Elon Musk Makes Sense to Me (Eric Weinstein) | AI Podcast Clips

Let me ask a silly question, but. We'll say it with a straight face. Impossible. So let me mention Elon Musk. What are your thoughts about, he's more, you're more on the physics theory side of things. He's more on the physics engineering side of things in terms of SpaceX efforts. What do you think of his efforts to get off this planet? Well, I think he's the other guy who's semi-serious about getting off this planet. I think there are two of us who are semi-serious about getting off the planet. What do you think about his methodology and yours? When you look at them. Don't, I don't wanna be against Elon, because I was so excited that your top video was Ray Kurzweil and then I did your podcast. We had some chemistry, so it zoomed up. And I thought, okay, I'm gonna beat Ray Kurzweil. So just as I'm coming up on Ray Kurzweil, you're like, and now, Alex Fridman special, Elon Musk, and he blew me out of the water. So I don't wanna be petty about it. I wanna say that I don't, but I am. Okay, but here's the funny part. He's not taking enough risk. Like, he's trying to get us to Mars. Imagine that he got us to Mars, the moon, and we'll throw in Titan. And nowhere good enough. The diversification level is too low. Now, there's a compatibility. First of all, I don't think Elon is serious about Mars. I think Elon is using Mars. As a narrative, as a story, as a dream. To make the moon jealous. To make the, no. No. I think he's using it as a story to organize us, to reacquaint ourselves with our need for space, our need to get off this planet. It's a concrete thing. He's shown that, many people think that he's shown that he's the most brilliant and capable person on the planet. I don't think that's what he showed. I think he showed that the rest of us have forgotten our capabilities. So he's like the only guy who has still kept the faith. And is like, what's wrong with you people? So you think the lesson we should draw from Elon Musk is there's a capable person within a lot of us. Elon makes sense to me. In what way? He's doing what any sensible person should do. He's trying incredible things and he's partially succeeding, partially failing. To try to solve the obvious problems before. Duh. You know, but he comes up with things like, you know, I got it. We'll come up with a battery company, but batteries aren't sexy. So we'll make a car around it. Like, great. You know, or any one of a number of things. Elon is behaving like a sane person. And I view everyone else as insane. And my feeling is that we really have to get off this planet. We have to get out of this, we have to get out of the neighborhood. Dilingual a little bit, do you think that's a physics problem or an engineering problem? I think it's a cowardice problem. I think that we're afraid that we had 400 hitters of the mind, like Einstein and Dirac, and that era is done. And now we're just sort of copy editors. So is some of it money? Like, if we become brave enough to go outside the solar system, can we afford to, financially? Well, I think that that's not really the issue. The issue is, look what Elon did well. He amassed a lot of money. And then he plowed it back in, and he spun the wheel, and he made more money. And now he's got FU money. Now the problem is, is that a lot of the people who have FU money are not people whose middle finger you ever wanna see. I wanna see Elon's middle finger. I wanna see what he's gonna do. What do you mean by that? Or like when you say, fuck it, I'm gonna do the biggest possible thing. He's gonna do whatever the fuck he wants. Yeah. Right? Fuck you, fuck anything that gets in his way that he can afford to push out of his way. And you're saying he's not actually even doing that enough. No, I'm. He's not going. Please, I'm gonna go. Elon's doing fine with his money. I just want him to enjoy himself, have the most, you know, Dionysian. But you're saying Mars is playing it safe. He doesn't know how to do anything else. He knows rockets. Yeah. And he might know some physics at a fundamental level. Yeah. I guess, okay, just let me just go right back to it. How much physics do you really, how much brilliant breakthrough ideas on the physics side do you need to get off this planet? I don't know. And I don't know whether, like in my most optimistic dream, I don't know whether my stuff gets us off the planet. But it's hope. It's hope that there's a more fundamental theory that we can access, that we don't need, you know, whose elegance and beauty will suggest that this is probably the way the universe goes. Like you have to say this weird thing, which is this I believe. And this I believe is a very dangerous statement. But this I believe, I believe that my theory points the way. Now, Elon might or might not be able to access my theory. I don't know what he knows. But keep in mind, why are we all so focused on Elon? It's really weird. It's kind of creepy too. Why? He's just the person who's just asking the obvious questions and doing whatever he can. But he makes sense to me. You see, Craig Venter makes sense to me. Jim Watson makes sense to me. But we're focusing on Elon because he somehow is rare. Well, that's the weird thing. Like we've come up with a system that eliminates all Elon from our pipeline. And Elon somehow snuck through when they weren't quality adjusting everything.


Devon Larratt vs Lex Fridman: How to arm wrestle

Devin Larratt, you son of a bitch. That's Rueben. What's the matter? MIT got you pushing too many pencils? Alright, you got me. No. Now, if I wanted to beat you, what would I do here? It starts before we even arm wrestle. It's all postural, really. So, if I can make you come to me. Come to me. This is the first step. Always make me come to you. Exactly. It lets you use more muscles against less muscles. You target weakness, right? You target my fingertips, you get to use more muscles. So, you see this ridge? Yeah. Very important. The higher up this ridge is in my hand, the better. The more the pressure is in my fingers, as opposed to your fingers, the better. Right. What are some basics here that people should know? Maybe we'll start with a move called the post. The post, by itself, is not that powerful of a move. But it's a fantastic opening. Once you have a post established, the world completely opens up for you. The post is very simply an upwards drive of the knuckles. And it also climbs at the same time. Yes, exactly. And it's all about generating better angles. That's it. You're always trying, like if you open up your hand, you're always trying to get deeper as well. So, you're trying to get deeper and deeper. Climbing up and over. Both. That's it. Advancing that position all the time. So, you're always climbing. You're always climbing over and over. Yeah, that's the goal. Yeah, always climbing, always improving position. A, B, C. Always be climbing. So, basically, inside and outside, that will be your first division. So, when we're arm wrestling outside, basically, it's all these pulling motions. Pulling, pulling. And to some degree, they all attack the fingers. Okay? A standard top roll will look something like this. I'll start to apply pressure up into the fingers. I'll start to bend my wrist. And as I'm bending my wrist, you will be attempting to bend your wrist. And I will block you with something called pronation, which is this turning. And I'll carry through the fingers. And I'll take you all the way to the pin pad through the fingertips. It's a fantastic move for beating people. It's one of the best techniques in arm wrestling, the top roll. Most world champions, I would say, most use top roll. I've also seen you compliment somebody that, you know, it's not over here yet. No. You can hold some of the strongest people in the world like this. And I think you were complimenting somebody that they were able to do that against. Another way to talk about arm wrestling, so we started to touch on the top roll, the hook. So, a hook is basically wrist flexor driven. So, it's more about attacking the person's pronation. So, you establish a hook, and then you can pull out of it. Or you make it more of a press, and it's more shoulders coming right forward. And you're coming right in on the person. So, hooking and pressing, all the moves in arm wrestling are similar. Okay? Just subtle differences. But the roll results in an asymmetric. Like I'm losing your weight. Right. It feels like the hook is where. You get to stay in the match a little bit more. You stay in the match, yeah. You do, yeah. But a good arm wrestler isn't going to necessarily give you that. Like, they're not. You don't see a lot of world class matches where the guy is just pressing. You don't see it. The controversial King's move. What is that? Okay, so the King's move is actually, it's a defensive move. Okay, so the only thing I'm trying to do when I King's move is I'm trying to attack your arm angle through dragging. Okay? And I'm trying to target your fingers through pronation. I'm trying to do all the other things as well, but these are my anchors. Okay, so I'll fight for all the other stuff, but I'll give them away. This is the one that I hold. So, I will turn, right? And a lot of times there's a dipping. Okay? So, you're pulling also? Are you basically pulling me towards you? I'll try, but I may not be able to. So, normally a King's move is used against a very strong opponent. The King's move is throwing a big wrench into the sport, but this has been happening a lot as the sport evolves. The King's move is here to stay. Where do the injuries come from? Right here. That's the one. The bad one is the break. Is the break. Of what? Humerus. Oh, wow. Right here. Yeah. So, literally break your arm. Test your frame, man. We're like, and we're not a bicep tear. It can happen. Bicep tears can happen. I'd say that the break is the one that's. It's a literal arm break. Oh, yeah. And when it goes, it's like. Yep. Wow. The strongest arm I've ever felt in my life. And he's not even trying. I'm very specialized. I'm a super specialized athlete. Ladies and gentlemen, Devin Larratt. This is a huge honor. Thank you so much. Hey, for me too. Right there......


The biggest chess game ever

This is a game of chess, played by two AI engines, each far better at chess than any human on earth. The question I had is what happens when you let these machines battle it out on an infinite chess board with infinite pieces. Each move calculated and performed on an 8x8 subset that in isolation is a totally legal position with two kings, but in the big picture is just a tiny subset of a much bigger game. Where each checkmate destroys the king and the deserted pieces move on in search of another neighboring victim. Pieces get captured and sacrificed, in other words the usual game of chess, but on a much bigger board. The play is extended arbitrarily outwards, but here we focus on just this subset of about 6,000 squares. On the bottom is the counter showing the number of kings and checkmate victories. Let me mention some details. The boards are initialized with the middle game position from one of 30,000 famous grandmaster games, including Magnus Carlsen, Bobby Fischer, Gasparov, Spassky, Tal, Karpov and so on. The selection of which 8x8 subset to use for computing the next move is made randomly among all the boards that have legal chess positions. Optimizing the selection process is something that could be formulated as a reinforcement learning problem and would make this meta game of infinite chess very interesting. In fact, there are fascinating questions here about the generalizability of AI engines, far beyond the scale and the constraints of the game they were trained on. Chess is just a game, but when you start to remove the constraints and increase the scale, it becomes something else, something more like the game of life played on an infinite chessboard. Thank you for watching!


Best feature of Python | List Comprehensions

This is a list comprehension that allows you to define one list in terms of another list and is reminiscent of set notation from mathematics. The elegance, simplicity and power of this notation makes it, in my opinion, the best feature of Python. Now what does it mean to be a best feature of a programming language? To me, it's a feature that makes you fall in love with the language and a feature that makes you enjoy programming in the language for many years. So the basic notation is a for loop over some input list nums and a function like f of x that computes something for each element x in nums. In addition, there's a filter conditional like if g of x, some function, some expression that filters the elements of nums and only keeps the ones that pass this conditional. Let's look at an example, input list nums 1, 2, 3, 4. The list comprehension squares each element of nums, so x times x for x in nums. So that creates a list that contains 1, 4, 9 and 16. Simple, beautiful. And now to add a filter to keep just the even numbers, we can add into the filter conditional of the list comprehension x modulo 2 equals 0. And then the result is the squaring of the elements that pass the filter, which is 4 and 16. Now some would argue that you can achieve the same kind of results with for loops or more direct comparisons, the map and filter functions, which are also available in Python. So what would that look like to square each element in the list? You can have a lambda function that does the squaring and a map that applies that lambda function to each element of nums. That's the second line in the code here. And the third line, you can add a filter to that. So first apply a filter with a lambda function that does the modulo 2 equals 0 conditional. And then on top of that, on the elements that pass the filter, you again do the map function of the lambda that squares each element. Now I believe this is also beautiful and powerful notation, but to me it's not nearly as elegant and Pythonic and readable as the list comprehension notation. Now I already did a video on the most controversial Python feature, which in my opinion is the Walrus operator. It comes into play nicely with list comprehensions. Now if we take some difficult to compute function like fibo here, which computes the nth element of the Fibonacci sequence, the one line ternary operator recursive implementation of the function, written by me, untested. I leave it to you as homework to test if this actually works. And I threw it in there to give a shout out to two other things I enjoy, which is recursion and the ternary operator. The if-else notation of which in Python I think is another beautiful design choice that makes an otherwise cryptic looking ternary operator actually readable to our human brains. And so if we take then another definition of nums that goes from 1 to 6, we can create a basic list comprehension that applies the fibo function to each element of nums, resulting in the familiar Fibonacci sequence of 1, 1, 2, 3, 5, 8. Now if we wanted to also add a conditional, which is where the Walrus operator comes in, we can compute fibo x and assign it to the variable y via the Walrus operator's assignment expression, and then do the modules 2 equals 0 check to keep just the even elements of the Fibonacci sequence. And then in the actual output of the list comprehension, we can just use the variable y as opposed to re-computing the fibo function. So the result of this list comprehension that uses the Walrus operator is 2 and 8. So list comprehension actually creates a list objects, computes all the elements in the list, and stores the entire list in memory, while the generator expression stores just the iterable object and computes every element in the list one at a time as it's being queried. So for most people, list comprehension is probably the default choice. It's used when the size of the list is not crazy large, especially when you want to reiterate over the list multiple times. It is faster than generator expressions, depending on the context, it could be two to three times faster. So if speed is essential, you want to use these. And if you need different list methods, like especially the slicing notation, you should be using list comprehension. On the other hand, you should use generator expressions when the range of the sequence is large or infinite, or if you want to construct iterable generator objects, which are great to impress your friends with. I should mention I'm really grateful for the sponsors that support these videos and the podcast, in this case, 8sleep. So if you enjoy these, click the links in the description to get a discount and to support my efforts. Thanks for listening, and remember, try to learn something new every day.


Kai-Fu Lee: Question for AGI - What is the Difference Between Me and You? | AI Podcast Clips

So I know you believe, as many do, that we're far from creating an artificial general intelligence system. But say, once we do, and you get to ask her one question, what would that question be? What is it that differentiates you and me?


Jim Keller: Abstraction Layers from the Atom to the Data Center | AI Podcast Clips

So let's get into the basics before we zoom back out. How do you build a computer from scratch? What is a microprocessor? What is a microarchitecture? What's an instruction set architecture? Maybe even as far back as what is a transistor? So the special charm of computer engineering is there's a relatively good understanding of abstraction layers. So down at the bottom you have atoms, and atoms get put together in materials like silicon or dope silicon or metal, and we build transistors. On top of that, we build logic gates, and then functional units like an adder, a subtractor, an instruction parsing unit, and then we assemble those into processing elements. Modern computers are built out of probably 10 to 20 locally organic processing elements, or coherent processing elements, and then that runs computer programs. So there's abstraction layers, and then software, there's an instruction set you run, and then there's assembly language C, C++, Java, JavaScript, there's abstraction layers, essentially from the atom to the data center. So when you build a computer, first there's a target, like what's it for, how fast does it have to be, which today there's a whole bunch of metrics about what that is. And in an organization of a thousand people who build a computer, there's lots of different disciplines that you have to operate on. Does that make sense? So there's a bunch of levels of abstraction, and organizational I can tell, and in your own vision, there's a lot of brilliance that comes in at every one of those layers. Some of it is science, some of it is engineering, some of it is art. What's the most, if you could pick favorites, what's the most important, your favorite layer on these layers of abstractions? Where does the magic enter this hierarchy? I don't really care. I'm somewhat agnostic to that. So I would say for relatively long periods of time, instruction sets are stable. So the x86 instruction set, the ARM instruction set. What's an instruction set? So it says, how do you encode the basic operations? Load, store, multiply, add, subtract, conditional, branch. There aren't that many interesting instructions. If you look at a program and it runs, 90% of the execution is on 25 opcodes, 25 instructions. And those are stable. What does it mean stable? Intel architecture has been around for 25 years. It works. It works. And that's because the basics are defined a long time ago. Right now, the way an old computer ran is you fetched instructions and you executed them in order. Do the load, do the add, do the compare. The way a modern computer works is you fetch large numbers of instructions, say 500. And then you find the dependency graph between the instructions. And then you execute in independent units those little micrographs. So a modern computer, like people like to say, computers should be simple and clean. But it turns out the market for simple, clean, slow computers is zero. Right? We don't sell any simple, clean computers. Now, you can, there's, how you build it can be clean. But the computer people want to buy, that's say in a phone or a data center, fetches a large number of instructions, computes the dependency graph, and then executes it in a way that gets the right answers. And optimizes that graph somehow. Yeah. They run deeply out of order. And then there's semantics around how memory ordering works and other things work. So the computer sort of has a bunch of bookkeeping tables that says what order do these operations finish in or appear to finish in. But to go fast, you have to fetch a lot of instructions and find all the parallelism. Now, there's a second kind of computer, which we call GPUs today. And I call it the difference, there's found parallelism, like you have a program with a lot of dependent instructions. You fetch a bunch and then you go figure out the dependency graph and you issue instructions out of order. That's because you have one serial narrative to execute. Which in fact is in, can be done out of order. Did you call it a narrative? Yeah. Wow. So yeah, so humans think in serial narrative. So read a book, right? There's a, you know, there's a sentence after sentence after sentence. And there's paragraphs. Now you could diagram that. Imagine you diagrammed it properly. You said, which sentences could be read in any order, any order without changing the meaning, right? That's a fascinating question to ask of a book. Yeah. Yeah, you could do that. Right. So some paragraphs could be reordered, some sentences can be reordered. You could say, he is tall and smart and X, right? And it doesn't matter the order of tall and smart. But if you say the tall man is wearing a red shirt, what colors, you know, like you can create dependencies, right? Right. And so GPUs on the other hand run simple programs on pixels. But you're given a million of them. And the first order, the screen you're looking at doesn't care which order you do it in. So I call that given parallelism. Simple narratives around the large numbers of things where you can just say it's parallel because you told me it was. So found parallelism where the narrative is sequential but you discover like little pockets of parallelism versus. Turns out large pockets of parallelism. Large. So how hard is it to discover? Well, how hard is it? That's just transistor count, right? So once you crack the problem, you say, here's how you fetch 10 instructions at a time, here's how you calculated the dependencies between them, here's how you describe the dependencies, here's, you know, these are pieces, right? So once you describe the dependencies, then it's just a graph, sort of, it's an algorithm that finds, what is that? I'm sure there's a graph theory, theoretical answer here that's solvable. In general, programs, modern programs that human beings write, how much found parallelism is there in them? About 10x. What does 10x mean? So if you execute it in order. Versus, yeah. You would get what's called cycles per instruction and it would be about, you know, three instructions, three cycles per instruction because of the latency of the operations and stuff. And in a modern computer, executes it, but like 0.2, 0.25 cycles per instruction. So it's about, we today find 10x. And there's two things. One is the found parallelism in the narrative, right? And the other is to predictability of the narrative, right? So certain operations, they do a bunch of calculations and if greater than one, do this, else do that. That decision is predicted in modern computers to high 90% accuracy. So branches happen a lot. So imagine you have a decision to make every six instructions, which is about the average, right? But you want to fetch 500 instructions, figure out the graph and execute them all in parallel. That means you have, let's say if you fix 600 instructions and it's every six, you have to fetch, you have to predict 99 out of 100 branches correctly for that window to be effective. Okay, so parallelism, you can't parallelize branches. Or you can. You can predict. What does predict a branch mean? So imagine you do a computation over and over, you're in a loop. So while n is greater than one, do. And you go through that loop a million times. So every time you look at the branch, you say, it's probably still greater than one. And you're saying you could do that accurately. Very accurately. My mind is blown. How the heck do you do that? Wait a minute. Well, you want to know? This is really sad. 20 years ago, you simply recorded which way the branch went last time and predicted the same thing. Right. Okay. What's the accuracy of that? 85%. So then somebody said, hey, let's keep a couple of bits and have a little counter. So when it predicts one way, we count up and then pins. So say you have a three-bit counter. So you count up and then you count down. And you can use the top bit as the assigned bit. So you have a assigned two-bit number. So if it's greater than one, you predict taken. And less than one, you predict not taken. Right? Or less than zero, whatever the thing is. And that got us to 92%. Oh. Okay. You know what? It gets better. This branch depends on how you got there. So if you came down the code one way, you're talking about Bob and Jane. Right? And then said, does Bob like Jane? It went one way. But if you're talking about Bob and Jill, does Bob like Jane? You go a different way. Right? So that's called history. So you take the history and a counter. That's cool. But that's not how anything works today. They use something that looks a little like a neural network. So modern, you take all the execution flows. And then you do basically deep pattern recognition of how the program is executing. And you do that multiple different ways. And you have something that chooses what the best result is. And then you have a supercomputer inside the computer. That's trying to predict branching. That calculates which way branches go. So the effective window that is worth finding grass in gets bigger. Why was that going to make me sad? Because that's amazing. It's amazingly complicated. Oh. Well, here's the funny thing. So to get to 85% took a thousand bits. To get to 99% takes tens of megabits. So this is one of those. To get the result, to get from a window of, say, 50 instructions to 500, it took three orders of magnitude or four orders of magnitude more bits. Now, if you get the prediction of a branch wrong, what happens then? You flush the pipe. You flush the pipe. So it's just the performance cost. But it gets even better. Yeah. You're getting the look at stuff that says, so they executed down this path, and then you had two ways to go. But far, far away, there's something that doesn't matter which path you went. So you took the wrong path. You executed a bunch of stuff. Then you had the mispredicting. You backed it up. But you remembered all the results you already calculated. Some of those are just fine. Like if you read a book and you misunderstand a paragraph, your understanding of the next paragraph sometimes is invariant to that understanding. Sometimes it depends on it. And you can kind of anticipate that invariance. Yeah, well, you can keep track of whether the data changed. And so when you come back to a piece of code, should you calculate it again or do the same thing? Okay, how much of this is art and how much of it is science? Because it sounds pretty complicated. Well, how do you describe a situation? So imagine you come to a point in the road where you have to make a decision. And you have a bunch of knowledge about which way to go. Maybe you have a map. So you want to go the shortest way, or do you want to go the fastest way, or do you want to take the nicest road? So there's some set of data. So imagine you're doing something complicated like building a computer, and there's hundreds of decision points all with hundreds of possible ways to go. And the ways you pick interact in a complicated way. Right. And then you have to pick the right spot. So is that art or science? I don't know. You avoided the question. You just described the Robert Frost problem of Road Less Taken. I described the Robert Frost problem? That's what we do as computer designers. It's all poetry. Okay. Great. Yeah, I don't know how to describe that because some people are very good at making those intuitive leaps. It seems like there's combinations of things. Some people are less good at it, but they're really good at evaluating the alternatives. Right. And everybody has a different way to do it. And some people can't make those leaps, but they're really good at analyzing it. So when you see computers are designed by teams of people who have very different skill sets, and a good team has lots of different kinds of people, I suspect you would describe some of them as artistic, but not very many. Unfortunately or fortunately. Unfortunately. Well, you know, computer design is hard. It's 99% perspiration. And the 1% inspiration is really important. But you still need the 99. Yeah, you got to do a lot of work. And then there are interesting things to do at every level of that stack. At the end of the day, if you run the same program multiple times, does it always produce the same result? Is there some room for fuzziness there? That's a math problem. So if you run a correct C program, the definition is every time you run it, you get the same answer. Yeah, well, that's a math statement. That's a language definitional statement. So for years when we first did 3D acceleration of graphics, you could run the same scene multiple times and get different answers. Right. Right. And then some people thought that was okay and some people thought it was a bad idea. And then when the HPC world used GPUs for calculations, they thought it was a really bad idea. Now, in modern AI stuff, people are looking at networks where the precision of the data is low enough that the data is somewhat noisy. And the observation is the input data is unbelievably noisy. So why should the calculation be not noisy? And people have experimented with algorithms that say can get faster answers by being noisy. Like as a network starts to converge, if you look at the computation graph, it starts out really wide and then it gets narrower. And you can say is that last little bit that important or should I start the graph on the next rev before we whittle it all the way down to the answer. Right. So you can create algorithms that are noisy. Now, if you're developing something and every time you run it, you get a different answer, it's really annoying. And so most people think even today, every time you run the program, you get the same answer. No, I know, but the question is that's the formal definition of a programming language. There is a definition of languages that don't get the same answer, but people who use those, you always want something because you get a bad answer and then you're wondering is it because of something in the algorithm or because of this. And so everybody wants a little switch that says no matter what, do it deterministically. And it's really weird because almost everything going into modern calculations is noisy. So why do the answers have to be so clear? Right, so where do you stand? I design computers for people who run programs. So if somebody says I want a deterministic answer, like most people want that. Can you deliver a deterministic answer I guess is the question? Yeah, hopefully, sure. What people don't realize is you get a deterministic answer even though the execution flow is very undeterministic. So you run this program 100 times, it never runs the same way twice, ever. And the answer, it arrives at the same answer. But it gets the same answer every time. It's just amazing.


Roomba vs Autonomous Vehicles: Vision not Lidar is the Future for Robotics - iRobot CEO | AI Clip

Another industry in which, I would say the only other industry in which there is automation actually touching people's lives today is autonomous vehicles. What the vision you just described of using computer vision and using cheap camera sensors, there's a debate on that of LIDAR versus computer vision. And sort of, Elon Musk famously said that LIDAR is a crutch that really in the long term, camera only is the right solution, which echoes some of the ideas you're expressing. Of course, the domain in terms of its safety criticality is different. But what do you think about that approach in the autonomous vehicle space? And in general, do you see a connection between the incredible real world challenges you have to solve in the home with Roomba? And I saw a demonstration of some of them, corner cases, literally, and autonomous vehicles. So there's absolutely a tremendous overlap between both the problems, you know, a robot vacuum and autonomous vehicle are trying to solve and the tools and the types of sensors that are being applied in the pursuit of the solutions. In my world, my environment is actually much harder than the environment in automobile travels. We don't have roads, we have t-shirts, we have steps, we have a near infinite number of patterns and colors and surface textures on the floor. Especially from a visual perspective. So the way the world looks is infinitely variable. On the other hand, safety is way easier on the inside. My robots, they're not very heavy, they're not very fast. If they bump into your foot, you think it's funny. And you know, and autonomous vehicles kind of have the inverse problem. And so that for me saying vision is the future, I can say that without reservation. For autonomous vehicles, I think I believe what Elon's saying about the future is ultimately going to be vision. Maybe if we put a cheap lighter on there as a backup sensor, it might not be the worst idea in the world. So the stakes are much higher. The stakes are much higher. You have to be much more careful thinking through how far away that future is. Right. But I think that the primary environmental understanding sensor is going to be a visual system.


Sean Carroll: Arrow of Time

We talked a little bit about error of time last time, but in many worlds that there is a kind of implied error of time, right? So you've talked about the error of time that has to do with the second law of thermodynamics, that's the error of time that's emergent or fundamental, we don't know, I guess. No, it's emergent. Does everyone agree on that? Well, nobody agrees with everything. They should. They should. They should. So that error of time, is that different than the error of time that's implied by many worlds? It's not different, actually, no. In both cases, you have fundamental laws of physics that are completely reversible. If you give me the state of the universe at one moment in time, I can run the clock forward or backward equally well. There's no arrow of time built into the laws of physics at the most fundamental level. But what we do have are special initial conditions 14 billion years ago near the Big Bang. In thermodynamics, those special initial conditions take the form of things were low entropy, and entropy has been increasing ever since, making the universe more disorganized and chaotic and that's the error of time. In quantum mechanics, these special initial conditions take the form of there was only one branch of the wave function and the universe has been branching more and more ever since. Okay, so if time is emergent, so it seems like our human cognitive capacity likes to take things that are emergent and assume and feel like they're fundamental. So if time is emergent and locality, is space emergent? Yes. Okay. But I didn't say time was emergent, I said the arrow of time was emergent. Those are different. What's the difference between the arrow of time and time? Are you using arrow of time to simply mean they're synonymous with the second law of thermodynamics? No, but the arrow of time is the difference between the past and future. So there's space, but there's no arrow of space. You don't feel that space has to have an arrow, right? You could live in thermodynamic equilibrium. There'd be no arrow of time, but there'd still be time. There'd still be a difference between now and the future or whatever. So if nothing changes, there's still time. Well things could even change. Like if the whole universe consisted of the earth going around the sun, it would just go in circles or ellipses, right? Things would change, but it's not increasing entropy. There's no arrow. If you took a movie of that and I played you the movie backward, you would never know. So the arrow of time can theoretically point in the other direction for brief, briefly. To the extent that it points in different directions, it's not a very good arrow. I mean, the arrow of time in the macroscopic world is so powerful that there's just no chance of going back. When you get down to tiny systems with only three or four moving parts, then entropy can fluctuate up and down.


Leonard Susskind: Is Ego Powerful or Dangerous in Science? | AI Podcast Clips

♪♪ Jumping back to Feynman just for a second, he had a little bit of an ego. Yes. Do you think ego is powerful or dangerous in science? I think both. Both. I think you have to have both arrogance and humility. You have to have the arrogance to say, I can do this. Nature is difficult. Nature is very, very hard. I'm smart enough. I can do it. I can win the battle with nature. On the other hand, I think you also have to have the humility to know that you're very likely to be wrong on any given occasion. Everything you're thinking could suddenly change. Young people can come along and say things you won't understand, and you'll be lost and flabbergasted. So I think it's a combination of both. You better recognize that you're very limited, and you better be able to say to yourself, I'm not so limited that I can't win this battle with nature. It takes a special kind of person who can manage both of those, I would say. And I would say there's echoes of that in your own work. A little bit of ego, a little bit of outside-of-the-box, humble thinking. I hope so. So was there a time where you felt, you looked at yourself and asked, am I completely wrong about this? Oh yeah. About the whole thing or about specific things? The whole thing. Wait, which whole thing? Me and me and my ability to do this thing. Oh, those kinds of doubts. First of all, did you have those kinds of doubts? No, I had different kinds of doubts. I came from a very working-class background, and I was uncomfortable in academia for a long time. But they weren't doubts about my ability. They were just the discomfort in being in an environment that my family hadn't participated in, I knew nothing about as a young person. I didn't learn that there was such a thing called physics until I was almost 20 years old. So I did have certain kinds of doubts, but not about my ability. I don't think I was too worried about whether I would succeed or not. I never felt this insecurity, am I ever going to get a job? That had never occurred to me that I wouldn't.


Self-replicating Python code | Quine

This Python code shown above, when executed, generates itself, shown below. This is what's called a quine. It is the kind of self-replicating system which is fascinating both philosophically and technically. The term quine was introduced by Douglas Hofstadter in his book, Geidel, Escher, Bach. The idea of quines has echoes of self-replication in biological cells where the cell contains data in the form of DNA and code in the form of biological processes which replicate that DNA. In the same way, the computational self-replication of a quine contains two elements. One is the code that performs the replication and two is the data that contains all the raw materials for performing the replication process. So like I said, most quines have a data part and a code part. So let's look at this elegant Python quine. The data part is the string of the program and the code part is the print statement that prints that program. So let's look at a basic example of the structure. The string is test %r, where %r is the repper function that returns a printable representation of the object that's passed. So when we execute this command of assigning the string to the variable s and then print the string s after passing itself to itself in a repper version of itself. And so the output of this code is test %r with the test %r in quotes, which is the repper version of the argument. Now we can move back to our quine with the same exact structure of a string, but this time the string contains also the print statement with a double percent sign, which when printed results in a single percent sign. So this code, once again, after you pass the repper version of the string to itself, results in a print of the entirety of the code. The recursive nature of this self-replicating piece of code has the beauty and the power that echoes the process of biological cell replication. I should briefly mention that there's a lot of ways of creating quines. The one I showed is the one I believe is the most elegant. There's also cheating ways of creating quines, which is, first of all, an empty program in many programming languages, including Python, is a quine itself. Nothingness, in a way, is forever effectively replicating itself. Also another obvious way is to put the code of the program into a file and then to print the contents of the file in which the code resides. Now this is actually officially considered to be cheating and not allowed to be in a quine. The quine has to be self-sustained. Now let me wrap up this video with the idea of an intron, which is code that you can inject within a quine that will replicate as part of the replication procedure, but does not actually contribute directly to the function of the quine. So it rides along for free, like a virus or a non-functional part of the genome in a DNA, without directly contributing to the replication of the code. Now this particular piece of code does more than just provide an intron. It takes the input from the user and adds that as an intron into the quine. Now the exact function is used here for brevity. So let's run this code. When we run it, it's waiting for the user input. If we just press enter, it generates the code itself, so it's a proper quine. If we run it again and provide input, like we live in a simulation, what you see is that the variable t now has a we live in a simulation string attached to it, and the entire program remains a proper quine. So if we copy it and run this program, it waits for the input. Now if we keep the same input, it generates the quine again successfully. Now we can run the same kind of quine with the we live in a simulation, and instead provide a new text, 42. The resulting output is a quine as well, with the variable t now assigned the number 42. Now to me, quines and introns make me think about the source code of our own life here on earth. And so I hope you may find this interesting and beautiful as I do. I may very well make a few more videos about quines, like multi-quines, polygon quines, quine relays, and other things that I find interesting in the space of computer science, mathematics, physics, artificial intelligence, machine learning, and even psychology, history, philosophy, biology, chemistry, and so on. So if you enjoy this kind of thing, consider subscribing, and remember, try to learn something new every day.


The Big Nap: Coronavirus and World War II - Eric Weinstein and Lex Fridman | AI Podcast Clips

Do you see a connection between World War II and the crisis we're living through right now? Sure. The need for collective action, reminding ourselves of the fact that all of these abstractions, like everyone should just do exactly what he or she wants to do for himself and leave everyone else alone, none of these abstractions work in a global crisis. And this is just a reminder that we didn't somehow put all that behind us. When I hear stories about my grandfather who was in the army, and so the Soviet Union where most people die when you're in the army, there's a brotherhood that happens, there's a love that happens. Do you think that's something we're going to see here? A sense of community? Well, we're not there. I mean, what the Soviet Union went through. I mean, the enormity of the war on the Russian doorstep, this is different. What we're going through now is not, we can't talk about Stalingrad and COVID in the same breath yet. We're not ready. And the sort of, you know, the sense of like the great patriotic war and the way in which I was very moved by the Soviet custom of newlyweds going and visiting war memorials on their wedding day. And on your happiest day of your life, you have to say thank you to the people who made it possible. We're not there. We're just restarting history. I've called this, on the Rogan program, I called it the great nap. The 75 years with very little by historical standards in terms of really profound disruptions. And so- When you call it the great nap, you mean lack of deep global tragedy? Well, lack of realized global tragedy. So I think that the development, for example, of the hydrogen bomb, you know, was something that happened during the great nap. And that doesn't mean that people who lived during that time didn't feel fear, didn't know anxiety, but it was to say that most of the violent potential of the human species was not realized. It was in the form of potential energy. And this is the thing that I've sort of taken issue with, with the description of Steven Pinker's optimism, is that if you look at the realized kinetic variables, things have been getting much better for a long time, which is the great nap. But it's not as if our fragility has not grown, our dependence on electronic systems, our vulnerability to disruption. And so all sorts of things have gotten much better. Other things have gotten much worse, and the destructive potential has skyrocketed. Is tragedy the only way we wake up from the big nap? Well, no, you could also have, you know, jubilation about positive things, but it's harder to get people's attention. Can you give an example of a big global positive thing that could happen? I think that when, for example, just historically speaking, HIV went from being a death sentence to something that people could live with for a very long period of time, it would be great if that had happened on a Wednesday, right? Like all at once, like you knew that things had changed. And so the bleed in somewhat kills the sort of the Wednesday effect where it all happens on a particular day at a particular moment. I think if you look at the stock market here, you know, there's a very clear moment where you can see that the market absorbs the idea of the coronavirus. I think that with respect to positives, the moon landing was the best example of a positive that happened at a particular time or recapitulating the Soviet American link up in terms of Skylab and Soyuz, right? Like that was a huge moment when you actually had these two nations connecting in orbit. And so, yeah, there are great moments where something beautiful and wonderful and amazing happens, you know, but it's just, there are fewer of them. That's why as much as I can't imagine proposing to somebody at a sporting event, when you have like 30,000 people waiting and you know, like she says yes, it's pretty exciting. So I think that we shouldn't discount that. So how bad do you think it's going to get in terms of the global suffering that we're going to experience with this crisis? I can't figure this one out. I'm just not smart enough. Something is going weirdly wrong. They're almost like two separate storylines. In one storyline, we aren't taking things nearly seriously enough. We see people using food packaging lids as masks who are doctors or nurses. We hear horrible stories about people dying needlessly due to triage. And that's a very terrifying story. On the other hand, there's this other story which says there are tons of ventilators someplace. We've got lots of masks, but they haven't been released. We've got hospital ships where none of the beds are being used. And it's very confusing to me that somehow these two stories give me the feeling that they both must be true simultaneously and they can't both be true in any kind of standard way. And I don't know whether it's just that I'm dumb, but I can't get one or the other story to quiet down. So I think weirdly, this is much more serious than we had understood it. And it's not nearly as serious as some people are making it out to be at the same time, and that we're not being given the tools to actually understand, oh, here's how to interpret the data, or here's the issue with the personal protective equipment is actually a jurisdictional battle or a question of who pays for it rather than a question of whether it's present or absent. I don't understand the details of it, but something is wildly off in our ability to understand where we are. So that's policy, that's institutions. What about, do you think about the quiet suffering of millions of people that have lost their job? Is this a temporary thing? I mean, what I'm, my ear is not to the suffering of those people who have lost their job or the 50% possibly of small businesses that are gonna go bankrupt. Do you think about that quiet suffering? Well, and how that might arise itself? Could be not quiet too. I mean, it could be a depression. This could go from recession to depression, and depression could go to armed conflict and then to war. So it's not a very abstract causal chain that gets us to the point where we can begin with quiet suffering and anxiety and all of these sorts of things and people losing their jobs and people dying from stress and all sorts of things. But look, anything powerful enough to put us all indoors in a, I mean, think about this as an incredible experiment. Imagine that you proposed, hey, I want to do a bunch of research. Let's figure out what changes in our emissions profiles for our carbon footprints when we're all indoors or what happens to traffic patterns or what happens to the vulnerability of retail sales as Amazon gets stronger, et cetera, et cetera. I believe that in many of those situations, we're running an incredible experiment. And am I worried for us all? Yes. There are some bright spots, one of which is that when you're ordered to stay indoors, people are going to feel entitled. And the usual thing that people are going to hit when they hear that they've lost your job, there's this kind of tough love attitude that you see, particularly in the United States. Oh, you lost your job. Poor baby. Well, go retrain. Get another one. I think there's going to be a lot less appetite for that because we've been asked to sacrifice, to risk, to act collectively. And that's the interesting thing. What does that reawaken in us? Maybe the idea that we actually are nations and that your fellow countrymen may start to mean something to more people. It certainly means something to people in the military, but I wonder how many people who aren't in the military start to think about this as like, oh yeah, we are kind of running separate experiments and we are not China. So you think this is kind of a period that might be studied for years to come? From my perspective, we are a part of the experiment, but I don't feel like we have access to the full data, the full data of the experiment. We're just like little mice in a large... Does this one make sense to you, Lex? I'm romanticizing it and I keep connecting it to World War II. So I keep connecting to historical events and making sense of them through that way or reading The Plague by Camus. Almost kind of telling narratives and stories, but I'm not hearing the suffering that people are going through because I think that's quiet. Everybody's numb currently. They're not realizing what it means to have lost your job and to have lost your business. There's kind of a... I'm afraid how that fear will materialize itself once the numbness wears out. And especially if this lasts for many months, and if it's connected to the incompetence of the CDC and the WHO and our government and perhaps the election process. My biggest fear is that the elections get delayed or something like that. So the basic mechanisms of our democracy get slowed or damaged in some way that then mixes with the fear that people have that turns to panic, that turns to anger, that anger. Can I just play with that for a little bit? Sure. If in fact all of that structure that you grew up thinking about, and again, you grew up in two places, right? So when you were inside the US, we tend to look at all of these things as museum pieces. Like how often do we amend the constitution anymore? And in some sense, if you think about the Jewish tradition of Simchat Torah, you've got this beautiful scroll that has been lovingly hand drawn in calligraphy that's very valuable. And it's very important that you not treat it as a relic to be revered. And so one day a year, we dance with the Torah and we hold this incredibly vulnerable document up. And we treat it as if it was Ginger Rogers being led by Fred Astaire. Well, that is how you become part of your country. In fact, maybe the election will be delayed. Maybe extraordinary powers will be used. Maybe any one of a number of things will indicate that you're actually living through history. This isn't a museum piece that you were handed by your great great grandparents.


Why is the Simulation Interesting to Elon Musk? (Nick Bostrom) | AI Podcast Clips

Let me ask, one of the popularizers, you said there's many through this, when you look at sort of the last few years of the simulation hypothesis, just like you said, it comes up every once in a while, some new community discovers it and so on. But I would say one of the biggest popularizers of this idea is Elon Musk. Do you have any kind of intuition about what Elon thinks about when he thinks about simulation? Why is this of such interest? Is it all the things we've talked about or is there some special kind of intuition about simulation that he has? I mean, you might have a better... I think, I mean, why it's of interest, I think it's like seems fairly obvious why, to the extent that one think the argument is credible, why it would be of interest. It would, if it's correct, tell us something very important about the world in one way or the other, whichever of the three alternatives for a simulation that seems like arguably one of the most fundamental discoveries, right? Now, interestingly, in the case of someone like Elon, so there's like the standard arguments for why you might want to take the simulation hypothesis seriously, the simulation argument, right? In the case that if you are actually Elon Musk, let us say, there's a kind of an additional reason in that what are the chances you would be Elon Musk? It seems like maybe there would be more interest in simulating the lives of very unusual and remarkable people. So if you consider not just simulations where all of human history or the whole of human civilization are simulated, but also other kinds of simulations, which only include some subset of people, like in those simulations that only include a subset, it might be more likely that that would include subsets of people with unusually interesting or consequential lives. So if you're Elon Musk, it's more likely that you're a human being. Like, if you're Donald Trump, or if you are Bill Gates, or you're like some particularly like distinctive character, you might think that that adds... I mean, if you just think of yourself into the shoes, right? It's got to be like an extra reason to think, that's kind of... So interesting. So on a scale of like Farmer in Peru to Elon Musk, the more you get towards the Elon Musk, the higher the probability... You'd imagine that would be some extra boost from that. There's an extra boost. So he also asked the question of what he would ask an AGI, saying the question being, what's outside the simulation? Do you think about the answer to this question, if we are living in a simulation, what is outside the simulation? So the programmer of the simulation? Yeah. I mean, I think it connects to the question of what's inside the simulation in that if you had views about the creatures of the simulation, it might help you make predictions about what kind of simulation it is, what might happen, what happens after the simulation, if there is some after, but also like the kind of setup. So these two questions would be quite closely intertwined. But do you think it would be very surprising to like, is the stuff inside the simulation, is it possible for it to be fundamentally different than the stuff outside? Yeah. Like, another way to put it, can the creatures inside the simulation be smart enough to even understand or have the cognitive capabilities or any kind of information processing capabilities enough to understand the mechanism that created them? They might understand some aspects of it. I mean, it's a level of, it's kind of, there are levels of explanation, like degrees to which you can understand. So does your dog understand what it is to be human? Well, it's got some idea, like humans are these physical objects that move around and do things. And like a normal human would have a deeper understanding of what it is to be a human. And maybe some very experienced psychologist or a great novelist might understand a little bit more about what it is to be human. And maybe superintelligence could see right through your soul. So similarly, I do think that we are quite limited in our ability to understand all of the relevant aspects of the larger context that we exist in. But there might be hope for some. I think we understand some aspects of it, but how much good is that if there's like one key aspect that changes the significance of all the other aspects? So we understand maybe seven out of 10 key insights that you need. But the answer actually varies completely depending on what number eight, nine, and 10 insight is. It's like whether you want to... Suppose that the big task were to guess whether a certain number was odd or even, like a 10 digit number. And if it's even, the best thing for you to do in life is to go north. And if it's odd, the best thing for you is to go south. Now we are in a situation where maybe through our science and philosophy, we figured out what the first seven digits are. So we have a lot of information, right? Most of it we figured out, but we are clueless about what the last three digits are. So we are still completely clueless about whether the number is odd or even, and therefore whether we should go north or go south. I feel that's an analogy, but I feel we're somewhat in that predicament. We know a lot about the universe. We've come maybe more than half of the way there to kind of fully understanding it, but the parts we're missing are plausibly ones that could completely change the overall upshot of the thing, and including change our overall view about what the scheme of priorities should be or which strategic direction would make sense to pursue. Yeah, I think your analogy of us being the dog, trying to understand human beings is an entertaining one and probably correct. The closer the understanding tends from the dog's viewpoint to us human psychologists' viewpoint, the steps along the way there will have completely transformative ideas of what it means to be human. So the dog has a very shallow understanding. It's interesting to think that, to analogize that a dog's understanding of a human being is the same as our current understanding of the fundamental laws of physics in the universe.


Charlie Munger: Investing in AI

You made some comments a few months ago that you're not quite sure what to think about that IBM investment. Well, I'm not sure how it's going to come out, but of course I like the fact they're doing it. And any kind of artificial intelligence is very much in the vote. It may well work. It's just I have no way of predicting. I don't think we're paying much for the opportunity. The opportunity to be in shares of IBM? I think that there's not much optimism baked into the current price of the shares. What is it that you like about the artificial intelligence there? I like the idea of using artificial intelligence because we're so short of the real thing.


Stuart Russell: AI Winter

So what happens in these things is that technology comes out and it's happening now with deep learning. Everyone says, if I don't get hold of this technology and build up a group within my company that knows how to do it, then I'm going to be left behind. So they start investing in the technology without any evidence that it actually works to solve their problem on the assumption that if they don't, they'll be left behind. There's a potential gain here, we can't afford to lose it. And then they start to hear stories that, oh, this other company, you know, they tried six times and it just fails, right? Didn't work for their problem. Then they start to lose faith very quickly. All those companies that haven't yet had a success can switch overnight from thinking it's essential to, we better get out of this, otherwise we're going to look foolish. So that's sort of what happened with the expert system in the late 80s, which is a shame because by then we already had the technological solutions that would have alleviated a lot of those difficulties.


Language or Vision - What's Harder? (Ilya Sutskever) | AI Podcast Clips

So, incredibly, you've contributed some of the biggest recent ideas in AI, in computer vision, language, natural language processing, reinforcement learning, sort of everything in between. Maybe not GANs. There may not be a topic you haven't touched, and of course, the fundamental science of deep learning. What is the difference to you between vision, language, and, as in reinforcement learning, action, as learning problems, and what are the commonalities? Do you see them as all interconnected, or are they fundamentally different domains that require different approaches? Okay, that's a good question. Machine learning is a field with a lot of unity, a huge amount of unity. What do you mean by unity? Like overlap of ideas? Overlap of ideas, overlap of principles. In fact, there's only one or two or three principles, which are very, very simple, and then they apply in almost the same way, in almost the same way to the different modalities, to the different problems. And that's why today, when someone writes a paper on improving optimization of deep learning and vision, it improves the different NLP applications, and it improves the different reinforcement learning applications. Reinforcement learning, so I would say that computer vision and NLP are very similar to each other. Today, they differ in that they have slightly different architectures. We use transformers in NLP, and we use convolutional neural networks in vision. But it's also possible that one day this will change and everything will be unified with a single architecture. Because if you go back a few years ago in natural language processing, there was a huge number of architectures for every different tiny problem had its own architecture. Today, there's just one transformer for all those different tasks. And if you go back in time even more, you had even more and more fragmentation and every little problem in AI had its own little subspecialization and sub, you know, little set of collection of skills, people who would know how to engineer the features. Now it's all been subsumed by deep learning. We have this unification. And so I expect vision to be communified with natural language as well. Or rather, I shouldn't say expect, I think it's possible. I don't wanna be too sure because I think on the convolutional neural net, it's very computationally efficient. RL is different. RL does require slightly different techniques because you really do need to take action. You really do need to do something about exploration. Your variance is much higher. But I think there is a lot of unity even there. And I would expect, for example, that at some point there will be some broader unification between RL and supervised learning where somehow the RL will be making decisions to make the supervised learning go better. And it will be, I imagine one big black box and you just throw every, you know, you shovel things into it and it just figures out what to do with whatever you shovel at it. And reinforcement learning has some aspects of language and vision combined almost. There's elements of a long-term memory that you should be utilizing and there's elements of a really rich sensory space. So it seems like the, it's like the union of the two or something like that. I'd say something slightly differently. I'd say that reinforcement learning is neither, but it naturally interfaces and integrates with the two of them. Do you think action is fundamentally different? So yeah, what is interesting about, what is unique about policy of learning to act? Well, so one example, for instance, is that when you learn to act, you are fundamentally in a non-stationary world because as your actions change, the things you see start changing. You experience the world in a different way. And this is not the case for the more traditional static problem where you have some distribution and you just apply a model to that distribution. You think it's a fundamentally different problem or is it just a more difficult, it's a generalization of the problem of understanding? I mean, it's a question of definitions almost. There is a huge, I mean, no, there's a huge amount of commonality for sure. You take gradients, you try, you take gradients, we try to approximate gradients in both cases. In some, in the case of reinforcement learning, you have some tools to reduce the variance of the gradients, you do that. There's lots of commonality. You use the same neural net in both cases. You compute the gradient, you apply Adam in both cases. So, I mean, there's lots in common for sure, but there are some small differences which are not completely insignificant. It's really just a matter of your point of view, what frame of reference you, how much do you want to zoom in or out as you look at these problems. Which problem do you think is harder? So people like Noam Chomsky believe that language is fundamental to everything. So it underlies everything. Do you think language understanding is harder than visual scene understanding or vice versa? I think that asking if a problem is hard is slightly wrong. I think the question is a little bit wrong and I want to explain why. So what does it mean for a problem to be hard? Okay, the non-interesting, dumb answer to that is there's a benchmark and there's a human level performance on that benchmark. And how is the effort required to reach the human level benchmark? So from the perspective of how much until we get to human level on a very good benchmark. Yeah, I understand what you mean by that. So what I was going to say that a lot of it depends on, you know, once you solve a problem, it stops being hard. And that's always true. And so whether something is hard or not depends on what our tools can do today. So, you know, you say today, true human level language understanding and visual perception are hard in the sense that there is no way of solving the problem completely in the next three months, right? So I agree with that statement. Beyond that, I'm just, I'd be, my guess would be as good as yours, I don't know. Oh, okay, so you don't have a fundamental intuition about how hard language understanding is. Well, I think, I know I changed my mind. I'd say language is probably going to be harder. I mean, it depends on how you define it. Like if you mean absolute top-notch, 100% language understanding, I'll go with language. And so, but then if I show you a piece of paper with letters on it, is that, you see what I mean? It's like you have a vision system, you say it's the best human level vision system. I show you, I open a book and I show you letters. Will it understand how these letters form into word and sentences and meaning? Is this part of the vision problem? Where does vision end and language begin? Yeah, so Chomsky would say it starts at language. So vision is just a little example of the kind of structure and fundamental hierarchy of ideas that's already represented in our brain somehow that's represented through language. But where does vision stop and language begin? That's a really interesting question. A question. So one possibility is that it's impossible to achieve really deep understanding in either images or language without basically using the same kind of system. So you're going to get the other for free. I think it's pretty likely that yes, if we can get one, our machine learning is probably that good that we can get the other. But I'm not 100% sure. And also, I think a lot of it really does depend on your definitions. Definitions of like perfect vision. Because reading is vision, but should it count? Yeah, to me, so my definition is if a system looked at an image and then a system looked at a piece of text and then told me something about that and I was really impressed. That's relative. You'll be impressed for half an hour and then you're gonna say, well, I mean, all the systems do that, but here's the thing they don't do. Yeah, but I don't have that with humans. Humans continue to impress me. Is that true? Well, the ones, okay, so I'm a fan of monogamy, so I like the idea of marrying somebody, being with them for several decades. So I believe in the fact that yes, it's possible to have somebody continuously giving you pleasurable, interesting, witty, new ideas, friends. Yeah, I think so. They continue to surprise you. The surprise, it's that injection of randomness seems to be a nice source of, yeah, continued inspiration, like the wit, the humor. I think, yeah, that would be, it's a very subjective test, but I think if you have enough humans in the room. Yeah, I understand what you mean. Yeah, I feel like I misunderstood what you meant by impressing you. I thought you meant to impress you with its intelligence, with how well it understands an image. I thought you meant something like, I'm gonna show it a really complicated image and it's gonna get it right, and you're gonna say, wow, that's really cool. Our systems of January 2020 have not been doing that. Yeah, no, I think it all boils down to the reason people click like on stuff on the internet, which is it makes them laugh. So it's like humor or wit or insight. I'm sure we'll get that as well.


Richard Feynman on Computation (Stephen Wolfram) | AI Podcast Clips

When you were at Caltech, did you get to interact with Richard Feynman at all? Do you have any memories of Richard? We worked together quite a bit actually. In fact, both when I was at Caltech and after I left Caltech, we were both consultants at this company called Thinking Machines Corporation, which was just down the street from here actually. It was ultimately an ill-fated company, but I used to say this company is not going to work with the strategy they have. Dick Feynman always used to say, what do we know about running companies? Just let them run their company. He was not into that kind of thing. He always thought that my interest in doing things like running companies was a distraction, so to speak. For me, it's a mechanism to have a more effective machine for actually figuring things out and getting things to happen. Did he think of it, because essentially what you did with the company, I don't know if you were thinking of it that way, but you're creating tools to empower the exploration of the university. Did he understand that point? The point of tools? I think not as well as he might have done. He was actually my first company, which was involved with more mathematical computation kinds of things. He had lots of advice about the technical side of what we should do and so on. Do you have examples, memories, or thoughts? Oh yeah, yeah. In the business of doing sort of, one of the hard things in math is doing integrals and so on. He had his own elaborate ways to do integrals and so on. He had his own ways of thinking about getting intuition about how math works. His meta idea was, take those intuitional methods and make a computer follow those intuitional methods. Now, it turns out, for the most part, like when we do integrals and things, what we do is we build this kind of bizarre industrial machine that turns every integral into products of major g functions and generates this very elaborate thing. Actually, the big problem is turning the results into something a human will understand. It's not, quote, doing the integral. Actually, Feynman did understand that to some extent. I'm embarrassed to say, he once gave me this big pile of calculational methods for particle physics that he worked out in the 50s. He said, it's more used to you than to me type thing. I was like, I've intended to look at it and give it back. I still have my files now. But that's what happens when it's finiteness of human lives. Maybe if he'd lived another 20 years, I would have remembered to give it back. But I think that was his attempt to systematize the ways that one does integrals that sharpen particle physics and so on. Turns out the way we've actually done it is very different from that way. What do you make of that difference, Eugene? So Feynman was actually quite remarkable at creating intuitive frameworks for understanding difficult concepts. I'm smiling because the funny thing about him was that the thing he was really, really, really good at is calculating stuff. But he thought that was easy because he was really good at it. And so he would do these things where he would do some complicated calculation in quantum field theory, for example, come out with a result. Wouldn't tell anybody about the complicated calculation because he thought that was easy. He thought the really impressive thing was to have the simple intuition about how everything works. So he invented that at the end. And because he'd done this calculation and knew how it worked, it was a lot easier. It's a lot easier to have good intuition when you know what the answer is. And then he would just not tell anybody about these calculations. And he wasn't meaning that maliciously, so to speak. He thought that was easy. And that led to areas where people were just completely mystified and they kind of followed his intuition, but nobody could tell why it worked. Because actually the reason it worked was because he'd done all these calculations and he knew that it would work. And he and I worked a bit on quantum computers, actually, back in 1980, 81, before anybody had heard of those things. And the typical mode of, I mean, he always used to say, and I now think about this because I'm about the age that he was when I worked with him. And I see that people are one third my age, so to speak. And he was always complaining that I was one third his age and therefore various things. But he would do some calculation by hand, blackboard and things, come up with some answer. I'd say, I don't understand this. I do something with a computer and he'd say, I don't understand this. So there'd be some big argument about what was going on. But it was always, and I think actually many of the things that we sort of realized about quantum computing that were sort of issues that have to do particularly with the measurement process are kind of still issues today. And I kind of find it interesting. It's a funny thing in science that there's a remarkable, it happens in technology too, there's a remarkable sort of repetition of history that ends up occurring. Eventually things really get nailed down, but it often takes a while and it often things come back decades later. Well, for example, I could tell a story actually happened right down the street from here. When we were both at Thinking Machines, I had been working on this particular cellular automaton called Rule 30 that has this feature that from very simple initial conditions, it makes really complicated behavior. So and actually of all silly physical things, using this big parallel computer called the connection machine that that company was making, I generated this giant printout of Rule 30 on actually on the same kind of printer that people use to make layouts for microprocessors. So one of these big, large format printers with high resolution and so on. So okay, so print this out lots of very tiny cells. And so there was sort of a question of how some features of that pattern. And so it was very much a physical, on the floor with meter rules trying to measure different things. So, so Feynman kind of takes me aside. We've been doing that for a little while and takes me aside and he says, I just want to know this one thing. He says, I want to know, how did you know that this Rule 30 thing would produce all this really complicated behavior that is so complicated that we're, you know, going around with this big printout and so on. And I said, well, I didn't know. I just enumerated all the possible rules and then observed that that's what happened. He said, oh, I feel a lot better. You know, I thought you had some intuition that he didn't have that would let one. I said, no, no, no, no intuition, just experimental science. So that's such a beautiful sort of dichotomy there of that's exactly what you showed is you really can't have an intuition about an irreducible, I mean, you have to run it. Yes, that's right. That's so hard for us humans and especially brilliant physicists like Feynman to say that you can't have a compressed, clean intuition about how the whole thing works. Yes, yes. No, he was, I mean, I think he was sort of on the edge of understanding that point about computation. And I think he found that, I think he always found computation interesting. And I think that was sort of what he was a little bit poking at. I mean, that intuition, you know, the difficulty of discovering things like even you say, oh, you know, you just enumerate all the cases and just find one that does something interesting, it sounds very easy. Turns out like I missed it when I first saw it because I had kind of an intuition that said it shouldn't be there. So I had kind of arguments, oh, I'm going to ignore that case because whatever. How did you have an open mind enough? Because you're essentially the same person as Richard Feynman, like the same kind of physics type of thinking. How did you find yourself having a sufficiently open mind to be open to watching rules and them revealing complexity? Yeah, I think that's an interesting question. I've wondered about that myself because it's kind of like, you know, you live through these things and then you say, what was the historical story? And sometimes the historical story that you realize after the fact was not what you lived through, so to speak. And so, you know, what I realized is I think what happened is, you know, I did physics kind of like reductionistic physics where you're thrown in the universe and you're told go figure out what's going on inside it. And then I started building computer tools and I started building my first computer language, for example. And a computer language is not like, it's sort of like physics in the sense that you have to take all those computations people want to do and kind of drill down and find the primitives that they can all be made of. But then you do something that's really different because you're just saying, okay, these are the primitives. Now, you know, hopefully they'll be useful to people. Let's build up from there. So you're essentially building an artificial universe in a sense where you make this language, you've got these primitives, you're just building whatever you feel like building. And that's, and so it was sort of interesting for me because from doing science where you're just thrown in the universe as the universe is, to then just being told, you know, you can make up any universe you want. And so I think that experience of making a computer language, which is essentially building your own universe, so to speak, is, you know, that's kind of the, that's what gave me a somewhat different attitude towards what might be possible. It's like, let's just explore what can be done in these artificial universes rather than thinking the natural science way of let's be constrained by how the universe actually is. Yeah. By being able to program, essentially you've, as opposed to being limited to just your mind and a pen, you now have, you've basically built another brain that you can use to explore the universe by, computer program, you know, is a kind of a brain. Right. And it's, well, it's, or a telescope or, you know, it's a tool. It lets you see stuff. But there's something fundamentally different between a computer and a telescope. I mean, it just, I'm hoping not to romanticize the notion, but it's more general. The computer is more general than the telescope. It is. It's more general. And this point about, you know, people say, oh, such and such a thing was almost discovered at such and such a time. The distance between, you know, the building, the paradigm that allows you to actually understand stuff or allows one to be open to seeing what's going on. That's really hard. And, you know, I think in, I've been fortunate in my life that I've spent a lot of my time building computational language, and that's an activity that in a sense works by sort of having to kind of create another level of abstraction and kind of be open to different kinds of structures. But, you know, it's, it's always, I mean, I'm fully aware of, I suppose, the fact that I have seen it a bunch of times of how easy it is to miss the obvious, so to speak, that at least is factored into my attempt to not miss the obvious, although it may not succeed.


When will the 200 billionth person be born?

Current population of Earth is 7.8 billion people. Since about 50,000 years ago, when modern Homo sapiens first appeared, the number of people who have lived is 109 billion, which naturally leads to the other number that gives me pause. Since the origin of modern humans, 101 billion people have died. 101 billion stories of suffering, of triumph, of death in infancy, of living beyond 100 years old, of wars, of hatred, of love and beauty. The data sources, estimates, and projections for this video are from the United Nations and the Population Reference Bureau. So the natural question I had when looking at these numbers is, what are the next milestones? When will the 110th, the 150th, the 200 billionth person be born? And of course, let me say that predicting the future of anything beyond 10 to 20 years is very difficult. So this is more of an exercise of building intuition about the prospects of human civilization. This is the plot of the world population on the y-axis in billions from 0 to 12 and the x-axis from the 1500s to 2100. We'll reach 8 billion people in 2022 and 10 billion people in 2056. Based on UN projections, the population of Earth should level out by 2100 at about 11 billion people. By the way, there are a lot of estimates of Earth carrying capacity, the median of those estimates being around 10 billion people. So there's a lot of debates on whether we'll eventually exceed the human carrying capacity of Earth. It should also be noted that since we're living in the times of COVID, the estimates for excess deaths in 2020 should be anywhere from 0 to 20%. In several studies of the topic, the estimate rests at about 20% excess deaths. So 20% more deaths than otherwise would be expected for a non-pandemic year. But when we look at 2020 as a whole, it's very possible it levels out to much closer to 0% excess deaths given the effects of lockdown on the patterns of human behavior. Now we take these projections of the world population and the birth rates estimated by the United Nations and talk about the number of people who have ever lived projected out into the future. Now we hit the 50 billion person born mark just about a century and a half after the common era began. And we hit the 100 billionth mark just before 1950. The 110th billion person will be born in 2028. The 120th billion in 2102. The 150th billion about three centuries from now in 2340. And the 200 billionth person will be born in 2736, which is about seven centuries from now. Unless, as described in the global catastrophic risk survey about existential risk, that estimates the probability of the destruction of human civilization in a century at a terrifying 19%. And if we take that out to 2736, the probability that the 200 billionth person will never be born, at least on earth at a terrifying 77%. Of course, it's possible that the 200 billionth person will be born on Mars, or maybe even Proxima b, the closest earth like planet outside of our solar system that we currently know. Hope you enjoy these little videos. They're easy and fun to make. Give me a chance to talk about some cool ideas. So if you enjoy these, subscribe, and remember, try to learn something new every day.