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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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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

119,751 Aufrufe • vor 1 Monat

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

279,329 Aufrufe • vor 5 Tagen

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

87,139 Aufrufe • vor 2 Monaten

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

13,419 Aufrufe • vor 22 Tagen

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 Aufrufe • vor 2 Monaten

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

269,381 Aufrufe • vor 21 Tagen

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

12,623 Aufrufe • vor 18 Tagen

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

562,582 Aufrufe • vor 2 Monaten

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

432,172 Aufrufe • vor 1 Monat

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

116,231 Aufrufe • vor 4 Monaten

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

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

215,185 Aufrufe • vor 4 Monaten

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

121,240 Aufrufe • vor 12 Tagen

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 Aufrufe • vor 8 Monaten

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

175,753 Aufrufe • vor 1 Monat

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

50,353 Aufrufe • vor 1 Monat

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

10,329 Aufrufe • vor 1 Monat

This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius

This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius

Sahil Bloom

213,822 Aufrufe • vor 1 Jahr

Gilbert Strang, MIT professor: "Citadel and OpenAI both pay $400K+ for people fluent in one branch of math. I taught it to the whole world from an MIT classroom, for free." this free MIT lecture is the whole engine under the "AI" everyone suddenly worships. strip the branding off a neural network and this is what's underneath: linear algebra, matrices multiplied over and over. the man teaching it spent fifty years at MIT and gave every lecture away for nothing. and what it does is simple. it lets you work with thousands of numbers at once instead of one at a time. how every asset moves against every other, the hidden factors driving a whole market, the guts of a neural net, all of it is just matrices. it is the grammar every model speaks, and it hasn't changed in decades. which is the entire point of the article above. renaissance was already running on this exact math in 1988. the "AI revolution" didn't build the engine, it just rebranded it. here is what the pitch skips. everyone's model runs on the same linear algebra, so the math was never the edge. the matrices don't care whose data goes in. cleaner data, and the discipline to trust the output, are the only parts anyone was ever really paying for.

Gilbert Strang, MIT professor: "Citadel and OpenAI both pay $400K+ for people fluent in one branch of math. I taught it to the whole world from an MIT classroom, for free." this free MIT lecture is the whole engine under the "AI" everyone suddenly worships. strip the branding off a neural network and this is what's underneath: linear algebra, matrices multiplied over and over. the man teaching it spent fifty years at MIT and gave every lecture away for nothing. and what it does is simple. it lets you work with thousands of numbers at once instead of one at a time. how every asset moves against every other, the hidden factors driving a whole market, the guts of a neural net, all of it is just matrices. it is the grammar every model speaks, and it hasn't changed in decades. which is the entire point of the article above. renaissance was already running on this exact math in 1988. the "AI revolution" didn't build the engine, it just rebranded it. here is what the pitch skips. everyone's model runs on the same linear algebra, so the math was never the edge. the matrices don't care whose data goes in. cleaner data, and the discipline to trust the output, are the only parts anyone was ever really paying for.

Rossst.03

33,926 Aufrufe • vor 25 Tagen

🚨Tom Emmer is exactly right: “CBDC is the ultimate surveillance tool that should NEVER have a place in this country.” Listen to EVERY single word he just said. 🔥💪 “We don’t have to guess how they would use it. All you got to do is look at the digital yuan; the Chinese are already using it. They surveil their citizens, they’re building scores based on your habits, what you get to buy at the grocery store, how you travel. This is the ULTIMATE form of surveillance … a Central Bank Digital Currency will give the leftists in this country exactly what they want!”

🚨Tom Emmer is exactly right: “CBDC is the ultimate surveillance tool that should NEVER have a place in this country.” Listen to EVERY single word he just said. 🔥💪 “We don’t have to guess how they would use it. All you got to do is look at the digital yuan; the Chinese are already using it. They surveil their citizens, they’re building scores based on your habits, what you get to buy at the grocery store, how you travel. This is the ULTIMATE form of surveillance … a Central Bank Digital Currency will give the leftists in this country exactly what they want!”

Rep. Keith Self

37,172 Aufrufe • vor 2 Monaten

What you're looking at started as something that most people drive past without a second thought. - Industrial - boring - overlooked And someone looked at it and saw something no one else was seeing. That's the whole game here. You don't have to invent something brand new to make serious money. You just have to see potential where everyone else sees limitations. You have to look at what already exists and imagine it just a little differently. So when you turn something familiar into something unexpected, it becomes shareable and memorable. It becomes the thing that people can't stop talking about because it's different in a world where everything feels the same. People will literally travel hours just to stay somewhere like this because different is the experience now. Nobody cares about another cookie cutter hotel room that they've seen a thousand times, but this gets filmed and posted and sent to friends with the caption "you have to see this place" And suddenly you're booked out months in advance because you gave something people can't get anywhere else. I love that the raw materials here aren't special, it's just space that was sitting there unused and cheap to acquire The success of this is found in the willingness to look at what everyone else ignored and ask "what could this become?" Instead of just accepting what currently is. That's where the opportunity is. There are thousands of: - shipping containers rotting in fields - old grain silos sitting empty - warehouses no one's using - barns that haven't been touched in decades And every single one of them could become something people would pay to experience if someone just had the vision to see it through. You don't need to build it from scratch. You just need to reimagine what's already sitting in front of you. Because the world is full of overlooked things waiting for someone to look at them differently. And I don't care if AI generated the video because the inspiration you get from it is still the same.

Chris Koerner

18,208 Aufrufe • vor 5 Monaten