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ELON: AI HAS DRAINED HUMAN KNOWLEDGE DRY - NOW DRINKING FROM ITS OWN WELL "You take the entire internet, all books ever written, all the interesting videos, and you distill that down into tokens, essentially bits of information. We've now exhausted all of that; the cumulative sum of human... show more

Mario Nawfal

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1,938,926 次观看 • 11 个月前 •via X (Twitter)

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ELON: THE CUMULATIVE SUM OF HUMAN KNOWLEDGE HAS BEEN EXHAUSTED IN AI 'We've now exhausted basically the cumulative sum of human knowledge has been exhausted in AI training. That happened, basically, last year.” Source: Elon Musk Mark Penn CES

ELON: THE CUMULATIVE SUM OF HUMAN KNOWLEDGE HAS BEEN EXHAUSTED IN AI 'We've now exhausted basically the cumulative sum of human knowledge has been exhausted in AI training. That happened, basically, last year.” Source: Elon Musk Mark Penn CES

Mario Nawfal

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Elon Musk On What It Takes To Build A Competitive AI Model Elon Musk breaks down the three factors that decide whether a foundation model can compete, and why the next frontier isn't human data at all. Speaking with Garry Tan, President and CEO of Y Combinator, Elon lays out what's actually required to build a large foundation model that's competitive: "You've got to get a lot of GPUs and have them train coherently and stably. Then it's like, what unique access to data do you have? I guess distribution matters to some degree as well like, how do people get exposed to your AI? Those are critical factors." But there's a problem with the data part of that equation. Echoing what a friend in the field has said, Elon explains that the industry has essentially run out of human-generated pre-training data: "You run out of tokens pretty fast, certainly of high-quality tokens. And then you need to essentially create synthetic data, and be able to accurately judge the synthetic data that you're creating, to verify: is this real synthetic data, or is it a hallucination that doesn't actually match reality?" That verification step is the hard part: "Achieving grounding in reality is tricky. But we are at the stage where there's more effort put into synthetic data. Right now we're training Grok 3.5, which is a heavy focus on reasoning." On reasoning, Garry Tan adds an interesting detail from researchers he's spoken to: hard science, particularly physics textbooks is very useful for training reasoning, whereas social science is "totally useless" for it. Elon's response: "Yes, that's probably true." He then points to where all of this is heading: "Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics. So, things like the Optimus humanoid robot."

Elon Musk On What It Takes To Build A Competitive AI Model Elon Musk breaks down the three factors that decide whether a foundation model can compete, and why the next frontier isn't human data at all. Speaking with Garry Tan, President and CEO of Y Combinator, Elon lays out what's actually required to build a large foundation model that's competitive: "You've got to get a lot of GPUs and have them train coherently and stably. Then it's like, what unique access to data do you have? I guess distribution matters to some degree as well like, how do people get exposed to your AI? Those are critical factors." But there's a problem with the data part of that equation. Echoing what a friend in the field has said, Elon explains that the industry has essentially run out of human-generated pre-training data: "You run out of tokens pretty fast, certainly of high-quality tokens. And then you need to essentially create synthetic data, and be able to accurately judge the synthetic data that you're creating, to verify: is this real synthetic data, or is it a hallucination that doesn't actually match reality?" That verification step is the hard part: "Achieving grounding in reality is tricky. But we are at the stage where there's more effort put into synthetic data. Right now we're training Grok 3.5, which is a heavy focus on reasoning." On reasoning, Garry Tan adds an interesting detail from researchers he's spoken to: hard science, particularly physics textbooks is very useful for training reasoning, whereas social science is "totally useless" for it. Elon's response: "Yes, that's probably true." He then points to where all of this is heading: "Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics. So, things like the Optimus humanoid robot."

High Signal AI

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🚨 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

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Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.

Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.

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"Is it Bitter Lesson-pilled?" — the most used, and most misused, phrase in AI right now. Richard Sutton sums it up on the latest Training Data with Khurram Javed , Sonya Huang 🐥, Alfred Lin "Don't be distracted by human knowledge, as AI traditionally has been many times. Instead, focus on learning methods that will scale with computation, like search and like learning."

"Is it Bitter Lesson-pilled?" — the most used, and most misused, phrase in AI right now. Richard Sutton sums it up on the latest Training Data with Khurram Javed , Sonya Huang 🐥, Alfred Lin "Don't be distracted by human knowledge, as AI traditionally has been many times. Instead, focus on learning methods that will scale with computation, like search and like learning."

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ELON MUSK: " AI and Robotics is the only path to amazing abundance, which is not to say it can't go wrong hopefully, but I think it'll probably go right, and it'll be a future that you love. And it's the best future I can think of. And then we go beyond the moon, beyond Mars."

ELON MUSK: " AI and Robotics is the only path to amazing abundance, which is not to say it can't go wrong hopefully, but I think it'll probably go right, and it'll be a future that you love. And it's the best future I can think of. And then we go beyond the moon, beyond Mars."

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🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."

🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."

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Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

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“My prediction is, in the benign scenario of the future, the robots will make so many robots and AI that they will saturate all human needs. So if you had a robot that could take care of and protect elderly parents, I think that would be an amazing thing” —Elon Musk

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Elon Musk just put a 12-month countdown on the end of human cognitive dominance. Musk: “I think we might have AI that is smarter than any human by the end of this year. And I would say no later than next year.” Not a decade. Not five years. This year. Or next. A machine that surpasses the smartest human who has ever lived. Every Nobel laureate. Every genius. Every person at the absolute peak of human intellectual capability. Eclipsed. Within twelve months. Think about the smartest person you have ever met in your life. The one whose mind made you feel like you were operating on a different level. That person. Gone past. This year. But that’s just the first threshold. The second one is where the human mind stops being able to process the implications. Musk: “Probably by 2030 or 2031, call it five years from now, AI will be smarter than all of humanity collectively.” Not smarter than any individual. Smarter than every human being alive. Combined. Eight billion minds. Centuries of accumulated knowledge. The entire cognitive output of our species. Surpassed by a single system inside of five years. For all of recorded history, human intelligence was the most powerful force on earth. Every civilization. Every discovery. Every advancement in the human story. All of it produced by biological minds working at the edge of their capability. That era has an end date now. And the nation that builds the system crossing that threshold first doesn’t just win the AI race. It dictates the terms of every race that comes after it. The countdown Musk is describing isn’t a prediction anymore. It’s the last chapter of a story that took 300,000 years to write.

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Bill Maher said tonight that Elon Musk has been the smartest on the subject of AI. "I am very close to the cutting edge in AI and it scares the hell out of me. By the time we are reactive with AI regulation it is too late. AI is a fundamental existential risk for human civilization, and I don't think people fully appreciate that." - Elon Musk in 2017/2018

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222,817 次观看 • 5 个月前

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

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Elon Musk explains how we can achieve abundance for all with AI and robotics “With AI and robotics, this is the path to abundance for all If you want to solve global poverty or give everyone a very high standard of living, the only way to do that is AI and robotics" if AI and robotics can handle the production of goods and services at near-zero cost, we could effectively eliminate poverty Elon Musk is going to lead humanity into an age of Amazing Abundance

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"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

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ELON: EVEN OPEN-SOURCE AI IS GETTING BETTER HUMAN TESTS THAN HUMANS "We're getting to the point where even open-source AI can pass human captcha tests: 'Are you a human? Identify all the traffic lights in this picture.' You're like, okay, it's going to have no problem doing that. In fact, it'll do it better than a human and faster than a human. So, at the point at which it's better passing human tests than humans, then, what tests actually make sense?" Source: Interview with Rishi Sunak, November 2023

Mario Nawfal

42,071 次观看 • 1 年前