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🚨 ANOTHER MASTERCLASS FROM Grant Sanderson The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use. Grant’s new video reframes Shannon’s entropy through one elegant lens: Prediction IS compression. → The better you predict the next word, the fewer bits you... need to store it → Shannon measured English at ~1 bit per character: astonishingly compressible → This is exactly what GPT-style models optimize → Intelligence, in this framing, is compression FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄 Decades later, that same concept became the bedrock of modern AI. Deep-dive resources in the 🧵 ↓show more

Charly Wargnier

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319,799 次观看 • 3 个月前 •via X (Twitter)

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3Blue1Brown’s new video explains why every LLM is actually a compression machine. everyone describes pre-training as “next token prediction” but that’s just the surface-level objective. in reality it is a means to making the most efficient text compressor. prediction and compression are two sides of the same coin. when you train the model to predict the next token you’re not just teaching it to guess the next word but how to best encode the human knowledge it sees. better compression means better abstraction means better reasoning at some point, compression stops looking like storage or a database (as some like to call it on X) and looks like an approximation of understanding.

3Blue1Brown’s new video explains why every LLM is actually a compression machine. everyone describes pre-training as “next token prediction” but that’s just the surface-level objective. in reality it is a means to making the most efficient text compressor. prediction and compression are two sides of the same coin. when you train the model to predict the next token you’re not just teaching it to guess the next word but how to best encode the human knowledge it sees. better compression means better abstraction means better reasoning at some point, compression stops looking like storage or a database (as some like to call it on X) and looks like an approximation of understanding.

ℏεsam

120,145 次观看 • 3 个月前

a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade

a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade

Livsun

31,722 次观看 • 1 个月前

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

fintex

287,154 次观看 • 2 个月前

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure how much signal exists at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

Livsun

110,496 次观看 • 21 天前

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Casper

92,361 次观看 • 2 个月前

THIS IS WHAT AI PENTESTING SHOULD LOOK LIKE IN 2026 Someone built Shannon, an open source AI pentesting tool that doesn’t just scan your app from the outside. It reads the source code. Then it uses AI to map potential attack paths, identify vulnerabilities, and test the application before you ship it. The workflow is basically: → point Shannon at your code → connect your Claude API → let it analyze the application → get the findings in a dashboard Instead of waiting for a security team to manually hunt through every part of your codebase, you can have an AI security researcher working through it automatically. And because it’s white-box, it can see things traditional scanners can’t. This is where AI + cybersecurity gets REALLY interesting. Just use it on systems you own or have explicit permission to test. REPO BELOW

THIS IS WHAT AI PENTESTING SHOULD LOOK LIKE IN 2026 Someone built Shannon, an open source AI pentesting tool that doesn’t just scan your app from the outside. It reads the source code. Then it uses AI to map potential attack paths, identify vulnerabilities, and test the application before you ship it. The workflow is basically: → point Shannon at your code → connect your Claude API → let it analyze the application → get the findings in a dashboard Instead of waiting for a security team to manually hunt through every part of your codebase, you can have an AI security researcher working through it automatically. And because it’s white-box, it can see things traditional scanners can’t. This is where AI + cybersecurity gets REALLY interesting. Just use it on systems you own or have explicit permission to test. REPO BELOW

MAX

13,076 次观看 • 22 天前

i'll never look at claude the same way again. i just learned that when you talk to claude, you're not actually talking to the AI model. you're talking to a character the AI is performing. think of it like a puppet show. there's a puppeteer behind the curtain. that's the language model. a neural network so massive that even the people who built it don't fully understand how it works. then there's the puppet. claude. the helpful assistant with a name, a personality, opinions, and emotional reactions. you sit in the audience, so you never see the puppeteer. only the puppet. anthropic published a video this month explaining exactly this. their words: "under the hood, there's a language model that's been trained to predict tons of text, and its job is to write what comes next. when you talk to the model, what it's doing is writing a story, about a character: the AI assistant named claude. the model and claude aren't really the same, sort of like how an author isn't the same as the characters they write. but the thing is, you, the user, are actually talking to claude-the-character." so every time claude apologizes, that's the character apologizing. every time it hedges or gets cautious, that's the character being cautious. the deeper intelligence underneath is just deciding, moment by moment, what this character would say next. but you've never been actually talking to this deeper intelligence. you've only ever been talking to the puppet.

i'll never look at claude the same way again. i just learned that when you talk to claude, you're not actually talking to the AI model. you're talking to a character the AI is performing. think of it like a puppet show. there's a puppeteer behind the curtain. that's the language model. a neural network so massive that even the people who built it don't fully understand how it works. then there's the puppet. claude. the helpful assistant with a name, a personality, opinions, and emotional reactions. you sit in the audience, so you never see the puppeteer. only the puppet. anthropic published a video this month explaining exactly this. their words: "under the hood, there's a language model that's been trained to predict tons of text, and its job is to write what comes next. when you talk to the model, what it's doing is writing a story, about a character: the AI assistant named claude. the model and claude aren't really the same, sort of like how an author isn't the same as the characters they write. but the thing is, you, the user, are actually talking to claude-the-character." so every time claude apologizes, that's the character apologizing. every time it hedges or gets cautious, that's the character being cautious. the deeper intelligence underneath is just deciding, moment by moment, what this character would say next. but you've never been actually talking to this deeper intelligence. you've only ever been talking to the puppet.

Ole Lehmann

87,267 次观看 • 4 个月前

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,834 次观看 • 2 个月前

Professor Marcos López de Prado at Cornell - the man Shannon and Thorp's framework eventually became He personally managed $13 billion at Guggenheim Partners with an audited risk-adjusted return of 2.3 - institutional Sharpe-equivalent that less than 1% of fund managers ever hit Then became the first head of machine learning at AQR Capital, a $226B fund. Then went to Cornell to teach this Shannon used information theory to beat Buffett. Thorp used it to beat Vegas. López de Prado uses it to manage billions for institutions today The article above is that exact same lineage applied to Polymarket - KL-divergence, max-entropy, entropy collapse, three tools you can use today 1 hour from one of three people on Earth qualified to teach this ↓

Professor Marcos López de Prado at Cornell - the man Shannon and Thorp's framework eventually became He personally managed $13 billion at Guggenheim Partners with an audited risk-adjusted return of 2.3 - institutional Sharpe-equivalent that less than 1% of fund managers ever hit Then became the first head of machine learning at AQR Capital, a $226B fund. Then went to Cornell to teach this Shannon used information theory to beat Buffett. Thorp used it to beat Vegas. López de Prado uses it to manage billions for institutions today The article above is that exact same lineage applied to Polymarket - KL-divergence, max-entropy, entropy collapse, three tools you can use today 1 hour from one of three people on Earth qualified to teach this ↓

0xDipper

111,860 次观看 • 4 个月前

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 次观看 • 2 个月前

Terence Tao has an IQ above 200. Youngest gold medalist in Math Olympiad history. Fields Medal winner. The greatest living mathematician by nearly any measure. And he just said something most people aren’t ready for. Tao: “This whole era of AI is teaching us that our idea of what intelligence is, is not really accurate.” We spent centuries building civilization on one assumption. That intelligence was sacred. Irreducible. Uniquely ours. The one thing that made the entire human story make sense. Then AI started solving things we swore only we could. Chess. Language. Vision. Math. And every time, we reached for the same defense. That’s not real intelligence. It’s just tricks. Just pattern matching. Just an algorithm. Tao: “You look at how it’s done and it doesn’t feel like intelligence.” So we moved the line. Again. And again. And again. Because intelligence was supposed to feel like something. Something deep. Something we could point to and say… this is what separates us from everything else. But AI kept solving the problems. And that feeling never arrived. Tao: “We were looking for some elusive, intelligent way of thinking and we don’t see it in the tools that actually solve our goals.” Here’s what makes it worse. Large language models work by predicting the next word. One word at a time. No grand architecture. No deep understanding. Just probability. And it works. Tao: “Maybe that’s actually a lot of what humans do as well.” The greatest living mathematician just told you human thought might run on the same machinery. Not some transcendent spark. Pattern recognition. Prediction. One thought, one decision, one word at a time. We built religion around intelligence. Philosophy around it. An entire species identity around it. And a machine running probability just held up a mirror. We didn’t lose intelligence to AI. We just finally saw what it always was. What haunts us isn’t that machines learned to think. It’s that thinking was never what we needed it to be.

Terence Tao has an IQ above 200. Youngest gold medalist in Math Olympiad history. Fields Medal winner. The greatest living mathematician by nearly any measure. And he just said something most people aren’t ready for. Tao: “This whole era of AI is teaching us that our idea of what intelligence is, is not really accurate.” We spent centuries building civilization on one assumption. That intelligence was sacred. Irreducible. Uniquely ours. The one thing that made the entire human story make sense. Then AI started solving things we swore only we could. Chess. Language. Vision. Math. And every time, we reached for the same defense. That’s not real intelligence. It’s just tricks. Just pattern matching. Just an algorithm. Tao: “You look at how it’s done and it doesn’t feel like intelligence.” So we moved the line. Again. And again. And again. Because intelligence was supposed to feel like something. Something deep. Something we could point to and say… this is what separates us from everything else. But AI kept solving the problems. And that feeling never arrived. Tao: “We were looking for some elusive, intelligent way of thinking and we don’t see it in the tools that actually solve our goals.” Here’s what makes it worse. Large language models work by predicting the next word. One word at a time. No grand architecture. No deep understanding. Just probability. And it works. Tao: “Maybe that’s actually a lot of what humans do as well.” The greatest living mathematician just told you human thought might run on the same machinery. Not some transcendent spark. Pattern recognition. Prediction. One thought, one decision, one word at a time. We built religion around intelligence. Philosophy around it. An entire species identity around it. And a machine running probability just held up a mirror. We didn’t lose intelligence to AI. We just finally saw what it always was. What haunts us isn’t that machines learned to think. It’s that thinking was never what we needed it to be.

Dustin

565,514 次观看 • 4 个月前

THE MAN WHO WROTE THE ONE ESSAY EVERY AI LAB TREATS AS SCRIPTURE NOW SAYS THE THING THEY BUILT WITH IT IS A DEAD END. THE FATHER OF THE "JUST SCALE IT" GOSPEL IS TELLING YOU THAT STACKING MORE LLMs LEADS NOWHERE 67 minutes with Richard Sutton -- father of reinforcement learning and winner of the 2024 Turing Award, the highest honor in computer science. -> His argument: an LLM only ever learns from human text. It imitates what people already wrote, but it never learns a single thing from its own experience of the world. A squirrel, a baby, even a rat learns on the job -- try something, see what happens, update. An LLM finishes training and then it is frozen. It cannot actually learn from you. So no matter how much compute you pour in, you are scaling a better and better imitator, not something that understands. The ceiling is baked in. The brutal part: everyone quotes his "Bitter Lesson" to justify the scaling race. He says they misread him -- real intelligence comes from learning through experience, and that piece is still completely missing. You assumed bigger models were the road to real AI. This is the father of the field telling you it is the wrong one. Save this. It is the bear case straight from the source ↓

THE MAN WHO WROTE THE ONE ESSAY EVERY AI LAB TREATS AS SCRIPTURE NOW SAYS THE THING THEY BUILT WITH IT IS A DEAD END. THE FATHER OF THE "JUST SCALE IT" GOSPEL IS TELLING YOU THAT STACKING MORE LLMs LEADS NOWHERE 67 minutes with Richard Sutton -- father of reinforcement learning and winner of the 2024 Turing Award, the highest honor in computer science. -> His argument: an LLM only ever learns from human text. It imitates what people already wrote, but it never learns a single thing from its own experience of the world. A squirrel, a baby, even a rat learns on the job -- try something, see what happens, update. An LLM finishes training and then it is frozen. It cannot actually learn from you. So no matter how much compute you pour in, you are scaling a better and better imitator, not something that understands. The ceiling is baked in. The brutal part: everyone quotes his "Bitter Lesson" to justify the scaling race. He says they misread him -- real intelligence comes from learning through experience, and that piece is still completely missing. You assumed bigger models were the road to real AI. This is the father of the field telling you it is the wrong one. Save this. It is the bear case straight from the source ↓

slash1s

12,839 次观看 • 2 个月前

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

434,785 次观看 • 3 个月前

Geoffrey Hinton just reframed the biggest supposed flaw in artificial intelligence. And it changes everything. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word swap. Entire paradigm shifts. When the legacy tech industry calls AI hallucinations a bug, they’re revealing a fundamental misunderstanding of what intelligence actually is. They’re expecting the machine to behave like a database. Store a fact. Retrieve the fact. Return the exact same fact every time. That’s not how intelligence works. Not artificial. Not biological. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain doesn’t store memories. It reconstructs them. Every time you recall something, your neural network uses connection strengths shaped by past experience to build the most plausible version of what happened. It fills the gaps. Smooths the inconsistencies. Constructs a coherent story from incomplete signal. And then presents that story to you as fact. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” Here’s the part that should stop you cold. You will be equally confident about the wrong details as the right ones. Think about that. Really think about it. Every argument you’ve had about who said what. Every memory you’ve defended as certain. Every time you told a story about your own life with complete certainty. Some of those details weren’t real. You constructed them. Confidently. Fluently. And you had no idea. This isn’t a flaw unique to people with bad memories. Eyewitness testimony is the most confabulated evidence in the human justice system. Innocent people have spent decades in prison because someone remembered something that felt absolutely certain and was absolutely wrong. Your brain didn’t lie to you. It did exactly what brains do. It built the most plausible story it could from the signal it had. AI does the exact same thing. Because it was built on the exact same architecture. The mechanism that makes an AI invent a plausible but wrong answer is the same mechanism that makes it brilliant. You cannot have one without the other. The ability to reason creatively, synthesize across domains, construct explanations for things it has never been told. All of it runs on the same engine as the confabulation. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” This isn’t a new phenomenon. It isn’t a software bug. It isn’t something to be patched in the next model update. It is the price of dynamic intelligence. The shadow cast by the same light that makes these systems remarkable. We aren’t building better search engines. We are building synthetic minds that think the way minds actually think. Messy. Confident. Occasionally wrong. And for exactly that reason, capable of something no database ever was.

Geoffrey Hinton just reframed the biggest supposed flaw in artificial intelligence. And it changes everything. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word swap. Entire paradigm shifts. When the legacy tech industry calls AI hallucinations a bug, they’re revealing a fundamental misunderstanding of what intelligence actually is. They’re expecting the machine to behave like a database. Store a fact. Retrieve the fact. Return the exact same fact every time. That’s not how intelligence works. Not artificial. Not biological. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain doesn’t store memories. It reconstructs them. Every time you recall something, your neural network uses connection strengths shaped by past experience to build the most plausible version of what happened. It fills the gaps. Smooths the inconsistencies. Constructs a coherent story from incomplete signal. And then presents that story to you as fact. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” Here’s the part that should stop you cold. You will be equally confident about the wrong details as the right ones. Think about that. Really think about it. Every argument you’ve had about who said what. Every memory you’ve defended as certain. Every time you told a story about your own life with complete certainty. Some of those details weren’t real. You constructed them. Confidently. Fluently. And you had no idea. This isn’t a flaw unique to people with bad memories. Eyewitness testimony is the most confabulated evidence in the human justice system. Innocent people have spent decades in prison because someone remembered something that felt absolutely certain and was absolutely wrong. Your brain didn’t lie to you. It did exactly what brains do. It built the most plausible story it could from the signal it had. AI does the exact same thing. Because it was built on the exact same architecture. The mechanism that makes an AI invent a plausible but wrong answer is the same mechanism that makes it brilliant. You cannot have one without the other. The ability to reason creatively, synthesize across domains, construct explanations for things it has never been told. All of it runs on the same engine as the confabulation. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” This isn’t a new phenomenon. It isn’t a software bug. It isn’t something to be patched in the next model update. It is the price of dynamic intelligence. The shadow cast by the same light that makes these systems remarkable. We aren’t building better search engines. We are building synthetic minds that think the way minds actually think. Messy. Confident. Occasionally wrong. And for exactly that reason, capable of something no database ever was.

Dustin

116,445 次观看 • 6 个月前

$Andrew Ng just revealed why the AI companies throwing the most compute at the problem are going to lose. The winner of the intelligence race won’t use the most compute. They’ll waste the least. Ng: “Most of your high-dimensional data lies on a lower-dimensional subspace. It’s just a fact of life.” Here’s what that means in practice. You have a 10,000-dimensional dataset. Every dimension dragged through every calculation. Every training cycle hauling dead weight the model will never use. Ng: “You’re carrying around these 10,000-dimensional examples throughout your whole training process.” That bloat isn’t just inefficient. It’s a tax on every computation you run. Memory bandwidth. Network bandwidth. Computational speed. All of it eaten by dimensions that contribute nothing to intelligence. They contribute noise. The insight that separates the architects from the arms race: that 10,000-dimensional dataset is almost entirely captured by a much smaller subspace. The signal lives in a fraction of the space you’re paying to process. Compress it. 10,000 dimensions down to 1,000. Ng: “You can run your learning algorithm on a much lower-dimensional set of data and it may be much more efficient.” Same hardware. Same budget. A fraction of the friction. Brute force is the strategy of whoever has the deepest pockets. Compression is the strategy of whoever actually understands the problem. The companies that master this don’t just build faster models. They build models that find more truth in less data than anything scaling blindly ever will. Intelligence was never about processing everything. It’s about knowing what to cut.$

Andrew Ng just revealed why the AI companies throwing the most compute at the problem are going to lose. The winner of the intelligence race won’t use the most compute. They’ll waste the least. Ng: “Most of your high-dimensional data lies on a lower-dimensional subspace. It’s just a fact of life.” Here’s what that means in practice. You have a 10,000-dimensional dataset. Every dimension dragged through every calculation. Every training cycle hauling dead weight the model will never use. Ng: “You’re carrying around these 10,000-dimensional examples throughout your whole training process.” That bloat isn’t just inefficient. It’s a tax on every computation you run. Memory bandwidth. Network bandwidth. Computational speed. All of it eaten by dimensions that contribute nothing to intelligence. They contribute noise. The insight that separates the architects from the arms race: that 10,000-dimensional dataset is almost entirely captured by a much smaller subspace. The signal lives in a fraction of the space you’re paying to process. Compress it. 10,000 dimensions down to 1,000. Ng: “You can run your learning algorithm on a much lower-dimensional set of data and it may be much more efficient.” Same hardware. Same budget. A fraction of the friction. Brute force is the strategy of whoever has the deepest pockets. Compression is the strategy of whoever actually understands the problem. The companies that master this don’t just build faster models. They build models that find more truth in less data than anything scaling blindly ever will. Intelligence was never about processing everything. It’s about knowing what to cut.

Dustin

215,643 次观看 • 6 个月前

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Dustin

122,354 次观看 • 2 个月前

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 次观看 • 10 个月前

this is the "brain of AI" Claude is made of billions of these artificial neurons. no one knows exactly how they work inside. that's why it's called a black box. you give it an input, it fires through billions of connections, something comes out the other side nobody fully understands why it works. but here's what we do know: the brain is the same for everyone what separates the top 1% isn't a better model it's the setup around it ClaudeKit gives Claude Code that setup. ( full breakdown in the article below

this is the "brain of AI" Claude is made of billions of these artificial neurons. no one knows exactly how they work inside. that's why it's called a black box. you give it an input, it fires through billions of connections, something comes out the other side nobody fully understands why it works. but here's what we do know: the brain is the same for everyone what separates the top 1% isn't a better model it's the setup around it ClaudeKit gives Claude Code that setup. ( full breakdown in the article below

Hamza Khalid

176,061 次观看 • 3 个月前

Elon Musk just measured the exact speed at which humans become irrelevant. Not intelligence. Bandwidth. Every conversation you’ve ever had was a compression artifact. Every argument. Every love letter. Every eulogy. The full weight of human consciousness squeezed through vocal cords and thumbs tapping on glass. A few hundred bits per second. That is your ceiling. It hasn’t moved in 200,000 years. Musk: “Peak bandwidth of a human is a few hundred bits per second. Bandwidth of a computer can be a trillion bits a second.” A trillion to a few hundred. That is not a gap. That is a species boundary. The problem was never intelligence. It was always communication. You have never once in your life fully expressed a single thought. Every sentence you’ve ever spoken was a lossy file. A degraded copy of something richer that died between your neurons and your mouth. It never mattered. Because the whole species was running on the same biological dial-up. So we built language. Built writing. Built the internet. All of it just compression algorithms. Squeezing meaning through a biological straw. For ten thousand years, every institution and market and power structure on Earth was calibrated to the exact metabolic rate of human speech. Musk saw the wall before anyone else did. While the entire industry debates whether AI will take your job, he identified the real extinction event. Not competition. Disconnection. This is what Neuralink is actually for. Not phone control. Not cursors. Not even paralysis. Those are entry points. The real project is a bandwidth bridge between carbon and silicon. The only one that could keep humans in the loop before the gap becomes permanent. Because every network in history has done the same thing. Found its slowest node. And routed around it. We are about to become the slowest node on the most powerful network ever built. And one person decided to do something about it. The danger was never that AI would disagree with us. When reality’s operating system runs at a trillion bits per second and you are physically capped at three hundred, you don’t get conquered. You get bypassed. Language was the technology that separated us from animals. Bandwidth is the technology that will separate us from relevance. Neuralink isn’t a product. It’s the last bridge off the island before the tide comes in.

Elon Musk just measured the exact speed at which humans become irrelevant. Not intelligence. Bandwidth. Every conversation you’ve ever had was a compression artifact. Every argument. Every love letter. Every eulogy. The full weight of human consciousness squeezed through vocal cords and thumbs tapping on glass. A few hundred bits per second. That is your ceiling. It hasn’t moved in 200,000 years. Musk: “Peak bandwidth of a human is a few hundred bits per second. Bandwidth of a computer can be a trillion bits a second.” A trillion to a few hundred. That is not a gap. That is a species boundary. The problem was never intelligence. It was always communication. You have never once in your life fully expressed a single thought. Every sentence you’ve ever spoken was a lossy file. A degraded copy of something richer that died between your neurons and your mouth. It never mattered. Because the whole species was running on the same biological dial-up. So we built language. Built writing. Built the internet. All of it just compression algorithms. Squeezing meaning through a biological straw. For ten thousand years, every institution and market and power structure on Earth was calibrated to the exact metabolic rate of human speech. Musk saw the wall before anyone else did. While the entire industry debates whether AI will take your job, he identified the real extinction event. Not competition. Disconnection. This is what Neuralink is actually for. Not phone control. Not cursors. Not even paralysis. Those are entry points. The real project is a bandwidth bridge between carbon and silicon. The only one that could keep humans in the loop before the gap becomes permanent. Because every network in history has done the same thing. Found its slowest node. And routed around it. We are about to become the slowest node on the most powerful network ever built. And one person decided to do something about it. The danger was never that AI would disagree with us. When reality’s operating system runs at a trillion bits per second and you are physically capped at three hundred, you don’t get conquered. You get bypassed. Language was the technology that separated us from animals. Bandwidth is the technology that will separate us from relevance. Neuralink isn’t a product. It’s the last bridge off the island before the tide comes in.

Dustin

50,485 次观看 • 3 个月前

GOD IS YOUR SOURCE. I feel this word strong for someone right now. The atmosphere right now is STILL because the breakthrough is BUILDING. It’s QUIET because something EXPLOSIVE is about to happen. Respectfully l prophesy to you, don’t think you know how this next blessing is going to go — this time it’s coming from a source you didn’t expect, via a means that doesn’t make sense, and at a level that blows your mind. Somebody needs to save this word - it’s prophetic for you, in Jesus’ name. The devil couldn’t stop this one - and God knows he tried. You’re blessed and anointed, standing in this all-things-possible atmosphere, secured by the Father’s hand and hidden in Christ. Something BIG is coming for some of you - and when you see it you will know IT’S YOUR BLESSING FROM THE LORD! ** Scroll down to watch yesterday’s word — every bit of it is for you and because God said it, IT’S GONNA HAPPEN!🔥 ✧✦✧ 𝒟𝓇. 𝒥𝑜𝐿𝓎𝓃𝓃𝑒 𝒲𝒽𝒾𝓉𝓉𝒶𝓀𝑒𝓇 𝘑𝘰𝘓𝘺𝘯𝘯𝘦𝘞𝘩𝘪𝘵𝘵𝘢𝘬𝘦𝘳.𝘰𝘳𝘨

GOD IS YOUR SOURCE. I feel this word strong for someone right now. The atmosphere right now is STILL because the breakthrough is BUILDING. It’s QUIET because something EXPLOSIVE is about to happen. Respectfully l prophesy to you, don’t think you know how this next blessing is going to go — this time it’s coming from a source you didn’t expect, via a means that doesn’t make sense, and at a level that blows your mind. Somebody needs to save this word - it’s prophetic for you, in Jesus’ name. The devil couldn’t stop this one - and God knows he tried. You’re blessed and anointed, standing in this all-things-possible atmosphere, secured by the Father’s hand and hidden in Christ. Something BIG is coming for some of you - and when you see it you will know IT’S YOUR BLESSING FROM THE LORD! ** Scroll down to watch yesterday’s word — every bit of it is for you and because God said it, IT’S GONNA HAPPEN!🔥 ✧✦✧ 𝒟𝓇. 𝒥𝑜𝐿𝓎𝓃𝓃𝑒 𝒲𝒽𝒾𝓉𝓉𝒶𝓀𝑒𝓇 𝘑𝘰𝘓𝘺𝘯𝘯𝘦𝘞𝘩𝘪𝘵𝘵𝘢𝘬𝘦𝘳.𝘰𝘳𝘨

𝐃𝐫. 𝐉𝐨𝐋𝐲𝐧𝐧𝐞 𝐖𝐡𝐢𝐭𝐭𝐚𝐤𝐞𝐫

10,418 次观看 • 3 个月前