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Marcus Hutter

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2020-02-26
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2020-02-26
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  1. “Pretty used to sell it out to Matthew. So you believe that you understand now why this phenomenon happens. But I give you a different example. I didn't play too much with this Converse game of life, but a little bit more with fractals and with the Mandalon set in this beautiful patterns. Just look Mandelbrot set. And, well, when the computers were really slow and just had a black and white monitor and I programmed my own programs in assembler to.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “That is truly amazing. And it's the prime example probably to demonstrate that very simple rules can lead to very rich phenomena. And people, you know, sometimes, you know, how is chemistry and biology so rich? I mean, this can't be based on simple rules. But no, we know quantum electrodynamics describes all of chemistry. And we come later back to that. I claim intelligence can be explained or described in one single equation, this very rich phenomenon. You asked also about whether, you know, I understand this phenomenon. Probably not. And there's this saying you never understand really things, you just get used to them.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “The seller laudomata and especially the Conway's game of life is really great because this ruler is so simple you can explain it to every child and even by hand you can simulate a little bit and you see this beautiful patterns emerge and people have proven that it's even Turing complete you cannot just use a computer to simulate game of life but you can also use game of life to simulate any computer”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “May become complex, and that may be counterintuitive, but there's a very nice analogy, the book, the library of all books. So imagine you have a normal library with interesting books and you go there, great, lots of information and quite complex. So now I create a library which contains all possible books, say, of 500 pages. So the first book just has AAA over all the pages. The next book, AAA, and ends with B. And so on. I create this library of all books. I can write a super short program which creates this library. So this library which has all books has zero information content. And you take a subset of this library and suddenly you have a lot of information in there.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “systems or by initial conditions which may be complex. So now if we don't take the whole universe with just a subset, you know, just take planet Earth. Planet Earth cannot be compressed into a couple of equations. This is a hugely complex system.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “But also, if you don't have noise, you have chaotic phenomena which are effectively like noise. So we can't get away with statistics even then. I mean, think about rolling a dice and forget about quantum mechanics and you know exactly how you throw it. But I mean, it's still so hard to compute the trajectory that effectively it is best to model it as coming out with a number with probability 1 over 6. But from this set of philosophical Kolmogorov complexity perspective, if we didn't have noise, then arguably you could describe the whole universe as standard model plus general activity. I mean, we don't have a theory of everything yet, but sort of assuming we are close to it or have it plus the initial conditions, which may hopefully be simple. And then you just run it and then you would reproduce the universe. But that's spoiled by noise or by chaotic.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “I would say it makes our life as a scientist really, really much harder. I mean, think about without noise, we wouldn't need all of the statistics.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “Get the thing going and then it will reproduce our universe. There's a problem with noise. We can come back to that later possibly.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “That's a tricky and difficult question. As I said before, I believe that the whole universe based on the evidence we have is very simple, so has a very short description.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Is called the Kolmogorph complexity, and arguably that is the information content in the data set. I mean, if the data set is very redundant or very boring, you can compress it very well. So the information content Should be low, and you know, it is lower according to this difference.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “Okay, Kolmogorov complexity is a notion of simplicity or complexity. And it takes the compression view to the extreme. So I explained before that if you have some data sequence, just think about a file and a computer and best sort of, you know, just a string of bits. And we have data compressors like we compress big files into zip files with certain compressors. And you can also produce self-extracting archerfs. That means as an executable, if you run it, it reproduces your original file without needing an extra decompressor. It's just a decompressor plus the archive together in one. Now there are better and worse compressors and you can ask what is the ultimate compressor? So what is the shortest possible self extracting archive you could produce for a certain data set which reproduces the data set? And the length of this”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “Well, at least all of science I see endeavor of compression, not all of humanity maybe. And, well, there are also some other aspects of science like experimental design, right? I mean, we create experiments specifically to get extra knowledge. And this is, that is then part of the decision-making process. Once we have the data to understand the data is essentially compression. So I don't see any difference between compression. Understanding and prediction.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “Essentially, I've already explained it so it compression means for me finding short programs for the data or the phenomenon at hand. You could interpret it more widely as finding simple theories which can be mathematical theories or maybe even informal, like just in words. Compression means finding short descriptions, explanations, programs for the data.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  14. “Yes, yeah. Well, in solomon of induction, precisely what you do is so you combine, so looking for the shortest program is like applying Opax razor, like looking for the simplest theory. There's also Epicorus principle, which says if you have multiple hypotheses, which equally well describe your data, don't discard any of them. Keep all of them around you. You never know. And you can put that together and say, okay, have a bias toward simplicity, but I don't rule out the larger models. And technically what we do is we weigh the shorter models higher and the longer models lower and you use a Bayesian technique, you have a prior, which is... Precisely 2 to the minus the complexity of the program, and you weigh all this hypothesis and take this mixture, and then you get also the stochasticity in.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “There are simpler models. There's a model print1 loop that also explains the data. And if you push it to the extreme, you are looking for the shortest program, which if you run this program, reproduces the data you have. It will not stop. It will continue naturally. And this you take for your prediction. And on the sequence of ones, it's very plausible, right? That print1 loop is the shortest program. We can give some more complex examples like one, two, three, four, five. What comes next? The short program is again a counter. And so that is roughly speaking how salom of induction works. The extra twist is that it can also deal with noisy data. If you have, for instance, a coin flip, say a biased coin, which comes up head with 60% probability, then it will predict, it will learn and figure this out. And after a while it predict, oh, the next coin.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “In simple terms. So assume we have a data sequence, make it very simple. The simplest one say 1111 and you see if 101s. What do you think comes next? The natural art, I must speed up a little bit. The natural answer is, of course, you know, one. And the question is why? Well, we see a pattern there. Okay, there's a one and we repeat it. And why should it suddenly after 100 ones be different? So what we're looking for is simple explanations or models for the data we have. And now the question is a model has to be presented in a certain language, in which language do we use. In science, we want formal languages and we can use mathematics or we can use programs on a computer, so abstractly on a Turing machine, for instance, or can be a general purpose computer. And there are, of course, lots of models of you can say, maybe it's 100 ones and then 100 zeros and 100 ones. That's a model, right?”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  17. “Yeah, so that's a theory which I claim, and claimed a long time ago, that this solves the big philosophical problem of induction. And I believe the claim is essentially true. And what it does is the following. Okay, for the picky listener, induction can be interpreted narrowly and wildly. Narrow means inferring models from data. And widely means also then using these models for doing predictions. So predictions are also part of the induction. So I'm a little bit sloppy sort of with the terminology and maybe that comes from Resolomanov, you know, being sloppy. Maybe I shouldn't say that. They can't complain anymore. So let me explain a little bit this theory.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “I guess mostly, in general, many things can be explained by an evolutionary argument. And there's some artifacts in humans which are just artifacts and not evolutionary necessary. But with this beauty and simplicity, it's, I believe, At least the core is about like science, finding regularities in the world, understanding the world, which is necessary for survival, right? You know, if I look A bush, right? And I just see noise, and there is a tiger, right? And eats me, then I'm dead. But if I try to find a pattern and we know that humans are prone to find more patterns in data than they are, like the Mars face and all these things. But these bias towards finding patterns, even if they are non, but I mean, it's best, of course, if they are, helps us for survival.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “Is the mechanism to finding these rules. And actually, in a more quantitative sense, and we come back to that later in Keroso-Somlom deduction, you can rigorously prove that if we assume that the world is simple is the best you can do in a certain sense.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “I believe that Occam's Razor is probably the most important principle in science. I mean, of course, we logically deduction, we do experimental design. But science is about finding, understanding the world, finding models of the world. And we can come up with crazy, complex models which explain everything, but predict nothing. But the simple model seem to have predictive power. It's a valid question why. There are two answers to that. You can just accept it. That is the principle of science. And we use this principle and it seems to be successful. We don't know why, but it just happens to be. Or you can try, you know, find another principle which explains or comes razor. And if we start with the assumption that the world is governed by simple rules, then there's a buyer. Towards simplicity and applying Occam's razor.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “So out comes Razor says that you should not multiply entities beyond necessity, which sort of if you translate it into proper English means, and in the scientific context means that if you have two theories or hypotheses or models which equally well describe the phenomenon of your study or the data, you should choose the more simple one.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “No, I strongly believe, and I'm pretty convinced that the universe is inherently beautiful, elegant, and simple and described by these equations. And we're not just picking that. I mean, if there were some phenomena which cannot be neatly described, scientists would try that, right? And, you know, there's biology which is more messy, but we understand that it's an emergent phenomena. And it's complex systems, but they still follow the same rules, right, of quantum electrodynamics. All of chemistry follows that. And we know that. I mean, we cannot compute everything because we have limited computational resources. No, I think it's not a bias of the humans, but it's objectively simple. I mean, of course, you never know, you know, maybe there's some corners very far out in the universe or super, super tiny below the nucleus of atoms or, well, parallel universes which are not nice and simple. But there's no evidence for that. And we should apply our”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “Okay, I have a big question first. I think it's very interesting hypothesis or idea. And I have a background in physics. So I know a little bit about physical theories, the standard model of particle physics and general relativity theory. And they are amazing and described virtually everything in the universe. And they're all in a sense computable theories. I mean, they're very hard to compute. And it's very elegant, simple theories which describe virtually everything in the universe. So there's a strong indication that somehow the universe is computable, but it's a plausible hypothesis.”

    2020-02-26 · Lex Fridman Podcast · #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source