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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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1,938,848 次观看 • 10 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

146,526 次观看 • 1 年前

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

21,696 次观看 • 1 个月前

🚨 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 次观看 • 2 个月前

Elon Musk: As human intelligence plateaus, AI will scale to harness most of the power of the galaxy Elon: "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute, and then ultimately most of the power of the galaxy." “ Once you think about artificial intelligence not as sort of this destination that you reach, but really as part of the overall escalation of intelligence, you know, human intelligence has also scaled as the population has increased, and we've been able to store more and more information.” “Because of population declines and low birthrate, human intelligence is somewhat plateauing and will actually decline.” “I think that we might have AI smarter than any single human at anything as soon as next year.” “And then probably within five (years), like say 2030, probably AI is smarter than the sum of all humans.” Friedberg: “ Do you think humans are on the decline because the AI is evolving?” “Do you think there's this evolution of the ecosystem on earth that's underway, that we don't really understand the structure of what's going on?” Elon: “ Yeah, maybe we implicitly know that it's coming.” Elon Musk david friedberg

Elon Musk: As human intelligence plateaus, AI will scale to harness most of the power of the galaxy Elon: "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute, and then ultimately most of the power of the galaxy." “ Once you think about artificial intelligence not as sort of this destination that you reach, but really as part of the overall escalation of intelligence, you know, human intelligence has also scaled as the population has increased, and we've been able to store more and more information.” “Because of population declines and low birthrate, human intelligence is somewhat plateauing and will actually decline.” “I think that we might have AI smarter than any single human at anything as soon as next year.” “And then probably within five (years), like say 2030, probably AI is smarter than the sum of all humans.” Friedberg: “ Do you think humans are on the decline because the AI is evolving?” “Do you think there's this evolution of the ecosystem on earth that's underway, that we don't really understand the structure of what's going on?” Elon: “ Yeah, maybe we implicitly know that it's coming.” Elon Musk david friedberg

The All-In 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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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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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."

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."

Wide Awake Media

144,618 次观看 • 7 个月前

🚨 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."

The All-In Podcast

597,321 次观看 • 1 年前

“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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Mars University

26,159 次观看 • 3 个月前

Hot take from my panel on decentralized AI at UC Berkeley: Your data is worthless. The idea that you'll get rich selling your data to train AI is a fantasy. The frontier is high-quality synthetic data, not your dumb tweets. Second hot take: the government censorship of AI has not started yet. It will come soon. And when it does, decentralized AI will matter in the same way that the rise of financial surveillance made Bitcoin matter. Spicy back and forth with Avichal - Electric ϟ Capital and Franklin Bi 👇

Hot take from my panel on decentralized AI at UC Berkeley: Your data is worthless. The idea that you'll get rich selling your data to train AI is a fantasy. The frontier is high-quality synthetic data, not your dumb tweets. Second hot take: the government censorship of AI has not started yet. It will come soon. And when it does, decentralized AI will matter in the same way that the rise of financial surveillance made Bitcoin matter. Spicy back and forth with Avichal - Electric ϟ Capital and Franklin Bi 👇

Haseeb ＞|＜

81,012 次观看 • 1 年前

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

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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222,808 次观看 • 4 个月前

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.

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.

Dustin

102,904 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.”

TBPN

18,811 次观看 • 1 年前

"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.

"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.

Box

66,225 次观看 • 1 个月前

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

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

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

442,841 次观看 • 29 天前

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.

The All-In Podcast

80,524 次观看 • 6 个月前