocuSign invests $15 million in AI contract discovery startup Seal Software 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.
DocuSign is investing heavily in AI. Literally. The San Francisco provider of electronic signature and digital transaction management services today announced that it’s putting $15 million toward Seal Software, a seal contract discovery and analytics startup that uses machine learning to find and parse contracts, building on an existing partnership between the two companies.
It follows DocuSign’s acquisition of intellectual property rights from machine learning startup Appuri in December 2017, and its purchase of text search and document indexing startup SpringCM last September. And it comes after Bay Area-based Seal — which was founded in 2010 by Kevin Gidney and Ulf Zetterberg, and which recently reported growth of more than 85 percent year-on-year — raised $30 million from Toba Capital, bringing its total raised to $43 million.
“We are thrilled by DocuSign’s confidence in Seal Software as a partner and now as a strategic investor, as we build the next generation of agreement discovery and analysis tools using artificial intelligence,” cofounder and CEO Ulf Zetterberg said. “Working together, we will continue to unlock the full potential of all the agreements that are pervasive across every size and type of business.” As per the agreement inked last year, Seal’s technologies are distributed through DocuSign’s platform extensions program. They underlie DocuSign’s Total Search service, which allows customers to centralize and organize digital agreements using metadata and search inside them using natural language terms, and DocuSign’s Intelligent Insights, which taps AI algorithms to automatically extract “mission-critical” legal concepts like indemnification and warranty.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Using these in tandem, DocuSign says, a customer might identify agreements in contracts that overlap and clauses that aren’t managed, which could allow for renegotiation and cost savings. “AI lets organizations analyze their agreements for hidden risks and opportunities in new ways,” said DocuSign chief product officer Ron Hirson. “As we have continued to invest in adding intelligence to our suite of products, this investment in Seal’s discovery and analytics is just another step in making our Agreement Cloud offering smarter.” Today caps off a newsy month for DocuSign. Last week, it announced Agreement Cloud, a solutions suite comprising three new products — DocuSign Gen for Salesforce, DocuSign Click, and DocuSign ID Verification — that enable users to automatically generate contracts from within Salesforce, quickly capture consent and standard agreement terms, and verify government-issued IDs and European electronic IDs used in sensitive transactions.
DocuSign has raised more than $500 million since 2003, and as of February, it had 450,000 customers and “hundreds of millions” of users in over 180 countries.
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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15,468 | 2,019 |
"The growth of cognitive search in the enterprise, and why it matters | VentureBeat"
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"https://venturebeat.com/2019/12/14/the-growth-of-cognitive-search-in-the-enterprise-and-why-it-matters"
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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 growth of cognitive search in the enterprise, and why it matters Share on Facebook Share on X Share on LinkedIn AI 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.
Enterprises typically have countless data buckets to wrangle (upwards of 93% say they’re storing data in more than one place), and some of those buckets invariably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends, while a separate Veritas report found that 52% of all information stored by organizations is of unknown value. The opportunity cost of this unused data is substantial — the Veritas report pegs it as a cumulative $3.3 trillion by the year 2020, if the current trend holds.
That’s perhaps why this year saw renewed interest from the corporate sector in AI-powered software-as-a-service (SaaS) products that ingest, understand, organize, and query digital content from multiple sources. “Keyword-based enterprise search engines of the past are obsolete. Cognitive search is the new generation of enterprise search that uses [AI] to return results that are more relevant to the user or embedded in an application issuing the search query,” wrote Forrester analysts Mike Gualtieri, Srividya Sridharan, and Emily Miller in a comprehensive survey of the industry published in 2017.
Emerging products Microsoft kicked the segment into overdrive in early November by launching Project Cortex, a service that taps AI to automatically classify and analyze an organization’s documents, conversations, meetings, and videos. It’s in some ways a direct response to Google Cloud Search, which launched July 2018. Like Project Cortex, Cloud Search pulls in data from a range of third-party products and services running both on-premises and in the cloud, relying on machine learning to deliver query suggestions and surface the most relevant results. Not to be outdone, Amazon last week unveiled Amazon Kendra , which taps a library of connectors to unify data sources, including file systems, websites, Box, DropBox, Salesforce, SharePoint, relational databases, and more.
Of course, Google, Amazon, and Microsoft aren’t the only cognitive search vendors on the block. There’s IBM, which offers a data indexing and query processing service dubbed Watson Explorer, and Coveo, which uses AI to learn users’ behaviors and return results that are most relevant to them. Hewlett-Packard Enterprise’s IDOL platform supports analytics for speech, images, and video, in addition to unstructured text. And both Lucidworks and Squirro leverage open source projects like Apache Solr and Elasticsearch to make sense of disparate data sets.
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 cognitive search market is exploding — it’s anticipated to be worth $15.28 billion by 2023, up from $2.59 billion in 2018, according to Markets and Markets — and it coincides with an upswing in the adoption of AI and machine learning in the enterprise. But it’s perhaps more directly attributable to the wealth of telemetry afforded by modern corporate digital environments.
AI under the hood AI models like those at the heart of Amazon Kendra, Project Cortex, and Cloud Search learn from signals, or behavioral data derived from various inputs. These come from the web pages that employees visit or the videos they watch online, or their online chats with support agents and public databases of support tickets. That’s not to mention detailed information about users, including job titles, locations, departments, coworkers, and potentially all of the documents, emails, and other correspondences they author.
Each signal informs an AI system’s decision-making such that it self-improves practically continuously, automatically learning how various resources are relevant to each person and ranking those resources accordingly. Plus, because enterprises have far fewer data sources to contend with than, say, a web search engine, the models are less expensive and computationally time-consuming to train.
The other piece of the puzzle is natural language processing (NLP), which enables platforms like Amazon Kendra to understand not only the document minutiae, but the search queries that employees across an organization might pose — like “How do I invest in our company’s 401k?” versus “What are the best options for my 401k plan?” Not every platform is equally capable in this regard, but most incorporate emerging techniques in NLP, as well as the adjacent field of natural language search (NLS). NLS is a specialized application of AI and statistical reasoning that creates a “word mesh” from free-flowing text, akin to a knowledge graph, to connect similar concepts that are related to larger ideas. NLS systems understand context in this way, meaning they’ll return the same answer regardless of how a query is phrased and will take users to the exact spot in a record where that answer is likely to be found.
Cognitive search: the new normal In short order, cognitive search stands to become table stakes in the enterprise. It’s estimated that 54% of knowledge workers are already interrupted a few times or more per month when trying to get access to answers, insights, and information. And the volume of unstructured data organizations produce is projected to increase in the years to come, exacerbating the findability problem.
“Productivity isn’t just about being more efficient. It’s also about aggregating and applying the collective knowledge of your organization so that together you can achieve more,” wrote Microsoft 365 corporate vice president Jared Spataro in a recent blog post. “[Cognitive search systems enable] business process efficiency by turning your content into an interactive knowledge repository … to analyze documents and extract metadata to create sophisticated content models … [and to] make it easy for people to access the valuable knowledge that’s so often locked away in documents, conversations, meetings, and videos.” 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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15,469 | 2,019 |
"Evisort raises $15 million to automate contract creation and management | VentureBeat"
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"https://venturebeat.com/2019/12/19/evisort-raises-15-million-to-automate-contract-creation-and-management"
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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 Evisort raises $15 million to automate contract creation and management 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.
Contract management is a time- and money-sucking endeavor for enterprises, regardless of their size or industry. According to a 2016 survey conducted by Apptus, 39% of legal departments are forced to rely on non-lawyers to manage their contracts, while 50% take a week or longer to turn out documents like non-disclosure agreements. Moreover, only 40% of legal departments say they have an automated contract management tool.
This state of affairs prompted a team of Harvard Law and MIT researchers — Amine Anoun, Jake Sussman, and Jerry Ting — to found Evisort , a startup developing software to help businesses categorize, search, and act on documents of any type. The trio pioneered AI that understands both meaning and context in legal language, eliminating the need for manual data entry and parsing. In the roughly three years since it opened for business, Evisort has attracted customers like Brooks Brothers, Cox Automotive, Fujitsu, TravelZoo, Sweetgreen, and over 100 others, for which it collectively manages more than $40 billion worth of corporate contracts. Now it’s raising fresh venture capital ahead of a planned expansion in the coming year.
Evisort today announced that it has secured $15 million in a funding round co-led by Vertex Ventures and Microsoft’s M12, with participation from Amity Ventures and Serra Ventures. This brings the startup’s total raised to nearly $20 million, following a $4.5 million seed round in February 2019, and CEO Ting says it will be used to grow Evisort’s team, expand its product offerings, and launch a new R&D office in Montreal, Quebec with upwards of 10 technical team members.
Evisort’s Amazon Web Services-hosted (and optionally on-premises) platform — which handles contract generation and review, in addition to management — automatically tracks expiring documents and syncs with all repositories where those documents might be located, after which it generates metadata across all contracts using AI and machine learning algorithms. The algorithms categorize clauses related to indemnification, confidentiality, limitation of liability, and termination and label them. Then they identify risky contracts by flagging non-standard provisions.
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 procurement side of the equation, Evisort’s bespoke systems analyze and aggregate contracts while powering robust searching (by clauses, contract types, and key terms) and reporting (such that contracts are automatically renewed). Ting says they work with most document formats, from PDF to Word, thanks to a document recognition AI trained on millions of files. This AI and platform support the tagging and categorization of an unlimited number of document and provision types and integrate with existing services like DropBox, OneNote, Salesforce, Evernote, and Asana.
Evisort is swift enough to work through a 20-page document in as little as “seconds,” Ting claims. Plus, it lets clients create and manage different levels of user access privileges, such as for admins and read-only users, and define which users have access to certain documents and folders.
Evisort certainly isn’t the only contract management solutions provider vying for a slice of the $2.9 billion market. Rivals include Concord , which raised $25 million for its digital contract visualization and collaboration tools last October, and Icertis , which raised a whopping $115 million in July. That’s not to mention incumbent DocuSign, which invested $15 million in AI contract discovery startup Seal Software.
But M12 corporate vice president and global head believes there’s room for growth yet. “Simply put, Evisort is creating the new command center for the operations team for mid-to-large enterprises,” he said. “Evisort’s AI algorithms draw out the deepest insights for legal, procurement, and financial teams and are pushing the envelope on AI 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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15,470 | 2,021 |
"Lexion raises $11M to expand its AI-powered contract management platform | VentureBeat"
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"https://venturebeat.com/2021/06/17/lexion-raises-11m-to-expand-its-ai-powered-contract-management-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 Lexion raises $11M to expand its AI-powered contract management platform 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.
Lexion , a platform leveraging AI to streamline legal contract workloads, today announced that it closed an $11 million series A funding round led by Khosla Ventures with participation from Madrona Venture Group and Wilson Sonsini. The capital brings the company’s total raised to $15.2 million to date, and cofounder and CEO Gaurav Oberoi says that it’ll be used to support the development of a workflow module designed to help legal teams manage contract intake, negotiations, approvals, and signatures.
According to Gartner, legal departments will increase their technology spend 300% by 2025, and yet will only realize 30% of the benefit of their contract lifecycle management (CLM) software because of complexities with requirements gathering, change management, and user adoption. Many CLMs require extensive time and resources to drive value. According to a Clio survey, when they use CLMs, lawyers spend only 2.3 hours a day on billable tasks and collect an average of only 1.6 hours of their billable time.
Incubated at the Allen Institute for AI in 2019, Lexion offers an NLP system that turns contract text into structured data and delivers it in a repository with search, reporting, alerts, permissions, and integrations. Beyond this, the startup provides a range of legal support services that aim to help firms increase efficiencies and recognize more revenue.
Lexion’s three founders — Oberoi, Emad Elwany, and James Baird — met at the Allen Institute for AI, where they were exploring ways to commercialize AI research, specifically NLP. Oberoi built several software-as-a-service businesses, most recently at SurveyMonkey, while Elwany helped to build NLP products at Microsoft, including the company’s AI-powered calendar scheduler and conversational AI platform.
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-house counsel are skilled individuals who want to work on high value tasks for their business. Yet, as much as 50% of their week can be spent doing relatively low value tasks like fetching contracts for teammates, answering trivial contract questions repeatedly, and spending weeks reading through contracts to create reports for the business,” Oberoi told VentureBeat in an email Q&A. “Lexion eliminates these low-value tasks by turning contracts into structured information, unlocking huge timesaving value for entire businesses.” AI inside Lexion’s platform centralizes contracts in one place, essentially converting them from Word-like documents into databases with columns like “parties,” “effective date,” and “payment terms.” A search box and connectors for Slack, Microsoft Teams, Salesforce, Coupa, and other enterprise systems make contracts easier to find, while reporting tools let users answers questions like “What vendor or customer contracts will expire or renew next month?” and “Which customers have limits on fee increases?” without having to read through thousands of pages.
Lexion claims to have trained over 125 different machine learning models to extract key insights, terms, and clauses from commercial contracts such as orders, mergers and acquisitions, statements-of-work, contractor agreements, employment contracts, and venture financing documents. Its system can recognize when a single PDF contains several contracts glued together, like a master agreement followed by an order form. And it can track dates of contracts and deliver reminders of upcoming events like auto-renewal cancellation, notice deadlines, and expiration dates.
“We’ve invested heavily, not just into NLP to understand prose, but also into understanding tabular structures, handwriting, and fixing optical character recognition issues,” Oberoi said.
In Q3, Lexion — which competes with Cortical , Pactum , LinkSquares , Evisort , Contractbook , and Agiloft — plans to release the aforementioned workflow module, which Oberoi says was designed based on feedback from over 50 general counsel, attorneys, and contract managers across a range of businesses. It’ll provide a dashboard to manage intake and tasks while letting the rest of the company email in requests, redlines, and follow-up questions.
“The consistent themes we heard were that other CLMs failed because they required far too much planning and requirements gathering up front, then they had to be configured based on those requirements that took substantial time and consulting fees, and then a huge change management and training process was needed to roll it out — and all this, only if there was broad executive buy-in to make it happen,” Oberoi said. “It’s surprising that in 2021 most departments in companies have standard allocations in the budget to buy software-as-a-service products, but legal does not.” In the past 6 months, Seattle, Washington-based Lexion says it’s seen rapid growth, increasing revenues 400% and bringing on major brands like OfferUp, Blue Nile, and Outreach. The platform now has tens of thousands of users across segments including software, consumer packaged goods, and biotech and expects to triple revenue over the next year as it expands its workforce from 31 employees to more than 60.
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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15,471 | 2,021 |
"LinkSquares nabs $40M to expand its AI-powered contract platform | VentureBeat"
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"https://venturebeat.com/2021/07/14/linksquares-nabs-40m-to-expand-its-ai-powered-contract-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 LinkSquares nabs $40M to expand its AI-powered contract platform 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.
LinkSquares , a contract management and analytics tool for legal and finance teams, today announced that it raised $40 million in a series B funding round led by Sorenson Capital. The company, whose total funding stands at $61.4 million, says it’ll use the financing to expand its workforce, advance its technology, and develop strategic business partnerships.
During the pandemic, legal departments and contract negotiators faced a critical period of transformation. Legal was expected to contribute data-driven analyses while contending with the gap between executed contract analytical platforms and legal request business process flows. More than half of the world’s major companies face lost revenue and missed business opportunities as a result of inefficiencies in their handling of contracting processes, according to an EY Law survey.
“Just this past year, legal and compliance teams raced to analyze their business exposure to major events across PDFs, paper contracts, and other documents. The pandemic caused a surge in interest from prospective customers who needed digital contract management. These companies couldn’t afford to use on-premises solutions locked away in an office anymore, and they needed our help to ensure fully remote implementations that were quick and painless, and we came through,” LinkSquares CEO Vishal Sunak told VentureBeat via email.
According to Sunak, the inspiration for LinkSquares came during his experiences with the manual work associated with contracts over the course of the acquisition business continuity firm Datto planned during Sunak’s time at Backupify, a cloud data backup company. Datto hoped to migrate Backupify’s customer data to its cloud infrastructure, but the team first had to understand each signed customer contract and determine if Datto had the right to move the data without permission.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Above: The LinkSquares platform.
“The idea to review each contract, read the provision related to data transfer, and store the answer seemed straightforward — at first. In reality, because Backupify had negotiated more than 2,000 contracts, the act of finding all the contracts and looking for the provision language was an impossible undertaking,” Sunak explained. “When our team explored the types of contract management products available for post-signed contracts, there was a spark of innovation: most of the tools that could surface answers and insights from executed agreements focused on contracts that hadn’t been finalized yet, mainly in the pre-signature stage. And so, LinkSquares and its AI for signed contracts was born.” AI-powered contract management LinkSquares’ platform performs searches for keywords, contract terms, and phrases across documents using AI. It extracts data from contracts (e.g., parties, effective and termination dates, payment terms, governing states, and limitations and liabilities), and its email-based notification function reminds teams of important dates and obligations. Optical character recognition transforms scanned PDFs into a searchable format, while custom user roles let admins control data access. And a clause library enables real-time searches for contract clauses.
“We’re starting to understand the real benchmarks of what is being agreed to — and how people are agreeing to it — in their contracts. Because we’re an AI company we’re starting to see macro trends in legal language emerge,” Sunak said. “Modern legal teams need to be data-driven and move beyond their perception as a cost center and gatekeeper. Corporate counsels and mergers and acquisitions teams rely on LinkSquares to elevate their internal value, reputation, confidence, and productivity by eliminating time spent on manual and ineffective processes.” LinkSquares’ customers include over 400 brands like Fitbit, Twilio, TGI Fridays, Wayfair, and Cogito. Growth over the last two years exceeded 1,000%, and the company recently announced a technology partnership with Xerox PARC (Palo Alto Research Center), the R&D lab behind laser printing and electronic ink.
“Cogito estimates saving $30,000 per year [with LinkSquares, while] Asurion reduced time spent on searching for information by 50%,” Sunak said. “We overcame a lot of adversity this past year — with employees and customers — and we needed new levels of empathy and flexibility for a workforce that was 100% remote overnight. And we had customers who had a lot of uncertainty about their financial situations, so I personally worked something flexible out with several. That way, no one had to choose between paying their own employees and maintaining access to their critical legal insights.” Sunak expects that Boston, Massachusetts-based LinkSquares’ annual recurring revenue will grow 100% year-over-year by the end of 2021, up from “well over” $10 million. Other backers in the company’s latest round included Catalyst Investors, Xerox, Bottomline Technologies, DraftKings’ founders and key legal and compliance executives, Hyperplane Venture Capital, MassMutual Ventures, and First Ascent Ventures.
There’s no shortage of startups developing AI-driven contract creation and management tools. Others in the $2.9 billion market include Concord , which raised $25 million for its digital contract visualization and collaboration tools in 2019. That’s not to mention Icertis , which recently snagged $115 million; DocuSign, which invested $15 million in AI contract discovery startup Seal Software; and Evisort , which nabbed tens of millions to develop its solutions.
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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15,472 | 2,020 |
"FPGA chips are coming on fast in the race to accelerate AI | VentureBeat"
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"https://venturebeat.com/2020/12/10/fpga-chips-are-coming-on-fast-in-the-race-to-accelerate-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 Sponsored FPGA chips are coming on fast in the race to accelerate AI Share on Facebook Share on X Share on LinkedIn Presented by Intel AI is hungry, hyperscale AI ravenous. Both can devour processing, electricity, algorithms, and programming schedules. As AI models rapidly get larger and more complex (an estimated 10x a year), a recent MIT study warns that computational challenges, especially in deep learning, will continue to grow.
But there’s more. Service providers, large enterprises and others also face unrelenting pressures to speed up innovation, performance, and rollouts of neural networks and other low-latency, data-intensive applications, often involving exascale cloud and High-Performance Computing (HPC). These dueling demands are driving technology advances and adoption of a growing universe of Field Programmable Gate Arrays (FPGAs).
Early leader gains a new edge In the early days of exascale computing and AI, these customer-configurable integrated circuits played a key role. Organizations could program and reprogram FPGAs onsite to handle a range of changing demands. As time went on, however, their performance and market growth got outpaced by faster GPUs and specialized ASICs.
Now, innovations like high-speed AI tensor logic blocks, configurable embedded SRAM, and lightning-fast transceivers and interconnects are putting this early leader back in the race. Technology advances provide a great balance of performance, economy, flexibility, and scale needed to handle today’s AI challenges, says Ravi Kuppuswamy, general manager of Custom Logic Engineering at Intel.
“FPGAs offer hardware customization with integrated AI and can be programmed to deliver performance similar to a GPU or an ASIC,” explains Kuppuswamy. “ The reprogrammable, reconfigurable nature of an FPGA lends itself well to a rapidly evolving AI landscape, allowing designers to test algorithms quickly and get to market fast and scale quickly.
Consider the el Stratix 10 NX FPGA.
Introduced in June, the company’s first AI-optimized FPGA family was designed to address the rapid rise in AI model complexity. New architectural changes brought the existing Stratix 10 in the same ballpark as GPUs. The new FPGA family delivers up to a 15x increase in operations-per-second over its predecessor. The boost gives exascale customers a viable FPGA option for quickly developing customized, highly differentiated end products. The new FPGA is optimized for low latency and high-bandwidth AI, including real-time processing such as video processing, security and network virtualization.
The ability of FPGAs to deliver higher compute density while reducing development time, power, and total cost of ownership is deepening and expanding its role as the architecture of choice for small-and medium-batch data center AI requiring high performance and heavy data flows.
Global FPGA market to double by 2026 The growing importance is reflected in rising global sales. GrandView Research projects a 9.7% compound annual growth rate (CAGR) from 2020 to 2027.
The firm points to several major drivers, including adoption across data centers and HPC systems. Other analysts forecast similar growth, with estimates of global sales between $4 billion and $13 billion , fueled by growing demand in AI and ML. McKinsey expects FPGAs will handle 20% of AI training in 2025 , up from nearly nothing in 2017.
Image credit: Verified Market Research Analysts agree: FPGAs will have broad appeal across industries, especially wireless communications, cloud service providers (CSPs), cybersecurity systems, aerospace and defense, automotive, and others. Not all adoption will be for AI, but industry watchers say more and more will.
Inside key FPGA innovations To better understand the appeal, and how advances in FPGAs can help organizations better handle current AI challenges, let’s take a closer look at key innovations in the Intel Stratix 10 NX FPGA.
High-performance AI Tensor (matrix) blocks.
AI is computationally intensive. To enhance the arithmetic functionality of the new FPGA, Intel and partner Microsoft rearchitected the device to accelerate data center AI workloads. They replaced the existing embedded DSP (digital signal processing) blocks with a new type of AI-optimized tensor arithmetic block that delivers high compute density.
Explains Deepali Trehan, general manager and senior director of FPGA Product Marketing at Intel: “The challenge was to keep in place all of the good things in the device — memory, logic, routing, transceivers, HBM — and fit the new AI Tensor blocks into the same location that the previous DSP block sat, so the FPGA could be brought into production much quicker, with lower risk.” The AI Tensor Blocks contain dense arrays of lower-precision multipliers typically used in AI applications. Architects increased the number of multipliers and accumulators to 30 each, up from two in the DSP block. The design is tuned for common matrix-matrix or vector-matrix multiplications used in a wide range of AI computations and convolutional neural networks (CNNs). The single AI Tensor Block achieves up to 15X more INT8 throughput than standard DSP block in its predecessor, enabling significantly increased AI for both small and large matrix sizes.
Near-compute memory.
Integrated 3D stacks of high-bandwidth (HBM2) DRAM memory allow large, persistent AI models to be stored on-chip. That results in lower latency helps prevent memory-bound performance challenges in large models.
The ability to mix and match components makes it easier to customize a wider range of FPGA chips for a diverse array of AI and hyperscale applications.
Image credit: Intel High-bandwidth networking and connectivity.
Slow I/O can choke AI. What good is super-fast math processing and memory if they’re bottlenecked in the interconnects between chips and chiplets or CPUs and accelerators? So another key advance focuses on reducing or eliminating bandwidth connectivity as a limiting factor in multi-node (“mix-and-match”) FPGA designs.
To speed networking and connectivity, the new Intel Stratix 10 NX adds up to four 57.8 Gbps PAM4 transceivers to implement multi-node AI inference solutions. Multiple banks of high-speed transceivers enable distributed or unrolled algorithms across the datacenter. The device also incorporates hard IP such as PCIe Gen3 x16 and 10/25/100G Ethernet MAC/PCS/FEC.
Support for super-fast CXL , faster transceivers, and Ethernet can be added by swapping out these modular tiles connected by EMIB.
Taken together, these interlocking innovations let the FPGA better handle larger, low-latency models needing greater compute density, memory bandwidth, and scalability across multiple nodes, while enabling reconfigurable custom functions.
Tech help for many AI challenges Technology innovations in today’s FPGAs enable improvements in many common AI requirements: Overcoming I/O bottlenecks.
FPGAs are often used where data must traverse many different networks at low latency. They’re incredibly useful at eliminating memory buffering and overcoming I/O bottlenecks — one of the most limiting factors in AI system performance. By accelerating data ingestion, FPGAs can speed the entire AI workflow.
Providing acceleration for high performance computing (HPC) clusters.
FPGAs can help facilitate the convergence of AI and HPC by serving as programmable accelerators for inference.
Integrating AI into workloads.
Using FPGAs, designers can add AI capabilities, like deep packet inspection or financial fraud detection, to existing workloads.
Enabling sensor fusion.
FPGAs excel when handling data input from multiple sensors, such as cameras, LIDAR, and audio sensors. This ability can be extremely valuable when designing autonomous vehicles, robotics, and industrial equipment.
Adding extra capabilities beyond AI.
FPGAs make it possible to add security, I/O, networking, or pre-/post-processing capabilities without requiring an extra chip, and other data-and compute-intensive applications.
Microsoft expands pioneering use Exascale cloud service providers are already deploying the latest FPGAs, often on supercomputers. They’re accelerating service-oriented tasks, such as network encryption, inference and training, memory caching, webpage ranking, high-frequency trading, video conversion, and improving overall system performance.
Take Microsoft. In 2010, the company pioneered using FPGAs on Azure and Bing to accelerate internal workloads such as search indexing and software defined networking (SDN).
In 2018, they reported a 95% gain in throughput, 8x speed increase with 15% less power, and a 29% decrease in latency on Microsoft Azure Hardware integrated with Project Brainwave , a deep learning platform for real-time AI inference in the cloud and on the edge.
Today, the company says Microsoft Azure is the world’s largest cloud investment in FPGAs. Microsoft continues expanding its use of FPGAs for deep neural networks (DNN) evaluation, search ranking, and SDN acceleration to reduce latency and free CPUs for other tasks.
Image credit: Microsoft The FPGA-fueled architecture is economical and power-efficient, according to Microsoft, with a very high throughput that can run ResNet 50, an industry-standard DNN requiring almost eight billion calculations, without batching. That means AI customers do not need to choose between high performance or low cost, the company says.
The company is continuing its partnership with Intel to develop next-generation solutions for its hyperscale AI. “As Microsoft designs our real-time multi-node AI solutions, we need flexible processing devices that deliver ASIC-level tensor performance, high memory and connectivity bandwidth, and extremely low latency,” explains Doug Burger, technical fellow, Microsoft Azure Hardware Top applications for FPGAs Many data center applications and workloads will benefit from the new AI optimizations in Intel FPGAs. Among them: Natural Language Processing, including speech recognition and speech synthesis.
NLP models are typically large and getter larger. The need to detect, recognize, and understand the context of various languages, followed by translation to the target language is a growing use for language translation applications, a common NLP workload. These expanded workload requirements drive model complexity, which results in the need for more compute cycles, more memory, and more networking bandwidth, but at very low latencies so as not to break a conversational-like flow. Compared to GPUs, FPGAs excel in handling low batch (single words or phrases) with low latency and high performance.
Security including deep packet inspection, congestion control identification, and fraud detection.
The FPGAs enable real-time data processing applications where every micro-second matters. The device’s ability to create custom hardware solutions with direct ingestion of data through transceivers and deterministic, low latency compute elements enable microsecond-class real-time performance.
Real-time video analytics including content recognition, video pre-and post-processing, and video surveillance. The new FPGAs excel here because of their hardware customization ability, which allows implementation of custom processing and I/O protocols for direct data ingestion.
Business benefits: Performance and TCO How do these technological advances translate into specific benefits for organizations? Customer experiences show that optimized FPGAs offer several advantages for deep learning applications and other AI workloads: High real-time performance and throughput.
FPGAs can inherently provide low latency as well as deterministic latency for real-time applications. That means, for example, video can bypass a CPU and be directly ingested into the FPGA. Designers can build a neural network from the ground up and structure the FPGA to best suit the model. In general, the more the FPGA can do with the data before it enters the CPU, the better, as the CPU can then be used for higher priority tasks.
Value and cost.
FPGAs can be reprogrammed for different functionalities and data types, making them one of the most cost-effective hardware options available. Furthermore, FPGAs can be used for more than just AI. By integrating additional capabilities onto the same chip, designers can save on cost and board space. FPGAs have long product life cycles, so hardware designs based on FPGAs can have a long product life, measured in years or decades. This characteristic makes them ideal for use in industrial defense, medical, automotive, and many others.
Above: The new FPGAs meet the biggest, expanding needs of today’s service providers and enterprises.
Image credit: Allied Market Research Reusability and upgradability are big pluses.
Design prototypes can be implemented on FPGA, verified, and implemented on an ASIC. If the design has faults, a developer can change the HDL code, generate bit stream, program to FPGA, and test again. While ASICs may cost less per unit than an equivalent FPGA, building them requires a non-recurring expense (NRE), expensive software tools, specialized design teams, and long manufacturing cycle.
Low power consumption.
FPGAs are not usually considered “low power”. Yet on cost per watt, they can match or beat fixed-function counterparts, especially ASICs and ASSPs (application-specific standard product s) that have not been optimized. With FPGAs, designers can fine-tune the hardware to the application, helping meet power efficiency requirements. FPGAs also accommodate multiple functions, delivering more energy efficiency from the chip. It’s possible to use a portion of an FPGA for a function, rather than the entire chip, allowing the FPGA to host multiple functions in parallel. Besides enabling power savings, Intel Hyperflex FPGA Architecture also reduces IP size, freeing resources for greater functionality.
Bottom line: FPGAs for high bandwidth, low latency and power For all their new advantages, FPGAs are not a do-everything chip for AI, notes Jason Lawley, technical marketing director of XPU at Intel. The spatial architecture of FPGAs is ideal for delivering data to customized, optimized and differentiated end products, he says. But as the company’s new vision makes clear, organizations also need scalar, vector, and matrix processors.
“This breadth lets companies choose the right balance of power, performance and latency for the workload,” explains Lawley.
Further, selecting the best chip, for data center, cloud or edge is not a one-time choice. “Increasingly, ” notes Lawley, “developers will be able to select the right architecture for their challenge, then have the flexibility to change if requirements change.” Intel’s OneAPI, a simplified cross-architecture programming model, ties together the different processor architectures. So software developed for one processor type can be used without rewriting for another. So, too will, new, scalable, open hardware and software infrastructure help developers speed development and deployment.
Other technological developments are helping drive adoption. Intel’s advanced packaging, including Embedded Multi-die Interconnect Bridge (EMIB) and the industry-first Foveros 3D stacking technology, are enabling new approaches in FPGA architecture. High-density interconnects enable high bandwidth at low power, with I/O density on par with or better than competitive approaches.
Once an unexciting part in the engineering toolbox, FPGAs are again becoming a popular chip choice for speeding development and processing for low-latency deep learning, cloud, search and other computationally-intensive applications. Today’s FPGAs offer a compelling combination of power, economy, and programmable flexibility for accelerating even the biggest, most complex, and hungriest models.
With workloads expected to increase in both size and breadth over the next decade, smart use of spatial and other architectures will be the key to competitive differentiation and success, especially for exascale companies.
Dig deeper: FPGAs for AI FPGA Technology Day 2020 Intel FPGA Resource Center Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].
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"Track API activity with AWS CloudTrail, Amazon's newest cloud service | VentureBeat"
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"https://venturebeat.com/2013/11/13/amazon-aws-cloudtrail"
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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 Track API activity with AWS CloudTrail, Amazon’s newest cloud service Share on Facebook Share on X Share on LinkedIn Andy Jassy, senior vice president of AWS, unveils CloudTrail onstage at AWS re:Invent in Las Vegas 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.
As apps on Amazon Web Services (AWS) become increasingly complex and integrated, AWS API calls come from more places and people. It can get confusing — and tracking all that information can be valuable for compliance aid, security analytics, operational troubleshooting, and resource life cycle tracking.
To help developers and IT follow it all, Amazon just unveiled AWS CloudTrail , which records calls made to the AWS APIs and publishes the resulting log files (in JSON format) to a storage bucket in AWS S3, Amazon Web Services’ cloud storage offering.
CloudTrail lets you keep track of what actions users have taken over a specific period of time and of which users have accessed a specific resource. You can also see the source IP address of that activity.
There’s no special charge for CloudTrail, just regular S3 and SNS (Simple Notification Service) pricing.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Amazon made a fun flow chart to demonstrate where CloudTrail fits in the AWS ecosystem. You can check that out below.
Related articles The Amazon Web Services API debate shows no sign of cooling Amazon plans to lower the cost of processing big data in the cloud NASA’s piping a bunch of earth science data to Amazon’s cloud Amazon embraces cloud gaming (and other 3D graphics applications) 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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15,474 | 2,021 |
"Amazon Managed Grafana for AWS hits general availability | VentureBeat"
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"https://venturebeat.com/2021/09/01/amazon-managed-grafana-for-aws-hits-general-availability"
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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 Managed Grafana for AWS hits general availability Share on Facebook Share on X Share on LinkedIn Grafana dashboard visualizing demo wind farm with data from AWS IoT SiteWise 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.
Let the OSS Enterprise newsletter guide your open source journey! Sign up here.
Amazon today announced the general availability of Amazon Managed Grafana , a fully managed offering that provides Amazon Web Services (AWS) cloud customers an easy way to deploy Grafana alongside other AWS services.
Released in 2014, Grafana is an open source platform that helps businesses like PayPal and JPMorgan Chase take all their existing data, wherever it resides, and build dashboards to visualize the unified data in a single dashboard.
On top of the open source incarnation, Grafana Labs offers enterprise-grade cloud and on-premises services with additional support and features, along with plugins for Elasticsearch, Jira, Datadog, Splunk, AppDynamics, Oracle, MongoDB, Snowflake, and more.
Above: AWS data sources for Grafana With the Amazon-flavored incarnation, Grafana now easily integrates with AWS data sources like Amazon CloudWatch, Amazon Elasticsearch Service, and the Amazon Managed Service for Prometheus. The tight integration means companies can create Grafana dashboards in AWS without having to manage any of the provisioning, setup, or maintenance processes.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! First announced in preview last December, Amazon Managed Grafana was created in direct partnership with Grafana Labs, and today’s announcement comes a week after Grafana Labs announced a fresh $220 million round at a $3 billion valuation.
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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15,475 | 2,021 |
"Kubeshop wants to be a Kubernetes product pipeline | VentureBeat"
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"https://venturebeat.com/2021/09/17/kubeshop-wants-to-be-a-kubernetes-product-pipeline"
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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 Kubeshop wants to be a Kubernetes product pipeline 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.
Let the OSS Enterprise newsletter guide your open source journey! Sign up here.
A new startup accelerator and incubator aims to provide a pipeline for products and projects for the Kubernetes ecosystem by “nurturing and funding” open source software development.
Founded out of Boston in March, Kubeshop is majority-owned by venture capital firm Insight Partners, alongside Veeam cofounder Ratmir Timashev, who sold his data backup and recovery platform (to Insight Partners) for $5 billion last year.
As one of the world’s most popular and powerful open source projects , Kubernetes needs little introduction. Emanating from Google in 2014 and later hosted by the Cloud Native Computing Foundation (CNCF), Kubernetes is an open source orchestration platform that automates many of the resource-intensive manual processes involved in managing containerized applications — it ultimately helps accelerate development velocity and agility.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Containers, for the uninitiated, are software packages consisting of all the components needed to help companies operate in public clouds or private datacenters and solve the problem of getting software to play ball when its moved between environments. A recent Forrester report commissioned by Capital One noted that enterprise container adoption was growing as part of a broader transition to the cloud and microservices.
Flywheel effect Kubernetes has been adopted by countless companies beyond Google, from Bose, Box, and BlackRock to IBM, Huawei, and Spotify. As with other successful open source projects, numerous companies have built commercial services on top of Kubernetes — and investors are taking note.
In the past 18 months alone, Kubernetes operations management platform Rafay raised $25 million ; Carbon Relay ( now StormForge) raised $63 million to automate Kubernetes app deployment; Kubernetes observability platform Pixie Labs raised $9.15 million before being snapped up by New Relic ; and Kubernetes security platform StackRox raised $26.5 million before Red Hat swooped in and bought the company in January. Elsewhere, Cisco bought at least two Kubernetes startups last year, while Rapid7 also got in on the Kubernetes acquisition action.
Put simply, Kubernetes is hot and getting hotter, which is where Kubeshop comes into play.
Kubeshop cofounder and CEO Dmitry Fonarev said Kubernetes is no longer just a niche technology used by advanced engineering teams and is now enjoying wider usage. He also elaborated on the factors behind this shift.
“More developers and DevOps teams are realizing its [Kubernetes] power and scale,” Fonarev told VentureBeat. “Because Kubernetes is complex — the expansion of tooling in the space makes it easier to onboard newcomers and operate and integrate with existing CI/CD workflows and the rest of the developer and DevOps ecosystem.” This creates something of a “ flywheel effect ” — which investors love.
“At a strategic level, some wise people believe that what operating systems did to the computer industry a few decades ago, Kubernetes is doing to cloud and SaaS infrastructure software now,” Fonarev added.
Power of 3 Just six months after its formal foundation, Kubeshop has three developer-focused open source projects in production.
Kusk , which is targeted at API developers; Kubtest , which integrates testing frameworks into Kubernetes application development; and Monokle , which is essentially a manifest IDE for Kubernetes.
Above: Monokle UI In terms of how Kubeshop’s projects get off the ground in the first place, Fonarev said he and CTO cofounder Ole Lensmar have “many years of experience” in the developer, testing, and DevOps space and so have good ideas themselves, although they also look externally. Moreover, while some of their projects may ultimately flounder, Kubeshop is not putting all its eggs in one Kubernetes basket.
“We generate ideas internally, some ourselves and some by hiring creative and innovative SMEs, then we build autonomous teams around each one of these,” Fonarev explained. “When — or if — those projects prove to be successful [and] have enough of the community and traction, we may spin them off as standalone commercial companies.” Each Kubeshop project has its own individual team and leader, though Kubeshop does share some resources across projects, such as developer relations, infrastructure, and general expertise. Looking to the future, Fonarev said the team is aiming to have as many as 10 projects running by the end of 2022, and perhaps double that the following year. And while there could be scope to extend coverage in the future, the current focus is firmly on Kubernetes.
“We believe that Kubernetes represents a strategic evolution in the software industry,” Fonarev said. “At the same time, it is complex, and overall it’s not used by many developers. Most of the tooling in the space has been built for DevOps and for the ‘experts.’ We are focusing on developers and testers — and most importantly, making it easier for people to get started.” It’s still very early days for Kubeshop and its triumvirate of open source projects, but Fonarev said they are in discussions with several enterprise companies, including a bank. However, he’s adamant that Kubeshop isn’t really focused on “selling” at the moment — that will come later. “Our focus is around getting people to adapt, use, validate, and provide feedback to inform the projects’ evolution,” he said.
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"Google taps AI to identify COVID-19 vaccine name variations | VentureBeat"
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"https://venturebeat.com/2021/06/29/google-taps-ai-to-identify-covid-19-vaccine-name-variations"
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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 taps AI to identify COVID-19 vaccine name variations 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 May during its Google I/O 2021 developer conference, Google demoed multitask unified model (MUM) , a system trained on 75 languages at once that can simultaneously understand different forms of information including text, images, and videos. Today, Google revealed that it’s using MUM to identify variations in the names of COVID-19 vaccines across multiple languages, which the company claims has improved Google Search’s ability to surface information about COVID-19 vaccines for users around the world.
As Google notes, the COVID-19 vaccines released to date — including those from AstraZeneca, Moderna, and Pfizer — go by different names depending on the country and region of origin. There are roughly hundreds of COVID-19 vaccine names globally, not all of which have historically risen to the top of Search when users would type in phrases like “new virus vaccines,” “mrna vaccines,” and “AZD1222.” MUM, which can transfer knowledge between languages and doesn’t need to be explicitly taught how to complete certain tasks, helped Google engineers to identify more than 800 COVID-19 name variations in over 50 languages, according to Google Search VP Pandu Nayak. With only a few examples of “official” vaccine names, MUM was able to find interlingual variations “in seconds” compared with the weeks it might take a human team.
Above: Google’s MUM is being used to surface COVID-19 vaccine information across different languages in Search.
“This first application of MUM has helped to provide users around the world with important information in a timely manner,” Nayak said in a blog post translated from Japanese. “We look forward to making search more convenient through the use of MUM in the future. Early testing has shown that MUM not only improves existing systems, but also helps develop new methods of information retrieval.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Future work Google previously applied AI to the problem of providing projections of COVID-19 cases, deaths, ICU utilization, ventilator availability, and other metrics useful to policymakers and health care workers. In August 2020, in partnership with Harvard, the company released models that forecast COVID-19–related developments over the next 14 days for U.S. counties and states.
MUM has potential beyond vaccine name identification, particularly in situations where it can lean on context and more in imagery and dialogue turns. For example, given a photo of hiking boots and asked “Can I use this to hike Mount Fuji?”, MUM can comprehend the content of the image and the intent behind the query, letting the questioner know that hiking boots would be appropriate and pointing them toward a lesson in a Mount Fuji blog.
MUM can also understand questions like “I want to hike to Mount Fuji next fall, what should I do to prepare?” Because of its multimodal capabilities, MUM realizes that “prepare” could encompass things like fitness training as well as weather. The model, then, could recommend that the questioner bring a waterproof jacket and give pointers to go deeper on topics with relevant content from articles, videos, and images across the web.
“We’re in the early days of exploiting this new technology,” Prabhakar Raghavan, senior VP at Google, said onstage at Google I/O. “We’re excited about its potential to solve more complex questions, no matter how you ask … MUM is changing the game with its language understanding capabilities.” 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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15,477 | 2,021 |
"How computer vision works -- and why it's plagued by bias | VentureBeat"
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"https://venturebeat.com/2021/08/11/how-computer-vision-works-and-why-its-plagued-by-bias"
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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 computer vision works — and why it’s plagued by bias 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’s no secret that AI is everywhere, yet it’s not always clear when we’re interacting with it, let alone which specific techniques are at play. But one subset is easy to recognize: If the experience is intelligent and involves photos or videos, or is visual in any way, computer vision is likely working behind the scenes.
Computer vision is a subfield of AI, specifically of machine learning. If AI allows machines to “think,” then computer vision is what allows them to “see.” More technically, it enables machines to recognize, make sense of, and respond to visual information like photos, videos, and other visual inputs.
Over the last few years, computer vision has become a major driver of AI. The technique is used widely in industries like manufacturing, ecommerce, agriculture, automotive, and medicine, to name a few. It powers everything from interactive Snapchat lenses to sports broadcasts, AR-powered shopping, medical analysis, and autonomous driving capabilities. And by 2022, the global market for the subfield is projected to reach $48.6 billion annually, up from just $6.6 billion in 2015.
The computer vision story follows that of AI overall. A slow rise full of technical hurdles. A big boom enabled by massive amounts of data. Rapid proliferation. And then growing concern over bias and how the technology is being used. To understand computer vision, it’s important to understand how it works, how it’s being used, and both the challenges it overcame and the ones it still faces today.
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 computer vision works Computer vision allows computers to accomplish a variety of tasks. There’s image segmentation (divides an image into parts and examines them individually) and pattern recognition (recognizes the repetition of visual stimuli between images). There’s also object classification (classifies objects found in an image), object tracking (finds and tracks moving objects in a video), and object detection (looks for and identifies specific objects in an image). Additionally, there’s facial recognition, an advanced form of object detection that can detect and identify human faces.
As mentioned, computer vision is a subset of machine learning, and it similarly uses neural networks to sort through massive amounts of data until it understands what it’s looking at. In fact, the example in our machine learning explainer about how deep learning could be used to separate photos of ice cream and pepperoni pizza is more specifically a computer vision use case. You provide the AI system with a lot of photos depicting both foods. The computer then puts the photos through several layers of processing — which make up the neural network — to distinguish the ice cream from the pepperoni pizza one step at a time. Earlier layers look at basic properties like lines or edges between light and dark parts of the images, while subsequent layers identify more complex features like shapes or even faces.
This works because computer vision systems function by interpreting an image (or video) as a series of pixels, which are each tagged with a color value. These tags serve as the inputs the system process as it moves the image through the neural network.
Rise of computer vision Like machine learning overall, computer vision dates back to the 1950s. Without our current computing power and data access, the technique was originally very manual and prone to error. But it did still resemble computer vision as we know it today; the effectiveness of first processing according to basic properties like lines or edges, for example, was discovered in 1959.
That same year also saw the invention of a technology that made it possible to transform images into grids of numbers , which incorporated the binary language machines could understand into images.
Throughout the next few decades, more technical breakthroughs helped pave the way for computer vision. First, there was the development of computer scanning technology, which for the first time enabled computers to digitize images. Then came the ability to turn two-dimensional images into three-dimensional forms. Object recognition technology that could recognize text arrived in 1974, and by 1982, computer vision really started to take shape. In that same year, one researcher further developed the processing hierarchy, just as another developed an early neural network.
By the early 2000s, object recognition specifically was garnering a lot of interest. But it was the release of ImageNet , a dataset containing millions of tagged images, in 2010 that helped propel computer vision’s rise. Suddenly, a vast amount of labeled, ready-to-go data was available for anyone who wanted it. ImageNet was used widely, and most of the computer vision systems that have been built today relied on it. But while computer vision systems were popular at this point, they were still turning up a lot of errors. That changed in 2012 when a model called AlexNet , which used ImageNet, significantly reduced the error rate for image recognition, ushering in today’s field of computer vision.
Computer vision’s bias and challenges The availability of ImageNet was transformative for the growth and adoption of computer vision. It quite literally became the basis for the industry. But it also scarred the technology in ways that are having a real impact today.
The story of ImageNet reflects a popular saying in data science and AI: “garbage in, garbage out.” In jumping to take advantage of the dataset, researchers and data scientists didn’t pause to consider where the images came from, who chose them, who labeled them, why they were labeled as they were, what images or labels may have been omitted, and the effect all of this might have on how their technology would function, let alone the impact it would have on society and people’s lives. Years later, in 2019, a study on ImageNet revealed the prevalence of bias and problematic labels throughout the dataset.
“Many truly offensive and harmful categories hid in the depth of ImageNet’s Person categories. Some classifications were misogynist, racist, ageist, and ableist. … Insults, racist slurs, and oral judgements abound,” wrote AI researcher Kate Crawford in her book Atlas of AI.
And even besides these explicitly obvious harms (some of which have been removed — ImageNet is reportedly working to address various sources of bias), curious choices in terms of categories, hierarchy, and labeling have been found throughout the dataset. It’s now widely criticized for privacy violations as well, as people whose photos were used in the dataset didn’t consent to being included or labeled.
Data and algorithmic bias is one of the core issues of AI overall, but it’s especially easy to see the impact in some computer vision applications. Facial recognition technology, for example, is known to misidentify Black people , but its use is surging in retail stores.
It’s also already common in policing , which has prompted protests and regulations in several U.S. cities and states.
Regulations overall are an emerging challenge for computer vision (and AI in general). It’s clear more of it is coming (especially if more of the world follows in the European Union’s path), but it’s not yet known exactly what such regulations will look like, making it difficult for researchers and companies to navigate in this moment. “There’s no standardization and it’s uncertain. For these types of things, having clarification would be helpful,” said Haniyeh Mahmoudian, DataRobot’s global AI ethicist and a winner of VentureBeat’s Women in AI responsibility and ethics award.
Computer vision has some technical challenges as well. It’s limited by hardware, including cameras and sensors. Additionally, computer vision systems are very complex to scale. And like all types of AI, they require massive amounts of computing power (which is expensive) and data. And as the entire history of computer vision makes clear, good data that is representative, unbiased, and ethically collected is hard to come by — and incredibly tedious to tag.
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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"Neuron7 employs open source AI tools for field service across devices | VentureBeat"
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"https://venturebeat.com/2021/08/13/neuron-ai-targets-ai-driven-field-service-across-devices"
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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 Neuron7 employs open source AI tools for field service across devices 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.
Let the OSS Enterprise newsletter guide your open source journey! Sign up here.
Neuron7.ai emerged from stealth this week to reveal its platform that combines various open source AI technologies to automate field service across many types of devices. The product’s promise earned the company $4.2 million in seed funding from Nexus Venture Partners and Battery Ventures.
Naturally, there’s already a fair number of organizations attempting to apply AI to a wide range of field service issues, from optimizing traffic routes to encouraging customers to engage bots rather than humans to resolve an issue.
It’s not likely AI platforms are going to replace the need for field technicians anytime soon, given all the issues that might be encountered once a device is deployed. However, AI will clearly play a significant role in enabling a limited number of field service technicians to support a much wider range of devices deployed anywhere in the world.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Neuron7.ai is building a platform that consumes the recommendations created by open source AI engines and models. The aim is to make AI technologies accessible to organizations that typically don’t have the resources required to build AI models that specifically address the unique needs of a field service team, said Neuron7.ai CEO Niken Patel.
Open source AI tools The Neuron7 platform ingests structured and unstructured data from a wide range of sources, including product and service manuals, knowledge bases, technician notes, customer relationship management (CRM) systems, and messaging systems such as Slack. It then applies various open source AI engines based on frameworks such as TensorFlow to determine how to best remediate a performance issue or an outright device failure, said Patel.
Designed as a software-as-a-service (SaaS) application, Neuron7’s goal is to make AI accessible to organizations that need to optimize field service across an increasing array of devices that require remote support by technicians, Patel said. Technicians can’t be expected to be experts on every potential issue or parameter for all those different devices — “No one can be an expert on every device,” he said.
In addition to aggregating all the data that technicians require to resolve an issue as soon as possible, Patel said, Neuron7 captures the unique knowledge and expertise of the technicians that service the devices to ultimately make the AI platform more accurate. That capability mitigates turnover issues that occur when experienced technicians leave an organization and new ones are onboarded.
Investing in service Pricing for the Neuron7 platform is based on a subscription model, with tiers that depend on the number of data sets that need to be trained. However, Patel said the company is hoping to shift to a pricing model that is based more on the outcomes enabled by the platform.
Angel investors, early backers, and advisors of the company include Akash Palkhiwala, CFO at Qualcomm; Ashish Agarwal, CEO of Neudesic Global Services; Kintan Brahmbhatt, general manager for Amazon Podcasts; and Anand Chandrasekaran, executive vice president for Five9.
In the age of COVID-19, organizations are looking for ways to automate service management as much as possible to reduce the number of technicians they need to dispatch. Achieving that goal requires organizations to provide customer support technicians with as much relevant data as possible so they can resolve any issues remotely. The challenge is that the devices being deployed in B2C and B2B environments are becoming more complex, Patel said. As more complex devices are connected within an internet of things (IoT) application environment, the need to augment technicians with an AI platform becomes more pressing, he added.
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 open source helps companies establish a culture of “doing” (VB Live) | VentureBeat"
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"https://venturebeat.com/2021/09/02/how-open-source-helps-companies-establish-a-culture-of-doing-vb-live"
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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 Live How open source helps companies establish a culture of “doing” (VB Live) Share on Facebook Share on X Share on LinkedIn Presented by DataStax Open source is an engine for innovation, offering reliability, scalability, and security for IT leaders. Learn how open source can help companies activate data in real time, improve customer relationships, and transform their business in this VB Live event.
Register here for free.
The world’s application innovation is now based on open source software.
It has come to transform enterprise infrastructure — and as the ecosystem gets bigger, the innovation comes faster. Just look at the way Microsoft helped accelerate the progress of Linux.
As we look ahead to the next decade, or what some have started to call the decade of data, companies trying to execute on their data strategy are joining that open source ecosystem, says Bryan Kirschner, vice president of strategy at DataStax. In recent research conducted by the company, they found that 96% of today’s data leaders are embracing more open source software, and 69% of all companies as a whole.
While data technology doesn’t have to be open source, best-of-breed technologies like Kafka, Sparc, Apache, and Cassandra, and emerging technologies like Pulsar and Flank, are all open source. That trend will just continue to accelerate over the next decade. Open source lets companies leap directly toward creating value and driving innovation.
“Facebook built Cassandra because they couldn’t find a solution for what they wanted to accomplish,” Kirschner says. “But you as the end user, you don’t want to be solving a problem that’s been solved somewhere else by somebody else before. Leverage the technologies that often were built to expand the boundaries of what was possible.” What’s unique about every enterprise is no one else has your customer relationships and the data they generate, he says. You have unique interactions with your customers, and domain knowledge in your industry. It’s about taking that data and figuring out how to use that data to create more value in those interactions or make your operations and processes more efficient.
“Focus on what’s unique to you, what’s in your domain knowledge,” he says. “The more types of interactions, the more data you generate, the more you can drive that data back into analytics, intelligence, recommendations, personalization, and so on. That’s what’s special. The infrastructure, the technology, they don’t solve problems that have already been solved.” How to start leveraging open source For companies that are interested in diving into the open source world, Kirschner notes that it’s an incredibly safe, secure, and rich ecosystem which offers a tremendous amount of choice. You can choose the level of autonomy you prefer, from Apache-licensed technology that lets you take the code and run it yourself, to 100 percent as-a-service offerings, like Astra DB, which is multi-cloud DBaaS built on Apache Cassandra™, so that your people don’t need to learn it if they don’t know it already.
Leaders in the space are also strong believers in best of breed — being able to deploy and use the best of breed today, but having their eye out for what’s cutting edge tomorrow.
“You’re going to come up with new and different ways of operating the business,” he explains. “It’s important to follow that commitment to best of breed. Expect that new tools will emerge that may make your wild and crazy ideas possible. Stay plugged into the ecosystem. Keep looking at what’s next, do proof of concepts, and stay proactive as the open source ecosystem evolves.” Getting buy-in: Open source and a culture of innovation “Technical practitioners have been getting excited about open source for as long as I can remember,” Kirschner says. “For folks on the business side who aren’t familiar with open source and its history and dynamics, it’s an opportunity for CIOs or CTOs, folks on the tech side, to explain why this is valuable and exciting, why you want to go down this path, and help them share that enthusiasm.” If you have a culture that encourages experimentation and innovation, open source is fantastic for the frictionless, “give it a try, see if it works” kind of innovation — and if you don’t have that culture, you should encourage it, he adds.
One of the big takeaways of the DataStax “State of the Data Race 2021” report was that the enemy of progress, pre-COVID, was talk. Debate can be endless — you make a lot more progress by shipping code, shipping MVPs, doing proof of concepts.
“What has come out of this disruption is we have just cut months and months off of the time it took us to do anything, because we have a bias for action,” he says. “It grew out of necessity, but once you prove that bias for action works, it sticks. That doesn’t require open source, but open source is a fantastic platform to help embed the culture of doing into your organization.” Register for free here.
You’ll learn: How and why key open source technologies should play a central role in your data strategy role How to leverage leading and emerging technologies to drive your own data strategy forward Best practices for aligning cultural as well as technical patterns for success to accelerate innovation Speakers: Al Gillen , Group VP, Software Development and Open Source, IDC Bryan Kirschner , Vice President Strategy, DataStax More industry thought-leaders to come! 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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"Open source services market on course to become $50B industry | VentureBeat"
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"https://venturebeat.com/2021/09/21/open-source-services-market-on-course-to-become-50b-industry"
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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 Open source services market on course to become $50B industry Share on Facebook Share on X Share on LinkedIn Concept illustration for open source software 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.
Let the OSS Enterprise newsletter guide your open source journey! Sign up here.
The open source services market, pegged at $21.7 billion in 2021, is on course to more than double within five years. That’s according to a new report from MarketsandMarkets, which predicts that the “professional” and “managed” open source services segments will grow 130% to $50 billion by 2026.
“Open source” refers to software that is made freely available for anyone to access, copy, and modify. It adheres to a collaborative, community-led philosophy that lowers both the bar to entry and the cost of building software. It also gives larger enterprises the freedom to deploy software wherever they wish, including on-premises — offering them greater control over customer data and allowing developers to more easily integrate the software into their existing systems and workflows.
On the flip side, switching to open source software might present compatibility issues, while inherent vulnerabilities and the threat of supply chain attacks can be another cause for concern. This is why the open source services market is predicted to flourish in the coming years, with MarketsandMarkets citing cloud computing adoption and broader digital transformations as a driving force behind this push. Enterprise-grade open source services support helps assure companies that their software stack is robust, secure, and kept up-to-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! Notable players in the open source services space include Red Hat, which IBM acquired for $34 billion in 2018; Cisco; Oracle; Suse; and Databricks.
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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"84% of tech workers report their products aren't inclusive | VentureBeat"
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"https://venturebeat.com/2021/07/27/84-of-tech-workers-report-their-products-arent-inclusive"
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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 84% of tech workers report their products aren’t inclusive 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.
New research on inclusivity in tech reveals some concerning results: 84% of all tech employees acknowledge their products are not inclusive, according to a report from the Capgemini Research Institute.
What’s more, the firm found stark differences between the opinions of leadership executives and underrepresented employees including women and “ethnic minorities” when it comes to inclusion practices.
When presented with positive statements meant to evaluate equity and inclusivity, executives overwhelmingly felt the statements were true for their organizations, while very few underrepresented employees felt similarly. For example, 85% of executive leaders said they believe their organizations provide equitable opportunities for career development and promotions to every employee, but only 18% of the underrepresented employees agreed. Similar numbers were reported for all seven inclusivity statements posed by the research team. Elizabeth Kiehner, VP of enterprise transformation at Capgemini Invent, told VentureBeat, “That’s an incredibly wide perception gap.” “These gaps demonstrate that true accountability needs to be established across the majority of organizations,” she said. “There’s an enormous opportunity to focus on inclusivity and appropriately recalibrating in order to create an improved reality for all in this pressing moment.” The urgency of inclusive tech Not only are diverse and inclusive teams more profitable , but they’re more likely to create inclusive products. Specifically, organizations with advanced inclusive practices are four times more likely to create inclusive products, according to Capgemini, which evaluated inclusivity by considering an organization’s training, growth opportunities, and to what extent employees feel comfortable offering their perspectives, among other factors.
The tech industry has seen example after example of products and services hitting the market with obvious harms or blind spots. There have been sexist Google features and racist Snapchat filters , for example, but the list could go on and on. Time after time, people wonder, “why didn’t anyone notice this?” Across subfields, technologists are sounding the alarm about the dangers of homogenous teams that can’t and don’t account for the experiences of underrepresented groups. Overall, only 26% of computing-related jobs are held by women; just 3% are held by African American women, 6% by Asian women, and 2% by Hispanic women. And studies show these women, especially women of color, feel invisible at work.
To put a number to it, only 16% of women and ethnic minority tech employees surveyed by Capgemini believe they’re well represented in tech teams.
“When tech teams themselves are not inclusive, or when tech team members do not even feel a sense of belonging, the design suffers,” Kiehner said.
And the stakes are only increasing as technology — especially increasingly powerful technologies like AI — become more integrated into our lives. Capgemini found consumers are aware of tech-based discrimination, and most have experienced it. Among women of color, nearly half were offered lower credit facility for certain banking products, according to the report. The use of facial recognition, which is known to misidentify Black people , is already common among law enforcement and is even surging in retail stores.
Researchers have also uncovered biases baked into AI-powered corporate interview tools that are costing people jobs. Timnit Gebru, the former co-lead of Google’s ethical AI team, who says she was fired after refusing to rescind research about the risks of deploying large language models, once summed up the issue succinctly in a now often-quoted Facebook post: “I’m not worried about machines taking over the world. I’m worried about groupthink, insularity, and arrogance in the AI community.” How tech teams can more inclusive Both Kiehner and the report share some guidance for improving inclusivity: drive fairness in AI systems , keep underrepresented users at the heart of design, reevaluate hiring processes for bias, reduce workplace microaggressions, and so on. In addition to taking such actionable steps, Kiehner says teams need to set “inclusive practices as a standard.” And the report concludes by expressing the urgency of the issue at hand.
But while the above steps are indeed necessary, it’s important to acknowledge that this issue runs deeper than a checklist. The lack of diversity and inclusion in tech — and the product dangers and abuses that can result from it — reflect pervasive racism, sexism, ableism, and other discriminations in our society at large. Building and empowering teams that actually represent our society is the only way forward.
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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"Why AI ethics needs to address AI literacy, not just bias | VentureBeat"
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"https://venturebeat.com/2021/08/13/why-ai-ethics-needs-to-address-ai-literacy-not-just-bias"
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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 AI ethics needs to address AI literacy, not just bias 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.
Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, we’re diving deeper into conversations with this year’s winners , whom we honored recently at Transform 2021.
Check out last week’s interview with the winner of our AI research award.
When you hear about AI ethics , it’s mostly about bias. But Noelle Silver, a winner of VentureBeat’s Women in AI responsibility and ethics award, has dedicated herself to an often overlooked part of the responsible AI equation: AI literacy.
“That’s my vision, is that we really increase literacy across the board,” she told VentureBeat of her effort to educate everyone from C-suites to teenagers about how to approach AI more thoughtfully.
After presenting to one too many boardrooms that could only see the good in AI, Silver started to see this lack of knowledge and ability to ask the important questions as a danger. Now, she’s a consistent champion for public understanding of AI, and has also established several initiatives supporting women and underrepresented communities.
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 excited to present Silver with this much-deserved award. We recently caught up with her to chat more about the inspiration for her work, the misconceptions about responsible AI, and how enterprises can make sure AI ethics is more than a box to check.
This interview has been edited for brevity and clarity.
VentureBeat: What would you say is your unique perspective when it comes to AI? What drives your work? Noelle Silver: I’m driven by the fact that I have a house full of people who are consuming AI for various reasons. There’s my son with Down syndrome, and I’m interested in making the world accessible to him. And then my dad who is 72 and suffered a traumatic brain injury, and so he can’t use a smartphone and he doesn’t have a computer. Accessibility is a big part of it, and for the products I have the opportunity to be involved in, I want to make sure I’m representing those perspectives.
I always joke about how when we first started on Alexa, it was a pet project for Jeff Bezos. We weren’t consciously thinking about what this could do for classrooms, nursing homes, or people with speech difficulties. But all of those are really relevant use cases Amazon Alexa has now invested in. I always quote Arthur C. Clarke, who said, “Any sufficiently advanced technology is indistinguishable from magic.” And that’s true for my dad. When he uses Alexa, he’s like, “This is amazing!” You feel that it mystifies him, but the reality is there’s someone like me with fingers on a keyboard building the model that supports that magic. And I think being transparent and letting people know there are humans making them do what they do, and the more diverse and inclusive those humans can be in their development, the better. So I took that lesson and now I’ve talked to hundreds of executives and boards around the world to educate them about the questions they should be asking.
VentureBeat: You’ve created several initiatives championing women and underrepresented communities within the AI community, including AI Leadership Institute, Women in AI, and more. What led you to launch these groups? And what is your plan and hope for them in the near future and the long run? Silver: I launched the AI Leadership Institute six years ago because I was being asked, as part of my profession, to go and talk to executives and boards about AI. And I was selling a product, so I was there to, you know, talk about the art of the possible and get them excited, which was easy to do. But I found there was really a lack of literacy at the highest levels. And the fact that those with the budgets didn’t have that literacy, it made it dangerous that someone like me could tell a good story and tap into the optimistic feels of AI and they couldn’t recognize that’s not the only course. I tell the good and the bad, but what if it’s someone who’s trying to get them to do something without being as transparent? And so I started that leadership institute with the support of AWS, Alexa, and Microsoft to just try and educate these executives.
A couple years later, I realized there was very little diversity in the boardrooms where I was presenting, and that concerned me. I met Dr. Safiya Noble , who had just written Algorithms of Oppression about the craziness that was Google algorithms years ago. You know, you type “CEO” and it only shows you white males — those types of things. That was a signal of a much larger problem, but I found that her work was not well known. She wasn’t a keynote speaker at the events that I was attending; she was like a sub session. And I just felt like the work was critical. And so I started Women in AI just to be a mechanism for it. I did a TikTok series on 12 African American women in AI to know, and that turned into a blog series, which turned into a community. I have a unique ability, I’ll say, to advocate for that work, and so I felt it was my mission.
VentureBeat: I’m glad you mentioned TikTok because I was going to say, even besides the boardroom discussions, I’ve seen you talking about building better models and responsible AI everywhere from TikTok to Clubhouse and so on. With that, are you hoping to reach the masses, get the average user caring, and get awareness bubbling up to decision makers that way? Silver: Yeah, that’s right. Last year I was part of a LinkedIn learning course on how to spot deepfakes, and we ended up with three million learners. I think three or four of the videos went viral. And this wasn’t YouTube with its elaborate search model that will drive traffic or anything, right. So I started doing more AI literacy content after that because it showed me people want to know about these emerging technologies. And I have teenagers, and I know they’re going to be leading these companies. So what better way to avoid systemic bias than by educating them on these principles of inclusive engineering, asking better questions, and design justice? What if we taught that in middle or high school? And it’s funny because my executives are not the ones I’m showing my TikTok videos to, but I was on the call with one recently and I overheard her seventh grade daughter ask, “Oh my gosh. Is that the Noelle Silver?” And I was like, you know, that’s when you’ve got it — when you’ve got the seventh grader and the CEO on the same page.
VentureBeat: The idea of responsible AI and AI ethics is finally starting to receive the attention it needs. But do you fear — or already feel like — it’s becoming a buzzword? How do we make sure this work is real and not a box to check off? Silver: It’s one of those things that companies realize they have to have an answer for, which is great. Like good, they’re creating teams. The thing that concerns me is, but like how impactful are these teams? When I see something ethically wrong with a model and I know it’s not going to serve the people it’s meant to, or I know it’s going to harm someone, when I pull the chain as a data scientist and say “we shouldn’t do this,” what happens then? Most of these ethical organizations have no authority to actually stop production. It’s just like diversity and inclusion — everything is fine until you tell me this will delay going to market and we’ll lose $2 billion in revenue over five years. I’ve had CEOs tell me, “I’ll do everything you ask, but the second I lose money, I can’t do it anymore. I have stakeholders to serve.” So if we don’t give authority to these teams to actually do anything, they’re going to end up like many of the ethicists we’ve seen and either are going to quit or get pushed out.
VentureBeat: Are there any misconceptions about the push for responsible AI you think are important to clear up? Or anything important that often gets overlooked? Silver: I think the biggest is that people often just think about ethical and responsible AI and bias, but it’s also about how we educate the users and communities consuming this AI. Every company is going to be data-driven , and that means everyone in the company needs to understand the impact of what that data can do and how it should be protected. These rules barely exist for the teams that create and store the data, and they definitely don’t exist for other people inside a company who might happen to run into that data. AI ethics isn’t just reserved just for the practitioners; it’s much more holistic than that.
VentureBeat: What advice do you have for enterprises building or deploying AI technologies about how to approach it more responsibly? Silver: The reason I went to Red Hat is because I actually do believe in open source communities where different companies come together to solve common problems and build better things. What happens when health care meets finance? What happens when we come together and share our challenges and ethical practices and build a solution that reaches more people? Especially when we’re looking at things like Kubernetes , which almost every company is using to launch their applications. So being part of an open source community where you can collaborate and build solutions that serve more people outside of your limited scope, I feel like that’s a good thing.
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"Black teen barred from skating rink by inaccurate facial recognition - The Verge"
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"https://www.theverge.com/2021/7/15/22578801/black-teen-skating-rink-inaccurate-facial-recognition"
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/ Tech / Reviews / Science / Entertainment / More Menu Expand Menu Tech / Artificial Intelligence / Policy Black teen barred from skating rink by inaccurate facial recognition Black teen barred from skating rink by inaccurate facial recognition / ‘To me, it’s basically racial profiling,’ the teen’s mother said By Dave Gershgorn | Share this story A facial recognition algorithm used by a local roller skating rink in Detroit wouldn’t let teen Lamya Robinson onto the premises, and accused her of previously getting into a fight at the establishment.
But Robinson had never even been to the rink.
The facial recognition system had incorrectly matched her to another patron, she told Fox 2 Detroit.
The rink removed her from the building and put her outside alone, her family says.
“To me, it’s basically racial profiling,” Juliea Robinson, her mother, told the TV station. “You’re just saying every young Black, brown girl with glasses fits the profile and that’s not right.” The harms of facial recognition systems deployed in businesses and by police have been slowly coming to light as the technology is more widely used. Research into these algorithms has shown that they are far less accurate when distinguishing between the faces of Black people, women, and children, which might help explain the error faced by Lamya Robinson.
The highest-profile case of facial recognition leading to a wrongful arrest was also in Detroit, in the case of Robert Williams. Williams was arrested and detained for 30 hours in January 2020 , after being accused of shoplifting from a Shinola watch store. He testified in front of the House Judiciary Committee, urging for legislators to adopt a moratorium on the technology introduced as legislation in June 2020.
“I don’t want anyone to walk away from my testimony thinking that if only the technology was made more accurate, its problems would be solved,” Williams said in his testimony. “Even if this technology does become accurate at the expense of people like me, I don’t want my daughters’ faces to be part of some government database.” The disparity in racial and gender accuracy, as well as the invasive nature of the technology, has led to civil rights organizations and politicians calling for bans. The American Civil Liberties Union has called for nationwide bans and is suing the Detroit Police Department on behalf of Williams for its misuse of the technology. Some states like Maine have already begun to limit police use of the technology. However, only Portland, Oregon , currently has laws limiting how private businesses can use facial recognition.
Civil rights nonprofit Fight for the Future announced that more than 35 other organizations had joined it in demanding that retailers stop using facial recognition in their stores. The group reiterated its position today after the report of Lamya Robinson’s experience getting kicked out of the skating rink.
“This is exactly why we think facial recognition should be banned in public places,” wrote Fight for the Future’s director of campaign and operations Caitlin Seeley George in a press release. “It’s also not hard to imagine what could have happened if police were called to the scene and how they might have acted on this false information.” OpenAI board in discussions with Sam Altman to return as CEO Sam Altman fired as CEO of OpenAI Screens are good, actually Windows is now an app for iPhones, iPads, Macs, and PCs What happened to Sam Altman? Verge Deals / Sign up for Verge Deals to get deals on products we've tested sent to your inbox daily.
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"Fireflies raises $5 million for its AI assistant for meetings | VentureBeat"
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"https://venturebeat.com/2019/10/29/fireflies-raises-5-million-for-its-ai-assistant-for-meetings"
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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 Fireflies raises $5 million for its AI assistant for meetings Share on Facebook Share on X Share on LinkedIn Fireflies.ai cofounders Krish Ramineni and Sam Udotong 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.
Fireflies.ai announced the close of a $5 million seed round today to grow its business and continue its effort to create an AI assistant dedicated to surfacing action items and next steps from what people say in meetings.
The seed round was led by Canaan Partners along with F7 Ventures and angel investors like former Slack CPO April Underwood, former Slack CMO Bill Macaitis, and Salesforce director of search Susan Kimberlin.
The funding will be used to grow the company’s machine learning and engineering ranks.
Fireflies launched the text-to-speech transcription service one year ago in October 2018.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Alongside services like Fireflies and startups like Voicea , automated transcription is increasingly becoming the norm. Automated transcription has become available in Microsoft Teams and even the Recorder app for Google’s Pixel 4.
Fireflies plans to be different by focusing on the recognition of questions in meetings, assigned tasks, major topics, and sentiment analysis.
Fireflies is also growing its language understanding for specific workflows for occupations like recruiters or people in sales.
“The other thing that we’re looking at that’s really important is adoption among teams, building for teams, building workflows, where people can access collaborate, like take it beyond this Recording transcription and being able to record audio,” Fireflies.ai cofounders Krish Ramineni told VentureBeat in a phone interview.
Fireflies connects with VoIP services for video calls as well like Zoom, Google Meet, and Cisco’s WebEx. After the meeting, flagged insights can be shared in team communication apps like Slack, Salesforce, or Hubspot.
Fireflies was founded by Ramineni and Sam Udotong in July 2016. The company has offices in Hyderabad, India and San Francisco and currently has 16 employees, Ramineni 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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"Apparent racial bias found in Twitter photo algorithm | VentureBeat"
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"https://venturebeat.com/2020/09/20/apparent-racial-bias-found-in-twitter-photo-algorithm"
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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 Apparent racial bias found in Twitter photo algorithm 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.
An algorithm Twitter uses to decide how photos are cropped in people’s timelines appears to be automatically electing to display the faces of white people over people with darker skin pigmentation. The apparent bias was discovered in recent days by Twitter users posting photos on the social media platform. A Twitter spokesperson said the company plans to reevaluate the algorithm and make the results available for others to review or replicate.
JFC @jack https://t.co/Xm3D9qOgv5 — Marco Rogers (@polotek) September 19, 2020 Twitter scrapped its face detection algorithm in 2017 for a saliency detection algorithm , which is made to predict the most important part of an image. A Twitter spokesperson said today that no race or gender bias was found in evaluation of the algorithm before it was deployed “but it’s clear we have more analysis to do.” Twitter engineer Zehan Wang tweeted that bias was detected in 2017 before the algorithm was deployed but not at “significant” levels. A Twitter spokesperson declined to clarify why there’s a gap in descriptions of bias found in the initial bias assessment and said the company is still gathering details about the assessment that took place before the algorithm’s release.
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 wonder if Twitter does this to fictional characters too.
Lenny Carl pic.twitter.com/fmJMWkkYEf — Jordan Simonovski (@_jsimonovski) September 20, 2020 On Saturday, algorithmic bias researcher Vinay Prabhu, whose recent work led MIT to scrap its 80 Million Tiny Images dataset , created a methodology for assessing the algorithm and was planning to share results via the recently created Twitter account Cropping Bias.
However, following conversations with colleagues and hearing public reaction to the idea, Prabhu told VentureBeat he’s reconsidering whether to go forward with the assessment and questions the ethics of using saliency algorithms.
“Unbiased algorithmic saliency cropping is a pipe dream, and an ill-posed one at that. The very way in which the cropping problem is framed its fate is sealed, and there is no woke ‘unbiased’ algorithm implemented downstream that could fix it,” Prabhu said in a Medium post.
Prabhu said he’s also reconsidering the assessment because he’s concerned some people may use experimentation results to claim an absence of racial bias. That’s what he said happened with initial assessment results.
“At the end of the day, if I do this extensive experimentation … what if it only serves to embolden apologists and people who are coming up with pseudo intellectual excuses and appropriating the 40:52 ratio as proof of the fact that it’s not racist? What if it further emboldens that argument? That would be exactly contrary to what I aspire to do. That’s my worst fear,” he said.
Twitter chief design officer Dantley Davis said in a tweet this weekend that Twitter should stop cropping images altogether.
VentureBeat asked a Twitter spokesperson about potentially getting rid of image cropping in Twitter timelines, about ethical questions surrounding the use of saliency algorithms, and what datasets were used to train the saliency algorithm. A spokesperson declined to respond to those questions but said Twitter employees are aware people want more control in image cropping and are considering a number of options.
Updated 10:09 a.m. September 21 to include responses from Twitter and Vinay Prabhu.
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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"Video chat apps tout 'inclusive' AI features | VentureBeat"
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"https://venturebeat.com/2021/06/07/video-chat-apps-tout-inclusive-ai-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 Video chat apps tout ‘inclusive’ AI features 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.
( Reuters ) — Video conferencing services have for years boasted that their technology is “intuitive” to use or “integrated” to function with other tools, but now vendors such as Google and Cisco can hardly go a blog post without trumpeting a different attribute: “inclusive.” The latest buzzword, and the product development that accompanies it, shows how tech companies are newly focused on assuring Black users and other persons of color that online chat products will not leave them out in the cold. The changes stem in part from the rise of the Black Lives Matter movement — which has prompted vendors and customers alike to think beyond the needs of a white, English-speaking audience — and the pandemic, which created a large “remote” workforce heavily dependent on technology.
Alphabet’s Google this month plans to deploy an artificial intelligence (AI) feature that addresses the longstanding issue of darker skin tones being under-illuminated in video chats.
Cisco Systems in January launched a gesture-recognition feature to display a thumbs-up in Webex, taking pains to assure that skin tones would not affect performance. LogMeIn’s GoToMeeting, Microsoft’s Teams and Facebook’s Workplace are adding translation or pronunciation options in what they describe as an equity push.
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! “Technology is fundamentally today just not as inclusive as you want it to be,” said Jeetu Patel, senior vice president and general manager for Cisco security and collaboration. Setting “values and principles that a product should stand for” has become essential, he said.
The tech industry has long been under fire for a poor record on workplace diversity and a failure to recognize the ways in which product design can perpetuate discrimination.
Worries about bias in video conferencing picked up last September after Colin Madland, a doctoral student at the University of Victoria in British Columbia, Canada, tweeted a screenshot of a Black colleague appearing headless when deploying a virtual background on Zoom.
Studies of other AI cropping systems have shown they generate more errors with darker skin, in part because the data used to train them mostly included lighter examples.
Zoom’s chief diversity officer, Damien Hooper-Campbell, told Reuters: “Bias was not at play, but rather a combination of the user’s distance from the camera, use of headphones, and seating position.” Madland said the problem subsided after his colleague purchased a green screen and some “snazzy lighting.” For Zoom and its rivals, delivering on inclusion could provide an edge while vying for post-pandemic deals with clients — which are facing their own reckonings on diversity.
Global spending on cloud-based conferencing is forecast to reach $5.41 billion this year, up from $5.02 billion in 2020, according to tech consultancy Gartner. It does not track market share, but analysts cite Zoom and Cisco as the leaders.
‘Not equally represented’ The upcoming Google Meet feature tackles the problem of people appearing darker because of their surroundings, for example when sitting in front of a window, said Niklas Blum, a Google product leader involved with Meet.
“Users with dark skin tones are not equally represented, and we want to build products for everyone,” he said.
The AI separates users from their background, determines whether they are underexposed regardless of their skin tone, brightens the picture accordingly, and finally merges the background and foreground.
Meet’s virtual waiting room will prompt users to activate the lighting adjustment when it detects they could benefit from it, said Stéphane Hulaud, product lead for video quality and processing in Meet.
Blum and Hulaud said Meet first identified the representation issues in video when launching a low-light enhancement for mobile calls well before the pandemic. Developing the latest feature took considerable time, but it led Meet to establish product inclusion testing procedures and mandate them for all of its work.
Google is pursuing additional fixes, too. Meet is pitching laptop makers and operating systems on sharing greater control over cameras’ white balance and exposure. Internally, Google adopted minimum light reflectiveness requirements for conference room designs.
At Cisco, the new gesture-recognition option, when turned on, lets users hold up their thumb on camera for about a second to generate a virtual thumbs-up on screen.
Cisco trained its AI to focus on the shape outline and movement of the gesture in time and space, reducing potential issues from variance in skin tone, said Keith Griffin, a distinguished engineer at the company.
Praying hands — for “thank you” — are among possible new gesture options to come, with a feature that interprets sign language an eventual goal. Webex also expects to add skin-tone options beyond yellow for the icons.
Mike Sharp, LogMeIn’s chief product officer for unified communications and collaboration, said education clients have driven some of the company’s “inclusion” updates.
For instance, forthcoming support for Spanish, Mandarin and other languages in voicemail prompts and transcriptions will benefit an unidentified California school district that wanted to better engage with its community, Sharp said.
Facebook Workplace, a suite of business communication tools, last month said video town hall hosts soon would see name pronunciations for workers posing written questions.
Product head Ujjwal Singh said the pointers, which are AI-generated but editable, aimed to help executives at clients including Nestle SA and Booking Holdings Inc properly address colleagues and promote inclusion.
“I don’t want to mispronounce it to thousands of employees and look like I’m not in touch with the company,” 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.
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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"New York City Council votes to prohibit businesses from using facial recognition without public notice | VentureBeat"
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"https://venturebeat.com/2020/12/10/new-york-city-council-votes-to-prohibit-businesses-from-using-facial-recognition-without-public-notice"
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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 York City Council votes to prohibit businesses from using facial recognition without public notice 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.
New York City Council today passed a privacy law for commercial establishments that prohibits retailers and other businesses from using facial recognition or other biometric tracking without public notice. If signed into law by NYC Mayor Bill de Blasio, the bill would also prohibit businesses from being able to see biometric data for third parties.
In the wake of the Black Lives Matter movement, an increasing number of cities and states have expressed concerns about facial recognition technology and its applications. Oakland and San Francisco in California and Somerville, Massachusetts are among the metros where law enforcement is prohibited from using facial recognition. In Illinois, companies must get consent before collecting biometric information of any kind, including facial images. New York recently passed a moratorium on the use of biometric identification in schools until 2022, and lawmakers in Massachusetts have advanced a suspension of government use of any biometric surveillance system within the commonwealth. More recently, Portland, Maine approved a ballot initiative banning the use of facial recognition by police and city agencies.
The New York City Council bill, which was sponsored by Bronx Council Member Ritchie Torres (D), doesn’t ban the use of facial recognition technologies by businesses outright. However, it does impose restrictions on the ways brick-and-mortar locations like retailers, which might use facial recognition to prevent theft or personalize certain services, can deploy it. Businesses that fail to post a warning about collecting biometric data must pay $500. And businesses found selling data will face fines of $5,000.
In this respect, the bill falls short of Portland, Oregon’s recently passed ordinance regarding biometric data collection, which bans all private use of biometric data in places of “public accommodation,” including stores, banks, restaurants, public transit stations, homeless shelters, doctors’ offices, rental properties, retirement homes, and a variety of other types of businesses (excepting workplaces). The law is scheduled to take effect on January 1, 2021.
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 commend the City Council for protecting New Yorkers from facial recognition and other biometric tracking. No one should have to risk being profiled by a racist algorithm just for buying milk at the neighborhood store,” Surveillance Technology Oversight Project executive director Albert Fox Cahn said. “While this is just a first step toward comprehensively banning biometric surveillance, it’s a crucial one. We shouldn’t allow giant companies to sell our biometric data simply because we want to buy necessities. Far too many companies use biometric surveillance systems to profile customers of color, even though [the systems] are biased. If companies don’t comply with the new law, we have a simple message: ‘We’ll see you in court.'” Numerous studies and VentureBeat’s own analyses of public benchmark data have shown facial recognition algorithms are susceptible to bias. One issue is that the datasets used to train the algorithms skew white and male. IBM found that 81% of people in the three face-image collections most widely cited in academic studies have lighter-colored skin. Academics have found that photographic technology and techniques can also favor lighter skin, including everything from sepia-tinged film to low-contrast digital cameras.
“Given the current lack of regulation and oversight of biometric identifier information, we must do all we can as a city to protect New Yorkers’ privacy and information,” said Council Member Andrew Cohen (D), who chairs the Committee on Consumer Affairs. Crain’s New York reports that the committee voted unanimously in favor of advancing Torres’ bill to the full council hearing earlier this afternoon.
The algorithms are also often misused in the field, which tends to amplify their underlying biases. A report from Georgetown Law’s Center on Privacy and Technology details how police feed facial recognition software flawed data, including composite sketches and pictures of celebrities who share physical features with suspects. The New York Police Department and others reportedly edit photos with blur effects and 3D modelers to make them more conducive to algorithmic face searches. And police in Minnesota have been using biometric technology from vendors including Cognitec since 2018, despite a denial issued that year, according to the Star Tribune.
Amazon, IBM, and Microsoft have self-imposed moratoriums on the sale of facial recognition systems. But some vendors, like Rank One Computing and Los Angeles-based TrueFace, are aiming to fill the gap with customers like the City of Detroit and the U.S. Air Force.
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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"Industrial systems under siege from ransomware | VentureBeat"
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"https://venturebeat.com/2021/06/04/industrial-systems-under-siege-from-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 Industrial systems under siege from ransomware 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.
While millions of blissfully unaware Americans grilled burgers and hot dogs over the Memorial Day weekend to celebrate the unofficial start of summer, security teams at the primary source of all that backyard barbecue fare were battling a red-hot crisis.
IT systems at JBS, the world’s largest meat processor, were showing signs of a ransomware infection. Then came the ransom demand, reportedly from a Russian ransomware-as-a-service syndicate known as REvil.
Uncertain how many systems were compromised and fearing the worst, JBS officials pulled the plug on servers supporting IT and OT (operational technology) systems in the U.S., Australia, and Canada, effectively shutting down beef production across North America on the Sunday before the holiday.
The story is, by now, a familiar one. According to threat intelligence firm Group-IB , the number of ransomware attacks grew by more than 150% last year, with the average ransom demand per case more than doubling in the same period. The latest wrinkle, however, is the type of companies criminals are increasingly targeting: companies like JBS. Rather than focus on better-defended financial institutions and government agencies, ransomware gangs are turning their sights on blue-collar enterprises, the working-class companies, the makers of things.
Large, global companies built around pursuits such as manufacturing, oil and gas processing, energy distribution, and food production have some key commonalities. Most feature a blend of cutting-edge IT systems that run the business alongside more utilitarian industrial controls and operational technology that handles the machines, levers, switches, sensors, gauges, and all manner of controllers that comprise the fabric of modern industry.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Consider JBS. While slaughterhouses are an enterprise as old as time, JBS, like all modern meat producers, relies on IT and internet connectivity throughout its plants to manage myriad recording keeping and quality controls — product sorting and tracking, equipment status and temperatures, health and safety documentation.
According to Beef Central , some of the challenges JBS is dealing with in the wake of the shutdown “include what happens to thousands of chilled carcasses from cattle slaughtered on Friday that have yet to be boned-out. Attempts will be made to bone those bodies out … using manual record keeping, documentation, and sortation.” What these tech-driven industrial firms also share is an Achilles heel. Legacy OT/ICS systems, particularly those interconnected with more modern IT, can be notoriously difficult to protect from misuse. And when an attack compromises one part of an industrial firm, the fear of increased contamination and further damage often requires costly shutdowns of entire plants. The result sends shockwaves through the supply chain and the economy at large. Such companies make the perfect ransomware victims: large, well-heeled, easily exploitable, and financially motivated to get their facilities back up and running quickly.
Other OT/ICS attacks just this year include: January 2021: Ransomware forced global paper and packaging giant WestRock of Atlanta to shut down production at several of its 300 plants and resort to manual processes to maintain the business, which serves major clients like General Motors, Home Depot, and Heinz. Two weeks into the incident, the company reported mill system production at “approximately 85,000 tons lower than plan.” March 2021: Chicago-based MillerCoors suffered a suspected ransomware attack that left the brewing behemoth unable to access systems that control beverage production and shipments. While the outage lasted only a few days, the disruption to operations and deliveries was significant enough to warrant disclosure to the Securities and Exchange Commission.
May 2021: Colonial Pipeline, owner of 5,500 miles of pipeline carrying gasoline, diesel, and jet fuel from Texas to much of the East Coast, shut down its OT systems in response to a ransomware attack targeting its IT network. The multi-day outage crippled 29 refineries and 267 distribution terminals, and sparked price hikes and gas hoarding across the mid-Atlantic.
Pundits have been quick to assume that the broad economic disruption these incidents create signals a shift in motivation for attackers toward more state-sponsored activism. The fact is, cybercriminals remain chiefly motivated by money , something the industrial firms have and are willing to part with in the wake of an attack. That the governments which harbor such criminals may enjoy the ensuing political chaos is, for now, mostly a side effect.
Hiding in plain sight The problems that plague security in industrial OT systems begin with a lack of visibility. The elements of industrial control systems are small, proprietary, widely dispersed, and typically not well documented or inventoried. In most organizations, OT has its own budget, its own users and aficionados, and is managed by teams separate from the larger corporate IT leadership structure.
When it comes to assessing and mitigating risk to the company at large, OT is a landscape of blind spots.
The CISO may intuitively know there are embedded systems sprinkled throughout the facilities, but naming them and describing their weaknesses is a test most would fail. When ranking security posture for OT/ICS systems at large companies, “90% or more would be poor to fair,” Ron Brash, director of cybersecurity insights at Verve Industrial Protection , told VentureBeat. “OT sites, which are often revenue generators, or the systems used for billing, reservations, inventory tracking, and so on, are severely neglected.” Brash said OT systems security suffers from a host of technical, financial, and cultural factors such as post-acquisition consolidation, uneven budgeting for process control versus technology infrastructure, and the ever-present priority on maintaining operations at all costs.
“Businesses are literally in the business of being in the black,” Brash said. “If you have systems that can’t run without data, resources that need to be fed with work in order to generate revenue, product that can’t get to market, then it’s simple. There is no IT or OT, there’s just [degrees of] safety, reliability, and productivity.” These shortcomings are exacerbated by what has come to be known as “IT-OT convergence,” the inexorable interconnection of ICS/OT wares with backend business IT systems. Strengthening the ties between OT and IT is undeniably beneficial for organizations looking to increase efficiency, productivity, and profitability. Knowing not just how a product is made, but where it is going, who is paying for it, and how many more will be needed next month is critical, particularly in industries such as Just In Time (JIT) manufacturing, where margins are thin and speed is essential. The melding of IT and OT systems defines digital transformation at many industrial firms, and leveraging the combined power of data, connectivity, and physical output has evolved into its own form of competitive advantage in many verticals.
“IT/OT convergence is accelerating because it unlocks business value in terms of operations efficiency, performance, and quality of services,” Yaniv Vardi, CEO of OT/ICS security vendor Claroty , told VentureBeat. “It’s good for business and it’s here to stay. But fully realizing the benefits requires mitigating the cyber risks that come along with it.” “IT-OT convergence actually started decades ago,” Brash said, “and most organizations now are tightly coupled to these integrated systems. There are benefits, but we need to get a handle on the implications. We have to control the environment, the flows of data, and secure the systems that are crucial to operations. We need to get better at protecting them and being able to effectively recover at scale.” Old tech, new threats Even when all the constituent parts of an OT environment — embedded systems, I/O devices, specialized networking gear, etc. — are accounted for, the technology at work can introduce its own brand of security deficiencies. Most of what lives in an OT environment are small-but-functional computers running stripped-down versions of Linux, Windows, or some proprietary operating system.
Beyond their specialized functionality, OT wares differ from mainstream IT in the way they are treated both by the vendors that sell them and the enterprises that deploy them. It’s not unusual for OT/ICS devices to be sold with hard-coded (and simplistic) administrative passwords, for example. While the typical office laptop lasts for a couple of years and is subjected to routine security updates, OT devices can be deployed for decades without benefit of a single software patch. Even OT devices that can be patched often aren’t due to concerns about system fragility and the cost of maintenance downtime.
“There’s a 25-year gap between the state of IT and OT security,” Vardi said. “Many production environments run on legacy OT equipment that was never designed to be connected to the internet. Connecting an OT environment to the IT network means introducing an operating system that might be nearly old enough to vote, with no means of patching its vulnerabilities.” That gap is particularly concerning for machines tasked with critical, often dangerous tasks like regulating pipeline pressures, checking machine operating temperatures, locking facility doors, or measuring contaminants in air and water supplies.
One way organizations comfort themselves when considering OT/ICS risk is with the much-overhyped “air gap.” Conceptually at least, systems with no logical connection to any other systems or the outside world should remain mostly safe from harm. The air-gapping approach ignores the possibility of insider attacks or compromises introduced by others with physical access to OT devices, of course, but the concept held up for the most part until IT-OT convergence and the emerging industrial internet of things (IIoT) became the norm. Today, true air gaps in OT are vanishingly rare. If the Iranian nuclear facility in Natanz couldn’t rely on its air-gap defense , it’s a safe bet most commercial manufacturers can’t either.
Another common strategy is to lean on what’s known as “security by obscurity.” The approach posits that arcane systems like SCADA and ICS are not well known to the majority of criminals. Reconnaissance on these systems has traditionally been difficult, and detailed descriptions of vulnerabilities and exploits typically stayed in the hands of OT/ICS specialists. This is no longer the case, however.
Over the past two years, the number of advisories issued by the Cybersecurity and Infrastructure Security Agency (CISA) describing vulnerabilities in ICS-related systems jumped more than 50%. Criminals have taken notice.
“The recent cyberattacks on both Colonial Pipeline and JBS are only a teaser of what’s to come,” Vardi said.
Strategic defense of OT/ICS If the problem with OT security stems from it being siloed and poorly understood, the solution, experts say, is to approach risk assessment and mitigation holistically across all of the organization’s technology assets, whether they live in the office or on the factory floor. The effort begins with acknowledging the scope and idiosyncratic nature of OT systems woven throughout the business.
The rest relies heavily on security fundamentals and due diligence.
“Organizations need to practice security in breadth and security in depth to make sure that holes in the IT environment don’t allow ransomware to get into the OT networks,” Vardi said. “This includes implementing strong authentication for all OT users, segmenting their network, and ensuring complete visibility into all systems.” Brash endorses implementing cybersecurity basics in order to reduce risk to manageable levels while simultaneously “leveraging and operationalizing the many technology investments already present within the majority of organizations.” “Certainly for the actual ICS/OT assets this may be harder, but the majority of risk comes from the IT side. OT is generally collateral damage,” Brash added.
Organizations looking for help protecting OT/ICS and blended industrial and IT environments can turn to some purpose-built guidance and established security frameworks. Late last month, the Department of Homeland security issued a security directive specifically for pipeline owners like Colonial. The document borrows heavily from the more general NIST Cybersecurity Framework (CSF) and spells out both reporting requirements and voluntary controls designed to mitigate risk from a ransomware attack.
Just this week, the White House issued its own set of defensive best practices for private-sector organizations. The guidance calls for broader use of multi-factor authentication, endpoint detection and response (EDR) capabilities, regularly tested business continuity and disaster recovery (BCDR) protocols , and a commitment to system patching and testing.
“The U.S. government is working with countries around the world to hold ransomware actors and the countries who harbor them accountable, but we cannot fight the threat posed by ransomware alone,” said Anne Neuberger, deputy national security advisor for cyber and emerging technology. “The private sector has a distinct and key responsibility.” For Vardi, NIST remains the gold standard for protection of all systems regardless of location or function. “The [NIST CSF] is arguably the most comprehensive and revered security framework,” he said. “Its flexibility, common lexicon, and emphasis on business drivers have fueled its adoption and recognition as a true requirement across industries globally.” Brash said technical controls as described by NIST or in the more detailed and OT-Specific ISA/IEC 62443 standards definitely play a vital role in defending at-risk companies. He added, however, that true resilience in the face of the ransomware scourge should also include a reconsideration of processes and structure at many manufacturing and production firms in order to make systems less susceptible to disruption.
“If your worst nightmare is that you can’t schedule product to be in a pipeline, produce tracking numbers, load goods onto pallets, or get them onto trucks, then we are doing risk management, distribution resource planning, and business continuity planning wrong,” Brash said. “Ransomware is merely a symptom of the actual condition affecting most organizations.
“The good news is that we just need to rethink those processes and retrofit those organizations to get us towards the path of treatment,” he said.
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"Proofpoint and the Ponemon Institute: Losses due to phishing have almost quadrupled since 2015 | VentureBeat"
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"https://venturebeat.com/2021/08/18/proofpoint-and-the-ponemon-institute-losses-due-to-phishing-have-almost-quadrupled-since-2015"
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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 Proofpoint and the Ponemon Institute: Losses due to phishing have almost quadrupled since 2015 Share on Facebook Share on X Share on LinkedIn Phishing fraud 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.
Costs from phishing attacks have tripled since 2015. The average annual cost of phishing has increased from $3.8 million in 2015 to $14.8 million in 2021. This is because phishing has a low entry barrier for cybercriminals with a high-value return. These emails are very easy to create, require little technical knowledge and most importantly, depend solely on one user clicking to succeed.
Phishing attacks not only had direct financial consequences but these attacks also increase the likelihood of a data breach, decrease employee productivity and increase the likelihood of a business disruption, all contributing additional costs. In fact, huge amounts of time and investment are spent dealing with the consequences of a phishing scam. Employee productivity losses are among the costliest to organizations, increasing from an average of $1.8 million in 2015 to $3.2 million in 2021.
Of note is how minimal the losses are from ransomware payments in the grand scheme of things. Ransomware annually costs large organizations $5.66 million. Of that, $790,000 accounts for the paid ransoms themselves. There is a much larger context to the costs these attacks can inflict.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Security training and awareness programs are the best remedy for addressing the dangers posed by phishing attacks. According to respondents, these programs can reduce phishing expenses by more than 50 percent on average. Users are a critical target in phishing attacks and the best defense is a people-centric approach to security.
The Ponemon Institute’s 2021 Cost of Phishing Study sponsored by Proofpoint surveyed 600 IT and IT security practitioners to better understand the risk and financial consequences of phishing. For the first time in this year’s study it looks at the threats and costs created by business email compromise (BEC), identity credentialing and ransomware in the workplace.
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"Civilization VI: Rise and Fall review -- loyalty has a price | VentureBeat"
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"https://venturebeat.com/2018/02/08/civilization-vi-rise-and-fall-review"
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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 Review Civilization VI: Rise and Fall review — loyalty has a price Share on Facebook Share on X Share on LinkedIn Queen Wilhelmina is all about commerce.
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My new favorite aspect of Civilization is watching my empire’s symbol flash under a city as it prepares to transfer its Loyalty from its leader to me.
I’m playing Poundmaker, the leader of the Cree, one of the great Native American peoples. Renown traders, I’ve built my empire in such a way that I’m not just trading gold, food, and production with neighboring cities, but I’m also sending pressure with just how much better my civilization is than theirs.
My amusement parks, cities, military, and other wonders first convince the Netherlands’ capital to join my civilization. Then comes their other cities. At last, their last one falls, not to the sword, but with a mighty stroke of the pen.
Now the Cree are not only larger, but Poundmaker’s people are putting out more than twice the gold, culture, and production than before. And I haven’t even launched a war to do so.
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 is Rise and Fall, the first expansion for 2016’s Civilization VI. Firaxis biggest addition to this long-lived PC strategy-game series is the Loyalty system, which measures how likely citizens of your empire (and others) are to stick with your leadership … or rebel and either found a free city or join another civilization. Governors help manage Loyalty as well. Rise and Fall also adds Eras and Historic Moments, which work together to bring about either ages of glory or woe. Emergencies rally the world against the actions of unruly rulers.
It fixes some of my issues with Civilization VI, giving your new paths to victory. Yet in the 40 or so hours I’ve put in so far, the busywork of managing your districts and the religion mechanics remain irritants, and if you didn’t like them before, Rise and Fall isn’t going to make those any more enjoyable for you.
Above: When a city is turning toward your civilization, your symbol flashes below it.
What you’ll like Loyalty gives you options My biggest compliant with Civilization VI is that you couldn’t entice cities to join your empire. Loyalty now gives you that option. In my first run, I had trouble making this work, as my Korean civilization’s cities were just too far away from those of others. I watched as cities changed hands between other civilizations, but I was never able to take advantage of the situation.
Poundmaker of the Cree helped me take advantage of the Loyalty system. With a combination of trade routes, which enables Poundmaker to claim a few extra open hexes on the map, and a focus on building amenities, my Loyalty put out enough pressure on The Netherlands to, as I described, first take their capital, then wipe them off the map, city by city. I found this to be a more satisfying round of Civilization VI that I had ever played, even when taking mods into account.
But Loyalty swings both ways. You want to keep your people happy. Governors can help with this. These are new units you recruit as your civilization marches through time. You base them in one city, either one of yours or a city state, and they can affect you empire in a multitude of ways: boosting loyalty, making the burg they’re based in a military strongpoint, improving your economy, making strides in science, and so on. In my playthroughs, it delighted me that I didn’t end up keeping governors in the same cities, as I feared. I moved most of them around to give my burgs the benefits they needed — or the influence I craved, when it came to city states.
Above: Rise and Fall even turns conquering barbarians into an epic moment.
An epic journey Rise and Fall’s Era system tracks the historic moments your civilization experiences. Some are trivial, such as the first time you defeat a barbarian or research a tech. But these do more than show your path from small village to globe-spanning empire — they also net your era points, which factor into whether you get a Golden Age or a Dark Age.
Now, if you’re struggling with generating enough Loyalty, it’s important to avoid Dark Ages. These cause it to decay faster than normal, so not only are you losing out on enticing outside cities to join your civ, you could be at risk at your own citizens rebelling against your rule. You’re not doing anything different from what you’d be doing in any standard game of Civ — but you’re reaping extra rewards for it.
I’ve found it rather easy to avoid Dark Ages. You earn era points from so many activities, even successful combats. Stay busy building your cities and wonders, recruiting great people, and destroying barbarians and other invaders, and you stand a fair shot at earning a Golden Era. The trick is doing enough of these that you earn a Herioc Age. With these, you get three Dedications instead of one, giving you multiple bonuses for the next era. My civilizations surged each time I earned one, and my science victory with Korea wouldn’t have happened without the Heroic Age that helped me zoom through the industrial tech tree and open up modern-era choices.
Above: I failed to stop Shaka here, and he profited greatly from his actions.
Red alert Emergencies are Civ VI’s take on emergent quests. These crop up when another leader is being naughty, like when they drop a nuke, converts the holy city of another empire, or takes over a city-state protectorate. These are fun. When you accept an Emergency, the other leaders have a limited amount of time to resolve it. Do so, and you split a pot of gold with those who assist. Fail, and the transgressor reaps the wealth.
In my run with the Cree, my Civ surged after resolving an Emergency. I got 1,800 gold for liberating an allied city state from Australia, and I used it to build a number of Amenities — which boosted the Loyalty in my civ and enabled me to start converting the Netherlands’ cities to my empire.
What you won’t like The AI needs help Firaxis still needs to address the behaviors of world leaders. I’d have friendly relationships — even alliances! — with some, and they’d scold me for actions. Once, Saladin declared war on me despite us having declared a friendship the turn before. Then he never attacked me. He didn’t even attempt to loot my traders near his empire. I just continued my progression to my science victory.
The AI remains far too generous to its friends in trading, offering me what I feel is twice the value for my spare luxuries. I felt like I was robbing them — that’s how good the terms of some of the proposed deals were.
I also found that the AI kept breaking its promises to me, regardless of the situations I found myself in. Friends would promise to stop settling near me or spying on my cities and districts, but it was like they were crossing their fingers behind their backs. I once found a spy of Poundmaker’s attempting to drain my coffers after he twice promised to stop syping on me.
Shaka, when the falls fell I hate one aspect of Shaka’s design. One of his two agendas is that he hates women leaders. He’s going to be antagonistic toward Korea’s Seondeok or the Netherlands’ Wilhelmina (or Civ VI’s other women), and that means you’re going to have someone who has one agenda against you should you play a women-lead Civ and Shaka’s in your game. It feels so limiting to me.
Above: Queen or governess? Leader … or Mary Poppins? I don’t like how the Wilhelmina looks in Civ VI’s cartoony style. She runs a civ! She was one of the Dutch’s most inspirational leaders, rallying her subjects during both World Wars. But she looks like one of the rejects interviewing for the governess job in Mary Poppins.
Conclusion Civilization is at its best when it enables you to tell your own stories. But at release, Civ VI didn’t do such a good job at that. Rise and Fall fixes this in many ways, giving your better ways to expand your civilization without resorting to combat. It adds a quest-like element with a significant penalty or reward, and most of its news leaders add variety to the game.
Firaxis will need to watch how the AI reacts as thousands of fans start playing and spreading their own stories and respond with patches to fix the issues. And we’ll have an even better idea of what Rise and Fall does after a few months, once modders get their hands on the new mechanics and deliver tweaks and all-new creations based on the Civ sandbox.
Score: 82/100 Civilization VI: Rise and Fall is out now for PC. The publisher gave GamesBeat a Steam code for the purposes of this review.
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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"Humankind preview: The end of Civilization? | VentureBeat"
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"https://venturebeat.com/2019/11/12/humankind-preview-the-end-of-civilization"
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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 Preview Humankind preview: The end of Civilization? Share on Facebook Share on X Share on LinkedIn An early city in Humankind looks oddly familiar 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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At first glance, Humankind seems familiar, perhaps a little too much so. Start a game, and there’s your initial tribe on a beautiful hex-based map, looking almost exactly like Civilization has since 2009. That’s not inherently misleading: This is a game that’s supposed to look like Civilization, because it has the same motivation.
But it’s also not quite Civilization … and that has the potential to be a really good thing.
The end of Civilization To get at why that is, it’s worth discussing what Civilization has become. When initially developed in the early 1990s, Sid Meier’s Civilization became the poster child for what all strategy games could be. It was wildly ambitious — attempting to model the entirety of human history, as well as the near future. It was also a damn good strategy game, with a simulation of great powers taking over the world and engaging with one another in alliances and wars.
But over time, especially starting with Civ 5, instead of playing in a living world — a simulation — Civ has become about making a long-term plan and sticking to it, with external pressure coming from enemies who might want to disrupt that plan. You’d pick a Civ and try to push toward a one of half-a-dozen victory conditions, instead of adapting 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 isn’t inherently a bad concept, but it is a relatively niche one, and now Civ is no longer the dominant force in strategy games. Paradox Interactive games like Europa Universalis 4 have moved in, with a focused simulation of a specific historical era and no rigid endgame. Total War, Civ’s only real long-term competitor for the greatest strategy series, has had an uneven but largely fruitful decade. And in the specific niche of the “4X” strategy game , Amplitude Studios, with its excellent Endless Legend and largely successful Endless Space 2, are starting to compete with Firaxis and Civilization on their home turf.
‘The journey matters more than the destination’ Thus, in a sense, Humankind can only be understood in relation to Civilization. This might sound limited, but there’s actually a lot of room in the anti-Civ genre — despite Civilization’s monumental place in the strategy game universe, it’s only inspired a handful of direct competitors of turn-based games covering the entirety of history. Humankind is that, and as it’s Amplitude that’s developing Humankind, this makes it worthy of interest.
As such, the core of the pitch of Humankind is this: If Civilization has become to enamored with long-term planning, with an endgame focus, with dividing into good plans versus bad plans; then Humankind is an attempt to muddy those waters. It is an attempt to make a game about all of human history that’s about making the most about the position players are in at any given time — about reacting and adapting instead of merely executing.
Above: A developed city in Humankind At the macro level, Humankind dives into the gray areas with its victory conditions. Or rather, that it doesn’t have express “conditions” for a variety of victories. Instead, it keeps score via “fame” — a marker that’s hidden throughout the game, and one that the developers said “might surprise you” when you win. Fame comes from being the biggest or best or smartest at key points. A conceptual example Amplitude gave me was how the Mongols had the largest contiguous land empire in human history, conquering most of settled Eurasia except for the peninsulas of Indochina, India, Arabia, and Western Europe. That sort of “fame” could get the Mongols closer to victory, even if their empire didn’t last for long beyond that era.
So Humankind is meant to encourage taking advantage of what you can when you can. Building out the greatest empire you can in the moment, even if it might overextend you long-term becomes potentially worthwhile. And this philosophy of working with what you have, and making short-term choices alongside long-term ones is supported by the entire framework of the game.
Stacking the deck Perhaps the key mechanic of Humankind that sets it apart from almost every other grand strategy game is that, instead of choosing which faction you want at the start, you pick a different culture across six different eras. So you might pick the Babylonians early on, and head down a path that leads you the Germans.
Each culture has its own aesthetic elements, like architecture and city names. But they also have practical effects, like units and general bonuses. If you pick the Phoenicians early on, you’ll get buffs that help you develop a seafaring nation, which you could stack by taking later seafaring cultures — perhaps the English. Or you could adjust if you needed more industrial or military or food-producing power.
Above: Hoplite art for Amplitude’s upcoming Humankind I find a lot to like about the culture draft idea, both conceptually and pragmatically. “Most civilizations are a succession of cultures,” Amplitude said, which seemed both historically accurate and interesting from a gameplay perspective. I loved seeing the variety of different options, and Humankind seemed to do a good job of offering options outside the Eurocentric model of civilization as a concept that starts in the Middle East and shifts into Europe. Each era seemed to have cultures from the Americas (like the Olmecs), Africa (Nubians), and East Asia.
In fact, as a bit of a Sinophile, I asked, noting the Zhou in the first era, if it was possible to pick Chinese cultures throughout — Zhou to Han to Song to Ming to Qing to PRC. Not quite, I was told, although there were three of those (Zhou, Ming, and PRC). But there was also the option to “transcend” an era by not picking a new culture — gaining points but missing out on the culture’s buffs.
The developers also mentioned an interesting quirk, where cultures would be drafted — you couldn’t have two players both playing the Romans in the same era. It wasn’t clear how exactly this would work, but I was amused by the idea that this would be like a history Auto Chess , with scarcity playing a role in building a full composition. And I do have concerns that some cultures may be, or seem to players, so powerful that they become default choices, though that’s something that testing and patches will have to determine.
Above: Drafting a culture in Humankind The living world My biggest concern right now, however, is the map. Much of this is likely due to the pre-alpha state of the build I saw, but it seemed lacking in personality in its natural state, and fairly dull when exploited. This is particularly worrisome because grand strategy games tend to be entirely map-focused — it represents what you’re playing with, where, and how. Civilization has increasingly become about playing the map, while Amplitude’s previous planet-based game, Endless Legend, had arguably the greatest map I’ve ever seen.
The two problems I saw were these: early in the game, when the map was unfilled, it was overly simple. Just some mountains and plains and forests — no resources or points of interest, nothing to add individual flair or personality. I asked and was told that resources, a key part of the Endless games, would be coming, and later in the demo, I did see some volcanoes. So these sorts of things can be expected to added.
Second, a grand strategy game tends to show when its geography is being inhabited and exploited by humans — buildings, mines, irrigation, and so on. The late-game part of the demo I was shown had multiple provinces fully developed, and they tended to look dull and similar throughout. Part of the joy of playing a grand strategy game is seeing your plans come to fruition on the map — recent Civilizations, for all their flaws, have been superb at that — so Humankind seemed a little disappointing on that front. But once again, this is the sort of pre-alpha status of game development I’d expect to be cleared up. It’s just that I want to make sure that it fully has the personality of the Endless games before I give it credit for that.
There are good or potentially interesting things about the map. It’s divided into provinces, like Endless Legend or Age of Wonders: Planetfall, which has shown itself to be an effective way of reducing micromanagement and creating some interesting strategic decisions. I’ll be interested to see what that looks like in a historical game aimed at simulating something like the real world.
Second, the demo showed some examples of single cities in provinces merging with other provinces to create super-cities. I didn’t see how this worked in practice, but the potential for reducing micromanagement is present.
Above: The start of a military conflict in Humankind Finally, the province model seemed to have potential for interesting military situations. Amplitude showed me a late-game battle, where the player-controlled army was attacking a weaker AI force, but attacking a fort up a hill. Like Endless Legend, armies are single entities on the map, whose individual units spread out onto a province for tactical combat. Unlike Endless Legend, players have direct control over each unit — something I’m skeptical might slow the game down, but certainly offers tactical depth.
But what really caught my eye, or ear, was the idea that these battles often wouldn’t necessarily end the turn they were started, if there wasn’t a full victory. The assault on the fort that I was shown was clearly something that could take time — and, the developers mentioned, could have reinforcements coming, both from the sides engaged as well as from allies. Suddenly, the possibility of a grand strategy game simulating the Western Front in World War I — a rather memorable event in human history and not one I’ve ever seen a game like this come close to modeling — became quite real.
Conclusion Humankind seems like a game aimed directly at me. Obviously I’m in favor of this as a method of game development. But is there a market beyond me for people who’ve spent decades with Civilization, know its strengths and weaknesses, and are looking for a game that might fix the latter without losing the former? This is the next step I’d love to see from Humankind — can it build general appeal and have its own personality like Amplitude’s previous games? 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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15,492 | 2,019 |
"Iterable raises $60 million to optimize omnichannel campaigns with AI | VentureBeat"
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"https://venturebeat.com/2019/12/10/iterable-raises-60-million-to-optimize-omnichannel-campaigns-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 Iterable raises $60 million to optimize omnichannel campaigns with AI 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.
Executing cross-channel marketing campaigns isn’t easy no matter what resources a brand has at its disposal. According to a recent survey , only 30% of marketers are highly confident in their ability to deliver a mulichannel strategy, followed by 67% who are only somewhat confident. Be that as it may, 95% of salespeople say they consider multichannel marketing important for customer targeting, which is likely why an estimated 51% of companies use at least eight channels to interact with customers.
That’s why Andrew Boni (who worked at Google on AdSense) and Justin Zhu (who built user growth systems at Twitter) cofounded Iterable, a startup developing a platform that enables brands to create, execute, and optimize cross-channel campaigns with flexibility. Roughly six years after the launch of its first product, the San Francisco-based company today announced that it has raised $60 million in series D funding led by Viking Global Investors, with participation from Index Ventures, CRV, Blue Cloud Ventures, Harmony Partners, and Stereo Capital.
The cash infusion — which brings Iterable’s total raised to over $140 million — comes shortly after Iterable opened offices in London and Denver. CEO Zhu says it will fund the expansion of its headcount to 400 employees by year-end 2020.
Iterable’s tools tap machine learning algorithms to analyze users’ behavior and optimize the time, channel, and frequency to engage them. The tools automatically suss out the best time for conversion — gleaned through event data — and designate the channels users are most likely to convert in. Plus, they leverage real-time interaction data to cap messaging volume in order to avoid fatiguing users with promotions.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! From within Iterable’s control panel, marketing mangers can orchestrate welcome campaigns and trials, targeted sales, promotions, and product updates across mail, mobile push, SMS, in-app, web push, and direct mail channels. They’re able to deploy cart abandonment flows to drive more checkouts, and to define rules-based triggers that kick off post-purchase, as well as renewal sequences and more. Furthermore, using Iterable’s Workflow Studio component, users can build cross-channel segments with drag-and-drop filters and delay schedulers.
An analytics component — Iterable Insights — lets clients drill down into real-time user, behavioral, and event data from up to millions of users, and measure and fine-tune campaigns with an on-demand experimentation and A/B testing tool. Customers can be dynamically segmented and their preferences stored, thanks to support for profiles spanning hundreds of demographic and custom event data fields. And APIs and universal webhooks enable the retrieval of information from third- and first-party sources at scale.
The global omnichannel retail commerce platform market is expected to grow from $2.99 billion in 2017 to $11.01 billion by 2023, and Iterable isn’t the only firm vying for a slice of it. There’s 6sense, which in April raised $27 million for its cloud-hosted marketing and sales predictive analytics tools. There’s also Kustomer , a software-as-a-service (SaaS) provider that automates repetitive processes by applying analytics atop data from multiple sources; RedPoint , which offers products that analyze customer data with AI; and Punchh , a startup leveraging machine learning and omnichannel integrations to create customer journeys.
But despite the fierceness of the competition, Iterable has made a name for itself, attracting heavy-hitting customers like AT&T, Box, DoorDash, ShopRunner, FabFitFun, SeatGeek, and Zillow. In another show of strength, this latest fundraising round is the company’s second in less than 12 months. (Iterable previously nabbed $50 million in a series C that closed in March 2019.) 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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15,493 | 2,021 |
"Glia raises $78 million to digitize customer service interactions | VentureBeat"
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"https://venturebeat.com/2021/01/07/glia-raises-78-million-to-digitize-customer-service-interactions"
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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 Glia raises $78 million to digitize customer service interactions 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.
Customer service startup Glia today announced it raised $78 million. The funds will be used to expand departments across its organization, the company says, with a focus on product development and strategic acquisitions.
Multimodality is fast becoming the norm in the $350 billion customer service industry. According to research published last year by Vonage company NewVoiceMedia, three-quarters of customers prefer to have their queries handled by a live agent, while the remaining 25% favor chatbots and other self-service alternatives. As a result, more than 85% of banks have digital investments as their key priority, a recent Ernst & Young survey found, with the goal of increasing customer engagement.
Glia , a New York-based startup cofounded by Justin DiPietro, Carlos Paniagua, and now-CEO Dan Michaeli in 2012, aims to capitalize on the trend with an omnichannel customer service platform that supports text, phone calls, video chat, and more.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Glia matches customers with support staff by pairing video with messaging and voice. The company claims its chatbot framework, AI Management Platform, is a key differentiator. It integrates and tracks bots powered by IBM’s Watson, Amazon’s Lex, Google’s Dialogflow, and other natural dialogue backends, which managers can divvy up into teams.
Customers who opt for human help can participate, via Glia, in live sessions during which reps provide product tours and answer questions verbally or through text. Niftily, folks who dial in are assigned a unique ID that Glia uses to intelligently route them to the person they last spoke with.
No matter which medium customers choose in Glia, its CoBrowsing tool enables agents to walk people through apps and websites with a virtual mouse cursor. The company asserts that this option, in tandem with the rest of its suite, has delivered some clients 20% faster issue reduction and an 18% reduction in average handle time.
It’s been an eventful few years for Glia owing to the pandemic, which pushed financial institutions to reconfigure systems and double down on digital solutions for customer engagement. The company grew annual recurring revenue by 150% this year and now counts among its customers over 150 financial institutions, insurance companies, and fintech providers including Deutsche Bank, BNP Paribas, United Healthcare, and Berkshire Hathaway.
“The events of 2020 forced businesses to reimagine how they guide and connect with customers in a digital world,” Michaeli told VentureBeat via email. “When businesses temporarily shutter their brick-and-mortar presence, customers still require support as they turn to online alternatives. In fact, this virtual shift creates an even greater need for customer service and support as many consumers who have never done business online are forced to do so for the first time … Many of our customers are happy to find that they can configure Glia to provide business continuity for displaced service workers and virtual contact center staff.” In September 2019, Glia, which has around 100 employees across the U.S. and Europe, acquired Gigzolo, a startup developing recommendation algorithms and a platform that makes it easier for marketers to transact online with more than 16,000 event service providers. More recently, Glia inked a strategic partnership with LitLingo to enable the former’s customers to use the latter’s out-of-the-box AI models to monitor inbound and outbound communications between support agents and financial customers.
Insight Partners led Glia’s series C announced today, which had participation from Cooley LLP. It brings the company’s total raised to date to $107 million following a $20 million series B round in March 2019.
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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15,494 | 2,020 |
"How technology is helping dine-in restaurants reopen | VentureBeat"
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"https://venturebeat.com/2020/07/02/how-technology-is-helping-dine-in-restaurants-reopen"
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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 technology is helping dine-in restaurants reopen Share on Facebook Share on X Share on LinkedIn A phone with the menu is seen next to the qr code in order for customers to download the menu as the terrace is being set up at Les Marronniers Cafe 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.
The COVID-19 crisis has been a boon for ecommerce, with social distancing measures leading to a major uptick in online sales.
Brick-and-mortar businesses have had to adapt quickly, and deliveries and curbside pickup are very much the order of the day.
There is little question the pandemic will leave an indelible mark across the retail landscape. A number of big-name retailers — such as JC Penney — have filed for bankruptcy. Microsoft recently announced it has permanently closed all of its physical retail stores.
And restaurants that once relied on diners have had to embrace delivery and pickups.
But as lockdown measures ease and the world returns to some semblance of normalcy, technology is playing a crucial role in enabling people to dine in safely.
Table service Amazon-backed Deliveroo this week announced a new “ table service ” option that enables restaurants to accept orders and payments from diners through the Deliveroo mobile app. Moving forward, restaurants that join Deliveroo’s platform will be able to tap its delivery network — with the usual commission fees attached. But the app will now double as a payment system and digital menu for dine-in customers too. Deliveroo is offering this service at 0% commission, presumably in hopes that new signups will be tempted to use its delivery network.
Above: Deliveroo’s new table service option This is a notable move for Deliveroo, whose food delivery business is built almost entirely on its transport infrastructure. But this latest effort is very much in keeping with the ways other tech companies are adapting to support the “new normal.” Deliveroo’s new offering builds on a movement that was already underway. McDonald’s was offering table service in some markets through its own app long before COVID-19. And while “fast food” and “table service” might have seemed odd bedfellows, the technology certainly makes sense in today’s world.
Restaurants are leveraging a range of tech options to encourage customers to sit down for a coffee or a nice lunch. QR code menus are surging in popularity, as outlets across France and elsewhere plaster their tables with square stickers diners can scan with their smartphones, bringing the menu directly to their device.
Above: A waiter applies a QR code so customers can download the menu at Les Marronniers Cafe in Paris, France.
On the payment side, PayPal recently rolled out QR code contactless payments in 28 markets, enabling farmers’ markets, cafes, and other establishments to accept payments while minimizing contact.
Completely new platforms are also emerging to help restaurants and diners conduct business from a safe distance.
TableDrop , for example, offers a new consumer app that taps a user’s location to automatically load the menu from the venue they are in, though restaurants, bars, and cafes need to sign up for the service in advance.
Though the world is still very much in flux, there is a growing eagerness to get society up and running as soon as possible, but only if it’s safe to do so. Whatever that looks like, it’s clear technology will play a huge role in making it happen.
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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15,495 | 2,021 |
"Researchers develop algorithm to identify well-liked brands | VentureBeat"
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"https://venturebeat.com/2021/07/01/researchers-develop-algorithm-to-identify-well-liked-brands"
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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 algorithm to identify well-liked brands Share on Facebook Share on X Share on LinkedIn A woman looks at the Facebook logo on an iPad in this photo illustration.
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Measuring sentiment can provide a snapshot of how customers feel about companies, products, or services. It’s important for organizations to be aware: 86% of people say that authenticity is a key factor when deciding what brands they like and support. In an Edelman survey, 81% of consumers said that they need to be able to trust a brand in order to buy products from them.
While sentiment analysis technology has been around for a while , researchers at the University of Maryland’s Robert H. Smith School of Business claim to have improved upon prior methods with a new system that leverages machine learning. They say that their algorithm, which sorts through social media posts to understand how people perceive brands, can comb through more data and better measure favorability.
Sentiment analysis isn’t a perfect science , but social media provides rich signals that can be used to help shape brand strategies.
According to CommSights , 46% of people have opted to use social media in the past to extend their complaints to a particular company.
“There is a vast amount of social media data available to help brands better understand their customers, but it has been underutilized in part because the methods used to monitor and analyze the data have been flawed,” Wendy W. Moe, University of Maryland associate dean of master’s programs, who created the algorithm with colleague Kunpeng Zhang, said in a statement. “Our research addresses some of the shortcomings and provides a tool for companies to more accurately gauge how consumers perceive their brands.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Algorithmic analysis Zhang’s and Moe’s method sifts through data from posts on a brand’s page, including how many users have expressed positive or negative sentiments, “liked” something, or shared something. It predicts how people will feel about that brand in the future, scaling to billions of pages of user-brand interaction data and millions of users.
The algorithm specifically looks at users’ interactions with brands to measure favorability — whether people view that brand in a positive or negative way. And it takes into account biases, inferring favorability and measuring social media users’ positivity based on their comments in the user-brand interaction data.
Zhang and Moe say that brands can apply the algorithm to a range of platforms, such as Facebook, Twitter, and Instagram, as long as the platforms provide user-brand interaction data and allow users to comment, share, and like content. The algorithm importantly doesn’t use private information, like user demographics, relying instead on user-brand publicly available interaction data.
“A brand needs to monitor the health of their brand dynamically,” Zhang said in a statement. “Then they can change marketing strategy to impact their brand favorability or better respond to competitors. They can better see their current location in the market in terms of their brand favorability. That can guide a brand to change marketing [practices].” Zhang’s and Moe’s research is detailed in the paper “Measuring Brand Favorability Using Large-Scale Social Media Data,” which will be published in the forthcoming issue of the journal Information Systems Research.
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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15,496 | 2,021 |
"Western Digital launches edge servers for rugged 5G networks in harsh environments | VentureBeat"
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"https://venturebeat.com/2021/06/15/western-digital-launches-edge-servers-to-support-rugged-5g-networks-in-harsh-environments"
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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 Western Digital launches edge servers for rugged 5G networks in harsh environments Share on Facebook Share on X Share on LinkedIn Ultrastar Edge server.
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Western Digital has launched its Ultrastar Edge servers to support 5G networks and other communications in harsh environments.
If you’ve ever planning a trip to the Alaskan wilderness and want to stream live video of a bear chasing after you to your friends back home, you might want to take one of these along. The servers can deliver high speeds and capacity for real-time analytics, AI, deep learning, ML training and inference, and video transcoding at the edge.
In all seriousness, these Ultrastar Edge servers can be lifesavers when it comes to creating rugged networks in harsh remote environments, the company said.
An expansion for WD The servers are part of Western Digital’s effort to provide the foundation for the world’s essential data infrastructure. Western Digital will use the servers to bring computing power closer to where data is generated for faster processing, lower latency, and real-time decision-making, even when disconnected.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! With the growing adoption of 5G, internet of things, and the cloud, businesses and consumers expect super-fast performance with their applications. This is creating demand for new, distributed intelligent architectures outside of core datacenters to help ingest, analyze, and transform data at the edge. In addition, organizations are running applications in extremely remote locations, such as deserts, seas, or jungles, and are driving the need for ruggedized compute and storage where networks can be expensive, intermittent, or nonexistent.
Designed for cloud service providers, telcos, and system integrators, Western Digital’s Ultrastar Edge servers are meant to be easy to transport and deploy and scale in the field, at colocation (colo) facilities, in factories, or in remote datacenters. The new family includes the Ultrastar Edge-MR, an extremely rugged, stackable, and transportable server for military and specialized field teams working in harsh remote environments, and the Ultrastar Edge, a transportable two-unit, rack-mountable server with a portable case for colos and edge datacenters. Both solutions are now sampling and can be ordered with general availability beginning in the fourth quarter.
Why it matters Above: Western Digital’s Ultrastar Edge server is rugged.
Manoj Sukumaran, senior analyst for datacenter compute at Omdia, said in a statement that more computing capacity is needed at edge locations as latency-sensitive applications proliferate. He said he expects server deployments at edge locations to double through 2024, totaling an estimated 5 million units, as they are an essential component in enabling new innovations and products, cloud services, remote campuses, CDNs, and virtually any vertical industry that relies on IoT, sensor, or remote data.
In the world of 5G and IoT, computing must happen at the edge — closer to devices, end-users, and the machines that are generating the data — he said, particularly where latency and bandwidth are essential factors for success. He said he is glad to see Western Digital enter the market, as the industry needs reliable products from trusted vendors.
Kurt Chan, VP of datacenter platforms at Western Digital, said in a statement that as a storage technology leader, Western Digital is constantly anticipating how it can continue to serve customers’ needs. The growth in data creation at the edge, the opportunities to extract value from that data, and the total available markets and customers innovating and doing work at the edge give the company an opportunity to launch its servers.
The specs Above: Western Digital’s Ultrastar Edge from the rear view.
The Ultrastar Edge-MR is a rugged, stackable solution that is designed and tested in accordance with MIL-STD-810G-CHG-1 standards for limits of shock and vibration. It also conforms to the MIL-STD-461G standard for electromagnetic interference. And the unit is rated IP32 to provide protection against water and debris. Whether conducting a military operation, doing research in the Amazon, or analyzing data during oil and gas explorations, the Ultrastar Edge-MR can handle extremes, Western Digital said. Both Ultrastar Edge solutions also feature the Trusted Platform Module 2.0, a tamper-evident enclosure built to meet FIPS 140-2 Level 2 security standard to help store, secure, transfer, and disseminate sensitive data. Aeon Computing is one of several partners.
The core of each Ultrastar Edge solution is a durable, high-speed server that supports up to 40 cores with two Intel Xeon Scalable Processors. It also has an Nvidia T4 graphics processing unit (GPU), and eight Ultrastar NVMe SSDs providing up to 61TB of storage. It features two 50Gb Ethernet connections or one 100Gb Ethernet connection for sending critical data back to the cloud or datacenter when connected.
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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15,497 | 2,021 |
"KPMG: Car makers will lose $100B in 2021 due to semiconductor shortage | VentureBeat"
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"https://venturebeat.com/2021/06/03/kpmg-car-makers-will-lose-100b-in-2021-due-to-semiconductor-shortage"
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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 KPMG: Car makers will lose $100B in 2021 due to semiconductor shortage Share on Facebook Share on X Share on LinkedIn KPMG says the chip shortage will cost car makers $100 billion in lost sales.
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The semiconductor shortage is putting the brakes on global automakers, and it could cost them $100 billion in lost revenues in 2021, according to a report by accounting firm KPMG.
The $100 billion in lost revenue is taken away from the approximately $2.1 trillion automotive market was expected to make in 2021, compared to its revenues of $1.65 trillion in 2020.
Nothing has demonstrated more clearly how much our economy and way of life depend on semiconductors than its current global shortage, KPMG’s principal chip adviser Scott Jones wrote in the report. A series of events — a perfect storm of natural and man-made events, including the COVID-19 pandemic — has created a serious shortage of semiconductor products that is expected to last well into 2022. Technological advancements and the increasing need for chips across industries have only worsened the shortage.
Chipmakers like TSMC and Intel are investing billions of dollars into new factories to boost production, but it takes time to build those factories, and new supplies will come too late to help automakers and other customers suffering shortages in 2021.
Slow-motion wreck Above: KPMG said a perfect storm caused the chip shortage.
It’s kind of a slow-motion train wreck. At the start of the pandemic, most automakers slashed orders of nearly all the components of their vehicles in anticipation of a major drop-off in demand for auto sales. Meanwhile, other semiconductor customers continued or increased purchases of semiconductors to meet the increased demand from work-from-home requirements, driving the need for more PCs, cloud compute capacity, and communications infrastructure.
When demand for vehicles started to accelerate, automakers were stuck behind a large backlog of orders from other larger semiconductor customers who had continued orders throughout the pandemic. Semiconductor companies were already running at near full capacity from the surge in those orders and had no ability to support the resuming automotive orders. Now, a shortage of a relatively inexpensive chip could hold off production of a $50,000 car.
KPMG saw that companies and manufacturers that use semiconductors cut production and trimmed earnings forecasts, with automakers clearly hit the hardest. While automakers account for only about 10% of global semiconductor sales, KPMG estimates that they will suffer about 80% of the $125 billion in lost sales due to the shortage — about $100 billion. Many leading automakers are predicting that production will be affected through the end of the year; semiconductor executives expect a 2022 recovery.
Beyond the more than $100 billion in lost revenue, KPMG estimated automakers will have to spend upward of $10 billion to $15 billion in a one-time cost to rebuild buffer inventory, upgrade the supply chain, and secure more capacity for the future. Additionally, KPMG expects an annualized cost increase on semiconductors to impact automakers by $1 billion to $2 billion annually.
While these costs and expected losses are large, automakers are mitigating the impact by shifting their production and focus to more profitable vehicle platforms. This shift aligns with current pandemic-driven consumer demand and expected emerging demands as many consumers shift to electronics-driven, smarter, and autonomous cars.
What went wrong for automakers? Above: A whipsaw in demand for chips.
Many automakers lack the close relationships with semiconductor suppliers that other customers have developed over time, Jones said. Despite the rising importance of semiconductors in their products, they haven’t adjusted their supply chain to reflect how critical semiconductors have become. They mostly rely on third parties, such as tier-1 suppliers, to deal with chipmakers, and they generally lack visibility into, and understanding of, the supply chain.
There are no quick fixes, but industry and government entities all over the world are taking steps to improve the long-term situation.
How can automakers avoid being caught short next time? Above: Cars use a lot more electronics than they once did.
The pandemic was a once-in-a-century event — hopefully — but now it will be prudent for car makers to consider what to do next. Jones said they will have to collaborate more closely with semiconductor manufacturers and foundries. Instead of relying on tier-1 suppliers or on indirect supply-chain management, automakers should collaborate directly with semiconductor manufacturers.
They should consider making investments in chipmaking capacity, he said. Automakers can take an active role in ensuring that there is enough capacity for the chips they need by reserving capacity, guaranteeing demand, or making direct investments to increase capacity.
They should also use data-driven supply chain decision-making. As supply chains become more complex, planning by spreadsheet is no longer adequate. “Cognitive planning” can configure and integrate supply chain activities using machine learning and artificial intelligence.
And automakers should overhaul the process for selecting, designing, and sourcing. In the longer term, they can avoid supply problems by reducing reliance on custom parts and instead use standard parts that can be modified or updated via software. Selecting hardware that works with open source software can reduce costs and guarantee access to multiple suppliers.
They should also address organizational barriers to optimize the semiconductor supply chain. For example, they could use dedicated centralized teams to oversee electronics and semiconductor supply chains. The shortage is expected to last well into 2022; however, now is the time to plan for the long term to avoid problems in the future.
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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15,498 | 2,021 |
"Low code: A promising trend or a Pandora’s Box? | VentureBeat"
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"https://venturebeat.com/2021/07/24/low-code-a-promising-trend-or-a-pandoras-box"
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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 Guest Low code: A promising trend or a Pandora’s Box? 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 analyst community is having a field day with hype around “ low code.
” IDC has predicted that there will be more and more low code used and that the worldwide population of low-code developers will grow with a CAGR of 40.4% from 2021 to 2025.
Gartner predicted that low code will increase nearly 30% from 2020 to reach $5.8 billion in 2021.
Forrester has also jumped on the low-code hype wagon and forecasted that by the end of 2021, 75% of application development will use low-code platforms.
Low code requires little coding skill and uses visual, pre-packaged templates, point and click, and drag-and-drop software techniques. It’s a bit like using Lego blocks to build software applications to solve problems related to procedures or small processes. Requiring minimal IT support, such low code applications can be implemented rapidly and are cost effective too.
A few low code pros and cons The development of low code is likely to be welcome by many business users, especially those who are a bit tech savvy and have had to wait for months for their IT department to address minor change requests. The relatively low cost of deploying low code and the faster/easier application delivery will also be appreciated.
On the other hand, there are several issues with low code that will only become clear with time. For example, using low code to solve multiple current problems will likely result in integration problems down the road. Then, even though there is some level of security built into most low code apps, as various users in multiple departments use low code to solve minor problems, the likelihood of future security issues increases. Next, the widespread use of low code in an organization may lead to an even larger number of data silos as users develop minor changes such as new screens to meet their own departmental needs. Also, due to the increasing number of low-code vendors, the sheer number of platforms may eventually result in fragmentation that will need to be addressed at some point in the future. What is even more noteworthy is the relationship — or lack thereof — between low-code and hyperautomation.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Low code and hyperautomation Gartner introduced the term hyperautomation to describe the combination of different technologies to automate an organization’s processes in a unified, integrated way. Forrester calls this approach digital process automation (DPA), and IDC has used the label intelligent process automation (IPA) for the very same thing. In each case, analysts are referring to the integrated deployment of technologies such as robotic process automation (RPA), intelligent business process management (IBPM), machine learning, and artificial intelligence (AI) to streamline and orchestrate as many tasks, activities, and processes as possible.
Hyperautomation promotes collaboration across both departments and vendor platforms. The practice of having users independently use low code to develop minor changes is contrary to the core principles of hyperautomation. Instead of breaking down silos, low-code reinforces departmental silos and is likely to create even more data silos. The low-code movement has produced so called “citizen developers” — a new term for business users acting to satisfy their own information management needs with low code. It’s too early to tell whether citizen developers will act as “cowboys,” satisfying just their own selfish information needs potentially at the expense of others or whether they will consider the big picture. We’ll know in the next few years.
Low code and technical debt Technical debt is the extra development work that arises when an IT group chooses to use easy-to-implement code in the short run instead of applying the best (and sometimes more difficult) solution. Many organizations are mired in technical debt due to short-term thinking, changing requirements, and the simple fact that, for decades, IT groups have responded to the self-serving needs of individual departments as opposed to thinking systemically. Low code is likely to exacerbate the challenge of reducing technical debt for the following reasons : As multiple citizen developers implement small scale, low-code solutions, version control will likely become increasingly problematic.
When a low code solution is working fine — then all is good — but once problems arise — then testing and debugging can be challenging, as it’s difficult to see under the covers of pre-packaged templates.
As enterprise applications often need to connect with distributed systems, the integration of low-code solutions can be tricky.
What will the future hold? Let’s face it: Users need tools, too. Low code clearly offers short-term benefits. For quite some time, users have been turning to tools such as custom Excel spreadsheets to address the shortcomings of enterprise systems such as ERP and CRM. Low code offers a quick and easy-to-implement alternative at relatively low cost. Will low code satisfy the needs of knowledge workers outside of IT in implementing small-scale fixes, or will it open a Pandoras Box of integration issues? But the widespread use of low code may lead to a set of management challenges.
How will IT management react when citizen developers create applications that don’t scale well and then turn these over to the IT group? What will be the backup plan when a citizen developer leaves the company and no one knows how to support the application they developed? Digital transformation is a complex undertaking with a dismal track record: As few as 30% of such initiatives succeed. Consider the Gartner Magic Quadrant below and note that the number of firms offering low-code solutions is steadily growing. What will the proliferation of low-code vendors mean to the development of an aligned and effective digital strategy? Will proprietary low-code systems lead to a significant risk of vendor lock-in? When departments do not collaborate, transformation efforts suffer. According to a 2020 report from Accenture, an amazing 75% of 1,500 global senior and C-level executives saw different business functions competing against each other instead of collaborating on digitization efforts. This lack of collaboration contributed to 64% of companies failing to see revenue growth from their digital investments, marking a clear connection between silo behavior and a lack of success with digital.
The bottom line is this: No one can see the big picture when individual departments just look at their own technology needs. In the absence of departmental collaboration, it’s hard to create the right context for focusing on customer experience and end-to-end process performance. Unless it is deployed with care, low code is not likely to promote a systemic view; instead, it is likely to reinforce short-term thinking.
Andrew Spanyi is President of Spanyi International.
He is a member of the Board of Advisors at the Association of Business Process Professionals and has been an instructor at the BPM Institute.
He is also a member of the Cognitive World Think Tank on enterprise AI.
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"The 2020 data and AI landscape | VentureBeat"
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"https://venturebeat.com/2020/10/21/the-2020-data-and-ai-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 Guest The 2020 data and AI landscape 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.
When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet many companies in the data ecosystem have not just survived but in fact thrived.
Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown ). And Palantir , an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown ).
Meanwhile, other recently IPO’ed data companies are performing very well in public markets.
Datadog , for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here ). When I hosted CEO Olivier Pomel at my monthly Data Driven NYC event at the end of January 2020, Datadog was worth $12 billion. A mere eight months later, at the time of writing, its market cap is $31 billion.
Many economic factors are at play, but ultimately financial markets are rewarding an increasingly clear reality long in the making: To succeed, every modern company will need to be not just a software company but also a data company. There is, of course, some overlap between software and data, but data technologies have their own requirements, tools, and expertise. And some data technologies involve an altogether different approach and mindset – machine learning, for all the discussion about commoditization, is still a very technical area where success often comes in the form of 90-95% prediction accuracy, rather than 100%. This has deep implications for how to build AI products and companies.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Of course, this fundamental evolution is a secular trend that started in earnest perhaps 10 years ago and will continue to play out over many more years. To keep track of this evolution, my team has been producing a “state of the union” landscape of the data and AI ecosystem every year; this is our seventh annual one. For anyone interested in tracking the evolution, here are the prior versions: 2012 , 2014 , 2016 , 2017 , 2018 and 2019 ( Part I and Part II ).
This post is organized as follows: Key trends in data infrastructure Key trends in analytics and enterprise AI The 2020 landscape — for those who don’t want to scroll down, here is the landscape image Let’s dig in.
Key trends in data infrastructure There’s plenty going on in data infrastructure in 2020. As companies start reaping the benefits of the data/AI initiatives they started over the last few years, they want to do more. They want to process more data, faster and cheaper. They want to deploy more ML models in production. And they want to do more in real-time. Etc.
This raises the bar on data infrastructure (and the teams building/maintaining it) and offers plenty of room for innovation, particularly in a context where the landscape keeps shifting (multi-cloud, etc.).
In the 2019 edition , my team had highlighted a few trends: A move from Hadoop to cloud services to Kubernetes + Snowflake The increasing importance of data governance, cataloging, and lineage The rise of an AI-specific infrastructure stack (“MLOps”, “AIOps”) While those trends are still very much accelerating, here are a few more that are top of mind in 2020: 1. The modern data stack goes mainstream.
The concept of “modern data stack” (a set of tools and technologies that enable analytics, particularly for transactional data) has been many years in the making. It started appearing as far back as 2012, with the launch of Redshift, Amazon’s cloud data warehouse.
But over the last couple of years, and perhaps even more so in the last 12 months, the popularity of cloud warehouses has grown explosively, and so has a whole ecosystem of tools and companies around them, going from leading edge to mainstream.
The general idea behind the modern stack is the same as with older technologies: To build a data pipeline you first extract data from a bunch of different sources and store it in a centralized data warehouse before analyzing and visualizing it.
But the big shift has been the enormous scalability and elasticity of cloud data warehouses (Amazon Redshift, Snowflake, Google BigQuery, and Microsoft Synapse, in particular). They have become the cornerstone of the modern, cloud-first data stack and pipeline.
While there are all sorts of data pipelines (more on this later), the industry has been normalizing around a stack that looks something like this, at least for transactional data: 2. ELT starts to replace ELT.
Data warehouses used to be expensive and inelastic, so you had to heavily curate the data before loading into the warehouse: first extract data from sources, then transform it into the desired format, and finally load into the warehouse (Extract, Transform, Load or ETL).
In the modern data pipeline, you can extract large amounts of data from multiple data sources and dump it all in the data warehouse without worrying about scale or format, and then transform the data directly inside the data warehouse – in other words, extract, load, and transform (“ELT”).
A new generation of tools has emerged to enable this evolution from ETL to ELT. For example, DBT is an increasingly popular command line tool that enables data analysts and engineers to transform data in their warehouse more effectively. The company behind the DBT open source project, Fishtown Analytics, raised a couple of venture capital rounds in rapid succession in 2020. The space is vibrant with other companies, as well as some tooling provided by the cloud data warehouses themselves.
This ELT area is still nascent and rapidly evolving. There are some open questions in particular around how to handle sensitive, regulated data (PII, PHI) as part of the load, which has led to a discussion about the need to do light transformation before the load – or ETLT (see XPlenty, What is ETLT? ). People are also talking about adding a governance layer, leading to one more acronym, ELTG.
3. Data engineering is in the process of getting automated.
ETL has traditionally been a highly technical area and largely gave rise to data engineering as a separate discipline.
This is still very much the case today with modern tools like Spark that require real technical expertise.
However, in a cloud data warehouse centric paradigm, where the main goal is “just” to extract and load data, without having to transform it as much, there is an opportunity to automate a lot more of the engineering task.
This opportunity has given rise to companies like Segment, Stitch (acquired by Talend), Fivetran, and others. For example, Fivetran offers a large library of prebuilt connectors to extract data from many of the more popular sources and load it into the data warehouse. This is done in an automated, fully managed and zero-maintenance manner. As further evidence of the modern data stack going mainstream, Fivetran, which started in 2012 and spent several years in building mode, experienced a strong acceleration in the last couple of years and raised several rounds of financing in a short period of time (most recently at a $1.2 billion valuation). For more, here’s a chat I did with them a few weeks ago: In Conversation with George Fraser, CEO, Fivetran.
4. Data analysts take a larger role.
An interesting consequence of the above is that data analysts are taking on a much more prominent role in data management and analytics.
Data analysts are non-engineers who are proficient in SQL, a language used for managing data held in databases. They may also know some Python, but they are typically not engineers. Sometimes they are a centralized team, sometimes they are embedded in various departments and business units.
Traditionally, data analysts would only handle the last mile of the data pipeline – analytics, business intelligence, and visualization.
Now, because cloud data warehouses are big relational databases (forgive the simplification), data analysts are able to go much deeper into the territory that was traditionally handled by data engineers, leveraging their SQL skills (DBT and others being SQL-based frameworks).
This is good news, as data engineers continue to be rare and expensive. There are many more (10x more?) data analysts, and they are much easier to train.
In addition, there’s a whole wave of new companies building modern, analyst-centric tools to extract insights and intelligence from data in a data warehouse centric paradigm.
For example, there is a new generation of startups building “KPI tools” to sift through the data warehouse and extract insights around specific business metrics, or detecting anomalies, including Sisu, Outlier, or Anodot (which started in the observability data world).
Tools are also emerging to embed data and analytics directly into business applications. Census is one such example.
Finally, despite (or perhaps thanks to) the big wave of consolidation in the BI industry which was highlighted in the 2019 version of this landscape, there is a lot of activity around tools that will promote a much broader adoption of BI across the enterprise. To this day, business intelligence in the enterprise is still the province of a handful of analysts trained specifically on a given tool and has not been broadly democratized.
5. Data lakes and data warehouses may be merging.
Another trend towards simplification of the data stack is the unification of data lakes and data warehouses. Some (like Databricks) call this trend the “data lakehouse.” Others call it the “Unified Analytics Warehouse.” Historically, you’ve had data lakes on one side (big repositories for raw data, in a variety of formats, that are low-cost and very scalable but don’t support transactions, data quality, etc.) and then data warehouses on the other side (a lot more structured, with transactional capabilities and more data governance features).
Data lakes have had a lot of use cases for machine learning, whereas data warehouses have supported more transactional analytics and business intelligence.
The net result is that, in many companies, the data stack includes a data lake and sometimes several data warehouses, with many parallel data pipelines.
Companies in the space are now trying to merge the two, with a “best of both worlds” goal and a unified experience for all types of data analytics, including BI and machine learning.
For example, Snowflake pitches itself as a complement or potential replacement, for a data lake. Microsoft’s cloud data warehouse, Synapse, has integrated data lake capabilities.
Databricks has made a big push to position itself as a full lakehouse.
Complexity remains A lot of the trends I’ve mentioned above point toward greater simplicity and approachability of the data stack in the enterprise. However, this move toward simplicity is counterbalanced by an even faster increase in complexity.
The overall volume of data flowing through the enterprise continues to grow an explosive pace. The number of data sources keeps increasing as well, with ever more SaaS tools.
There is not one but many data pipelines operating in parallel in the enterprise. The modern data stack mentioned above is largely focused on the world of transactional data and BI-style analytics. Many machine learning pipelines are altogether different.
There’s also an increasing need for real time streaming technologies, which the modern stack mentioned above is in the very early stages of addressing (it’s very much a batch processing paradigm for now).
For this reason, the more complex tools, including those for micro-batching (Spark) and streaming (Kafka and, increasingly, Pulsar) continue to have a bright future ahead of them. The demand for data engineers who can deploy those technologies at scale is going to continue to increase.
There are several increasingly important categories of tools that are rapidly emerging to handle this complexity and add layers of governance and control to it.
Orchestration engines are seeing a lot of activity. Beyond early entrants like Airflow and Luigi, a second generation of engines has emerged, including Prefect and Dagster, as well as Kedro and Metaflow. Those products are open source workflow management systems, using modern languages (Python) and designed for modern infrastructure that create abstractions to enable automated data processing (scheduling jobs, etc.), and visualize data flows through DAGs (directed acyclic graphs).
Pipeline complexity (as well as other considerations, such as bias mitigation in machine learning) also creates a huge need for DataOps solutions, in particular around data lineage (metadata search and discovery), as highlighted last year, to understand the flow of data and monitor failure points. This is still an emerging area, with so far mostly homegrown (open source) tools built in-house by the big tech leaders: LinkedIn (Datahub), WeWork (Marquez), Lyft (Admunsen), or Uber (Databook). Some promising startups are emerging.
There is a related need for data quality solutions, and we’ve created a new category in this year’s landscape for new companies emerging in the space (see chart).
Overall, data governance continues to be a key requirement for enterprises, whether across the modern data stack mentioned above (ELTG) or machine learning pipelines.
Trends in analytics & enterprise ML/AI It’s boom time for data science and machine learning platforms (DSML). These platforms are the cornerstone of the deployment of machine learning and AI in the enterprise. The top companies in the space have experienced considerable market traction in the last couple of years and are reaching large scale.
While they came at the opportunity from different starting points, the top platforms have been gradually expanding their offerings to serve more constituencies and address more use cases in the enterprise, whether through organic product expansion or M&A. For example: Dataiku (in which my firm is an investor) started with a mission to democratize enterprise AI and promote collaboration between data scientists, data analysts, data engineers, and leaders of data teams across the lifecycle of AI (from data prep to deployment in production). With its most recent release, it added non-technical business users to the mix through a series of re-usable AI apps.
Databricks has been pushing further down into infrastructure through its lakehouse effort mentioned above, which interestingly puts it in a more competitive relationship with two of its key historical partners, Snowflake and Microsoft. It also added to its unified analytics capabilities by acquiring Redash, the company behind the popular open source visualization engine of the same name.
Datarobot acquired Paxata, which enables it to cover the data prep phase of the data lifecycle, expanding from its core autoML roots.
A few years into the resurgence of ML/AI as a major enterprise technology, there is a wide spectrum of levels of maturity across enterprises – not surprisingly for a trend that’s mid-cycle.
At one end of the spectrum, the big tech companies (GAFAA, Uber, Lyft, LinkedIn etc) continue to show the way. They have become full-fledged AI companies, with AI permeating all their products. This is certainly the case at Facebook (see my conversation with Jerome Pesenti, Head of AI at Facebook ). It’s worth nothing that big tech companies contribute a tremendous amount to the AI space, directly through fundamental/applied research and open sourcing, and indirectly as employees leave to start new companies (as a recent example, Tecton.ai was started by the Uber Michelangelo team).
At the other end of the spectrum, there is a large group of non-tech companies that are just starting to dip their toes in earnest into the world of data science, predictive analytics, and ML/AI. Some are just launching their initiatives, while others have been stuck in “AI purgatory” for the last couple of years, as early pilots haven’t been given enough attention or resources to produce meaningful results yet.
Somewhere in the middle, a number of large corporations are starting to see the results of their efforts. They typically embarked years ago on a journey that started with Big Data infrastructure but evolved along the way to include data science and ML/AI.
Those companies are now in the ML/AI deployment phase, reaching a level of maturity where ML/AI gets deployed in production and increasingly embedded into a variety of business applications. The multi-year journey of such companies has looked something like this: Source: Dataiku As ML/AI gets deployed in production, several market segments are seeing a lot of activity: There’s plenty happening in the MLOps world, as teams grapple with the reality of deploying and maintaining predictive models – while the DSML platforms provide that capability, many specialized startups are emerging at the intersection of ML and devops.
The issues of AI governance and AI fairness are more important than ever, and this will continue to be an area ripe for innovation over the next few years.
Another area with rising activity is the world of decision science (optimization, simulation), which is very complementary with data science. For example, in a production system for a food delivery company, a machine learning model would predict demand in a certain area, and then an optimization algorithm would allocate delivery staff to that area in a way that optimizes for revenue maximization across the entire system. Decision science takes a probabilistic outcome (“90% likelihood of increased demand here”) and turns it into a 100% executable software-driven action.
While it will take several more years, ML/AI will ultimately get embedded behind the scenes into most applications, whether provided by a vendor, or built within the enterprise. Your CRM, HR, and ERP software will all have parts running on AI technologies.
Just like Big Data before it, ML/AI, at least in its current form, will disappear as a noteworthy and differentiating concept because it will be everywhere. In other words, it will no longer be spoken of, not because it failed, but because it succeeded.
The year of NLP It’s been a particularly great last 12 months (or 24 months) for natural language processing (NLP), a branch of artificial intelligence focused on understanding human language.
The last year has seen continued advancements in NLP from a variety of players including large cloud providers (Google), nonprofits (Open AI, which raised $1 billion from Microsoft in July 2019) and startups. For a great overview, see this talk from Clement Delangue, CEO of Hugging Face: NLP—The Most Important Field of ML.
Some noteworthy developments: Transformers, which have been around for some time, and pre-trained language models continue to gain popularity. These are the model of choice for NLP as they permit much higher rates of parallelization and thus larger training data sets.
Google rolled out BERT, the NLP system underpinning Google Search, to 70 new languages.
Google also released ELECTRA , which performs similarly on benchmarks to language models such as GPT and masked language models such as BERT, while being much more compute efficient.
We are also seeing adoption of NLP products that make training models more accessible.
And, of course, the GPT-3 release was greeted with much fanfare. This is a 175 billion parameter model out of Open AI, more than two orders of magnitude larger than GPT-2.
The 2020 data & AI landscape A few notes: To view the landscape in full size, click here.
This year, we took more of an opinionated approach to the landscape. We removed a number of companies (particularly in the applications section) to create a bit of room, and we selectively added some small startups that struck us as doing particularly interesting work.
Despite how busy the landscape is, we cannot possibly fit every interesting company on the chart itself. As a result, we have a whole spreadsheet that not only lists all the companies in the landscape, but also hundreds more.
[Note: A different version of this story originally ran on the author’s own web site.] Matt Turck is a VC at FirstMark , where he focuses on SaaS, cloud, data, ML/AI and infrastructure investments. Matt also organizes Data Driven NYC , the largest data community in the US.
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"ThoughtSpot adds support for Databricks 'lakehouse' to analytics platform | VentureBeat"
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"https://venturebeat.com/2021/05/26/thoughtspot-adds-support-for-databricks-lakehouse-to-analytics-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 ThoughtSpot adds support for Databricks ‘lakehouse’ to analytics platform 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.
ThoughtSpot has expanded the number of backend data sources that can be accessed via its cloud-based analytics platform to include the Databricks cloud service based on the Apache Spark framework.
A ThoughtSpot for Databricks offering now makes it possible to directly run queries through the ThoughtSpot search engine against a Databricks Lakehouse , a data architecture that combines the features of data lakes and data warehouses , according to Databricks.
For nearly a decade, ThoughtSpot has been making the case for an alternative approach to analytics that eliminates the need to rely on a data analyst or IT professional to construct a dashboard. Instead, it presents end users with a search interface through which they can employ natural language to query multiple backend data repositories.
That approach enables end users to interrogate data in a more interactive fashion that is not constrained by the limitations of how a dashboard was constructed, said Seann Gardiner, senior vice president of business development for ThoughtSpot. Users can launch those search queries in a single click, accessing backend data via SQL or levying Databricks’ machine learning algorithms for data science teams.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Organizations can also embed ThoughtSpot within third-party applications using low-code tools to create an alternative to dashboards, Gardiner said.
More flexibility means better decisions Databricks is now one of several backend platforms that ThoughtSpot now supports. Rather than standardizing on a visualization tool optimized for a single platform, Gardiner said, ThoughtSpot lets end users launch queries against multiple backend data sources using an interface that provides a familiar consumer-grade experience.
For the future, ThoughtSpot is also evaluating how to use speech interfaces to make its platform even more accessible to end users, Gardiner said. “Certainly, speech is the next generation of this,” he said.
Visualization tools that IT teams build generally limit queries to a set of frequently asked questions. A search engine approach makes it easier for end users to explore data by asking questions in a more open-ended manner, where the next query can be informed by the answer to a previous query. That’s especially critical for business conditions that are highly volatile. End users don’t want to wait a week for the IT team to construct a new type of query every time they want to investigate a trend that is currently beyond the scope of the data visualization tool.
As analytics continues to evolve, IT teams are becoming less bogged down in daily tasks. They still play a critical role in provisioning a platform, but the days when business users waited for IT teams to email reports to them based on a set of canned queries derived from a set of pre-defined key performance indicators (KPIs) have come to an end. IT personnel can be reallocated to more pressing tasks that have yet to be automated.
Business leaders, in the meantime, will hopefully be able to make better fact-based decisions faster. One of the reasons many business leaders still tend to rely on gut instinct is they are not always sure what question to ask. A more iterative approach to launching queries makes it simpler for business leaders to explore relationships between data without having to perfectly frame their initial query.
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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"Bias in AI isn't an enterprise priority, but it should be, survey warns | VentureBeat"
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"https://venturebeat.com/2021/07/22/bias-in-ai-isnt-an-enterprise-priority-but-it-should-be-survey-warns"
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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 Bias in AI isn’t an enterprise priority, but it should be, survey warns 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 global survey published today finds nearly a third (31%) of respondents consider the social impact of bias in models to be AI’s biggest challenge. This is followed by concerns about the impact AI is likely to have on data privacy (21%). More troubling, only 10% of respondents said their organization has addressed bias in AI, with another 30% planning to do so sometime in the next 12 months.
Conducted by Anaconda, whose platform provides access to curated instances of open source tools for building AI models, the survey of 4,299 individuals includes IT and business professionals, alongside students and academics. It suggests IT organizations are now exercising more influence over AI, with nearly a quarter of respondents (23%) noting data science teams report up through the IT organization. Approval of the AI platforms employed by IT teams ranked third (45%) after performance (60%) and memory (46%).
Respondents said they spend about 39% of their time on data prep and data cleansing, which is more than the time spent on model training, model selection, and deployment combined.
Among respondents responsible for deploying AI models in production environments , the top challenges cited are security (27%), recoding models from Python or R to another programming language (24%), managing dependencies and environments (23%), and recoding models from other languages into Python or R (23%). Python remains the dominant language (63%) employed by data science teams, while a full 87% said they are employing open source software to some degree.
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 worst AI myths The top two biggest data science myths cited are 1) that having access to lots of data leads to greater accuracy (33%) and 2) the perception that data scientists don’t know how to code (31%).
The survey suggests there is also a long way to go in terms of embedding AI within business workflows. Only 39% of respondents said many decisions are based on insights surfaced by their data science efforts. A little over a third (35%) said some decisions are influenced by their work. Only 36% said their organization’s decision-makers are very data literate and understand the stories told by visualizations and models. Just over half (52%) said decision-makers as mostly data literate but need some coaching.
However, the percentage of individuals across an organization that will be employing data science is only going to increase in the months ahead, said Anaconda CEO Peter Wang. “You don’t need to know data science to use data science,” he said.
AI spending drops over 2020 In the short term, however, the survey suggests investment in AI fell somewhat in the last year. More than a third of respondents said they saw a decline in AI investments in the wake of the economic downturn brought on by the COVID-19 pandemic. Only just over a quarter of respondents (26%) said their organization actually increased investments in AI.
Nearly half of respondents (45%) said reduced investments manifested themselves in the form of reduced budgets. Nearly half (47%) said their teams did not grow, while 39% said members of their teams were actually laid off. Just over a third (35%) said projects were put on hold or had their deadlines extended.
Just under a third of respondents (32%) said they expect to be looking for a new job in the next 12 months.
There’s no doubt organizations of all sizes are engaging to the best of their ability in what is rapidly becoming an AI arms race. But not all processes lend themselves equally well to AI, so the issue is not just how to build AI models but where best to apply them.
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 'Great Rehire' with data intelligence | VentureBeat"
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"https://venturebeat.com/2020/10/21/navigating-the-great-rehire-with-data-intelligence"
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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 Sponsored Navigating the ‘Great Rehire’ with data intelligence Share on Facebook Share on X Share on LinkedIn Presented by Hiretual Although some are cutting their hiring investments and downsizing their recruiting teams, it’s actually getting more expensive to attract suitable talent and convert them into new hires.
The average cost-per-applicant (CPA) in the U.S. has gone up 60% from last year, an uptick caused by specific pandemic-driven factors.
The CARES Act implemented this April gave unemployed workers a $600 boost in weekly benefits — a sustainable sum that has discouraged individuals from going back to work. Another important fact to note is that many companies are looking for professional workers that have not been affected by unemployment the way the hourly workforce has. Given the current economy, a majority of those in the professional talent pool are not taking any risks and would rather stay at their current roles.
Job openings are on the rise again, but this doesn’t necessarily mean long-term optimism for employers. To keep talent attraction costs at a sustainable level, hiring teams need the right resources to build pipelines during the upcoming stage of job recovery — a giant spike in hiring, otherwise known as the ‘Great Rehire.’ Succeeding in the ‘Great Rehire’ It took three years for the unemployment rate to drop below the 8% baseline after the 2008 recession because employers were unprepared for such drastic changes in the job scopes for many roles across the board.
The need for some jobs had been completely erased, and employers had to shift talent resources to functions they may not have spent as much on as before. Similarly, one of the biggest impacts of the pandemic has been forced digital transformation for all businesses — it’s no longer a ‘nice-to-have’, it’s a necessity. We’re seeing tech jobs lead the pack with a 13.4% month-over-month growth leaning toward roles in IT staffing, software, and digital operations.
Taking the lessons we’ve learned from the last recession, hiring teams must start preparing for both immediate and long-term business needs now before an all out war for talent begins in 2021. The continued evolution of recruitment technology will become pivotal for this strategy.
During the last recession, a new generation of recruitment was boosted — online recruitment via LinkedIn. The success of LinkedIn in addressing what employers lacked helped the company far exceed expectations during its IPO debut at the end of the recession.
Similarly, the Great Rehire will be spearheaded by a new generation of technology to help employers navigate an online recruitment market that has evolved far beyond the scope of just LinkedIn.
Moving beyond a talent database I say this often — there is a stark difference between a data-driven team and an intelligence-driven team.
What we’re currently seeing in data-driven hiring teams is the 80/20 dilemma.
We’re spending 80% of our time finding and organizing data from platforms like LinkedIn and GitHub, job boards like Glassdoor and Indeed and resumes collected during recruitment marketing events. That leaves us only 20% of remaining time to spend analyzing that data for pipeline-building.
To prepare for the massive hiring surges in 2021 and effectively compete for talent with other companies, employers need to spend their time actually acting on the data they’ve collected. Instead of relying on data availability, employers need to start adopting a data intelligence approach that brings talent data points together. This infrastructure acts as a powerful middleware between external online databases like LinkedIn to in-house systems like an ATS or CRM.
At Hiretual , we call this recruiting with a “ central talent data system.
” This centralized loop of data actively recognizes and acts on structured and unstructured data to enrich old candidate information with newly sourced online data, remove duplicate data entries and provide teams with a dashboard of talent pool insights powered by AI/ML pattern recognition.
Southeast Asian ride-share giant, Grab, uses this approach to develop consistent and real-time engagement with local, regional, and global talent. Grab’s hiring team uses Hiretual as their talent data system to drive sourcing through efficient pattern recognitions and iterative searches. The team has successfully deployed a sourcing strategy that leverages real-time visibility into applications in their ATS, inbound recruitment marketing leads in their CRM, and open web communities like Stack Overflow, GitHub, and Kaggle.
Simplifying talent acquisition with contextual data Grab oils their recruitment machine with a strong integrated framework powered by NLP-based data fusion. It is a knowledge graph for talent acquisition that informs the hiring process by analyzing queries and answering questions. So, rather than being additive to existing workflows, this well-integrated infrastructure consolidates processes within your tech stack.
The billions of entities and trillions of edges embedded within this graph gives rise to a scalable and responsive infrastructure for data federation, processing, and self-expansion. Ultimately, this will remove hours spent manually cleaning and organizing large volumes of multisource data to consolidate and simplify your hiring process.
Hiring teams can now use that 80% of their time to bring the human element back into recruiting. By optimizing existing strategies based on identified patterns from an online talent pool, more effort can be spent on heightened candidate engagement and a better candidate experience to bring talent attraction costs back down.
Making recruiting people-focused again Data intelligence doesn’t dehumanize recruitment, instead it does quite the opposite. It creates more time for hiring teams to focus on personalization. Messages become less cookie-cutter, outreach becomes more intentional, and employers are able to reach a more diverse and inclusive scope of job seekers for current and future goals.
The companies that succeed in the Great Rehire will be the ones that best understand their candidates’ needs and their own organizational needs. This is the future of AI in recruitment, and it’s already here –let’s make that leap and welcome it.
Hiretual helps hiring teams centralize talent management to build robust, diverse and inclusive workforces with AI technology — learn more about us here.
Steven Jiang is CEO/Co-Founder at Hiretual.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].
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 wants you to make ‘smart choices’ when it comes to food | VentureBeat"
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"https://venturebeat.com/2013/05/30/google-wants-you-to-make-smart-choices-when-it-comes-to-food"
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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 wants you to make ‘smart choices’ when it comes to food Share on Facebook Share on X Share on LinkedIn Google today announced the addition of nutrition information for more than 1,000 fruits, vegetables, meats, and meals to its search results.
Hey, how many calories are there in that cup of popcorn? Google will tell you: “It’s 31 calories per cup.” Google today announced the addition of nutrition information for more than 1,000 fruits, vegetables, meats, and meals to its search results.
The nutrition information will also be available through voice search on both web and mobile.
“From the basics of potatoes and carrots to more complex dishes like burritos and chow mein, you can simply ask, ‘How much protein is in a banana?’ or ‘How many calories are in an avocado?’ and get your answer right away,” blogged Ilya Mezheritsky, product manager at Google.
The feature launched today in English, and is expected to roll out in United States over the next 10 days.
It is powered by Google’s Knowledge Graph, which pulls together all the related information – even different names at times – from across the web.
“For example, when you ask for ‘summer squash carbs’, we include ‘zucchini’ as a relevant food in the dropdown, because it is a type of summer squash,” read Google’s Search blog.
The search giant plans to add more features, foods, and languages, over time.
Image Credit: epSos.de /Flickr 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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"Salesforce looks for a future beyond its walls with rising VC investments and acquisitions | VentureBeat"
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"https://venturebeat.com/2017/03/25/salesforce-looks-for-a-future-beyond-its-walls-with-rising-vc-investments-and-acquisitions"
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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 Salesforce looks for a future beyond its walls with rising VC investments and acquisitions Share on Facebook Share on X Share on LinkedIn Sales 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.
When it comes to investments and acquisitions, Salesforce has made headlines lately for deals that didn’t happen. The company’s rebuffed $26 billion bid for LinkedIn last year and a run at Twitter that never got off the ground would have ranked by far as Salesforce’s largest deals.
But those non-deals overshadowed the things Salesforce has been doing, namely ramping up its venture capital investments to grow an ecosystem of partners as well as making the most acquisitions ever in its history last year. Even as Salesforce itself continues to grow organically, these aggressive moves are a sign of how the company continues to look beyond its own walls for how and where it might grow.
In a recent conversation, Alex Kayyal , European head for Salesforce Ventures , said nurturing an expanding ecosystem of partners and developers has become a critical part of the company’s success and strategy.
“Salesforce has been on a phenomenal growth trajectory,” he said. “But so much of what we do is partnering with others. And it’s just blown my mind to see the power of that ecosystem.” Perhaps the most visible part of that ecosystem is the Salesforce AppExchange , which now has 3,500 apps that extend the company’s core platform. It is this rapidly growing ecosystem that research firm IDC dubbed the “Salesforce Economy” in a report last September. According to IDC, that ecosystem could be responsible for creating 1.9 million jobs by 2020 and contributing $389 million to global GDP.
To foster and continue that momentum, Salesforce has opened its wallet.
Salesforce Ventures was officially launched in 2011, but the company actually began making venture investments back in 2009. In a recent securities filing, Salesforce noted that it has minority investments in over 180 companies as of January 31, 2017.
According to CB Insights , Salesforce is the third largest corporate VC, behind Google Ventures and Intel Capital.
The size of the investments reached such a point this past year that Salesforce felt the need to warn investors about the potential risks in its securities filings: “Because of the inherent risk in investing in early-to-late stage technology companies, our individual investments are subject to a risk of partial or total loss of investment capital.” As part of that expanding portfolio, Salesforce announced a separate $100 million fund in 2015 to target European startups. Overall, the company has made 30 investments in Europe, including 15 in the 2 years since the fund was announced. Kayyal said that, in general, the company’s attention was drawn by the rapidly accelerating ecosystem in Europe and the emergence of numerous startup hubs across the continent.
“The pace and level of innovation we are seeing on this side of the pond is incredible,” he said. “And we’ve been really excited about the reception we’ve seen.” Salesforce Ventures also looks primarily for companies that are doing some sort of cloud-based service, typically B2B. The emergence of a number of cloud-based startups in Europe was another reason the company decided to focus more effort here. And while the venture arm operates somewhat independently, it’s also aware of the company’s shifting priorities, such as a growing interest in artificial intelligence, Kayyal said.
In addition to the money, the company tries to foster a true sense of partnership between the portfolio companies, to create what Kayyal calls a “mesh.” And it’s also looking for companies that can get help it get into new markets or technologies. That was the case, for instance, with its investment last year in France’s IoT startup Sigfox , which is building a communications network for connected devices.
Over the past eight years, Salesforce has built a solid track record. It has seen 50 of its investments get acquired and 7 have IPOs. The VC investment is not necessarily a scouting operation for acquisitions, however. Salesforce makes it clear that any of its companies are free to pursue any attractive offers.
That said, it has acquired a handful of its investments. And in general, Salesforce has also been making more M&A deals. Last year was the biggest in its history.
Those acquisitions in 2016 included: SteelBrick, which develops apps to automate the quote-to-cash process, for an undisclosed sum.
MetaMind, which developed natural language processing and image recognition for cloud services, for an undisclosed sum.
BeyondCore, which made tools to analyze structured data sources, for an undisclosed sum.
Demandware, which expanded its position in CRM and pursue the digital commerce market, for $2.9 billion.
Quip, Inc., which developed productivity tool, for $412.0 million.
Krux Digital, Inc., which created a data management platform, for $741.8 million.
Before 2016, Salesforce’s biggest deal was for ExactTarget in 2014 for $2.5 billion.
All this speaks to the company’s growing appetite and ambition. Its ability to balance these expanding investments and digest these acquisitions may determine whether Salesforce can succeed in striking in a balance that few maturing companies manage to find: Maintain an innovative culture, continue to grow its core services, and be ready to adapt as markets and technologies evolve in sometimes sudden and expected ways.
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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"Zoom launches Zapps to bring third-party apps into video calls | VentureBeat"
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"https://venturebeat.com/2020/10/14/zoom-launches-zapps-to-bring-third-party-apps-into-video-calls"
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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 Zoom launches Zapps to bring third-party apps into video calls 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.
Zoom has become an indispensable tool for businesses, schools, and even health care providers during the pandemic. Now the company is extending its utility with the launch of new app integrations it calls Zapps.
Zapps are third-party applications that integrate into Zoom’s existing workflow so users can more easily access information and collaborate while on video calls. Simultaneously, Zoom is also launching OnZoom, an integrated online events platform.
While Zoom’s existing app marketplace allows developers to bring its functionality into their own apps, the new Zapps marketplace brings third-party app functionality into Zoom. The company plans to launch with some 35 Zapps partners later this year, including Atlassian, Dropbox, HubSpot, Salesforce, and Slack. Zoom also expects education partners like Coursera and Kahoot to enhance virtual learning, which could be particularly timely as lockdowns stretch on indefinitely.
Zoom claims Zapps will enable companies to drive more growth and revenue and anticipates opening the platform to all developers after this year. As of April 2020, Zoom has about 300 million daily meeting participants. (Unlike daily active users, “meeting participants” can count the same user more than once.) VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Chorus.ai has had native integration with Zoom since 2019 and is now one of Zoom’s official Zapps launch partners. Chorus.ai brings conversational intelligence to meetings, allowing sales teams to review existing customer emails in the Zoom client, transcribe meetings in real time, and analyze recorded calls. Chorus.ai also keeps track of who talks and when, with features designed to improve engagement and teamwork. If a team member is less active, for example, someone can tag them and encourage them to speak up.
Above: Chorus.ai “Zapp” integrated into Zoom Chorus.ai CEO Jim Benton compares the launch of Zapps to Apple’s App Store launch. “[Apple] opened up their API so that others could build apps. We went a year and a half with iPhones that just had the baseline apps that Apple had — you know, calculator, messaging, music, photos. And it was remarkable. We were blown away by what we could do,” Benton said in an interview with VentureBeat. “And then they opened it up, and we saw companies like Box reinvent themselves. We saw companies like Pandora suddenly thrive, where they had a beautiful player. And we saw companies like Sonos no longer have to build the hardware they had. I think that this is a very powerful moment.” Zoom product lead Ross Mayfield told reporters in a briefing that Zapps could present “a new model of collaborative app distribution, adoption, and engagement.” Mayfield said he expects to see a “rise of Zoom startups and apps for collaborative social experiences we’ve just begun to imagine.” For Zoom, the Zapps launch represents a major move against chief competitors Microsoft Teams and Google Meet.
By the end of this year, the first round of Zapps will be available for all Zoom users, whether they’re on free or paid plans.
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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"Zoom CEO Eric Yuan on the challenges of adapting an enterprise product for consumers | VentureBeat"
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"https://venturebeat.com/2020/12/03/zoom-ceo-eric-yuan-on-the-challenges-of-adapting-an-enterprise-product-for-consumers"
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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 Zoom CEO Eric Yuan on the challenges of adapting an enterprise product for consumers Share on Facebook Share on X Share on LinkedIn Zoom launches end-to-end encryption 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.
There’s been a lot of talk about the consumerization of IT in the workplace. But in the case of Zoom, the pandemic forced the high-flying video conferencing service to rapidly shift gears in the other direction, with some painful lessons along the way.
Zoom founder and CEO Eric Yuan said despite the company’s culture of trying to see the world through its customers’ eyes, many of its assumptions were called into question when a wide range of consumers suddenly began using the platform for everything from distance learning to virtual cocktails with friends.
“On the one hand, we were very excited, because after many years of hard work your dream is coming true of helping people stay connected,” Yuan said. “But then suddenly you have 30 times more growth than you were expecting, so how do you handle that? You’ve got to work harder.” Yuan spoke on the second day of the Web Summit mega-conference, where the impact of the pandemic on work and technology has been a big theme this year.
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! Founded in 2011, Zoom initially targeted the kind of customers who had been using Cisco’s WebEx. Yuan had been an early WebEx employee and stuck with the company when Cisco acquired it. But after several years, he found himself losing motivation because customers seemed increasingly unhappy with the product.
“The year before I left, every day I did not want to go to the office because I did not see a single happy WebEx customer,” Yuan said.
He knew that despite a large number of video conferencing options, the company wasn’t meeting business customers’ needs. Still, he had a famously hard time raising money for his new startup, with a long list of venture capitalists taking a pass. Looking back, he doesn’t blame them.
“I think they were not wrong,” he said. “Because it was indeed crowded. Nobody thought the world needed another video service. However, I spent a lot of time talking to the customers. I knew the market potential was big because nobody liked the existing products.” Time proved him right, and Zoom went public in 2019.
At one point, it was the best-performing tech IPO of 201 9.
The video conferencing company priced its IPO at $36 a share and saw it pop 72% on the first day of trading before eventually reaching $102.30 per share in June. It closed in late December at $66.64 per share, as investors felt it faced growing uncertainty heading into 2020.
Then the pandemic happened. The company had been preparing for growth, but nothing like the tidal wave that hit.
Zoom reorganized around the new reality, and Yuan had to rethink how to onboard employees as the company sustained a rapid hiring pace. A year ago, Zoom had 2,400 employees, and now it has 3,400. Like many companies, it had to devise new ways to impart its culture to employees who had never met in person while making sure the new hires felt integrated into the virtual workplace.
More dramatically, consumers flocked to Zoom, which went from business tool to cultural touchstone, becoming practically synonymous with video calls. People were no longer Skyping. They were Zooming.
Initially, the company was overjoyed. Zoom’s daily meeting participants exploded to more than 200 million in March from a previous high of 10 million. As VentureBeat’s Emil Protalinski wrote in April : “Let’s put those numbers into context. Skype’s daily active users grew by 70% month over month.
Microsoft Teams’ daily active users jumped 110% in four months.
Zoom’s daily active users exploded 1,900% in three months.” The stock market applauded, driving Zoom’s stock up to $559 per share in mid-October. But there were stumbles and some black eyes as the company struggled to adapt.
While Zoom offered a certain ease of use, some of the settings left consumers vulnerable to “Zoombombing,” when an uninvited participant crashes a Zoom video. The company had to change the defaults on its K-12 program settings so teachers had more control over who enters a virtual classroom and what can be shared. The Electronic Frontier Foundation even issued a consumer’s guide to Zoom settings.
Security proved to be an even bigger embarrassment. Success brought more scrutiny , and soon the company seemed to be facing daily headlines over security flaws, privacy breaches, lawsuits, and investigations.
Eventually, Zoom announced it would stop all new feature development until these problems were fixed. In October, Zoom announced a gradual rollout of end-to-end encryption for all customers.
Looking back on this period, Yuan said the fundamental problem was a failure to see the world from the eyes of these new consumers. Security, for instance, had typically been something addressed by the IT teams of enterprise clients.
“Normally, the enterprise IT team working together with us would enable or disable some features,” Yuan said. “With consumers, we have to take this approach further, and so we’re learning quickly. And that’s why we have to change our internal approach.” A post-pandemic world With vaccines arriving, Zoom is again facing uncertainly. Over the past six weeks, news about vaccine progress has sent its stock plunging, while new vaccine obstacles can push it right back up. Clearly, Wall Street isn’t convinced the world is going to continue using video conferencing at the same rate once schools and offices reopen.
Yuan generally agrees, but he’s more optimistic that the exposure to video conferencing will lead to some fundamental and enduring changes. In terms of business travel, for instance, he argues that it’s going to be hard to justify many traditional trips, given their expense, time requirements, and environmental impact.
As for offices, he believes a hybrid model will emerge as companies experiment with different mixes of remote work.
“Maybe today and tomorrow we all can work in the office,” he said. “And next week, we can all work from home. It shows you can have more time, and you have some control to be with your family and to get what you want.” Yuan sees more dramatic changes as 5G networks become ubiquitous over the next decade and augmented reality allows for richer interactions over video. These tools should bridge more of the distance between real-world and virtual meetings.
“We’ll feel like we’re sitting in that same Starbucks and drinking a coffee together,” he said. “Anyone, no matter where they are, will always feel like they’re in the same place together.” 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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"Zoom boosts its app ecosystem with $100M venture fund | VentureBeat"
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"https://venturebeat.com/2021/04/19/zoom-boosts-its-apps-ecosystem-with-100m-fund"
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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 Zoom boosts its app ecosystem with $100M venture fund 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.
Zoom has announced a new $100 million venture fund designed to “stimulate growth” of its burgeoning ecosystem of third-party app integrations.
The announcement comes after a whirlwind 12 months for the video-communications platform company. Zoom has more than doubled in value over the past year, with businesses forced to embrace cloud-based tools as they rapidly transitioned to remote work.
The Salesforce playbook Back in October, Zoom launched its new Zoom Apps platform for third-party developers to integrate their apps into Zoom. This is designed to make it easier for teams to collaborate and access data while on video calls — integrations include everything from whiteboarding to cloud storage services. And this is essentially what the new $100 million fund will support. Zoom said it will invest between $250,000 and $2.5 million in growth-stage companies looking to develop tools and products that “will become core to how Zoom customers meet, communicate, and collaborate,” according to a statement.
In many ways, Zoom is following the Salesforce playbook in terms of how it’s pushing to develop a vibrant ecosystem built around its core product — first through embracing third-party integrations and then through investing in them directly. Zoom has invested in startups before — in 2019 it backed hardware startup Neat — but this latest fund goes some way toward establishing Zoom as a more formal investor.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! To qualify for funding, companies must have a market-ready product with evidence of at least some early traction. Perhaps more importantly, their product must be focused on helping improve the Zoom experience in some way, either through Zoom Apps, SDKs, APIs, or even hardware products.
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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15,508 | 2,021 |
"Otter.ai automatically joins and transcribes calendared Zoom meetings | VentureBeat"
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"https://venturebeat.com/2021/05/19/otter-ai-automatically-joins-and-transcribes-calendared-zoom-meetings"
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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 Otter.ai automatically joins and transcribes calendared Zoom meetings 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.
AI-powered transcription company Otter.ai has announced a new integration that automatically joins, records, and transcribes scheduled Zoom meetings.
The Los Altos, California-based company, which raised a fresh $50 million tranche of funding just a few months ago, has offered integrations with Zoom for a while ( as well as with Google Meet). However, this latest tie-up goes further by carrying out all the manual steps involved in joining a meeting, transcribing it, and sharing notes with all users.
Assist Otter Assistant, as the new feature is called, connects with a user’s Google or Outlook calendar (once permissions have been granted) to see when a Zoom meeting is due to start. It then joins the call and starts recording on schedule, with no manual actions required.
For transparency, Otter Assistant shows up on the call as a participant. And each other participant in the call can view the live meeting notes, with support for making notes and highlighting text visible to everyone.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Above: Otter Assistant One key differentiator from the existing Zoom integration is that this now works with all Zoom calls, regardless of whether the user is the official host.
This launch also serves as a major boost to Zoom’s burgeoning app ecosystem , something the company has been keen to encourage to make its platform more useful and, ultimately, stickier.
Zoom itself has also been on something of a feature launch spree of late, having recently brought Alexa for Business to Zoom conference room calls and rolled out a new “immersive view” to position remote participants in the same virtual room.
The Otter Assistant is available as part of Otter.ai’s business plan , which costs $20 per user per month.
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 smart way to approach your IP strategy | VentureBeat"
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"https://venturebeat.com/2020/02/09/the-smart-way-to-approach-your-ip-strategy"
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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 Guest The smart way to approach your IP strategy 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.
Strategy is almost a foul word today, the result of too many costly, high-level strategy projects in which considerable effort concludes with a fancy presentation that is neither actionable nor practical. All too often, such projects fail to provide adequate information for companies to make sound strategic decisions, defeating the whole purpose. Weeks or months of staff time and large sums of money get squandered without tangible benefits to show for it.
In my job role, I develop commercialization and intellectual property strategies for technologies developed by PARC researchers. This includes conducting technology assessments, valuations and competitive landscape analyses, along with creating IP licensing programs or new businesses to promote the use of PARC’s technologies.
To be effective, IP strategies must be actionable and decision-driven. They also need to be more holistic. The methodical process starts by identifying the critical business decisions you need to make and the business outcomes you want to achieve over the next couple of years. Should you enter a new market? Invest or divest in a certain product or service? License a technology, or spin out a new business from it? Which approach will produce the most successful commercialization of a certain technology? Once you have a set of key decisions, the next step will be to define what information you need to make the decisions and what must hold true for you to achieve the desired outcomes. Some of the information and actions will relate directly to intellectual property, and some will involve other parts of the business — for example, a startup project that will need to initiate another funding round in 18 months and is looking for a valuation of $100 million (the outcome). What actions should they take between now and then to achieve this goal (key business decisions)? Next, it is important to identify main success drivers and/or value drivers. The success drivers are unique for each business and the market it operates in, but they typically fall under one or more of the following categories: talent, technology, market power, brand, and business model. For instance, take two different cloud services companies: Company A is in the very early stages of productization of a new cloud service that is based on a new software architecture that allows for better scalability. A beta version of the product has just been released, and primary users are cloud computing enthusiasts. Company B has also developed a technology that enables a highly scalable cloud computing platform, but compared to company A, they are approaching series C funding and have three large enterprise accounts that generate recurring revenue. Although Company A and B are tech-based companies operating in the same market with similar value propositions, the success and value drivers are likely different. For company A, the talent is probably the key driver at this stage, whereas Company B’s main driver is likely the technology or the business model.
Once you’ve determined these factors for success, it’s essential to conduct a gap analysis to assess the current situation and design the best path forward to reach your goals. Each gap analysis is based on a series of probing questions to identify the state of your company’s competitive position in the market. Questions such as what are our biggest strengths today? What weaknesses or shortcomings make us potentially vulnerable? What does the market really want, and is anyone serving that need? Then from that assessment, where is there a market opening that’s ripe for new opportunities? Next comes the process of choosing the most effective actions to enhance your strengths and guard against your weaknesses. These actions should be ranked in terms of their likely impacts and their overall feasibility for achieving the desired outcome through successful execution. Remember that detailed planning serves as a roadmap, but if you can’t execute on your plan, you’re certain to veer off course along the way.
Let’s consider the case of Company A, which has determined its key driver for a successful exit involves its talent. In this case, investments should be made towards hiring and retaining the best people to further the business, rather than filing for expensive patent applications.
In the other case of company B, the main driver for success involves its proprietary software and supporting technologies. If the company’s ability to prevent rivals from copying that software is weak, then its investments and actions should be applied to shore up this potentially fatal flaw. For this reason, Company B should intelligently invest in trade secret, patent, and copyright protection.
In the end, an IP strategy has no intrinsic value in itself – it only has value by enabling you to make good decisions and achieve your desired business goals. Yet to realize a positive outcome, IP strategies must be actionable. Without a solid plan of action, all the fancy, hand-waving presentations in the world won’t produce an effective result.
Lisa Rythen Larsson is an interdisciplinary commercialization strategy manager in the Intellectual Capital Management team for PARC , a Xerox company.
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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15,510 | 2,021 |
"How Hugging Face is tackling bias in NLP | VentureBeat"
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"https://venturebeat.com/2021/08/25/how-hugging-face-is-tackling-bias-in-nlp"
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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 Hugging Face is tackling bias in NLP 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.
Given that natural language processing (NLP) is a subset of artificial intelligence (AI), models need to train on large volumes of data. Unfortunately, many researchers are unable to access or develop the models and datasets necessary for robust systems — they are mostly the purview of large technology giants.
Hugging Face , the winner of VentureBeat’s Innovation in Natural Language Process/Understanding Award for 2021, is looking to level the playing field. The team, launched by Clément Delangue and Julien Chaumond in 2016, was recognized for its work in democratizing NLP, the global market value for which is expected to hit $35.1 billion by 2026.
This week, Google’s former head of Ethical AI Margaret Mitchell joined the team.
There are many reasons to democratize access to NLP , says Alexander (Sasha) Rush, associate professor at Cornell University and a researcher at Hugging Face. In addition to the technology being shaped and developed by just a few large tech companies, the language can be overly focused on English, he pointed out in an email interview. Also, “text data can be particularly sensitive to privacy or security concerns,” Rush said, “users often want to run their own version of a model,” he added.
Today, Hugging Face has expanded to become a robust NLP startup, known primarily for making open-source software such as Transformers and Datasets, used for building NLP systems. “The software Hugging Face develops can be used for classification, question answering, translation, and many other NLP tasks,” Rush said. Hugging Face also hosts a range of pretrained NLP models, on GitHub, that practitioners can download and apply for their problems, Rush added.
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 datasets challenge One of the many projects that Hugging Face works on, is related to datasets. Given that datasets are essential to NLP — “Every system from translation to question answering to dialogue starts with a dataset for training and evaluation,” Rush said — their numbers have been growing.
“As NLP systems have started to become more accurate there has been a large growth in the number and size of datasets produced by the NLP community, both by academics and community practitioners,” Rush pointed out. According to Rush, chief scientist Thomas Wolf developed Datasets “to help standardize the distribution, documentation and versioning of these datasets, while also making them easy and efficient to access.” Hugging Face’s Datasets project is a community library of natural language processing, which has collected 650 unique datasets from more than 250 global contributors. Datasets has facilitated a large variety of research projects. “In particular we are seeing new use cases where users run the same system across dozens of different datasets to test generalization of models and robustness on new tasks. For instance, models like OpenAI’s GPT-3 use a benchmark of many different tasks to test ability to generalize, a style of benchmarking that Datasets makes possible and easy to do,” Rush said.
Addressing diversity and bias Datasets is just one of the many projects Hugging Face is working on; the startup also tackles larger questions related to the field of AI. To address the challenge of increasing diversity of language-related datasets, the startup is making adding datasets as easy as possible so that any community member can do so, Rush said. Hugging Face is also hosting joint community events with interest groups such as Bengali NLP and Masakhane , a grassroots NLP community for Africa.
Bias in AI datasets is a known problem, and Hugging Face is tackling the challenge by strongly encouraging users to write extensive documentation, including known biases, when they add a dataset. Hugging Face provides users with a template and guide for this process. “We do sometimes ask users to reconsider adding datasets if they are problematic,” said Yacine Jernite, a research scientist at Hugging Face, via email. “This has only happened in rare cases and through a direct conversation with the person who suggested the dataset.” In one instance, a community member was looking to add problematic jokes from Reddit, so Hugging Face talked to the user, who took them down.
Hugging Face is also knee-deep in a project called BigScience , an international, multi-company, multi-university research project with over 500 researchers, designed to better understand and improve results on large language models. “The project is multi-faceted as well, incorporating both engineering aspects of how to produce larger, more accurate models with groups studying social and environmental impact and data governance,” Rush 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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15,511 | 2,014 |
"What’s in a container? You don’t know. And that’s a problem | VentureBeat"
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"https://venturebeat.com/2014/12/16/whats-in-a-container-you-dont-know-and-thats-a-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 Guest What’s in a container? You don’t know. And that’s a problem 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.
How much do you really know about application containers? Not the technical features or benefits, as those are readily circulating across technology communities. From extreme application portability and flexibility to simplified IT operations, application containers appear to be god’s gift to IT. But, again, ask the question: What do you really know about application containers? Specifically, your application containers? An application packaging and delivery technology, Linux (application) containers isolate applications from all but the needed components of a host operating system, enabling applications to be easily developed and deployed across multiple systems. Built from open source technologies, including CGroups and SELinux, containers themselves include many different pieces, not all of which are readily transparent or accessible to the consumer. Add that to the fact that many containers come from vendors that don’t maintain or support the software that is included, and application containers rapidly move from “download and run” to, “Wait a minute, what’s in this thing?” Background checks required To help answer that all important question, IT needs to first know the provenance, or origin, of a container. Containerized applications from a known and trusted source, like an established independent software vendor or developer, are more likely to be built with stability and security in mind than those created by a shop that is more concerned with just “churning out code.” Containers built by a trusted source are also more likely to have other inherent values, like a clear and concise manifest or “packing list” that details exactly what is in the container, whereas other containers may come with no content listing or a dubious (at best) description of the containerized application. The most thorough “packing list” will also explain from where the contents came, when and where they passed though others hands, and what versions are included. Not unlike an international shipping manifest, the container will assert that the contents have not been tampered with through the delivery process.
Handle with care Contrary to what you may read or hear, containers do require some level of maintenance.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Containers are not intended to be deployed and left alone. Just like any other application stack, they require updating (or wholesale replacing) with fixes for everything from security flaws to stability issues. End users may not know when such a fix is needed or, if they do, may not know where to find a fix or how it should be applied to their environment. Even though the container may come from a trusted source, without a notification system in place and guidance on how to apply the fix, an IT shop could easily end up running a compromised application container without knowing it, essentially throwing up a “welcome mat” for would-be digital interlopers.
This end up The last component needed to determine what’s in the container is certification, something that is currently lacking in the container ecosystem today. Beyond coming from a trusted source and including some kind of maintenance agreement, containers should be verified to work on a given host. Especially if it’s a production system, certification entails support, so in the event that something unforeseen happens with a certified container on a critical system, the developer or provider will be on tap to help resolve the issue.
If you’re thinking “Hey, these answers are what most IT shops require from standard application vendors today,” then you’re right. Just because containers are new(ish) and deliver delightful benefits, they don’t get a pass when it comes to trust, maintenance, and support. They are ultimately still application stacks, many of which will be mission-critical, and will be running on production systems.
That’s why we need to remember that when asking, “What’s in the container?” the answer is an application stack. And just like any other application stack, would a reputable IT shop deploy an untrusted, orphaned and unsupported application stack onto a production system? Um, no.
Answering this question should be paramount for vendors entering the container space. It’s still an application, just in a fancy box, so treat it like one.
Lars Herrmann is senior director of strategy at Red Hat.
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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15,512 | 2,020 |
"Red Hat: Shift to Kubernetes and microservices is happening faster than expected | VentureBeat"
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"https://venturebeat.com/2020/04/28/red-hat-shift-to-kubernetes-and-microservices-is-happening-faster-than-expected"
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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 Red Hat: Shift to Kubernetes and microservices is happening faster than expected 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 push to reinvent the way developers create applications for the internet has gathered significant momentum, catching even some of its most ardent supporters by surprise.
But even as the popularity of infrastructure based on the Kubernetes platform and microservices surges, the adoption has inevitably brought to light the massive challenges big businesses and large organizations face in overhauling unwieldy infrastructure. To help IT managers navigate this transition, products and services that enable simultaneous management of legacy and new systems are gaining in popularity.
Such “hybrid” products will be one of the main themes of the Red Hat Summit that runs today and Wednesday, a recognition of how critical this area has become to companies trying to modernize their cloud-based infrastructure. Speaking on the eve of the event, Red Hat’s Joe Fernandes said he’s among those excited by the progress being made by microservices, but he remains realistic about the challenges such a shift presents.
“This evolution is happening even faster than I expected,” said the vice president of Red Hat’s Core Cloud Platforms. “If you look at the adoption of containers, or Kubernetes, compared to past innovations, like virtualization back in the early 2000s, it’s really grown tremendously fast just over the last five 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! So-called microservices , aka cloud native computing, break applications into smaller, self-contained units or “containers,” which can significantly reduce costs and time needed to write, deploy, and manage each one. Proponents say such an approach to web development is faster, more stable, more open, and makes it easier for users to change cloud platforms.
Read more: Kubernetes and microservices: A developers’ movement to make the web faster, stable, and more open Kubernetes , originally a Google project and now an open source service managed by the Linux Foundation’s Cloud Native Computing Foundation, has become one of the most popular tools for managing containers. Born of the open source movement, Red Hat was one of the earliest supporters of Kubernetes.
In 2015, Red Hat released the first version of its OpenShift Container Platform, which incorporates that hybrid approach to manage web infrastructure. At today’s summit, the company announced OpenShift 4.4, which includes new metric and monitoring tools that centralize reporting across multiple cloud operations. The company also unveiled Advanced Cluster Management for Kubernetes, which aims to simplify deploying and managing clusters of Kubernetes-based applications that are running across several cloud services.
Fernandes said that as tools like Kubernetes and microservices become more prevalent, companies are more comfortable running applications across different services such as Google Cloud, Microsoft Azure, and Amazon Web Services. While that helps them avoid becoming overly reliant on a single partner, it also creates challenges in continuing to manage the multi-cloud approach.
“The new challenge becomes how to manage all of these environments,” Fernandes said. “Now from a single management console you can import or inventory all these different clusters.” OpenShift will also begin offering a preview of a new virtualization feature that’s designed to help companies ease the transition from apps running on virtual machines to Kubernetes.
“Companies have been adopting containers over the last several years and moving apps that were previously running in virtual machines to containers,” Fernandes said. “But virtual machines aren’t going away. So what we wanted to do is explore how you could use Kubernetes to manage both types of workloads. And this allows them to migrate VM-based apps to containers over time.” As quickly as things are moving, Fernandes said it’s important to recognize that overhauling a company’s web development infrastructure is a massive undertaking that will take years even in the most motivated enterprises. And that means the hybrid approach will by necessity be the default for the foreseeable future.
“These large organizations have tens of thousands of applications right now,” he said. “They can’t turn over their application portfolio overnight. They end up with a hybrid collection of applications that span from very traditional to very modern and cloud native. And they need to be able to manage both for now, and we see that being the case for a long time.” 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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15,513 | 2,021 |
"Copado raises $96 million for Salesforce-native DevOps | VentureBeat"
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"https://venturebeat.com/2021/02/17/copado-raises-96-million-for-salesforce-native-devops"
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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 Copado raises $96 million for Salesforce-native DevOps Share on Facebook Share on X Share on LinkedIn Copado 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.
Copado , a developer operations (DevOps) platform built for Salesforce, has closed a $96 million series B round co-led by Insight Partners and Salesforce Ventures. This follows an initial $26 million series B investment Copado announced last summer.
The Salesforce ecosystem could be at least 4 times larger than Salesforce itself, according to some estimates , as myriad third parties build sizable businesses off the back of the platform. In the past three weeks alone, we’ve seen OwnBackup raise $167.5 million at a $1.4 billion valuation to bring cloud data backups to Salesforce, while fledgling startup Scratchpad secured $13 million for a productivity workspace aimed at Salesforce-powered sales teams.
Salesforce, which is now a $230 billion company in its own right, may attribute much of its success over the past 20 years to its thriving partnership ecosystem, which Copado is helping expand.
While Salesforce assists with many of the processes associated with DevOps, such as hosting and managing infrastructure, Salesforce DevOps teams still have to manage their deployment pipeline, or the steps and environments they go through to get their Salesforce configuration and code into production.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Founded out of Madrid, Spain in 2013, Copado promises enterprises an integrated Salesforce-native platform that covers each part of the DevOps process, including agile planning, continuous delivery, automated testing, and compliance. “It automates how companies build software in the cloud at scale,” Copado CEO Ted Elliott told VentureBeat.
Copado is ultimately about increasing product releases, reducing bugs, and enhancing companies’ return on their SaaS cloud spending.
Above: Copado: Deployment dashboard Custom fit The broader DevOps market, which was pegged at $5.2 billion in 2018, has plenty of tools to help developer and operations teams function cohesively and ship quality software on schedule. But Copado is betting that Salesforce DevOps teams need custom-fit tools.
“Low-code cloud platforms like Salesforce work very differently than traditional development platforms. This prevents developers from working with popular code-based DevOps solutions,” Elliott said. “Copado is the only DevOps platform that was purpose-built for the speed and technology of SaaS clouds.” Multinational chemical company Linde used Copado’s platform to “move from manually intensive development processes to a fully automated and transparent delivery pipeline,” Elliott said, one that “aligns multiple teams on a single set of business goals.” Copado has now raised $117 million since its inception. With its fresh cash injection, which included investments from Lead Edge Capital, ISAI Cap Venture, and Perpetual Investors, the company is now well-financed to double down on its existing Salesforce support while also expanding its horizons.
“Based on customer demand, Copado is increasingly being used to deliver multi-cloud projects,” Elliott said. “This investment allows Copado to continue to expand and support additional clouds and platforms outside of Salesforce as our customers build more sophisticated and integrated customer experiences.” 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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15,514 | 2,021 |
"Sonar, which monitors companies' Salesforce tech stack for changes, raises $12M | VentureBeat"
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"https://venturebeat.com/2021/03/30/sonar-which-monitors-companies-salesforce-tech-stack-for-changes-raises-12m"
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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 Sonar, which monitors companies’ Salesforce tech stack for changes, raises $12M Share on Facebook Share on X Share on LinkedIn Sonar: Dashboard view 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.
Salesforce has emerged as a formidable force in the cloud-based enterprise software sphere — a $200 billion colossus upon which countless others have built their own billion-dollar businesses.
In the past couple of months alone, we’ve seen OwnBackup raise $167.5 million at a $1.4 billion valuation to power cloud data backups for Salesforce; Scratchpad lock down $13 million to develop a productivity workspace for Salesforce teams; and Copado secure $96 million for Salesforce-native DevOps.
According to some estimates , the Salesforce ecosystem could be at least 4 times larger than the company itself, a factor that has played no small part in Salesforce’s success over the past two decades.
Against this backdrop, Sonar today announced it has raised $12 million from a slew of big-name investors, including David Sacks’ Craft Ventures and Slack’s venture capital fund, to bring “X-ray vision” to Salesforce by helping sales teams visualize how all their data is connected and used across related systems.
“It’s essentially a living, searchable dictionary that shows how your entire tech stack works together and automatically documents every change to your data,” Sonar CEO and cofounder Brad Smith told VentureBeat. “This allows teams to scope and execute their work fast, work confidently without the risk of taking critical systems offline due to breakages, and collaborate effortlessly around change-management projects, digital transformation, systems integrations, building new processes, and more.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Above: Search, navigate, and explore dependencies and apex code in Sonar Although Sonar is now focused purely on Salesforce, and showing how other systems are mapped to Salesforce fields and processes, later this year it plans to “start serving more operations teams across the enterprise,” according to Smith. This will include expanding to marketing platforms such as Marketo, HubSpot, and Pardo, as well as finance systems such as Netsuite.
Small change “Change management” is perhaps the key to understanding what Sonar actually does. When someone in a company alters any fields or automations in a critical system, it can be difficult to see how this might impact other processes that rely on it — but the smallest changes can break integrations, lead to inaccurate reports, and — perhaps more importantly — prevent sales teams from closing deals. For a platform like Salesforce that thrives on integrations, Sonar shows its users how all their data is joined together and issues real-time alerts when anything that might impact them is changed.
Sonar can be used before changes are made to determine how (or whether) they should be implemented, and it can also provide visibility into problems that occur as a result of changes after they have been made. This is particularly important for teams that are transitioning any of their existing workflows or integrations, as Sonar helps them reverse-engineer processes that may have been set up a while ago by someone who is no longer at the company and understand how everything fits together.
“Sonar gives you complete situational awareness so you can understand the impact of changes before they happen and correct problems if and when they occur,” Smith said. “This helps teams prioritize their work, work faster, and execute changes safely. In the event someone does make a change that causes processes to break, Sonar will alert you to exactly what broke and why.” Above: Sonar: Dashboard view At its core, Sonar is designed to help revenue and operations teams avoid making blind changes and then scrambling to fix problems after. According to Smith, Sonar does something “similar to what GitHub does for software engineers,” in terms of how it documents every change you make and records the full history and context around it. But rather than focusing on codebases, Sonar is concerned with integrations across a company’s tech stack, allowing non-technical teams to handle at least some of the heavy lifting behind the scenes.
Sonar is available as a standalone web application, though the company also offers a browser extension that overlays data on top of Salesforce to help visualize dependencies across every field.
Founded out of Atlanta, Georgia in 2018, Sonar had previously raised $3.7 million. It said that over the past year its customer count and revenue have increased by 1,000% and 3,000%, respectively. It has also signed up notable enterprise clients, including OneLogin and Carta.
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"Cloud data backup and recovery platform OwnBackup raises $240M | VentureBeat"
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"https://venturebeat.com/2021/08/10/cloud-data-backup-and-recovery-platform-ownbackup-raises-240m"
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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 Cloud data backup and recovery platform OwnBackup raises $240M Share on Facebook Share on X Share on LinkedIn OwnBackup homepage 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.
OwnBackup , a platform that provides data backup and recovery services for cloud software providers such as Salesforce , has raised $240 million in a series E round of funding at a $3.35 billion valuation.
Cloud spending continues to surge as part of companies’ digital transformation efforts, spurring the need to develop a robust data backup and recovery plan. Putting 100% faith in third-party infrastructure is never a good idea, particularly in light of the OVH datacenter disaster earlier this year.
Elsewhere, a recent cloud threat report published by Oracle and KPMG found that three-quarters of organizations had experienced data loss from a cloud service on more than one occasion.
At the application level, SaaS platforms such as Salesforce usually offer disaster recovery tools in the event of a systemwide catastrophe — however, this generally doesn’t work at an individual account level, meaning customers can’t recover or restore specific individual data items on-demand. This falls under what is known as a “shared responsibility” model, where the platform owner (e.g. Salesforce) is responsible for infrastructure-level security and disaster recovery while the paying customer is responsible for managing things like permissions and passwords and generally safeguarding all their data at the account level.
This situation has led to a slew of investments in the data backup and recovery space. Notable players include Druva, which recently raised $147 million at a $2 billion valuation , and backup-as-a-service platform Rewind , which raised $15 million earlier this year and expanded its backup and recovery support to GitHub and Trello.
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 nutshell, OwnBackup serves businesses such as Adecco, AECOM, Aston Martin, Delivery Hero, and Navy Federal Credit Union with the tools to create daily backups and speedily restore all their SaaS data. The company had already raised around $267 million since its inception in 2015, including a $167.5 million round just seven months ago.
“There has been a broad misconception that SaaS platforms take care of data backup and protection,” OwnBackup CEO Sam Gutmann told VentureBeat. “Since SaaS customers are responsible for their data, they are driving demand for easy backup solutions. We’re here to empower our customers to own and protect their data on any cloud platform.” Own your backups OwnBackup has so far put most of its focus on providing data backup and recovery services for Salesforce, and by extension Salesforce ecosystem companies, such as Veeva and Ncino.
But with another $240 million in the bank, the company is well positioned to extend support to other clouds, starting with Microsoft later this year. Initially it will help Dynamics 365 customers meet “complex regulatory requirements” and “eliminate data disruptions” due to user and integration errors.
“We’re empowering customers with complete ownership of their data,” Gutmann said.
While it might be technically possible for SaaS customers to develop their own data backup solutions, specialized third-party platforms such as OwnBackup — which work in partnership with cloud companies — make it much easier, allowing enterprises to allocate their IT resources to other initiatives.
“SaaS applications were not created with restoring lost or corrupted data in mind, and there is no easy or effective way for customers to back up the data themselves,” Gutmann continued. “Yet data loss and corruption occur frequently.” Moreover, even where SaaS providers do provide backup functionality, that doesn’t really address the bigger problem of how to get the data back into the system after a catastrophe occurs.
“Those who rely on a SaaS provider’s backup capabilities, or try to back up data themselves, quickly discover the true challenge isn’t backing up the data, it’s restoring it,” Gutmann continued. “Isolating what damage was done is very difficult, and restoring just the impacted data to a specific point in time is practically impossible.” If it was just about backing up data from across multiple clouds, it would be relatively easy to create a holistic solution covering all clouds rather than introducing support on a cloud-by-cloud basis. But data recovery is the key here.
“Every cloud has a different API, and while backing up is ‘relatively’ easy across clouds, time to recovery is what the customers care about — that is where there is a lot of cloud-specific nuance,” Gutmann added. “Our precision-restore capabilities are focused on getting those nuances right for each cloud, which can mean the difference between minutes versus days to get the recovery done — and done properly.
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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"Observe.ai raises $54 million to analyze call center conversations | VentureBeat"
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"https://venturebeat.com/2020/09/15/observe-ai-raises-54-million-to-analyze-call-center-conversations"
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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 Observe.ai raises $54 million to analyze call center conversations 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.
Observe.ai , a startup developing AI call center software, today closed a $54 million funding round. A spokesperson told VentureBeat the funds will be used to accelerate growth and R&D as Observe expands its go-to-market efforts.
With customer representatives in India , the U.S., and elsewhere ordered to work from home during the pandemic, companies are turning to AI to bridge the resulting gaps in service. The solutions aren’t perfect — human teams will always be needed, even when chatbots are deployed — but COVID-19 has accelerated the demand for AI-powered contact center messaging. Amazon recently launched an AI contact center product — Contact Lens — into general availability alongside several third-party solutions.
And Google continues to expand Contact Center AI , which automatically responds to customer queries and hands them off to a person when necessary.
Observe, which CEO Swapnil Jain cofounded out of his bedroom in Bengaluru, India in 2017, is another beneficiary of increased demand. The company claims to have brought on more than 150 customers since the pandemic began, including Pearson, Alcon Laboratories, and Concentrix. Observe added over 20,000 agent licenses to its platform during that same time frame, which contributed to a 600% boost in revenue. The company also added 100 employees across India and the U.S. cities of Dallas and San Francisco.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Observe claims its speech transcription achieves higher accuracy on some metrics than Google and Amazon (over 80%), courtesy of algorithms trained on millions of calls weekly. The platform can pick up terms specific to businesses and identify types of silence in calls (e.g. dead air and hold time), making note of their causes, lengths, and contexts. Moreover, Observe provides insight into sentiment by tagging instances of “strong emotions” displayed toward businesses and agents, frequent mentions, shifts in topic, and anomalies — all while automatically redacting sensitive data from both audio and transcripts.
With Observe, agents and managers can automate tasks like completing forms for customers and playing and scoring calls from a single view. They’re also afforded access to a transcript editor that lets them leave personalized tips within transcripts themselves. Dubbed Evaluation Forms, the editor aims to deliver actionable feedback via comments and timestamps on calls, like why agents scored well and where they can improve.
Beyond Google and Amazon, Observe has competition in Boston-based call center analytics startup Cogito.
There’s also Augment , a company developing a platform that assists customer service agents at large enterprises; Mattersight , which uses unique voice signatures to personalize customer experiences; and DigitalGenius, LivePerson, Verint , and Nice Technologies.
Above: Observe.ai: Agent feedback, including adherence to compliance phrases But Observe has confidence in its product’s robustness. According to Jain, Observe’s engineering team spent time in the Philippines — where the industry generates over $25 billion — to study call center operations. Future plans include real-time coaching, omnichannel support, and interaction analytics capabilities, as well as a virtual assistant that will handle repetitive calls to free up agents.
Menlo Ventures led this week’s series B, which brings the company’s total raised to $88 million, following a $26 million funding round in December 2019. Next47 Ventures and NGP Capital also participated in the round.
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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15,517 | 2,021 |
"Adobe extends AI-infused customer analytics platform to offline data | VentureBeat"
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"https://venturebeat.com/2021/04/27/adobe-extends-ai-infused-customer-analytics-platform-to-offline-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 Adobe extends AI-infused customer analytics platform to offline 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.
Adobe today unfurled an enhanced Adobe Customer Journey Analytics cloud service that enables organizations to apply AI to data from both online and offline sources to gain deeper insights into customer behavior.
Available as an extension to the Adobe Experience Platform, the Adobe Customer Journey Analytics service extends the analytics capabilities based on its Sensei AI platform that is already widely employed to track online engagements into the realm of data collected offline.
That omnichannel approach to tracking a customer journey is becoming more critical as customers begin to return to retail outlets as more people receive COVID-19 vaccinations, said John Bates, director of product management for Adobe. He spoke during an Adobe Summit event.
Democratizing analytics Adobe Customer Journey Analytics provides a unified view of a complete customer journey using real-time analytics dashboards that can be accessed from anywhere on any type of device, including via a mobile application for accessing Adobe dashboards that was also unveiled today.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! That approach is intended to democratize the advanced analytics enabled by the Adobe Sensei platform as an alternative to relying solely on data scientists to sift through data. Alerts that are generated by Adobe Customer Journey Analytics will automatically surface insights that would have otherwise gone unnoticed, Bates said.
In comparison, legacy analytics applications assume the end user knows what queries should be launched to answer a set of already known questions. Adobe Customer Journey Analytics “will help you identify the unknown unknowns,” said Bates.
Over time those alerts, currently available in preview mode, will become more personalized as the Adobe Sensei platform identifies what data is being accessed most often, added Bates.
Tracking customer data Adobe is also making it easier to collect and process data. A data views feature allows organizations to ingest more of their data in its original format, then apply logic at the time of reporting. That capability will make it easier for organizations to slice and dice data without having to first normalize it into a specific format. The goal is to enable organizations to track customer behaviors more easily whenever they see fit, noted Bates.
As part of that effort, data collection tasks that once occurred on a mobile device or in a browser are being shifted to the Adobe server that is part of Adobe Experience Platform Collection Enterprise. The Adobe Experience Platform Edge Network then makes it possible to receive and send an event or individual piece of data in milliseconds in a way that enables privacy and governance controls to be applied.
In effect, Adobe is making a case for extending the level of visibility that many organizations have into online customer behavior to include offline activities such as the number of times they visit a mall. Customers may never return to retail outlets in the same numbers they did prior to the pandemic, but it’s also clear many have become more adept at moving back and forth between an online application and a brick-and-mortar experience. It’s not uncommon these days for shoppers to employ a mobile application within a retail outlet to check, for example, inventory availability before deciding whether to make a purchase now at the store or later online.
Adobe isn’t trying to replace data scientists. In many instances, Bates noted, data collected by the Adobe cloud offerings will be shared with data scientists that will be using tools from Adobe and others to analyze massive amounts of data. However, it’s also apparent business executives want to be able to identify and track different types of customer journeys in near real time. The challenge and the opportunity now is to enable that capability in the most frictionless way possible.
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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15,518 | 2,021 |
"Facebook’s metaverse for work draws hope, but mostly skepticism | VentureBeat"
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"https://venturebeat.com/2021/08/20/facebooks-metaverse-for-work-draws-hope-but-mostly-skepticism"
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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 Facebook’s metaverse for work draws hope, but mostly skepticism 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.
Mark Zuckerberg let the public in on his “metaverse” ambitions just a few weeks ago, setting off a firestorm of discourse around the kinds of virtual worlds found in Snow Crash and Ready Player One — and what it would look like if Facebook created one in real life. Yesterday, we got a look at where the company is on this journey: a virtual conference room.
Depicted as a legless floating figure, Facebook’s founder yesterday appeared in such an environment to unveil Horizon Workrooms, a VR-powered app for Oculus Quest 2.
The experience allows colleagues to represent themselves as cartoon-like avatars and work together in animated 3D virtual spaces. To some hoping for an alternative to Zoom, it’s exciting stuff. But many founders, executives, and employees are wary, citing objections to Facebook’s data privacy practices and known issues with misinformation/disinformation, hate speech, and violence-inducing content (such as the bomb threat unfolding near the Capitol just shortly after the Workrooms news broke, which the company allowed to livestream for hours ).
And beyond uneasiness about Facebook’s ownership of the platform, some people aren’t sold on the concept or sure it’s really solving a problem. There’s also a feeling VR is still more novel than it is useful.
Prolific tech founder and investor Mark Cuban, who says he’s “very active and involved in VR,” told VentureBeat the technology has “one fatal flaw: There isn’t one single daily use application.” He explained: “People are always amazed when they try VR goggles. But when they take them off, there rarely is a compelling reason to use them again and again. Until there is a daily use case so that people want to have their goggles readily available, Facebook will struggle getting people to buy them and become comfortable with them, whether it’s for work or play.” 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! VR for the workplace The concept of escaping Zoom fatigue by immersing ourselves in virtual worlds isn’t totally novel, and Facebook certainly isn’t the only one doing it. In March, Microsoft launched Mesh, which lets colleagues interact with avatars in AR-powered virtual spaces and even pass “objects.” There’s also Holopod, Imverse, and Spaceform, which are all vying for a piece of the virtual meeting market with holograms, avatars, and the like. Spatial is another competitor, with a product that enables holographic-style virtual meetings on Oculus Quest 2.
Earlier this year, Ericsson published a report suggesting the “dematerialized office” will take hold by 2030, meaning people will interact professionally entirely in virtual spaces. Additionally, IDC recently forecast worldwide spending on AR and VR to grow from just over $12 billion in 2020 to $72.8 billion in 2024. That figure encompasses much more than workplace apps, but we know commercial use cases are making up a growing portion of AR and VR spending worldwide. In the Asia Pacific region, spending on commercial VR/AR technology has already passed that of consumer spending. The primary use cases, however, are training, industrial maintenance, and retail showcasing.
Zuckerberg said during his briefing that Facebook employees have been using Workrooms for internal meetings for about six months. Almost a fifth of Facebook employees — nearly 10,000 — are now working on VR- and AR-related projects and technologies, but the general public isn’t as sold on the use case. According to a recent survey from software studio Myplanet, VR in the workplace is among the least accepted uses for the technology — 49% of respondents expressed discomfort at the idea, while more were in favor of using VR for gaming, movies, education, “travel,” and calls with friends or family. And as Cuban mentioned, even those who love VR don’t necessarily see themselves using it regularly in the long term.
Jana Boruta, director of events and experiential marketing at HashiCorp, told VentureBeat her company recently created a virtual world for its employee summit, complete with meeting areas and activities. “Employees were able to walk around the world we designed as 2D avatars with their actual faces showing on the screen,” she said.
But people stopped logging in after a day or two “after the nostalgia wore off,” according to Boruta. She said her team found people enjoyed this experience, but not as a permanent way to connect with each other.
“The questions I’m asking myself around Facebook’s Horizon Workrooms are: What does attending a meeting in a VR setting with colleagues actually accomplish? Can you create meaningful connections with a person’s avatar versus seeing someone’s actual face and features? Will this just be a fun tool that could actually have negative results, such as becoming a distraction?” she said.
Ben Lamm, a prolific founder, most recently of machine learning company Hypergiant, had a similar gut reaction to the product. “I’m not sure it really adds value over the current collaborative video tools in the market,” he said. “At its core, it’s also a sales tool to expand Oculus 2 hardware sales and make people more addicted to the Facebook universe. Facebook has historically failed us in numerous ways in the past. I’m not ready to let it also take over my work life.” Anyone but Facebook Facebook was early to virtual reality, paying $2 billion for headset startup Oculus VR in 2014. But the company is still widely associated with the raft of issues on its main social network, with privacy concerns, in particular, spanning every part of Facebook as a company. For this reason, Lamm’s feelings were echoed by many founders, executives, and employees VentureBeat spoke to about Horizon Workrooms. Some people are interested in the concept, but not if it’s coming from Facebook.
“Given Facebook’s past history with data privacy, I’m not sure they’re the right player to lead this effort,” said Angela Benton, founder and CEO of user-provided data company Streamlytics , an all-remote company of 13 employees.
She finds the idea of the metaverse “quite powerful” and believes virtual remote work environments like Horizon Workrooms will catch on. But she’s looking toward a decentralized future and doesn’t think user data associated with any metaverse-type company should be centralized with Facebook. “I don’t think this is something that I’d invest in for my team,” she said.
BiggerPockets founder Joshua Dorkin, who now advises several startups, agreed that Facebook has a trust problem and that this will impact organizations’ willingness to adopt Horizon Workrooms.
“Given all of the trust issues that people have with Facebook — thanks to their history around tracking, invasions of privacy, disinformation, and beyond — I find it hard to believe that corporations will be quick to jump in and adopt their new VR technology,” he told VentureBeat.
Post-Zoom remote work Peter Allen Clark, a tech and business editor for Time Magazine , feels similarly, saying he “definitely has lingering privacy and harassment concerns.” He also got a chance to try the app. And like VentureBeat’s own Dean Takahashi, who also demoed Horizon Workrooms , Clark really appreciated the audio experience. But mostly, he just enjoyed the change of scenery.
“The more I think on it, I did have a pretty good experience in that space with that tech,” Clark told VentureBeat. “It’s tough to lean too much onto that, because it could just be a novelty. But after a year and a half of remote working, it was honestly refreshing to have a new way to experience a conference call.” He added that he’s not sure if he’d want all of his calls to take place like this, but he’d “sure like some of them [to].” It’s hard to imagine this launch receiving as much buzz without the backdrop of the pandemic (and as Lamm puts it, Zuckerberg’s “co-opting” of the term metaverse). In fact, the positive reactions to Horizon Workrooms largely invoked the pandemic and a desire for alternate ways to collaborate remotely.
“Maybe I’m the minority here, but I’d much prefer this over Zoom,” Taylor Lorenz, a technology reporter for the New York Times , tweeted following the app’s unveiling. “There’s just something about physical presence and shared space you undeniably [lose] over Zoom. If we’re heading to a remote-forward future, having those shared spaces is key, especially when you’re doing creative work. Seems like a step in the right direction at least.” Peter Bailis, CEO and founder of data analytics company Sisu Data, told VentureBeat “there’s real promise in more immersive experiences like Facebook’s Horizon Workspaces or Google’s Project Starline ,” a video chat tool that makes it appear as if the person you’re chatting with is right in front of you in 3D.
“There’s something tangible about in-person collaboration that is hard to replicate, and this VR approach could provide a compelling intermediate option, without the time, hassle, and expense involved in full-time, in-person workplaces,” he said, adding that he doesn’t think we’ve yet fully optimized our existing places for virtual work.
Dorkin echoed this sentiment. “I think the VR tools have the potential to bring people together,” he said. “But I’ll be waiting for someone else to develop their own version.” 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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15,519 | 2,021 |
"The Linux Foundation takes control of open source Magma wireless ecosystem | VentureBeat"
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"https://venturebeat.com/2021/02/03/the-linux-foundation-takes-control-of-open-source-magma-wireless-ecosystem"
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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 Linux Foundation takes control of open source Magma wireless ecosystem 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.
The Linux Foundation announced today that an open source Magma platform for building wireless networks will now be managed under its auspices.
Originally developed by Facebook, the founding members of the Magma consortium include Arm, Deutsche Telekom, Facebook, FreedomFi, Qualcomm, the Institute of Wireless Internet of Things at Northeastern University, the OpenAirInterface Software Alliance, and the Open Infrastructure Foundation (formerly the OpenStack Foundation ).
Magma provides organizations with a modular approach to creating an access-agnostic mobile packet core that comes bundled with network automation and management tools that use open source software. The goal is to make it simpler for both enterprise IT organizations and carriers to set up a wireless network that can be deployed on standard IT servers rather than proprietary network infrastructure.
Up until today, Magma has been governed by Facebook. The goal is to create a vendor-neutral governing structure for the project that will encourage more organizations to participate and deploy the platform, said Arpit Joshipura, general manager for networking and edge at the Linux Foundation.
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 Linux Foundation has launched a series of networking initiatives that are all intended to enable telecommunications carriers to deploy programmable network services based on virtual machines and containers. The goal is to make it simpler for carriers to more rapidly provision network services in an age where IT teams routinely provision infrastructure resources in minutes. In contrast, carriers are still heavily dependent on legacy proprietary network infrastructure that is still programmed manually. Carriers are now determining to what degree they want to replace that physical infrastructure with either proprietary software-defined networks (SDNs) or equivalent open source software.
Magma extends that effort by making it easier to deploy a wireless network in, for example, a remote area that extends the reach of a carrier’s service. Alternatively, enterprise IT organizations can deploy their own wireless network that can be integrated with services provided by a carrier. “The goal is to plug Magma into a larger ecosystem,” said Joshipura.
Wireless networks based on Magma are designed to integrate with existing LTE networks in addition to providing the foundation for delivering 5G services. FreedomFi, for example, has deployed Magma on a set of radio access gateways that enable organizations to cost-effectively deploy their own private 5G network, said FreedomFi CEO Boris Renski. “It’s a new architecture,” he said. “The total cost of deploying a private 5G network is dropping.” Use cases for a private 5G network span everything from connecting drilling platforms in an oil field to providing access to wireless networks in remote areas not served well by carriers, such as Indian reservations, Renski said.
Despite the availability of smartphones that are touted as being 5G enabled, the backend infrastructure that carriers rely on is still being modernized. In many cases, enterprise IT organizations that deploy their own private 5G network may be able to make more dedicated bandwidth available to a narrow range of devices than a carrier can currently provide.
Regardless of approach, the need for increased bandwidth at the network edge will soon become acute. Many organizations are building applications that assume a level of bandwidth will be available by the time those applications are ready to be deployed. If that bandwidth isn’t available at the levels advertised, the performance of those applications will suffer.
In the meantime, enterprise IT organizations might want to evaluate how dependent they want to be on carriers to deliver those 5G services and beyond. After all, the 5G services provided by carriers are considerably more expensive than existing LTE services that many organizations are already paying a premium to receive.
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"Commercial and open source GraphQL company Apollo raises $130M | VentureBeat"
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"https://venturebeat.com/2021/08/17/commercial-and-open-source-graphql-company-apollo-raises-125m"
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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 Commercial and open source GraphQL company Apollo raises $130M Share on Facebook Share on X Share on LinkedIn Apollo GraphQL homepage 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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Apollo , the GraphQL company that serves as the data graph layer connecting modern apps to the cloud, has raised $130 million in a series D round of funding at a valuation of more than $1.5 billion.
GraphQL, for the uninitiated, was developed inside Facebook as a data query language for APIs back in 2012, constituting part of its transition from mobile web wrappers to fully native mobile apps. Facebook open-sourced the project in 2015 , and GraphQL was later shifted under the control of the GraphQL Foundation, which in turn was taken over by the Linux Foundation. Today, GraphQL is used by a host of companies beyond Facebook, from Netflix, Lyft, and Shopify to Amazon, GitHub, and Atlassian.
In a nutshell, GraphQL helps support the burgeoning API economy and the push towards microservices — software built from smaller purpose-built components that are easier to maintain compared to giant monolithic applications. GraphQL APIs give developers flexibility to query specific data from any number of disparate sources, and makes cross-platform app development simpler across the web, Android, iOS, and IoT.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Business of open source So where does Apollo come into all of this? The core Apollo graph platform unifies APIs, microservices, and databases into what is known as a data graph (though it now just refers to this as “graph”), which can be easily queried with GraphQL and put to use in client-side applications. The platform allows users to see everything that they need to build new software-based experiences in a single place, including the types of data that exist, the relationships between them and relevant documentation, what services rely on that data, when they last changed, and who’s using them.
“The graph empowers developers to deliver better experiences, faster, by providing a single place they can get all the data they need, without tightly coupling to each and every service,” Apollo cofounder and CEO Geoff Schmidt told VentureBeat. “The graph is the biggest transformation to application development since the native app and the cloud.” Before all this, developers would usually have to generate lots of boilerplate code to access features that were spread across myriad REST APIs, meaning that they would spend a considerable amount of time finding and combining the right endpoints.
Most of the tools a developer needs to leverage the Apollo platform to connect to a GraphQL API are freely available via open source libraries on GitHub.
On top of all this, the proprietary cloud-based Apollo Studio serves as a cloud management platform for engineering teams to manage their graph lifecycles. “Apollo tracks GraphQL schemas in a hosted registry to create a central source of truth for everything in your graph,” Schmidt explained. “Developers can explore data, collaborate on queries, observe usage, and deliver schema changes with agility and confidence.” Many of the Apollo Studio features are also available for free, but Team and Enterprise plan subscribers pay to access things like alerts, client registry, schema checks, single sign-on, roles and permissions management, and more.
The story so far Apollo’s recent history goes back to 2011, when Schmidt cofounded a company called Meteor Development Group (MDG), which was designed to help app developers build full stack Javascript apps more quickly with the open source Meteor framework. While Meteor became profitable and popular in its own right, MDG released the Apollo GraphQL data stack in 2016 shortly after Facebook open-sourced the project. Three years later, in 2019, Schmidt and his two cofounders sold Meteor to focus all their efforts on Apollo and GraphQL.
Prior to now, Apollo had raised around $53 million including its $22 million series C round two years ago , and with another $130 million in the bank from investors including Insight Partners, Andreessen Horowitz, Matrix Partners, and Trinity Ventures, the company is well-financed to capitalize on the growing uptake of GraphQL in the enterprise. As with just about every other popular open source project out there, there is real demand for support and managed services, which is why Apollo has lured in big-name customers such as Walmart, PayPal, Volkswagen, Expedia, and the New York Times.
“Organizations can’t leave GraphQL adoption unmanaged and as a completely grassroots effort,” Schmidt said. “Otherwise they risk delivering ad-hoc, siloed, redundant multiple graphs which can result in conflicts and breaking changes. GraphQL without strategy ultimately prevents organizations from realizing the full value of adopting GraphQL.” The company claims it has seen a 274% growth in enterprise revenue in the past 12 months, with more than 30% of Fortune 500 companies now among its customer base.
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"80% of tech could be built outside IT by 2024, thanks to low-code tools | VentureBeat"
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"https://venturebeat.com/2021/06/14/80-of-tech-could-be-built-outside-it-by-2024-thanks-to-low-code-tools"
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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 80% of tech could be built outside IT by 2024, thanks to low-code tools 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 looks like no-code and low-code tools are here to stay. Today, Gartner released new predictions about technology products and services, specifically who will build them and the impact of AI and the pandemic. The research firm found that by 2024, 80% of tech products and services will be built by people who are not technology professionals. Gartner also expects to see more high-profile announcements of technology launches from nontech companies over the next year.
“The barrier to become a technology producer is falling due to low-code and no-code development tools,” Gartner VP Rajesh Kandaswamy told VentureBeat. When asked what kinds of tech products and services these findings apply to, he said “all of them.” Overall, Kandaswamy sees enterprises increasingly treating digital business as a team sport, rather than the sole domain of the IT department.
For this research, Gartner defined technology professionals as those whose primary job function is to help build technology products and services, using specific skills like software development testing and infrastructure management. This includes IT professionals and workers with specialized expertise such as CRM, AI, blockchain, and DevOps.
But instead of tech professionals driving what’s next, Gartner predicts a democratization of technology development that includes citizen developers, data scientists, and “business technologists,” a term that encompasses a wide range of employees who modify, customize, or configure their own analytics, process automation, or solutions as part of their day-to-day work. Aside from non-IT humans, AI systems that generate software will also play a significant role, according to Gartner.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! From low-code to AI Tools used for low-code development — such as drag-and-drop editors, code-generators, and the like — allow non-technical users to achieve what was previously only possible with coding knowledge. But by automating and abstracting some of the underlying technical processes — and by making the use of coding or scripting optional — these tools now make it easier for more people to customize features and functions in various applications. What’s more, AI has the potential to automate and improve many aspects of software development, from evaluating needs to deployment.
“For instance, machine learning features for helping coding are available. One example is Microsoft’s Intellicode,” Kandaswamy told VentureBeat. “While such tools are in their infancy, we expect their sophistication to improve and [that] they’ll help reduce the barriers for those without specialized skills to develop useful technology products and services.” Overall, this trend toward democratizing technology development is driven by a new category of buyers outside of the traditional IT enterprise, Gartner says. Total business-led IT spend averages up to 36% of the formal IT budget, according to the firm’s findings.
Pandemic impact From retail to financial services, more companies are increasing efforts to embrace digital transformation. As they do so, they’re more often entering markets related to, or in competition with, traditional technology providers. By 2024, Gartner predicts over one-third of technology providers will be competing with non-technology providers.
In driving digital transformation , the pandemic only accelerated this shift, according to Gartner. Cloud services, digital business initiatives, and remote services rapidly expanded as a direct result of the crisis, opening the door to new possibilities in integration and optimization.
In 2023, Gartner anticipates that $30 billion in revenue will be generated by products and services that did not exist pre-pandemic.
Correction: An earlier version of this post said Gartner predicts over one-third of technology providers will be competing with non-technology providers by 2042, rather than 2024.
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"UN: renewable resources can outpace global energy demand by 2020 | VentureBeat"
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"https://venturebeat.com/2011/05/04/united-nations-renewables-bible"
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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 UN: renewable resources can outpace global energy demand by 2020 Matthew Lynley 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.
Wind, solar and four other forms of renewable resources have the potential to outstrip energy demand by 2020 and replace fossil fuels as a power source, according to a new report by the United Nations.
The report is called the “Renewables Bible,” and will serve as a reference guide for renewable energy growth. The report indicates that there’s enough potential for the six renewable energy sources — which also include geothermal power, biomass fuel, hydropower and power harnessed from oceanic waves — can grow 20-fold over the next decade. The United Nations examined 164 scenarios to come to the conclusion in a comprehensive survey of the current renewable energy environment.
But in reality, only around 2.5 percent of that potential growth will happen based on the current growth trajectory for renewable energy, according to the report. That’s because a complete shift to renewable energy sources will cost global markets around $12.3 trillion by 2030. Global markets will have to invest around $5.1 trillion over the next decade and an as much as an extra $7.1 trillion between 2020 and 2030 to complete the shift.
Most of the scenarios examined by the United Nations still pointed to a substantial increase in the amount of renewable energy deployed by 2020 and 2030. Global markets added around 140 gigawatts of power from renewable sources between 2008 and 2009, bringing the world total up to around 300 gigawatts. That’s mostly dominated by biomass energy sources, which account for around 10 percent of renewable energy generation.
Paris-based International Energy Agency said that a total of $20 trillion needs to be spent on energy infrastructure to expand it and meet demand by 2030. Right now, renewable energy sources account for around 13 percent of global energy usage. In some of the best scenarios, renewable energy would account for up to 77 percent of global energy usage by 2050.
[Photo: s-ariga ] 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 Azure Sphere launches in general availability | VentureBeat"
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"https://venturebeat.com/2020/02/24/microsoft-azure-sphere-launches-in-general-availability"
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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 Azure Sphere launches in general availability Share on Facebook Share on X Share on LinkedIn The Microsoft Azure logo.
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In April 2018, nearly two years ago, Microsoft announced Azure Sphere , a program to better secure the 41.6 billion internet for things (IoT) devices expected to be connected to the internet by 2025. Now, following a lengthy preview, the tech giant is this week launching Azure Sphere in general availability.
Eligible customers will be able to sign up in the coming days. Azure Sphere doesn’t have ongoing fees associated with it, but there’s a one-time cost for a chip (as little as less than $8.65) that includes access to all of Sphere’s components, plus OS updates for the lifetime of the chip. Alternatively, developers can license Visual Studio and Microsoft’s Azure IoT services to develop apps for Sphere “more efficiently, according to Azure IoT CVP Sam George.
“We live in an increasingly connected world,” added George, who noted last year that Microsoft’s Azure IoT software-as-a-service (SaaS) suite grew nearly 150% year-over-year from 2018 to 2019 and gained over 100 new features. “At Microsoft, we are committed to providing a trusted, easy-to-use platform that allows our customers and partners to build seamless, smart, and secure solutions regardless of where they are in their IoT journey.” Azure Sphere For the uninitiated, Azure Sphere is a high-level software-as-a-service (SaaS) platform with built-in communication features for cross-industry IoT devices. It comprises integrated hardware built around a secured silicon chip; the Azure Sphere OS, a custom Linux-based operating system; and the Azure Sphere Security Service, a cloud-based service that provides continuous security.
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 Azure Sphere OS runs on Azure Sphere-certified chips and connects to the Azure Sphere Security Service, and it’s designed to provide a platform for IoT app development — including both high-level and real-time capable apps. It’s the first operating system running a Linux kernel and the second Unix-like operating system that Microsoft has publicly released, interestingly, the other being the decades-old and discontinued Xenix.
Above: Avnet’s Azure Sphere MT3620 starter kit, which packs a MediaTek chip.
Azure Sphere-certified chips and hardware support two implementations: greenfield and brownfield. Greenfield involves designing and building IoT devices with a supported (and optionally cellular-enabled) chip produced or soon-to-be-produced by MediaTek, NXP, and Qualcomm. As for brownfield, it calls for an Azure Sphere “guardian” device — the bulk of which are produced by Avnet and AI-Link — to connect existing hardware to the internet.
Azure Sphere hardware is available in several configurations, including Wi-Fi modules, development kits and dev boards (from partners like Seeed Studios and USI), and the aforementioned guardian. Speaking of, the guardian module — which doesn’t directly connect to networks — is a peripheral with a built-in Azure Sphere-certified chip that runs the Azure Sphere OS and the Azure Sphere Security Service, both of which can be configured via a Wi-Fi or Ethernet connection.
Security A core component of Azure Sphere is the above-mentioned Security Service, a cloud-based service that enables maintenance, updates, and control for Azure Sphere-certified chips. It establishes a connection between devices and the internet and various ancillary cloud services and ensures secure boot, authenticating device identity, integrity, and root of trust while certifying the device is running a vetted codebase. The Azure Sphere Security Service additionally provides the channel by which Microsoft automatically downloads and installs Azure Sphere OS updates and app updates across deployed devices.
At the hardware level, complementing the Security Service, there’s Pluton. It’s a Microsoft-designed security subsystem that implements root of trust for Azure Sphere via a combination of techniques: A custom-designed security processor core Cryptographic engines A hardware-based random number generator A public and private key generator Asymmetric and symmetric encryption An elliptic curve digital signature algorithm verification for secure boot Measured boot in silicon to support remote attestation with a cloud service Various tampering counter-measures App development App development on Azure Sphere OS is relatively straightforward. Using the Azure Sphere SDK for Linux or Windows and samples and solutions open-sourced on GitHub, developers can deploy apps that make use of peripherals on Azure Sphere-certified chips. The apps in question run atop a primary processor core with access to external networking or a lower-powered core as a real-time capable app, with real-time capable apps running either on bare metal or with a real-time operating system, and they can be distributed to Azure Sphere devices through the same mechanism as Azure Sphere OS updates.
Of course, development isn’t confined to Azure. The chips work with other public, private, and hybrid cloud environments including Amazon Web Services and Google Cloud, which no doubt appealed to Azure Sphere’s early adopters.
Here’s a few of them: Microsoft’s own datacenter team tapped Azure Sphere guardian modules to connect equipment and systems for the first time and build new systems.
Qiio developed an Azure Sphere-based IoT deployment solution that combines hardware, cellular connectivity, and cloud services.
Vitamix incorporated Azure Sphere into its IoT Module, a retrofit device that allows users to remotely program Vitamix blenders.
Elettrone is in the process of building an Azure Sphere energy monitoring solution to reduce waste in commercial and residential properties.
Starbucks partnered with Microsoft to deploy Azure Sphere across its existing equipment in stores globally using guardian modules.
Gojo , the brand behind Purell, plans to integrate Azure Sphere with motion detectors and connected dispensers in healthcare facilities.
Leoni , which develops cable systems for the automotive sector and other industries, uses Azure Sphere with integrated sensors to actively monitor cable conditions, creating intelligent and connected cable systems.
Microsoft in IoT In 2018, Microsoft committed $5 billion to intelligent edge innovation by 2022 (an uptick from the $1.5 billion it spent prior to 2018) and pledged to grow its IoT partner ecosystem to over 10,000. It’s borne fruit in Azure IoT Central, a cloud service that enables customers to quickly provision and deploy IoT apps, and IoT Plug and Play, which provides devices that work with a range of off-the-shelf solutions. Microsoft’s investment has also bolstered Azure Sphere; Azure Security Center, its unified cloud and edge security suite; and Azure IoT Edge, which distributes cloud intelligence to run in isolation on IoT devices directly.
Microsoft has competition in Google’s Cloud IoT, a set of tools that connect, process, store, and analyze edge device data. Not to be outdone, Amazon Web Services’ IoT Device Management tracks, monitors, and manages fleets of devices running a range of operating systems and software. And Baidu’s OpenEdge offers a range of IoT edge computing boards and a cloud-based management suite to manage edge nodes, edge apps, and resources such as certification, password, and program code.
But the Seattle company has ramped up its buildout efforts as of late, most recently with the acquisition of Express Logic , a San Diego, California-based developer of real-time operating systems (RTOS) for IoT and edge devices powered by microcontroller units. Separately, it’s partnered with companies like DJI, SAP, PTC, Qualcomm, and Carnegie Mellon University for IoT and edge app development.
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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"Microsoft launches Cloud for Sustainability to help companies track emissions | VentureBeat"
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"https://venturebeat.com/2021/07/14/microsoft-launches-cloud-for-sustainability-to-help-companies-track-emissions"
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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 launches Cloud for Sustainability to help companies track emissions 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.
At its Inspire 2021 conference today, Microsoft announced Microsoft Cloud for Sustainability, a new service in preview designed to help companies measure and manage their carbon emissions by setting sustainability goals. It includes software-as-a-service offerings that can discover and connect to real-time data sources, provide carbon accounting, and deliver insights for organizations to take action.
According to Accenture , 94% of the top CEOs cite sustainability as important or very important to the future success of their businesses. But measuring the overall environmental impact of an organization is particularly challenging. Organizations need to be able to record their footprint, report to stakeholders, reduce their resource usage, and remove their footprint through carbon offsets or recycling. McKinsey found in a recent survey that most companies are struggling to factor sustainability into the “hard” areas of their business, like supply chain, indicating that there’s a potential to drive further integration.
Cloud for Sustainability aims to pave the way by allowing companies to report on carbon emissions from the cloud, devices, and apps as part of their environmental footprint. Customers can connect emissions data sources into one view and use Cloud for Sustainability to publish a scorecard to track progress. Moreover, they can pinpoint areas and audit to see if they’re meeting emission reduction goals. For instance, if an HVAC system isn’t on schedule to meet its reduction target, the task can be assigned to operations to make improvements to reach that target.
Datacenters contribute 0.3% to global carbon emissions, according to a Nature paper. And it’s believed that machine learning models in particular have contributed to the adverse trend. In June 2020, researchers at the University of Massachusetts at Amherst released a report estimating that the amount of power required for training and searching a certain model involves the emissions of roughly 626,000 pounds of carbon dioxide , equivalent to nearly 5 times the lifetime emissions of the average U.S. car.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! “At some level, every industry is undergoing sustainable digital transformation,” Microsoft EVP and chief commercial officer Judson Althoff said in a press release. “With Cloud for Sustainability, we’re creating a whole new category, going beyond capturing data to helping customers aggregate sustainability data in an actionable way … [The service] enables any organization more easily and effectively to record, report, reduce, and replace their emissions.” Growth in sustainability The launch of Cloud for Sustainability comes as more than two out of three North American consumers claim they’re more likely to favor socially responsible brands, according to the National Retail Federation. Meanwhile, Salesforce reports that 78% of people believe companies are responsible for fighting climate change.
Cloud for Sustainability competes with Salesforce’s Sustainability Cloud, which similarly allows companies to analyze carbon emissions from energy usage by measuring and managing plans. Sustainability Cloud ships with preloaded datasets from the U.S. EPA, IPCC, and others to assess carbon accounting while tracking energy patterns and emission trends, exposing the environmental impact with visualization and dashboards.
Not to be outdone, earlier this year , Microsoft cofounded the Green Software Foundation , a nonprofit established with the Linux Foundation to build an ecosystem of people, standards, tooling, and practices to reduce carbon emissions caused by software development. It aims to help advance the information and communications technology segments targets for reducing greenhouse gas emissions by 45% by 2030, in line with the Paris Climate Agreement.
In 2020, Microsoft pledged to become carbon negative by the year 2030, whereby the company will eliminate more carbon from the atmosphere than it generates. By 2025, Microsoft intends to remove all carbon it has emitted either directly or by electrical consumption since its founding in 1975, an effort it will pay for with an expanded internal carbon fee, both for direct emissions and supply and value chain 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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"Friends With Holograms: Using VR to train social workers to sniff out child abuse | VentureBeat"
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"https://venturebeat.com/2019/10/15/friends-with-holograms-using-vr-to-train-social-workers-to-sniff-out-child-abuse"
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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 Friends With Holograms: Using VR to train social workers to sniff out child abuse 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.
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Social workers have to make important decisions every day when investigating child abuse. Trained social workers are the best at shouldering that responsibility, but they aren’t always available to train new workers.
That’s where virtual reality could make a difference.
Accenture worked with a New York-based digital agency, Friends With Holograms , to create a VR training program dubbed Accenture Avenues (from Accenture Virtual Experience Solution). They created a VR training program that improves the teaching and immerses the trainee in a realistic environment. It also uses voice recognition as an input system to further increase realism.
The idea is to help caseworkers refine their skills in a real-world setting, interviewing a child, a mother, and an admittedly scary-looking father. Only the immersive storytelling and interactivity of VR and voice could make this training so impactful, said Cortney Harding, founder of Friends With Holograms, in an interview with VentureBeat. (I’m going to moderate a session on enterprise VR at Greenlight Insights ‘ Virtual & Augmented Reality Strategy Conference during XRS Week on Thursday.) Above: The cast of Accenture Avenues.
The voice recognition — where you say something that the program will record as your answer to a question — helps make the training reach a wider group of people. Harding even tried the VR training on her non-techie parents, and they loved it.
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! “It’s gotten an incredibly positive response so far,” Harding said. “And I’m really excited about the future. I showed my parents. Then, we went to dinner, and the entire dinner conversation was about them talking about it, which was incredible. This is really groundbreaking technology, and we’ve found a good way to use it.” Above: The Friends With Holograms founders with friends at SXSW.
The social worker VR program was a 20-minute training program, with a subtle narrative. Depending on how you asked a question, you got a different answer. It uses actual video of actors and actresses, rather than animated characters, to make it feel more real, Harding said.
“Our core belief is that everything has to be as realistic as possible,” she said. “So it’s all voice-activated. We don’t use controllers or remote controls at all.” The hope is to completely transform how caseworkers hone their data-gathering and decision-making skills. Accenture Avenues creates the foundation for a highly skilled workforce that can rapidly discuss the decision points all practiced caseworkers face.
Harding believes that training is the killer application for VR and augmented reality technology, which hasn’t yet got its traction in the consumer mass markets. Enterprise is coming on strong, and it could form the bridge for VR to take off in markets where early adopters don’t mind paying some of the higher costs.
Harding started doing this work with Pamela Jaber, head of ideation at Friends With Holograms, in 2017, and now her firm has done work for Accenture, Coca-Cola, DDI, Verizon, Unity, and the U.S. Air Force.
With Accenture, Friends With Holograms has now premiered two chapters of the training program, with chapter two most recently debuting at the American Public Health Service Association.
Above: Cortney Harding of Friends With Holograms For DDI, Friends With Holograms and Strivr worked on a VR training program that helps leaders develop their soft skills through memorable VR scenarios. The goal may be to help the leaders gain empathy for others, or give them a safe space to practice emotionally charged conversations.
Harding believes the payoff from the VR training is obvious. But it isn’t always easy to measure the nuances, such as how much faster someone might learn a task in VR compared to other methods.
Johnson & Johnson recently said that training surgeons using Oculus VR headsets is paying off in huge ways. An independent study by the Imperial College London showed that 83% of surgeons who trained with VR could then go into the lab environment with minimal guidance. With such training with traditional methods, the percentage was zero.
“What is the savings of having doctors and med students who can perform a procedure better, without asking for help?,” Harding said. “That’s huge. Think of the one that’s going to save you in the future. What is the savings of not getting sued for sexual harassment?” Above: Friends With Holograms at SXSW.
On sexual harassment, Harding said VR would be ideal for running trainees through scenarios, like when a work group goes out drinking and someone makes a dirty joke. Within VR, you would be able to pick up on people’s responses, and see if someone you’re joking with is uncomfortable and maybe it’s time to change the subject.
“That’s where the real learning is going to happen. And that’s where the real change is going to happen,” Harding said. “And that’s the type of thing where you’re going to save money. The last thing any company wants is a huge sexual harassment lawsuit.” To create these VR experiences, Harding’s team sits down with clients to find out the problem they’re trying to solve. VR can help solve it in a more authentic and memorable way, she says. Harding’s team has 12 people, with roughly half of the staffers being female. The company is self-funded.
“We work hard on diversity, and we work hard on making it really cinematic,” Harding said.
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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"Automattic buys WooCommerce to get into e-commerce -- its largest acquisition to date | VentureBeat"
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"https://venturebeat.com/2015/05/19/automattic-buys-woocommerce-to-get-into-ecommerce-its-largest-acquisition-to-date"
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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 Automattic buys WooCommerce to get into e-commerce — its largest acquisition to date 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.
Automattic , the company behind WordPress.com, is buying WooCommerce , a popular e-commerce platform built on WordPress with over $1 million in monthly recurring revenue.
Automattic chief and WordPress creator Matt Mullenweg announced the deal today, but declined to share the terms of the deal. Here’s what we know: WooCommerce’s 55 employees are joining Automattic, WooCommerce is currently profitable, and there are no plans (yet) for WooCommerce’s plug-and-play e-commerce service to land on the for-profit WordPress.com.
It’s noteworthy that WooCommerce competes with e-commerce platforms like Shopify (which is going public) and Squarespace.
We spoke to Mullenweg about the deal this morning, one month before his company turns 10 [the interview was abridged by us for readability’s sake]: VentureBeat: Will any of this tech make it into WordPress.org? Mullenweg : It’s actually exclusively there. It’s already open source GPL. We don’t have plans currently for it to make it to WordPress.com VB: Really? I had assumed it would be automatically built into WordPress.com.
Mullenweg : I think it’s a little bit further down the line. There’s a lot to do with the plugin already.
VB: What about Squarespace? Does this acquisition make you more competitive? Mullenweg : Absolutely. I would like to think everything we do makes us more competitive. If you look at the data, people are still using WordPress over Squarespace.
We’ve been thinking about this for a really long time — the best way to bring e-commerce to the WordPress world… VB: Automattic turns ten next month — what’s next? Mullenweg : We’ll talk about that soon. We’re really focused on Woo today. We have two major lines: WordPress.com, Jetpack, and this is adding a third. It’s far and away our biggest acquisition. It’s six times larger than anything we’ve done before.
VB : This deal seems to be about making WordPress a one-click tool.
Mullenweg: It’s definitely not easy to do. We have teams inside Automattic working on it. The challenge is bringing it to a wider audience. Things like e-commerce definitely bring us closer … it’s thousands and thousands of small improvements every day.
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"WordPress.com parent Automattic raises $300 million from Salesforce at a $3 billion valuation | VentureBeat"
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"https://venturebeat.com/2019/09/19/wordpress-com-parent-automattic-raises-300-million-from-salesforce-at-a-3-billion-valuation"
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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 WordPress.com parent Automattic raises $300 million from Salesforce at a $3 billion valuation Share on Facebook Share on X Share on LinkedIn WordPress for iOS 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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Automattic , the company best known for WordPress.com and its contributions to the open source, non-hosted WordPress alternative, has raised $300 million in a series D round of funding from Salesforce’s venture capital (VC) arm Salesforce Ventures.
This is Automattic’s first outside investment since its $160 million series C round back in 2014, which valued the company at $1.16 billion. Off the back of its latest investment, Automattic said it now has a valuation of $3 billion.
Founded in 2005, Automattic offers a range of tools and services beyond its core WordPress.com offering, including Tumblr, which it bought a few months back , long-form content platform Longreads , note-taking app Simplenote , a drag-and-drop file-sharing platform called CloudUp , a collaborative translation tool called GlotPress , and a survey-creating platform called Crowdsignal ( formerly PollDaddy ).
While neither Salesforce nor Automattic have provided details of any planned integrations or product alignment between their respective services, Automattic did confirm that today’s news represents more than funding. “We’ll be working with each other to explore possible opportunities beyond the investment,” a spokesperson told VentureBeat.
Reading between the lines, it’s not hard to see why Salesforce would invest such a gargantuan sum in a company best known for blogging. WordPress currently powers one-third of the web, which includes everything from small-time bloggers to publishers and online retailers. And several products in Automattic’s arsenal hint at the reasons Salesforce has elected to invest in the company.
Customer management A few months back, Automattic acquired ZBS CRM , which is a customer relationship management (CRM) plugin for WordPress — the duo previously announced plans to integrate ZBS CRM deeper into WordPress. Automattic also operates an ecommerce plugin called WooCommerce, which it acquired four years ago.
Online retailers using WordPress and WooCommerce will likely want to use a CRM system to keep track of their customers, which is why ZBS CRM already offers extensions that enable WooCommerce users to connect their online store to a CRM.
Moreover, several third-party plugins bridge Salesforce and websites built on WordPress software.
It’s clear there is a great deal of value in integrating CRM systems with WordPress websites, and Salesforce is the biggest CRM player in the game. Salesforce also has a track record in the open source realm , most recently open-sourcing its Lightning Web Components JavaScript framework. Given the open and extensible nature of WordPress on the web, the companies seem like a good fit.
“Through Salesforce’s investment and partnership with Automattic, we look forward to deepening our commitment to WordPress and the open web,” said Salesforce president and chief product officer Bret Taylor in a press release.
From Automattic’s perspective, the fresh cash injection will help it “scale its business” and grow its own investments across the WordPress ecosystem, including WordPress.com, WooCommerce, Jetpack, Tumblr, and the premium WordPress VIP offering.
“I met Marc Benioff (Salesforce CEO and cofounder) earlier this year, and it became obvious to both of us that Salesforce and Automattic shared a lot of principles and philosophies,” added Automattic CEO and WordPress co-creator Matt Mullenweg in a personal blog post.
“Marc is a mindful leader, and his sensibilities and sense of purpose feel well aligned with our own mission to make the web a better place. He also helped open my eyes to the incredible traction WordPress and WP VIP has seen in the enterprise market, and how much potential there still is there.” Today’s announcement comes several months after Automattic introduced a new suite of products called Happy Tools , designed for companies with a remote or globally distributed workforce. Indeed, these tools were created in-house for companies that operate much like Automattic, which has no centralized office and claims more than 900 employees working across 71 countries.
“The Salesforce funding is also a vote of confidence for the future of work,” Mullenweg continued. “Distributed work is going to reshape how we spread opportunity more equitably around the world.” With its latest round, Automatic has now raised more than $600 million, with previous investors including True Ventures, Tiger Global Management, Insight Partners, and Iconiq Capital.
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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"Automattic's WordPress VIP acquires Parse.ly to bring web content analytics to the enterprise | VentureBeat"
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"https://venturebeat.com/2021/02/08/automattics-wordpress-vip-acquires-parse-ly-to-bring-web-content-analytics-to-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 Automattic’s WordPress VIP acquires Parse.ly to bring web content analytics to the enterprise Share on Facebook Share on X Share on LinkedIn WordPress.com from Automattic 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.
WordPress VIP (WPVIP) , Automattic’s enterprise-focused content management platform, has acquired Parse.ly , an engagement analytics platform aimed at online publishers. Terms of the deal were not disclosed.
While Automattic is perhaps better known as the parent company of WordPress.com, it also operates a host of third-party services, including WPVIP, which is a managed hosting service for enterprises and high-traffic websites. WPVIP — an official Automattic subsidiary, with some 150 employees and its own leadership and development teams — allows companies such as Salesforce, Microsoft, Slack, Spotify, and Facebook to access a dedicated team of WordPress experts alongside a suite of enterprise-grade plugins, integrations, and APIs.
After a prolonged beta period, Parse.ly launched its flagship “Dash” product in 2012 to help online publishers gain insights into what people are reading on their site and how they got there. A few years later, the company introduced a whole new platform that factored in all the ways publishers seek revenue beyond advertising, such as paywalls, events, research reports, ecommerce, and more. And in 2018, it launched Currents , which uses AI to measure real-time audience attention. The New York-based company had raised around $13 million in outside funding.
Above: Parse.ly dashboard WPVIP said it will integrate Parse.ly’s real-time content analytics and content recommendation smarts into its own analytics toolset, benefiting WPVIP’s existing customer base, while Parse.ly’s customers will gain immediate access to WPVIP. All in all, this acquisition will make both WPVIP and Parse.ly much stickier propositions, with the new combined unit spearheaded by existing WordPress VIP CEO Nick Gernert.
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 acquisition comes 16 months after Automattic raised $300 million from Salesforce , an investment that highlighted Automattic’s intentions for the enterprise sphere. Not long before that, Automattic acquired ZBS CRM , a customer relationship management plugin for WordPress. Automattic also operates an ecommerce plugin called WooCommerce, which it acquired in 2015.
It’s clear Automatic has been investing in its enterprise aspirations for a while now, and Parse.ly fits neatly into those plans.
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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"Element bolsters decentralized team messaging with $30M raise | VentureBeat"
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"https://venturebeat.com/2021/07/27/element-bolsters-decentralized-team-messaging-with-30m-raise"
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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 Element bolsters decentralized team messaging with $30M raise Share on Facebook Share on X Share on LinkedIn Element / Matrix cofounders Matthew Hodgson (left) and Amandine Le Pape (right) 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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Element , the company behind an end-to-end encrypted team messaging platform powered by the Matrix protocol, has raised $30 million in a series B round of funding from a slew of notable backers, including Skype cofounder Jaan Tallinn ‘s Metaplanet , WordPress.com parent company Automattic, Protocol Labs , and Notion.
In a week that saw Salesforce officially taking Slack under its wing, the gargantuan $27.7 billion acquisition serves as a timely reminder of the importance that team chat and collaboration tools play in a world that has rapidly embraced remote work.
In tandem, open source software is infiltrating just about every nook and cranny of the business world, a combination that puts four-year Element in a strong position.
London-based Element develops an open source messaging client on top of Matrix, a decentralized open standards-based communication protocol created inside Amdocs by Matthew Hodgson and Amandine Le Pape in 2014. The duo departed Amdocs in 2017 to focus entirely on growing and commercializing Matrix, first through a company called New Vector , which developed a Matrix hosting service and a cross-platform Slack alternative called Riot. In 2018, the Matrix.org Foundation came into being to cement Matrix’s development as a neutral not-for-profit entity, while last year Hodgson and Le Pape rebranded both New Vector and the Riot app as Element.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Above: Element: An instant message app built on Matrix So what exactly is Matrix, and how is Element looking to capitalize on it? Like email Matrix can perhaps best be compared to something like a telephone network or email, insofar as it’s an interoperable communication system that doesn’t lock people into a closed ecosystem. With email, you can send a message to another person regardless of what service provider or client (app) they’re using — Yahoo, Gmail, Outlook, and ProtonMail users can all message each other just fine. The Matrix protocol is striving to achieve a similar goal, but with modern internet-based messaging in its crosshairs — in a Matrix world, WhatsApp users can easily communicate with Slack and Skype users.
Because Element is built on Matrix, it essentially serves as a catalyst for the growth of the broader Matrix network. Anyone using Element is participating in an open and global network of tens of millions of users, spread across thousands of deployments from different organizations. It also means that someone that’s using Element isn’t locked into Element — they can switch to any other Matrix-powered client and not lose any data.
Separately, a method called “ bridging ” opens up support for third-party apps not built on the Matrix protocol, including Telegram, Slack, WhatsApp, and rival open source messaging tools such as Mattermost.
Earlier this year, a new startup called Beeper entered the fray, courtesy of Eric Migicovsky, who sold his previous Pebble smartwatch business to Fitbit back in 2016.
Beeper is a universal chat app built on Matrix, and it uses bridging to relay messages between more than a dozen chat apps including iMessage, WhatsApp, Slack, Twitter, Hangouts, and Facebook Messenger.
Above: Beeper: A universal chat app News also emerged last week that Gematik , Germany’s national agency responsible for digitization the country’s health care system, was switching to Matrix.
This followed several years of siloed digital transformation that resulted in the various health care bodies unable to effectively communicate with each other, while also raising questions over the security and privacy of the systems they had chosen to transmit confidential medical data. Some 150,000 separate organizations constitute Germany’s health care system, spanning hospitals, local doctors, clinics, insurance companies, and more.
Switching to Matrix affords the different parties some flexibility in terms of the specific apps that they use. They may have different use cases, but by adhering to a common standard, all the apps will still be able to talk to each other.
“This is a big undertaking, and there are 15 vendors so far involved — including Element,” Element CEO and CTO Hodgson told VentureBeat. “There will be a wide range of apps compatible with the system from different vendors.” Several other commercial companies have previously built products on top of Matrix, including Ericsson’s Contextual Communication Cloud , a managed service that lets enterprises integrate advanced communication and collaboration services into their applications. And French giant Thales launched an instant message service for businesses called Citadel.Team , which is not too dissimilar to Element.
Element also recently renewed its French government contract, through which it targets its messaging and collaboration toolset to more than 5.5 million civil servants across France, while last year it signed a new contract with Dataport , selling commercial licenses to Germany’s education system.
All in all, it seems like Matrix has more than a little momentum, claiming 190% growth in the past 12 months, with more than 35 million “addressable users” across 75,000-plus deployments. And as the main commercial entity pushing the Matrix protocol, Element is well-positioned to capitalize on this surge with a continued focus on data privacy and ownership.
Data sovereignty Data sovereignty is the concept of letting organizations choose where and how their data is hosted, and thus which laws apply to its governance — this is a massive deal for highly regulated industries and nation states. And in a world that is increasingly turning to the cloud and private infrastructure, many situations lead organizations to feel uneasy handing over all their data to a single big company.
“When communication is centralized, it becomes a very appealing target for abuse — whether that’s through propaganda, surveillance, censorship, or worse,” Element’s new investor (and Skype cofounder) Tallinn said in a statement. “Consumers need rescuing from surveillance capitalism, and organisations need a secure neutral way to communicate. Matrix is the most advanced platform to provide that missing communication layer.” Salesforce’s multi-billion dollar Slack acquisition last week is a good reminder of what’s at stake. Data belonging to businesses that had gone all-in on Slack is now owned by Salesforce, whether they like it or not. And one of the reasons why Slack decided to sell to Salesforce in the first place was due to Microsoft’s aggressive bundling of Teams as part of its broader Office software suite, something that could soon face antitrust scrutiny in Europe.
The point is, this whole episode shines a spotlight on the power and control “big tech” can wield, something that a decentralized “open” protocol like Matrix helps to solve.
“Salesforce’s acquisition of Slack is excellent news for Matrix,” Hodgson said. “It makes it crystal clear that when you are using Slack or Teams, your data is hurtling — encrypted only in transit and rest (i.e. not end-to-end) — into the big-tech clouds of Salesforce or Microsoft. Organizations of all shapes and sizes are frantically looking for alternatives which give them full sovereignty over their own data, rather than having to blindly trust that these tech giants will act as good custodians.” The same principle applies to the myriad other enterprise team communication tools out there that adhere to an open source ethos, such as Slack-alternative Mattermost ; Brazil-based Rocket.Chat , which offers various hosted and self-hosted tiers on top of its core open source chat platform, and which raised $19 million just a few months back; and Kandra Labs, which has developed a bunch of commercial products on top of open source chat platform Zulip.
Element, meanwhile, offers various pricing plans depending what the customer needs. The company offers Element Matrix Services (EMS), for example, which promises all the data sovereignty benefits of an on-premises deployment, except as a hosted service.
But what does that mean, exactly? “It’s hosted in that you get a dedicated instance just for you, run by us in a geography of your choice, which you can point your DNS at,” Hodgson explained. “Combined with end-to-end encryption, this means you have full ownership and control over your conversations — you have effectively just outsourced the administration of your server to the people who wrote it in the first place.” Element had previously raised around $18 million, and with its latest cash injection the company is now well-financed not only to bolster its own product, but also the foundational Matrix protocol that now powers a host of open source and commercial services. And in the process, it might just go some way toward breaking down communication silos.
“Element’s funding means there’s continued significant investment in the Matrix protocol, which hugely benefits the entire Matrix ecosystem,” Hodgson said. “Matrix will do for communications what the web did for information sharing. And just like the web, it’s an open standard, decentralized and universal.” 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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"Peak.AI raises $12 million to bolster enterprise AI adoption | VentureBeat"
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"https://venturebeat.com/2020/04/23/peak-ai-raises-12-million-to-bolster-enterprise-ai-adoption"
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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 Peak.AI raises $12 million to bolster enterprise AI adoption 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.
Peak.AI , a startup developing AI solutions for enterprise customers, today announced that it has raised $12 million in extended series A funding. The fresh capital will fuel Peak’s growth, commercial expansion, and R&D, according to CEO Richard Potter, and will come as up to 25% of companies report experiencing a 50% failure rate in deploying AI models.
Despite the promise of AI, the corporate sector’s adoption curve hasn’t been as steep as some had predicted. A survey of publicly traded U.S. retailers’ earnings calls found that only nine of about 50 companies had started to discuss an AI strategy, and a separate study — from Genesys — found that 68% of workers aren’t yet using tools that leverage AI.
Peak aims to simplify implementation with a subscription-based software-as-a-service offering that spans infrastructure, data processing, workflow, and applications. Its customers — brands like Pepsi and Marshalls — supply their data, which Peak’s platform ingests through built-in connectors to accomplish things like optimizing supply and demand and supporting fulfillment processes, courtesy of a library of configurable AI engines.
Once AI engines go live, their predictive and prescriptive outputs can be exposed through APIs or explored, visualized, and exported with Peak’s Data Studio. The platform can handle data sets of virtually any size running on Amazon Web Services, and it serves models in an always-on fashion so that they self-improve over time. It also screens all ingested data through an algorithm to identify and anonymize any personally identifiable information.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Peak’s team optionally works with customers to define objectives, quantify opportunities using a sample of data, and scope out a business case for sign-off and launch. It’ll take care of kick-off and onboarding, as well as operationalizing, and it’ll configure the solutions to individual user needs.
There’s no shortage of fully managed AI solutions with substantial venture backing.
H2O recently raised $72.5 million to further develop its platform that runs on bare metal or atop existing clusters and supports a range of statistical models and algorithms. And Cnvrg.io — which recently launched a free community tier — has raised $8 million to date for its end-to-end AI model tracking and monitoring suite.
But Peak claims its platform is more performant than rival offerings. It says it has helped customers achieve a 28% uplift in marketing revenues, a 4 times increase in return on capital employed, and a 147-ton reduction in CO2 emissions through optimized logistics and resource planning.
MMC Ventures and Praetura Ventures led the series A round, which brings Manchester-based Peak’s total funding to $18 million. The company was founded in December 2014 by CEO Richard Potter, David Leitch, and Atul Sharma and has additional offices in Jaipur and Edinburgh.
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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"Peak.AI raises $21 million to drive enterprise AI adoption | VentureBeat"
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"https://venturebeat.com/2021/02/17/peak-ai-raises-21-million-to-drive-enterprise-ai-adoption"
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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 Peak.AI raises $21 million to drive enterprise AI adoption 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.
Peak.AI , a startup developing AI solutions for enterprise customers, today announced that it closed a $21 million series B round. The funds, which bring Peak’s total raised to date to $43 million, will drive the company’s R&D and commercial expansion in the U.S. and India, according to CEO Richard Potter.
The global enterprise AI market size was valued at $4.68 billion in 2018 and is projected to reach $53.06 billion by 2026, according to Allied Market Research.
But the corporate sector’s adoption curve hasn’t been as steep as some had predicted despite the promise of AI. A survey of publicly traded U.S. retailers’ earnings calls found that only 9 of about 50 companies had started to discuss an AI strategy, and a separate study from Genesys shows that 68% of workers aren’t yet using tools that leverage AI.
Peak aims to simplify the implementation of AI systems with a subscription-based software-as-a-service (SaaS) offering that spans infrastructure, data processing, workflow, and applications. Its customers — brands like Pepsi and Marshalls — supply their data, which Peak’s platform ingests through built-in connectors to accomplish things like optimizing supply and demand and supporting fulfillment processes, courtesy of a library of configurable AI engines.
Once AI engines go live, their predictive and prescriptive outputs can be exposed through APIs or explored, visualized, and exported with Peak’s Data Studio. The platform can handle datasets of virtually any size running on Amazon Web Services, and it serves models in an always-on fashion so that they self-improve over time. Peak also screens all ingested data through an algorithm to identify and anonymize any personally identifiable information.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Peak’s team optionally works with customers to define objectives, quantify opportunities using a sample of data, and scope out a business case for sign-off and launch. The company can take care of deployment and onboarding as well as operationalizing, and it can configure a solution to an individual user’s needs.
There’s no shortage of managed AI development platforms with venture backing.
H2O recently raised $72.5 million to further develop its platform that runs on bare metal or atop existing clusters and supports a range of statistical models and algorithms.
Cnvrg.io — which recently launched a free community tier — has raised $8 million to date for its end-to-end AI model tracking and monitoring suite.
But Peak, which claims that revenues doubled over the past 12 months thanks to customer wins in Europe, the U.S., the Middle East, and Asia, asserts that its platform is more performant. The company says it has helped customers achieve a 5% increase in total company revenues, a doubling of return on advertising spend, a 12% reduction in inventory holdings, and a 5% reduction in supply chain costs.
“It’s becoming impossible to run a business without AI. Modern businesses are complex and operate in an ever-changing world,” Potter said in a statement. “Our software empowers day-to-day decision makers across businesses, and we’re proud to be working with household names such as PrettyLittleThing, KFC, and PepsiCo, and other industry leaders like Marshalls and Speedy Hire. We’re delighted to have secured this new funding in an oversubscribed round.” Oxx led Peak’s latest fundraising round with participation from existing investors MMC Ventures and Praetura Ventures and new investor Arete. The company, which was founded in December 2014 by Potter, David Leitch, and Atul Sharma, has additional offices in Jaipur and Edinburgh and plans to hire 130 employees in the coming year.
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"Sonoa Systems, a service-oriented architecture co., raises up to $16M | VentureBeat"
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"https://venturebeat.com/2006/11/27/sonoa-systems-a-service-oriented-architecture-co-raises-up-to-16m"
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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 Sonoa Systems, a service-oriented architecture co., raises up to $16M Editor Share on Facebook Share on X Share on LinkedIn Sonoa Systems , a Santa Clara service-oriented architecture (SOA) start-up, has raised up to $16 million in a second round of funding, according to LightReading.
Juniper Networks, SAP Ventures and Japanese integrator Net One Systems joined the round, which was led by existing investors Bay Partners and Norwest Venture Partners, according to the report.
(This story was first posted 11/22) 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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"KPMG survey finds execs worry about AI hype -- but they can address it | VentureBeat"
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"https://venturebeat.com/2021/05/26/kpmg-survey-finds-execs-are-worried-about-ai-hype-but-they-can-overcome-it"
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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 Sponsored KPMG survey finds execs worry about AI hype — but they can address it Share on Facebook Share on X Share on LinkedIn Presented by KPMG AI is more hype, less reality, say three-quarters of the executives surveyed in a 2021 study by KPMG, “ Thriving in an AI World: Unlocking the Value of AI across 7 Industries.
” And half think AI is moving too fast in their industry, even as they wish their company was moving faster. The hurdles to implementing AI are high, from a fundamental misunderstanding of what AI actually is and what it can do, to the significant lack of expertise available to help AI-curious organizations get their footing, says Dr. Ellen Campana, head of enterprise AI at KPMG LLP.
“The overhype is a real concern,” Campana says. “There are a lot of people trying to get into the game. Many have had a bad experience along the way, because they have put their trust in a group or person that didn’t have a lot of experience with AI, or they didn’t have a clear understanding of what to expect when launching an AI program.” At least some of the hype is warranted, says Campana. According to KPMG research, AI is becoming ubiquitous — 92% percent of the execs that have implemented the technology in their organization believe in its ability to deliver value and make their organization run more efficiently. There is clear confidence that AI has the potential to solve some of their industry’s biggest challenges. Yet there are still long-standing hurdles to overcome.
The long-term impact of the hype cycle This isn’t the first time people felt that AI was moving too fast and too much was being promised, Campana says. The term ‘AI Winter’ was coined as a way to describe those periods of boom and bust, specifically when interest in artificial intelligence faded during the technology’s long history — the last was in the 90s, during the dot-com boom.
Despite the public discourse, work has never stopped on advancing the technology. In 2012, breakthroughs in machine learning began to renew interest in the tremendous potential of AI. But those earlier hype cycles have had a long-term impact, Campana says, leaving a painful dearth of experienced and knowledgeable AI experts.
“Because the training programs were shut down due to that AI Winter, the people who are trained to do this work are scarce,” she explains. “It feeds into the current sense of things being overhyped, because there’s not a lot of people with deep training, but there’s a big market demand for it. This leads to a lot of variability in the advice people are hearing.” Many of those companies that are having unfortunate experiences with AI now have unrealistic expectations about the technology — they haven’t been educated about what to expect. Campana points to the surprisingly common idea that AI is something that you install, load up, and run.
“People expect things to happen quickly, and seem to believe and expect that AI will just know things,” she says. “Improving literacy of these AI systems and improving people’s knowledge about what can and can’t be done is key.” The real promise of AI, and the risks Due to major breakthroughs in business platforms and tools, AI is prevailing across industries, Campana says.
“AI excels when you find a way for the human and the computer to collaborate efficiently,” she says. “If you divide the tasks based on the characteristics that each participant is good at, then there’s a lot of promise that together, people and machines can do a lot more.” Campana and her colleagues have been engaged in a broad variety of AI projects in their work at KPMG. To address the impact that COVID-19 has had on the supply chain, they’ve been using AI to reorganize and reconfigure the food distribution system and optimize it despite disrupted supply chains and changing markets.
In technology, they’ve been working on automating identification of commercial leakage in contracts, and for financial institutions, they’re applied AI to determine whether companies are in compliance with regulations.
Their AI solutions also deal with earnings calls in order to understand how companies are talking about their finances, the implications for stock market valuation, and whether companies are communicating accurately. In health care, they are helping to optimize customer experience at payer organizations which are being flooded with calls at an unprecedented level. And in education, conversational AI is helping to distribute the technology children need in order to keep learning, particularly during the pandemic.
The risk in implementing solutions like these, or the thousands of others available, is underestimating both the need to participate actively in developing the systems, and the need to find expertise, Campana says.
As well, people will sometimes listen to marketing from technology vendors rather than seeking out third-party intelligence. That can lead to spending money on the wrong technology, or investing in a system or vendor that doesn’t understand how to maintain and keep the system current. Or companies can run into a problem with their AI implementation because their vendor has told them they don’t need a particular component, but that may be because the vendor doesn’t provide it.
“Companies need to be aware of the difference between marketing and implementation,” Campana says. “They’re sometimes turning to a technology vendor for advice about strategy, which is not something to do. They should either develop their own strategy considering multiple perspectives or come and ask for help to develop strategies. But they definitely need to make sure that they have a strategy that’s independent of a particular vendor view.” Implementing a hype-proof AI strategy Nearly eight in 10 executives in the KPMG survey reported that AI is functional in their organization, and a majority who are using it say it’s delivering value beyond what was promised. But how does a CTO get company buy-in without stirring up the fear that that they’re overpromising, or buying into the hype? Campana says that you can start small, with a proof-of-concept project if necessary. It’s important to know that it will need to scale, and to have a plan for how that can realistically happen.
For C-suite stakeholders, it’s critical to continuously monitor progress and provide reporting while offering concrete, practical evidence as the project evolves. That includes documenting performance improvements as well as outlining opportunities for improvement, all the while keeping executives informed of the iterative process.
Of course, that also includes impact on the bottom line: it’s vital to identify KPIs that are about money. For instance, documenting call deflection in conversational AI, or identifying commercial leakage in contracts, so the efforts can be tied back to the business value, even in the early stages.
“We’re not waiting until three months down the line when [execs] say, ‘We invested a million dollars in your AI initiative, what have you got to show for it?’” Campana says. “You have to show them the iterative improvement. They have to understand that it’s not something that gets installed and just works. It gets better because you teach it.” In order for that to happen — for the AI to learn and get better –it’s imperative to get buy-in from internal teams and have them actively participate in the shift to AI. For instance, if you’re implementing an AI solution to help contracts for commercial leakage, the group that handles that issue needs to be actively involved in helping the AI learn what to look for — not just the vendor, and not just a consulting group.
“A consulting group can help accelerate and make things more efficient, and so can a technology team, but they will need the help of the people on the ground,” Campana explains. “They need to spend time making sure that those people are bought in and understand that the goal is to make their lives better, not to replace them.” For all levels of the company, implementing AI successfully takes patience as well — or in other words, understanding that AI isn’t like a lot of other IT domains where you install the software, configure it, and then it just pretty much works. It is, instead, a process.
That requires the most important part of a successful AI implementation: AI literacy, or combating the long-term damage caused by the last AI Winter. That’s the most important piece of advice Campana has for her clients about realizing the promise of AI.
“You need to pay attention to data literacy and AI literacy from the bottom to the top of the organization as you begin your AI journey,” she says. “If employees know what to look for, they’ll know how they could delegate their least favorite tasks to a computer. That means innovation opportunities that drive a lot of efficiency gains, coming from the people who are doing the work.” Dig Deeper: Read the entire 2021 KPMG study, “Thriving in an AI World.” Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].
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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"Juniper projects voice assistants will play a bigger role in shopping | VentureBeat"
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"https://venturebeat.com/2021/08/03/juniper-projects-voice-assistants-will-play-a-bigger-role-in-shopping"
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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 Juniper projects voice assistants will play a bigger role in shopping 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.
Customers are increasingly making purchases through voice assistants — to the tune of $19.4 billion by 2023, according to projections. A new study from Juniper Research finds that ecommerce transactions will increase from $4.6 billion this year as voice assistant devices with screens improve the efficiency of the checkout process.
“[G]rowing the size and accessibility of the content domain libraries will be critical to increasing the number of transactions processed by voice assistant services. In turn, this will increase the value proposition of voice commerce to third-party retailers and generate new revenue streams for voice assistant platforms,” the report reads.
Juniper expects that the global install base of smart speakers will rise by over 50% between 2021 and 2023. Similarly, Statista expects global smart speaker revenue will see an uptick, reaching $35.5 billion by 2025. While smartphone-based assistants will remain dominant in terms of usage, the rising number of standalone smart speakers means the potential for commerce will grow — supporting the adoption of new monetization strategies.
In 2020, 23 million consumers used voice assistants to make purchases, according to a survey by the publication PYMNTS and Visa. That was a 45% increase from 2018 and an 8% gain since 2019.
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 pandemic and desire for contactless shopping options have fueled a rise in ecommerce and accelerated the shift toward omnichannel retail experiences.
According to Opus Research, retailers are increasingly installing voice-enabled kiosks and contact centers. And 73% of retail respondents to the survey considered search via voice to be a top benefit of voice assistants.
According to a study by Gartner , brands that redesign their websites to support voice search stand to increase their digital commerce revenue by 30%. Forty-one percent of consumers would prefer a voice assistant over a website or app while shopping online, Capgemini reports.
And the Opus study found that online shoppers who use voice spend $136 more on average than those who shop solely online.
Barriers to adoption Some experts disagree with the findings in the Juniper report, disputing the notion that voice will become a major ecommerce revenue stream. A recent eMarketer study , for example, found that more than half of U.S. adults have never shopped for goods via voice and have no interest in trying voice shopping.
Even the Juniper report encourages leaders in the voice assistant space — particularly Amazon, Apple, and Google — to open up their ecommerce services to third-party retailers, in addition to leveraging their own ecosystems. A key hurdle to attracting third-party retailers is the absence of a screen in many smart speakers, according to Juniper, which limits the contextual information that can be presented to users.
The report also recommends implementing omnichannel retail strategies, where users’ interactions are managed across multiple channels, to enable retailers to display detailed info on a product. “Users will generally use voice assistants to initially explore a product, before completing the purchase via a device with a screen,” Meike Escherich, one of the report’s coauthors, said in a statement. “Voice assistant platforms must ensure that the user experience is so seamless that transactions are carried out via these platforms, rather than requiring additional devices.” 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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"Some FDA-approved AI medical devices are not 'adequately' evaluated, Stanford study says | VentureBeat"
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"https://venturebeat.com/2021/04/12/some-fda-approved-ai-medical-devices-are-not-adequately-evaluated-stanford-study-says"
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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 Some FDA-approved AI medical devices are not ‘adequately’ evaluated, Stanford study says 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.
Some AI-powered medical devices approved by the U.S. Food and Drug Administration (FDA) are vulnerable to data shifts and bias against underrepresented patients. That’s according to a Stanford study published in Nature Medicine last week, which found that even as AI becomes embedded in more medical devices — the FDA approved over 65 AI devices last year — the accuracy of these algorithms isn’t necessarily being rigorously studied.
Although the academic community has begun developing guidelines for AI clinical trials, there aren’t established practices for evaluating commercial algorithms. In the U.S., the FDA is responsible for approving AI-powered medical devices, and the agency regularly releases information on these devices including performance data.
The coauthors of the Stanford research created a database of FDA-approved medical AI devices and analyzed how each was tested before it gained approval. Almost all of the AI-powered devices — 126 out of 130 — approved by the FDA between January 2015 and December 2020 underwent only retrospective studies at their submission, according to the researchers. And none of the 54 approved high-risk devices were evaluated by prospective studies, meaning test data was collected before the devices were approved rather than concurrent with their deployment.
The coauthors argue that prospective studies are necessary, particularly for AI medical devices, because in-the-field usage can deviate from the intended use. For example, most computer-aided diagnostic devices are designed to be decision-support tools rather than primary diagnostic tools. A prospective study might reveal that clinicians are misusing a device for diagnosis, leading to outcomes that differ from what would be expected.
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’s evidence to suggest that these deviations can lead to errors. Tracking by the Pennsylvania Patient Safety Authority in Harrisburg found that from January 2016 to December 2017, EHR systems were responsible for 775 problems during laboratory testing in the state, with human-computer interactions responsible for 54.7% of events and the remaining 45.3% caused by a computer. Furthermore, a draft U.S. government report issued in 2018 found that clinicians not uncommonly miss alerts — some AI-informed — ranging from minor issues about drug interactions to those that pose considerable risks.
The Stanford researchers also found a lack of patient diversity in the tests conducted on FDA-approved devices. Among the 130 devices, 93 didn’t undergo a multisite assessment, while 4 were tested at only one site and 8 devices in only two sites. And the reports for 59 devices didn’t mention the sample size of the studies. Of the 71 device studies that had this information, the median size was 300, and just 17 device studies considered how the algorithm might perform on different patient groups.
Partly due to a reticence to release code, datasets, and techniques, much of the data used today to train AI algorithms for diagnosing diseases might perpetuate inequalities, previous studies have shown. A team of U.K. scientists found that almost all eye disease datasets come from patients in North America, Europe, and China, meaning eye disease-diagnosing algorithms are less certain to work well for racial groups from underrepresented countries. In another study , researchers from the University of Toronto, the Vector Institute, and MIT showed that widely used chest X-ray datasets encode racial, gender, and socioeconomic bias.
Beyond basic dataset challenges, models lacking sufficient peer review can encounter unforeseen roadblocks when deployed in the real world. Scientists at Harvard found that algorithms trained to recognize and classify CT scans could become biased toward scan formats from certain CT machine manufacturers. Meanwhile, a Google-published whitepaper revealed challenges in implementing an eye disease-predicting system in Thailand hospitals, including issues with scan accuracy. And studies conducted by companies like Babylon Health , a well-funded telemedicine startup that claims to be able to triage a range of diseases from text messages, have been repeatedly called into question.
The coauthors of the Stanford study argue that information about the number of sites in an evaluation must be “consistently reported” in order for clinicians, researchers, and patients to make informed judgments about the reliability of a given AI medical device. Multisite evaluations are important for understanding algorithmic bias and reliability, they say, and can help in accounting for variations in equipment, technician standards, image storage formats, demographic makeup, and disease prevalence.
“Evaluating the performance of AI devices in multiple clinical sites is important for ensuring that the algorithms perform well across representative populations,” the coauthors wrote. “Encouraging prospective studies with comparison to standard of care reduces the risk of harmful overfitting and more accurately captures true clinical outcomes. Postmarket surveillance of AI devices is also needed for understanding and measurement of unintended outcomes and biases that are not detected in prospective, multicenter trial.” 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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"FAA approves small drones in the U.S. to fly over people and at night | VentureBeat"
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"https://venturebeat.com/2020/12/29/faa-approves-small-drones-in-the-u-s-to-fly-over-people-and-at-night"
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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 FAA approves small drones in the U.S. to fly over people and at night 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.
( Reuters ) — Small drones will be allowed to fly over people and at night in the United States, the Federal Aviation Administration (FAA) said on Monday, a significant step toward drones’ use for widespread commercial deliveries.
The FAA said its long-awaited rules for the drones, also known as unmanned aerial vehicles, will address security concerns by requiring remote identification technology in most cases to enable their identification from the ground.
Previously, small drone use was limited to flights over people who were directly participating in the operation, under a covered structure, or inside a stationary vehicle — unless operators had obtained a waiver from the FAA.
The rules will take effect 60 days after publication in the federal register in January. Drone manufacturers will have 18 months to begin producing drones with Remote ID, and operators will have an additional year to provide Remote ID.
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 are other, more complicated rules that allow for operations at night and over people for larger drones in some cases.
“The new rules make way for the further integration of drones into our airspace by addressing safety and security concerns,” FAA administrator Steve Dickson said. “They get us closer to the day when we will more routinely see drone operations, such as the delivery of packages.” Companies have been racing to create drone fleets to speed deliveries.
The United States has over 1.7 million drone registrations and 203,000 FAA-certificated remote pilots.
For at-night operations, the FAA said drones must be equipped with anti-collision lights. The final rules allow operations over moving vehicles in some circumstances.
Remote ID is required for all drones weighing 0.55 lb (0.25 kg) or more and is required for smaller drones under certain circumstances, like flights over open-air assemblies.
The new rules eliminate requirements that drones be connected to the internet to transmit location data but do require that they broadcast remote ID messages via radio frequency broadcast. Without the change, drones could have been barred from use in areas without internet access.
The Association for Unmanned Vehicle Systems International said Remote ID will function as “a digital license plate for drones … that will enable more complex operations,” while operations at night and over people “are important steps toward enabling integration of drones into our national airspace.” One change since the rules were first proposed in 2019 requires that small drones not have any exposed rotating parts that could lacerate human skin.
United Parcel Service (UPS) said in October 2019 that it won the government’s first full approval to operate a drone airline.
Last year, Alphabet’s Wing, a sibling of search engine Google, was the first company to get U.S. air carrier certification for a single-pilot drone operation.
In August, Amazon’s drone service received federal approval allowing the retailer to begin testing commercial deliveries through its drone fleet.
Walmart said in September it would run a pilot project for delivery of grocery and household products through automated drones but acknowledged “it will be some time before we see millions of packages delivered via drone.” ( Reporting by David Shepardson, editing by Nick Zieminski and Howard Goller.
) 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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"Zelros raises $11 million for AI that personalizes insurance plans | VentureBeat"
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"https://venturebeat.com/2021/02/24/zelros-raises-11-million-for-ai-that-personalizes-insurance-plans"
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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 Zelros raises $11 million for AI that personalizes insurance plans 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.
Insurance tech startup Zelros today announced that it raised $11 million in series A funding led by BGV. The company plans to use the capital to scale operations across Europe and expand into North America, a spokesperson told VentureBeat.
The insurance tech market is red hot. Online platform Next Insurance , which targets small business owners with a focus on specific niches, recently raised $250 million. In March 2019, Washington, D.C.-based workers’ compensation insurer Pie Insurance brought in $45 million, just a month after CoverHound nabbed $58 million. And in June 2020, Planck raised $16 million for its AI-powered commercial risk insurance platform.
For its part, Paris-based Zelros, which was founded in 2016, employs AI to provide advisors and policyholders with advice on choosing the right coverage for their needs. The platform ingests claims, quotes, voice calls, underwriting documents, and other kinds of data via connectors to cover tasks like claim handling and facilitate the deployment and monitoring of AI pipelines in production.
Zelros taps natural language understanding technology to capture information from voice conversations between policyholders and insurance advisors or contact center representatives. The platform surfaces contextualized recommendations in real time and at the end of customer calls, storing newly detected information to enrich customer relationship management databases. Meanwhile, Zelros’ computer vision system extracts key fields from insurance-specific documents, while a separate set of algorithms prioritizes products based on business priorities like revenue, number of subscriptions, and product mix.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Zelros can run simulations and A/B tests to analyze the business impacts of new AI settings. Insurers get profiles of customers drawn from their histories in the form of a summary of data and analyses based on predictions for answers to requests. These profiles can be used to implement learning paths for advisors and to define missions and business goals to be achieved.
Zelros claims to have seen two consecutive years of triple-digit revenue growth, with a customer base that’s grown to include 15 of the leading insurance providers in Europe across property and casualty and life insurance business lines. In 2020 alone, the company reviewed over 20 million policyholder-specific situations and issued over two million personalized recommendations.
“Digital transformation is fundamentally changing how businesses operate, and with insurtech funding reaching an all-time high of $7.1 billion in 2020, the insurance industry is no exception,” cofounder and CEO Christophe Bourguignat said in a press release. “At Zelros, we focus one hundred percent of our attention on developing res AI-driven technology to improve and advance the insurance industry. We’re passionate about helping insurance players transform into technology-first companies, and the support of BGV, Capgemini, and all of our investors will empower us to accomplish our mission.” Beyond BGV, new investors ISAI Cap Venture and Plug and Play and previous investors HI INOV, 42CAP, and Astorya.vc also participated in Zelros’ funding round announced today. In Q3 2021, Zelros cofounder and COO Damien Philippon will relocate to Montreal and launch the company’s North American headquarters, which will employ five employees by the end of the year.
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"How the Pandemic Changed The Way We Develop Products For the Better | VentureBeat"
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"https://venturebeat.com/2021/07/24/how-the-pandemic-changed-the-way-we-develop-products-for-the-better"
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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 the Pandemic Changed The Way We Develop Products For the Better 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 post was written by Ian Ferguson, Formlabs’ SLS Engineering Lead It was late January 2020 when we first got a call from our factory in Shenzhen, China that the government was shutting down facilities due to the spread of an unknown virus.
We were prepared for the two-week shutdown. Our supply chain had the redundancies in place to enable our factory to go offline without impacting the delivery of our printers and resins to our customers. We were ready for those two weeks, but no one could have been ready for that unknown virus, COVID-19, to completely shut down the world not just for weeks, but for months.
No company was in a good position to shut down operations for an unknown period of time, but it was particularly bad timing for us. Beyond merely ensuring the ongoing production of our current product lineup, we were also in the midst of bringing two new flagship printers to market, the Form 3L and Fuse 1. Under normal circumstances, this would involve a constant presence of design and manufacturing engineers at the factory working closely with the contract manufacturers to run constant tests, iron out problems, and ramp up production. We had never tried to handle this essential process remotely and, frankly, were unsure we could.
For the product teams, launching these new printers and various new resins went beyond overcoming logistical issues caused by the new normal of remote work. We were forced to rethink and re-approach the processes and workflows that we have had in place for the better part of a decade.
And that is just what we did. By digitally transforming our previously more hands-on workflows and strategies, we were able to meet our production goals and create greater efficiencies along the way. Here are a few things we’ve learned over the past year that we’ll be carrying on with long after the pandemic ends.
Digitizing our workflows Historically, we’ve been very deliberate about spending time and money to send team members to our factories to bring our products to market. It’s not easy (or cheap) to send dozens of engineers and product designers across the globe to oversee the production process.
Going virtual during the pandemic showed us that the same quality of work could be achieved over a Zoom call and in-person on the manufacturing line. By utilizing remote collaboration tools, we saved a lot of time and money from not sending engineers around the world.
Beyond just video calls and collaborative to-do lists in Asana, we also implemented 24/7 webcams at specific points on the production line to better monitor manufacturing. Engineers could call into those webcams to watch what was going on at any given time. This enabled our team to have that same hands-on approach to our work without needing to have boots on the ground in the factory.
The team also used Onshape , a cloud-based design collaboration software, to tweak designs remotely. With Onshape, sharing designs for specific parts within the context of the whole assembly was an easy and secure way to spend less time on technical drawings.
Our team also went beyond improving how we collaborate — we also digitized how we test our products. During the pandemic, Formlabs developed its own manufacturing line software to perform calibration tests. The software also uploads to the cloud all of the data that comes back from these tests. This lets Formlabs track individual printer status in real-time, find issues remotely, and debug problems without having to be there.
The calibration steps we perform are a big part of our secret sauce that makes our printers work. After analyzing this data we would regularly “secretly” solve problems by connecting to printers remotely over the internet to update code, fix files on the printer that might be wrong for some reason, or grab data that might not have made it into the cloud We had to eat our own dog food The Fuse 1 is a complex machine that requires hundreds of custom components to operate. When the supply chain crumbled, we encountered an obstacle common among our customers: we had a need for unique parts, but no way to get them manufactured.
Naturally, we turned to 3D printing to design, test, and produce these custom parts. In fact, we turned to the Fuse 1 itself. Dozens of the internal components of the Fuse 1 — including sensor covers, door handles and more — were produced on early versions of the Fuse 1.
We didn’t need a pandemic to know that 3D printing is a powerful way to prototype and ultimately produce end-use custom parts, but it did help us empathize with our customers at a new level. It also helped us integrate our own technology into our product design and manufacturing process more deeply than ever before.
Looking Ahead Nobody could have been prepared for what the past year had in store for the world. Despite the ups and downs, we were able to remain agile and focused on our mission to drive our goals forward, and ultimately, accomplish them, which included bringing our industry-leading 3D printers to market. What we learned over the past year will continue to impact and improve our product development workflows when life is back to “normal.” Perhaps the biggest lesson that we’ve learned, however, is that agility and flexibility are the hallmarks of a resilient team. Instilling these values within a company’s culture ensures that no matter what the world throws at us, we will be ready to overcome it and continue to deliver for our customers.
Formlabs is expanding access to digital fabrication, so anyone can make anything. Headquartered in Somerville, Massachusetts with offices in Germany, Japan, China, Singapore, Hungary, and North Carolina, Formlabs is the professional 3D printer of choice for engineers, designers, manufacturers, and decision makers around the globe. Formlabs products include the Form 3, Form 3B, Form 3L, and Form 3BL powered by an advanced form of stereolithography (SLA) called Low Force Stereolithography (LFS)™ 3D printing, Form Wash and Form Cure post-processing solutions, Fuse 1 SLS 3D printer, and Form Cell manufacturing solution. Through its Factory Solutions offering for industrial users, Formlabs provides the factories of tomorrow with the flexibility and versatility needed for demanding, evolving industrial applications. Formlabs also develops its own suite of high-performance materials that continue to push the boundaries for 3D printing, as well as best-in-class 3D printing software. For more information visit formlabs.com.
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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"What to expect for cybersecurity investment as we emerge from the pandemic | VentureBeat"
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"https://venturebeat.com/2021/07/18/what-to-expect-for-cybersecurity-investment-as-we-emerge-from-the-pandemic"
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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 What to expect for cybersecurity investment as we emerge from the pandemic 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.
As we emerge from the global pandemic and return to robust economic growth, the cybersecurity industry is on fire and venture capitalists are taking notice. While the industry has seen steady growth over the past decade, since 2019, industry expansion has accelerated at a breakneck pace. This is particularly true when you look at industry growth and investment in 2020 and in the first quarter of 2021. We look at what’s driving demand, dive into the life of a cybersecurity startup, examine target markets, and scan the horizon for signs of what’s in store for the future.
What’s driving interest? Major breaches are continually making headlines, and the security risks created by an increasingly remote workforce are leading companies and individuals to rapidly increase their spending on cybersecurity protections. In fact, research firm Gartner forecasts that spending on cybersecurity will surpass $150 billion in 2021, an increase of 12.4% over last year.
Where is innovation happening? This surge in interest in cybersecurity has led to a wave of startups popping up in this space, looking to take advantage of this incredible opportunity. According to a Crunchbase report , 2020 was a record-breaking year for the cybersecurity industry with six new cybersecurity unicorns. Just a few months into 2021, we have surpassed that record with nine new cybersecurity unicorns already.
That same Crunchbase report also noted a record year for investment in the cybersecurity space in 2020 with $7.8 billion invested globally, nine times greater than what the industry saw just 10 years ago. This year is already on pace to smash the record-breaking industry investment of 2020.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Case study: Dover Microsystems Dover Microsystems is a case in point, a cybersecurity startup based in Waltham, Massachusetts, led by co-founder Jothy Rosenberg.
With cybercrime estimated to cost $6 trillion in 2021 , a business will likely fall victim to ransomware every 11 seconds , according to Cybersecurity Ventures. A global car manufacturer recently spent a reported $2.1 billion on responding to the hack that occurred during the demonstration of a new vehicle. Customers don’t know what to do, so they keep adding layers of defensive software, cluttering up their software stack and slowing down their products. This makes the problem worse: software has up to 50 bugs per 1,000 lines of source code.
Dover believes that the only way to stop 95% of attacks that come over the network is in silicon, where it cannot be subverted over the network. The result is CoreGuard, a unique, disruptive solution to the failure of cybersecurity defense across all our computing systems in all vertical market segments. It integrates with leading processor architectures to monitor every instruction executed to ensure that it complies with a defined set of security, safety, and privacy rules. If an instruction violates a rule, CoreGuard stops it from executing and notifies the host processor in real-time of the exact offending line in the source code that was exploited.
While formed more than five years ago, Dover leveraged lean capital to develop a minimum viable product, sell multiple proofs of concept, and then begin commercial shipment. Looking forward, Dover intends to sell into the B2B as well as the B2G spaces, which are markets that are forecasted to see significant growth in the coming years.
Demand triggers for the cybersecurity market What is leading investors to pour money into the cybersecurity industry? There is an increase in demand for cybersecurity products driven by several factors.
One of the major factors is today’s remote workforce. The pandemic forced companies to pivot as employees worked from home, a trend that does not look to be going away anytime soon. With a remote workforce and sensitive data moving through the cloud, there are serious security concerns. This has led to more cloud security startups looking to provide solutions to companies seeking ways to protect their data. Gartner research showed 41% growth in end user spending on cloud security between 2020 and 2021.
Companies are also handling more data than ever before, making them more attractive to hackers looking to steal that data or hold it for ransom. We are seeing an alarming number of data breaches and ransomware attacks facing U.S. companies. According to Risk Based Security, “the total number of records compromised in 2020 exceeded 37 billion, a 141% increase compared to 2019 and by far the most records exposed in a single year since we have been reporting on data breach activity.” Already in 2021, we have seen high-profile breaches and ransomware attacks impacting the D.C. police department, the Colonial Pipeline, and meat producer JBS, and there are surely many more to come in the second half of the year.
Scanning the cybersecurity horizon These factors have created an ideal environment for cybersecurity startups looking to offer their products, services and solutions to companies and individuals demanding greater protection. Because the demand is only increasing, investment in this area is also on the rise. The Crunchbase report highlighted the increase in deal value in just the past three years. In 2017, the average deal value was around $6.9 million. In 2020, that number jumped 73% to an average of $11.9 million per deal. This shows a greater appetite for investment in this sector that is sure to keep growing.
With 2021 already poised to outpace record-breaking 2020 in cybersecurity spending and investment, this industry will be one to continue to watch long-term.
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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"Hybrid multiclouds promise easier upgrades, but threaten data risk | VentureBeat"
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"https://venturebeat.com/2021/05/13/hybrid-multi-clouds-promise-easier-upgrades-but-threaten-data-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 Hybrid multiclouds promise easier upgrades, but threaten data 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.
Enterprises see hybrid multicloud as a promising path to new customers and digital transformation — and as a quick on-ramp to rejuvenating IT and driving new revenue models. But many enterprises err badly as they migrate decades-old legacy systems to public, private, and community clouds, accidentally allowing bad actors access to their company’s most valuable data.
Marketing claims promise enterprises they can continue to get security and value out of datacenters if they choose hybrid cloud as their future. For many enterprises, the opposite is true. Hybrid multicloud brings greater risk to data in transit and at rest, opening enterprises to more cyber threats and malicious activity from bad actors than they ever encountered before.
Getting hybrid cloud security right is hard By definition, a hybrid cloud is an IT architecture comprising legacy IT systems integrated with public, private, and community-based cloud platforms and services. Gartner defines hybrid cloud computing as policy-based and coordinated service provisioning, use, and management across a mixture of internal and external cloud services. Hybrid clouds’ simple definition conflicts with the complexity of making them work securely and at scale.
What makes hybrid multicloud so challenging to get right from a security standpoint is how dependent it is on training people and keeping them current on new integration and security techniques. The more manual the hybrid cloud integration process, the easier it is to make an error and expose applications, network segments, storage, and applications.
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 pervasive are human-based errors in configuring multiclouds ? Research group Gartner predicts this year that 50 percent of enterprises will unknowingly and mistakenly expose some applications, network segments, storage, and APIs directly to the public, up from 25% in 2018. By 2023, nearly all (99%) of cloud security failures will be tracked back to manual controls not being set correctly.
What defines the dark side of hybrid multiclouds? The promises of hybrid multiclouds need to come with a disclaimer: Your results may vary depending on how deep your team’s expertise is on multiple platforms extending into compliance and governance. Hybrid multiclouds promise to provide the following under ideal conditions that are rarely achieved in organizations today: Integrate diverse cloud platforms and infrastructure across multiple vendors with little to no degradation in data latency, vendor lock-in, or security lapses.
Autonomously move workloads and data at scale between legacy, third-party legacy on-premises systems, and the public cloud.
Support and securely scale for edge computing environments as enterprise spending is surging in this area today. Bain’s analysis of IDC data anticipates spending on edge computing infrastructure and environments will grow at a 35% CAGR between 2019 and 2024, compared with approximately 2.5% growth of nonpublic cloud spending.
Enterprises need to work their way through the dark side of hybrid multiclouds to see any benefits. While the challenges are unique to the specific enterprise’s legacy systems, previous results in public, private, and hybrid cloud pilots and proofs-of-concept are a reliable predictor of future results.
The roots of risk In reality, hybrid multicloud platforms are among the riskiest and most challenging to get right of any IT infrastructure. According to Bain’s Technology Report 2020:Taming the Flux , the average organization relies on 53 different cloud platform services that go beyond basic computing and storage.
Bain’s study found that CIOs say the greater the complexity of multicloud configurations, the greater the security and downtime risks their entire IT infrastructures are exposed to. CIOs also told Bain their organizations are struggling to develop, hire, and retain the talent needed to securely operate one cloud infrastructure at scale, let alone several.
That heads a list of indicators that innovative enterprises are seeing as they work to improve their hybrid multicloud security. The indicators include: Lack of ongoing training and recertification. Such training helps to reduce the number and severity of hybrid cloud misconfigurations. As the leading cause of hybrid cloud breaches today, it’s surprising more CIOs aren’t defending against misconfigurations by paying for their teams to all get certified. Each public cloud platform provider has a thriving sub-industry of partners that automate configuration options and audits. Many can catch incorrect configurations by constantly scanning hybrid cloud configurations for errors and inconsistencies. Automating configuration checking is a start, but a CIO needs a team to keep these optimized scanning and audit tools current while overseeing them for accuracy. Automated checkers aren’t strong at validating unprotected endpoints, for example.
Automation efforts often overlook key factors. It is necessary to address inconsistent, often incomplete controls and monitoring across legacy IT systems. That is accompanied by inconsistency in monitoring and securing public, private, and community cloud platforms.
Lack of clarity on who owns what part of a multicloud configuration continues because IT and the line of the business debate who will pay for it. Addressing the lack of clarity regarding each cloud instance is the responsibility of a business IT leader or the core IT team. Line of business leaders’ budgets are charged for hybrid multicloud integration projects that digitally transform a business model. But data and IT governance, security, and reliability can fall on the line between the business and IT, creating confusion — and opening the door for bad actors searching for gaps in hybrid cloud configurations.
Accountability lines between cloud providers and customers get blurred as well. With cloud providers taking on more responsibility for managing all aspects of hardware and software co-hosted in their datacenters, there’s more confusion than ever on who covers the gaps in system and cybersecurity configurations.
The overhyped benefits of cloud elasticity and paying-as-you-go for computing resources can obscure the overall picture. Important details too often get buried in complex, intricate cloud usage reporting invoices from public cloud providers. It’s easy to get lost in these lengthy reports and overlook essential cloud security options. Later in this series of articles, I’ll address the limitations and misconceptions of the Shared Responsibility Model.
Mind the multicloud gaps Lack of compliance and governance are the most costly errors enterprises are making today when it comes to hybrid multicloud deployments. Not only are they paying the fines for lack of compliance, but they’re also losing customers forever when their data is compromised in a breach. Gaps between legacy systems and public, private, and community clouds that provide bad actors an open door to exfiltrate customer data violate the California CCPA laws and the EU’s GDPR laws.
Enterprises can achieve more real-time visibility and control across all cloud instances by standardizing on a small series of monitoring tools. That means trimming back, to better ensure assorted tools don’t conflict with each other.
How quickly any given business can keep reinventing itself and digitally transform how it serves customers depends on how quickly IT can adapt. Leaders must understand that hybrid multicloud is an important strategy, but the hype doesn’t match the reality. Too many organizations are leaving wide gaps between cloud platforms.
The recent high-profile SolarWinds breach exposed hybrid multicloud’s weaknesses and showed the need for Zero Trust frameworks. In the next article in this series, I’ll look at the lessons learned from the SolarWinds hack and how greater understanding can help strengthen compliance and governance of any hybrid cloud initiative.
Machine learning and terrain analytics show promising potential to identify and troubleshoot hybrid multicloud security gaps as well, and this too will be explored in the upcoming series.
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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"Zapier: Automation helped small businesses survive the pandemic | VentureBeat"
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"https://venturebeat.com/2021/05/03/zapier-automation-helped-small-businesses-survive-the-pandemic"
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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 Zapier: Automation helped small businesses survive the pandemic 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 past year was marked with a lot of uncertainty for businesses. A new study from Zapier , which allows users to automate tasks for web applications, found that 63% of small businesses said automation helped them survive COVID-19.
Above: The findings from the 2021 Zapier State of Business Automation Report.
With a specific focus on the small and medium-sized business community — more than 3,000 partners apps on its platform for small businesses — key findings include: Despite the common narrative of automation taking over jobs, it isn’t a competitor against humans. The future of automation is about how technology can support humans, especially in the small and medium-sized businesses. Software automation continues to grow and it enables workers to be more efficient, which gives humans time back to do things only people can do.
Automation is essential software for small and medium-sized businesses.
Sixty-three percent of SMBs say automation allowed their company to quickly pivot as a result of the pandemic — whether it was bringing their goods and services online or changing their business model completely. That benefit isn’t likely to shrink, either: even as things get back to “ normal ,” we’ll live much more of our lives online. Small businesses are already using technology to prepare for that reality: 66% say automation is now essential for running their 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! Why is automation essential? SMBs rely on software to perform specific functions, like capturing leads from Facebook Lead Ads. Rather than spend time manually sending data from one place to another, SMBs use automation to connect different software and create scalable systems and processes.
While automation helps SMBs eliminate repetitive, everyday tasks, there are bigger benefits as well: 88% of SMBs say automation allows them to compete with larger companies by allowing them to move faster, close leads quickly, spend less time on busywork, reduce errors, and offer better customer service.
By identifying repetitive tasks that take the most time, SMBs can develop a strategy to automate manual and repetitive processes and free up time for more creative or strategic tasks. In fact, nearly 70% of SMB employees say using automation software has helped them be more productive at work.
The future of automation is already here. Many SMBs are using automation to increase worker productivity and happiness, create efficient and scalable systems, and compete with larger businesses.
Zapier surveyed 2,000 U.S. knowledge workers from small and medium businesses [fewer than 250 total employees] on whether or not workflow automation tools are being used at their company. This survey was completed online in March 2021 and responses were random, voluntary, and completely anonymous.
Read more in Zapier’s full report 2021 Zapier State of Business Automation Report 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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"Productivity API and automation platform Nylas raises $120 million | VentureBeat"
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"https://venturebeat.com/2021/06/17/productivity-api-and-automation-platform-nylas-raises-120-million"
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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 Productivity API and automation platform Nylas raises $120 million Share on Facebook Share on X Share on LinkedIn Nylas in operation (illustration) 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.
Nylas , an application programming interface (API) platform that helps businesses and developers integrate key productivity features — such as email and calendar functionality — into their apps, has raised $120 million in a series C round of funding led by Tiger Global Management.
The raise comes amid a booming API economy , as organizations are using APIs to improve their efficiency by tapping into the wider digital ecosystem. Rather than consuming internal resources developing their own infrastructure from scratch, businesses can use purpose-built APIs created by third parties to bring login authentication to their finance apps, privacy to their data-hungry health care apps , or customer service support to their ecommerce apps.
Productivity Nylas offers APIs spanning email, calendar, contacts, and scheduling and includes integrations with all the major services from the likes of Microsoft, Google, AOL, Yahoo, and IMAP. So rather than having to develop multiple integrations for all these different services, a resource-intensive endeavor that requires ongoing maintenance and management, a CRM software maker (for example) can use Nylas’ email API to bypass all these hurdles and let sales teams send and receive emails from any provider directly through the CRM.
Among its client base is cloud communications juggernaut Dialpad , which uses Nylas to offer end users full create, read, update, and delete (CRUD) functionality spanning email, calendar, and contacts. This ensures all data is constantly synchronized in both directions between Dialpad and the service provider accounts (e.g. Google or Microsoft).
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Above: Dialpad uses Nylas’s API Automation for the people In the past year, Nylas has launched a new neural API.
This stems from its first acquisition last March , when it snapped up June.ai , an AI-enabled email client that automatically organizes a user’s inbox. As part of Nylas, the technology helps aggregate, analyze, and process vast swathes of data gleaned from emails, calendars, and contacts, taking unstructured data and transforming it into something more usable.
“The Nylas neural API takes unstructured data and uses intelligence and machine-learning algorithms to trigger automations that can save the average person one full day’s worth of work each week,” Nylas cofounder and CEO Gleb Polyakov told VentureBeat.
For example, “categorizer” distinguishes machine-to-human from human-to-human messages while also detecting bounced emails and automated out-of-office responses.
“Customers use this feature to automatically pause sales cadences based on detecting out-of-office emails and restart them based on the dates found in the email,” Polyakov explained. “Customers also use the categorizer API to trigger business-specific automations — for instance, ecommerce customers use a categorizer to detect a shipping confirmation-related email and send custom notifications to their customers.” Elsewhere, the neural API can extrapolate key data found in email signatures to automatically update address books or even trigger workflows based on a job title change, while sentiment analytics helps users figure out whether an email was positive, negative, or neutral. Meanwhile, a “clean conversations” feature helps filter out “non-essential” content from messages to get to the nuts and bolts of a conversation.
“Emails are often polluted and hard to read through since they contain past responses, signatures, legal language, and so on,” Polyakov said “The clean conversations API makes it easy for developers to access the most important part of any email — the body — via a single API call.” Above: Nylas: Clean conversations API Nylas had previously raised around $55 million, almost half of which came via its series B round last June.
Its latest investment included a host of notable old and new investors, including Slack (via Slack Fund), Stripe cofounders Patrick and John Collison, Nest founder Tony Fadell, Klarna CEO Sebastian Siemiatkowski, Citi Ventures, 8VC, Round13 Capital, and Owl Rock Capital.
With another $120 million in the bank, Nylas said it plans to continue investing in its product, including boosting security, components, and automation — such as AI and ML, sentiment analysis, natural language processing (NLP), and more.
“There’s a lot of work a developer has to do to build modern software — everything from integrating backend APIs, mining and filtering data, orchestrating workflows, and building rich front-end user experiences,” Polyakov said. “We’re developing products and services across all of these areas to help developers ship value faster to their end 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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"Phantom Covert Ops -- How nDreams is making a covert stealth game in a kayak | VentureBeat"
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"https://venturebeat.com/2019/05/21/phantom-covert-ops-how-ndreams-is-making-a-covert-stealth-game-in-a-kayak"
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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 Phantom Covert Ops — How nDreams is making a covert stealth game in a kayak 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.
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Who makes a covert operations stealth game where you’re in a kayak ? It sounds like a dumb idea, but I played a preview of Phantom Covert Ops recently on the Oculus Quest virtual reality headset.
And it was fun.
nDreams is making the title for Oculus Studios. Coming out this year, the game turns the player into an elite covert operative who sneaks into a flooded Russian military compound. You have to sneak around the hostile wetlands, take out your targets with silencer, and disable enemy installations like a satellite tower. If you’re caught, you’re pretty much dead.
I spoke with Lewis Brundish, the game director at nDreams, about how the studio tackled the task of making this unusual stealth game for the Oculus Quest and Oculus Rift VR headsets.
The game is coming out later this year. Here’s an edited transcript of our interview.
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! Above: Phantom Covert Ops game director Lewis Brundish.
GamesBeat: How long have you been working on Phantom Covert Ops? Lewis Brundish: The concept started probably around 18 months ago, maybe two years. I forget the exact dates because it’s something that came together quite naturally out of prototyping and various ideas we had.
GamesBeat: What have you done before in VR? Brundish: We’ve been working in VR for five years. As soon as the Oculus DK1 came into the studio, everyone knew that’s what they wanted to do. We saw the potential there. We’ve done a bunch of VR games. We’ve done the “Perfect” games, like Perfect Beach, those experiences. We did The Assembly, which was a narrative-driven game a few years back. Shooty Fruity is probably the latest one. We published Bloody Zombies. We’ve been around in VR for a long time. This is our fifth game, something like that.
GamesBeat: What led you into this stealth-military space? Brundish: For a while, we’ve been wanting to make a really substantial VR game, something you can sit down and play for extended periods of time and lose yourself in. We came up with the idea of this military theme and this kayak movement as a way of giving you a game where you could play comfortably for extended periods of time.
We went through loads of iterations on how the boat would work. We went through boats with motors and two-person kayaks with other people in them. We were just prototyping different ideas. We didn’t know if any of them would work. But as soon as we got near the version you played today, we knew it would work. Then the stealth game wrapper fell very neatly around that.
Above: You can plant bombs in Phantom Covert Ops GamesBeat: I don’t think anyone has done kayaking in a stealth game before.
Brundish: Yeah, it’s unique. It’s quite a unique selling point.
GamesBeat: You could turn it into a sports game later.
Brundish: Right, we’ve got all the tech. It’s one of those things—I hope you can attest from playing the demo, but you hear it and think, “Oh, that’s an interesting idea.” But when you play it – this is how we felt in the studio – it just works. It immediately works. The two things complement each other really well. When you pick the right setting, like this flooded Cold War naval base in the early ‘90s, once the environment is built so it’s waterlogged and there are channels everywhere and the ground is a bad place to be, it just clicks immediately with the boat. The stealth mechanics of hiding in the reeds, timing your movement to get past lights, going underneath the guards, it all clicks really well.
GamesBeat: I shot out a light to get to the radio tower.
Brundish: Yeah, that’s the thing. It’s great to have more people playing the game, because the way we designed this, we tried to go in at the start with—everything has to work exactly as the player would expect. That’s not just—the movement has to really tactile and one-to-one and working as you’d think. But then the environment—if you can see anything in the environment that you feel like you should be able to do, then we want to make sure you can do it.
It’s cool to hear that you discovered you can shoot out lights. You can shoot fire extinguishers as well, to create distractions. You can throw your ammo clips in the water to distract guards. You can grab things in the environment and pull them, move them. Anything that players want to do, we try to make sure that can happen. In early playtests of the game we had our C4 remote explosives that you could place and detonate. We found out that some people were trying to shoot them to detonate them instead of using the trigger. So, okay, we’ll make that work. The next person tried throwing them in the air and shooting them in midair. That wasn’t working, so we had to make that work.
That’s how we’ve designed it. It’s great getting the feedback from you guys playing today. We’ve not had any yet, but if anyone tells us about something that they wanted to do and couldn’t, we’ll try to make sure that happens.
Above: Phantom Covert Ops GamesBeat: How much more work do you have to do? Brundish: We’re targeting a release this year. But there’s still a lot to do. We don’t have a specific number of hours we’re shooting for. We want it to be narrative-driven. The game is going to take place over the course of a single night in this one location. You’ll get there just as the sun is going down, and then the events of the story will play out throughout the night. The sun is rising at the end, just as you reach the finale.
We also want to make sure the missions are full of replay value. You saw at the end of the demo that you get these medals. We award medals for pacifism and speed, but we also award medals for killing everyone. We want to have real, meaningful replay value, where players want to come back trying different equipment, trying different approaches, and trying to unlock everything.
GamesBeat: How do the guards sense you, or otherwise figure out there’s somebody there? Does the sound of the water alert them? Brundish: Because we’re quite early in development right now, a lot of that stuff is still coming online. But things that will play into that are how close you are to lights, how illuminated you are, and also how much noise you’re making. Possibly even how much movement you’re creating. These are all things we’re working on.
Above: Phantom Covert Ops GamesBeat: Is stealth VR becoming its own genre, do you think? Brundish: We’ve found that, just like with horror—when horror games came to VR it was a real step up for that genre. The level of fear and visceral reaction that people felt was something they’d never experienced on other platforms. We really believe that stealth is the next big genre that can be lifted up in that way.
I love stealth games, and when you play a stealth game on a TV, you can understand that the situation is perilous or tense. You’re hiding in a bush while a guard is coming and you get that you’re close to being found. But you don’t feel it the way you do in VR, when you’re crouching in the reeds and trying to stay silent and there’s a torch passing right over your head, a guard standing right next to you. As soon as we hit on that we thought, “Wow, this is really cool.” We think stealth is going to be a big thing in VR.
Above: Phantom Covert Ops puts you in a stealth kayak GamesBeat: How many kinds of environments do you have? Brundish: Narratively the game all takes place around this flooded military installation on the coast of the Black Sea. There’s obviously quite a lot of diversity in what we can do with that. You have the forests surrounding the area. You have rocky canyon areas. You have the docks of the naval base. You have ruins, these abandoned buildings. In the demo you played today you started in the woods and went through a base. There was a shipwreck where you could paddle through the interior. There’s tons of variety, but it all comes together to form this cohesive location.
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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Join the GamesBeat community! Enjoy access to special events, private newsletters and more.
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"Osso VR raises $27M to train surgeons via simulations | VentureBeat"
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"https://venturebeat.com/2021/07/07/osso-vr-raises-27m-to-train-medical-professionals-via-surgery-simulations"
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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 Osso VR raises $27M to train surgeons via simulations Share on Facebook Share on X Share on LinkedIn Trainees can operate on patients in virtual reality with Osso VR.
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Osso VR has raised $27 million to train doctors and other medical professionals using virtual reality surgery simulations.
The San Francisco-based Osso VR said its numerous hospitals have validated its virtual reality surgical training and assessment platform as a training tool for surgeons.
GSR Ventures led the second institutional round of funding, with participation from SignalFire, Kaiser Permanente Ventures, OCA Ventures, Scrum Ventures, Leslie Ventures, and Anorak Ventures.
Osso VR’s surgical training technology provides on-demand, educational experiences that are effective, repeatable, and measurable to help surgeons reach proficiency with emerging surgical techniques and technologies, the company said.
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! Pandemic growth Above: Surgeons use hand controllers in simulated surgery in VR.
Osso VR grew rapidly during 2020 to meet the increased demand in virtual training thanks to the pandemic. The company says it works with industry leaders like Johnson & Johnson, Stryker, and Smith & Nephew.
As part of the recent growth, the company recently expanded into additional specialties, such as orthopedics, endoscopy, and interventional procedures. Osso VR has more than 120 modules and 10 specialties in its surgical training library.
Sunny Kumar, a partner at GSR Ventures, said in a statement that Osso VR is poised to transform how surgeons are trained on new devices and surgical procedures. He said the Osso platform’s level of immersion provides an experience that mirrors the operating room in a manner more efficient, accessible, and effective than any surgical training platform that’s come before.
Osso VR’s platform has high visual fidelity to ensure that every aspect of surgery, from anatomical detail to the OR environment, enhances the training experience. Osso VR employs the world’s largest medical illustration team and alums from Industrial Light & Magic, Electronic Arts, Microsoft, and Apple.
Lots of training Above: A surgical team in Osso VR.
With nearly 30,000 training sessions completed on the platform, working out to an average of 22,000 minutes of training a month, Osso claims its VR’s platform is proven to significantly affect surgical performance. In two recent randomized peer-reviewed studies, surgeons training with Osso VR showed anywhere from a 230% to a 306% improvement in overall surgical performance compared to traditional training.
Osso VR is available in more than 20 countries, and all top five orthopedic medical device companies are using Osso VR as their VR training partner. The platform is available in multiple languages including English, Japanese, Spanish, German, and French. More than 20 global hospital residency programs use it, including Brown University, Hospital for Special Surgery, Johns Hopkins University, and Rush University.
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.
Join the GamesBeat community! Enjoy access to special events, private newsletters and more.
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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"Entrepreneur Corner Roundup: The state of the VC world and tech’s human problem | VentureBeat"
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"https://venturebeat.com/2009/11/21/entrepreneur-corner-roundup-the-state-of-the-vc-world-and-techs-human-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 Entrepreneur Corner Roundup: The state of the VC world and tech’s human problem Chris Morris Share on Facebook Share on X Share on LinkedIn Here’s the latest from VentureBeat’s Entrepreneur Corner: 5 ways VC firms can stop shooting themselves in the foot – Venture Capital firms drill the need to create basic credibility into the companies they invest in – but often fail to take their own advice. Laura Grimmer, CEO of Articulate Communications (which works with VC firms), lists five things they could do to build a better pipeline of prospective portfolio companies.
After VC cash? Show ‘em what you’ve learned – When the time came for Cafepress to seek its second round of funding, the company went about it in a slightly different way. Serial entrepreneur Steve Blank, a longtime board member of the company, offers a look at how they did it – and what the reaction was from investors.
Boston Millennia’s Callow on the state of the VC industry – Dana Callow has been watching the Venture Capital business morph and change for years. In this Q&A, the managing partner of Boston Millennia Partners discusses the VC shakeout, fields that have him excited and what entrepreneurs seeking capital should be doing.
Finding a buyer for your start-up – It’s hard enough to walk away from the business you built from scratch. It’s even harder to find an ideal buyer. John Ovrom founder and CEO of Exit and Answers, examines the two most common ways people begin the process when they decide to sell.
Tech mishaps and the human problem – Engineers might cringe when they hear it, but every tech problem is tied to a human problem. Serial entrepreneur Eric Ries, in this lecture given at Stanford University, demonstrates how to narrow things down and learn the root cause.
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.
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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"AI leaders talk intersectionality, microaggressions, and more at Transform Women in AI Breakfast | VentureBeat"
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"https://venturebeat.com/2021/07/12/ai-leaders-talk-intersectionality-microaggressions-and-more-at-transform-women-in-ai-breakfast"
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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 leaders talk intersectionality, microaggressions, and more at Transform Women in AI Breakfast 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.
At VentureBeat’s third annual Women in AI Breakfast at Transform 2021, leaders in AI and machine learning across industries came together to discuss some of the most urgent questions in the tech sector today, including what responsible AI and engineering means, and the roles and responsibilities of corporates, academia, governments, and society as a whole in getting more diverse voices into the tech sector, and more.
This year the breakfast, presented by Capital One, was moderated by Noelle Silver, the founder of Women in AI.
The breakfast opened with a talk about what inclusive engineering means for the tech sector — and why it can’t just be the responsibility of those who are underrepresented.
Diversity must encapsulate diversity of race and ethnicity and religion, but also economic diversity, diversity of experience, and diversity of education, said Teuta Mercado, director of the responsible AI program at Capital One. And inclusive engineering means ensuring that all voices are represented. Many corporations are starting to do the outreach necessary to bring more diversity into this field with recruiting efforts and imperatives, she noted.
“At Capital One, inclusive engineering means ensuring that our products and our services really reflect our customer base, and are accessible to all,” said Mercado. “Banking customers represent all facets of society, and everyone needs credit services, so it’s really important that we have diverse teams that are working on and building AI and machine learning — so that we can build for all our customers, and not just for some.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Kathy Baxter, principal architect of ethical AI practice at Salesforce, has a background in psychology and human factors engineering, noting that she’s benefited from the strong mix of genders seen in psychology and user experience research.
“So, when moving into AI, I’ve been able to leverage those different experiences of working across roles,” she said. “Seeking out people with a large variety of expertise and disciplines to contribute and participate in our discussions of what it means to create fair and equitable AI and identifying unintended consequences of AI systems.” Tiffany Deng, who leads the program management team for Responsible AI at Google, explained that her time in the U.S. Army as an intelligence officer prepared her for what she does today: bringing different voices to the table in order to find solutions.
“That is what inclusive engineering is about — it’s about giving voice to the underrepresented, and ensuring not only that their voice is heard, but it’s acted upon,” Dang said. “And then thinking about going into communities and understanding how a problem may present itself differently in that subtle context, as opposed to what we already think or what we’ve already seen.” “At the World Economic Forum, we think about inclusive engineering globally,” said Kay Firsh Butterfield, head of AI and machine learning, and executive committee member of the World Economic Forum.
Companies need to have multi-stakeholder teams so when they’re thinking about policy, that should include academics, nonprofits, governments, and businesses in the room — but that’s not enough, she said.
“We’ve been doing some work with a number of the big tech companies to ask what does responsible use of technology actually look like,” she said. “One of the things we know is that you need diverse product teams. We should all be at the table, with different backgrounds, so that our products use good, non-biased AI.” When talking about increasing the diversity of teams, it comes back to the pipeline issue, or encouraging people of marginalized backgrounds or underrepresented minorities to participate, Baxter added.
“I don’t want us putting all of the onus on women and other minorities to have to push their way into the door, and I feel like that’s what often comes of these discussions,” she said. “Instead we need to look within our own companies, when we are hiring. Really taking significant time and effort to ensure we have a diversity of sources.” That means not hiring the very first person who meets the job criteria, but continuing to talk to a wide range of people who might be interested in these jobs — which takes time and effort, but needs to be done. And then, once individuals are in the companies, it’s making sure that the environment is actually inclusive.
“Too often, there are the micro aggressions and toxicities that not only impact an individual’s ability to participate in these conversations, but also to keep them long-term, so that we lose the contributions that they could have given to the company,” Baxter said.
“There are so many different perspectives around every single day that we’ve always tried to tap into, in order to come to a better solution for our users,” Deng said. “A thing that really keeps me going is this idea of intersectionality.” We all have different facets, and identify in very different ways, and companies need to continue to build upon that in truly inclusive ways for these different communities, to ensure that all voices brought into the fold are heard, she said.
“It’s not enough just to have the women. We have to have men who are in positions of power, also supporters, who also understand that there is a role in their companies for us — and that they actually begin to do much better if they have diversity within their country, their cabinet, or within the C-suite,” Firth Butterfield said.
So many have struggled against bias in their field, Mercado said, but many things have kept her going.
“One, the network: just surrounding yourself with women and men, people who care about what they’re doing, who are passionate about ethical AI or passionate about just doing what’s right for our customers for people who are using the products,” she said. “And then the biggest thing for me is working for a company that has a culture of empowerment, allowing you to do the right thing.” Diversity and inclusion in AI, the erasure of bias in machine learning algorithms, and inclusive engineering is an issue that directly impacts the bottom line, but it’s so much bigger than that, said Deng.
“Me being a Black woman, I think about my children and how can I make the world better for them, and how can I make the world safer for them,” she said. “AI is omnipresent; it’s a part of how we go to school, how we district, how things and services are distributed in our communities. And so I want to make that better. This is really an inflection point, and a really great opportunity for us to have deep impact, in all those different types in places in society.” Don’t miss the full discussion , from how bias has impacted women’s careers in male-dominated fields to how men often miscalculate what women can bring to the table, to how everyone must continue pushing traditional boundaries of these sectors, building truly inclusive community and ensuring that all perspectives are considered at every moment of the design process, and more.
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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"Capital One exec shares why building diverse teams in AI and finance is so crucial | VentureBeat"
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"https://venturebeat.com/2021/07/29/capital-one-exec-shares-why-building-diverse-teams-in-ai-and-finance-is-so-crucial"
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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 Capital One exec shares why building diverse teams in AI and finance is so crucial Share on Facebook Share on X Share on LinkedIn With the conclusion of Transform 2021 , we were very glad to see that our sessions addressing diversity, equity, and inclusion initiatives in AI were among the most well-attended. Our third Women in AI Breakfast, presented by Capital One, kicked off the entire five-day event. Teuta Mercado, Director, Responsible AI Program at Capital One, provided the opening remarks. We’re excited to share her views on diversity as part of a series showcasing Transform speakers — and the work she’s involved with to advance the responsible use of AI.
See the others in the series: Intel’s Huma Abidi, Redfin’s Bridget Frey, Salesforce’s Kathy Baxter , McAfee’s Celeste Fralick.
and ThoughtSpot’s Cindi Howson.
VB: Can you tell us about your background, and your current role at your company? TM: I lead Capital One’s Responsible AI program, and I’ve found that my background in law and as a compliance professional has allowed me to approach this field with a unique perspective. I spend a lot of time solving consumer-related issues and looking for the best outcomes for consumers. For me, inclusive engineering is really ensuring that our products and our services reflect our customer base and are accessible to all. Our customers represent all facets of society. So, when I think about diversity, I think of it in many different forms — diversity of race, ethnicity and religion, but also economic diversity and diversity of experience, education and more.
It’s important that we have diverse teams working on AI and machine learning so we can build products for all of our customers, rather than for a homogeneous group of people. Corporations are doing a lot of outreach in this space, which is fantastic. For example, Capital One is working to bring more diversity into this field, with recruiting efforts and imperatives around Diversity, Inclusion, Belonging, and Equity. There’s also a personal responsibility we all own — to encourage others, to seek other perspectives, and to create teams that are inclusive.strong>VB: Can you tell us about the diversity initiatives you’ve been involved in? TM: The Responsible AI program at Capital One seeks to advance our ethical practices and increase the unassailability of our machine learning products. We do that through robust relationships across the company and developing a base of support to empower our practitioners. The Responsible AI program seeks to advance a culture where everyone, not just those directly responsible for machine learning, but everyone involved across the board, is responsible for ethical AI.
Inclusion at Capital One begins with our Business Resource Groups, of which we have many. These groups are established as forums for employees to celebrate their shared culture, to support one another, and to encourage continuous learning to meet business objectives. Part of this network is our Women in Technology Business Resource Group, which is comprised of women and allies with the mission of accepting nothing less than an inclusive environment in technology that is approachable and welcoming to all.
Our Women in Tech group brings women and allies together to elevate the focus on women technologists. It examines how diversity is critical to obtaining and keeping the best talent and emphasizes the importance of addressing the decline in the number of women in technology throughout the pipeline.
VB: What’s kept you moving in this space? TM: There are so many things that have kept me going. One is the network of professionals and surrounding yourself with people who care about what they’re doing, who are passionate about ethical AI — that makes all the difference. A big thing for me has also been working for a company that has a culture of empowerment, a culture that not only allows but encourages you to do the right thing. That’s why it’s so crucial that companies have mechanisms that make empowerment part of everyday work life.
VB: What advice would you give to someone trying to get into this industry? TM: Your path may not be a straight shot. It may be a journey — you might start out doing one thing and you don’t know where it’s going to lead you. It is important to recognize that you bring something to the table that others may not. You are an important voice.
For example, what led me to my current role was understanding and recognizing what I really enjoyed doing in my previous roles, such as the pieces that were consumer-focused and where I could see my work in application in helping people. Having exposure and consulting on different products and platforms or models really helped me to see that I could make an impact if I continued to focus on the customer — this led me to where I am today.
My advice is to think about what you love doing, what excites you, and if you’re shifting gears and are interested in AI or machine learning, there are so many things you can do to enter this space. Engage with the professionals around you, both within and outside of your organization. Network and see what’s out there, what people are working on and what they are learning, then determine the unique skill sets that you can bring to the space.
I also recommend taking classes to understand machine learning. Even though my role at the time didn’t necessarily call for it, I knew that I was interested in machine learning and I had a passion for it, so I went outside my role and pursued training. Immersing yourself in the literature that lives in the space you’re entering will help fuel your growth mindset. There are so many opinions and perspectives — you’ll never stop learning.
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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"Sumo Logic raises $110 million to orchestrate cloud apps with AI | VentureBeat"
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"https://venturebeat.com/2019/05/08/sumo-logic-raises-110-million-to-orchestrate-cloud-apps-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 Sumo Logic raises $110 million to orchestrate cloud apps with AI Share on Facebook Share on X Share on LinkedIn One of Sumo Logic's dashboards.
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There’s an enormous market for log management software, one potentially worth $1.2 billion by 2022. It’s not all that surprising — demand for products that automate the orchestration of labyrinthine systems won’t likely decline anytime soon. And most log-tracking solutions don’t require significant infrastructural changes to implement, which no doubt appeals to DevOps teams.
That’s probably why Sumo Logic , a cloud-native, machine data analytics platform delivering continuous app intelligence, had no trouble attracting tens of millions of dollars in its latest round of funding. The company today announced that it has raised $110 million in a series G funding round led by Battery Ventures, with contributions from Tiger Global Management and Franklin Templeton. This brings the company’s total capital raised to $345 million, following a $75 million funding round in June 2017 and an $80 million round in June 2015, and it’s Sumo Logic’s largest round to date.
“We are excited to join Sumo Logic at this important moment in the industry-wide transition to cloud computing,” said Franklin Templeton’s Jonathan Curtis. “As the only cloud-native DevSecOps platform built to address the challenges of digital businesses, we believe Sumo Logic is well positioned to capture a significant share of its market.” The infusion follows a breakout year in which Sumo Logic notched over $100 million in revenue and hit the 2,000-customer mark. It’s now valued at over $1 billion, has more than 500 employees, and counts among its client base Airbnb, Pinterest, The Pokémon Co., Samsung SmartThings, Zuora, AB InBev, Adobe, Alaska Airlines, BBC Genesys, Hearst Media, Infor, Levi’s Marriott, Pitney Bowes, and USA Today.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! CEO Ramin Sayar says the funds will be used to expand Sumo Logic’s engineering, sales, and global operations teams, with an emphasis on extending the platform analytics capabilities of its various services. “Sumo Logic’s growth is driven by the shift to digital business and cloud adoption across all industries and companies of all sizes,” added Sayar. “We have proven that we are the platform of choice for not only cloud-native companies, but also enterprise companies and their cloud migration initiatives.” To that end, Sumo Logic’s software-as-a-service (SaaS) platform — which spans 150 apps and integrations — provides analytics and insights to help clients build, run, and secure apps and cloud infrastructures. A network of clustered nodes ingests hundreds of petabyes of data and more than 20 million searches each day to troubleshoot issues in real time and act on (and prevent) cyberattacks and breaches, with over 75 prebuilt security solutions.
Sumo Logic’s machine leaning algorithms alert DevOps team members or leaders if there’s a problem through customizable and shareable dashboards and reports, or through third-party tools like Zendesk, Slack, PagerDuty, Jira, and Service Now. Alternatively, teams can boil down log lines with powerful built-in functions like LogReduce and LogCompare.
Service plans are priced flexibly — Sumo Logic averages data ingested over the month, providing pricing it claims is 30% less on average than peak pricing — and it is secure. Disks have AES 256 encryption and keys are rotated daily per customer, and the company claims its platform is the only one that’s compatible with SOC 2 Type 2, PCI DSS 3.2, Privacy Shield, CSA Star, and HIPAA certifications.
“We have been tracking the Sumo Logic team for some time and admire the company’s early understanding of the massive cloud-native opportunity and the rise of new, modern application architectures,” said Battery Ventures’ Dharmesh Thakker. “The company’s fast-growing business dovetails with Battery’s larger thesis on OpenCloud — open source and cloud-native technologies working with, not against, new cloud distribution models — and we are thrilled to partner with the team to capture the significant opportunity ahead.” Speaking of capturing opportunities, Sumo Logic hasn’t been resting on its laurels. A little over a year ago, it acquired FactorChain to bolster its security toolset, and it recently launched a beta cloud security information and event management (SIEM) solution alongside Global Intelligence Service, an enterprise analytics insights tool for AI and benchmarking workloads. Sumo Logic also rolled out enhancements to better monitor and troubleshoot container architectures such as Docker, Kubernetes, and Amazon Elastic Kubernetes Service (EKS).
Sumo Logic, which was founded in 2010, is based in Redwood City, California, and backed by Accel Partners, DFJ, Greylock Partners, IVP, Sapphire Ventures, and Sequoia Capital, in addition to the participants in this latest funding round.
The company competes with Splunk, Loggly, and LogRhythm in a sector that’s predicted to be worth $11.4 billion by 2022, and one that’s predictably cutthroat. LogRhythm has raised over $100 million to date for its log management, security, and event management tools, while Harness just last month secured $60 million.
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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"As Sumo Logic readies for IPO, can it threaten Splunk? | VentureBeat"
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"https://venturebeat.com/2020/09/14/as-sumo-logic-readies-for-ipo-can-it-threaten-splunk"
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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 Guest As Sumo Logic readies for IPO, can it threaten Splunk? Share on Facebook Share on X Share on LinkedIn Sumo Logic CEO Ramin Sayar 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.
tldr: No. At least not yet ….
Sumo Logic is filing for its IPO this week. Yes, the world may be plagued by a pandemic, forest fires, chaotic weather patterns with 100-degree temperatures and snowfalls (on the same day, yes), but let us take a pause to consider the upcoming market battle between Sumo Logic and Splunk.
Mount Vesuvius may be exploding and spewing fire in our face, but on our way to our extinction, we sorely need to check our log data, our stocks, and IRR growth.
So here we go.
By now, we have accepted that we are all data hogs, collecting and saving every bit and byte, logs, trickles, and streams. ( Here is my recent post on Snowflake and its meteoric growth ). We are addicts and hoarders all in one. Both Sumo Logic and Splunk do a similar thing — gather all your data and let you analyze it, more or less.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! But Sumo Logic is all SaaS, while Splunk is still shifting to SaaS. So can the new IPO kid in town, Sumo Logic, be a strong competitor to Splunk? Considering that both are playing in a $50 billion total addressable market, according to Sumo Logic’s S1 filings, I am pretty sure there is room for more than one player.
Splunk got off to an early head start but was an “on-prem” product. “It was almost eight years ago when I first saw Splunk’s product – I was blown away” says a VP of Engineering at a $4 billion company. “It was a well designed product and we loved it.” Those of you who are old enough may recall that Splunk started as a data ingesting dinosaur, which then turned into a nimble bird, flying in these hybrid clouds. I got a laugh when I dug out its S1 offering from January 2012, where Splunk identified SaaS as a risk, saying if customers demand software that provides operational intelligence via a “Software-as-a-Service” business model, the company’s business could be adversely affected. I’m sure the founders of Sumo saw that opportunity and jumped on it at a good time. But Splunk has turned into a cloud-first company, without compromising its growth rate. It is now pulling in $1.93 billion ARR (Q2 2021). In its most recent earnings call in Sep 2020, CEO Doug Merritt shared that the annual recurring revenue (ARR) from SaaS subscriptions is growing at a healthy ~50%, and 53% of its total bookings are now via subscriptions. This increase is a non-trivial feat. To make this shift while maintaining growth is like swapping out an airplane engine while flying at 30,000 feet.
Splunk now pulls in $1 billion ARR from cybersecurity Almost 50% of Splunk’s revenue comes from its cybersecurity offerings. (The rest comes from its other business areas of IT Ops and application observability). Sumo Logic’s security business has potential, but it’s too early to tell how it will play its cards. For one, the company acquired JASK (in which I was an investor), and with it, it acquired a modern-day autonomous SOC platform and some established security leaders who came from Arcsight, Anomali, and Netflix.
Meanwhile, Splunk is gearing up, and its newest offering is Mission Control — a unified SaaS platform that was released in Q2 2020. Mission Control can help perform advanced detections and investigations and streamline security operations processes in the cloud. Yet a lot remains to be done, thanks to the ever-growing complexity of security data sources. Dhiraj Sharan, Founder of Query.ai (in which I am an investor), said “Splunk has clearly demonstrated its chops in the security marketplace, but security data continues to remain fragmented across products and platforms.” Above: Sixteen data feeds and counting – the messy world of a security analyst. Source: Query.ai A survey of more than 200 security leaders by Panaseer shows that enterprise security teams spend an average of 36% of their time manually producing reports, yet 89% of these organizations have concerns about the lack of visibility and insight into trusted data. Grunt work includes extracting, moving, cleaning, and merging data, as well as making, formatting, and presenting calculations. Security leaders are concerned that their team productivity is adversely impacted because of time spent on reporting, according to the survey.
COVID tailwinds and price wars COVID has accelerated most cloud and technology companies’ growth, and I’m no genius in saying that Splunk and Sumo will benefit over the midterm. As a SaaS offering, Sumo has a built-in advantage, and Splunk is rapidly catching up. Splunk’s Merritt remarked on the recent earnings call that the company will reach its cloud mix revenue target of 60% two years ahead of schedule.
Anticipating price wars, Splunk has shifted into a new pricing model. It’s data volume pricing caused much heartburn for its customers, and it’s now shifting to instance-based pricing. As a strategy, the company played its hand very well by skimming the cream as a first move. Now, with others stepping into the arena, and customers fatigued with cost runoffs, Splunk is moving towards instance-based pricing. This will help it retain customers in the short run. Splunk also has a significant advantage over Sumo with integrations and well over a thousand apps, entrenching it in the ecosystem in a solid way. Rapid production innovation (it can offer machine learning across its entire platform) and embracing open source offerings give it an edge in the long run.
But in the meantime, the growth of data volumes, the complexity of data types and sources, and disparate security tools will generate plenty of opportunities in the market. That is good for all the players in this sector.
Mahendra Ramsinghani is founder of Secure Octane , with investments in cybersecurity and cloud infrastructure companies like Query.ai , CyberGRX , and Accurics He is the author of two books The Business of Venture Capital and Startup Boards (co-authored with Brad Feld ).
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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"The fintech app revolution: Building apps to feed a new financial generation (webinar) | VentureBeat"
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"https://venturebeat.com/2015/08/13/the-fintech-app-revolution-building-apps-to-feed-a-new-financial-generation-webinar"
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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 Webinar The fintech app revolution: Building apps to feed a new financial generation (webinar) Share on Facebook Share on X Share on LinkedIn We’re now in the midst of a global fintech transformation. The latest trends in consumer experiences for devices such as mobile, wearables, and other online advancements are steadily increasing consumer expectations for fintech innovations. In parallel, a generational shift in users is unfolding and meeting their needs may be even more complicated than ever — yet, this will open up more and more opportunities to engage in new ways.
From smart watches to mobile devices, new technologies offer quicker and easier ways to check account balances or provide alerts and loyalty rewards. Most significantly, perhaps, the ability to make payments via wearable devices using mobile wallets such as Apple Pay means financial institutions can play a greater role in financial management and purchasing decisions.
This means that fintech startups are poised to be powerful contributors to the growth of innovation in the banking sector. In fact, many believe that the non-bank services are destined to take over where financial institutions have lost ground.
This convergence of major trends — cloud computing, open software, generational shifts, and big data/analytics — means that it is easier than ever for small, innovative technology startups to quickly turn their ideas into marketable products across multiple channels.
Don’t miss out! Register here for free.
For this webinar, we’re calling on all innovative fintech startups to join us to get essential insights on what it will take to succeed and thrive in this era of disruption. Our panelists include successful disruptors who have lived through the transformation and understand the needs of all stakeholders: the consumers driving the revolution, the banks who hold the critical consumer data, and the fintech startup community headstrong on change.
What you’ll learn: What’s driving change in fintech How to prepare for the coming wave of new technology How to prepare for Generation Z Why collaboration is the path forward to create killer fintech applications Speakers: Josh Gordon-Blake , SVP of Global Partnerships at Pangea Dr. Richard M. Smith , Founder and CEO of TradeStops Elizabeth Gunderson , Senior Manager of Customer Success at Yodlee Interactive This webinar is sponsored by Yodlee.
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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"Browser benchmark battle January 2020: Chrome vs. Firefox vs. Edge vs. Brave | VentureBeat"
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"https://venturebeat.com/2020/01/15/browser-benchmark-battle-january-2020-chrome-firefox-edge-brave"
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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 Browser benchmark battle January 2020: Chrome vs. Firefox vs. Edge vs. Brave 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.
It’s been some 18 months since our last browser benchmark battle.
What better time to get the latest results than the start of a new year? Over the past year and a half, Google Chrome has continued to dominate market share , Mozilla Firefox has doubled down on privacy , Microsoft Edge has embraced Chromium , and Brave launched out of beta.
Users, developers, and businesses alike want to know which browser performs best. A single benchmark that definitively tests desktop browsers does not exist. As such, we ran eight separate benchmarks to give you a broader overview of what you can expect. We used Windows 10 in order to maintain a common platform, and because that’s what the majority of desktop users browse on.
Setup Like for the last time around, we used a Surface Laptop (Intel Core i5-7200U, 8GB of RAM, 256GB SSD). Most people rely on laptops rather than desktops nowadays. Plus, the desktop we used in the past has become too outdated.
We split off a new 100GB partition for a fresh install of Windows 10 Pro (64-bit), downloaded the browsers, and ran Windows Update a few times. We then ran all eight tests on each browser, taking screenshots along the way. We used the latest browser versions available for Windows 10 at the time of testing: Chrome 79 , Firefox 72 , Edge 79 , and Brave 1.2.
Please remember that if you try to replicate the tests, your numbers will differ because you’re using a different computer. You will not get the same figures, but you may get similar results if you try multiple browsers. The exact numbers aren’t important: How they compare between browsers within a given test is what counts.
Results And finally, the part you’ve been waiting for (click on an individual test to see the nitty-gritty details): SunSpider: Edge wins! Octane: Chrome wins! Kraken: Firefox wins! JetStream: Edge wins! MotionMark: Edge wins! Speedometer: Edge wins! Basemark: Brave wins! WebXPRT: Firefox wins! The Chromium version of Edge did a lot better given that the stable release only arrived today. We were expecting improvements, but not so many outright wins. That said, browser performance was solid across all four contestants — each browser won at least one test. Performance of course shouldn’t be your only consideration when picking your preferred app for consuming internet content.
As long as you’re using a browser that receives regular updates (and all four of these meet that criteria), you can expect performance to be solid. There is certainly room for improvement, but Chrome, Firefox, and now Edge, as well as Brave, are all quite capable.
1 2 3 4 5 6 7 8 9 View All 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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"Health records join the API economy as new rules go live | VentureBeat"
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"https://venturebeat.com/2021/07/01/health-records-join-the-api-economy-as-new-rules-go-live"
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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 Health records join the API economy as new rules go live 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 beginning of July marked the launch of an ambitious U.S. experiment to drive digital transformation across health care through open APIs. Proponents believe this could unlock opportunities for new digital health services and applications.
This first major step promises to open medical data for patients through APIs , making it easy to search through the system and improve coordination across primary care providers, medical specialists, and insurance organizations.
The U.S. Centers for Medicare and Medicaid Services (CMS) wields a big stick as it drives this effort. The department is threatening to cut off payments for non-compliance. CMS manages all federal insurance payments to health care providers across the U.S.
Plans have been underway for almost a decade to bring health care into the mainstream of system development, but API innovation has lagged.
Building a faster process U.S.
health care communication has typically been a slow and methodical process, which may be one reason why America spends twice as much as other developed countries. One big challenge has been an ancient system of data exchange based on the Electronic Data Interchange (EDI) standard developed in the 1960s.
While other U.S. industries have migrated to more flexible APIs, the medical industry has been stuck with an outdated approach for exchanging even simple data across providers.
It often takes days for a hospital to get information about insurance authorization or complex situations, said Ruby Raley, vice president of health care and life sciences at Axway, an API tools provider. The new APIs will allow health care providers to process these requests in real time.
Over the next two years, additional rules will kick in to further drive health care system transformation. These include: A Payer-to-Payer Data exchange, which will require health plans to share data with other plans when a member transitions to a different health plan A No Surprises Act to address unanticipated medical bills for emergency services and out-of-network events The Prior Authorization rule, which builds on ongoing changes to improve prior authorization processes The Transparency in Coverage rule, which will give consumers access to pricing information through an internet-based self-service tool, while requiring most group health plans and health insurance issuers to disclose price and cost-sharing information A linchpin of the new interoperability efforts is the Fast Healthcare Interoperability Resources (FHIR) framework. FHIR provides a method for consistently representing data and makes it easier to share the same data across multiple payers, providers, and medical specialists coordinating a patient’s health care. In addition, FHIR standardizes how data and elements are formatted for exchanging clinical, claims, and pharmacy records.
Health care providers must also comply with new security requirements such as OpenID Connect for credentials and OAuth 2.0 for authorizations. They must also make sure they do not block information from someone who has a right to access it.
Open APIs auger new opportunities Health care organizations have traditionally approached integration from a project-based perspective, in which integration specialists would create a custom integration for specific requirements. The new rules promise to open opportunities for a self-service marketplace of APIs, Raley said. This will make it easier to integrate data across existing electronic medical records and new AI algorithms , digital twins , and home health equipment to detect and treat disease.
The interoperability rule would drive expanded use of health information portals and improve conversations between clinicians and patients.
The jury is still out as to whether these rules drive meaningful change for patients. Some providers will merely do the bare minimum, while others will explore creative ways to save money while improving patient outcomes. But significant change may be in the offing, Raley predicts.
“We can see this as a chance to lay a foundation and build things as we’ve never been able to do before,” she said.
This initial step is the beginning of what Raley expects to be a five-year journey that will ultimately change every aspect of patient portals, collaboration with partners, and health care business processes.
Demand for data will increase as consumers become comfortable accessing their personal health information using apps. New tooling for health care records can be expected to emerge to manage data throughput and expand capacity as the demands on systems increase.
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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"How to avoid drowning in application sprawl | VentureBeat"
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"https://venturebeat.com/2021/07/03/how-to-avoid-drowning-in-application-sprawl"
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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 Guest How to avoid drowning in application sprawl 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.
As new and better technology and solutions emerge, a product or environment can become irrelevant overnight. Think about your own personal devices. If you looked through your apps right now, are there some that you forgot you have? If you have a continuous improvement mindset, this is an exciting time — rapid advancements and the flexibility of microservices provide extremely efficient and custom IT environments. When a business need is recognized, leaders are able to find a highly specialized solution and get to market faster than ever before. Continuous adoption of new technologies without getting rid of old ones leads to application sprawl.
And application sprawl is like quicksand: It will stop you from moving forward.
The most common reasons for abandoning an application include: Societal changes: Social changes, such as a change in the business model or consumer choices can lead to leaving one application for a new one. Even political situations can render things obsolete — like a sales tax change or a new regulation that changes data policies and renders applications unsafe or non-compliant.
Rapid innovation: Simply put, better solutions with new capabilities are developed. For example, if a new CRM software is created with better-targeted functionality than an older version, businesses naturally will gravitate to newer, more efficient options — without ever fully retiring previous solutions.
Technological progress: Advances in technologies like AI, machine learning, RPA, and IoT are enabling businesses to do more with less, making prior applications unnecessary or redundant. This is different than just newer versions of older software, because these technologies bring in completely new advancements and capabilities not previously possible.
A cycle of continuous abandonment is usually an indicator of a forward-thinking organization, but it has to be balanced with strong management, organization, and retirement of old technologies or IT leaders will quickly feel as if they are drowning in applications. Every time you add a new solution, it’s important to intentionally remove any older ones you no longer need. A company that fails to do this will overburden IT, increase discord in the organization, reduce its ability to adapt to changing business needs, and open itself up to security threats.
The burden on IT.
The sharp growth in applications means IT teams have more to manage. The increased workload demands team members focus on the most pressing issues first, leaving less time for planning and innovation. When the IT team starts to feel overwhelmed by their environment, they are naturally going to pull back on adding anything new, halting innovation.
Increased discord.
The problem of an overburdened IT compounds when other departments start to feel like the IT team is not supporting their needs. Many have the ability to purchase and implement the solution they want without informing IT at all, which is increasingly common. Departments that operate in silos might have very similar needs but adopt different, overlapping solutions. As the issue grows, it can create confusion for general business employees, who might not always know what the most up-to-date or correct applications are to use. Instead they spend time across disparate applications, creating a roadblock for efficiency and productivity. This compounds as tenured employees leave and applications are forgotten, unknown to the IT team.
Impediment to progress.
Note that technology sprawl is not limited to applications, but to features within applications as well. When applications and even features are not discarded, over time the technology spreads widely, creating a headache for IT as they must manage things like dependencies on obsolete technology. This makes them beholden to the technology, rather than forward progress; a failure to discard holds the business back from modernization like cloud migration.
Security risks.
When applications are no longer in use, they are no longer a priority for updates or monitoring. This is even more true if an app was unknown to the IT department in the first place — even though it’s still connected to other business applications, business data, and internal IT systems. Hackers now have a prime way to sneak into your IT stack through back doors and forgotten applications that haven’t been maintained with the proper and most up-to-date security measures.
To stay innovative and protect against data breaches or leaks, you need to balance the adoption of new technologies with the retirement of old ones. Yet, the process of retiring requires effort and carries a risk of downtime, data loss, or impacting other applications. Many are hesitant to slow down their time to market with this kind of clean-up work, so it is put off to another day.
Similarly, some IT shops assume the upfront cost and time commitment of retirement is too high in contrast to the cost of just letting an old app run in the background. But “out of sight, out of mind” will catch up with you, resulting in a negative impact much sooner than you might realize.
Businesses need to start looking at how to track applications — for example, through a company app directory — and add continuous retirement to the application lifecycle. As IT environments become increasingly more complex with the addition of microservices and citizen development through low code and no code, the organization’s commitment to retiring obsolete technology is key to future growth and innovation. Bringing less code, fewer features, or fewer apps to internal customers is not traditionally a celebrated part of IT culture, but it should be, and continuous retirement can be just that vehicle.
As the pace of innovation continues to rapidly increase, we’re seeing new capabilities, applications and solutions that hold incredible potential for the future of businesses. It’s understandable — and encouraged — that businesses keep up with this pace. But it’s critical to do so without letting application sprawl hold the company back. It’s natural to move on to the next new technology, especially when it promises to drive new revenues or increase performance. Where continuous improvement is an admirable business model focused on driving this innovation, continuous retirement is a necessary counterweight. The risks to business productivity, innovation, and security are far too great to ignore.
Aater Suleman is Vice President of Cloud Transformation at NTT DATA Services.
He is also co-founder and CEO of Flux7, an NTT DATA Company. He is a frequent speaker at events including AWS re:Invent, Dockercon, TechWell, and O’Reilly and regularly conducts corporate workshops on digital transformation. He is a Technology Council Contributor for Forbes and a professor at the University of Austin.
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"Hackers co-opt Microsoft's anti-phishing feature for phishing attacks | VentureBeat"
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"https://venturebeat.com/2021/07/21/hackers-co-opt-microsofts-anti-phishing-feature-for-phishing-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 Exclusive Hackers co-opt Microsoft’s anti-phishing feature for phishing 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.
Sometimes, security features don’t go as planned. Email security company Vade has discovered that a Microsoft 365 setting intended to protect enterprise users has been co-opted by malicious actors, who are instead using it to launch sophisticated and automated phishing attacks.
Hackers specifically are exploiting the custom login page feature, which many businesses have in place to thwart phishing attempts. Tomorrow, Vade will publish a research paper detailing the findings, including a step-by-step of how the intrusions are occurring and more broad overview of the state of phishing attacks.
Thomas Briend, a senior sales engineer at Vade who uncovered the tactic while reviewing proof of concepts with prospective end clients, told VentureBeat this campaign is automated to target certain individuals while ignoring others, “suggesting that the person or individuals responsible did their homework.” He added that the same attack can “wear many disguises” and use different links, content, and calls to action. Vade doesn’t currently have specific metrics on how widespread the tactic is, but the company confirmed the attack has been successful in impacting businesses including a European airline and regional newspaper.
“Automation is already in the wild and becoming more common in phishing, because creating individual attacks can be very time-consuming for cybercriminals and often result in a low ROI,” he said. “With automated attacks like this one, a cybercriminal essentially presses play, sits back, and reaps the benefits. Low-tech hackers will keep sending phishing emails that even poor filters can detect, but the sophisticated ones are professionals, with high levels of organization.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Behind the tactic The idea behind Microsoft 365’s custom login pages is that if employees ever land on a generic login page that doesn’t have the company’s branding, they can easily recognize that something is wrong. But hackers are using this trust to their advantage and have discovered how to convincingly replicate enterprise custom login pages, direct users to them, and gain access by hiding in plain sight.
They’re able to do this because the logos and backgrounds that differentiate customized pages are actually public. Briend explained they’re available through API calls, which are technical requests anybody can make as long as they provide an email address. “Through this approach, one can pull the logo and background picture of any organization running on Microsoft 365,” he said.
Vade called this a “big misstep” by Microsoft.
Briend said it’s likely Microsoft built these API endpoints for legitimate reasons, but without realizing they could be abused to build customized phishing pages.
“As far as I know, this is a first in terms of API abuse,” he said. “Maybe [it] will lead to more thorough review in the design and availability of future API endpoints, not necessarily just for Microsoft, but also for other vendors and service providers.” Microsoft told VentureBeat it is not aware of any vulnerabilities, nor has Vade reported such a vulnerability to the company. Once the report is shared with, Microsoft says it will investigate any claims and take appropriate action to protect customers. The company added that Office 365 and Outlook.com employ a multi-layered email filtering defense to protect customers from the latest phishing, spoofing, and impersonation attacks, as does its SmartScreen browser security feature in Edge.
Securing the enterprise According to Vade’s report, Microsoft is consistently one of the most impersonated brands in phishing attacks and is the most impersonated overall since 2018. In the first six months of 2021 alone, Vade found 12,777 Microsoft phishing URLs.
To protect themselves, Briend said, enterprises should consider the defensive solutions they’re using and determine if cybercriminals can identify them. If an enterprise is protecting Microsoft 365 with an email gateway or cloud-based email security solution, for example, he says a simple MX record search can reveal the domain of the solution to the hacker, who can then use that information to reverse engineer and bypass it.
Beyond that, he said step one is to evaluate the email security for Microsoft 365 and determine if it has the ability to both identify and remediate this type of attack. Enterprises should ensure that security solutions thoroughly inspect not just the elements of emails themselves, but also the page any URLs link to. This is important for avoiding an attack technique called “time bombing,” wherein malicious actors deliver emails uninfected and then create redirects to the phishing pages after the fact.
“Any defensive solution must be able to follow that link all the way through to the end — to the phishing page — and to inspect the page from top to bottom: the text, the images, the code,” Briend said. “Additionally, because no security solution catches 100% of attacks, when it comes to email, enterprises need the ability to continue to scan after delivery with both automatic and assisted remediation.” Briend added that enterprises should keep employees informed of these threats — especially these types of social engineering techniques.
A semi-annual or otherwise infrequent training isn’t enough, he said, because there are new attacks and techniques every day. “This should be an ongoing effort,” he said.
Update at 9:48am Pacific: This post was updated to include information provided by Microsoft. It was also updated to note that Vade’s report also covers phishing more broadly.
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"Punchh raises $40 million for AI that promotes customer engagement and loyalty | VentureBeat"
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"https://venturebeat.com/2019/11/14/punchh-raises-40-million-for-ai-that-promotes-customer-engagement-and-loyalty"
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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 Punchh raises $40 million for AI that promotes customer engagement and loyalty Share on Facebook Share on X Share on LinkedIn Punchhs cloud-hosted solutions suite.
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.
Punchh, a startup leveraging machine learning and omnichannel integrations to create customer journeys, today revealed that it’s closed a $40 million series C round co-led by Adams Street Partners and Sapphire Ventures. AllianceBernstein also contributed to the tranche, which brings Punchh’s total raised to nearly $70 million following a $20 million series B in January 2018 and a $7.5 million series A in 2014.
According to CEO Shyam Rao, the bulk of the funding will be put toward augmenting Punchh’s AI capabilities as it expands into new verticals like convenience stores and other physical retail segments. “Consumers expect ubiquity of experiences online and off. In-store retail remains extremely popular and is one of the most powerful relationship building channels ever created,” said Rao, who cofounded Punchh in 2010 with Aditya Sanghi, Jitendra Gupta, and Sastry Penumarthy. “Our platform gives retailers an unparalleled understanding of how customers engage with their brand in the real world, along with the ability to use that understanding to create AI-powered experiences that keep customers coming back for more.” Punchh’s products aim to supercharge same-store sales by integrating with existing point-of-sale and ecommerce systems, enabling them to collect in-store and online data that inform customer profiles. AI algorithms tap these profiles to generate targeted marketing campaigns and offers with the goal of promoting loyalty, which in turn incorporates a range of engagement channels including online forms, Wi-Fi marketing, and bounceback offers via email, SMS, or printed receipts.
Punchh ships with a wealth of tools that allow its customers to manage and maintain marketing lists, including message throttling (which sets the number and frequency of messages sent to their customers) and a detector that removes suspicious or fraudulent users. From a unified dashboard, they’re able to orchestrate campaigns and create promotion codes and coupons on the fly, or tap one of Punchh’s bespoke services for greater customization.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Speaking of those services, Punchh Loyalty lets brands build flexible, stackable loyalty programs with spend and visit-based rewards and tiered memberships, plus a machine learning component that captures reviews and converts their sentiment into quantifiable metrics. As for Punchh Offers, it taps AI-powered promotions and trackable coupons in real time, eligibility for which brands can determine based on predefined or custom criteria or segments.
Punchh says its clients experience an 85% uptick in offer redemption rates on average and an 84% boost in loyalty participation rates, as well as a 58% loyalty check lift. It claims to have reached over 125 million consumers across 200 corporations with 80,000 locations, including Focus Brands (which owns brands like Carvel, Cinnabon, and Auntie Anne’s), Yum (Taco Bell, KFC, Pizza Hut), TGI Fridays, and Casey’s General Stores, the last of which was announced today.
There’s no shortage of cloud-based management platforms that promise to unify and make actionable customer data, as evidenced by the customer journey analytics segment’s growth to nearly $25.93 billion.
Boston-based customer data and engagement SessionM raised $23.8 million in July for its loyalty solutions, while Mountain View startup CleverTap nabbed $35 million for AI that tracks customer engagement. But despite the competition, Sapphire Ventures managing director Jai Das asserts that Punchh’s suite is more holistic than most.
“Analysts predict ecommerce will account for just 10 percent 1 of total retail sales in 2019, which means about 90% of transactions are still taking place in store,” said Das. “There’s tremendous opportunity in brick-and-mortar retail, and brands are looking for ways to better understand their customers and build data-driven relationships that translate into increased customer lifetime value. Punchh’s solutions allow retailers to do that in a highly scalable manner, which is why they’re trusted by so many leading brands, and why we’re so optimistic on their long-term growth.” Above: Guest profiles in Punchh.
Adams Street Partners partner Robin Murray, who plans to join the company’s board of directors, added, “Punchh is the undisputed leader in this category. They work with the biggest brands, have the most sophisticated technology, and drive real results for their customers. While everyone else got distracted by maximizing ecommerce, Punchh took the best technologies and practices from that space and applied them to physical retail. Now the world is coming back around — just look at Amazon’s purchase of Whole Foods — and Punchh is already 10 steps ahead of the game.” In addition to its Silicon Valley headquarters, Punchh has a second U.S. office in Austin, Texas and global offices across Canada, India, the U.K., and Singapore. Currently, its global workforce numbers north of 320 people.
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"6Sense, which uses AI to power account engagement, raises $125M | VentureBeat"
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"https://venturebeat.com/2021/03/30/6sense-which-uses-ai-to-power-account-engagement-raises-125m"
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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 6Sense, which uses AI to power account engagement, raises $125M 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.
Account engagement startup 6Sense today announced that it closed a $125 million series D funding round led by D1 Capital Partners, valuing the company at $2.1 billion post-money. 6Sense says that the investment will bolster its growth and product initiatives, particularly in the areas of machine next-best-action prediction, data insights, and AI-powered orchestration capabilities.
Business-to-business buyers are typically 57% of the way to a buying decision before they engage with sales departments. Moreover, only 23% of executives are confident in the speed at which they’re gaining accurate insights.
Motivated by the idea that AI might have a role to play in helping seal the deal, five entrepreneurs — Amanda Kahlow, Dustin Chang, Premal Shah, Shane Moriah, and Viral Bajaria — cofounded 6Sense in 2013.
6Sense’s product captures intent signals from known and anonymous sources including the web, creating customer segments by account, behavioral intent, or a combination of those two factors. 6Sense identifies contacts and builds out targeted buyer lists, helping to prioritize outreach sales efforts and boost conversions with machine learning-based fit scores. The platform also triggers marketing communications through apps like Marketo and Eloqua in response to sales prospects’ demands. Moreover, it enables salespeople to engage with buying teams via multichannel, multitouch campaigns.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Automation is now seen as essential among marketers to bolster the outreach of campaigns, in part because of its ability to better target customer communications. According to a recent HubSpot survey , email automation campaigns are among the top three tactics used by marketers to improve performance. And in 2017, Salesforce reported that 67% of sales leaders used a marketing automation platform.
Under the hood of 6Sense’s platform is a demand graph that captures signals and automatically connects them to sales prospects. Algorithms ingest historical intent data to reconstruct account-based buyer journeys for any given business, monitoring the demand graph and analyzing changes in intent to score hundreds of millions of accounts and people every day.
6Sense recently launched Segment Performance Reports and Custom Talking Points, two features that enable marketers to analyze changes in account engagement and progression through buying stages and provide guided conversation points based on buyer intent, role, and fit. February 2020 saw the introduction of Next Best Actions, which leverages AI to present business development representatives with a prioritized list of actions to engage buying teams within a target account.
6Sense competes to a degree with ZoomInfo. Other startups operating in the segment include Demandbase , which has raised over $150 million with backing from high-profile investors, as well as Lattice Engines and Leadspace.
But 6Sense backers aren’t concerned, and they have some reason to be optimistic. The year 2020 was the company’s third straight year of 100% revenue growth, 6Sense says, driven by “significant increases” in pipeline, revenue, average sale price, and deal velocity and a twofold increase in customer base size. (Brightcove and Cognizant are among 6Sense’s clients.) While global ad spend was predicted to have fallen 10.2% year-on-year in 2020, ad agencies including Magna say they expect to see ad spend to rise over 7% in 2021 to around $612 billion total, with digital media seeing growth exceeding 10%.
“We’re grateful for our success leading the account-based sales and marketing category — and humbled by the confidence our customers and investors have in 6Sense — but our vision has always been bigger and bolder,” said CEO Jason Zintak. “There is an enormous opportunity to redesign the way business-to-business companies go to market. We believe we have the platform, data, team, and investment partners to be the foundation for business-to-business revenue technology.” Forrester predicts that spend for marketing automation tools will grow “vigorously” over the next few years, reaching $25.1 billion annually by 2023 from $11.4 billion in 2017. It’s estimated that 55% of marketing decision-makers plan to increase their spending on marketing technology including AI and machine learning, with one-fifth of the respondents expecting to increase by 10% or more.
“We invest heavily in sales and marketing technology, and 6Sense is truly one-of-a-kind,” Sapphire Ventures partner Rajeev Dham, a 6Sense investor, told VentureBeat. “We’ve always viewed 6Sense as a market leader with the ability to execute on their bold vision of transforming sales and marketing with data-driven insights and orchestration capabilities. 6Sense is already the leading account-based sales and marketing platform, and they are poised to define and deliver the future of revenue technology that every B2B organization needs.” Beyond D1 Capital Partners and Sapphire Ventures, Insight Partners and Tiger Global participated in 6Sense’s latest funding round. It brings the 300-employee, San Francisco, California-based company’s total raised to date to over $225 million.
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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"Canalys: Cybersecurity spending is projected to rise 10% in 2021 | VentureBeat"
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"https://venturebeat.com/2021/01/19/canalys-cybersecurity-spending-is-projected-to-rise-10-in-2021"
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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 Canalys: Cybersecurity spending is projected to rise 10% in 2021 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.
Global cybersecurity spending is expected to grow 10% in 2021 as new types of threats emerge along with an increasing volume of attacks. With enterprises adapting their infrastructure to new cloud architectures and new work configurations, the need to address potential vulnerabilities is taking on greater urgency.
Those figures come from the latest Global Security Forecast by research firm Canalys.
The report predicts worldwide spending of $60.2 billion on security products and services in 2021.
That investment includes beefing up areas such as “endpoint security, network security, web and email security, data security, vulnerability and security analytics, and identity access management,” according to the report.
Canalys offers the caveat that the percentage increase could drop to as low as 6.6% if the pandemic keeps most businesses closed for extended periods, a dynamic that could hamper work in this area.
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 such investments are seen as essential, they only seem to be slowing a rising tide of attacks, at best. According to Canalys, hacking hit a historic high last year. The report says 12 billion records were compromised in some fashion last year, and ransomware attacks rose 60%.
What are the culprits? Everything from misconfiguring cloud databases to phishing campaigns. There also seems to be a growing ability to target the weakest links in the security chain: poorly trained remote workers.
“The biggest threats are always those not yet known. The discovery of the SUNBURST advanced persistent threat campaign at the end of 2020, stemming from malicious code injected into the widely used SolarWinds Orion IT management platform and subsequent infiltration into other systems, highlights this,” Canalys chief analyst Matthew Ball said in a statement. “Cybersecurity professional services engagements in response to this latest issue will be one of many factors contributing to sustained investment this year, especially in newer solutions to mitigate emerging threats.” Among the cybersecurity categories predicted to grow the fastest: web and email security by 12.5% in 2021, vulnerability and security analytics by 11.0%, and endpoint security by 10.4%.
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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"'Antivirus is dead': The rising enterprise security threats for 2021 and how to protect against them | VentureBeat"
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"https://venturebeat.com/2021/02/22/antivirus-is-dead-the-rising-enterprise-security-threats-for-2021-and-how-to-protect-against-them"
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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 ‘Antivirus is dead’: The rising enterprise security threats for 2021 and how to protect against them 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.
2020 was unprecedented in nearly every way, and cyberattacks were no exception. The CrowdStrike 2021 Global Threat Report from cloud-native cybersecurity company CrowdStrike states this was “perhaps the most active year in memory.” For enterprises specifically, the report uncovers rising threats to watch in the coming year. Malicious actors furthered their shift toward attacks on high-value targets such as enterprises, known as “big game hunting,” which has become increasingly popular in recent years because of the more lucrative payday potential. Malicious actors also developed new tools and procedures and formed alliances to bolster the strength and reach of their attacks. Most significantly, they increasingly integrated blackmail and extortion techniques into ransomware operations.
Malicious actors have escalated their efforts over the last 18 months, CrowdStrike senior VP Adam Meyers told VentureBeat. They want “to steal as much data as they can get their hands on. Then they’ll say ‘If you don’t pay us, we’re going to release all this sensitive data,’ which could have reputational or even regulatory impact.” Cyber criminals also exploited the COVID-19 pandemic, preying on fears, targeting the health sector, and taking advantage of the abrupt switch to remote work. According to the report, 71% of cybersecurity experts surveyed said they’re more worried about ransomware attacks as a result of COVID-19. Additionally, 2020 saw what is perhaps the most sophisticated and far-reaching supply-chain attack in history.
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 best defense for enterprises is to be informed of the evolving threats, act quickly in the event of an attack, and be proactive with advanced security solutions. “You have to have a next-gen solution. Antivirus is dead,” Meyers said.
The findings in the 40-page report, which tracks and analyzes the activity of the world’s major cyber adversaries, were compiled using machine learning, firsthand observations from the company’s frontline cyber analysts, and insights from crowdsourced threat measurement, the company said. Here are the trends, threats, and security best practices for targeted enterprise, according to the report.
Cyber criminals exploit the COVID-19 crisis The health care sector faces significant security threats in a typical year, and the stakes related to the pandemic only brought increased attention, particularly to pharmaceutical companies, biomedical research companies, and government entities.
While early objectives for targeted intrusion actors may have included acquiring information on infection rates or country-level responses, the aim quickly shifted to vaccine development. Malicious actors based in China, North Korea, and Russia all targeted vaccine research, CrowdStrike said. In total, at least 104 health care organizations were infected with ransomware in 2020.
COVID-19 also proved effective for phishing, a technique that’s typically most successful when it taps into human emotions like hope, fear, and curiosity. Phishing scams targeted the federal COVID-19 relief plan for businesses (PPE), financial assistance, and other government stimulus packages. They also pretended to offer information on testing and treatment and impersonated medical bodies, including the World Health Organization (WHO) and U.S. Centers for Disease Control and Prevention (CDC).
Lastly, the abrupt shift to remote work thrust many enterprises into a security situation for which they were not prepared. The sudden use of personal computers for work, for example, means many people are working on devices that may have already been infected with malware. Another risk comes with sharing devices between family members, who may not be aware of security threats they encounter.
“The biggest impact is that it increased the attack surface,” Meyers said, referring to the sum of entry points a malicious actor can use to gain access.
Enterprises at greatest risk: private and government health care entities, newly remote organizations.
Nation-states go after IP Beyond vaccine development, nation-state actors also targeted enterprises across sectors for intellectual property (IP). The report suggests they’re not letting up and will continue in 2021, echoing sentiments from around the industry.
China specifically has a “shopping list” of technologies it’s looking to develop and is using economic espionage to leapfrog the existing technology, especially in AI and machine learning. Some nation-state actors are also interested in accessing cybersecurity companies’ own toolkits that could aid them in further attacks, as happened in the case of FireEye.
Another threat comes from bilateral agreements or joint-venture purchases with companies based in other countries, which nation-state actors look to capitalize on. Beyond IP, a company’s negotiating strategies, expansion plans, and bottom lines are all potential targets.
Enterprises at greatest risk: clean energy, medical technology, digital agriculture, cybersecurity, mining/limited-supply resources, and emerging technologies.
Supply-chain attacks reach new heights While supply-chain attacks are nothing new, 2020 saw one that some cybersecurity experts are calling “the hack of the decade.” A nation-state actor breached the network of IT software provider SolarWinds , maintaining access for 264 days and attacking customers through stealthy malware hidden in multiple software updates. The SEC identified at least 18,000 potential victims of the attack , including top-tier companies and governments. The actor even studied and downloaded Microsoft’s source code for authenticating customers.
Supply-chain attacks are uniquely damaging because of their domino effect, in which one intrusion can enable further breaches of multiple downstream targets.
“The scope, depth, and length of time this was out there, I would say, is unprecedented,” Meyers said, adding that supply-chain attacks, specifically in software, are what keep him up at night.
Ransomware meets extortion Among increased ransomware activity, 2020 also saw the accelerated integration of data extortion and blackmail techniques, a practice the report warns will likely grow this year. This echoes another recent report from data protection specialist Acronis, which declared “2021 will be the year of extortion.” A large part of this was the introduction of dedicated leak sites (DLSs), which are dark web posts where malicious actors detail — with proof — the exact data they’ve stolen, aiming to increase pressure on targets to meet ransom demands. One notable example was the attack on New York-based law firm Grubman Shire Meiselas & Sacks. The responsible criminal group dropped posts hinting it had files of companies and celebrities including Madonna, Bruce Springsteen, Facebook, and more, eventually releasing a 2.4GB archive containing Lady Gaga’s legal documents. Overall, this approach was adopted by at least 23 major ransomware operators in 2020. The average ransom paid was $1.1 million.
Threat actors deployed new data extortion techniques. This includes going after non-traditional targets within organizations, such as hypervisors like VMware ESXi. They’re also staggering the release of stolen data, which in the case of enterprises with high brand recognition can generate news and social media buzz that adds pressure to ransom negotiations. Threat actors also collaborated on extortion campaigns, forming alliances such as the self-proclaimed Maze Cartel. This could evolve into hosting each other’s victims’ data, increasing the risk it will be shared or sold and making it more difficult to negotiate the full removal or destruction of stolen data.
New ransomware variants and families were also introduced, and one actor launched ransomware-as-a-service (RaaS). The report also details the increased use of access brokers, wherein hackers who gain backend access to enterprises simply sell it directly to malware actors. This eliminates the time spent identifying targets and gaining access, allowing them to deploy more malware faster.
Enterprises at greatest risk: Although most ransomware operations are opportunistic, the industrial, engineering, and manufacturing sectors were especially targeted in 2020. Technology and retail sectors are also at high risk.
How enterprises can defend against threats According to Meyers, these are the five things enterprises should be doing.
Secure the enterprise.
This means following best practices and having multiple safeguards, including solid vulnerability management, consistent patch cycles, and “the principle of least privilege.” Prepare to defend.
CrowdStrike recommends a 1-10-60 rule: Identify an attack within one minute, respond to it within 10 minutes, investigate it, and prevent the attacker from carrying out their objective within one hour. Either cross-layer detection (XDR) or endpoint detection and response (EDR) should be in place, according to Meyers.
Have a next-gen solution.
Antivirus needs to have seen a threat before, but machine learning-based solutions can decipher threats without having ever seen them. This difference is crucial with the growing rate of ransomware today.
Training and practice.
Get executives, directors, and board members together and develop a response plan. Know everyone you’ll need to call and don’t wait to handle attacks on the fly.
Intelligence.
Be aware of the threats, their techniques, and tools, as well as which specific threats target your industry and geolocation.
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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"NoSQL database company Couchbase confidentially files for IPO | VentureBeat"
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"https://venturebeat.com/2021/03/12/nosql-database-company-couchbase-confidentially-files-for-ipo"
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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 NoSQL database company Couchbase confidentially files for IPO Share on Facebook Share on X Share on LinkedIn Couchbase homepage 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.
( Reuters ) — Database software firm Couchbase has registered for a stock market debut that could come in the first half of this year and value it at as much as $3 billion, according to people familiar with the matter.
The company has achieved more than $100 million in annual revenue, one of the sources said. The sources requested anonymity because the initial public offering (IPO) filing with the U.S. Securities and Exchange Commission is confidential and has not yet been made public.
Couchbase declined to comment.
Couchbase helps corporate customers such as Comcast and eBay manage databases on web and mobile applications through its NoSQL cloud database service. It has thrived as demand for data storage and processing has soared because of remote working during the COVID-19 pandemic.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Founded in 2011, Couchbase has raised $294 million from investors thus far. It last raised $105 million at a valuation of $580 million in May 2020, according to PitchBook data. GPI Capital, North Bridge Venture Partners, and Accel are among its backers.
The company had eyed an IPO back in 2016, after it raised $30 million. It said at the time it expected that to be its last round before going public.
MongoDB, another database company and a competitor of Couchbase, went public in 2017 and now commands a $20 billion market capitalization. Snowflake, a cloud-based data-warehousing company, went public last year at a $33 billion valuation, the largest software IPO in history.
The U.S. IPO market remains welcoming, with 62 operating companies listed so far this year, according to data provider Refinitiv.
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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"Rockset integrates real-time analytics platform with relational databases | VentureBeat"
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"https://venturebeat.com/2021/04/15/rockset-integrates-real-time-analytics-platform-with-relational-databases"
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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 Rockset integrates real-time analytics platform with relational databases 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.
Rockset today announced it has integrated its analytics database with both MySQL and PostgreSQL relational databases to enable organizations to run queries against structured data in real time.
Rather than having to shift data into a cloud data warehouse to run analytics, organizations can now offload analytics processing to a Rockset database running on the same platform, Rockset CEO Venkat Venkatramani told VentureBeat. The Rockset platform is based on Facebook-developed RocksDB, an open source log structured database engine based on a key/value store that has been extended to support SQL queries.
The approach enables organizations to offload queries to an indexing engine that can process sub-second queries while transactions continue to be processed using a relational database, Venkatramani added. The issue many organizations face today is that they already have extensive investments in open source relational databases. Neither MySQL nor PostreSQL are designed to process analytics at scale, which is one reason so many organizations have either adopted a NoSQL database or a data lake in the cloud. Replacing those databases with a proprietary relational database that can also process analytics in real time would be cost-prohibitive for many.
A fresh approach Rockset is making a case for an alternative approach based on a Converged Index that can be employed to analyze structured relational data, as well as semi-structured, geographical, and time-series data in real time. Complex analytical queries can be scaled to include JOINS with other databases , data lakes, or event streams. All fields are entered into a converged index that includes an inverted index, a columnar index, and a row index.
In addition to integrations with open source relational databases, the company also provides connectors to MongoDB, DynamoDB, Kafka, Kinesis, Amazon Web Services (AWS), and Google Cloud Platform, among others.
As organizations collect data in real time, they increasingly also need to analyze it in real time, Venkatramani said. “Batch-based workloads are becoming real-time workloads,” he added.
Moving data into a data lake using a batch-oriented process only provides a means to process a larger amount of historical data, Venkatramani said. IT organizations may still have a need for a data lake, but real-time analytics are going to be at the heart of most digital business processes, Venkatramani noted.
Rockset earlier this year published the results of a Star Schema Benchmark test showing millisecond-latency query performance against the Star Schema Benchmark (SSB). The company claims it’s the only vendor to publish benchmarks showing it can execute queries up to 9.4 times faster than rivals while simultaneously ingesting 1 billion events a day with one second of data latency.
The company last fall raised an additional $40 million to grow its workforce and accelerate product development and research while bolstering its go-to-market efforts.
Future of real-time platforms It’s not clear to what degree batch-oriented processes that have dominated IT architecture for decades will give way to real-time platforms. Historically, the data organizations have applied analytics to is usually several hours to a day old because the underlying database has typically been updated overnight using a batch-oriented process. Today organizations want to be able to continuously apply analytics to, for example, clickstream data from social media feeds — in real time, as it’s being processed. Other use cases include supply chain logistics and delivery tracking systems, gaming leaderboards, fraud detection systems, health and fitness trackers, and ecommerce applications.
Of course, the days when organizations standardized on one database platform are long over. The challenge now is weaving a polyglot set of databases together in a way that allows an organization to take advantage of the capabilities of multiple platforms optimized for varying classes of workloads.
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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"Lack of AI implementation may have cost enterprises $4.26T, Signal AI finds | VentureBeat"
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"https://venturebeat.com/2021/06/10/lack-of-ai-implementation-may-have-cost-enterprises-4-26t-signal-ai-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 Lack of AI implementation may have cost enterprises $4.26T, Signal AI 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.
AI’s potential impact on the U.S. economy could reach into the trillions of dollars, according to a report published this week.
Signal AI , which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster.
According to the Signal AI survey, 96% of business leaders said they believe AI decision augmentation will transform decision-making, with 92% agreeing companies should leverage AI to augment their decision-making processes.
More than three-quarters of respondents (79%) also noted that their organizations are already using AI technologies to help make decisions.
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 general, 96% of business leaders said they believe they can leverage AI to improve their business decision-making processes, with 80% noting they already feel they have too much data to weigh when making decisions. On average, 63% of respondents said they spend upwards of 40 hours a week on decisions.
Reputations and expectations More than two-thirds of respondents (69%) ranked data higher than instinct in terms of influence on business decisions, even though many execs have been skeptical of the quality of data being employed within analytics and business intelligence (BI) applications.
Arguably the most surprising survey result is that just over 85% ranked reputation as a bigger priority than profit margins, Signal AI CEO David Benigson said. There’s a growing appreciation for the impact reputation has on both profitability and revenues, he noted.
But some business leaders may have unrealistic AI expectations, Benigson reported. “Just like with other technologies, they are overestimating the impact of AI in the short term and underestimating it in the longer term,” he said.
Estimating the potential revenue impact of AI is an inexact science. But a lot of complex business processes are occurring in near real time that are impossible for humans to optimize with AI augmentation.
The challenge is building AI models that accurately reflect those business processes. Many of the data science teams that have been hired to build AI models lack a deep understanding of the process they are being tasked with automating. Many AI models, as a consequence, never get deployed in a production environment.
Nevertheless, the volume of AI models being deployed continues to increase. The next big challenge for organizations will be the maintenance of all those AI models, many of which are subject to drift as new data sources become available. This means an AI model may not be as efficient as it once was because it needs to be retrained or replaced altogether.
Regardless of the path forward, AI models will increasingly become just another type of artifact to be incorporated into the application development process. The challenge will be aligning the efforts of application developers with the data science teams that build AI models to ensure neither is waiting for the other to finish a project before an application can be deployed.
In the meantime, business leaders may want to temper their AI expectations. Implementing an AI model is roughly akin to hiring a junior member of a team that needs some time to learn how processes work. Unlike a human, however, that AI model never takes a day off, quits, or forgets what it learns unless it is retrained. The only real issue is that when an AI model does make a mistake it may be at a level of scale that is difficult for the business to recover from unless the proper guardrails are in place.
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 Intel is leveraging AI to drive sales | VentureBeat"
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"https://venturebeat.com/2021/05/01/how-intel-is-leveraging-ai-to-drive-sales"
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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 Intel is leveraging AI to drive sales 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 pandemic greatly accelerated businesses’ digital transformation efforts. This is particularly true in the marketing domain, where enterprises began to embrace automation and enablement technologies. When McKinsey surveyed 1,500 executives across industries and regions in 2018, 66% said addressing skills gaps related to automation and digitization was a “top 10” priority. Forrester predicts that 57% of business-to-business sales leaders will invest more heavily in tools with automation.
While Intel might be best known for its chip business, it’s among the companies embracing this automation and digitization. The company expedited plans to apply AI throughout the customer lifecycle over the past 12 months, particularly on the marketing side of the house, where the goal was to tap AI to help identify and solve selling pain points. Intel also sought to adopt predictive tech to give its sellers a competitive edge, ideally months ahead of a potential close with buyers.
As Jake Tatel, global director of sales enablement and productivity at Intel, told VentureBeat via email, Intel started its AI marketing tech journey about five years ago. The company’s analytics teams started collecting a wide range of data from prospect websites and social media accounts. Then they combined it with Intel’s own website activity and overlaid it with customer buying patterns to drive actionable insights.
“Our internal team has really challenged itself to look at use cases where we could utilize data that we’re able to amass from customer buying patterns, in addition to how potential buyers are engaging across all our properties — whether that be the website, our training properties, or other Intel-owned channels,” Tatel said. “While tapping the data we have internally is a key part of the equation, it was also important for us to weave it together with publicly available information, like prospect websites and social media data. We have a complex ecosystem, so it was critical for us to take a wide view of it all to figure out the best way to stitch it together for continuous, real-time scanning.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Sales Assist and Autonomous Sales Two applications sprung out of the analytics team’s early work: Sales Assist and Autonomous Sales. Sales Assist provides insights — referred to as assists — to account managers. These assists broaden managers’ opportunities to interact with customers so that they can cover more accounts. Meanwhile, Autonomous Sales creates automatic sales motions, which are actions that Intel offers customers and partners through emails, website ads, and newsletters. Autonomous Sales operates daily and automatically, without human intervention, and applies to all partners’ accounts — even if they aren’t covered by an Intel sales team.
Sales Assist now has over 1,500 users at Intel and has provided more than 17,000 assists relating to nearly 5,000 accounts, 80% of which account managers have taken action on. For its part, Autonomous Sales delivers a yearly volume of about 30,000 emails to more than 10,000 contacts within Intel’s customers, with an open rate averaging around 36% and a first-time purchase conversion rate of 16%.
Together, Tatel says that the applications are generating “significant” business value for Intel, with the incremental contribution of Sales Assist estimated to be greater than $100 million per year. Autonomous Sales is helping to create $30 million in new sales, he says. And in 2020, Sales Assist and Autonomous Sales together delivered more than $168 million.
“Salespeople won’t adopt any technology if it doesn’t actually serve their needs or help them do their job more efficiently. So, we made it a top priority to ensure that the ‘assists’ being delivered to the sales organization were actionable,” Tatel said. “We did this by building in a feedback loop that informed the development team on whether the recommendations and insights being delivered were helpful. This fostered collaboration between the sales and development team, and made it so we could actually increase pipeline via the assists.” Sales AI Intel created Sales Assist in 2017, built on the company’s broader Sales AI platform. Sales AI — which is made up of the modules Sense, Reason, Interact, and Learn — is designed to collect and interpret customer and ecosystem data and translate it into useful recommendations.
The Sense module continuously scans, mines, and collects data about Intel’s customers from a variety of sources. The data reflects interactions and engagements between customers including billing information, past opportunities, and first-party data engagements on Intel.com in addition to responses to communications and any affiliations with partnership programs. Sales AI also incorporates external data like a customer’s or partner’s website, news mention, social media information, and so on, regardless of their specific connection with Intel.
Tatel says that Sense runs on millions of webpages, tweets, sales transactions, and customer and partner engagements, transforming them into thousands of data points on over 750,000 companies. In 2020, Sense scraped 15 million webpages and monitored over 347,000 million tweets.
Above: The Intel Sales AI Sense module extracts an enormous amount of data.
Sales AI employs a number of web-mining techniques to collect data. Leveraging natural language processing (NLP), the platform identifies cross-reference information about products, brands, advertisements, verticals, and other key variables. It then extracts data about the industries in which the customer is operating, such as automotive, communications, or health care. Thanks to pretrained language models including BERT and GloVe , Sales AI can understand the customer’s role (e.g., manufacturer, integrator, or reseller) and the technologies it’s using, according to Tatel.
“The Sales Assists AI engine automatically tags content and web pages using NLP and then notifies the seller whenever there is product intent of interest which is significantly different from a usual behavior pattern,” Tatel explained. “The approach imitates the way a seller thinks and acts, identifying potential opportunities and helps them to proactively engage customers at the right time.” Sales AI also analyzes the text, website structure, links, and images in a customer’s website to learn more about them. When the platform spots mentions of a competitor’s product, it generates an assist, notifying the account manager than there might be an opportunity for conversion.
Mining for insights Once the Sense module gathers a customer’s digital representation, the Reason module uses this data to begin mining, correlating, and generating business insights. An insight — a prediction of a likely sales motion — might be a suggestion that a customer needs an existing Intel product, for example, based on a new direction published on their website. Or a customer might announce an acquisition of a smaller company in a new business line, and an Intel salesperson could recommend what they need to grow this business line.
According to Tatel, Reason can deduce insights from one or multiple data sources by attempting to identify a customer’s goals, needs, organizational changes, and shifts in focus areas. This enables Sales AI to identify trends, analyzing customers’ purchasing history and product-related activities on Intel’s platform and monitoring information regarding product lifecycles, examining past purchase patterns to identify the customers that might be impacted.
“We’re combining the ‘human intelligence; of our sales team with the AI in a unique way,” Tatel said. “For example, Sales Assist can now spot when a customer has a product that’s end-of-life and can recommend an alternative product for a salesperson to suggest to the customer to aid in their transition or make sure they have the latest and greatest solution.” For sales teams at Intel, Sales AI predicts topics of interest to a company by connecting between different webpages using links and users’ journeys to build a topic network. The platform can identify changes in this network over time, alerting salespeople with an insight when a customer shows a shift in interest toward a new industry or technology. And it looks for “unusual behavior,” such as a sudden increase in download activity from Intel’s Research and Development Center.
Recommending products To assist with product recommendation, Sales AI offers a recommender system that considers multiple customer objectives. Combining three components — features creation, a recommender model, and an optimization model — the recommender system provides updated recommendations while focusing on revenue opportunities, according to Tatel.
The recommender model considers products that a customers did or didn’t purchase in the past, plus the customer’s expected volume of purchases and their internal priorities. This nets a ranked list of products, which is updated weekly based on the customer’s activities and characteristics. The optimization model refines the list by taking into account sales strategies and feedback, so that it doesn’t always recommend the same products.
The Sales AI Interact module takes over at this stage. Working with results from Reason, it aims to push recommendations to customers at the right time, way, and format, from web to email. The Learn module feeds information from customer and partner interactions into the algorithms powering Reason and the rest of the Sales AI modules throughout, allowing them to self-improve over time.
Looking ahead In the future, Intel plans to broaden the applicability of Sales Assist by expanding the number of account types that receive assists. The company also plans to add the ability for Sales Assist to recommend specific actions to sales representatives, like sending a customer a link to products they recently viewed on Intel.com.
“Now that Sales Assist is rolled out more broadly, we’re continuously looking for new ways to add intelligence into our sales processes, and have started to look at off-the-shelf solutions … to help with content enablement,” Tatel said. “We’re going to be taking all the learnings from building our own internal AI-powered sales tool. These tools only work — and will only be adopted — if the insights they deliver are insightful and actionable. So, we’ll be building in similar feedback loops and beta programs to ensure they’re fine-tuned for success.” 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.
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"Endgame raises $12.5M to flag sales opportunities | VentureBeat"
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"https://venturebeat.com/2021/07/13/endgame-raises-12-5m-to-flag-sales-opportunities"
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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 Endgame raises $12.5M to flag sales opportunities 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.
Endgame , a self-described customer observability platform, today announced it has raised $12.5 million in a funding round, with contributions from Naomi Ionita, Menlo Ventures, Kara Nortman, Upfront Ventures, Sandhya Hedge, and Unusual Ventures. CEO Alex Bilmes said the funds, which bring the company’s total raised to $17 million, will be used to grow the team and invest in R&D.
Product-led growth, where users adopt free or trial products, has attracted attention and investment, particularly in the enterprise. Traditional go-to-market activities involve an abundance of tooling, with the customer relationship management market expected to reach $96.5 billion by 2028, according to Grand View Research. But dedicated software platforms remain in relatively short supply, despite the value captured with product-led motions.
Endgame claims to help by letting sales teams take action on prospective and current accounts to realize increases in revenue. The company’s platform analyzes real-time signals from a number of data sources, including product adoption, user behavior, transactional data, sales activity, and more, showing salespeople how users and accounts and are using products and whether those products are “sales-ready.” “Endgame makes it easy for businesses to observe what’s happening in their trial or free motion, prioritize sales-ready accounts and users based on behavioral signals, and act on them to drive more revenue faster. There has been no good solution for this,” Bilmes told VentureBeat via email. “At my previous company, I drove my engineering team crazy trying to build an in-house solution, hacking together one-off customer data tools, using Segment, extract, transform, and load scripts, spreadsheets, Google Data Studio, and an ever-increasing number of Zapier workflows. Then I had to do this again when I led growth at Puppet. It was painful, but in realizing how widespread the problem was, and with no good solution on the market, I created the product I always wished I’d had.” VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Automation inside Endgame shows reps prospects and opportunities with the strongest signals. The aim is to increase conversion likelihood via the right action from Endgame’s dashboard or by triggering the engagement tools teams already use.
“A core value proposition of Endgame is helping enterprises uncover hidden signals in their first-party customer data through data science and machine learning. Endgame identifies which data signals are highly correlated with conversion and what behaviors in the customer journey lead to the best long-term outcomes,” Bilmes explained. “Use cases include being able to understand what accounts are likely to convert with a nudge from sales, which accounts will convert on their own, and what customer behaviors increase conversion likelihood.” According to Bilmes, Endgame is working with design partners like Figma, Loom, Airtable, and Clubhouse. Endgame is at the pre-revenue stage, but the startup plans to hire “across functions” and expects to double its nine-person workforce within the next 12 months.
“The pandemic has dramatically accelerated customer and investor interest in Endgame. Since salespeople were unable to fly to meet with prospects and customers onsite, businesses have doubled down on their investment into the self-service business models that Endgame supports,” Bilmes added.
Other investors in Los Angeles, California-based Endgame’s series A include Puppet CEO Yvonne Wassenaar, GitHub CTO Jason Warner, and Intercom founder Des Traynor.
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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"Hungryroot delivers AI-powered grocery experience | VentureBeat"
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"https://venturebeat.com/2021/07/17/hungryroot-delivers-ai-powered-grocery-experience"
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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 Hungryroot delivers AI-powered grocery experience Share on Facebook Share on X Share on LinkedIn Asparagus - Food 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.
There’s Netflix for movies.
Stitch Fix for clothes.
Hungryroot , an AI-powered delivery service, hopes to occupy a similar niche for online groceries in the United States.
The recommender system uses a collaborative filtering, supervised learning model to match consumer preferences to foods. Customers answer questions about their dietary habits, the kinds of foods they (and family members) like, the family size, budget, and more. On a weekly basis, the Hungryroot algorithm predicts the groceries the customer might like. Once the customer approves the list, a box ships from one of three Hungryroot locations. Customers also receive a set of recipes, also predicted by the algorithm, that use the week’s ingredients.
Neil Saunders, the managing director of GlobalData’s retail division, has seen grocery retailers of all stripes lean into AI as a way of better forecasting demand. “With the disruption from the pandemic and more people buying groceries online, demand forecasting has become increasingly difficult for retailers and AI can help them make sense of the data and make more accurate decisions about what to stock,” Saunders says.
The AI-powered grocery challenge Hungryroot works on a collaborative filtering model much like Netflix, learning from customer likes over time and pooling their preferences with others’. But AI-based recommendations for groceries are challenging, says CTO Dave Kong. For one thing, Netflix can recommend movies from a near-infinite queue. There are no additional constraints. Food, on the other hand, is not a consumable entity like movies.
Food is perishable.
Your choices depend upon inventory and on how much you can fit in the box.
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 consumers who like horror movies can feed on films in that genre for a while, the same need not apply to food. Feed consumers pasta three weeks in a row and they might complain. “The first step is to dissect the problem better for each customer. For example, what does variety mean to each customer ? Is it different items (i.e. types of pasta like spaghetti vs. penne), or different dish types (i.e. pasta vs. salad vs. stir-fry, etc.),” Kong says.
Hungryroot is also trying to figure out what repetition means to the customer. “Are they looking for similar recipes and items to their last order, even if it’s two weeks ago, or does the skipped week matter to them? We can then focus on the right AI approaches depending on what we learn,” he says. “Understanding repetition and variety is the key to success in the food model that is not a factor anywhere else.” The other challenge is that the number of customers who might like the exact same recipes using the exact same ingredients is not as large as movie buffs liking a genre. Consumer food preferences need to be digested at a much more granular level: salty, different types of protein, texture, and more.
The Hungryroot factorization machine crunches 60 different parameters (that number continues to increase) into its model. And data sources aren’t limited to only what customers say or do — Hungryroot also relies upon additional sources, like nutritional data.
A pleasant side dish: reducing waste The Hungryroot algorithm optimizes recommendations not just for an individual user, but across the board for all its customers. Tweaking what’s in the box just a bit — if a customer likes one kind of white fish, they might like a similar one in large supply at Hungryroot — can help optimize food distribution across all boxes, cutting down waste, Kong says.
In addition, the AI-powered grocery suggestion algorithm itself is smart and helps Hungryroot to predict how much of each kind of food to buy. Since customer preferences are known, it’s easier to forecast demand and manage inventory. Saunders agrees. “The main advantage for brands is that they get better at providing customers what they want and have enough stock to satisfy demand. With regular grocery delivery, one of the most frustrating things is bad substitutions or unwanted products. If AI helps brands to understand what customers want they have a greater chance of building loyalty and repeat business,” Saunders says.
Hungryroot also makes sure to keep customers’ pantry purchases in mind: While every recipe might need salt, customers don’t need to buy salt every week.
Growing appetite for AI grocery delivery Customers have responded well to Hungryroot: The startup is up 133% year-on-year for active customers. In June 2021, Hungryroot raised $40M in a series C funding round.
The algorithm has a high success rate. Consumers buy 72% of the AI-powered grocery deliveries. Kong expects including more unsupervised learning in addition to the supervised learning model. “We believe a neural-network model that is great at factoring in temporal information and excels at pattern recognition is the key to creating a successful and effective AI-enabled grocery service,” Kong says. “If we can nail the right level of predictability and variety for each and every customer, then we’ve solved the hardest problem with AI-enabled grocery shopping.” 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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"SugarCRM taps real-time sentiment analysis for customer service | VentureBeat"
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"https://venturebeat.com/2021/07/20/sugarcrm-taps-real-time-sentiment-analysis-for-customer-service"
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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 SugarCRM taps real-time sentiment analysis for customer 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.
SugarCRM has boosted the artificial intelligence (AI) capabilities of its SugarPredict engine for marketing and sales intelligence, adding a sentiment analysis tool that determines prospects’ “emotional state and intent.” SugarPredict is available on the Cupertino, California-based company’s Sugar Sell and Sugar Market platforms for automating and assisting with sales and marketing tasks. It’s also embedded in SugarCRM’s SugarLive multichannel customer communications application, the company said in a statement.
SugarLive is “designed to enable sales and service personnel to track the details of each customer interaction as it’s happening and effortlessly access customer information across all touch points and channels at the exact moment it’s needed,” SugarCRM said.
The omnichannel SugarLive customer service tool is available to SugarCRM customers with Sugar Serve licenses and provides real-time intelligence to customer service agents who are interacting with prospects online or on the phone.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Sentiment analysis works by using natural language processing (NLP) and AI to score the state of mind of a prospective or existing customer to determine best next steps, the company said. These could include escalating an interaction to a supervisor, presenting a “save-the-sale” offer, or moving to an upsell attempt.
Making the best first impression SugarCRM chief technology officer Rich Green said sentiment analysis is intended to help SugarPredict users better handle crucial first interactions with prospective customers in the marketing process.
“You rarely get a second chance to make a great impression with a customer; it’s profoundly important to get each and every interaction right and connect on a deeply human level,” he said. “Sales and service professionals are under a great deal of pressure, as a customer’s business can be won or lost in a single misstep.” Sentiment analysis is the latest in a series of AI-driven, marketing-centric improvements to SugarPredict. SugarCRM first released the software in January as a sales tool for SugarCRM’s Sugar Sell users. SugarPredict for sales is billed as an automated cloud software solution that can intelligently enrich the customer data entered into CRM systems to ensure quality and consistency.
In May, SugarCRM made SugarPredict available to users of its Sugar Market marketing intelligence platform to help marketing teams with automated predictive lead scoring.
Sugar Market is a customer lead-cultivating platform that helps marketing teams with tasks like quantifying how website visitors interact with digital marketing materials; assisting in the creation of emails, landing pages, and forms; qualifying leads with lead-scoring models; and aligning with sales via automated hand-offs of qualified leads.
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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"Ad-blocking browser Brave launches out of beta | VentureBeat"
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"https://venturebeat.com/2019/11/13/ad-blocking-browser-brave-launches-out-of-beta"
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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 Ad-blocking browser Brave launches out of beta 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.
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Brave Software today launched its Brave browser out of beta. Brave version 1.0 has the same pitch: an open source browser that blocks ads but also offers a blockchain-based digital advertising platform. You can download Brave 1.0 now for Windows, macOS, Linux , Android , and iOS in 52 languages.
Brave Software is led by CEO Brendan Eich, best known for inventing JavaScript and cofounding Mozilla. The company launched Chromium-based open source browser Brave in January 2016. Despite being in beta for almost four years, Brave has garnered 8.7 million active users and 3 million daily active users, Eich told VentureBeat. For comparison, Chrome has over 1 billion active users, while Firefox has about 250 million active users. Still, this is a notable achievement given how difficult it is to differentiate a browser nowadays. Even more notable is that Brave has pushed other browsers to follow in its path of cleaning up the web.
Brave features Brave aggressively blocks everything it can. On by default, the Brave Shields feature blocks third-party ads, trackers, autoplaying videos, and device fingerprinting. Brave claims its browser as a result loads websites up to 3 to 6 times faster than competitors. It also drains less battery life and uses less memory.
VB Event The AI Impact Tour Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you! Major browsers have adopted Brave’s approach with trackers, autoplaying videos, and device fingerprinting, or are in the process of implementing similar approaches. None of them blocks ads by default, however; so Brave continues to enjoy a noticeable performance edge out of the box.
Brave Rewards and Brave Ads Brave Rewards is the company’s attempt to fund the web. You can use it to support your favorite publishers and content creators by sending Basic Attention Tokens ( BAT ) as tips, either directly as you browse or via recurring monthly payments. The program is currently supported by over 300,000 websites, a tiny but not insignificant fraction of the web. (Full Disclosure: VentureBeat is one of them, and we’ve earned about $175 to date.) Brave Ads goes further with a blockchain-based advertising model. First announced in June 2018, the digital ads platform rolled out in April to all Brave users.
In return for your attention, you receive 70% of the gross ad revenue, paid out with BAT. Brave keeps the remaining 30%. Brave Ads first rolled out to desktop, then Android, and now iPhones and iPads. When Brave Ads was still in beta, you had to opt into Brave Rewards and then turn on Ads. Now, when you opt into Brave Rewards, Brave Ads is enabled by default.
Since Brave Software can’t use tracking, Brave pushes ad catalogs (one per region and language) to your device. As you browse, Brave learns about your interests based on the content of sites you visit. It then locally matches the best available ad from the catalog. Using machine learning, Brave Software claims its ad system can deliver ads “at the right time” based on the user’s “behavior in the browser.” We asked Eich about adoption of ads in the ad-blocking browser. The company has run over 475 ad campaigns (averaging three to five ads) to date. Major brands include Intel, Pizza Hut, and Home Chef. More impressively, between 10% and 20% of Brave users (depending on the platform) have opted into Brave Ads in areas where ads are available. Brave Ads has so far delivered a 14% CTR (industry average is about 2%). Given the 70% revenue share given to Brave users, they’re making about $5/month in BAT. Brave Ads has rolled out to 22 more regions today — so we’ll check with Eich in a few months to see where the numbers are.
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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Subsets and Splits
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.