text
stringlengths 301
426
| source
stringclasses 3
values | __index_level_0__
int64 0
404k
|
|---|---|---|
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
much faster, smaller, and easier to run. Read on for why and how we’re making this change. 📖┆Differential storage: a key building block for a DuckDB-based data warehouse ✍ Joseph Hwang Today we’d like to talk about Differential Storage, a key infrastructure-level enabler of new capabilities and
|
medium
| 6,161 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
stronger semantics for MotherDuck users. Thanks to Differential Storage, features like efficient data sharing and zero-copy clone are now available in MotherDuck. Moreover, Differential Storage unlocks other features, like snapshots, branching and time travel which we’ll release in the coming
|
medium
| 6,162 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
months. 📖┆Improving Efficiency Of Goku Time Series Database at Pinterest (Part 2) ✍ Pinterest Engineering This 2nd blog post focuses on how Goku time series queries were improved. We will provide a brief overview of Goku’s time series data model, query model, and architecture. We will follow up
|
medium
| 6,163 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
with the improvement features we added including rollup, pre-aggregation, and pagination. 📖┆Scaling Models And Multi-Tenant Data Systems — ASDS Chapter 6 ✍ Jack Vanlightly What is scaling in large-scale multi-tenant data systems, and how does that compare to single-tenant data systems? How does
|
medium
| 6,164 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
per-tenant scaling relate to system-wide scaling? How do scale-to-zero and cold starts come into play? Answering these questions is chapter 6 of The Architecture of Serverless Data Systems. 📖┆How Figma’s databases team lived to tell the scale ✍ Sammy Steele Figma’s database stack has grown almost
|
medium
| 6,165 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
100x since 2020. This is a good problem to have because it means our business is expanding, but it also poses some tricky technical challenges 📖┆Data Engineering Best Practices — #2. Metadata & Logging ✍ Joseph M. Dealing with breaking pipelines, debugging why they failed, and putting up a fix are
|
medium
| 6,166 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
everyday tasks for a data engineer. 📖┆S3 is files, but not a filesystem ✍ Cal Paterson “Deep” modules, mismatched interfaces — and why SAP is so painful 📖┆Building data abstractions with streaming at Yelp ✍ Hakampreet Singh Pandher This blog post covers how we leverage Yelp’s extensive streaming
|
medium
| 6,167 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
infrastructure to build robust data abstractions for our offline and streaming data consumers. We will use Yelp’s Business Properties ecosystem (explained in the upcoming sections) as an example. 📖┆Airflow & Kestra: a Simple Benchmark ✍ Benoit Pimpaud This post compares Airflow and Kestra, focusing
|
medium
| 6,168 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
on installation, configuration, pipeline syntax, and performance. 📖┆Postgres Aurora DB major version upgrade with minimal downtime ✍ Jay Patel Our payment platform team had the unique challenge to upgrade our Aurora Postgres DB from v10 to v13. This DB was responsible for storing transactions
|
medium
| 6,169 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
within Lyft and contains ~400 tables (with partitions) and ~30TB of data. Upgrading the database in-place would have resulted in ~30 mins of downtime. Such significant downtime is untenable — it would cause cascading failures across multiple downstream services, requiring a large amount of
|
medium
| 6,170 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
engineering effort to remediate. 📖┆Apache Druid’s Architecture — How Druid Processes Data In Real Time At Scale ✍ Ben Rogojan Apache Druid has several unique features that allow it to be used as a real-time OLAP. Everything from its various nodes and processes that each have unique functionality
|
medium
| 6,171 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
that let it scale to the fact that the data is indexed to be pulled quickly and efficiently. 📖┆How to save millions by optimizing data pipeline shuffling ✍ Zach Wilson In this article we will be going over: — Why does shuffle happen and what SQL keywords trigger shuffle and which do not? — Some
|
medium
| 6,172 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
techniques you can use to minimize shuffle especially in Apache Spark 📖┆A Look Back at Key Trends in Data Infrastructure in 2023 by Four Industry Founders ✍ RisingWave Labs The discussion with the four founders of data infrastructure startups focused on key trends in the industry for 2023.
|
medium
| 6,173 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
📖┆Unlocking Kafka’s Potential: Tackling Tail Latency with eBPF ✍ Maciej Mościcki + Piotr Rżysko At Allegro, we use Kafka as a backbone for asynchronous communication between microservices. With up to 300k messages published and 1M messages consumed every second, it is a key part of our
|
medium
| 6,174 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
infrastructure. A few months ago, in our main Kafka cluster, we noticed the following discrepancy: while median response times for produce requests were in single-digit milliseconds, the tail latency was much worse. Namely, the p99 latency was up to 1 second, and the p999 latency was up to 3
|
medium
| 6,175 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
seconds. This was unacceptable for a new project that we were about to start, so we decided to look into this issue. In this blog post, we would like to describe our journey — how we used Kafka protocol sniffing and eBPF to identify and remove the performance bottleneck. ✏ Data The one thing that
|
medium
| 6,176 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
this job has taught me is that truth is stranger than fiction. — Predestination (2014) 📖┆Scalable Automated Config-Driven Data Validation with ValiData ✍ Bharadwaj Jayaraman ValiData is a scalable automated config-driven data validation tool extensively used in LinkedIn that compares metric values
|
medium
| 6,177 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
of test datasets against production or source-of-truth datasets and highlights differences in metric values across dimensions. 🤖 AI┆ML┆Data Science You know, Burke, I don’t know which species is worse. — Ripley, Aliens (1986) 📖┆How Meta tests products with strong network effects ✍ Analytics at Meta
|
medium
| 6,178 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
I’m a member of a team that’s been applying cluster experimentation to products with strong network effects, such as chat and calling, since 2018. Today, I’d like to give an overview of the challenges we face in these highly-interactive domains, and how one solution — cluster experiments — has
|
medium
| 6,179 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
become a go-to method for addressing these challenges. 📖┆Best practices for building LLMs ✍ Nitzan Gado + Oren Dar Intuit shares what they’ve learned building multiple LLMs for their generative AI operating system. 📖┆Improving ETAs with Multi-Task Models, Deep Learning, and Probabilistic Forecasts
|
medium
| 6,180 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
✍ Doordash Engineering Blog The DoorDash ETA team is committed to providing an accurate and reliable estimated time of arrival (ETA) as a cornerstone DoorDash consumer experience. We want to ensure that every customer can trust our ETAs, ensuring a high-quality experience in which their food
|
medium
| 6,181 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
arrives on time every time. 📖┆Building Meta’s GenAI Infrastructure ✍ Kevin Lee + Adi Gangidi + Mathew Oldham Marking a major investment in Meta’s AI future, we are announcing two 24k GPU clusters. We are sharing details on the hardware, network, storage, design, performance, and software that help
|
medium
| 6,182 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
us extract high throughput and reliability for various AI workloads. We use this cluster design for Llama 3 training. 🔥 Catch up …Next Saturday night, we’re sending you back to the future! — Dr. Emmett Brown, Back to the Future (1985) 📖┆LinkedIn Open Sources OpenHouse: A Control Plane for Managing
|
medium
| 6,183 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
Tables in a Data Lakehouse 📖┆OpenTable now changes name to Apache XTable 📖┆Announcing Apache Arrow DataFusion Comet 💠 Previously on Dimension Dimension is my sub-newsletter where I note down things I learn from people smarter than me in the data engineering field. Here are the 3 latest articles:
|
medium
| 6,184 |
Data Engineer, Data Engineering, Software Engineering, AI, Big Data.
Published on 2024, March 2: I spent 7 hours reading another paper to understand more about Snowflake’s internal. Here’s what I found. Published on 2024, March 9: If I could travel back to 5 years ago, what would I talk to myself about Docker? Published on 2024, March 16: I spent another 8 hours
|
medium
| 6,185 |
.
wholly original. It borrows from lessons we’ve read, heard of, learned from iconic firms and investors, but equally as much from influences from outside the VC world like Carlota Perez, Charlie Munger, Bruce Greenwald and Michael Mauboussin. Our approach doesn’t fit a lot of the traditional venture
|
medium
| 6,202 |
.
well as the target outputs y. So the term hidden layer refers to the fact that in the training set, the true values for these nodes in the middle are not observed. That is, you don’t see what they should be in the training set. You see what the inputs are. You see what the output should be. But the
|
medium
| 6,213 |
.
So this is analogous to how in logistic regression we have y hat equals a and in logistic regression which we only had that one output layer, so we don’t use the superscript square brackets. But with our neural network, we now going to use the superscript square bracket to explicitly indicate which
|
medium
| 6,221 |
.
parameters associated with them. So the hidden layer will have associated with it parameters w and b. And I’m going to write superscripts square bracket 1 to indicate that these are parameters associated with layer one with the hidden layer. We’ll see later that w will be a 4 by 3 matrix and b will
|
medium
| 6,225 |
.
that time. But in some of the output layers has associated with it also, parameters w superscript square bracket 2 and b superscript square bracket 2. And it turns out the dimensions of these are 1 by 4 and 1 by 1. And these 1 by 4 is because the hidden layer has four hidden units, the output layer
|
medium
| 6,227 |
.
📚GitHub Repository 📝Notebook Do you want to get into data science and AI and need help figuring out how? I can offer you research supervision and long-term career mentoring. Skype: themushtaq48, email: [email protected] Contribution: We would love your help in making coursesteach community even
|
medium
| 6,234 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
Biomechanics professor Anthony Lau, PhD, demonstrates the value and step-by-step use of Design Hour activities, using his own Chicken Bone Test. Edited by Brian Fiske · October 3, 2019 Anthony Lau, PhD Assistant Professor of Biomedical Engineering, The College of New Jersey When Anthony Lau was in
|
medium
| 6,249 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
first grade, an uncle from Hong Kong gave him a Game Boy and some games. The catch? They were in Japanese-a language Lau did not speak. Though that may sound daunting, Lau found it fascinating. “I had to learn how the games worked inductively, by observing and interacting with them,” he says. This
|
medium
| 6,250 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
provided foundational thinking skills that proved helpful as he proceeded through school, eventually earning a PhD in biomedical engineering. As areas of study go, biomedical engineering is also both daunting and fascinating. It involves the application of principles and theories of all disciplines
|
medium
| 6,251 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
of engineering to the human body and encompasses scientific developments ranging from artificial organs to prosthetic limbs. This is the world where Anthony Lau, PhD, assistant professor of engineering at The College of New Jersey, thrives-and he wants his students to thrive here, as well. Which is
|
medium
| 6,252 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
why he makes special use of the College’s “Design Hour” sessions to help students grasp biomechanical theories by putting them to the test. So what is the Design Hour? These required weekly 50-minute sessions are essentially mini-labs, where Lau can provide a hands-on activity that builds on the
|
medium
| 6,253 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
materials covered in lecture. It combines the best qualities of the traditional recitation hour and the three-hour lab, but it is more interactive than the former and less time-consuming than the latter. Lau says that the Design Hour is endemic to many courses at The College of New
|
medium
| 6,254 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
Jersey-especially engineering classes-because it gives instructors a chance to show, not just tell. “The Design Hour lets students see theories in practice,” Lau says. To demonstrate the value and use of the Design Hour, Lau has provided details on one activity he uses in his Biomechanics class,
|
medium
| 6,255 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
called the Chicken Bone Test. Below, he outlines the main points that make the approach so effective, while also detailing his steps for this particular activity. Context “From K-12, students are taught to memorize and regurgitate. That doesn’t apply to engineering, especially in my class. Design
|
medium
| 6,256 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
Hour activities are important because they allow students to explore the concepts from class and apply them to open-ended experimental problems.” - Anthony Lau, PhD Course: BME 343 Biomechanics Course description: This course is a comprehensive study of structure, function, and mechanical
|
medium
| 6,257 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
properties of biological soft and hard tissues from the cellular to whole body levels through the application of mechanical principles and experimental methods. Topics include cellular and tissue mechanics of several physiological systems, analysis of tissue remodeling, fatigue and fracture
|
medium
| 6,258 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
resistance, and mechanical properties of muscular and skeletal tissues, joints, and body kinematics. See resources shared by Anthony Lau, PhD >> The Chicken Bone Test: A Template for Design Hour Activities The Chicken Bone Test helps to show the benefits of the Design Hour because it allows
|
medium
| 6,259 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
students to apply principles of biomechanics to a real-world challenge. “This activity takes mechanical engineering theory and applies it to bone strength,” Lau explains. “That’s important since, if the body is not strong enough to bear the loads on it, people can get injured, especially during the
|
medium
| 6,260 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
aging process with osteoporosis.” Testing the force needed to break a chicken bone-the goal of this activity-is one way to have students engage in quantifying bone strength. “This activity includes quite a lot of inductive learning components, which require students to pull from everything they
|
medium
| 6,261 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
have learned in class,” he adds. For example, it requires that students apply previous learnings to a real-world experiment, then compare their findings to other students’ work. Here is step-by-step guidance on how Lau sets up this activity. Set a goal, and build lectures that feed into it Before
|
medium
| 6,262 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
students conduct the Chicken Bone Test, Lau delivers lectures on the concepts related to bone strength. For example, students already know that most types of material, including steel, aluminum, rubber, and bone have an elastic modulus (strength or stiffness) that can be measured. So, once they
|
medium
| 6,263 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
enter this particular Design Hour, they will have a good idea what Lau means when he says that the goal for the day is to find the elastic modulus of a chicken bone (in this case, a drumstick). Invite reflection with a memory-jogging worksheet Also prior to the lab, Lau ensures that students have
|
medium
| 6,264 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
absorbed the relevant material. To do this, he posts to Canvas a pre-lab memory-jogging worksheet, which lists the relevant concepts and key terms. For the Chicken Bone Test, for example, students review their understanding of (among other things) how a material’s elastic modulus is measured, as
|
medium
| 6,265 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
well as the beam theory, which involves how a structure responds to forces (for example, by bending). This paper is strictly for guidance, adds Lau. He does not provide all the answers, because he wants students to learn how to research. They may use textbooks, lecture notes, and online sources to
|
medium
| 6,266 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
complete it. (He recommends peer-reviewed journal articles from PubMed, which he says is the gold standard for searching biomedical research articles, and occasionally Google Scholar.) They may complete the paper at any time they wish, before they can begin the lab. “This simulates a real
|
medium
| 6,267 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
biomechanical experiment in a graduate research lab, where you would not run a test without knowing all the relevant experimental parameters,” Lau notes. Create a super-specific assignment that allows for divergent results To kick off the lab itself, Lau provides an assignment worksheet. This
|
medium
| 6,268 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
reiterates the goal of the activity and includes a series of open-ended questions that students must answer as they conduct the experiment. In this case, the queries are about the bone’s length, a cross-sectional area (from the shaft of the bone), and a moment of inertia (a calculation based on the
|
medium
| 6,269 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
shape of that cross-section). Encourage diverse results by providing unidentical materials Unlike some highly structured labs, this one results in similar but unique challenges, since each student is given a different chicken bone. All chicken bones are not of equal strength and length, so even
|
medium
| 6,270 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
when all students follow the same procedure, their results will be different. This encourages students to think for themselves and trust their own results, since they cannot copy others’ work even if they wanted to. Enlist students in lab prep to build skills (and humor) Rather than handing out
|
medium
| 6,271 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
clean, dry bones, Lau provides each student with a freshly cooked chicken drumstick, which he has barbecued right before class. Then he tells them to remove the meat in any way they like (he chooses to eat his). This provides a bit of fun and also reinforces that, to focus the test purely on the
|
medium
| 6,272 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
strength of a bone, it must be cleaned of any tissue. Have students guess at the results before finding them Before putting the bone through its paces, the students take physical measurements of its geometry. They use this data, along with what they learned in lectures and research, to make
|
medium
| 6,273 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
assumptions about their bone’s properties and estimate the force that will be needed to break it. Next, Lau has students use an Instron mechanical testing frame machine to perform a bending test on the bone. They place the bone across a span on the machine (which students can adjust based on the
|
medium
| 6,274 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
geometry of their particular bone), and the machine pushes on the middle of the bone, bending it until failure (breakage). Students analyze the force vs. deflection data recorded by the Instron to calculate the actual elastic modulus of the bone. Leave time for group discussion and teachable
|
medium
| 6,275 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
moments In the next class after students have conducted their experiment, student groups compare and discuss notes from their assignment sheets, and then the class reconvenes for a large-scale discussion. Students often ask Lau questions such as, “Is this answer right?” which provides him the
|
medium
| 6,276 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
opportunity to point out that each individual calculation is only as good as the quality of the initial assumptions, the experiment itself, and the analysis of the findings. “Nobody knows the answer, which is why we do the experiments-to get the knowledge,” he points out. Lau also reminds his
|
medium
| 6,277 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
students that it is important to review and recheck their work until they are confident of their results. “Often I see that a cohort of students makes a similar error in calculations, and thus there is a group of answers on the shared Google sheet that looks similar and ‘correct,’ even though they
|
medium
| 6,278 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
are not. This often makes other students doubt their own results, as they are not similar.” But those dissimilar answers may, in fact, not contain the calculation error. “I like this lab for many reasons, but especially for the class discussion after the experiment,” Lau says. “They compare their
|
medium
| 6,279 |
Education, Biochemistry, Critical Thinking, Higher Education, Teaching.
findings as a class, as well as anything they can find in the published literature, and you can see the lightbulbs in their heads going off. The learning has been effective.” Originally published at https://www.coursehero.com on October 3, 2019.
|
medium
| 6,280 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
👉There are different types of fear. 👈 “Any event that taxes your stamina and endurance will trigger physiological fear that will affect your breathing which will in turn affect your cognitive control which is needed to control your breathing.” It’s an unconscious vicious cycle… The only way to
|
medium
| 6,281 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
acclimate is to replicate. No one can magically manifest experience. This was boxing. Just boxing. Mayweather has trained his mind and body for this sport for decades. McGregor less than a year. I’m not referring to technique, I’m talking about physiology and mind-body communication. Conor started
|
medium
| 6,282 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
to really gas around rounds 7/8 — that’s approx 24 min of work, which is his physiological “gas tank time” acclimated from years of MMA. You can’t fool physiology — and physiology affects physics and ultimately psychology. Here’s an example you can relate to: You ever go for a run with someone who
|
medium
| 6,283 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
is more conditioned than you? They have better technique and they’re chatting while running. You pretend the pace is ok, but you just can’t seem to catch your breath, stride or rhythm? It’s like that but now someone is also punching you in the face 😬👊 [For those of you who haven’t boxed, trust me,
|
medium
| 6,284 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
just holding your hands up for all those rounds is actually exhausting.] The ‘fear’ I refer to is the body’s, not the mind’s. Vince Lombardi understood this when he said, “Fatigue makes cowards of us all.” Conor wasn’t mentally afraid of Floyd. This is all about understanding physiology and it’s
|
medium
| 6,285 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
affects on perfomance and psychology. FTR, I love both sports. But if you’ve never boxed, respectfully you need to STFU 😁🙏😉 — your opinion needs to be empirical not theoretical — so get in the ring and try it with an open mind. You’ll learn a lot about boxing, like how the rhythm and timing are
|
medium
| 6,286 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
completely different from MMA. How the pace is also very different that it changes your striking timing, balance and torque when you’re used to a wider stance. Go box, you’ll discover this and you’ll also learn a lot about yourself too. Despite all the trash talk what Conor did was epic and
|
medium
| 6,287 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
incredibly impressive. I don’t know many men that would get in the ring with a pro boxer let alone a master like Floyd Mayweather and even last a round! Conor, an amateur boxer who hadn’t boxed in years actually went after Mayweather. That was so mentally impressive. He actually hit Mayweather
|
medium
| 6,288 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
several times with clean, deliberate attacks and he slipped some very sharp and fast combos. For a guy with a record of zero pro fights vs TBE (regardless of his age) he did amazing. Respect Conor🙏 and Floyd, 50–0, that’s crazy 🙏 So remember, you can’t fake experience. Your body and mind are
|
medium
| 6,289 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
organic systems that interact and communicate with each other regardless of what you want, think or say before the fight. In closing, when it comes to any type of competition (sports, business, etc.) if you want to be great at something you need to study the subject and the movement, and then put
|
medium
| 6,290 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
in the time to teach your body and mind. You cannot fake experience. Coach Blauer [FYI, I talk and write about this all the time. Fighters and coaches need to dig deeper into this. A positive mental attitude will only take you so far in a contact sport.] Visit my website and blog to learn more
|
medium
| 6,291 |
Boxing, Conor Mcgregor, Floyd Mayweather, Training, MMA.
about my approach to managing fear, adaptive courage and mental toughness blauerspear About the photos… respectfully borrowed from Instagram accounts. There were no copyright mentions so I’m simply re-sharing them to support the story and it’s message 🙏
|
medium
| 6,292 |
AI, Artificial Intelligence, Manufacturing.
Photo by Clayton Cardinalli on Unsplash Imagine a world where manufacturing machinery could whisper their secrets, revealing the precise moment they might falter before any visible sign of trouble. This isn’t a page from science fiction; it’s the reality being forged in the foundries of modern
|
medium
| 6,293 |
AI, Artificial Intelligence, Manufacturing.
manufacturing, thanks to the magic of Artificial Intelligence (AI). But how exactly is AI rewriting the rules of maintenance in this age-old industry? Read also: Top 10 AI Integration In Manufacturing From Reactive Measures to Predictive Precision Gone are the days when machines ran until the point
|
medium
| 6,294 |
AI, Artificial Intelligence, Manufacturing.
of breakdown, only then to be met with the hurried hands of maintenance crews. The narrative shifted slightly with preventive maintenance, a more scheduled approach, yet often led to the over-servicing of machines, an inefficient use of resources. The real transformation began with the advent of
|
medium
| 6,295 |
AI, Artificial Intelligence, Manufacturing.
predictive maintenance, a strategy emboldened by AI, offering a glimpse into the future health of machinery, ensuring interventions are as timely as they are necessary. The Symphony of AI Technologies At the heart of this predictive prowess lie several AI technologies, each playing a vital role.
|
medium
| 6,296 |
AI, Artificial Intelligence, Manufacturing.
Machine Learning serves as the maestro, orchestrating patterns from sensor data to predict potential failures. Deep Learning adds layers to this complexity, handling intricate datasets with ease. Neural Networks bring a semblance of human cognition, deciphering the data’s cryptic messages, while
|
medium
| 6,297 |
AI, Artificial Intelligence, Manufacturing.
the Internet of Things (IoT) ensures a steady stream of data, keeping the entire system informed and interconnected. The Bounty of Benefits The boons of integrating AI into predictive maintenance are manifold. Foremost is the dramatic reduction in unexpected downtimes, keeping the wheels of
|
medium
| 6,298 |
AI, Artificial Intelligence, Manufacturing.
production smoothly turning. This precision care extends the lifespan of machinery and optimizes maintenance schedules, ensuring resources are judiciously allocated. The ripple effects extend to enhanced safety protocols and a nod towards environmental stewardship, minimizing waste and unnecessary
|
medium
| 6,299 |
AI, Artificial Intelligence, Manufacturing.
interventions. Charting the Challenges Yet, the path to AI-driven predictive maintenance is not devoid of obstacles. The quality and integrity of data are paramount; flawed inputs lead to misguided predictions. The upfront investment in technology and training can be daunting, but it’s a gateway to
|
medium
| 6,300 |
AI, Artificial Intelligence, Manufacturing.
unparalleled long-term gains, making the initial hurdles well worth the effort. Gazing into the Future As we peer into the future, the potential of AI in this domain only broadens. Digital twins promise a virtual counterpart for every physical asset, offering a risk-free arena for analysis and
|
medium
| 6,301 |
AI, Artificial Intelligence, Manufacturing.
experimentation. Augmented reality stands poised to revolutionize maintenance further, overlaying digital insights onto the physical world, guiding technicians with unparalleled precision. In Conclusion The melding of AI with predictive maintenance is not merely a technological upgrade; it’s a
|
medium
| 6,302 |
AI, Artificial Intelligence, Manufacturing.
revolution in the ethos of manufacturing maintenance. It’s a promise of greater efficiency, sustainability, and a safer work environment. The question now is not whether AI will continue to transform maintenance practices but how swiftly and effectively industries will embrace and leverage this
|
medium
| 6,303 |
Blockchain.
Please note: This posting for the purposes of formulating my thoughts-in-progress and gaining constructive feedback from the community. It does not reflect an official position. Our current draft policy definition for a trusted digital identity is an electronic representation of a person, used
|
medium
| 6,305 |
Blockchain.
exclusively by that same person, to receive valued services and to carry out transactions with trust and confidence. This policy definition is intended to convey the overall policy intent (making sure you have the right person with confidence before carrying out a transaction) but it is a bit vague
|
medium
| 6,306 |
Blockchain.
from a technical perspective. A more technical definition of trusted digital identity is required for implementation, so here is my first crack at one that is a bit more math-y and logical. In developing this definition, I have kept in mind that there are numerous approaches and schemes in
|
medium
| 6,307 |
Blockchain.
implementing a trusted digital identity (centralized, decentralized, hybrid, etc.) A trusted digital identity is the set of identifiers and verifiable claims accompanied by proofs of: 1) ownership, 2) control, and 3) agreement. Unpacking this more technical definition: Identifier: Anything (name,
|
medium
| 6,308 |
Blockchain.
numbers, symbols, etc.) that uniquely distinguishes a member of a population from another member. Verifiable Claim: a qualification, achievement, quality, or piece of information about an individual such as a name, government ID, home address, or university degree. Such a claim describes a quality
|
medium
| 6,309 |
Blockchain.
or qualities, property or properties of an individual which establish its existence and uniqueness. Identifiers and verifiable claims are broken out as separate considerations, because while a claim can be verifiable, to be useful, it needs to be attributable to someone (or something) who needs to
|
medium
| 6,310 |
Blockchain.
be identified in some way or another. Accompanying identifiers and verifiable claims are proofs: Proof of Ownership: the ability to prove the right to create, transfer or revoke something (e.g., an identifier, a digital or physical asset, etc.). Proof of Control: the ability to the prove the right
|
medium
| 6,311 |
Blockchain.
to use, or authorize the use of something. Proof of Agreement: the ability to prove something that has been agreed to— either by an authority (by fiat) or a community (by consensus) Similarly, the proofs are broken into separate considerations of ownership, control, and agreement allow for the
|
medium
| 6,312 |
Blockchain.
implementation options: So how does this work for trusted digital identity? Going back to the definition: a trusted digital identity is the set of identifiers and verifiable claims accompanied by proofs of: 1) ownership, 2) control, and 3) agreement. A example scheme could use comprise as trusted
|
medium
| 6,314 |
Blockchain.
digital identity Fully Centralized Scheme: Use a centralized identifier such as the Social Security Number/Social Insurance Number (SSN/SIN). Since the the SSN/SIN is centrally administered, it is actually owned by the SSA (in the US) or ESDC (in Canada). The SSN/SIN is issued to an individual who
|
medium
| 6,315 |
Blockchain.
the needs to prove control before it can be used by a service. For the verifiable claims, the central authority issues the claims, determines the rightful user, and determines the accuracy. Fully Decentralized Scheme: Use a Decentralized Identifier (DID) that is registered on a consensus platform
|
medium
| 6,316 |
Blockchain.
(e.g., distributed ledger). The individual proves control of the DID through decentralized cryptographic challenge/response methods. The individual asserts whatever they wish. Proof of agreement is done by means of a consensus platform, in this case, the individual asserted something at some point
|
medium
| 6,317 |
Blockchain.
in time. Hybrid Scheme. Use a centralized identifier such as the Social Security Number/Social Insurance Number (SSN/SIN). The individual proves control of the SSN/SIN through decentralized cryptographic challenge/response methods. For verifiable claims, they could be self-asserted by the
|
medium
| 6,318 |
Blockchain.
individual, through agreement by the community, and provided by a central authority. To conclude, a trusted digital identity, as a policy concept is intended to achieve the objective that you are dealing with the right person with confidence. As a technical concept, a trusted digital identity can
|
medium
| 6,319 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.