Pod Insights Lex Fridman Podcast #76 – John Hopfield: Physics View of the Mind and Neurobiology About this episode John Hopfield is 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 is perhaps best known for his work on associate 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.
EPISODE LINKS:
Now What? article: http://bit.ly/3843LeU
John wikipedia: https://en.wikipedia.org/wiki/John_Hopfield
Books mentioned:
– Einstein’s Dreams: https://amzn.to/2PBa96X
– Mind is Flat: https://amzn.to/2I3YB84
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584, follow on Spotify https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL, or support it on Patreon https://www.patreon.com/lexfridman.
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Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE:
00:00 – Introduction
02:35 – Difference between biological and artificial neural networks
08:49 – Adaptation
13:45 – Physics view of the mind
23:03 – Hopfield networks and associative memory
35:22 – Boltzmann machines
37:29 – Learning
39:53 – Consciousness
48:45 – Attractor networks and dynamical systems
53:14 – How do we build intelligent systems?
57:11 – Deep thinking as the way to arrive at breakthroughs
59:12 – Brain-computer interfaces
1:06:10 – Mortality
1:08:12 – Meaning of life
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Lex Fridman? The following is a conversation with John Hopfield, Professor 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,
0:42 Lex Fridman? 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 Podcasts, support it on Patreon, or simply connect with me on Twitter at LexFriedman, 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.
1:25 Lex Fridman? 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
2:09 Lex Fridman? 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.
2:52 Lex Fridman? 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.
3:08 Speaker 01 One of the things that very much intrigues me is the fact that neurons have all kinds of components, properties to them.
3:21 Speaker 01 In 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.
3:58 Lex Fridman? So the glitches become features in the biological neural network?
4:04 Speaker 01 They can. Look, let me take one of the things that I used to do research on.
4:12 Speaker 01 If you take things which oscillate, 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. And pedestrians walking across, pedestrians don't walk 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.
4:55 Speaker 01 And after a while, the bridge was oscillating back and forth. And the pedestrians were walking in step to it. And you could see it in the movies made out of the bridge. And the engineers made a simple minded 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 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 could 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, while they built stiffening for it.
5:38 Speaker 01 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. Other circumstances in which they won't. Well, they fire together, you can be sure that other cells are going to 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 the possibility for synchronizing them.
6:16 Lex Fridman? 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 how do you make sense of that ability to leverage all the different kinds of complexities in the, in the biological brain?
6:42 Speaker 01 Well, look, in the biological molecule level.
6:47 Speaker 01 You have a piece of DNA which encode 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, that molecule which is slightly different, had a function which helped any old chemical reaction was as important to the cell. You would go ahead and let that try and evolution would slowly improve that function. And so you have the possibility of duplicating, then having things drift apart.
7:32 Speaker 01 One of them retain the old function, the other one do something new for you. And there's evolutionary pressure to improve. Look, there isn't in computers too, but improvement has to do with closing some companies and opening some others. The evolutionary process looks a little different.
7:51 Lex Fridman? Yeah, similar timescale, perhaps.
7:55 Speaker 01 Much, much shorter in timescale.
7:57 Lex Fridman? Companies close, yeah, go bankrupt and are 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 all know, yeah, it's a fascinating dual correspondence there between biological.
8:19 Speaker 01 And companies have difficulty having a new product competing with an old product.
8:25 Lex Fridman? Yeah.
8:26 Speaker 01 And when IBM built its first PC, you probably read, 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.
8:55 Speaker 01 And biology finds other markets in a more adaptive way.
9:03 Lex Fridman? Yeah, it's better at it. It's better at that kind of integration.
9:08 Lex Fridman? So maybe you've already said it, but what to use the most beautiful aspect or mechanism of the human mind?
9:19 Lex Fridman? Is it the adaptive, the ability to adapt as you've described, or is there some other little quirk that you particularly like?
9:29 Speaker 01 Adaptation is everything when you get down to it. But the difference, there are differences between adaptation where your learning goes on only over generations, over evolutionary time, where your learning goes on at the time scale of one individual who must learn from the environment during that individual's lifetime.
9:56 Speaker 01 And biology has both kinds of learning in it.
10:01 Speaker 01 And the thing which makes neurobiology hard is that it's
10:08 Speaker 01 a mathematical system that were built on this other kind of evolutionary system.
10:15 Lex Fridman? What do you mean by mathematical system? Where's the math in the biology?
10:21 Speaker 01 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 thing, and the fact that
10:34 Speaker 01 biology is about a system which you can build in three dimensions.
10:43 Speaker 01 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.
11:13 Speaker 01 But there's a huge, the effect of computer structure on what is easy and what is hard is immense.
11:26 Lex Fridman? So.
11:26 Speaker 01 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, which is awfully stupid.
11:40 Lex Fridman? 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.
11:56 Speaker 01 That's right. That's right.
11:58 Lex Fridman? 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?
12:23 Speaker 01 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.
12:35 Speaker 01 And since I can't see the evolutionary process going on, I am in awe of it.
12:44 Speaker 01 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.
12:57 Lex Fridman? The human life's time scale is however thing you can tease apart and study.
13:05 Speaker 01 Yeah, you can do, there's developmental neurobiology, which understands how the connections and 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.
13:27 Lex Fridman? In just days and months, those early, early days of a human life are really interesting.
13:34 Speaker 01 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.
13:49 Lex Fridman? It's turnover.
13:52 Speaker 01 So what is, what, what, what is not effective, what is not wired well enough to use at the moment? Throw it out.
14:00 Lex Fridman? 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?
14:13 Lex Fridman? Decades, is it neuroscience, computer science, neurobiology, psychology? Physics, maybe math?
14:23 Lex Fridman? Maybe literature?
14:26 Speaker 01 Well, of course, I see the world always through a lens of physics. I grew up in physics.
14:33 Speaker 01 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.
14:46 Lex Fridman? Both of your parents are physicists?
14:47 Speaker 01 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 that you can do the mathematics of the relevant to the experiments, you also be able to understand how things work.
15:13 Lex Fridman? But that was, that was a few years ago. Did you change your mind at all through many decades of trying to understand the mind? Of studying in different kinds of way, not even the mind, just biological systems? You still have hope that physics that you can understand?
15:34 Speaker 01 There's a question of what do you mean by understand?
15:38 Lex Fridman? Of course.
15:39 Speaker 01 When I taught freshman physics, I used to say, I wanted to get 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. I expect some intuitive notion of understanding. And
16:14 Speaker 01 I don't know how to express that very well. I've never known how to express it well. And you run smack up against it when you do these, look at these simple neural nets, feed forward 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.
16:42 Lex Fridman? 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 feed forward neural networks, do you think they'll ever understand?
16:58 Speaker 01 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.
17:26 Lex Fridman? So feedback is essential. So we can talk even about recurrent, 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?
17:52 Speaker 01 Suppose you ask a neurosurgeon. When is somebody dead?
17:58 Lex Fridman? Yeah.
18:00 Speaker 01 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 going to 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. Just don't look in any detail at all at what individual neurons are doing.
18:31 Speaker 01 These rhythms are utterly absent from anything which goes on at Google.
18:41 Lex Fridman? Yeah, but the rhythms.
18:44 Speaker 01 But the rhythms what?
18:45 Lex Fridman? 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, is there, will it ever go on at Google? Do you have a hope? Because you're one of the seminal figures in both, launching both disciplines, both sides of the river.
19:18 Speaker 01 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 from neurobiology." And some other grand thing comes in and enables you to go a lot further.
20:00 Speaker 01 But we'll go into the sand again. And I think there could be generations of this evolution. And I don't know how many of them. And each one is going to get you further into what a brain does.
20:13 Speaker 01 In some sense, pass the Turing test longer and for more broad aspects.
20:23 Speaker 01 And how, how many of these are, there are going to have to be before you say,
20:28 Speaker 01 I've made something, I've made a human, I don't know.
20:33 Lex Fridman? But your senses, it might be a couple.
20:35 Speaker 01 My senses, it might be a couple more.
20:37 Lex Fridman? Yeah.
20:38 Speaker 01 And, um, going back to my, uh, brainwaves, as it were.
20:43 Lex Fridman? Yes.
20:47 Speaker 01 From the AI point of view, they, they, they would say, ah, maybe these are an heavy phenomenon and not important at all.
20:58 Speaker 01 The first car I had, a real wreck of a 1936 Dodge.
21:04 Speaker 01 Go above about 45 miles an hour and the wheels would shimmy.
21:08 Lex Fridman? Yeah.
21:10 Speaker 01 Good, good speedometer that.
21:14 Speaker 01 Now, nobody designed the car that way. The car is malfunctioning to have that. But in biology, if you, if, 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.
21:33 Lex Fridman? Yeah.
21:33 Speaker 01 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,
21:48 Speaker 01 how many transistors are there in your laptop these days?
21:52 Lex Fridman? Actually, I don't, I don't know the number. It's, it's.
21:55 Speaker 01 On the scale of 10 to the 10, I can't remember the number either.
21:58 Lex Fridman? Yeah.
22:01 Speaker 01 And all the transistors are somewhat similar. And most physical systems with that many parts, all of which are similar, have collective properties.
22:12 Lex Fridman? Yes.
22:13 Speaker 01 Sound waves in air, earthquakes, what have you, have collective properties, weather.
22:20 Speaker 01 There are no collective properties used in artificial neural networks, in AI.
22:28 Lex Fridman? Yeah, it's very.
22:29 Speaker 01 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, what they mean.
22:40 Lex Fridman? See, you're very right. I might have to return several times to neurobiology and try to make our transistors more messy.
22:50 Speaker 01 Yeah, yeah. At the same time, the simple ones will conquer, will conquer big aspects.
23:01 Speaker 01 And I think one of the most biggest surprises to me was
23:09 Speaker 01 how well learning systems, which are manifestly non-biological,
23:14 Speaker 01 how important they can be actually, and how important and how useful they can be in AI.
23:21 Lex Fridman? So if you 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.
23:52 Speaker 01 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, to what their voice sounds like, to 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, ten, 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 Jeff Hinton. I never, I never mentioned the name or anything very particular.
24:36 Speaker 01 But somehow, a few facts that are associated with this, with a particular person enables you to get a hold of the rest of the facts.
24:45 Lex Fridman? Yeah.
24:45 Speaker 01 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 at work, as far as I can see.
25:11 Lex Fridman? 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?
25:27 Speaker 01 What I made thirty-five 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."
25:50 Lex Fridman? Right.
25:51 Speaker 01 And that gives you something with which to think in physical terms, rather than only in mathematical terms.
26:00 Lex Fridman? So you've created these associative artificial networks?
26:04 Speaker 01 That's right. And now, if you, 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 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 every second for the next hour or what have you. Each, each look at you is a little bit different.
26:46 Speaker 01 I don't store all those second-by-second images. I don't store 3,000 images. I somehow compact this information. So I now have a view of you.
27:01 Speaker 01 Which I can use. It doesn't slavishly remember anything in particular, but it compacts the information into useful chunks, which are somehow these chunks, which are not just activities of neurons, bigger things than that, which are the real entities which are useful to you.
27:25 Lex Fridman? Useful to you to describe, to compress this information coming at you.
27:31 Speaker 01 And you have to compress it in such a way that if I get, 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, I've stored it somewhere already." It's gotta be something which is much more automatic in the machine hardware.
27:59 Lex Fridman? Right, so in the human mind, how complicated is that process, do you think? So you created, feels weird to be sitting with John Hopfield calling them Hopfield Networks, but...
28:12 Speaker 01 It is weird.
28:15 Lex Fridman? Yeah, but nevertheless, that's what everyone calls them. 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, as a, if you look at the mind, where you can't quite simplify it so perfectly, do you think that...
28:36 Speaker 01 Let me backtrack just a little bit.
28:39 Lex Fridman? Yeah.
28:40 Speaker 01 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 of a 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
29:24 Speaker 01 in order to understand the biology."
29:31 Speaker 01 Most artificial neural nets, they're all done as, first of all, I spend some time learning, not performing, and I turn off learning and I perform.
29:44 Lex Fridman? Right.
29:45 Speaker 01 That's not biology.
29:48 Speaker 01 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.
30:02 Speaker 01 And I can't just get by by saying I'll do the dynamics of activity with fixed synapses.
30:10 Lex Fridman? So the synaptic, the dynamics of the synapses is actually fundamental to the whole system?
30:15 Speaker 01 Yeah, yeah. And there's, there's not, 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.
30:31 Lex Fridman? So you're, you're kind of dancing beautifully between showing a lot of respect to physics and then also saying that physics cannot quite reach the, the complexity of biology. So where do you land? Or do you continuously dance between the two?
30:51 Speaker 01 I continuously dance between them because my whole notion of understanding
30:57 Speaker 01 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
31:18 Speaker 01 as 10 to the 32 molecules colliding in the atmosphere. I can simulate weather that way with a big enough machine. I'll simulate it accurately.
31:30 Speaker 01 It's no good for understanding. If I 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 of the molecules colliding. That's what I mean from the roots of physics by understanding.
32:05 Lex Fridman? So what did, again, sorry, but Hopfield Networks help you understand? What insight did give us about memory, about learning?
32:17 Speaker 01 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." But 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 is to say, "Ah, if I put part of the
32:59 Speaker 01 pattern in here, the other part of the pattern will complete over here." I could understand that physics if the right learning stuff had already been put in. And I 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.
33:21 Lex Fridman? 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, for error correction kind of thing. So this is kind of what the biology does, what we're talking about.
33:40 Speaker 01 Yeah, and what bi paper did was simply enable you, there are lots of ways of being robust.
33:51 Speaker 01 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 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, 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,
34:34 Speaker 01 there's a valley that you're following, flowing down, and if you get pushed a little bit off the valley, it's 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 is, can be, under some circumstances, an accurate metaphor. It's not the only metaphor. There are error correction schemes
35:19 Speaker 01 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.
35:31 Lex Fridman? So there's the physical metaphor that seems to work here.
35:36 Speaker 01 That's right, that's right.
35:38 Lex Fridman? 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.
35:55 Lex Fridman? 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 reinvigorated 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?
36:23 Speaker 01 It 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 Sonovsky 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.
37:03 Speaker 01 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.
37:25 Lex Fridman? Something computational?
37:27 Speaker 01 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.
37:47 Lex Fridman? What do you think about learning in this whole domain? Do you, do you think the aforementioned guy, Jeff Hinton, all the work there with back propagation, all, all the kind of learning that goes on in these networks?
38:05 Lex Fridman? How do you, if you 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?
38:28 Speaker 01 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 is the question of to what extent has the biology, which has some feed forward and some feedback, been captured by something which has
39:07 Speaker 01 got many more neurons, much more depth to the neurons?
39:14 Lex Fridman? 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?
39:24 Speaker 01 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 to you put, you reload something coming in. 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 chessboard for two minutes without any input whatsoever?
39:56 Lex Fridman? Yeah, that memory thing. That's fundamentally a feedback kind of mechanism. You're going back to something. Yes.
40:07 Lex Fridman? It's hard, it's hard, it's hard to understand, it's hard to introspect, let alone consciousness.
40:15 Speaker 01 Oh, let alone consciousness, yes, yes.
40:18 Lex Fridman? Because that's tied up in there, too. You can't just put that on another shelf.
40:24 Speaker 01 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 betters, as it were, their view on consciousness. It's been interesting collecting them.
40:39 Lex Fridman? What is consciousness? Let's try to take a brief step into that room.
40:47 Speaker 01 Well, I asked Marvin Minsky, if you want 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 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."
41:32 Lex Fridman? So his view that consciousness is not, that's just a little icing on the cake? The real cake is in the subconscious?
41:39 Speaker 01 Yeah, yeah. Subconscious, non-conscious.
41:42 Lex Fridman? Non-conscious, that's the better word, sir.
41:45 Speaker 01 It's only that Freud captured the other word.
41:47 Lex Fridman? Yeah, that's a confusing word, subconscious.
41:50 Speaker 01 Nicholas Chater wrote an interesting book. I think the title of it is "The Mind is Flat." Flat, and in a neural net sense, might be, flat is something which is a very broad neural net, without really any layers in depth. Whereas a 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."
42:34 Lex Fridman? Yeah, it's the weaving of the narrative around the things that have already been computed for you.
42:41 Speaker 01 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.
43:22 Speaker 01 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 cover-up, 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.
44:07 Speaker 01 And long afterward, some of the tapes, the secret tapes as it were, from which John 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.
44:32 Lex Fridman? By the way, that's a beautiful description of consciousness. Do you, like, where do you stand in your, today? So perhaps this changes day to day, but where do you stand on the importance of consciousness in our whole big mess of cognition?
44:59 Lex Fridman? Is it just a little narrative maker? Or is it actually fundamental to intelligence?
45:09 Speaker 01 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 Mendel and the peas.
45:22 Lex Fridman? Yeah.
45:23 Speaker 01 And how Mendel knew that there was something, and how biologists understood 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 it 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,
46:03 Speaker 01 thinking biologists did realize that there was a deep problem about inheritance. And Francis would have liked, would like 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, his book, what he wrote with Koch, I think.
46:33 Lex Fridman? Yeah, Christoph Koch, yeah.
46:35 Speaker 01 I find it unconvincing for this, for 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, whereas in neurobiology, once I understood the idea of a collective, a, 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's that I have something I might be able to contribute.
47:19 Lex Fridman? And right now, there's no way to grasp at consciousness from a physics perspective.
47:25 Speaker 01 From my point of view, that's correct.
47:29 Speaker 01 And of course, people, physicists, like everybody else, think very rudely about things. You have 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.
47:56 Lex Fridman? 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, how much is already deterministic about our universe. There's lots of different.
48:21 Speaker 01 And if you don't push quite that far, you can say essentially all of neural biology, which is relevant, can be captured by classical equations of motion.
48:31 Lex Fridman? Right.
48:34 Speaker 01 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 complexity is something which is, the physical complex systems is at least as badly understood as the physics of phase coherence in quantum mechanics.
49:04 Lex Fridman? 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?
49:23 Speaker 01 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 as just the motion of a point over time in this huge number of dimensions.
49:35 Lex Fridman? Right.
49:37 Speaker 01 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-
49:49 Lex Fridman? In a highly, highly dimensional space.
49:52 Speaker 01 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 if 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 Leoville's theorem, which says you can't contract everywhere. You have to, if you contract somewhere, you expand somewhere else. And in interesting physical systems, you get driven systems
50:35 Speaker 01 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.
50:48 Speaker 01 But basically, attractor networks are dynamics funneling down so 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 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.
51:24 Lex Fridman? 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?
51:42 Speaker 01 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.
52:04 Lex Fridman? You have to run it.
52:06 Speaker 01 You have to run it. There's a subset of systems which has what is actually known to the mathematicians as a Leapinov 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 without ever knowing what the Leapinov 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 pathways without having to follow the dynamics in infinite detail.
52:49 Speaker 01 You started rolling a ball at the top of a mountain, it's going to wind up at the bottom of a valley, you know that's true without actually watching the ball fall, roll down.
53:00 Lex Fridman? There are certain properties of the system that, when you can know that.
53:05 Speaker 01 That's right. And not all systems behave that way.
53:09 Lex Fridman? Most don't, probably.
53:12 Speaker 01 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 Leapinov function behind them, or an energy function behind them. It gives you a metaphor for thought.
53:33 Lex Fridman? 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?
53:57 Lex Fridman? 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?
54:09 Speaker 01 Well, you look at the simple networks, one pass networks.
54:19 Speaker 01 They don't support multiple hypotheses very well.
54:24 Speaker 01 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, take a physical action.
54:40 Lex Fridman? Almost like we were mentioning, playing chess, visualizing, simulating inside your head, different outcomes.
54:48 Speaker 01 Yeah, yeah. And
54:53 Speaker 01 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.
55:07 Speaker 01 It actually has parts of a dynamical system in which you're doing exploration
55:13 Speaker 01 in a way which is.
55:17 Lex Fridman? There's a creative element, like there's a.
55:20 Speaker 01 There's, there's, there's a creative element and.
55:25 Speaker 01 In a simple minded neural net,
55:30 Speaker 01 you have a constellation of instances from which you've learned.
55:40 Speaker 01 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,
56:04 Speaker 01 you have no way of knowing how the system is 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 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 could expect to get the answer right.
56:34 Lex Fridman? Yeah, what they call outside the distribution.
56:38 Speaker 01 That's right, that's right. And so if you see a ball rolling across the street in dusk,
56:46 Speaker 01 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.
56:58 Lex Fridman? And it is to our, there's something in your biology that allows that.
57:03 Speaker 01 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, it's...
57:18 Speaker 01 There's more to it than that. It gets back to my question of what is it to understand something?
57:26 Lex Fridman? Yeah.
57:29 Lex Fridman? 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?
57:55 Speaker 01 Well, look, there are obviously scientific questions in which the root to the answer to it comes through the analysis of one hell of a lot of data.
58:04 Lex Fridman? Right.
58:06 Lex Fridman? Cosmology, that kind of stuff.
58:08 Speaker 01 And that's never been the kind of problem
58:14 Speaker 01 in which I've had any particular insight. Though I must say, if you look at 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.
58:37 Speaker 01 He's used the encapsulation of the work of others in this regard.
58:45 Lex Fridman? But it ultimately boiled down to thinking through the problem. Like, what are the principles under which a particular phenomenon operates?
58:53 Speaker 01 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, 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.
59:30 Lex Fridman? There's a, 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.
59:45 Speaker 01 Yeah, I know there've 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.
1:00:05 Lex Fridman? 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?
1:00:36 Speaker 01 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.
1:00:54 Lex Fridman? One cell, yeah.
1:00:56 Speaker 01 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 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, the collective operation of things. It doesn't rely on this action potential of that cell, it relies on the collective
1:01:40 Speaker 01 properties of this set of cells connected with this kind of patterns, so on. And you're not going to succeed in seeing what those collective activities are without recording many cells at once.
1:01:55 Lex Fridman? The question is how many at once? What's the threshold? And that's the...
1:02:00 Speaker 01 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 very, it's fairly simple.
1:02:17 Speaker 01 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 that are controlling a limb, rather than a hundred thousand muscle fibers, each of which has to be individually controlled.
1:02:37 Speaker 01 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.
1:02:51 Speaker 01 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.
1:03:06 Lex Fridman? So you're saying your hope is, your advice for the engineers of the future is to embrace the large chaos of a messy, air-prone system like those of the biological systems? Like, that's probably the way to solve some of these...
1:03:23 Speaker 01 I think you'll be able to make better computation/robotics that way than by trying to force things into robotics where joint motors are powerful and stepping motors are accurate.
1:03:43 Lex Fridman? But then the physicists, the physicist 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.
1:03:56 Speaker 01 Well, you say that, and yet there's a lot of physics in the Naive Stokes equations, the equations of nonlinear hydrodynamics. Huge amount of physics in them. All the physics of atoms and molecules has been lost, but has 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.
1:04:31 Lex Fridman? They're out there.
1:04:32 Speaker 01 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.
1:04:44 Lex Fridman? Would you say that's one of the main open problems of our age, is to discover those equations?
1:04:52 Speaker 01 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.
1:05:21 Lex Fridman? 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 you're both a physicist and a dreamer. You have a sense that...
1:05:56 Speaker 01 Yeah, but I can only dream physics dreams.
1:06:00 Lex Fridman? Physics dreams.
1:06:02 Speaker 01 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.
1:06:17 Speaker 01 The linking between these being important ideas that Einstein might have had to think about the essence of time as he was thinking about time.
1:06:29 Lex Fridman? So, speaking of the essence of time in 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?
1:06:56 Speaker 01 It has really, because particularly as you get older and the body comes apart in various ways.
1:07:04 Speaker 01 I became much more aware of the fact that what is somebody is contained in the brain
1:07:16 Speaker 01 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,
1:07:31 Speaker 01 dreams, notepads, diaries, fractions of their thought does continue to live after they're dead and gone, after their body is dead and gone.
1:07:45 Speaker 01 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...
1:08:17 Speaker 01 What is death? Maybe different for people a generation ago and a generation further ahead.
1:08:25 Lex Fridman? Maybe we have become immortal under some definitions.
1:08:28 Speaker 01 Yeah, yeah.
1:08:32 Lex Fridman? Last easy question.
1:08:35 Lex Fridman? What is the meaning of life?
1:08:40 Lex Fridman? Looking back, you've studied the mind, us weird descendants of apes. What's the meaning of our existence on this little earth?
1:08:56 Speaker 01 Oh, that word meaning is as slippery as the word understand.
1:09:03 Lex Fridman? Interconnected somehow, perhaps.
1:09:09 Lex Fridman? Is there, it's slippery, but is there something that you, despite being slippery, can hold long enough to express?
1:09:20 Speaker 01 I've been amazed at how hard it is
1:09:25 Speaker 01 to define the things in a living system.
1:09:32 Speaker 01 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 a species gets a little bit fuzzy. And species exist in the absence of certain classes of environments. And pretty soon one winds up with the biology which the whole thing is living, but whether there's actually any element of it,
1:10:05 Speaker 01 which by itself would be said to be living.
1:10:09 Speaker 01 Is becomes a little bit vague in my mind.
1:10:13 Lex Fridman? 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. We're all kind of this complicated mess of biological systems at all different levels, where the human starts and when the human ends is unclear.
1:10:39 Speaker 01 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 on the spinal cord. And so we say, what is the essence of thought? Is it just going to be neocortex? Can't be, can't be.
1:10:58 Lex Fridman? Yeah, maybe to 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.
1:11:14 Speaker 01 Well, thank you for the challenge of talking with you. And it'll be interesting to see whether you can winnow five minutes, five minutes out of this, just coherent sense to anywhere.
1:11:24 Lex Fridman? 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, give it five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at @LexFriedman. And now let me leave you with some words of wisdom from John Hopfield in his article titled, Now What?
1:12:02 Lex Fridman? 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.
1:12:46 Lex Fridman? 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.
Previous #75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI Next #77 – Alex Garland: Ex Machina, Devs, Annihilation, and the Poetry of Science Page updated October 26, 2026 · transcript and summary are machine-generated
