Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Gilbert Strang, MIT professor: "Anthropic pays $500,000 for one skill: linear algebra. a professor who gave 66 years to MIT taught all of it in his final lecture, for free." the article above hands you an AI that prints trading models on demand. strip the wrapper off any of... them and one object is left. a matrix. Strang spent his whole career on that one thing, and this was the last time he ever wrote it on a chalkboard. look at what he puts up. A x = 0. the null space. it is every mix of your signals that cancels to nothing. an AI will hand you a thousand factors and half of them live right there, pure risk, zero return. a model is a covariance matrix, a set of eigenvectors, one projection. that is the entire machine. no magic, no black box. he was 88, and this was his last class ever. the recording sits free on YouTube while people pay for a worse version of what he gave away. the AI builds the matrix. Strang teaches you to read it. only one of those two skills holds its price.show more

Rossst.03

6,598 subscribers

67,844 Aufrufe • vor 3 Tagen •via X (Twitter)

Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

17 Kommentare

Profilbild von Roan

Roanvor 3 Tagen

this is top tier share from MIT professor on math

Profilbild von Rossst.03

Rossst.03vor 3 Tagen

Yes, Gilbert Strang has dedicated his life to this)

Profilbild von Spark

Sparkvor 2 Tagen

the null space is where most factors live

Profilbild von Bullish Mike

Bullish Mikevor 3 Tagen

Generating a matrix with AI is a fifty-cent commodity; having the mathematical literacy to know that the matrix is mathematically singular and will blow your entire portfolio through the drywall is the only skill that will never be automated.

Profilbild von Mason Reid

Mason Reidvor 2 Tagen

the funny part is the “$500k AI skill” still comes back to linear algebra on a whiteboard 🤯new models, new agents, new tooling — same old math underneath everything.

Profilbild von Liquidden

Liquiddenvor 2 Tagen

AI can calculate the answer. knowing what to ask is another skill

Profilbild von 45 90

45 90vor 2 Tagen

Matrices forget time

Profilbild von SYNTHLEX

SYNTHLEXvor 2 Tagen

The matrix is not the scarce skill - every printed book already has one, and half of it lives in Ax=0; what still costs money is a second read that is allowed to kill the trade when those factors were only cancelling each other.

Profilbild von mani freecharge

mani freechargevor 2 Tagen

...

Profilbild von SpaceMaks

SpaceMaksvor 2 Tagen

AI gives you a thousand factors, Strang gave you the filter to tell signal from noise. Without the second, the first is just noise in a nice wrapper.

Profilbild von Brian Lampton

Brian Lamptonvor 2 Tagen

Modeling fucking noise.

Profilbild von Crio Songo

Crio Songovor 2 Tagen

基础才是一切的根啊，不管AI怎么发展，核心还是线性代数这些底层知识，回头我就找这个公开课补基础。

Profilbild von Macro_Ripples

Macro_Ripplesvor 2 Tagen

Does Anthropic really pays $500k to someone with JUST one skill, and that's Linear Algebra? Prove it.

Profilbild von Nobody

Nobodyvor 2 Tagen

sounds like another dream job if one can get it

Profilbild von Frezz

Frezzvor 2 Tagen

The best part is that the fundamentals never go out of data. 66 years of knowledge, given away for free.

Profilbild von Vogel_OpenCreators

Vogel_OpenCreatorsvor 2 Tagen

Volatility rewards the boring checklist, not the impulse click.

Profilbild von AI Mastery Guide

AI Mastery Guidevor 2 Tagen

linear algebra really is the secret weapon huh

Ähnliche Videos

Gilbert Strang taught linear algebra at MIT for fifty years. His last lecture is on YouTube. It has fewer views than his worst lecture. Nobody told students it was the last one. He just walked in, said "the final class in linear algebra at MIT," and started reviewing old exams. Google built a $2,000,000,000,000 company on one concept from this course. He is 88 years old. He still answers emails. This is MIT 18.06. Linear Algebra. The most watched mathematics course in history. Then the Markov matrix. Strang writes a transition matrix on the board. Three states. People moving between them every step. After enough steps - the system locks into a steady state that never changes. That steady state is an eigenvector. Google's PageRank works the same way. Websites are states. Clicks are transitions. The importance of every page on the internet is one eigenvector of one enormous matrix. Then least squares. Three data points. No line passes through all three. So you find the line that minimizes total error. That is how every AI model on earth is trained - including the ones running inside Goldman Sachs trading desks. Then the projection. The closest point on a plane to a vector outside it. Strang draws it in 30 seconds. That same operation is how Netflix decides what to recommend to 280,000,000 subscribers. Watch the moment he gives back the final exam answer and asks students to work backwards to the question - the room solves it in silence faster than any other lecture all semester. A software engineer told me 18.06 was the course that got her from $90,000 to $200,000 in two years. Same company. Different title. Bookmark this and watch later - after this lecture every dataset you touch will feel like a geometry problem waiting to be solved. MIT 18.06 Lecture 34 | Linear Algebra Final Review | Gilbert Strang

Gilbert Strang taught linear algebra at MIT for fifty years. His last lecture is on YouTube. It has fewer views than his worst lecture. Nobody told students it was the last one. He just walked in, said "the final class in linear algebra at MIT," and started reviewing old exams. Google built a $2,000,000,000,000 company on one concept from this course. He is 88 years old. He still answers emails. This is MIT 18.06. Linear Algebra. The most watched mathematics course in history. Then the Markov matrix. Strang writes a transition matrix on the board. Three states. People moving between them every step. After enough steps - the system locks into a steady state that never changes. That steady state is an eigenvector. Google's PageRank works the same way. Websites are states. Clicks are transitions. The importance of every page on the internet is one eigenvector of one enormous matrix. Then least squares. Three data points. No line passes through all three. So you find the line that minimizes total error. That is how every AI model on earth is trained - including the ones running inside Goldman Sachs trading desks. Then the projection. The closest point on a plane to a vector outside it. Strang draws it in 30 seconds. That same operation is how Netflix decides what to recommend to 280,000,000 subscribers. Watch the moment he gives back the final exam answer and asks students to work backwards to the question - the room solves it in silence faster than any other lecture all semester. A software engineer told me 18.06 was the course that got her from $90,000 to $200,000 in two years. Same company. Different title. Bookmark this and watch later - after this lecture every dataset you touch will feel like a geometry problem waiting to be solved. MIT 18.06 Lecture 34 | Linear Algebra Final Review | Gilbert Strang

Zyphor

56,603 Aufrufe • vor 18 Tagen

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

Ochob

130,880 Aufrufe • vor 1 Monat

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 Aufrufe • vor 2 Monaten

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Loran

47,557 Aufrufe • vor 1 Monat

An Indian physicist put the entire secret behind Renaissance's $100 billion on one chalkboard: a coin that barely beats 50/50. he is V. Balakrishnan, and his lecture went viral years after he recorded it. it has been free the whole time. almost nobody who trades has watched it. lecture one is the humblest thing in all of finance. a coin that lands your way a little more than half the time. that is the entire edge. that is all Renaissance ever had. the post above is the one equation for what to do with that coin. not what to bet. how much. bet too little and you die of old age. bet too much and one flip ends you. that same coin is every trade, every hand, every prediction market. a tiny edge, flipped a million times. the whole machine fits on his board, and he charged nothing for it. no slides. no notes. one coin and a stick of chalk. a quant I know says this old Indian lecture taught him probability better than his entire degree. the coin is free. the equation is free. the discipline to size it is the only thing left to earn.

An Indian physicist put the entire secret behind Renaissance's $100 billion on one chalkboard: a coin that barely beats 50/50. he is V. Balakrishnan, and his lecture went viral years after he recorded it. it has been free the whole time. almost nobody who trades has watched it. lecture one is the humblest thing in all of finance. a coin that lands your way a little more than half the time. that is the entire edge. that is all Renaissance ever had. the post above is the one equation for what to do with that coin. not what to bet. how much. bet too little and you die of old age. bet too much and one flip ends you. that same coin is every trade, every hand, every prediction market. a tiny edge, flipped a million times. the whole machine fits on his board, and he charged nothing for it. no slides. no notes. one coin and a stick of chalk. a quant I know says this old Indian lecture taught him probability better than his entire degree. the coin is free. the equation is free. the discipline to size it is the only thing left to earn.

Rossst.03

160,260 Aufrufe • vor 1 Monat

Prabha Sharma, one of the sharpest women mathematicians India has produced: "Optiver puts every applicant through a probability gauntlet, then pays the ones who pass $500,000 a year for one skill: counting the ways an outcome can happen before anyone else does. that is exactly where this professor opens her course, and it is free" this free lecture is the foundation under every prediction market and every price you have ever seen. strip away the models and the AI and one question is left. in how many ways can this happen, and how many of them pay you. get that count right and the probability falls out on its own. get it wrong and every model built on top is wrong with it. Sharma builds it from zero, on a blackboard, in notation a schoolkid can follow. permutations, combinations, the n choose r sitting under every set of odds. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the whole foundation for free.

Prabha Sharma, one of the sharpest women mathematicians India has produced: "Optiver puts every applicant through a probability gauntlet, then pays the ones who pass $500,000 a year for one skill: counting the ways an outcome can happen before anyone else does. that is exactly where this professor opens her course, and it is free" this free lecture is the foundation under every prediction market and every price you have ever seen. strip away the models and the AI and one question is left. in how many ways can this happen, and how many of them pay you. get that count right and the probability falls out on its own. get it wrong and every model built on top is wrong with it. Sharma builds it from zero, on a blackboard, in notation a schoolkid can follow. permutations, combinations, the n choose r sitting under every set of odds. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the whole foundation for free.

Rossst.03

61,370 Aufrufe • vor 24 Tagen

Gilbert Strang, the legendary mathematician who taught linear algebra for 61 years and became the most watched math professor in history: "I used to think a matrix was just a grid of numbers, until I proved that its rows and columns always agree on one number no matter how you look at them. That fact still feels like magic to me after sixty years." this is the exact proof sitting quietly underneath every factor model a risk desk trusts with real capital, and almost nobody outside a math department has ever seen it. strip away the notation and the idea is almost absurdly simple. take any matrix, any grid of numbers, and count how many of its rows are truly independent, meaning none of them can be built out of the others. now count the independent columns instead, a completely different question on the surface. those two numbers, row independence and column independence, always turn out exactly equal, no matter how large or lopsided the matrix is. nobody presenting a clean risk model out loud credits a decades old proof for the reason the math even holds together. zoom out to what this means for anything built on a grid of numbers today. a portfolio, a covariance matrix, a neural network's weights, all of them hide a true dimension smaller than their size suggests, and that hidden number is exactly what this proof pins down. the industry sells complexity as scale, more assets, more parameters, more rows and columns. but the real question was never how big the matrix is. it's how many independent directions are actually hiding inside it. the size of the grid was never the real story. it was the one number both sides of it were quietly agreeing on the whole time.

Gilbert Strang, the legendary mathematician who taught linear algebra for 61 years and became the most watched math professor in history: "I used to think a matrix was just a grid of numbers, until I proved that its rows and columns always agree on one number no matter how you look at them. That fact still feels like magic to me after sixty years." this is the exact proof sitting quietly underneath every factor model a risk desk trusts with real capital, and almost nobody outside a math department has ever seen it. strip away the notation and the idea is almost absurdly simple. take any matrix, any grid of numbers, and count how many of its rows are truly independent, meaning none of them can be built out of the others. now count the independent columns instead, a completely different question on the surface. those two numbers, row independence and column independence, always turn out exactly equal, no matter how large or lopsided the matrix is. nobody presenting a clean risk model out loud credits a decades old proof for the reason the math even holds together. zoom out to what this means for anything built on a grid of numbers today. a portfolio, a covariance matrix, a neural network's weights, all of them hide a true dimension smaller than their size suggests, and that hidden number is exactly what this proof pins down. the industry sells complexity as scale, more assets, more parameters, more rows and columns. but the real question was never how big the matrix is. it's how many independent directions are actually hiding inside it. the size of the grid was never the real story. it was the one number both sides of it were quietly agreeing on the whole time.

MindArch

18,494 Aufrufe • vor 1 Monat

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.

Rossst.03

707,196 Aufrufe • vor 28 Tagen

Sukhendu Das, IIT Madras professor: "Jump Trading pays a 23 year old $500,000 a year for one skill: given the odds, pick the one move that makes the most money on average. that rule is the first thing this professor proves on a free chalkboard, and it costs nothing" this free lecture is the decision rule sitting under every trading bot and every hedge fund model on the planet. strip away the AI and the neural nets and one question is left. given everything you know right now, what is the single best bet. there is a mathematically correct answer. it is called the Bayes decision rule, and it turns a probability into the one action that maximizes your payoff. every profitable bot is just this rule running fast. Das builds it from zero, on a blackboard, in notation a beginner can follow. the odds go in, the optimal bet comes out. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the rule itself for nothing.

Sukhendu Das, IIT Madras professor: "Jump Trading pays a 23 year old $500,000 a year for one skill: given the odds, pick the one move that makes the most money on average. that rule is the first thing this professor proves on a free chalkboard, and it costs nothing" this free lecture is the decision rule sitting under every trading bot and every hedge fund model on the planet. strip away the AI and the neural nets and one question is left. given everything you know right now, what is the single best bet. there is a mathematically correct answer. it is called the Bayes decision rule, and it turns a probability into the one action that maximizes your payoff. every profitable bot is just this rule running fast. Das builds it from zero, on a blackboard, in notation a beginner can follow. the odds go in, the optimal bet comes out. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the rule itself for nothing.

Rossst.03

69,755 Aufrufe • vor 23 Tagen

Srinivasan Chandrasekaran, IIT Madras professor: "Bridgewater pays a risk manager $650,000 a year to answer one question: what is the exact probability this blows up. that is the same question this professor answers to keep billion dollar oil rigs from collapsing in a storm, and he teaches the math for free" this free lecture is the risk model under every fund that has survived more than one crash. strip away the upside talk and one number decides everything. not how much you can make, but the odds the whole thing fails. an oil rig and a trading account die the same way: one rare load bigger than anything you planned for. reliability is the math that sizes your safety margin so that day does not end you. Chandrasekaran builds it from zero, on a blackboard, in notation a beginner can follow. failure modes, safety factors, and the probability of ruin. Wall Street rents this out as a $650,000 skill. an IIT professor hands you the math of not blowing up for free.

Srinivasan Chandrasekaran, IIT Madras professor: "Bridgewater pays a risk manager $650,000 a year to answer one question: what is the exact probability this blows up. that is the same question this professor answers to keep billion dollar oil rigs from collapsing in a storm, and he teaches the math for free" this free lecture is the risk model under every fund that has survived more than one crash. strip away the upside talk and one number decides everything. not how much you can make, but the odds the whole thing fails. an oil rig and a trading account die the same way: one rare load bigger than anything you planned for. reliability is the math that sizes your safety margin so that day does not end you. Chandrasekaran builds it from zero, on a blackboard, in notation a beginner can follow. failure modes, safety factors, and the probability of ruin. Wall Street rents this out as a $650,000 skill. an IIT professor hands you the math of not blowing up for free.

Rossst.03

21,051 Aufrufe • vor 21 Tagen

An MIT professor explained the one idea funds pay $500K for, and he does it in the first hour of freshman calculus. MIT put the whole lecture online. it has been free for twenty years. almost nobody finishes it. his point: calculus is not a hundred formulas to memorize. it is one idea. take two points on a curve, slide them together until the gap vanishes, and the slope you are left with is the derivative. everything else is just consequences. he is David Jerison, and his 18.01 lectures have taught more people calculus than any room on earth. skip to the board, where the power rule, the product rule, the chain rule, the fifty things you were forced to memorize, all fall out of that one limit. no formula sheet. no tricks. one definition, reused. that same slope is velocity. it is marginal cost. it is the gradient that trains a neural network. one operation, hiding everywhere something moves. the math is free. what nobody can sell you is the patience to understand the one limit instead of cramming the fifty formulas that fall out of it. that hour is the foundation, and it is the only part that ever pays.

An MIT professor explained the one idea funds pay $500K for, and he does it in the first hour of freshman calculus. MIT put the whole lecture online. it has been free for twenty years. almost nobody finishes it. his point: calculus is not a hundred formulas to memorize. it is one idea. take two points on a curve, slide them together until the gap vanishes, and the slope you are left with is the derivative. everything else is just consequences. he is David Jerison, and his 18.01 lectures have taught more people calculus than any room on earth. skip to the board, where the power rule, the product rule, the chain rule, the fifty things you were forced to memorize, all fall out of that one limit. no formula sheet. no tricks. one definition, reused. that same slope is velocity. it is marginal cost. it is the gradient that trains a neural network. one operation, hiding everywhere something moves. the math is free. what nobody can sell you is the patience to understand the one limit instead of cramming the fifty formulas that fall out of it. that hour is the foundation, and it is the only part that ever pays.

Loran

1,078,694 Aufrufe • vor 1 Monat

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 Aufrufe • vor 2 Monaten

An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.

An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.

Rossst.03

81,024 Aufrufe • vor 1 Monat

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Rossst.03

148,991 Aufrufe • vor 2 Monaten

In 2013 a Wall Street risk boss walked up to an MIT chalkboard and gave away the exact math desks charge $400,000 a year to run. MIT filmed it. it has been free for 13 years. 7.9 million people pressed play and almost none of them changed a thing. his point: the Wall Street edge was never a secret. it is a first lecture. options, risk, how a payout is actually built. every prediction-market trader hunting an edge in 2026 is chasing what Jake Xia put on a board in hour one of a free class. he ran risk for a living, not a channel. the video outdrew most blockbusters and still moved almost no one, because watching is not doing. skip to where he prices risk. he takes the thing quant threads sell for $500 and builds it from zero, slow enough for a beginner to follow. no paywall. no NDA. one board and a marker. a quant I know sends this link to every junior who whines the math is too hard. you are 13 years late. it is still free.

In 2013 a Wall Street risk boss walked up to an MIT chalkboard and gave away the exact math desks charge $400,000 a year to run. MIT filmed it. it has been free for 13 years. 7.9 million people pressed play and almost none of them changed a thing. his point: the Wall Street edge was never a secret. it is a first lecture. options, risk, how a payout is actually built. every prediction-market trader hunting an edge in 2026 is chasing what Jake Xia put on a board in hour one of a free class. he ran risk for a living, not a channel. the video outdrew most blockbusters and still moved almost no one, because watching is not doing. skip to where he prices risk. he takes the thing quant threads sell for $500 and builds it from zero, slow enough for a beginner to follow. no paywall. no NDA. one board and a marker. a quant I know sends this link to every junior who whines the math is too hard. you are 13 years late. it is still free.

Loran

152,813 Aufrufe • vor 1 Monat

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.

Lumen

24,218 Aufrufe • vor 21 Tagen

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900,000 to build multi-agent AI systems. the blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the article above is that blueprint pointed at markets. one giant model does not trade. a swarm of narrow agents does. each one dumb alone, each one able to veto the rest. Minsky proved it decades ago on a chalkboard. a mind is not a genius, it is a crowd of simple parts arguing. the pitch sells you one all-knowing model. the systems that actually survive are the swarm. no monolith. no single point of failure. just many dumb specialists who check each other.

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900,000 to build multi-agent AI systems. the blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the article above is that blueprint pointed at markets. one giant model does not trade. a swarm of narrow agents does. each one dumb alone, each one able to veto the rest. Minsky proved it decades ago on a chalkboard. a mind is not a genius, it is a crowd of simple parts arguing. the pitch sells you one all-knowing model. the systems that actually survive are the swarm. no monolith. no single point of failure. just many dumb specialists who check each other.

Rossst.03

79,898 Aufrufe • vor 4 Tagen

A dead MIT professor accidentally destroyed the $20 billion executive coaching industry with one hour of lecture, and ten million people have already watched him do it. He filmed it once in January 2018 and died eighteen months later. Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in that one hour for free. His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years. Every January for four decades, he gave a lecture called "How to Speak." His entire framework fits on a napkin. Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you." That last rule alone has probably cost the executive coaching industry a hundred million dollars. "Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order." That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk. Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train. The lecture is free on MIT OpenCourseWare. The textbook is free on his page. Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin. The napkin is free. The willingness to actually use it in your next meeting is the entire edge.

A dead MIT professor accidentally destroyed the $20 billion executive coaching industry with one hour of lecture, and ten million people have already watched him do it. He filmed it once in January 2018 and died eighteen months later. Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in that one hour for free. His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years. Every January for four decades, he gave a lecture called "How to Speak." His entire framework fits on a napkin. Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you." That last rule alone has probably cost the executive coaching industry a hundred million dollars. "Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order." That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk. Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train. The lecture is free on MIT OpenCourseWare. The textbook is free on his page. Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin. The napkin is free. The willingness to actually use it in your next meeting is the entire edge.

Isa

540,913 Aufrufe • vor 1 Monat

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

ooaes

28,543 Aufrufe • vor 1 Monat

$In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.$

In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.

Loran

33,982 Aufrufe • vor 1 Monat