Joe Rogan Podcast Theme Music (Guitar)

Let me quickly say that I've been a fan of the JRE podcast since it first started 10 years ago. Joe's open-mindedness and just genuine curiosity was a breath of fresh air, especially to me as a scientist, but in general as a thinking person. He inspired me to be a better and especially kinder human being. So it was surreal for me to be on there a couple of times last year and I'll be on there again a third time on February 3rd. It feels like I'm Forrest Gump somehow finding myself shaking hands with John F. Kennedy. Anyway, I also enjoy playing guitar and I thought I'd play the intro theme plus a bit more to say thank you for the 10 years of JRE. As a wise man once said, train by day, Joe Rogan podcast by night, all day. So you


GSP teaches Lex Fridman how to street fight

In a street fight I would rather fight Francis and Gannou and fight the Bas Ruton in a street fight. Yeah, so very often people ask me the difference between a street fight and a fight and mixed martial art. The difference is in the street there is no referee and there is an instigator and there is the other person. The best in life for a street fight is always to not be the instigator because you have the element of surprise. So if you're in a heated argument with someone and then you feel that you potentially gonna be into a fight the best thing to do is to never show your center line. To always go on the side and put your hand up like this. You know that's one of the best thing to do. It's a self-defense tactic that it's used all around the world. Because from there the distance that I have to travel to cause a lot of damage to him is very minimal. You know it's very short. You know I can go boom, I can go boom and I'm protected because if he ever tried to do anything my hands are already up but I'm ready to respond at any aggression. So the first thing is if you're in an argument and you feel the heat is rising is to eat first. You don't want to fight but you want to eat first. You want to hit first you know. So it's either boom hit first depending the situation if you're someone who's much less physically strong than the aggressor you can use in the eyes, in the genitals, in the neck you know and then you can leave the scene. However if I'm like this and the minute he touched me like he declared war. Now I can go and perform a self-defense move. Striking not wrestling. Yes, yes. It's always striking first and leave the scene if you're for example a kid or someone who's not as physically doesn't have the physical strength of your aggressor. Of course I'm a UFC champion so that does not apply to me but the key is on a tactical we always use the element of surprise and when you strike, strike first and strike to cause the most damage as possible. The eyes, you can use the neck and do the genitals and then after you can leave the scene. That's the the the gold of having the element of surprise. Okay we were talking about knives. Yes. What about weapons are involved? Run faster? So weapons is very important if someone has a weapon and attack me for having my money I give him my money even if I'm Georges Saint-Pierre and I'm UFC champion. However if someone put a knife on my throat here and he's telling me to go in the trunk. No, I don't want to go in the trunk because I know it might it I've seen this movie and it's a bad ending. So things that I can do first it's always make sure that I try to make my hand as close as possible as the weapon and I try to be as close range as possible. If he's here I cannot do it because he's the distance is too far. If he's here that's idea so I can I can I can act like I want to look scared. Yeah please please please boom see here here I use my body and then I can go and break you know. So the idea is to use your entire body to deflect the weapon. So if the weapon is like this and the blade is coming out this way I use the element of surprise. You see I use my body not only grabbing him like this so if he tries to come back with the the knife it's solid then I can go and break. If he if it's the blade is pointing the other side it's same thing here here and here I can I can I can use my body always to smother the weapon. Controlling the wrist yeah but if it's out here. If it's out if it's out here you know and yes exactly it's too there's too much distance. You want to make sure you you you you get close to the weapon because that's what can cause the most damage. This is very important. There is other situation if the opponent if let's say you're a kid or someone come grab you by the body here you run by the body what I can do is grab the head and put my fingers inside the the eyes will with will make my opponent to release me immediately then I can I can go and leave. Yeah thumb in the eyes you you you push in the eyes. There is no rules. The eyes is always my favorite choice to go for because if you cannot see it's very hard to fight and normally the reflex for most people when they can't see they they they grab their eyes you know so it released the the the grip. I'm not gonna ask about the tie because I think you're wrong still. I think it's possible so like if you it's possible to use it as a same as for like a head smash like this kind of situation as opposed to a choke. I think it could be an advantage if it's a fake tie if it's if it's like something that can go like that can like a like a reptile that like a tail of a reptile that can go so if you try to pull my tie it goes out and now I know I get a head start. Exactly it's all about the element of surprise you want to to strike first the element of surprise in this street. George thank you so much for talking today. My pleasure. For looking for sharp men in black baby men in black.


John Hopfield: Physics View of the Mind and Neurobiology | Lex Fridman Podcast #76

The following is a conversation with John Hopfield, professor at Princeton, whose life's work weaved beautifully through biology, chemistry, neuroscience, and physics. Most crucially, he saw the messy world of biology through the piercing eyes of a physicist. He's perhaps best known for his work on associative neural networks, now known as Hopfield networks, that were one of the early ideas that catalyzed the development of the modern field of deep learning. As his 2019 Franklin Medal in Physics Award states, he applied concepts of theoretical physics to provide new insights on important biological questions in a variety of areas, including genetics and neuroscience, with significant impact on machine learning. And as John says in his 2018 article titled, "'Now What?' His accomplishments have often come about by asking that very question, now what? And often responding by a major change of direction." This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcast, support it on Patreon, or simply connect with me on Twitter, at Lex Friedman, spelled F-R-I-D-M-A-N. As usual, I'll do one or two minutes of ads now, and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as $1. Since Cash App does fractional share trading, let me mention that the order execution algorithm that works behind the scenes to create the abstraction of fractional orders is to me an algorithmic marvel. So big props to the Cash App engineers for solving a hard problem that in the end provides an easy interface that takes a step up the next layer of abstraction over the stock market, making trading more accessible for new investors and diversification much easier. So again, if you get Cash App from the App Store or Google Play, and use code LEXPODCAST, you'll get $10, and Cash App will also donate $10 to Thirst, one of my favorite organizations that is helping advance robotics and STEM education for young people around the world. And now, here's my conversation with John Hopfield. What difference between biological neural networks and artificial neural networks is most captivating and profound to you? At the higher philosophical level, let's not get technical just yet. One of the things that very much intrigues me is the fact that neurons have all kinds of components, properties to them. And evolutionary biology, if you have some little quirk in how a molecule works or how a cell works, and it can be made use of, evolution will sharpen it up and make it into a useful feature rather than a glitch. And so you expect in neurobiology for evolution to have captured all kinds of possibilities of getting neurons, of how you get neurons to do things for you. And that aspect has been completely suppressed in artificial neural networks. So the glitches become features in the biological neural network? They can. Look, let me take one of the things that I used to do research on. If you take things which oscillate, they have rhythms which are sort of close to each other, under some circumstances, these things will have a phase transition and suddenly the rhythm will, everybody will fall into step. There was a marvelous physical example of that in the Millennium Bridge across the Thames River, about, built about 2001. And pedestrians walking across, pedestrians don't walk, synchronize, they don't walk in lockstep, but they're all walking about the same frequency. And the bridge could sway at that frequency and the slight sway made pedestrians tend a little bit to lock into step. And after a while, the bridge was oscillating back and forth and the pedestrians were walking in step to it. You could see it in the movies made out of the bridge. And the engineers made a simple, minor mistake. They assumed when you walk, it's step, step, step, and it's back and forth motion. But when you walk, it's also right foot, left foot, side to side motion. And it's the side to side motion for which the bridge was strong enough, but it wasn't stiff enough. And as a result, you would feel the motion and you'd fall into step with it. And people were very uncomfortable with it. They closed the bridge for two years while they built stiffening for it. Now, nerves, look, nerve cells produce action potentials. You have a bunch of cells which are loosely coupled together producing action potentials of the same rate. There'll be some circumstances under which these things can lock together. Other circumstances in which they won't. Well, they fire together, you can be sure that the other cells are gonna notice it. So you can make a computational feature out of this in an evolving brain. Most artificial neural networks don't even have action potentials, let alone have the possibility for synchronizing them. And you mentioned the evolutionary process. So the evolutionary process that builds on top of biological systems leverages the weird mess of it somehow. So how do you make sense of that ability to leverage all the different kinds of complexities in the biological brain? Well, look, at the biological molecule level, you'd have a piece of DNA which encodes for a particular protein. You could duplicate that piece of DNA and now one part of it can code for that protein, but the other one could itself change a little bit and thus start coding for a molecule which is slightly different. Now, if that molecule was just slightly different, had a function which helped any old chemical reaction was as important to the cell, it would go ahead and let that try and evolution would slowly improve that function. And so you have the possibility of duplicating and then having things drift apart. One of them retain the old function, the other one do something new for you. And there's evolutionary pressure to improve. Look, there is in computers too, but it's improvement has to do with closing some companies and opening some others. The evolutionary process looks a little different. Yeah, similar time scale perhaps. Much shorter in time scale. Companies close, yeah, go bankrupt and are born. Yeah, shorter, but not much shorter. Some companies last a century, but yeah, you're right. I mean, if you think of companies as a single organism that builds and you all know, yeah, it's a fascinating dual correspondence there between biological- And companies have difficulty having a new product competing with an old product. Yeah. And when IBM built its first PC, you probably read the book, they made a little isolated internal unit to make the PC. And for the first time in IBM's history, they didn't insist that you build it out of IBM components, but they understood that they could get into this market, which is a very different thing by completely changing their culture. And biology finds other markets in a more adaptive way. In a more adaptive way. Yeah, it's better at it. It's better at that kind of integration. So maybe you've already said it, but what to use the most beautiful aspect or mechanism of the human mind? Is it the adaptive, the ability to adapt as you've described or is there some other little quirk that you particularly like? Adaptation is everything when you get down to it, but the difference, there are differences between adaptation where you're learning goes on only over generations and over evolutionary time, where you're learning goes on at the time scale of one individual who must learn from the environment during that individual's lifetime. And biology has both kinds of learning in it. And the thing which makes neurobiology hard is that a mathematical system, as it were, built on this other kind of evolutionary system. What do you mean by mathematical system? Where's the math and the biology? Well, when you talk to a computer scientist about neural networks, it's all math. The fact that biology actually came about from evolution and the fact that biology is about a system which you can build in three dimensions. If you look at computer chips, computer chips are basically two-dimensional structures, maybe 2.1 dimensions, but they really have difficulty doing three-dimensional wiring. Biology is, the neocortex is actually also sheet-like and it sits on top of the white matter, which is about 10 times the volume of the gray matter and contains all what you might call the wires. But there's a huge, the effect of computer structure on what is easy and what is hard is immense. So. And biology does, it makes some things easy that are very difficult to understand how to do computationally. On the other hand, you can't do simple floating-point arithmetic, so it's awfully stupid. Yeah, and you're saying this kind of three-dimensional, complicated structure makes, it's still math. It's still doing math. The kind of math it's doing enables you to solve problems of a very different kind. That's right, that's right. So you mentioned two kinds of adaptation, the evolutionary adaptation and the adaptation or learning at the scale of a single human life. Which do you, which is particularly beautiful to you and interesting from a research and from just a human perspective? And which is more powerful? I find things most interesting that I begin to see how to get into the edges of them and tease them apart a little bit and see how they work. And since I can't see the evolutionary process going on, I'm in awe of it, but I find it just a black hole as far as trying to understand what to do. And so in a certain sense, I'm in awe of it, but I couldn't be interested in working on it. The human life's time scale is however thing you can tease apart and study. Yeah, you can do, there's the developmental neurobiology which understands how the connections and how the structure evolves from a combination of what the genetics is like and the real, the fact that you're building a system in three dimensions. And in just days and months, those early days of a human life are really interesting. They are, and of course, there are times of immense cell multiplication. There are also times of the greatest cell death in the brain is during infancy. It's turnover. So what is not effective, what is not wired well enough to use the moment, throw it out. It's a mysterious process. From, let me ask, from what field do you think the biggest breakthroughs in understanding the mind will come in the next decades? Is it neuroscience, computer science, neurobiology, psychology, physics? Maybe math, maybe literature? Well, of course, I see the world always through a lens of physics. I grew up in physics and the way I pick problems is very characteristic of physics and of an intellectual background, which is not psychology, which is not chemistry and so on and so on. Now, both of your parents are physicists. Both of my parents were physicists and the real thing I got out of that was a feeling that the world is an understandable place and if you do enough experiments and think about what they mean and structure things so you can do the mathematics of the relevant to the experiments, you also be able to understand how things work. But that was a few years ago. Did you change your mind at all through many decades of trying to understand the mind, of studying it different kinds of ways? Not even the mind, just biological systems. You still have hope that physics, that you can understand? There's a question of what do you mean by understand? Of course. When I taught freshman physics, I used to say, I wanted to give physics to understand the subject, to understand Newton's laws. I didn't want them simply to memorize a set of examples to which they knew the equations to write down to generate the answers. I had this nebulous idea of understanding so that if you looked at a situation, you could say, oh, I expect the ball to make that trajectory or I expect some intuitive notion of understanding. And I don't know how to express that very well and I've never known how to express it well. And you run smack up against it when you look at these simple neural nets, feedforward neural nets, which do amazing things and yet you know contain nothing of the essence of what I would have felt was understanding. Understanding is more than just an enormous lookup table. Let's linger on that. How sure you are of that? What if the table gets really big? So, I mean, ask another way, these feedforward neural networks, do you think they'll ever understand? Could answer that in two ways. I think if you look at real systems, feedback is an essential aspect of how these real systems compute. On the other hand, if I have a mathematical system with feedback, I know I can unlayer this and do it. But I have an exponential expansion in the amount of stuff I have to build if I can solve the problem that way. So feedback is essential. So we can talk even about recurrent neural nets, so recurrence. But do you think all the pieces are there to achieve understanding through these simple mechanisms? Like back to our original question, what is the fundamental, is there a fundamental difference between artificial neural networks and biological? Or is it just a bunch of surface stuff? Suppose you ask a neurosurgeon, when is somebody dead? Yeah. They'll probably go back to saying, well, I can look at the brain rhythms and tell you this is a brain which is never gonna function again. This one is, this other one is one which if we treat it well, is still recoverable. And then just do that by some electrodes looking at simple electrical patterns which don't look in any detail at all at what individual neurons are doing. These rhythms are utterly absent from anything which goes on at Google. Yeah, but the rhythms. But the rhythms what? So, well, that's like comparing, okay, I'll tell you, it's like you're comparing the greatest classical musician in the world to a child first learning to play. The question I'm at, but they're still both playing the piano. I'm asking, will it ever go on at Google? Do you have a hope? Because you're one of the seminal figures in launching both disciplines, both sides of the river. I think it's going to go on generation after generation the way it has where what you might call the AI computer science community says, let's take the following. This is our model of neurobiology at the moment. Let's pretend it's good enough and do everything we can with it. And it does interesting things. And after a while, it sort of grinds into the sand and you say, ah, something else is needed for neurobiology and some other grand thing comes in and enables you to go a lot further. But we'll go into the sand again. And I think it could be generations of this evolution. I don't know how many of them, and each one is going to get you further into what our brain does. In some sense, past the Turing test, longer and more broad aspects. And how many of these are good there are going to have to be before you say, I've made something, I've made a human, I don't know. But your sense is it might be a couple. My sense is it might be a couple more. Yeah. And going back to my brain waves as it were, from the AI point of view, they would say, ah, maybe these are an heavy phenomenon and not important at all. The first car I had, a real wreck of a 1936 Dodge, go above 45 miles an hour and the wheels would shimmy. Yeah. Good speedometer that. Now, nobody designed the car that way. The car is malfunctioning to have that. But in biology, if it were useful to know when are you going more than 45 miles an hour, you just capture that and you wouldn't worry about where it came from. Yeah. It's going to be a long time before that kind of thing, which can take place in large complex networks of things, is actually used in the computation. Look, how many transistors are there in your laptop these days? Actually, I don't know the number. It's- It's on the scale of 10 to the 10. I can't remember the number either. Yeah. And all the transistors are somewhat similar. And most physical systems with that many parts, all of which are similar, have collective properties. Yes. Sound waves in air, earthquakes, what have you, have collective properties. Whether, there are no collective properties used in artificial neural networks, in AI. Yeah, it's very- If biology uses them, it's going to take us to more generations of things for people to actually dig in and see how they are used and what they mean. See, you're very right. I might have to return several times to neurobiology and try to make our transistors more messy. Yeah, yeah. At the same time, the simple ones will conquer big aspects. And I think one of the most biggest surprises to me was how well learning systems, which are manifestly non-biological, how important they can be actually, and how important and how useful they can be in AI. So, if we can just take a stroll to some of your work, that is incredibly surprising, that it works as well as it does, that launched a lot of the recent work with neural networks. If we go to what are now called Hopfield networks, can you tell me what is associative memory in the mind for the human side? Let's explore memory for a bit. Okay, what you mean by associative memory is, ah, you have a memory of each of your friends. Your friend has all kinds of properties from what they look like, what their voice sounds like, where they went to college, where you met them, go on and on, what science papers they've written. If I start talking about a five foot 10, wire-aided cognitive scientist who's got a very bad back, it doesn't take very long for you to say, oh, he's talking about Jeff Hinton. I never mentioned the name or anything very particular, but somehow a few facts that are associated with a particular person enables you to get a hold of the rest of the facts, or not the rest of them, another subset of them. And it's this ability to link things together, link experiences together, which goes under the general name of associative memory. And a large part of intelligent behavior is actually just large associative memories that work, as far as I can see. What do you think is the mechanism of how it works in the mind? Is it a mystery to you still? Do you have inklings of how this essential thing for cognition works? What I made 35 years ago was, of course, a crude physics model to show the kind, to actually enable you to understand, my old sense of understanding as a physicist, because you could say, ah, I understand why this goes to stable states, it's like things going downhill. Right. And that gives you something with which to think in physical terms, rather than only in mathematical terms. So you've created these associative artificial networks. That's right. And now if you look at what I did, I didn't at all describe a system which gracefully learns. I described a system in which you could understand how learning could link things together, how very crudely it might learn. One of the things which intrigues me as I reinvestigate that system now to some extent is, look, I'll see you every second for the next hour or what have you. Each look at you is a little bit different. I don't store all those second by second images. I don't store 3000 images. I somehow compact this information. So I now have a view of you, which I can use. It doesn't slavishly remember anything in particular, but it compacts the information into useful chunks, which are somehow, it's these chunks, which are not just activities of neurons, bigger things than that, which are the real entities which are useful to you. Useful to you to describe, to compress this information. Coming at you. And you have to compress it in such a way that if the information comes in just like this again, I don't bother to rewrite it, or efforts to rewrite it, simply do not yield anything because those things are already written. And that needs to be not, look this up, have I written this, have I stored it somewhere already? That'd be something which is much more automatic in the machine hardware. Right, so in the human mind, how complicated is that process, do you think? So you've created, feels weird to be sitting with John Hopfield calling him Hopfield Networks, but. It is weird. Yeah, but nevertheless, that's what everyone calls him, so here we are. So that's a simplification, that's what a physicist would do. You and Richard Feynman sat down and talked about associative memory. Now if you look at the mind, where you can't quite simplify it so perfectly, do you think that. Let me backtrack just a little bit. Yeah, biology is about dynamical systems. Computers are dynamical systems. You can ask, if you want to model biology, if you want to model neurobiology, what is the time scale? There's a dynamical system in which, fairly fast time scale in which you could say, the synapses don't change much during this computation, so I'll think of the synapses fixed and just do the dynamics of the activity. Or you can say, the synapses are changing fast enough that I have to have the synaptic dynamics working at the same time as the system dynamics in order to understand the biology. Most, if you look at the feed-forward artificial neural nets, they're all done as learnings. First of all, I spend some time learning, not performing, then I turn off learning and I perform. Right. That's not biology. And so as I look more deeply at neurobiology, even as associative memory, I've got to face the fact that the dynamics of a synapse change is going on all the time. And I can't just get by by saying, I'll do the dynamics of activity with fixed synapses. So the synaptic, the dynamics of the synapses is actually fundamental to the whole system. Yeah, yeah. And there's nothing necessarily separating the time scales. When the time scales can be separated, it's neat from the physicist's or the mathematician's point of view, but it's not necessarily true in neurobiology. So you're kind of dancing beautifully between showing a lot of respect to physics and then also saying that physics cannot quite reach the complexity of biology. So where do you land? Or do you continuously dance between the two? I continuously dance between them because my whole notion of understanding is that you can describe to somebody else how something works in ways which are honest and believable and still not describe all the nuts and bolts in detail. Weather. I can describe weather as 10 to the 32 molecules colliding in the atmosphere. I can simulate weather that way, I have a big enough machine. I'll simulate it accurately. It's no good for understanding. I just want to understand things. I want to understand things in terms of wind patterns, hurricanes, pressure differentials, and so on. All things as they're collective. And the physicist in me always hopes that biology will have some things which can be said about it which are both true and for which you don't need all the molecular details as the molecules colliding. That's what I mean from the roots of physics by understanding. So what did, again, sorry, but Hopfield Networks help you understand what insight did it give us about memory, about learning? They didn't give insights about learning. They gave insights about how things having learned could be expressed. How having learned a picture of you reminds me of your name. That would, it didn't describe a reasonable way of actually doing the learning. Or at least that if you had previously learned the connections of this kind of pattern would now be able to behave in a physical way which is a, oh, if I put part of the pattern in here, the other part of the pattern will complete over here. I can understand that physics if the right learning stuff had already been put in. And it could understand why then putting in a picture of somebody else would generate something else over here. But it did not have a reasonable description of the learning process. But even, so forget learning. I mean, that's just a powerful concept that sort of forming representations that are useful to be robust for error correction kind of thing. So this is kind of what the biology does that we're talking about. Yeah, and what my paper did was simply enable you, there are lots of ways of being robust. If you think of a dynamical system, you think of a system where a path is going on in time. And if you think of a computer, there's a computational path, which is going on in a huge dimensional space of ones and zeros. And an error correction system is a system which if you get a little bit off that trajectory, will push you back onto that trajectory again. So you get to the same answer in spite of the fact that there were things, the computation wasn't being ideally done all the way along the line. And there are lots of models for error correction. But one of the models for error correction is to say, there's a valley that you're following flowing down. And if you push a little bit off the valley, just like water being pushed a little bit by a rock, it gets back and follows the course of the river. And that basically the analog in the physical system, which enables you to say, oh yes, error-free computation and an associative memory are very much like things that I can understand from the point of view of a physical system. The physical system can be under some circumstances, an accurate metaphor. It's not the only metaphor. There are other error correction schemes, which don't have a valley and energy behind them. But those are correction schemes, which a mathematician may be able to understand, but I don't. So there's the physical metaphor that seems to work here. That's right, that's right. So these kinds of networks actually led to a lot of the work that is going on now in neural networks, artificial neural networks. So the follow-on work with restricted Boltzmann machines and deep belief nets followed on from these ideas of the Hopfield network. So what do you think about this continued progress of that work towards now re-revigorated exploration of feed-forward neural networks and recurrent neural networks and convolutional neural networks and kinds of networks that are helping solve image recognition, natural language processing, all that kind of stuff? It's always intrigued me that one of the most long-lived of the learning systems is the Boltzmann machine, which is intrinsically a feedback network. And with the brilliance of Hinton and Sanofsky to understand how to do learning in that. And it's still a useful way to understand learning and understand, and the learning that you understand in that has something to do with the way that feed-forward systems work. But it's not always exactly simple to express that intuition. But it always amuses me to see Hinton going back to the will yet again on a form of the Boltzmann machine, because really that which has feedback and interesting probabilities in it is a lovely encapsulation of something computational. Something both computational and physical. Computational in the, it's very much related to feed-forward networks. Physical in that Boltzmann machine learning is really learning a set of parameters for a physics Hamiltonian or energy function. What do you think about learning in this whole domain? Do you think the aforementioned guy, Jeff Hinton, all the work there with backpropagation, all the kind of learning that goes on in these networks, how do you, if we compare it to learning in the brain, for example, is there echoes of the same kind of power that backpropagation reveals about these kinds of recurrent networks, or is it something fundamentally different going on in the brain? I don't think the brain is as deep as the deepest networks go, the deepest computer science networks. And I do wonder whether part of that depth of the computer science networks is necessitated by the fact that the only learning that's easily done on a machine is feed-forward. And so there's the question of to what extent has the biology, which has some feed-forward and some feed-back, been captured by something which has got many more neurons, but much more depth than the neurons in it. So part of you wonders if the feedback is actually more essential than the number of neurons or the depth, the dynamics of the feedback. The dynamics of the feedback, look, if you don't have feedback, it's a little bit like building a big computer and running it through one clock cycle, and then you can't do anything until you reload something coming in. How do you use the fact that there are multiple clocks? How do I use the fact that you can close your eyes, stop listening to me, and think about a chess board for a few minutes without any input whatsoever? Yeah, that memory thing, that's fundamentally a feedback kind of mechanism. You're going back to something. Yes, it's hard to understand. Talk about introspect, let alone consciousness. Is that so? Let alone consciousness, yes, yes. Because that's tied up in there too. You can't just put that on another shelf. Every once in a while, I get interested in consciousness, and then I go and I've done that for years, and ask one of my bettors, as it were, their view on consciousness. It's been interesting collecting them. What is consciousness? Let's try to take a brief step into that room. Well, I asked Marvin Minsky, his view on consciousness, and Marvin said, consciousness is basically overrated. It may be an epiphenomenon. After all, all the things your brain does, which are actually hard computations, you do non-consciously. And there's so much evidence that even the simple things you do, you can make committed decisions about them. The neurobiologist can say, he's now committed. He's going to move the hand left before you know it. So his view that consciousness is not, that's just like little icing on the cake, the real cake is in the subconscious. Yeah, yeah. Subconscious, non-conscious. Non-conscious, what's the better word, sir? It's only that Freud captured the other word. Yeah, it's a confusing word, subconscious. Nicholas Chater wrote an interesting book. I think the title of it is, The Mind is Flat. Flat, in a neural net sense, might be flat is something which is a very broad neural net without really any layers in depth, or the deep brain would be many layers and not so broad. In the same sense that if you push Minsky hard enough, he would probably have said, "'Consciousness is your effort to explain to yourself that which you have already done.'" Yeah. Yeah, it's the weaving of the narrative around the things that have already been computed for you. That's right, and so much of what we do for our memories of events, for example, if there's some traumatic event you witness, you will have a few facts about it correctly done. If somebody asks you about it, you will weave a narrative which is actually much more rich in detail than that, based on some anchor points you have of correct things, and pulling together general knowledge on the other, but you will have a narrative. And once you generate that narrative, you are very likely to repeat that narrative and claim that all the things you have in it are actually the correct things. There was a marvelous example of that in the Watergate slash impeachment era of John Dean. John Dean, you're too young to know, had been the personal lawyer of Nixon. And so John Dean was involved in the coverup, and John Dean ultimately realized the only way to keep himself out of jail for a long time was actually to tell some of the truths about Nixon. And John Dean was a tremendous witness. He would remember these conversations in great detail, and very convincing detail. And long afterward, some of the tapes, the secret tapes as it were, from which Jane was recalling these conversations were published, and one found out that John Dean had a good but not exceptional memory. What he had was an ability to paint vividly and in some sense accurately the tone of what was going on. By the way, that's a beautiful description of consciousness. Do you, like where do you stand in your, today? So perhaps it changes day to day, but where do you stand on the importance of consciousness in our whole big mess of cognition? Is it just a little narrative maker, or is it actually fundamental to intelligence? That's a very hard one. When I asked Francis Crick about consciousness, he launched forward in a long monologue about Mendel and the peas. Yeah. And how Mendel knew that there was something, and how biologists understood that there was something in inheritance, which was just very, very different, and the fact that inherited traits didn't just wash out into a gray, but were this or this, and propagated, that that was absolutely fundamental to biology, and it took generations of biologists to understand that there was genetics, and it took another generation or two to understand that genetics came from DNA. But very shortly after Mendel, thinking biologists did realize that there was a deep problem about inheritance. And Francis would have liked to have said, and that's why I'm working on consciousness. But of course, he didn't have any smoking gun in the sense of Mendel. And that's the weakness of his position. If you read his book, which he wrote with Koch, I think. Yeah, Christoph Koch, yeah. I find it unconvincing for the smoking gun reason. So I go on collecting views without actually having taken a very strong one myself, because I haven't seen the entry point. Not seeing the smoking gun, from the point of view of physics, I don't see the entry point. Whereas in neurobiology, once I understood the idea of a collective, an evolution of dynamics, which could be described as a collective phenomenon, I thought, ah, there's a point where what I know about physics is so different from any neurobiologist that I have something that I might be able to contribute. And right now, there's no way to grasp at consciousness from a physics perspective. From my point of view, that's correct. And of course, people, physicists like everybody else, think very muddily about things. You ask the closely related question about free will, do you believe you have free will? Physicists will give an offhand answer and then backtrack, backtrack, backtrack, where they realize that the answer they gave must fundamentally contradict the laws of physics. Naturally, answering questions of free will and consciousness naturally lead to contradictions from a physics perspective. Because it eventually ends up with quantum mechanics, and then you get into that whole mess of trying to understand how much, from a physics perspective, how much is determined, already predetermined, much is already deterministic about our universe. There's lots of different- And if you don't push quite that far, you can say essentially all of neurobiology, which is relevant, can be captured by classical equations of motion. Because in my view of the mysteries of the brain are not the mysteries of quantum mechanics, but the mysteries of what can happen when you have a dynamical system, driven system with 10 to the 14 parts. That that complexity is something which is, that the physics of complex systems is at least as badly understood as the physics of phase coherence in quantum mechanics. Can we go there for a second? You've talked about attractor networks, and just maybe you could say what are attractor networks, and more broadly, what are interesting network dynamics that emerge in these or other complex systems? You have to be willing to think in a huge number of dimensions, because in a huge number of dimensions, the behavior of a system can be thought of as just the motion of a point over time in this huge number of dimensions. Right. And an attractor network is simply a network where there is a line, and other lines converge on it in time. That's the essence of an attractor network. That's how you- In a highly dimensional space. And the easiest way to get that is to do it in a highly dimensional space where some of the dimensions provide the dissipation, which means, which, look, I have a physical system, trajectories can't contract everywhere. They have to contract in some places and expand in others. There's a fundamental classical theorem of statistical mechanics, which goes under the name of Liouville's theorem, which says you can't contract everywhere. You have to, if you contract somewhere, you expand somewhere else. And it's an interesting physical systems. You get driven systems where you have a small subsystem, which is the interesting part, and the rest of the contraction and expansion, the physicists would say is entropy flow in this other part of the system. But basically, attractor networks are dynamics funneling down, so you can't be any, so if you start somewhere in the dynamical system, you will soon find yourself on a pretty well-determined pathway, which goes somewhere. You start somewhere else, you'll wind up on a different pathway, but you don't have just all possible things. You have some defined pathways, which are allowed and onto which you will converge. And that's the way you make a stable computer, and that's the way you make a stable behavior. So in general, looking at the physics of the emergent stability in these networks, what are some interesting characteristics that, what are some interesting insights from studying the dynamics of such high-dimensional systems? Most dynamical systems, most driven dynamical systems, by driven, they're coupled somehow to an energy source, and so their dynamics keeps going because it's coupling to the energy source. Most of them, it's very difficult to understand at all what the dynamical behavior is going to be. You have to run it. You have to run it. There's a subset of systems which has what is actually known to the mathematicians as a Lyapunov function. And those systems, you can understand convergent dynamics by saying you're going downhill on something or other. And that's what I found with ever knowing what Lyapunov functions were in the simple model I made in the early 80s, was an energy function so you could understand how you could get this channeling onto pathways without having to follow the dynamics in infinite detail. You started rolling a ball off of a mountain that's going to wind up at the bottom of a valley. You know that's true without actually watching the ball roll down. There are certain properties of the system that when you can know that. That's right. And not all systems behave that way. Most don't, probably. Most don't, but it provides you with a metaphor for thinking about systems which are stable and who to have these attractors behave even if you can't find a Lyapunov function behind them or an energy function behind them. It gives you a metaphor for thought. Speaking of thought, if I had a glint in my eye with excitement and said, you know, I'm really excited about this, something called deep learning and neural networks, and I would like to create an intelligent system and came to you as an advisor, what would you recommend? Is it a hopeless pursuit to use neural networks to achieve thought? Is it, what kind of mechanisms should we explore? What kind of ideas should we explore? Well, you look at the simple networks, one-pass networks, they don't support multiple hypotheses very well. As I have tried to work with very simple systems which do something which you might consider to be thinking, thought has to do with the ability to do mental exploration before you make it take a physical action. Almost like we were mentioning, playing chess, visualizing, simulating inside your head, different outcomes. Yeah, yeah. And now you could do that in a feed-forward network because you've pre-calculated all kinds of things. But I think the way neurobiology does it, it hasn't pre-calculated everything. It actually has parts of a dynamical system in which you're doing exploration in a way which is... There's a creative element. Like there's an... There's a creative element. And in a simple-minded neural net, you have a constellation of instances from which you've learned. And if you are within that space, if a new question is a question within this space, you can actually rely on that system pretty well to come up with a good suggestion for what to do. If on the other hand, the query comes from outside the space, you have no way of knowing how the system is going to behave. There are no limitations on what could happen. And so with the artificial neural net world is always very much, I have a population of examples. The test set must be drawn from the equivalent population. If the test set has examples which are from a population which is completely different, there's no way that you could expect to get the answer right. Yeah. And so- What they call outside the distribution. That's right. That's right. And so if you see a ball rolling across the street at dusk, if that wasn't in your training set, the idea that a child may be coming close behind that is not going to occur to the neural net. And it is to our, there's something in your biology that allows that. Yeah. There's something in the way of what it means to be outside of the population of the training set. The population of the training set isn't just sort of this set of examples. There's more to it than that. And it gets back to my own question of what is it to understand something? Yeah. You know, in a small tangent, you've talked about the value of thinking of deductive reasoning in science versus large data collection. So sort of thinking about the problem. I suppose it's the physics side of you of going back to first principles and thinking. But what do you think is the value of deductive reasoning in the scientific process? Well, there are obviously scientific questions in which the route to the answer to it comes through the analysis of one hell of a lot of data. Right. Cosmology, that kind of stuff. And that's never been the kind of problem in which I've had any particular insight. Though I must say, if you look at, I mean, cosmology is one of those. If you look at the actual things that Jim Peebles, one of this year's Nobel Prize physics ones from the local physics department, the kinds of things he's done, he's never crunched large data. Never, never, never. He's used the encapsulation of the work of others in this regard. Right. But ultimately it boiled down to thinking through the problem. Like what are the principles under which a particular phenomenon operates? Yeah, yeah. And look, physics is always going to look for ways in which you can describe the system in a way which rises above the details. And to the hard-dyed-in-the-wool biologist, biology works because of the details. And physics, to the physicists, we want an explanation which is right in spite of the details. And there will be questions which we cannot answer as physicists because the answer cannot be found that way. There's, I'm not sure if you're familiar with the entire field of brain-computer interfaces. That's become more and more intensely researched and developed recently, especially with companies like Neuralink with Elon Musk. Yeah, I know there have always been the interest both in things like getting the eyes to be able to control things or getting the thought patterns to be able to move what had been a connected limb which is now connected through a computer. That's right. So in the case of Neuralink, they're doing a thousand plus connections where they're able to do two-way, activate and read spikes, neural spikes. Do you have hope for that kind of computer brain interaction in the near or maybe even far future of being able to expand the ability of the mind of cognition or understand the mind? It's interesting watching things go. When I first became interested in neurobiology, most of the practitioners thought you would be able to understand neurobiology by techniques which allowed you to record only one cell at a time. One cell, yeah. People like David Hubel very strongly reflected that point of view and that's been taken over by a generation, a couple of generations later by a set of people who says, not until we can record from 10 to the four or 10 to the five at a time, will we actually be able to understand how the brain actually works. And in a general sense, I think that's right. You have to begin to be able to look for the collective modes, collective operations of things. It doesn't rely on this action potential of that cell. It relies on the collective properties of this set of cells connected with this kind of patterns and so on. And you're not going to succeed in seeing what those collective activities are without recording many cells at once. The question is how many at once? What's the threshold? And that's the- Yeah, and look, it's being pursued hard in the motor cortex. The motor cortex does something which is complex and yet the problem you're trying to address is fairly simple. Now, neurobiology does it in ways that differ from the way an engineer would do it. An engineer would put in six highly accurate stepping motors controlling a limb rather than 100,000 muscle fibers, each of which has to be individually controlled. And so understanding how to do things in a way which is much more forgiving and much more neural, I think would benefit the engineering world in a way that I think would benefit the engineering world. The engineering world, ah, touch. Let's put in a pressure sensor or two, rather than an array of a gazillion pressure sensors, none of which are accurate, all of which are perpetually recalibrating themselves. So you're saying your hope is, your advice for the engineers of the future is to embrace the large chaos of a messy, error-prone system like those of the biological systems. Like that's probably the way to solve some of these. I think you'll be able to make better computations slash robotics that way than by trying to force things into a robotics where joint motors are powerful and stepping motors are accurate. But then the physicists, the physicists in you will be lost forever in such systems because there's no simple fundamentals to explore in systems that are so large and messy. Well, you say that, and yet there's a lot of physics, in the Navier-Stokes equations, the equations of nonlinear hydrodynamics, huge amount of physics in them. All the physics of atoms and molecules has been lost, but it's been replaced by this other set of equations, which is just as true as the equations at the bottom. Now those equations are going to be harder to find in general biology. But the physicist in me says there are probably some equations of that sort. They're out there. They're out there. And if physics is going to contribute to anything, it may contribute to trying to find out what those equations are and how to capture them from the biology. Would you say that's one of the main open problems of our age is to discover those equations? Yeah, if you look at, there's molecules and there's psychological behavior. And these two are somehow related. They're layers of detail. They're layers of collectiveness. And to capture that in some vague way, several stages on the way up to see how these things can actually be linked together. So it seems in our universe, there's a lot of elegant equations that can describe the fundamental way that things behave, which is a surprise. I mean, it's compressible into equations. It's simple and beautiful. But it's still an open question whether that link is equally between molecules and the brain is equally compressible into elegant equations. But your sense, you're both a physicist and a dreamer. You have a sense that- Yeah, but I can only dream physics dreams. Yeah, physics dreams. There was an interesting book called Einstein's Dreams, which alternates between chapters on his life and descriptions of the way time might have been, but isn't. The linking between these being, of course, ideas that Einstein might've had to think about the essence of time as he was thinking about time. So speaking of the essence of time and your biology, you're one human, famous impactful human, but just one human with a brain living the human condition, but you're ultimately mortal, just like all of us. Has studying the mind as a mechanism changed the way you think about your own mortality? It has really, because particularly as you get older and the body comes apart in various ways, I became much more aware of the fact that what is somebody is contained in the brain and not in the body that you worry about burying. And it is to a certain extent true that for people who write things down, equations, dreams, notepads, diaries, fractions of their thought does continue to live after they're dead and gone, after their body is dead and gone. And there's a sea change in that going on in my lifetime between when my father died, when except for the things which were actually written by him as it were, very few facts about him will have ever been recorded. And the number of facts which are recorded about each and every one of us forever now, as far as I can see, in the digital world. And so the whole question of what is death may be different for people a generation ago and a generation further ahead. Maybe we have become immortal under some definitions. Yeah, yeah. Last easy question, what is the meaning of life? Looking back, you've studied the mind, us weird descendants of apes, what's the meaning of our existence on this little earth? Oh, that word meaning is as slippery as the word understand. Interconnected somehow, perhaps. Is there, it's slippery, but is there something that you, despite being slippery, can hold long enough to express? I've been amazed at how hard it is to define the things in a living system in the sense that one hydrogen atom is pretty much like another, but one bacterium is not so much like another bacterium, even of the same nominal species. In fact, the whole notion of what is the species gets a little bit fuzzy. And the species exists in the absence of certain classes of environments. And pretty soon one winds up with a biology which the whole thing is living, but whether there's actually any element of it, which by itself would be said to be living, it becomes a little bit vague in my mind. Yeah. So in a sense, the idea of meaning is something that's possessed by an individual, like a conscious creature. And you're saying that it's all interconnected in some kind of way that there might not even be an individual, or all kind of this complicated mess of biological systems at all different levels where the human starts and when the human ends is unclear. Yeah, yeah, and we're in neurobiology where the, oh, you say the neocortex is the thinking, but there's lots of things that are done in the spinal cord. And so what is the essence of thought? Is it just gonna be neocortex? Can't be, can't be. Yeah, maybe to understand and to build thought, you have to build the universe along with the neocortex. It's all interlinked through the spinal cord. John, it's a huge honor talking today. Thank you so much for your time. I really appreciate it. Well, thank you for the challenge of talking with you. And it'll be interesting to see whether you can win five minutes out of this, just coherent sense to anyone. Beautiful. Thanks for listening to this conversation with John Hopfield. And thank you to our presenting sponsor, Cash App. Download it, use code LEXPODCAST. You'll get $10 and $10 will go to FIRST, an organization that inspires and educates young minds to become science and technology innovators of tomorrow. If you enjoy this podcast, subscribe on YouTube, get five stars on Apple Podcast, support on Patreon, or simply connect with me on Twitter at Lex Friedman. And now let me leave you with some words of wisdom from John Hopfield in his article titled, Now What, choosing problems is the primary determinant of what one accomplishes in science. I have generally had a relatively short attention span in science problems. Thus, I have always been on the look out for more interesting questions, either as my present ones get worked out, or as it get classified by me as intractable, given my particular talents. He then goes on to say, what I have done in science relies entirely on experimental and theoretical studies by experts. I have a great respect for them, especially for those who are willing to attempt communication with someone who is not an expert in the field. I would only add that experts are good at answering questions. If you're brash enough, ask your own. Don't worry too much about how you found them. Thank you for listening and hope to see you next time.


Comma.ai Drive and Tour with George Hotz and Lex Fridman

George Hotz gave me a demo of the Kama 2 on a ride back to the Kama AI offices in San Diego. Then we took a quick tour of the office. I found both the Kama 2 system and the entire Kama AI team and operation very impressive. Plus I got a 3D printer named after me. As one robot to another, we're in this together my brother. And now on to the ride and tour with George Hotz. I'm here with uh driving with George Hotz and uh looking at the Kama 2. Trying to find a stretch of road where we can enable it. Okay just start looping back to the office. I mean I could enable it here but this isn't really where it shines. But yeah so we don't have we don't have red lights and stop signs yet. I don't want to ship it until we have a real uh holistic one. Until we have an end-to-end one. So you would be able to then like stop at a stop sign. So the longitudinal control. Did you just enable it? I just enabled it yeah. So this is the uh so we're gonna have to stop at this light. But um how do you enable it? Uh cruise control. So that's that's the the integrated knobs on the on the car. Yeah we have a good we'll go on the 163 a bit and that's that's that's a good test of uh whether your level two system's any good. Supercruise actually can't do it. Supercruise can't do it because they're they're not good enough with roll on the road. Autopilot can do it and we can do it. How's San Diego in terms of quality of roads and lane markings all that kind of stuff? Pretty mediocre like the rest of California. What's your favorite uh reason for using KAMA? Like when when do you find the most joy? Oh oh it's it's long trips. It's um you know driving from here to LA you'll do it without a disengagement almost. You can you can you know it's two hours to LA from here you could just go to LA uh get in your car and you just sit there and watch and it's it's a it's a great time. All right so we can we can engage here. So you know watch. So it doesn't technically require you to engage. It's just it's just it's just a little bit of a hassle. So it doesn't technically require perfect lane markings? No not at all. In fact you can do lane changes on roads that don't have lane markings because it's all end to end. So I have to get over three lanes. I'm just going to take control here to do it especially since there's a cop there. So let me just get over three lanes and then this road's a decent demo. Yeah so I think only us and Autopilot can do this road. Pretty curvy. Yeah and uh it's not just curvy it's also banked. And if you don't yeah if you don't do that well if you don't have a model that can detect that. So the longitudinal policy is actually just being done by the car. Hyundai has quite a competent ACC. Probably the best one. So we haven't really felt the urge to move to our gas and brakes. There's a few cute things where which ours will do better and more holistically than theirs. But um really it's about the turning. How hard is it to get it to be this smooth? Uh hard. Five years of engineering. All this end to end huh? We weren't detecting the lane lines. They're used but they're more used as uh see like right there. That's the stock system being too harsh on the brakes. If you're a human driving you would have been much more gradual on the brakes there. But we're really going to push to switch all the longitudinals to open pilot once we have end-to-end longitudinal and the same way we have end-to-end lateral. What's harder end-to-end longitudinal end-to-end lateral? We put a lot more work into end-to-end lateral because there's a lot more subtlety in that. The car companies have managed to build competent longitudinal. We have competent longitudinal using old school policies. But lateral there's so much subtlety. For example like when a lane splits out should you follow that split? Should you stay in the center? Should you stay to one side? Or so much subtlety there. And making it all smooth and not freak out. Yeah and this thing you know the reason I feel very confident keeping my hands off the wheel our torque limit is so much lower than Tesla's. So you can't can't do anything crazy? No. We do injection tests where one person sits in that seat with a joystick and the other person keeps their hands off the wheel and that person you can't look at them they're free to jam the joystick in any direction and if you don't feel you know safe in that maneuver they take back over here because we're getting off here. But um yeah. That stretch of road is a is a is a competent demonstration of open pilot. Yeah that was impressive. It's not the high end. Maybe when we get to the the city streets I'll engage on the city streets. Is uh city streets like a compelling use case or is that just kind of uh intuition builder? Without end-to-end longitudinal it's pretty uh like it's not really useful as product. So it's more just it's cool that it can do it and it's not really useful. Um so it's more just it's cool that it can do it and like we don't gate it off but most of open pilot uh is highways like most of the value real gain is is highways. Um but also well not just highways also like um like one lane each way kind of things like double yellow kind of roads. I was doing all like the mountains up in Colorado with this thing and just yeah you get to look at the mountains before it dries. Um so like I can engage it here. Nice um now remember the longitudinal policy is is Hyundai uh and it's not ideal for cities. But um so again we're going to go through this intersection with no lines. Oh yeah. See notice how there's no line there yet our placement is still almost human ideal. Yeah this is great. So there I mean there you see we did a stretch of you know a minute of city and perfectly human nothing sketchy. See places like that are where disengage on gas is really nice. Really easy to you know hand off you always know when that handoff is happening with the noise. It didn't like keep the ACC on or anything. God if it kept the ACC on and I make the turn and it's like trying to accelerate at a weird time you know just asking to have a bad time. I like the the sounds the visuals they clear when it's on and when it's off. Love it. Well done. Look at this. Let's stay on the bike. Damn that's impressive. That's really impressive. That's the intersections without thinking about it. Okay that was awesome. It's communicating the uncertainty with this movement a little bit. Yeah. I like it. Humans actually wobble a little more than you think too. You notice it a lot more in this. Yeah that's an interesting point. Yeah. Yeah true. I think Jesse Levinson mentioned this on one of his interviews. He's like so you know we have all these we have the safety driver sit in the car and mark every tiny mistakes it made and then we also had the safety driver who was like you know I think we have all these we have the safety driver sit in the car and mark every tiny mistakes it made and then we also had the safety driver sit in a car that was being human driven and mark all the little tiny mistakes it made. This is the Kama office in downtown. Kama office is right here. We're at Kama AI offices with Mr. George Hotz. Can you give us a little tour? It doesn't light up anymore. I don't know why. I think most of these dashboards are okay. We're almost at 30 million open pilot miles. Wow. It's a hell of a number. So here here's dailies, weeklies and uh so actually yeah dailies are like maybe not 2,000. 1.5. So those are used daily and then used weekly. Yeah. That's really cool. Weeklies are almost 2.5 and monthlies are a little over 3. This is how many devices are on our botnet in the last hour. Botnet last day, botnet last week. Is this all over the United States? This is no this is everywhere. So here's here's here's the maps. All right here's the map of the world. So we're everywhere in the world. So let's see. In the last 30 days we had 2,200 in the U.S. And then Korea, Canada, Taiwan. Japan's up there. This is our percent engaged. So percent of miles engaged is about 50 percent. Percent time engaged is about 30. Our disengagements are split pretty evenly between cancel, gas and break. Most of our segments are now coming from Comma 2s. Comma 2s. Comma 2s is the big the big breakout for this company. Yeah. Oh yeah, it's profitable. This is our live link to the Europe office. And come as a multinational corporation. George just erased a bunch of top secret things. Censorship, the censors came in. Yeah, yeah, yeah. Look the missions at the top of the board. Solve self-driving cars while delivering shippable intermediaries. Amen. What do we do badly? What can we do better? Right. Good questions. Open pilot 1.0, when is that coming up? When those things are done. PyTorch. Okay. I like how that's a bullet point. That's like move everything to PyTorch. Oh, sorry. It's not a data center, it's a compute cluster. For legal reasons, it's a compute cluster. It's a compute cluster, okay. That sounds sexier too. Data ingestion, multi-chain. Cool. So this is like a, This is the list of vision board for open pilot 1.0. It's not going to happen for a while. But the Comma 2 is going to get open pilot 1.0. It's like what I've said with the hardware. So this is all going to come to the Comma 2, just with OTAs. OTA, over the air. We're going to get it. Yeah. I mean, we're getting close on a lot of these. I'm going to start lowering the real touch timeout. Alex, you want to? This is Alex. Hi, nice to meet you. She's our COO. That's true. COO. Nice to meet you. She runs the back there stuff. What's the technical description of the back there stuff? You want to see? Yeah. I think it's more exciting than this stuff. It depends who you ask. We're ahead of all the secrets, right? Nick, do we have any secrets out? I'll blur it out. I think we have secrets right now. I'll blur the top secrets. This is production. Stress test. This is our final test. The Comma 2s hang out here for about 24 hours. We test them for their heat, their temperature, their light sensors. We do cosmetic checks, screen checks. Cool. Pretty exciting. You can see they all start over there as foams. Then they get retrofit. We have a beautiful house today. But you can see we have a class of 100 foams out right now. They're getting tested. Cool. It's kind of exciting, actually. Lex, this is Mitchell. He's the latest addition to our machine learning team. That's Chris, also known as Virtually Chris. He's a famous YouTuber. Oh, wow. How did fame change you? I introduced you as a famous YouTuber. Yes, I was the open pilot YouTuber. And then I got hired. Don't let it get to your head. Is that it? That's our head of hardware. Awesome. What's all the stuff that you guys are doing? What's that stage of the assembly? This is the final case. The case is going on. We have board testing. Then the retrofit foams get board put on them, soldered to them. We get the heat sink and the fan. And then we put the cases on them. Awesome. Yeah, they're taking the cases off. Screen's off here. Testing screens over here. We have an oven. 3D printer. I'm afraid to take it in there. It's very hot and loud. It's also the home of the compute cluster. It's not a data center. That's right. It's a compute cluster. Prepare yourself. The sound of productivity. Yeah. Oh my gosh, it's really hot. Fresh bag. This is what you see on the outside of the Comma 2's. I like how they have names. Meg. What's the purpose of the rings? They used to be for athletic activity. But now we have too many printers. We can't anymore. We just got about 20 printers in the last few months. They've come from all over the world. Literally. Poland, Germany, Peru. France. How long does it take to print a single one? We'll name number 49 for you. Thank you. We'll call him Lex. I told him he could be number 49. I can be. I'm working right now, but I promise 49 will work. It will be Lex number 49. I appreciate that. That would mean a lot to me. This is cool. Unfortunately, because we have to keep the bugs and dust out, they're covered, but... One U. I already forgot the original name. Compute cluster. These are our CPU machines. We have three racks that look like that. Two petabytes of spinning disks. We're in our 3080s. Our 3080s are in only desktop computers for non-business purposes. These are our GPU machines. What's in there? V100s. It's like a farm. Every day we have to go out and... 21st century farm. We have to hire a good farm-builder player to come farm us. I'm so proud of you. I farm the farmers every day. These are the ones that are ready to ship. These are all of our growing harnesses. These are all the cargo support. CAA, Bosch, Elevert, Subaru, Toyota, all the Hyundai's. All the way to J, Nissan, VW. All these are assembled by hand? That's cool. That's amazing. That's legit. That's awesome. That's the office. He took me for a ride. It was awesome. It was really surprising how well it worked. In the city a little bit too. It held up. He didn't show many secrets though. It means like, keep on driving, you know? That's why we replaced it with a heart. We're all big Elon fans. Elon's success is our success. If iOS succeeds, Android succeeds.