Pod Insights Lex Fridman Podcast Tomaso Poggio: Brains, Minds, and Machines About this episode Tomaso Poggio is a professor at MIT and is the director of the Center for Brains, Minds, and Machines. Cited over 100,000 times, his work has had a profound impact on our understanding of the nature of intelligence, in both biological neural networks and artificial ones. He has been an advisor to many highly-impactful researchers and entrepreneurs in AI, including Demis Hassabis of DeepMind, Amnon Shashua of MobileEye, and Christof Koch of the Allen Institute for Brain Science. Video version is available on YouTube https://www.youtube.com/watch?v=aSyZvBrPAyk. 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, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations.
Transcript 10,206 words · 2 speakers
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 Tomaso Poggio. He's a professor at MIT and is a director of the Center for Brains, Minds and Machines. Cited over 100,000 times, his work has had a profound impact on our understanding of the nature of intelligence in both biological and artificial neural networks. He has been an advisor to many highly impactful researchers and entrepreneurs in AI, including Demis Hassabis of DeepMind, Amnon Anshashwa of Mobileye, and Kristof Koch of the Allen Institute for Brain Science. This conversation is part of the MIT course on Artificial General Intelligence and the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at
0:44 Lex Fridman? Lex Fridman, spelled F-R-I-D. And now, here's my conversation with Tomaso Poggio.
1:09 Lex Fridman? You've mentioned that in your childhood, you've developed a fascination with physics, especially the theory of relativity, and that Einstein was also a childhood hero to you. What aspect of Einstein's genius, the nature of his genius, do you think was essential for discovering the theory of relativity?
1:29 Speaker 01 You know, Einstein was a hero to me, and I'm sure to many people, because he was able to make, of course, a major, major contribution to physics with simplifying a bit just a Gedankenexperiment, a thought experiment.
1:52 Speaker 01 You know, imagining communication with lights between a stationary observer and somebody on a train. And I thought, you know, the fact that just with the force of his thought, of his thinking, of his mind, it could get to something so deep in terms of physical reality, how time depend on space and speed. It was something absolutely fascinating. It was the power of intelligence, the power of the mind.
2:25 Lex Fridman? Do you think the ability to imagine, to visualize as he did, as a lot of great physicists do, do you think that's in all of us, human beings? Or is there something special to that one particular human being?
2:39 Speaker 01 I think, you know, all of us can learn and have, in principle, similar breakthroughs. There are lessons to be learned from Einstein. He was one of five PhD students at ETA, the Atgenossische Technische Hochschule in Zurich, in physics. And he was the worst of the five, the only one who did not get an academic position when he graduated, when he finished his PhD. And he went to work, as everybody knows, for the patent office.
3:19 Speaker 01 And so it's not so much that he worked for the patent office, but the fact that obviously he was smart, but he was not a top student, obviously was anti-conformist, was not thinking in the traditional way that probably his teachers and the other students were doing. So there is a lot to be said about, you know, trying to be, to do the opposite, or something quite different from what other people are doing. That's certainly true for the stock market. Never, never buy if everybody's buying.
3:52 Lex Fridman? And also true for science.
3:55 Speaker 01 Yes.
3:56 Lex Fridman? So you've also mentioned, staying on the theme of physics, that you were excited at a young age by the mysteries of the universe that physics could uncover, such, as I saw mentioned, the possibility of time travel. So the most out of the box question I think I'll get to ask today. Do you think time travel is possible?
4:20 Speaker 01 Well, it would be nice if it were possible right now. You know, in science, you never say no.
4:29 Lex Fridman? But your understanding of the nature of time?
4:32 Speaker 01 Yeah, it's very likely that it's not possible to travel in time. We may be able to travel forward in time. If we can, for instance, freeze ourselves or, you know, go on some spacecraft traveling close to the speed of light.
4:54 Speaker 01 But in terms of actively traveling, for instance, back in time, I find probably very unlikely.
5:02 Lex Fridman? So do you still hold the the underlying dream of the engineering intelligence? That we'll build systems that are able to do such huge leaps like discovering the kind of mechanism that would be required to travel through time? Do you still hold that dream or echoes of it from your childhood?
5:24 Speaker 01 Yeah, you know, I don't think whether there are certain problems that probably cannot be solved, depending what what you believe about the physical reality. Like, you know, maybe totally impossible to create energy from nothing or to travel back in time. But about making machines that can think as well as we do or better, or more likely, especially in the short and midterm, help us think better, which is in a sense is happening already with the computers we have. And it will happen more and more.
6:05 Speaker 01 But that I certainly believe, and I don't see in principle why computers at some point.
6:12 Speaker 01 Could not become more intelligent than we are, although the word intelligence is a tricky one and one who should discuss what I mean with that.
6:24 Lex Fridman? Intelligence, consciousness, words like love, all these are very, you need to be disentangled. So you've mentioned also that you believe the problem of intelligence is the greatest problem in science, greater than the origin of life and the origin of the universe. You've also, in a talk I've listened to, said that you're open to arguments against you. So what do you think is the most captivating aspect of this problem of understanding the nature of intelligence? Why does it captivate you as it does?
7:04 Speaker 01 Well, originally, I think one of the motivation that I had as, I guess, a teenager, when I was infatuated with theory of relativity, was really that I found that there was the problem of time and space and general relativity, but there were so many other problems of the same level of difficulty and importance that I could, even if I were Einstein, it was difficult to solve all of them. So what about solving a problem or solution allow me to solve all the problems? And this was, what if we could find the key
7:48 Speaker 01 to an intelligence, you know, 10 times better or faster than Einstein?
7:54 Lex Fridman? So that's sort of seeing artificial intelligence as a tool to expand our capabilities. But is there just an inherent curiosity in you in just understanding what it is in here that makes it all work?
8:11 Speaker 01 Yes, absolutely, you're right. So I was starting, I started saying this was the motivation when I was a teenager. But, you know, soon after, I think the problem of human intelligence became a real focus of, you know, of my science and my research because...
8:34 Speaker 01 I think he's, for me, the most interesting problem is really asking who we are. Right? He's asking not only a question about science, but even about the very tool we are using to do science, which is our brain. How does our brain work? From where does it come from? What are its limitations? Can we make it better?
9:03 Lex Fridman? And that in many ways is the ultimate question that underlies this whole effort of science. So you've made significant contributions in both the science of intelligence and the engineering of intelligence. In a hypothetical way, let me ask, how far do you think we can get in creating intelligent systems without understanding the biological, the understanding how the human brain creates intelligence? Put another way, do you think we can build a strong AI system without really getting at the core, the functionality, understanding the functionality of the brain?
9:42 Speaker 01 Well, this is a real difficult question. You know, we did solve problems like flying without really using too much of knowledge about how birds fly. It was important, I guess, to know that you could have things heavier than than air being able to fly like like birds. But beyond that, probably we did not learn very much, you know, some, you know, the
10:19 Speaker 01 Brothers Wright did learn a lot of observation about birds and designing their their aircraft. But, you know, you can argue we did not use much of biology in that particular case. Now, in the case of intelligence, I think that it's it's a bit of a bet right now. If you are, if you ask, okay, we we all agree we'll get at some point, maybe soon, maybe later, to a machine that is indistinguishable from my secretary, say, in terms of what I can ask the machine to do.
11:04 Speaker 01 I think we'll get there. And now the question is, you can ask people, do you think we'll get there without any knowledge about, you know, the human brain, or that the best way to get there is to understand better the human brain?
11:18 Lex Fridman? Yeah.
11:19 Speaker 01 Okay, this is, I think, an educated bet, that different people with different backgrounds will decide in different ways. The recent history of the progress in AI in the last, I would say, five years or 10 years has been the main breakthroughs, the main recent breakthroughs,
11:44 Speaker 01 really start from neuroscience.
11:49 Speaker 01 I can mention reinforcement learning as one, is one of the algorithms at the core of AlphaGo, which is the system that beat the kind of an official world champion of Go, Lee Sedol, two, three years ago in Seoul.
12:09 Speaker 01 That's one, and that started really with the work of Pavlov
12:16 Speaker 01 in the 1900s, Marvin Minsky in the 60s, and many other neuroscientists later on. And deep learning started, which is at the core again of AlphaGo and systems like autonomous driving systems for cars, like the systems that Mobili, which is a company started by one of my ex post doc, Amnon Shashua,
12:45 Speaker 01 that is the core of those things. And deep learning, really the initial ideas in terms of the architecture of these layered hierarchical networks, started with the work of Thorsten Wiesel and David Hubel at Harvard up the river in the 60s. So, recent history suggests that neuroscience played a big role in these breakthroughs. My personal bet is that there is a good chance they continue to play a big role, maybe not in all the future breakthroughs, but in some of them.
13:20 Lex Fridman? At least in inspiration.
13:21 Speaker 01 At least in inspiration, absolutely, yes.
13:24 Lex Fridman? So you studied both artificial and biological neural networks. You said these mechanisms that underlie deep learning, deep and reinforcement learning. But there is nevertheless significant differences between biological and artificial neural networks as they stand now. So between the two, what do you find is the most interesting, mysterious, maybe even beautiful difference as it currently stands in our understanding?
13:55 Speaker 01 I must confess that until recently, I found that the artificial networks too simplistic relative to real neural networks. But recently, I've been started to think that, yes, there are very big simplification of what you find in the brain. But on the other hand, are much closer in terms of the architecture to the brain than other models that we had that computer science used as model of thinking, which were mathematical logics, you know, Lisp, Prolog, and those kind of things. So in comparison to those, they're much closer to the brain.
14:40 Speaker 01 You have networks of neurons, which is what the brain is about. The artificial neurons in the models, as I said, caricature of the biological neurons. But they're still neurons, single units communicating with other units, something that is absent in the traditional computer-type models of mathematics, reasoning, and so on.
15:07 Lex Fridman? So what aspect would you like to see in artificial neural networks?
15:13 Lex Fridman? Added over time as we try to figure out ways to improve them.
15:17 Speaker 01 So one of the main differences and, you know, problems in terms of deep learning today, and it's not only deep learning, and the brain, is the need for deep learning techniques to have a lot of labeled examples. You know, for instance, for ImageNet, you have like a training set, which is one million images, each one labeled by some human in terms of which object is there. And it's, it's clear that in biology, a baby may be able to see million of images in
16:02 Speaker 01 the first years of life, but will not have million of labels given to him or her by parents or take, take caretakers. So how do you solve that? You know, I think there is this interesting challenge that today, deep learning and related techniques are all about big data, big data meaning a lot of examples labeled by humans. Whereas in nature, you have, so that this big data is N going to infinity, that's the best, you know, N meaning labeled data.
16:47 Speaker 01 But I think the biological world is more N going to one. A child can learn
16:53 Lex Fridman? It's a beautiful way to put it.
16:55 Speaker 01 From very small number of, you know, labeled examples. Like you tell a child, this is a car. You don't need to say, like in ImageNet, you know, this is a car, this is a car, this is not a car, this is not a car, one million times.
17:10 Lex Fridman? So and of course with AlphaGo and or at least the AlphaZero variants, there's because of the because of the world of Go is so simplistic that you can actually learn by yourself through self-play. You could play against each other. And the real world, I mean, the visual system that you've studied extensively is a lot more complicated than the game of Go. So on the comment about children, which are fascinatingly good at learning new stuff, how much of it do you think is hardware? And how much of it is software?
17:43 Speaker 01 Yeah, that's a good, a deep question is in a sense is the old question of nurture and nature. How much is in the gene? And how much is in the experience of an individual? Obviously, it's both that play a role. And I believe that the way evolution gives put prior information, so to speak, hardwired. It's not really hardwired, but that's essentially an hypothesis. I think what's going on is that evolution
18:24 Speaker 01 has, you know, almost necessarily, if you believe in Darwin, is very opportunistic. And and think about our DNA and the DNA of the rosophila. Our DNA does not have many more genes than the rosophila.
18:45 Lex Fridman? The fly.
18:46 Speaker 01 The fly, the fruit fly. Now, we know that the fruit fly does not learn very much during its individual existence. It looks like one of this machinery that it's really mostly not 100%, but you know, 95% hardcoded by the genes.
19:08 Speaker 01 But since we don't have many more genes than the rothophila, evolution could encode in us a kind of general learning machinery, and then had to give very weak priors. Like, for instance, let me give a specific example, which is recent work by a member of our Center for Brains, Minds and Machines. We know, because of work of other people in our group and other groups, that there are cells in a part of our brain, neurons, that are tuned to faces. They seem to be involved in face recognition.
19:50 Speaker 01 Now, this face area exists, seems to be present in young children and adults. And one question is, is there from the beginning? Is hardwired by evolution? Or, you know, somehow is learned very quickly?
20:12 Lex Fridman? So what's your, by the way, a lot of the questions I'm asking, the answer is, we don't really know. But as a person who has contributed some profound ideas in these fields, you're a good person to guess at some of these. So, of course, there's a caveat before a lot of the stuff we talk about. But what is your hunch? Is the face, the part of the brain that seems to be concentrated on face recognition, are you born with that? Or you just, is designed to learn that quickly? Like the face of the mother and stuff?
20:43 Speaker 01 My hunch, my bias was the second one. Learned very quickly. And it turns out that Marge Livingstone at Harvard has done some amazing experiments in which she raised baby monkeys, depriving them of faces during the first weeks of life. So they see technicians, but the technician have a mask.
21:10 Lex Fridman? Yes.
21:12 Speaker 01 And so when they looked at the area in the brain of these monkeys that were usually, you find faces, they found no face preference.
21:27 Speaker 01 So my guess is that what evolution does in this case is there is a plastic area, which is plastic, which is kind of predetermined to be imprinted very easily. But the command from the gene is not a detailed circuitry for a face template. Could be, but this will require probably a lot of bits. You had to specify a lot of connection of a lot of neurons. Instead, the command from the gene is something like imprint, memorize what you see most often in the first two weeks of life, especially in connection with food. And maybe nipples, I don't know.
22:11 Lex Fridman? Right, well, source of food. And so in that area is very plastic at first and then solidifies. It'd be interesting if a variant of that experiment would show a different kind of pattern associated with food than a face pattern, whether that could stick.
22:27 Speaker 01 There are indications that during that experiment, what the monkeys saw quite often were the blue gloves of the technicians that were giving to the baby monkeys the milk. And some of the cells, instead of being face sensitive in that area, are hand sensitive.
22:48 Lex Fridman? Oh, that's fascinating. Can you talk about what are the different parts of the brain? And in your view, sort of loosely, and how do they contribute to intelligence? Do you see the brain as a bunch of different modules? And they together come in the human brain to create intelligence? Or is it all one mush of the same kind of fundamental architecture?
23:21 Speaker 01 Yeah, that's, you know, that's an important question. And there was a phase in neuroscience back in the nineteen fifty or so, in which it was believed for a while that the brain was equipotential. This was the term. You could cut out a piece and nothing special happened apart, a little bit less performance. There was a surgeon, Lashley, who did a lot of experiments of this type with mice and rats
23:58 Speaker 01 and concluded that every part of the brain was essentially equivalent to any other one. It turns out that that's, that's really not true. It's, there are very specific modules in the brain, as you said. And, you know, people may lose the ability to speak if you have a stroke in a certain region, or may lose control of their legs in another region, or, so they're very specific. The brain is also quite flexible and redundant, so often it can correct things and, you know,
24:39 Speaker 01 the kind of take over functions from one part of the brain to the other. But, but, but really there are specific modules. So the answer that we know from this old work, which was basically on, based on lesions, either on animals or very often there were a mine of, well, there was a mine of very interesting data coming from, from the war, from different types of. Injuries. Injuries that soldiers had in the brain.
25:20 Speaker 01 And more recently, functional MRI, which allow you to, to check which part of the brain are active when you're doing different tasks.
25:36 Speaker 01 As you know, can replace some of this. You can see that certain parts of the brain are involved, are active.
25:46 Lex Fridman? Vision, language, yeah.
25:47 Speaker 01 Yeah, that's right.
25:49 Lex Fridman? But sort of taking a step back to that part of the brain that discovers that specializes in the face and how that might be learned. What's your intuition behind, you know, is it possible that the sort of from a physicist's perspective, when you get lower and lower, it's all the same stuff and it just, when you're born, it's plastic and it quickly figures out this part is going to be about vision, this is going to be about language, this is about common sense reasoning. Do you have any intuition that that kind of learning is going on really quickly or is it really kind of solidified in hardware?
26:26 Speaker 01 That's a great question. So there are parts of the brain like the cerebellum or the hippocampus that are quite different from each other. They clearly have different anatomy, different connectivity. Then there is the cortex, which is the most developed part of the brain in humans. And in the cortex, you have different regions of the cortex that are responsible for vision, for audition, for motor control, for language. Now, one of the big puzzles of this is that in the cortex is the cortex is the cortex
27:11 Speaker 01 is, looks like it is the same in terms of hardware, in terms of type of neurons and connectivity across these different modalities. So for the cortex, letting aside these other parts of the brain like spinal cord, hippocampus, cerebellum and so on.
27:35 Speaker 01 For the cortex, I think your question about hardware and software and learning and so on, it's, I think, is rather open. And, you know, I find very interesting for Lisa to think about an architecture, computer architecture, that is good for vision and at the same time is good for language. Seems to be, you know, so different problem areas that you have to solve.
28:06 Lex Fridman? But the underlying mechanism might be the same, and that's really instructive for
28:09 Speaker 01 It may be.
28:10 Lex Fridman? Artificial neural networks. So you've done a lot of great work in vision, in human vision, computer vision. And you mentioned the problem of human vision is really as difficult as the problem of general intelligence. And maybe that connects to the cortex discussion. Can you describe the human visual cortex and how the humans begin to understand the world through the raw sensory information? What's for folks enough familiar, especially on the computer vision side, we don't often actually take a step back, except saying with a sentence or two that one is inspired by the other.
28:53 Lex Fridman? What is it that we know about the human visual cortex? That's interesting.
28:57 Speaker 01 So we know quite a bit, at the same time, we don't know a lot. But the bit we know, you know, in a sense, we know a lot of the details and many we don't know, and we know a lot of the top level, the answer to the top level question, but we don't know some basic ones, even in terms of general neuroscience, forgetting vision. You know, why do we sleep?
29:26 Speaker 01 It's such a basic question. And we really don't have an answer to that.
29:31 Lex Fridman? Do you think, so taking a step back on that. So sleep, for example, is fascinating. Do you think that's a neuroscience question? Or if we talk about abstractions. What do you think is an interesting way to study intelligence? Or most effective on the levels of abstraction? Is it chemical? Is it biological? Is it electro-physical? Mathematical, as you've done a lot of excellent work on that side? Which psychology, sort of like, at which level of abstraction do you think?
30:00 Speaker 01 Well, in terms of levels of abstraction, I think we need all of them. It's one, you know, it's like if you ask me, what does it mean to understand a computer? Right? That's much simpler. But in a computer, I could say, well, I understand how to use PowerPoint. That's my level of understanding a computer. It's, it has reasonable, you know, it gives me some power to produce slides and beautiful slides. Now, you can ask somebody else, he says, well, I, I know how the transistor work that are inside the computer.
30:38 Speaker 01 I can write the equation for, you know, transistor and diodes and circuits, logical circuits. And I can ask this guy, do you know how to operate PowerPoint? No idea.
30:51 Lex Fridman? So do you think if we discovered computers walking amongst us full of these transistors that are also operating under Windows and have PowerPoint? Do you think it's, digging in a little bit more, how useful is it to understand the transistor? In order to be able to understand PowerPoint and these higher level
31:15 Speaker 01 Very good.
31:16 Lex Fridman? Intelligent processes?
31:17 Speaker 01 So I think in the case of computers, because they were made by engineers, by us, this different level of understanding are rather separate on purpose. You know, they are separate modules. So that the engineer that designed the circuit for the chips does not need to know what is inside PowerPoint. And somebody can write the, the software translating from one to the, to the other. So.
31:49 Speaker 01 In that case, I don't think understanding the transistor helped you understand PowerPoint, or very little. If you want to understand the computer, this question, you know, I would say you have to understand it at different levels, if you really want to build it one, right? But for the brain, I think these levels of understanding, so the algorithms, which kind of computation, you know, the equivalent of PowerPoint, and the circuits, you know, the transistors, I think they are much more intertwined with each other. There is not, you know, a neatly level of the software separate from the hardware.
32:33 Speaker 01 And so that's why I think, in the case of the brain, the problem is more difficult, more than for computers requires the interaction, the collaboration between different types of expertise.
32:46 Lex Fridman? The brain is a big hierarchical mess, so you can't just disentangle.
32:52 Speaker 01 I think you can, but it's much more difficult and it's not, you know, it's not completely obvious. And as I said, I think it's one of the, personally, I think is the greatest problem in science. So, you know, I think it's fair that it's difficult.
33:08 Lex Fridman? That's a difficult one. That said, you do talk about compositionality, and why it might be useful. And when you discuss why these neural networks, in artificial or biological sense, learn anything, you talk about compositionality. See, there's a sense that nature can be disentangled, or, well, all aspects of our cognition could be disentangled a little, to some degree. So why do you think, what, first of all, how do you see compositionality, and why do you think it exists at all in nature?
33:48 Speaker 01 I spoke about, I use the term "compositionality".
33:56 Speaker 01 When we looked at deep neural networks, multi-layers, and trying to understand when and why they are more powerful than more classical one-layer networks like linear classifier, kernel machines, so-called. And what we found is that in terms of approximating or learning or representing a function, a mapping from an input to an output, like from an image to the label in the image. If this function has a particular structure,
34:37 Speaker 01 then deep networks are much more powerful than shallow networks to approximate the underlying function. And the particular structure is a structure of compositionality. If the function is made up of functions of functions, so that you need to look on when you are interpreting an image, classifying an image, you don't need to look at all pixels at once, but you can compute something from small groups of pixels and then you can compute something on the output of this local computation, and so on.
35:21 Speaker 01 That is similar to what you do when you read a sentence. You don't need to read the first and the last letter, but you can read syllables, combine them in words, combine the words in sentences. So this is this kind of structure.
35:38 Lex Fridman? So that's as part of the discussion of why deep neural networks may be more effective than the shallow methods. And is your sense for most things we can use neural networks for?
35:53 Lex Fridman? Those problems are going to be compositional in nature, like, like language, like vision. How far can we get in this kind of way?
36:04 Speaker 01 Right. So here is almost philosophy.
36:08 Lex Fridman? Well, let's go there.
36:10 Speaker 01 Yeah, let's go there. So a friend of mine, Max Tegmark, who is a physicist at MIT.
36:17 Lex Fridman? I've talked to him on this thing. Yeah, and he disagrees with you, right? A little bit.
36:21 Speaker 01 Yeah, we, you know, we agree on most, but the conclusion is a bit different. He, his conclusion is that for images, for instance, the compositional structure of this function that we have to learn or to solve these problems comes from physics, comes from the fact that you have local interactions in physics between atoms and other atoms, between particle of matter and other particles, between planets and other planets, between stars and other, it's all local.
37:02 Lex Fridman? Yeah.
37:05 Speaker 01 And that's true, but you could push this argument a bit further. Not this argument, actually, you could argue that, you know, maybe that's part of the true, but maybe what happens is kind of the opposite, is that our brain is wired up as a deep network. So it can learn, understand, solve problems that have this compositional structure. And it cannot do, it cannot solve problems that don't have this compositional structure.
37:46 Speaker 01 So the problems we are accustomed to, we think about, we test our algorithms and so on, are this compositional structure because our brain is made up.
37:59 Lex Fridman? And that's in a sense, an evolutionary perspective that we've, so the ones that didn't have, that weren't dealing with the compositional nature of reality died off?
38:12 Speaker 01 Yes, but also could be, maybe the reason.
38:17 Speaker 01 Why we have this local connectivity in the brain? Like, simple cells in cortex looking only at the small part of the image, each one of them, and then other cells looking at the small number of these simple cells, and so on. The reason for this, maybe purely that it was difficult to grow long-range connectivity. So suppose it's, you know, for biology. It's possible to grow short-range connectivity, but not long-range also, because there is a limited number of long-range that you can. And so you have this limitation from the biology.
39:02 Speaker 01 And this means you build a deep convolutional network. This would be something like a deep convolutional network. And this is great for solving certain class of problems. These are the ones we find easy and important for our life. And yes, they were enough for us to survive.
39:22 Lex Fridman? And you can start a successful business on solving those problems, right? Like with Mobileye. Driving is a compositional problem. So on the learning task, I mean, we don't know much about how the brain learns in terms of optimization. But so the thing that's stochastic gradient descent is what artificial neural networks use for the most part to adjust the parameters in such a way that it's able to deal, based on the labeled data, it's able to solve the problem. So what's your intuition about why it works at all?
40:07 Lex Fridman? How hard of a problem it is to optimize a neural network, artificial neural network? Is there other alternatives? Yeah, just in general?
40:18 Lex Fridman? Your intuition is behind this very simplistic algorithm that seems to do pretty good, surprisingly.
40:23 Speaker 01 Yes, yes. So I find neuroscience, the architecture of cortex is really similar to the architecture of deep networks. So there is a nice correspondence there between the biology and this kind of local connectivity, hierarchical architecture. The stochastic gradient descent, as you said, is a very simple technique. It seems pretty unlikely that biology could do that from what we know right now about, you know, cortex and neurons and synapses.
41:05 Speaker 01 So it's a big question open whether there are other optimization learning algorithms that can replace stochastic gradient descent. And my guess is yes, but nobody has found yet a real answer. I mean, people are trying, still trying, and there are some interesting ideas. The fact that stochastic gradient descent is so successful, this has become clearly is not so mysterious.
41:44 Speaker 01 And the reason is that it's an interesting fact, you know, is a change in a sense in how people think about statistics. And this is the following, is that typically when you had data and you had, say, a model with parameters, you are trying to fit the model to the data, you know, to fit the parameter. Typically, the kind of, kind of crowd wisdom type idea was you should have at least, you know, twice the number of data than the number of parameters.
42:28 Speaker 01 Maybe 10 times is better. Now, the way you train neural network these days is that they have 10 or 100 times more parameters than data, exactly the opposite.
42:43 Speaker 01 And which, you know, it has been one of the puzzles about neural networks. How can you get something that really works when you have so much freedom?
42:57 Lex Fridman? From that little data, it can generalize somehow.
43:00 Speaker 01 Right, exactly.
43:01 Lex Fridman? Do you think the stochastic nature of it is essential, the randomness?
43:05 Speaker 01 So I think we have some initial understanding why this happens. But one nice side effect of having this over parameterization, more parameters than data, is that when you look for the minima of a loss function, like stochastic gradient descent is doing, you find, I made some calculations based on some old basic theorem of algebra called the Bezos theorem, that gives you an estimate of the number of solution of a system of polynomial equation.
43:43 Speaker 01 Anyway, the bottom line is that there are probably more minima for a typical deep networks than atoms in the universe. Just to say, there are a lot because of the over parameterization. More global minimum, zero minimum, good minimum. So it's not too su-
44:07 Lex Fridman? More global minima?
44:08 Speaker 01 Yeah, a lot of them. So you have a lot of solutions. So it's not so surprising that you can find them relatively easily. And this is, this is because of the over parameterization.
44:21 Lex Fridman? The over parameterization sprinkles that entire space with solutions that are pretty good.
44:26 Speaker 01 Yeah, it's not so surprising, right? It's like, you know, if you have a system of linear equation and you have more unknowns than equations, then you have, we know, you have an infinite number of solutions. And the question is to pick one, that's another story. But you have an infinite number of solutions. So there are a lot of value of your unknowns that satisfy the equations.
44:50 Lex Fridman? But it's possible that there's a lot of those solutions that aren't very good. What's surprising is that they're pretty good.
44:56 Speaker 01 Why can you pick one that generalizes well?
44:59 Lex Fridman? Yeah.
45:00 Speaker 01 But that's a separate question with separate answers.
45:03 Lex Fridman? Yeah. One theorem that people like to talk about that kind of inspires imagination of the power of neural networks is the universality, universal approximation theorem, that you can approximate any computable function with just a finite number of neurons in a single hidden layer. Do you find this theorem one surprising? Do you find it useful, interesting, inspiring?
45:29 Speaker 01 No, this one, you know, I never found it very surprising. It was known since the 80s, since I entered the field. Because it's basically the same as Viestras theorem, which says that I can approximate any continuous function with a polynomial of sufficiently, with a sufficient number of terms, monomials.
45:54 Lex Fridman? Yeah.
45:55 Speaker 01 It's basically the same and the proofs are very similar.
45:58 Lex Fridman? So your intuition was, there was never any doubt that neural networks in theory could be very strong approximations.
46:05 Speaker 01 Right. The question, the interesting question is that if this theorem
46:13 Speaker 01 says you can approximate, fine. But when you ask how many neurons, for instance, or in the case of polynomial, how many monomials I need to get a good approximation?
46:28 Speaker 01 Then it turns out that that depends on the dimensionality of your function, how many variables you have. But it depends on the dimensionality of your function in a bad way. It's for instance, suppose you want an error which is no worse than 10% in your approximation. You come up with a network that approximates your function within 10%. Then turns out that the number of units you need are in the order of 10 to the dimensionality, D, how many variables. So if you have, you know, two variables, these two, and you have 100 units and okay.
47:14 Speaker 01 But if you have, say, 200 by 200 pixel images. Now, this is, you know, 40,000, whatever.
47:23 Lex Fridman? We again go to the size of the universe pretty quickly.
47:26 Speaker 01 Exactly, 10 to the 40,000 or something.
47:30 Lex Fridman? Yeah.
47:31 Speaker 01 And so this is called the curse of dimensionality. Not, you know, quite appropriately.
47:39 Lex Fridman? And the hope is with the extra layers, you can remove the curse.
47:45 Speaker 01 What we proved is that if you have deep layers or hierarchical architecture with the local connectivity of that type of convolutional deep learning. And if you're dealing with a function that has this kind of hierarchical architecture, then you avoid completely the curse.
48:07 Lex Fridman? You've spoken a lot about supervised deep learning.
48:10 Speaker 01 Yeah.
48:11 Lex Fridman? What are your thoughts, hopes, views on the challenges of unsupervised learning with GANs, with generative adversarial networks? Do you see those as distinct, the power of GANs, do you see those as distinct from supervised methods in neural networks? Or are they really all in the same representation ballpark?
48:33 Speaker 01 GANs is one way to get estimation of probability densities, which is a somewhat new way that people have not done before. I, I don't know whether this will really play an important role in, you know, in intelligence or it's, it's interesting. I'm, I'm less enthusiastic about it than many people in the field. I have the feeling that many people in the field are really impressed by the ability to, of producing realistic looking images.
49:16 Speaker 01 In this generative way.
49:18 Lex Fridman? Which describes the popularity of the methods, but you're saying that while that's exciting and cool to look at, it may not be the tool that's useful for.
49:27 Speaker 01 Yeah.
49:28 Lex Fridman? For, so you describe it kind of beautifully. Current supervised methods go N to infinity in terms of the number of labeled points, and we really have to figure out how to go to N to one.
49:37 Speaker 01 Yeah.
49:38 Lex Fridman? And you're thinking GANs might help, but they might not be the right.
49:42 Speaker 01 I don't think for that problem, which I really think is important, I think they may help, they certainly have applications, for instance, in computer graphics and, you know, I did work long ago, which was a little bit similar in terms of saying, okay, I have a network and I present images and I can, so input is images and output is, for instance, the pose of the image, you know, a face, how much is smiling, is rotated 45 degrees or not. What about having a network that I train with the same data set, but now I invert input and output.
50:27 Speaker 01 Now, the input is the pose or the expression, a number, certain numbers, and the output is the image, and I train it. And we did pretty good, interesting results in terms of producing very realistic looking images. It was, you know, a less sophisticated mechanism, but the output was pretty, less than GANs, but the output was pretty much of the same quality. So I think for computer graphics type application, yeah, definitely GANs can be quite useful, and not only for that, but for.
51:09 Speaker 01 You know, helping, for instance, on this problem unsupervised example of reducing the number of labeled examples.
51:19 Speaker 01 I think people, it's like they think they can get out more than they put in.
51:26 Lex Fridman? You know. There's no free lunches.
51:29 Speaker 01 Yeah, right.
51:31 Lex Fridman? What do you think, what's your intuition? How can we slow the growth of end to infinity in supervised, end to infinity in supervised learning? So, for example, Mobileye has very successfully, I mean essentially annotated large amounts of data to be able to drive a car. Now, one thought is, so we're trying to teach machines, school of AI, and we're trying to, so how can we become better teachers, maybe? That's one, one way.
52:04 Speaker 01 No, you're, you're, you know, what I, I like that because one, again, one caricature of the history of computer science, you could say, is, begins with programmers, expensive.
52:21 Lex Fridman? Yep.
52:22 Speaker 01 Continue with labelers, cheap.
52:25 Lex Fridman? Yep.
52:26 Speaker 01 And the future will be schools like we have for kids.
52:31 Lex Fridman? Yeah. Currently, the labeling methods were not selective about which examples we, we teach networks with. So I think the focus of making one, networks that learn much faster is often on the architecture side, but how can we pick better examples with which to learn? Do you have intuitions about that?
52:56 Speaker 01 Well, that's part of the, the part of the problem, but the other one is, you know, if we look at biology, a reasonable assumption, I think, is, in the same spirit that I said, evolution is opportunistic and has weak priors. You know, the way I, I think the intelligence of a child, a baby may develop is by bootstrapping
53:32 Speaker 01 weak priors from evolution. For instance.
53:38 Speaker 01 You can assume that you have in most organisms, including human babies, built in some basic machinery to detect motion and relative motion.
53:55 Speaker 01 And in fact, there is, you know, we know all insects, from fruit flies to other animals, they have this.
54:06 Speaker 01 Even in the retina, in the very peripheral part, it's very conserved across species, something that evolution discovered early. It may be the reason why babies tend to look, in the first few days, to moving objects and not to not moving objects. Now, moving objects means, okay, they are attracted by motion. But motion also means that, motion gives automatic segmentation from the background. So because of motion boundaries, you know, either the object is moving or the eye of the baby is tracking the moving object and the background is moving, right?
54:49 Lex Fridman? Yeah, so just purely on the visual characteristics of the scene, that seems to be the most useful.
54:54 Speaker 01 Right, so it's like looking at an object without background.
55:01 Speaker 01 It's ideal for learning the object, otherwise it's really difficult. Because you have so much stuff. So suppose you do this at the beginning, first weeks.
55:12 Speaker 01 Then after that, you can recognize the object. Now they are imprinted, a number of them.
55:19 Speaker 01 Even in the background, even without motion.
55:22 Lex Fridman? So that's the, by the way, I just want to ask, on the object recognition problem. So there is this being responsive to movement and doing edge detection, essentially. What's the gap between being effectively effective at visually recognizing stuff, detecting where it is and understanding the scene? Is this a huge gap in many layers or is it close?
55:49 Speaker 01 No, I think that's a huge gap. I think present algorithm with all the success that we have and the fact that there are a lot of very useful, I think we are in a golden age for applications of low level vision and low level speech recognition and so on, you know, Alexa and so on. There are many more things of similar level to be done, including medical diagnosis and so on, but we are far from what we call understanding of a scene, of language, of actions, of people. That is, despite the claims, that's, I think, are very far.
56:34 Lex Fridman? We're a little bit off. So in popular culture and among many researchers, some of which I've spoken with, Stuart Russell and Elon Musk, in and out of the AI field, there's a concern about the existential threat of AI.
56:50 Speaker 01 Yeah.
56:51 Lex Fridman? And how do you think about this concern? And is it valuable to think about large scale, long term unintended consequences of intelligent systems we try to build?
57:08 Speaker 01 I always think it's better to worry first, you know, early rather than late.
57:14 Lex Fridman? So worry is good.
57:16 Speaker 01 Yeah, I'm not against worrying at all. Personally, I think that, you know, it will take a long time before there is real reason to be worried.
57:33 Speaker 01 But as I said, I think it's good to put in place and think about possible safety against... What I find a bit misleading are things like that have been said by people I know, like Elon Musk and what is Bostrom in particular, and what is his first name?
57:53 Lex Fridman? And Nick.
57:54 Speaker 01 Nick Bostrom, right. You know, and a couple of other people that, for instance, AI is more dangerous than nuclear weapons.
58:03 Lex Fridman? Right.
58:04 Speaker 01 Yeah, I think that's really wrong. That can be misleading, right? Because in terms of priority, we should still be more worried about nuclear weapons and, you know, what people are doing about it and so on than AI.
58:21 Lex Fridman? And you spoke about Demis Hassabis and yourself saying that you think it'll be about 100 years out before we have a general intelligence system that's on par with a human being. Do you have any updates for those predictions?
58:39 Speaker 01 Well, I think he said...
58:41 Lex Fridman? He said 20, I think.
58:42 Speaker 01 He said 20, right. This was a couple of years ago. I have not asked him again, so should I?
58:48 Lex Fridman? Your own prediction, what's your prediction about when you'll be truly surprised? And what's the confidence interval on that?
58:58 Speaker 01 You know, it's so difficult to predict the future, even the present sometimes.
59:04 Lex Fridman? It's pretty hard to predict, yeah.
59:05 Speaker 01 But I would be, as I said, this is completely... I would be more like Rod Brooks. I think he's about 200 years.
59:16 Lex Fridman? 200 years. When we have this kind of AGI system, artificial general intelligence system, you're sitting in a room with her, him, it...
59:29 Lex Fridman? Do you think it will be, the underlying design of such a system is something we'll be able to understand, it will be simple, do you think it'll be explainable, understandable by us, your intuition, again, we're in the realm of philosophy a little bit?
59:48 Speaker 01 Well, probably no, but it, again, it depends what you really mean for understanding. So I think, you know, we don't understand what, how deep networks work, I think we're beginning to have a theory now, but in the case of deep networks, or even in the case of the simple, simpler kernel machines or linear classifier, we really don't understand
1:00:25 Speaker 01 the individual units or so, we, but we understand, you know, what the computation and the limitations and the properties of it are, it's similar to many things, you know, we, what does it mean to understand how a fusion bomb works, how many of us, you know, many of us understand the basic principle, and some of us may understand deeper details.
1:00:57 Lex Fridman? In that sense, understanding is, as a community, as a civilization, can we build another copy of it?
1:01:03 Speaker 01 Okay.
1:01:04 Lex Fridman? And in that sense, do you think there'll be, there'll need to be some evolutionary component where it runs away from our understanding, or do you think it could be engineered from the ground up, the same way you go from the transistor to a powerpoint?
1:01:19 Speaker 01 Right, so many years ago, this was actually, let me see, forty, forty-one years ago, I wrote a paper with David Marr, who was one of the founding father of computer vision, computational vision.
1:01:37 Speaker 01 I wrote a paper about levels of understanding, which is related to the question we discussed earlier about understanding PowerPoint, understanding transistors and so on. And, you know, in that kind of framework, we had a level of the hardware and the top level of the algorithms. We did not have learning. Recently, I updated adding levels. And one level I added to those three was learning. So, and you can imagine, you could have a good understanding of how you construct a learning machine, like we do.
1:02:22 Speaker 01 But being unable to describe in detail what the learning machines will discover, right? Now, that would be still a powerful understanding if I can build a learning machine, even if I don't understand in detail. Every time, it learns something.
1:02:43 Lex Fridman? Just like our children, if they, if they start listening to a certain type of music, I don't know, Miley Cyrus or something, you don't understand why they came to that particular preference, but you understand the learning process. That's very interesting.
1:02:57 Speaker 01 Yeah, yeah.
1:02:58 Lex Fridman? So, on learning, for systems to be part of our world, it has a certain, one of the challenging things that you've spoken about is learning ethics, learning morals. And what, how hard do you think is the problem of, first of all, humans' understanding of ethics? What is the origin on the neural and low level of ethics? What is it at the higher level? Is it something that's learnable from machines in your intuition?
1:03:34 Speaker 01 I think, yeah, ethics is learnable, very likely. I, I think it's one of these problems where...
1:03:46 Speaker 01 Think understanding the neuroscience of ethics. You know, people discuss, there is an ethics of neuroscience.
1:03:58 Lex Fridman? Yeah, yes.
1:03:59 Speaker 01 You know, how a neuroscientist should or should not behave. Can think of a neurosurgeon and the ethics rule he has to be or he she has to be. But I'm more interested on the neuroscience of.
1:04:14 Lex Fridman? You're blowing my mind right now, the neuroscience of ethics, it's very meta.
1:04:18 Speaker 01 Yeah, and you know, I think that would be important to understand also for being able to to design machines that have, that are ethical machines in our sense of ethics.
1:04:32 Lex Fridman? And you think there is something in neuroscience, there's patterns, tools in neuroscience that could help us shed some light on ethics? Or is it mostly on the psychologists of sociology much higher level?
1:04:46 Speaker 01 No, there is psychology, but there is also, in the meantime, there are, there is evidence, fMRI, of specific areas of the brain that are involved in certain ethical judgment. And not only this, you can stimulate those area with magnetic fields and change the ethical decisions.
1:05:10 Lex Fridman? Yeah, wow.
1:05:13 Speaker 01 So that's work by a colleague of mine, Rebecca Sachs. And there is other researchers doing similar work. And I think, you know, this is the beginning, but ideally at some point we'll have an understanding of how this works and why it evolved, right?
1:05:35 Lex Fridman? The big why question, yeah, it must have some, some purpose.
1:05:39 Speaker 01 Yeah, obviously it has, you know, some social purposes is.
1:05:46 Speaker 01 Probably.
1:05:47 Lex Fridman? If neuroscience holds the key to at least illuminate some aspect of ethics, that means it could be a learnable problem.
1:05:54 Speaker 01 Yeah, exactly.
1:05:55 Lex Fridman? And as we're getting into harder and harder questions, let's go to the hard problem of consciousness. Is this an important problem for us to think about and solve on the engineering of intelligence side of your work, of our dream?
1:06:13 Speaker 01 You know, it's unclear. So, you know, again, this is a deep problem, partly because it's very difficult to define consciousness. And there is a debate among neuroscientists about whether consciousness, and philosophers, of course, whether consciousness is something that requires flesh and blood, so to speak.
1:06:47 Lex Fridman? Yeah.
1:06:48 Speaker 01 Or could be, you know, that we could have silicon devices that are conscious or up to statement like everything has some degree of consciousness and some more than others. This is like Giulio Tonioni and Fee.
1:07:11 Lex Fridman? We just recently talked to Christoph Koch.
1:07:13 Speaker 01 Okay.
1:07:14 Lex Fridman? So he-
1:07:14 Speaker 01 Yeah, Christoph was my first graduate student.
1:07:17 Lex Fridman? Do you think it's important to illuminate aspects of consciousness in order to engineer intelligent systems? Do you think an intelligent system would ultimately have consciousness? Are they interlinked?
1:07:35 Speaker 01 You know, most of the people working in artificial intelligence, I think, would answer, we don't strictly need consciousness to have an intelligent system.
1:07:47 Lex Fridman? That's sort of the easier question because this is a very engineering answer to the question.
1:07:52 Speaker 01 Yes.
1:07:53 Lex Fridman? Pass the Turing test, we don't need consciousness. But if you were to go,
1:07:58 Lex Fridman? do you think it's possible that we need to have- So that kind of self-awareness.
1:08:04 Speaker 01 Ah, we may, yes. So for instance, I, I personally think that when test a machine or a person in a Turing test, in an extended Turing test, you know, I think consciousness is part of what we require in that test, you know, implicitly to say that this is intelligent. Christoph disagrees, so.
1:08:34 Lex Fridman? Yes, he does.
1:08:35 Speaker 01 Yeah.
1:08:35 Lex Fridman? It, despite many other romantic notions he holds, he disagrees with that one.
1:08:41 Speaker 01 Yes, that's right. So, you know, we'll see.
1:08:46 Lex Fridman? Do you think, as a quick question, Ernest Becker's Fear of Death, do you think mortality and those kinds of things are important for, well, for consciousness and for intelligence? The finiteness of life? Finiteness of existence? Or is that just a side effect of evolutionary side effect that's useful to, for natural selection? Do you think this kind of thing that we're gonna, this interview is gonna run out of time soon? Our life will run out of time soon? Do you think that's needed to make this conversation good? And, and life good?
1:09:29 Speaker 01 You know, I never thought about it. It's a very interesting question. I think Steve Jobs, in his commencement speech at Stanford, argued that, you know, having a finite life was important for, for stimulating achievements. It was a different.
1:09:48 Lex Fridman? Yeah, live every day like it's your last, right?
1:09:50 Speaker 01 Yeah, yeah. So rationally, I don't think strictly you need mortality for consciousness, but.
1:10:02 Lex Fridman? Who knows? They seem to go together in our biological system.
1:10:05 Speaker 01 Yeah, yeah.
1:10:08 Lex Fridman? You've mentioned before and students are associated with AlphaGo mobilize the big recent success stories in AI. I think it's captivated the entire world of what AI can do. So what do you think will be the next breakthrough? And what's your intuition about the next breakthrough?
1:10:30 Speaker 01 Of course, I don't know where the next breakthrough is. I think that there is a good chance, as I said before, that the next breakthrough would also be inspired by, you know, neuroscience.
1:10:45 Speaker 01 But which one? I don't know.
1:10:49 Lex Fridman? And there's, so MIT has this quest for intelligence.
1:10:52 Speaker 01 Yeah.
1:10:52 Lex Fridman? And there's a few moonshots, which in that spirit, which ones are you excited about? What, which projects kind of?
1:11:01 Speaker 01 Well, of course, I'm excited about one of the moonshots, which is our Center for Brains, Minds and Machines, is the one which is fully funded by NSF. And it's a, it is about visual intelligence.
1:11:19 Lex Fridman? And that, that one is particularly about understanding?
1:11:23 Speaker 01 Visual intelligence, so the visual cortex and, and visual intelligence in the sense of how we look around ourselves and understand the world around ourselves, you know, meaning what, what is going on, how we could go from here to there without hitting obstacles.
1:11:49 Speaker 01 You know, whether there are other agents, people in the environment. These are all things that we perceive very quickly. And, and it's something actually quite close to being conscious, not quite, but you know, there is this interesting experiment that was run at Google X, which is in a sense is just a virtual reality experiment, but in which they had subjects sitting, say in a chair with goggles, like Oculus and so on.
1:12:26 Speaker 01 Earphones and they were seeing through the eyes of a robot nearby. Two cameras, microphones for receiving. So their sensory system was there. And the impression of all the subject, very strong, they could not shake it off, was that they were where the robot was. They could look at themselves from the robot and still feel they were where the robot is. They were looking at their body. Their self had moved.
1:13:05 Lex Fridman? Some aspect of scene understanding has to have ability to place yourself, have a self-awareness about your position in the world and what the world is. So we may have to solve the hard problem of consciousness.
1:13:21 Speaker 01 On their way, yes.
1:13:22 Lex Fridman? It's quite a moonshot. So you've been an advisor to some incredible minds, including Demis Hassabis, Christoph Koch, Amna Shashwal, like you said. All went on to become seminal figures in their respective fields. From your own success as a researcher and from perspective as a mentor of these researchers, having guided them, in the way of advice, what does it take to be successful in science and engineering careers? Whether you're talking to somebody in their teens, twenties and thirties, what does that path look like?
1:14:05 Speaker 01 It's curiosity and having fun. And I think it's important also having fun with other curious minds.
1:14:19 Lex Fridman? It's the people you're surrounded with too. So fun and curiosity. Is there... You mentioned Steve Jobs. Is there also an underlying ambition that's unique that you saw, or is it really does boil down to insatiable curiosity and fun?
1:14:35 Speaker 01 Well, of course, you know, it's being curious in an active and ambitious way, yes. And definitely, but I think sometime in in science, there are friends of mine who are like this, you know, there are some of the scientists like to work by themselves and kind of communicate only when they complete their work or discover something. I think I always found the the actual process of, you know, discovering something is more
1:15:20 Speaker 01 fun if it's together with other intelligent and curious and fun people.
1:15:26 Lex Fridman? So if you see the fun in that process, the side effect of that process will be that you'll actually end up discovering some interesting things.
1:15:32 Speaker 01 Yes.
1:15:33 Lex Fridman? So as you've led many incredible efforts here, what's the secret to being a good advisor, mentor, leader in a research setting? Is it a similar spirit? Or yeah, what what advice could you give to people, young faculty and so on?
1:15:53 Speaker 01 It's partly repeating what I said about an environment that should be friendly and fun and ambitious. And, you know, I think I learned a lot from some of my advisors and friends, and some were physicists. And there was, for instance, this behavior that was encouraged of when somebody comes with a new idea in the group, you're unless it's really stupid, but you are always enthusiastic. And then and you're enthusiastic for a few minutes, for a few hours, then you start.
1:16:35 Speaker 01 You know, asking critically a few questions, testing this. But you know, this is a process that is, I think is very, very good. This, you have to be enthusiastic. Sometimes people are very critical from the beginning. That's, that's, that's not.
1:16:53 Lex Fridman? You have, you have to give it a chance.
1:16:54 Speaker 01 Yes.
1:16:55 Lex Fridman? That seed to grow. That said, with some of your ideas, which are quite revolutionary. So there's, I've a witness, especially in the human vision side and neuroscience side. There could be some pretty heated arguments. Do you enjoy these? Is that a part of science and academic pursuits that you enjoy?
1:17:12 Speaker 01 Yeah.
1:17:13 Lex Fridman? Is it? Is that something that happens in your group as well?
1:17:18 Speaker 01 Yeah, absolutely. I also spent some time in Germany. Again, there is this tradition in which people are more forthright, less kind than here. So, you know, in the US, when you write a bad letter, you still say, this guy is nice.
1:17:39 Lex Fridman? Yes, yes.
1:17:42 Speaker 01 So.
1:17:43 Lex Fridman? Yeah, here in America, it's degrees of nice.
1:17:45 Speaker 01 Yes.
1:17:46 Lex Fridman? It's all just degrees of nice.
1:17:47 Speaker 01 Right, right. So as long as this does not become personal and it's really like, you know, a football game with its rules, that's great.
1:18:01 Lex Fridman? It's fun. So if you somehow find yourself in a position to ask one question of an oracle, like a genie, maybe a god, and you're guaranteed to get a clear answer, what kind of question would you ask? What, what would be the question you would ask?
1:18:21 Speaker 01 In the spirit of our discussion, it could be, how could be, how could I become 10 times more intelligent?
1:18:28 Lex Fridman? And so, but see, you only get a clear, short answer. So do you think there's a clear, short answer to that?
1:18:35 Speaker 01 No.
1:18:37 Lex Fridman? And that's the answer you'll get. So you've mentioned Flowers of Elgron.
1:18:43 Speaker 01 Oh, yeah.
1:18:45 Lex Fridman? There's a story that inspired you in your, in your childhood. As this story of a mouse, human achieving genius level intelligence and then understanding what was happening while slowly becoming not intelligent again. And this tragedy of gaining intelligence and losing intelligence. Do you think in that spirit, in that story, do you think intelligence is a gift? Or curse from the perspective of happiness and meaning of life? You, you tried to create an intelligent system that understands the universe. But on an individual level, the meaning of life, do you think intelligence is a gift?
1:19:27 Speaker 01 It's a good question.
1:19:34 Speaker 01 I don't know.
1:19:39 Lex Fridman? As one of this, as one, people consider the smartest people in the world in some, in some dimension, at the very least. What do you think?
1:19:51 Speaker 01 I don't know, it may be invariant to intelligence, let agree of happiness. It would be nice if it were.
1:20:00 Lex Fridman? That's the hope.
1:20:01 Speaker 01 Yeah.
1:20:03 Lex Fridman? You could be smart and happy and clueless and happy.
1:20:07 Speaker 01 Yeah.
1:20:08 Lex Fridman? As always, on the discussion of the meaning of life is probably a good place to end. Tomaso, thank you so much for talking today.
1:20:16 Speaker 01 Thank you. This was great.
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