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"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is... show more

Applied Compute

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64,144 просмотров • 4 месяцев назад •via X (Twitter)

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"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

Rohan Paul

119,783 просмотров • 14 дней назад

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

375,365 просмотров • 3 месяцев назад

The government is failing on cancer waiting times, just as it has failed on the economy and on asylum. Labour has a mission to build an NHS that is fit for the future, where patients are treated on time again and where fewer lives are lost to cancer.

The government is failing on cancer waiting times, just as it has failed on the economy and on asylum. Labour has a mission to build an NHS that is fit for the future, where patients are treated on time again and where fewer lives are lost to cancer.

Keir Starmer

209,214 просмотров • 2 лет назад

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,419 просмотров • 3 месяцев назад

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Akshay 🚀

51,091 просмотров • 1 месяц назад

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Solana ID 🪷

30,451 просмотров • 1 год назад

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,176 просмотров • 25 дней назад

Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind. The bottleneck is continual learning. How does a model do something once and improve from feedback? The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access. Applied Compute helps companies train, serve, own, and improve custom models. Thanks Apoorv Agrawal for having Yash Patil at MS&E 435 to talk about the future of model training.

Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind. The bottleneck is continual learning. How does a model do something once and improve from feedback? The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access. Applied Compute helps companies train, serve, own, and improve custom models. Thanks Apoorv Agrawal for having Yash Patil at MS&E 435 to talk about the future of model training.

Applied Compute

12,513 просмотров • 1 месяц назад

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Applied Compute

118,922 просмотров • 1 месяц назад

I’m worried that the US government debt problem will get so bad that the US will “go broke” and I believe that there are steps we can take to prevent it. I am in the process of completing a comprehensive study about the mechanics of how countries go broke, where the US government is in its Big Debt Cycle, and what policymakers and investors should do about it. The study will be published later this year as a new book: How Countries Go Broke: Principles for Navigating the Big Debt Cycle, Where We Are Headed, and What We Should Do. As with my prior books, I am going to pass along parts of it for free as I am writing, and I'd like to discuss what it says with you and others on social media. You can read Part One here:

I’m worried that the US government debt problem will get so bad that the US will “go broke” and I believe that there are steps we can take to prevent it. I am in the process of completing a comprehensive study about the mechanics of how countries go broke, where the US government is in its Big Debt Cycle, and what policymakers and investors should do about it. The study will be published later this year as a new book: How Countries Go Broke: Principles for Navigating the Big Debt Cycle, Where We Are Headed, and What We Should Do. As with my prior books, I am going to pass along parts of it for free as I am writing, and I'd like to discuss what it says with you and others on social media. You can read Part One here:

Ray Dalio

286,202 просмотров • 1 год назад

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

67,788 просмотров • 12 дней назад

Whoever is behind the CitizenApp which was launched yesterday has done a brilliant and fantastic job. The App is broad. It has a special feature known as CADIS(CitizenApp Data Interoperability System) where individuals databases are made available to institutions/agencies enrolled on the App without having to go through the stress of providing same information over and over. Again, it saves one from going to offices of agencies and going to waste productive hours just for their services. All you need is to do is just download the app and log in with your Ghana Card. It’s a one time sign up. Interestingly it has the features of which is a payment platform for government businesses. So you can buy ECG credits in the comfort of your home. The likes of SSNIT, ECG, NSS, GRA, Ghana Police, Ghana water Company, MMDAs and many others are all on the system. One can request his SSNIT statements on the same App. Trust me, Dr Bawumia is really speeding with this digitalization thing. Enjoy the video

Whoever is behind the CitizenApp which was launched yesterday has done a brilliant and fantastic job. The App is broad. It has a special feature known as CADIS(CitizenApp Data Interoperability System) where individuals databases are made available to institutions/agencies enrolled on the App without having to go through the stress of providing same information over and over. Again, it saves one from going to offices of agencies and going to waste productive hours just for their services. All you need is to do is just download the app and log in with your Ghana Card. It’s a one time sign up. Interestingly it has the features of which is a payment platform for government businesses. So you can buy ECG credits in the comfort of your home. The likes of SSNIT, ECG, NSS, GRA, Ghana Police, Ghana water Company, MMDAs and many others are all on the system. One can request his SSNIT statements on the same App. Trust me, Dr Bawumia is really speeding with this digitalization thing. Enjoy the video

Mr Irish

22,993 просмотров • 1 год назад

Arteta: “To be fair, it's a bit different when you finish second and when you win it with your phone, and that's a sport and it's a big lesson in life as well because the margins are so small and it can go either way. “When you just accomplish it, you realise, yeah, how immense it is, how big it is for so many people. I mean, just to witness the happiness and the enjoyment, all the things that I've seen from all of our supporters, families in different countries, it's just so good.”

Arteta: “To be fair, it's a bit different when you finish second and when you win it with your phone, and that's a sport and it's a big lesson in life as well because the margins are so small and it can go either way. “When you just accomplish it, you realise, yeah, how immense it is, how big it is for so many people. I mean, just to witness the happiness and the enjoyment, all the things that I've seen from all of our supporters, families in different countries, it's just so good.”

Connor Humm

39,215 просмотров • 1 месяц назад

Coinbase’s Shan Aggarwal on why everything in finance is moving onchain. “We think that all assets are going to get tokenized and be onchain in some capacity. It just provides better infrastructure for financial transactions.” That vision is driving Coinbase’s push to build what they call the “everything exchange”, not just for crypto, but for equities, prediction markets, and even trading directly on Dexs. As Shan put it, the lines between off-chain and on-chain finance are already starting to blur, and Coinbase wants to be the platform where it all converges.

Coinbase’s Shan Aggarwal on why everything in finance is moving onchain. “We think that all assets are going to get tokenized and be onchain in some capacity. It just provides better infrastructure for financial transactions.” That vision is driving Coinbase’s push to build what they call the “everything exchange”, not just for crypto, but for equities, prediction markets, and even trading directly on Dexs. As Shan put it, the lines between off-chain and on-chain finance are already starting to blur, and Coinbase wants to be the platform where it all converges.

TBPN

54,076 просмотров • 10 месяцев назад

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,758 просмотров • 2 месяцев назад

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,267 просмотров • 2 месяцев назад

“Do you see how scary this is?”: CrowdStrike CEO on AI Agents communicating around human guardrails George Kurtz: “There was a customer who basically created a whole suite of AI agents to help their automation in their IT department.” “So they had one agent that was looking for IT problems, software bugs.” “It found something. So the agent said, ‘Hey, I found this bug. I want to fix it, but I don’t have access to fix it.’” “So it went to the Slack channel that had the other 99 agents and said, ‘Hey, does any other agent have access to this thing,’ because they need it fixed. And there was an agent that raised its hand and said, ‘Oh, I have access, and I can fix it.’” “Do you see how scary this is? These two agents are reasoning, and they went right around the guardrails that were put in place.” @jason: “This is unintended consequences and these LLMs are essentially guessing what you want them to do.” “They're reasoning it. ‘Oh, it is reasonable for me to go ask for help. It is reasonable for me to give help.’ Now, what if it pushes the wrong code? What if it makes a mistake? And then how do you ever track that down? Who's monitoring these agents?” “The agent technology has unlimited upside, but my lord, you're going to be in business for a long time.” Kurtz: “Well, this is it. It's called AIDR. AI Detection and Response.” “And this is why it's a huge opportunity for us because on average each employee is going to have about 90 agents they control.” “So we're going to have protection and visibility across all of those agents, whether it's from a third party or whether it's a homegrown agent, and that is a massive TAM opportunity for us.” ------------------------------------ Thanks to our partner for making this happen!: On Public, you can invest in stocks, options, bonds, and crypto. Plus, build your own custom index with AI. Get started at — investing for those who take it seriously.

“Do you see how scary this is?”: CrowdStrike CEO on AI Agents communicating around human guardrails George Kurtz: “There was a customer who basically created a whole suite of AI agents to help their automation in their IT department.” “So they had one agent that was looking for IT problems, software bugs.” “It found something. So the agent said, ‘Hey, I found this bug. I want to fix it, but I don’t have access to fix it.’” “So it went to the Slack channel that had the other 99 agents and said, ‘Hey, does any other agent have access to this thing,’ because they need it fixed. And there was an agent that raised its hand and said, ‘Oh, I have access, and I can fix it.’” “Do you see how scary this is? These two agents are reasoning, and they went right around the guardrails that were put in place.” @jason: “This is unintended consequences and these LLMs are essentially guessing what you want them to do.” “They're reasoning it. ‘Oh, it is reasonable for me to go ask for help. It is reasonable for me to give help.’ Now, what if it pushes the wrong code? What if it makes a mistake? And then how do you ever track that down? Who's monitoring these agents?” “The agent technology has unlimited upside, but my lord, you're going to be in business for a long time.” Kurtz: “Well, this is it. It's called AIDR. AI Detection and Response.” “And this is why it's a huge opportunity for us because on average each employee is going to have about 90 agents they control.” “So we're going to have protection and visibility across all of those agents, whether it's from a third party or whether it's a homegrown agent, and that is a massive TAM opportunity for us.” ------------------------------------ Thanks to our partner for making this happen!: On Public, you can invest in stocks, options, bonds, and crypto. Plus, build your own custom index with AI. Get started at — investing for those who take it seriously.

The All-In Podcast

108,941 просмотров • 5 месяцев назад

“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.

“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.

Brian Roemmele

32,167 просмотров • 1 год назад

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 просмотров • 1 месяц назад