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An MIT professor fired a gun in a lecture hall to teach students one formula. The recording is online. The topic sounds boring on paper: velocity and acceleration. Then he pulls out a rifle. He wants the speed of a bullet. No modern sensors, just two wires and a...

47,612 görüntüleme • 2 ay önce •via X (Twitter)

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A Stanford neuroscientist has spent thirty years arguing that you have never made a decision. Not one. Every choice you are proud of and every mistake you still think about was the output of biology you did not pick and cannot see. He teaches an entire course on why, and it is free. The man is Robert Sapolsky, who spent decades in Kenya living among wild baboons. He opens with a man who woke up one morning as somebody else. Same brain, same family, same job. One gene had changed. He describes the case and leaves it unresolved, because the point is not the answer. The point is that you already reached for one. Then he goes after the way we reach. His argument is that every explanation you have ever heard for why a person did something is broken in the same way. It is genetic. It is cultural. It is hormones. It is childhood. Each of those is a bucket, and the buckets do not exist anywhere in nature. They exist because universities have separate departments and separate corridors, and the corridors decided what counts as an explanation. Nothing interesting happens inside a bucket. Everything interesting happens where two of them touch, and almost nobody is trained to look there. He asks the room about free will, about God, about evil, about whether biology explains who becomes religious, and whether things that happened before you were born shape your politics thirty years later. Then he refuses to answer any of it, and tells them what they will be able to judge for themselves in ten weeks. The lectures run through aggression, depression, language, schizophrenia, sexuality, and why any of it differs between two people at all. Twenty five hours. One lecture hall. Free.

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Jeff Bezos on the exact moment he realized he would never be a great physicist: "I really was determined to be a theoretical physicist. It's why I went to Princeton in the first place. And then I realized I was going to be a mediocre theoretical physicist. There were a few people in my classes, in quantum mechanics and so on, who could effortlessly do things that were so difficult for me." "Theoretical physics is not one of those fields where only the top few percent move the state of the art forward. It's one of those things where your brain has to be wired in a certain way." "My friend Joe and I were working on a very difficult partial differential equations problem set one night, and there was one problem we worked on for three hours. We made no headway whatsoever. And we looked up at each other at the same time and we said, Yasantha." "He stared at it for just a few seconds, maybe 10 seconds, and he said, cosine. And I said, what do you mean, cosine? He said, that's the answer. And I said, no, no, no, come on. And he said, let me show you. And he took out some paper and wrote down three pages of equations, everything canceled out, and the answer was cosine." "I said, Yasantha, did you do that in your head? And he said, oh no, that would be impossible. A few years ago I solved a similar problem, and I could map this problem onto that problem, and then it was immediately obvious that the answer was cosine." "You have an experience like that, you realize maybe being a theoretical physicist isn't what the universe wants you to be."

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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

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