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

Lex Fridman 0:00 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. They 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 (laughs) 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, @lexfridman, 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 FIRST, 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.

Lex Fridman 2:35 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.

John Hopfield 2:51 One of the things that very much intrigues me is the fact that neurons have all kinds of components, properties to them, and in evolutionary biology, if you have some little quirk in how y- y- 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 f- 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 like how you get neurons to do things for you, and that aspect has been completely suppressed in artificial neural networks.

Lex Fridman 3:38 Mm-hmm. So the glitches become features in the m- in the biological neural network?

John Hopfield 3:46 They, they, they can. Look, let, let me take one of the things that I used to do research on. If you take things which oscillate and 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, uh, built about 2001.

Lex Fridman 4:20 Mm-hmm.

John Hopfield 4:21 And pedestrians walking across, pedestrians don't walk s- synchronized, they don't walk in lock, 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.

Lex Fridman 4:43 Wow.

John Hopfield 4:43 And you could see it in the movies made out at the bridge. And the engineers made a simple-minded mistake, they had 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... 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-

Lex Fridman 5:17 (laughs)

John Hopfield 5:17 ... 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 at the same rate, there'll be some circumstances under which these things can lock together.

Lex Fridman 5:34 Mm-hmm.

John Hopfield 5:36 Other circumstances in which they won't. Well, if they fire together, y- you can be sure that the other cells are gonna notice it. So you could make a computational feature out of this in your... in an evolving brain. Most artificial neural networks don't even have action potentials, let alone have, have the poten- possibility for synchronizing them.

Lex Fridman 5:59 And you mentioned the evolutionary process. So there...... the evolutionary process that builds on top of biological systems leverages that, the, the weird mess of it somehow. So, w- how do you make sense of that ability to leverage all the different kinds of complexities in the, in the biological brain?

John Hopfield 6:24 Well, look, in the bi- at the biological molecule level, you have a piece of DNA which enc- would enc- encode for a particular protein. You could duplicate that piece of DNA and now one part of it encodes 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 an e- a function which helped any old chemical reaction, it was as important to the cell. It would go ahead and let that ev- try, and evolution would slowly im- 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 openings of others.

Lex Fridman 7:31 (laughs) Yeah.

John Hopfield 7:31 The evolutionary process looks a little different.

Lex Fridman 7:34 Yeah.

John Hopfield 7:34 Uh-

Lex Fridman 7:35 Similar time scale, perhaps. No-

John Hopfield 7:37 Much, much shorter in time scale.

Lex Fridman 7:39 Companies close, yeah, go bankrupt, and they're born. Yeah. Shorter. But not much shorter. Some, some company lasts a century. Couples... But yeah, you're right. I mean if you think of companies as a single organism that builds and you're on that, yeah, that's a fascinating, uh, dual correspondence there between biological organisms-

John Hopfield 8:02 And, and, and companies have difficulty having a new product competing with an old product.

Lex Fridman 8:07 Yeah.

John Hopfield 8:08 And, um...

Lex Fridman 8:09 (laughs)

John Hopfield 8:10 When IBM built its first PC, you've probably read th- 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.

Lex Fridman 8:25 Hmm.

John Hopfield 8:27 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.

Lex Fridman 8:44 (laughs) Yeah, it's better at it. It's better at that kind of integration.

Lex Fridman 8:50 So maybe you've already said it, but what to you is 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?

John Hopfield 9:11 Adaptation is everything when you get down to it. But the difference, uh, there's diff- there are differences b- between adaptation where your learning goes on only over generations and over evolutionary time, or as your learning goes on at the time scale of when an individual 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 it, a mathematical system as it were, built on this other kind of evolutionary system.

Lex Fridman 9:58 W- what do you mean by mathematical system? Where (laughs) , where is the math in the biology?

John Hopfield 10:03 Well, when you talk to a computer scientist about neural networks, it's all math.

Lex Fridman 10:08 (laughs)

John Hopfield 10:08 The fact that biology actually came about from evolution, and the thing, and the fact that biology is about a system which you can build in three dimensions.

Lex Fridman 10:23 Mm-hmm.

John Hopfield 10:25 If you look at computer chips, computer chips are basically two-dimensional structures-

Lex Fridman 10:32 Yeah.

John Hopfield 10:32 ...maybe 2.1 dimensions, but they really have difficulty doing three-dimensional wiring. Biology's, uh, biology is, the neocortex is actually also sheet-like and it sits on top of white matter which is about 10 times the volume of the gray matter, and contains all of what you might call the wires. But there's a hu- huge, the effect, the effect of computer structure on what is easy and what is hard is immense.

Lex Fridman 11:08 So-

John Hopfield 11:09 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 because it's awfully stupid.

Lex Fridman 11:23 Yeah. And you're saying this kind of three-dimensional complicated structure, uh, makes, it's still math, it's still doing math.

John Hopfield 11:31 It's-

Lex Fridman 11:32 The kind of math it's doing enables you to solve problems of a very different kind.

John Hopfield 11:38 That's right. That's right.

Lex Fridman 11:40 So you mentioned two kinds of adaptation, the evolutionary adaptation at the, and the adaptation or learning at the scale of a single human life. Which do you, uh, which is, uh, particularly beautiful to you and interesting from a research and from just a human perspective? And which is more powerful?

John Hopfield 12:05 I find things most interesting that I begin to see how to get into the edges- 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 w- I am in awe of it, but I find it just a, a, 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.

Lex Fridman 12:39 The human life's timescale is, however, a thing you can tease apart and study.

John Hopfield 12:47 Yeah. You can do the... there's developmental neurobiology which understands how the connections, uh, and s- how the structure evolves from a combination of what the genetics is like and the real, the fact that this is, you're building a system in three dimensions.

Lex Fridman 13:10 In, in just days and months. Those early, early days of a human life are really interesting.

John Hopfield 13:17 They are, and, uh, of course, there are times of immense cell multiplication. There are also times of the greatest cell death in the brain-

Lex Fridman 13:28 Hmm.

John Hopfield 13:29 ... is during infancy.

Lex Fridman 13:32 It's turnover. (laughs)

John Hopfield 13:34 So what is, what, what, uh, what is not effective, what is not wired well enough to use at the moment, throw it out.

Lex Fridman 13:42 It's a mysterious process.

Lex Fridman 13:45 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? (laughs)

John Hopfield 14:09 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, of physics and of a, uh, intellectual background which is not psychology, which is not chemistry and so on and so on.

Lex Fridman 14:28 Uh, both of your parents were physicists?

John Hopfield 14:30 Both of my parents were physicists, and, uh, 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 that you can do the mathematics, uh, of the ex- relevant to the experiments, you ought to be able to understand how things work.

Lex Fridman 14:56 But that was, that was a few years ago. Did you change your mind at all through, uh, many decades of trying to understand the mind, of studying it different kinds of way? Not even the mind, just biological systems. Do you still have hope that physics... that you can understand? (laughs)

John Hopfield 15:17 There's a question of, what do you mean by understand?

Lex Fridman 15:19 (laughs) Of course.

John Hopfield 15:21 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 for which they knew the, the equations to write down to generate the answers. I had this nebulous i- 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, 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 choose these, 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.

Lex Fridman 16:20 The-

John Hopfield 16:20 Understanding is more than just an enormous lookup table.

Lex Fridman 16:24 Let's linger on that, how sure you are of that. What if the table gets really big? So I mean, asked another way, these feedforward neural networks, do you think they'll ever understand?

John Hopfield 16:40 I 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-

Lex Fridman 16:59 (laughs) Yeah.

John Hopfield 16:59 ... before th- but, uh, but I have an exponential expansion in the amount of stuff I have to build if I're gonna solve the problem that way.

Lex Fridman 17:08 Right. So feedback is essential. So we can talk even about recurrent n- uh, neural net-

John Hopfield 17:13 Yeah, yeah.

Lex Fridman 17:13 ... so recurrence. But do you think all of 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?

John Hopfield 17:34 Suppose you ask a neurosurgeon, "When is somebody dead?"

Lex Fridman 17:39 (laughs) Yeah.

John Hopfield 17:42 He'll probably go back to saying, "Well, I can look at the brain rhythms and tell you this is a brain which is never going to function again. This is one that's a... this other one is one which if we, uh, treat it well, uh, is still recoverable." And then just, th- do that by sending electrodes and looking at simple elec- electrical patterns which don't look in any detail at all what individual neurons are doing.These rhythms are utterly absent from anything which goes on at Google. (laughs)

Lex Fridman 18:23 (inhales deeply) Yeah, yeah, but the rhythms-

John Hopfield 18:26 But the rhythms what?

Lex Fridman 18:28 So, well, that's like comparing... Okay. I'll tell you. It's like you're comparing, um, the, the greatest classical musician in the world to a child first learning to play. The question I'm a- but they're still both playing the piano. Uh, I'm, I'm asking, is there... Will it ever go on at Google? Do you have a hope? Because you were one of the seminal (laughs) figures in both, launching both disciplines, both sides of the, of the river. Uh-

John Hopfield 19:00 I think it's going to go on generation after generation-

Lex Fridman 19:05 (laughs)

John Hopfield 19:05 ... the way it has for what you might call the AI computer science community says, "Let's take the following. This is our modeled neurobiology at the moment. Let's pretend it's g- good enough and do everything we can with it." And it does interesting things and after a while, it, there are grinds into the sand. And you say, "Ah, something else is needed for neurobiology." And some other grand thing comes in and, and they usually go a lot further, but will go into the sand again. And I think it could be generations of this evolution, and I don't know how many of them, and each one is going to get you, uh, further into what a brain does, w- uh, and... In some sense, pass the Turing test longer and in more, more broad aspects. And how, how many of these are there, are going to have to be before you say, "I've made something, I've made a human-"

Lex Fridman 20:13 (laughs)

John Hopfield 20:13 "... I don't know."

Lex Fridman 20:15 But your sense is, it might be a couple? It might be-

John Hopfield 20:17 My sense is, it might be a couple more.

Lex Fridman 20:19 Yeah.

John Hopfield 20:20 And, um, going back to my, uh, brain waves as it were-

Lex Fridman 20:26 Yes.

John Hopfield 20:28 ... from the AI point of view, they've, they've, they would say, "Ah, maybe these are an epi phenomenon and not important at all." The first car I had, a real wreck of a 1936 Dodge, go above about 45 miles an hour and the wheels would shimmy.

Lex Fridman 20:51 Okay. Yeah.

John Hopfield 20:52 Good, good speedometer that.

Lex Fridman 20:55 (laughs)

John Hopfield 20:56 Now, uh, nobody designed the car that way. The car is malfunctioning to have that, but in biology, if you, i- if it, 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.

Lex Fridman 21:14 (inhales deeply) (sighs) Yeah, that-

John Hopfield 21:16 And it's gonna 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, the, um... How many transistors are there in your laptop these days?

Lex Fridman 21:35 Actually, I don't, I don't know the number. It's, it's-

John Hopfield 21:37 On the scale of 10 to the 10, I can't remember the number either.

Lex Fridman 21:40 Yeah, yeah.

John Hopfield 21:43 And all the transistors are somewhat similar. And most physical systems with that many parts, all of which are sim- similar, have collective properties.

Lex Fridman 21:54 Yes.

John Hopfield 21:55 Sound waves in air, earthquakes, what have you, have collective properties, weather. There are no collective properties used in artificial neural networks, in AI.

Lex Fridman 22:11 Yeah, it's very-

John Hopfield 22:12 I- I- if biology uses them, it's gonna take us to more generations of things to, for people to actually dig in and see how they are used and what they mean.

Lex Fridman 22:23 See, y- you're, you're very right. We might have to return several times to neurobiology and try to make our transistors more messy.

John Hopfield 22:33 Yeah, yeah. At the same time, the simple ones will cons- will conquer big aspects, and I think one of the most, uh, biggest surprises to me was, uh, how well learning systems, which are manifestly non-biological, how important they can be, actually, and in u- and how important and how useful they can be in AI.

Lex Fridman 23:03 So if we can just take a stroll to, uh, some of your work that is incredibly surprising, that it works as well as it does, that launched a lot of the, uh, recent work with neural networks. If we go to what are now called Hopfield networks, uh, can you tell me what is associative memory in the mind for the human side? Let's explore memory for a bit.

John Hopfield 23:34 Okay, what you mean by associative memory is, ah, you have a m- memory of each of your friends. Your friend has all kinds of properties from what they look like, as what their voice sounds like, to where they went to college, where you met them, uh, go on and on, what, what, what science papers they've written. And if I start talking about a 5'10", wiry, cognitive scientist who's got a very bad back, it doesn't take very long for you to say, "Oh, he's talking about Geoff Hinton." I never men- I never mentioned the name or anything very particular.... but somehow a f- a few facts that are associated with this, with a particular person, enables you to get a hold of the rest of the facts.

Lex Fridman 24:27 Yeah.

John Hopfield 24:27 Or n- or not the rest of them, o- another subset of them. And it's this ability to link things together, link experiences together, which it goes under the general name of associative memory. And a large part of intelligent behavior is actually just large associative memories at work, as far as I can see.

Lex Fridman 24:53 What do you think is the mechanism of how it works in the mind? Is it, is it a mystery to you still? Do you have inklings of how this essential thing for cognition works?

John Hopfield 25:10 What I made, uh, 35 years ago was, of course, a, uh, crude physics model to show the ki- just 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 down- downhill."

Lex Fridman 25:32 Right.

John Hopfield 25:34 And that gives you th- something with which to think in physical terms, rather than only in mathematical terms.

Lex Fridman 25:42 So you've created these associative artificial neural networks?

John Hopfield 25:47 Ye- ye- yeah, that's right. Uh, now, if you, if you look at what I did, I didn't at all describe a system which gracefully learns. I described a sy- system (laughs) in which you could d- understand how things, 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 see you, I'll see you if f- every second for the next f- hour or what have you.

Lex Fridman 26:26 (laughs)

John Hopfield 26:26 Each gl- each look at you is a little bit different. I don't store all those second by second images. I don't store 3,000 images. I somehow compact this information.

Lex Fridman 26:36 Mm-hmm.

John Hopfield 26:37 So I now have a, uh, view of you which can b- which I can u- use. It doesn't slavishly remember anything in particular, but it compacts the information into useful chunks, which are, uh, s- somehow it's these chunks which are not just activities of neurons from bigger things than that, which are the real entities which are s- which are useful, which are useful to you.

Lex Fridman 27:07 Useful descr- uh, useful to you to describe, to compress this information coming at- Coming at you.

John Hopfield 27:13 Yeah, yeah, yeah. And you have to compress it in such a way that if, if I get, if the information comes in just like this again, I don't bother to, bother to rewrite it.

Lex Fridman 27:22 Right.

John Hopfield 27:22 Or, or, or efforts to rewrite it simply do not yield anything because those things are already written. And that needs to be not looked this up, have I re- written it, so I started somewhere already. It's gotta be s- something which is much more automatic in the machine hardware.

Lex Fridman 27:42 Right. So, in the human mind, how complicated is that process do you think? So you, you've created... It feels weird to be sitting with John Hopfield calling him Hopfield networks, but, uh-

John Hopfield 27:55 It is weird.

Lex Fridman 27:55 (laughs) Yeah. But nevertheless, that's what everyone calls them, so, uh, here we are. Uh, so that's a s- a simplification. That's what a physicist would do. You and Richard Feynman sat down and talked about associative memory. Now, if you, as a, uh... If you look at the mind where you can't quite simplify it so perfectly, do you, do you think that-

John Hopfield 28:18 Well, let, l- l- let me back tr- track just a little bit.

Lex Fridman 28:22 Yeah.

John Hopfield 28:23 Biology is th- is about dynamical systems. Computers are dynamical systems. Um, you can ask... If you want to me- to model biology, if you want to model neurobiology, what is the time scale? There's a dynamical system in which, u- of a f- 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 as 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 ga- understand the biology." Most artif- If you look at the feedforward artificial neural nets, they're all done as learnings. Uh, first of all, I spend some time learning, not performing. Then I turn off learning, and I perform.

Lex Fridman 29:26 Right.

John Hopfield 29:27 That's not biology. And so y- as I look more deeply at neurobiology, even at associative memory, I've got to face the fact that the dynamics of synapse change is going on all the time, and I can't just get by by saying, "I'll do the a- dynamics of activity with fixed synapses."

Lex Fridman 29:52 So the, the synaptic- the dynamics of the synapses is actually fundamental to the whole system?

John Hopfield 29:58 Yeah, yeah.

Lex Fridman 29:59 Wow.

John Hopfield 30:00 And there's n- there's noth- 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.

Lex Fridman 30:13 So you're k- you're kind of dancing beautifully between showing a lot of respect to physics and then also, uh, saying that physics cannot quite reach the-... the complexity of biology. So where do you land? Or do you continuously dance between the, the two parts?

John Hopfield 30:33 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 s- 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 if I have a big enough machine. I'll simulate it accurately. It's no good for understanding. If I under- wanna understand things, I wanna understand things in terms of wind, wind patterns, hurricanes, pressure differentials, and so on, all things as they're collective. And-

Lex Fridman 31:28 Yeah.

John Hopfield 31:29 ... the physicist, 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 of the molecules colliding. That's what I mean from the roots of physics by understanding.

Lex Fridman 31:49 So what did, again, sorry, but Hopfield networks help you understand? What insight did it give us about memory, about learning?

John Hopfield 32:01 They didn't give insights about l- learning. They gave insights about how things having learned could be expressed, how having learned a picture of y- a picture of you reminds me of your name. That would put it... it didn't describe a reasonable way of actually doing the learning. It only said if you had previously learned the connections of this kind of pattern, would now be able to behave in a physical way which would say, "Ah, if I put, put the part of the pattern in here, the other patter- pa- part of the pattern will complete over here." I could understand that physics if the right learning stuff had already been put in, and you could understand why then putting in a picture of somebody else would generate something else over here.

Lex Fridman 32:56 Mm-hmm.

John Hopfield 32:57 But it didn't not under- it, it did not have a reasonable description of the learning process.

Lex Fridman 33:03 But even... so forget learning. I mean, that's just a powerful concept that sort of forming representations that are useful to be robust, you know, uh, for error correction kind of thing. So this is kinda what the biology does, what we're talking about is you-

John Hopfield 33:22 Yeah. And, and what my, my paper did was simply enable you... there are lot, there are lots of ways of being robust. Um, if you think of it, 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, in a huge dimensional space of ones and zeros. And an error correcting 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... that 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, f- flowing down and if you push, get pushed 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, in the physical system which enables you to say, "Oh, yes, error-free computation and an associative memory are very much like, like things that I could understand from the point of view of a physical system." The physical system is, 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 other error correction schemes which a mathematician may be able to understand but I don't.

Lex Fridman 35:13 So there's a, the physical metaphor that seems to, seems to work here.

John Hopfield 35:18 That, that's right. That's right.

Lex Fridman 35:20 So,

Lex Fridman 35:22 so these kinds of networks actually led to a lot of the work that is going on now in neural networks, uh, artificial neural networks. So the follow on work with, uh, restricted Boltzmann machines and deep belief nets, uh, followed on from the, from these ideas of the Hopfield network. So what, what do you think about this continued progress of that work towards now re- revigorated exploration of feedforward neural networks and recurrent neural networks and convolutional neural networks and, uh, kinds of networks that are helping solve image recognition, natural language processing, all that kind of stuff?

John Hopfield 36:05 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.

Lex Fridman 36:17 Mm-hmm.

John Hopfield 36:19 And, uh, with the brilliance of Hinden, Hinden and Sinofsky 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 feedforward systems work. But it's not always exactly simple to express that intuition. But it- it always amuses me, is the hint going- 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.

Lex Fridman 37:07 Something computational?

John Hopfield 37:10 Something both computational and physical. Computational in the- it's very much related to feedforward networks. Physical in that Boltzmann machine learning is really learning a set of parameters for a physics Hamiltonian or energy function.

Lex Fridman 37:28 Mm-hmm.

Lex Fridman 37:29 What do you think about learning in this whole domain? Do you, uh, do you think the aforementioned guy, uh, Geoff Hinton, all the work there with back propagation, all- all the kind of, um, learning that goes on in these networks, how do you, um... if you compare it to learning in the brain, for example, is there echoes of the same kind of power that back propagation reveals about these kinds of recurrent networks? Or is it something fundamentally different going on in the brain?

John Hopfield 38:10 I don't think the brain is, um, as deep as the deepest networks go, the deepest, uh, 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- is feedforward. And so there is the question of to what extent is the biology, which has some feedforward and some feedback, been captured by something which is... it's got many more neurons, bu- much more depth to the neurons in it.

Lex Fridman 38:56 So part of you wonders if the feedback is actually more essential than the number of neurons or the depth, the- the dynamics of the feedback?

John Hopfield 39:06 The dynamics of the feedback, look, if you don't have, if you don't have feedback, it's a little bit like building a big computer and having, running it through one clock cycle, and then you can't do anything 'til you put, 'til- 'til you reload something coming in. How- how do you use the fact that there are multiple clock cycles? How do I use the fact that you can close your eyes, stop listening to me, and think about a chess board for two minutes without any input whatsoever?

Lex Fridman 39:37 (laughs) Yeah. That memory thing, that's fundamentally a feedback kind of mechanism. You're going back to something. Yes. It's har- it's hard. It's hard to understand. It's hard to introspect,

Lex Fridman 39:54 let alone, uh, consciousness (laughs) -

John Hopfield 39:57 Oh-

Lex Fridman 39:58 ... 'cause that's all-

John Hopfield 39:58 ... let- let alone consciousness. Yes, yes.

Lex Fridman 40:01 'Cause that's tied up in there too. You can't just put that on anot- on another shelf. (laughs)

John Hopfield 40:06 Every once in a while, I get interested in consciousness, and then I go and, I've done that for years, and- and ask one of my betters, as it were, uh, their view on consciousness. It's been interesting collecting them.

Lex Fridman 40:22 What are- (laughs)

John Hopfield 40:23 Omega-

Lex Fridman 40:24 (laughs)

John Hopfield 40:24 ... uh-

Lex Fridman 40:24 What is consciousness? Let's- let's try to take a brief step into that room.

John Hopfield 40:30 Well, I asked Marvin Minsky this, your consciousness, and Marvin said, "Consciousness is basically overrated. It may be an epiphenomenon. After all, all the things your brain does which are, uh, which are actually hard computations, you do non-consciously."

Lex Fridman 40:52 Hm.

John Hopfield 40:55 And there's so much evidence that even the things, the simple things you do, you can make decisions, 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.

Lex Fridman 41:14 So his view that consciousness is not, that's just like little icing on the cake. The real cake is in the subconscious.

John Hopfield 41:21 Yeah. Yeah. Subconscious, non-conscious.

Lex Fridman 41:24 Non-conscious-

John Hopfield 41:25 You're a-

Lex Fridman 41:25 ... is the better word. Sorry.

John Hopfield 41:26 There's, it's only that Freud captured the other word.

Lex Fridman 41:29 Yeah. It's, that's a confusing word, subconscious.

John Hopfield 41:33 Nic- Nicholas Chater wrote an interesting book, um, I think the title of it is The Mind is Flat.

Lex Fridman 41:41 Hm. (laughs)

John Hopfield 41:43 And flat in-s- in a neural net sense might be, uh, flat as something which is of very broad neural net without really having any layers and depth, whereas a deep brain would be many layers and not so broad. In the same sense that, uh, 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."

Lex Fridman 42:14 (laughs) Yeah. It's the weaving of the narrative around the things that already been computed for you. Wh-

John Hopfield 42:23 That's right. And then- then 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-

Lex Fridman 42:50 Yeah.

John Hopfield 42:50 ... and pulling together general knowledge on the other, but you will have a narrative. And once you generate that narrative, you're 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, um, Watergate/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 cover-up and, uh, 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 in very convincing detail. And long afterward, some of th- some of the tapes, the secret tapes as it were fr- from which these k- John was re- Dean 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.

Lex Fridman 44:16 By the way, that's a beautiful description of consciousness. Uh, (laughs) do you en- like where d- where do you stand in your, uh, today (laughs) , so m- uh, perhaps this 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?

John Hopfield 44:51 That's a, that's a, a very hard one. When I asked Francis Crick about consciousness, he launched forward in a long monologue about-

Lex Fridman 45:03 (laughs)

John Hopfield 45:03 ... Mendel and the peas.

Lex Fridman 45:05 Yeah.

John Hopfield 45:06 And how Mendel knew that there was something, and how bi- biologists understood that there was something in inheritance which was just very, very different and the eth- the effect 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 they, but, but, but very shortly after Mendel, thinking biologists did realize that there was a deep problem about inheritance. And if, Francis would have liked to have, liked to have said, "And that's why I'm wor- working on consciousness," but of course he didn't have any smoking gun in the sense of Mendel. (sighs) And that's the weakness of his position.

Lex Fridman 46:10 (laughs)

John Hopfield 46:10 If you read his, his book on, which he wrote with Koch I think.

Lex Fridman 46:16 Yeah, Christoph Koch, yeah.

John Hopfield 46:17 Um, I find it unconvincing-

Lex Fridman 46:19 (laughs)

John Hopfield 46:19 ... for the, for smoking gun reason. (sighs) So I've gone 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, whereas in neurobiology once I understood the idea of a collective, a, an evolution of dynamics which could be described as a collec- 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 I might be able to contribute."

Lex Fridman 47:01 And right now there's no way to grasp at consciousness from a physics perspective?

John Hopfield 47:07 From my point of view, that's correct.

Lex Fridman 47:09 Yeah.

John Hopfield 47:11 And of course people, (sighs) physicists like a- e- everybody else think, think very muddily about things. You ask the closely

Narrator 47:20 (laughs)

John Hopfield 47:20 ... as related question about, um, free will, do you believe you have free will?

Lex Fridman 47:25 Mm-hmm.

John Hopfield 47:27 Physicists will give an offhand answer and then backtrack, backtrack, backtrack-

Lex Fridman 47:32 (laughs)

John Hopfield 47:32 ... when they realize that the answer they gave must fundamentally contradict the laws of physics.

Lex Fridman 47:38 Na- naturally answering questions of free will and consciousness naturally lead to contradictions from a physics perspective. 'Cause it eventually ends up with quantum mechanics and then you get into that whole mess of trying to understand how much, uh, from a physics perspective how much is determined, already predetermined, how much is already deterministic about our universe. And-

John Hopfield 48:02 And-

Lex Fridman 48:02 ... there's lots of different things.

John Hopfield 48:03 ... and, uh, 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.

Lex Fridman 48:13 Right. (laughs)

John Hopfield 48:16 Because it, in my view 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 10^14 parts. That that complexity is something which is... Th- th- that the physical complex systems is at least as badly understood-... as, as the physics of phase coherence in quantum mechanics.

Lex Fridman 48:46 Can we go there for a second? Uh, you've talked about attractor networks, uh, 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?

John Hopfield 49:05 You have to be willing to think in a huge number of dimensions, 'cause in a huge number of dimensions, the behavior of a system can be thought of as- as just the motion of a point over time in this huge number of dimensions.

Lex Fridman 49:17 (laughs) Right. Yeah.

John Hopfield 49:19 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-

Lex Fridman 49:31 In a highly, um, highly dimensional space.

John Hopfield 49:34 And the, and the easiest way to get that is to do it in a high dimensional space where some of the dimensions provide the dissipation which makes, which... like I have a physical system, trajectories can't c- 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 contr- if you contract somewhere, you, uh, expand somewhere else. To get, and is, in 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 will say it's entropy flow in this other part of the system. But ba- but basically, attractor networks are dynamics funneling down so that the, you can't be any... so that if you start somewhere in the dynamical system, you will soon find yourself on a pretty well determined pathway which goes somewhere. If you start somewhere else, you'll wind up on a different pathway. But you, 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.

Lex Fridman 51:06 So, in general, looking at the physics of the emergent stability in these net- in networks, what are some interesting characteristics that, um, what are some interesting insights from studying the dynamics of such high dimensional systems?

John Hopfield 51:25 Most dynamical systems, most dyn- dyn- driven dynamical systems, by driven they're coupled somehow to an energy source and so their dynamics keeps going because of this coupling to the energy source. Most of them, it's very difficult to uh, to understand at all what the dynamical, the dy- dynamical behavior is going to be.

Lex Fridman 51:46 Right. You have to run it. (laughs)

John Hopfield 51:49 You have, you have to run it. There's a s- there's a subset of systems which has what is accurately known to 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- without 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 on the, on the pathways without having to follow the dynamics in an in- infinite detail. You started r- roll- rolling a ball off the top of a mountain that's going to wind up in the bottom of a valley, you know that's true without actually watching the ball fall, roll down.

Lex Fridman 52:43 There are certain properties of the system that, uh, when you can know that.

John Hopfield 52:47 That, that, that's right. And not all systems behave that way. But-

Lex Fridman 52:53 Most don't probably.

John Hopfield 52:55 Most don't. But it provides you with a metaphor for thinking about systems which are stable and who do have these attractors behave even if you can't find the, a Lyapunov function behind them or an energy function behind them. It gives you a metaphor for thought.

Lex Fridman 53:15 Speaking of thought, if I had, uh, a glint in my eye with excitement and said, um, "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, uh, what would you recommend? Uh, is it a hopeless pursuit to use neural networks to achieve thought? Is it, uh, what kind of mechanisms should we explore? What kind of ideas should we explore?

John Hopfield 53:52 Well, you look at the sy- sy- the, um, simple networks, the one-pass networks, they don't support multiple hypotheses very well. As I th- 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, take a physical action.

Lex Fridman 54:22 Almost a, uh, like we were mentioning playing chess, visualizing, simulating inside your head different outcomes.

John Hopfield 54:30 Yeah. Yeah. And, um, now you could do that in a feedforward network because you've precalculated all kinds of things. But I think the way neurobiology does it hasn't precalculated everything.... it actually has parts of a dynamical system in which you're doing exploration in- in- in a way which is ...

Lex Fridman 54:59 There's a creative element, like there's an-

John Hopfield 55:02 There's- there's- there's- there's a creative element and in a s- in a simple-minded neural net, you have a- a, um, constellation of instances from which you've learned.

Lex Fridman 55:21 Mm-hmm.

John Hopfield 55:23 And if you are within that space, if a- if a- if a new- if a new question is a question within this space, you can actually rely on that re- per- 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's going to behave. There are no limitations on what could happen, and so the artificial neural net world is always very much, I have a- a population of examples. The test set must be drawn from this equivalent population. If the test set has examples which are from a population which is completely different, there's no way that you- y- you could expect to get the answer right.

Lex Fridman 56:16 Yeah, out-

John Hopfield 56:17 And so if-

Lex Fridman 56:17 ... what they call h- o- outside the distribution.

John Hopfield 56:20 That's right, that's right.

Lex Fridman 56:22 Uh-huh.

John Hopfield 56:22 And so if you see a- a ball rolling across the street at dusk, if that wasn't in your- your- your- your training set, the idea that a child may be coming close behind that is not going to occur to the neural net.

Lex Fridman 56:40 And it is to our... there's something in the neurobiology that allows that?

John Hopfield 56:45 Yeah. There's- there's something in the way of what it means to be outside of the p- of the- of the population of the training set. The population of the training set isn't just for the set of examples, it's- it's... there- there's more to it than that and it gets back to my own f- uh, question of what is it to understand something?

Lex Fridman 57:08 (laughs) Yeah.

Lex Fridman 57:12 You know, as- in a small tangent, you've talked about the value of thinking, of deductive reasoning in science versus large data collection, s- sort of-

John Hopfield 57:23 Yeah.

Lex Fridman 57:23 ... thinking about the problem. It's- I suppose it's the physics side of you of, uh, going back to first principles and thinking, but what do you think is the value of deductive reasoning in- in the scientific process?

John Hopfield 57:37 Well, look, there are obviously scientific questions in which the root of the answer to it comes through the an- analysis of one hell of a lot of data.

Lex Fridman 57:46 Right. Cosmology, that kind of stuff.

John Hopfield 57:50 And that's- that's never been the kind of problem in which I've had any particular insight, though I must say if you look at, um... cosmology is- was one of those, but if you look at the actual things that Jim Peebles, one of this year's Nobel Prize for the Phy- in 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- of- of the work of others in this regard.

Lex Fridman 58:25 Right. But it ultimately boiled down to, uh, thinking through the problem. Like, what are the principles under which a particular phenomena operates?

John Hopfield 58:35 Yeah, yeah. And look, physics is always gonna look for ways in which you can describe the system in a way which rises above the det- rises above the details, and to the hard-died, real biologist, biology works because of the details. In physics, the p- 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.

Lex Fridman 59:13 There's, uh... 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.

John Hopfield 59:29 Yeah, I know they've always been the i- interest both in, um, things like getting the eyes to be able to control things or getting the thought patterns to be able to move what had been, uh, uh, a conne- a connected limb which is now connected through a computer.

Lex Fridman 59:48 That's right. So, uh, in the case of Neuralink, they're doing a thousand plus connections where they're able to do two-way, activate and read spikes, neuro- 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?

John Hopfield 1:00:20 It's interesting watching things go, uh... when I first became interested in neurobiology-

Lex Fridman 1:00:27 (clears throat)

John Hopfield 1:00:27 ... most of the practitioners thought you would be able to understand neurobiology by techniques which allowed you to re- uh, record only one cell at a time.

Lex Fridman 1:00:36 One cell, yeah.

John Hopfield 1:00:38 People like, um, David Hubel very strongly reflected that point of view, and that's been taken over by a generation...... a couple of- a couple of generations later by a set of people who says, "Not until we c- can record from 10 to the 4, 10 to the 5 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 look, you have to begin to be able to look for the collective modes, collective operation of things. It doesn't rely on this action potential or that cell, it relies on the collective properties of this set of cells connected with this kind of pattern and so on. And you're not going to succeed in seeing what those collective activities are without recording many cells at once.

Lex Fridman 1:01:38 Uh, the question is how many at once. What's the threshold? And that's the- that's the-

John Hopfield 1:01:42 Yeah. And- and, um, 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 very- is fairly simple. Now neurobiology does it in ways that are different from the way an engineer would do it. An engineer would put in six highly accurate stepping motors for controlling a limb-

Lex Fridman 1:02:11 Right.

John Hopfield 1:02:11 ... rather than un- 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. The engineering world, ah, touch, let's put in a pressure sensor or two, let- rather than a- an array of- of a gazillion pressure- pressure sensors, none of which are accurate, all of which are perpetually recalibrating themselves.

Lex Fridman 1:02:48 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 big challenges.

John Hopfield 1:03:05 I think you'll be able to make better compu- computations/robotics that way than by trying to force things into a- into a robotics where joint motors are powerful and stepping motors are accurate.

Lex Fridman 1:03:25 But then the physicists- the physicist in you will be lost forever in such systems, 'cause there's no simple fundamentals to explore in systems that are so large and messy.

John Hopfield 1:03:38 Well, yes, you- you- you say that, and yet, um, there's a lot of physics in the Navier–Stokes equations, the equations of non- nonlinear hydrodynamics. Huge amount of physics in them. All of the physics of atoms and molecules has been lost, but it's been replaced by this other set of equations which is- is just as true as the equations at the bottom.

Lex Fridman 1:04:01 Yes.

John Hopfield 1:04:02 Now the di- those- those equations are going to be harder to find in neurobiology, but the physicist in me says there are probably some equations of that sort-

Lex Fridman 1:04:13 They're out there.

John Hopfield 1:04:14 ... which- They're- they're out there, and if physics is going to contribute anything, it may contribute to trying to find out what those equations are and how to capture them from the biology.

Lex Fridman 1:04:26 Would you say that's one of the main open problems of our age, is to discover those equations?

John Hopfield 1:04:34 Yeah. If you look at... There's molecules and there's psychological behavior.

Lex Fridman 1:04:41 Mm-hmm.

John Hopfield 1:04:42 And these two are somehow related. There are layers of detail, there are layers of collectiveness, and to capture th- to capture that in some vague way, several stages on the way up to see how these things ac- can actually be linked together.

Lex Fridman 1:05:04 So it seems in our universe there's a lot of- 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, eh, at the... But there isn't- it's still an open question whether that link is equally between molecules and the brain is equally compressible into elegant equations. But your- your sense, s- some- well, you're both a physicist and a dreamer. You have a sense that, uh-

John Hopfield 1:05:38 Yeah, but I can only- I can only dream physics dreams.

Lex Fridman 1:05:42 (laughs) Yeah, physics dreams.

John Hopfield 1:05:44 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, eh, thus, uh, the linking between these being important, uh, ideas that Einstein might have had to think about the essence of time as he was thinking about time.

Lex Fridman 1:06:11 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?

John Hopfield 1:06:38 It has really because it... 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.

Lex Fridman 1:07:25 Yeah.

John Hopfield 1:07:27 And, uh, 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 that are recorded about each and every one of us, forever now as far as I can see, in- 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.

Lex Fridman 1:08:07 Maybe we have become immortal under some definitions.

John Hopfield 1:08:10 Yeah. Yeah.

Lex Fridman 1:08:14 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?

John Hopfield 1:08:39 Oh, the word meaning is as slippery as the word understand. (laughs)

Lex Fridman 1:08:45 (laughs) Interconnected somehow, perhaps. Is there... It's slippery, but is there something that you, uh, despite being slippery, can hold long enough to express? (laughs)

John Hopfield 1:09:03 I've been amazed at how hard it is to define the things in a living system, uh, in the sense that one hydrogen atom is pretty much like another, but one bacterium is not so much like ano- like ano- another bacterium, even of the same nominal species. In fact, the whole notion of what is a species gets a little bit fuzzy. And do species exist in the absence of certain classes of environments? And pretty soon, one winds up with, um, 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 is, becomes a little bit vague in my mind.

Lex Fridman 1:09:54 (laughs) 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, uh, it's all interconnected in some kind of way, that there might not even be an individual. We're all kind of this complicated mess of biological systems at all different levels. Where- where the human starts and when the human ends is unclear.

John Hopfield 1:10:21 Yeah, yeah. And we're... In neurobiology, we're the, um... Oh, you say the neocortex does the thinking, but there's lots of things which are done in the spinal cord. And so we say, what is the essence of thought? Is it just gonna be neocortex? Can't be. Can't be. (laughs)

Lex Fridman 1:10:40 Yeah, maybe to, uh, to understand and to build thought, you have to build the universe along with the- 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.

John Hopfield 1:10:57 Well, thank you for the challenge of talking with you, and it'll be interesting to see whether you can winnow five mins- five minutes out of this-

Lex Fridman 1:11:03 Yeah.

John Hopfield 1:11:03 ... with just coherent sense to any winner.

Lex Fridman 1:11:05 (laughs) 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 enjoyed this podcast, subscribe on YouTube, give it five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter @LexFriedman. 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 lookout for more interesting questions, either as my present ones get worked out or as they 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.