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[00:00:00]

The following is a conversation with Matt Botnick, director of neuroscience research, A Deep Mind, he's a brilliant cross disciplinary mind, navigating effortlessly between cognitive psychology, computational neuroscience and artificial intelligence. Quick summary of the ads to sponsors the Jordan Harbage show and Magic Spoon Cereal. Please consider supporting the podcast by going to Jordan Harbinger Complex and also going to Magic Spoon dot com slash leks and using collects at checkout after you buy all of their cereal. Click the links, buy the stuff.

[00:00:40]

It's the best way to support this podcast and journeyman. If you enjoy this podcast, subscribe on YouTube or view it as fast as an app, a podcast, follow on Spotify, support on Patrón or connect with me on Twitter. Àlex Friedman spelled surprisingly without the E just F.R. ID man. As usual, although a few minutes of hours now and every naiades in the middle that can break the flow of the conversation, this episode is supported by the Jordan Harbinger show.

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Go to Jordan Hajigak complex. It's how he knows I sent you on that page. Subscribe to his podcast, an Apple podcast, Spotify, and you know where to look.

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I've been bingeing on his podcast. Jordan is a great interviewer and even a better human being. I recently listened to his conversation with Jack Barsky, former sleeper agent for the KGB in the 80s and author of Deep Undercover, which is a memoir that paints yet another interesting perspective on the Cold War era.

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Subscribe to his podcast how he knows I sent you. It's awesome. You won't regret it. This episode is also supported by magic spoon, low carb, keto friendly, super amazingly delicious cereal.

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I've been on Akito or very low carb diet for a long time now. It helps with my mental performance. It helps my physical performance. Even during this crazy push up, pull up challenge I'm doing, including the running. It just feels great. I used to love cereal. Obviously I can't have it now because most cereals have a crazy amount of sugar, which is terrible for you. So I quit it years ago. But Magic spoon, amazingly somehow is a totally different thing.

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How much of the human brain do you think we understand? I think we're at a weird moment in the history of neuroscience in the sense that there's a there I feel like we understand a lot about the brain at a very high level.

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But a very, very coarse level, when you say high level, what are you thinking? You're thinking functional? Yes, structurally. So in other words, what is what is the brain for? You know, what what what kinds of computation does the brain do? What kinds of behaviors would we have to. Would we have to explain if we were going to look down at the mechanistic level? And at that level, I feel like we understand much, much more about the brain than we did when I was in high school, but but it's a very it's almost like we're seeing it through a fog.

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It's only at a very coarse level. We don't really understand what the the neuronal mechanisms are that underlie these computations. We've gotten better at saying, you know, what are the functions that the brain is computing that we would have to understand, you know, if we were going to get down to the neuronal level.

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And at the other end of the spectrum, we you know, in the last few years, incredible progress has been made in terms of technologies that allow us to see, you know, actually literally see in some cases what's going on at the at the single unit level, even the dendritic level.

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And then there's this yawning gap in between. Oh, that's interesting. So at the high level. So that's almost a cognitive science. Yeah, yeah. And then at the neuronal level, that's neurobiology and neuroscience. Yeah. Just studying single neurons, the the the synaptic connections and all the dopamine, all of the kind of neurotransmitters.

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One blanket statement I should probably make is that.

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As I've gotten older, I have become more and more reluctant to make a distinction between psychology and neuroscience to me.

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The point of neuroscience is to study what the brain is for, if you if you if you're if you're a nephrologist and you want to learn about the kidney.

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You start by by saying, what is this thing for?

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Well, it seems to be for taking blood on one side that has metabolites in it that are that shouldn't be there, sucking them out of the blood while leaving the good stuff behind and then excreting that in the form of urine.

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That's what the kidney is for obvious. So the rest of the work is deciding how it does that.

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And this, it seems to me, is the right approach to take to the brain. You say, well, what is the brain for? The brain, as far as I can tell, is for producing behaviors from going it's for going from perceptual inputs to behavioral outputs and the behavioral outputs should be adaptive. So that's what psychology is about.

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It's about understanding the structure of that function. And then the rest of neuroscience is about figuring out how those operations are actually carried out at a at a mechanistic level.

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That's really interesting. But so unlike the kidney, the the brain, the gap between the electrical signal and behavior. So you truly see neuroscience as the science that that touches behavior, how the brain generates behavior or how the brain converts raw visual information into understanding like and like, you basically see cognitive science, psychology and neuroscience is all one size.

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Yeah. Is that a personal statement? I said I'm hopeful. Is that is that a hopeful or realistic statement? So certainly you will be correct in your feeling in some number of years, but that number of years could be two hundred, three hundred years from now.

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Well, well, there's a is that aspirational or is that pragmatic engineering feeling that you have?

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It's it's both in the sense that this is what I. Hope and expect will. Bear fruit over the coming decades, but it's also pragmatic in the sense that I'm not sure what we're doing in either in either psychology or neuroscience, if that's not the framing. I don't I don't I don't know what it means to understand the brain. If there's no if part of the. Enterprise is not about understanding the behavior that's being produced. I mean, yeah, but I would compare it to maybe astronomers looking at the movement of the planets and the stars and without any interest of the underlying physics.

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Right. And I would argue that at least in the early days, there is some value to just tracing the movement of the planets and the stars without thinking about the physics too much, because it's such a big leap to start thinking about the physics before we even understand even the basic structural elements of. Oh, I agree with that. I agree. You're saying in the end the goal should be. Yeah, deeply understand. Well, right. And I, I think so.

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I thought about this a lot when I was in grad school because a lot of what I studied in grad school psychology and I found myself a little bit confused about what it meant to it seemed like what we were talking about a lot of the time were virtual causal mechanisms like, oh, well, you know, attentional selection, then select some object in the environment.

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And that is then passed on to the motor.

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You know, information about that is passed on to the motor system. But these are these are virtual mechanisms.

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These are their metaphors.

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They're you know, there's no they're not there's no reduction to there's no reduction going on in that conversation to some physical mechanism that, you know, which is really what it would take to fully understand, you know, how how behavior is rising.

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But the causal mechanisms are definitely neurons interacting. I'm willing to say that at this point in history. So in psychology, at least for me personally, there was this strange insecurity about trafficking in these metaphors, which we're supposed to explain the function of the mind if you can't ground them in physical mechanisms than what you know, what is the what is the explanatory validity of these explanations?

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And I I managed to I managed to soothe my own nerves by thinking about the history of genetics research. So I'm very far from being an expert on the history of this field.

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But I know enough to say that, you know, Mendelian genetics preceded, you know, Watson and Crick.

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And so there was a significant period of time during which people were, you know, productively investigating the structure of inheritance, using what was essentially a metaphor, the notion of a gene, you know, and what genes do this and genes do that. But, you know, where are the genes there? There's sort of an explanatory thing that we made up. And we we ascribe to them these causal properties. There's a dominant, there's a recessive.

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And then then they recombine and and then later there was a kind of blank there that was filled in with with a with a physical mechanism. That connection was made.

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But it was worth having that metaphor, because that's that gave us a good sense of what kind of cause what kind of causal mechanism we were looking for.

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And the fundamental metaphor of cognition, you said, is the interaction of neurons. Is that what is the metaphor?

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No, no, the metaphor the metaphors we use in in cognitive psychology are things like attention to the way that memory works.

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You know, I, I retrieve something from memory. You know, a memory retrieval occurs.

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What is the Hatano that's not that's not a physical mechanism that I can examine in its own right.

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But if we if if but it's still worth having that that metaphorical level. Yes.

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So yeah, I misunderstood actually. So the higher level abstractions is the metaphor that's most useful. Yes, but. But what about. So how does that connect to the. The idea that that arises from interaction of neurons, well, even is the interaction of neurons also not a metaphor to you?

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Is or is it literally like that's no longer a metaphor. That's that's already that's already the lowest level of abstractions that could actually be directly studied. Well. I'm hesitating because I think what I want to say could end up being controversial. So what I want to say is, yes, the interaction of the interactions of neurons. That's not metaphorical. That's a physical fact. That's that's that's where the causal interactions actually occur. Now, I suppose you could say, well, you know, even that is metaphorical relative to the quantum events that underlie.

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So, you know, I don't want to go down that rabbit hole. So turtles on top of turtles. But there is there is a there's a reduction that you can do.

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You can say these psychological phenomena are can be explained through a very different kind of causal mechanism, which has to do with neurotransmitter release. And and so what we're really trying to do in neuroscience writ large, you know, as I say, which for me includes psychology, is to take these. Psychological phenomena and map them onto neural events, I think. Remaining forever at the level of.

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Description that is natural for psychology, for me personally would be disappointing, I want to understand how mental activity arises from neural neural activity.

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But the converse is also true. Studying neural activity without any sense of what you're trying to explain to me feels like at best, groping around, you know, at random.

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Now, you've kind of talked about this bridging the gap between psychology and neuroscience, but do you think it's possible? Look, my love is like I fell in love with psychology and psychiatry in general with Freud. And when I was really young and I hope to understand the mind. And for me, understanding the mind, at least the young age before discovered I and even neuroscience was to. Is psychology and do you think it's possible to understand the mind without getting into all the messy details of neuroses like you kind of mentioned to you, it's appealing to try to understand the mechanisms at the lowest level.

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But do you think that's needed? That's required to understand how the mind works? That's an important part of the whole picture, but I would be the last person on Earth to suggest that. That reality renders psychology in its own right. Unproductive, I trained as a psychologist, I, I, I am fond of saying that I have learned much more from psychology than I have from neuroscience.

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To me, psychology is a hugely important discipline. And and one thing that warms my heart is that. Ways of ways of investigating behavior that have been native to cognitive psychology since it's, you know, dawn in the 60s are starting to become they're starting to become interesting to researchers for a variety of reasons.

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And that's been exciting for me to see him maybe talk a little bit about what's what you see is. Beautiful aspects of psychology, maybe limiting aspects of psychology. I mean, maybe just started off as a science, as a field to me was when I understood what psychology is analytical psychology like the way it's actually carried out is really disappointing to see two aspects. One is how few, how small the NT is, how many, how small the number of subjects is in studies.

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And two, it was disappointing to see how controlled the entire how how much it was in the lab, how it wasn't studying humans in the wild. There was no mechanism for studying humans in the wild. So that's why I became a little bit disillusioned into psychology. And then the modern world of the Internet is so exciting to me. The Twitter data or YouTube data of human behavior on the Internet becomes exciting because the NT grows and then in the wild grows.

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But that's just my narrow sense. Do you have us optimistic or pessimistic, cynical view of psychology? How do you see the field broadly?

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When I was in graduate school, it was early enough that. There was still a thrill in seeing that there were ways of. There were ways of doing experimental science that provided insight to the structure of the mind.

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One thing that impressed me most when I was at that stage in my education was neuropsychology looking at looking at analyzing the behavior of populations who had brain damage of different kinds and trying to understand what what the what the specific deficits were that arose from a lesion in a particular part of the brain and the kind of experimentation that was done and that's still being done to get answers in that context was so creative and it was so deliberate.

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You know, it was good science. An experiment answered one question but raised another. And somebody would do an experiment that answered that question. And you really felt like you were narrowing in on some kind of approximate understanding of what this part of the brain was for different examples from memory of what kind of aspects of the mind could be studied in this kind of way.

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Oh, sure.

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I mean, the very detailed neuropsychological studies of language, language function, looking at production and reception and the relationship between, you know, visual function, you know, reading and auditory and semantic. And there were these beaute and still are these beautiful models that came out of that kind of research that really made you feel like you understood something that you hadn't understood stood before about how, you know, language processing is organized in the brain.

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But having said all that, um.

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You know, I I think, you know, I think you are I mean, I agree with you that the cost of doing highly controlled experiments is that you buy construction, miss out on the richness and complexity of the real world.

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One thing that so I I was drawn into science by what in those days was called connection ism, which is, of course, the, you know, what we now call deep learning.

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And at that point in history, neural networks were primarily being used in order to model human cognition.

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They weren't yet really useful for industrial applications. So you always follow neural networks in biological form. Beautiful.

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Oh, neural networks were very concretely the thing that drew me into science. I was handed. Are you familiar with the PDP books from from the 80s?

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When I was I went to medical school before I went into science.

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And I really just think, wow, I also I also did a graduate degree in art history.

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So I kind of explore our history. I understand that's just a curious, creative mind. But medical school with the dream of what if we take that slight tangent? What did you what did you want to be a surgeon?

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I actually was quite interested in surgery. I was interested in surgery and psychiatry. And I thought that must be I must be the only person on the planet who had who was torn between those two fields.

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And I said exactly that to my advisor in medical school who who turned out I found out later to be a famous psychoanalyst. And and he said to me, no, no, it's actually not so uncommon to be interested in surgery and psychiatry. And he conjectured that the reason that people developed these these two interests is that both fields are about going beneath the surface and kind of getting into the kind of secret. I mean, maybe you understand this as someone who was interested in psychoanalysis in the States, there's sort of a you know, there's a cliche phrase that people use now, you know, like NPR, the secret life of blankety blank.

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Right. You know, and that was part of the thrill of surgery was seeing, you know, the secret you know, the secret activity that's inside everybody's abdomen and thorax.

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It's a very poetic way to connect it to two disciplines that are very, practically speaking, different from each other. That's for sure. That's for sure. Yes. So, uh, so how do we get on to medical school? So so I was in medical school and I I was doing a psychiatry rotation and my kind of advisor in that rotation asked me what I was interested in. And I said, well, maybe psychiatry. He said, why?

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And I said, well, I've always been interested in how the brain works. I'm pretty sure that nobody's doing scientific research that addresses my interests, which are I didn't have a word for it then, but I would have said about cognition. And he said, well, you know, I'm not sure that's true. You might you might be interested in these books. And he pulled down the the PDB books from his shelf and they were still Shrink-wrapped.

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He hadn't read them, but he handed to him that he said, you feel free to borrow these. And that was you know, I went back to my dorm room and I just, you know, read them cover to cover. And what's PDP parallel distributed processing, which was the one of the original names for Deep Learning.

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And so I apologize for the romanticized question. But what what idea in the space of neuroscience and space of the human brain is to you the most beautiful, mysterious, surprising what what had always fascinated me, even when I was a pretty young kid, I think, was the.

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The the paradox that lies in the fact that the brain is so mysterious and so it seems so distant, but at the same time it's responsible for the the the the full transparency of everyday life.

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It's the brain is literally what makes everything obvious and familiar and and and there's always one in the room with you. Yeah.

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I used to teach when I taught at Princeton. I used to teach a cognitive neuroscience course. And the very last thing I would say to the students was, you know.

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People often when people think of scientific inspiration, the metaphor is often will look to the stars, you know, the stars will inspire you to wonder at the universe and, you know, think about your place in it and how things work. And and I'm all for looking at the stars, but I've always been much more inspired. And my sense of wonder comes from the not from the distant, mysterious stars, but from the extremely intimately close brain. Yeah.

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There's something just endlessly fascinating to me about that.

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The like Jessica said, the the one is close and yet distant in terms of our understanding of it. Do you are you also. Captivated by the fact that this very conversation is happening because two brains are communicating because the I guess what I mean is the subjective nature of the experience, if you can take a small tension into the the mystical of it, the consciousness or or when you are saying you're captivated by the idea of the brain, are you talking about specifically the mechanism of cognition or are you also just like at least for me, it's almost like paralyzing the beauty and the mystery of the fact that it creates the entirety of the experience, not just the reasoning capability, but the experience.

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Well. I definitely resonate with that, that latter thought and. I, I often find. Discussions of artificial intelligence to be disappointingly narrow, you know, speaking as someone who has always had an interest in in in art is just going to go there because it sounds like somebody who has an interest in art.

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Yeah, I mean, there, there, there there are many layers to, you know, to full bore human experience. And in some ways it's not enough to say, oh, well, don't worry.

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You know, we're talking about cognition, but we'll add emotion, you know.

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Yeah.

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There's there's there's an incredible scope to what humans go through in every moment and.

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And yes, so that's part of what fascinates me is that is that our brains are producing that. But at the same time, it's so mysterious to us how, like we literally our brains are literally in our heads producing this.

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And yet and yet there's it's so mysterious to us. And so in the scientific challenge of getting at the actual explanation for that is so overwhelming. That's just I don't know that certain people have fixations on particular questions and that's always just always been mine.

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Yeah, I would say the poetry that is fascinating and I'm really interested in natural language as well. And when you look at artificial intelligence community, it always saddens me how much we need try to create a benchmark for the community together around how much of the magic of language is lost.

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When you create a benchmark that there is something we talk about, experience the music, the language, the wit, the something that makes a rich experience, something that would be required to pass the spirit of the Turing test is lost in these benchmarks. And I wonder how to get it back in, because it's very difficult. The moment you tried to do like real good, rigorous science, you lose some of that magic when you try to study cognition in a rigorous scientific way.

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Feels like you're losing some of the magic, the seeing cognition and mechanistic way that I feel at this stage in our history. Well, OK, I agree with you.

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But at the same time, one one thing that I found really exciting about that first wave of deep learning models in cognition was.

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There was the fact that the people who are building these models were focused on the richness and complexity of human cognition, so an early debate in cognitive science, which I sort of witnessed as a grad student, was about something that sounds very dry, which is the formation of the past tense.

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But there were these two camps.

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One said, well, the the mind encodes certain rules. And it also has a list of exceptions because, of course, the rule is add.

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But that's not always what you do. So you have to have a list of exceptions.

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And and then there were the connection lists who, you know, evolved into the deep learning people who said, well, you know, if you look carefully at the data, if you look at actually look at corpora like language corpora, it's it turns out to be very rich because, yes, there are there are there's a you know, the there most verbs that and, you know, you just tack on and then there are exceptions.

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But there are also there's also there are there are rules that there's the exceptions aren't just random. There are certain clues to which which which verbs should be exceptional. And then there are exceptions to the exceptions.

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And there was a word that was kind of deployed in order to capture this, which was quasi irregular.

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In other words, there are rules, but it's it's messy. And there there's there structure, even among the exceptions. And and it would be yeah, you could try to write that. We could try to write down the structure in some sort of closed form, but really the right way to understand how the brain is handling all this. And by the way, producing all of this is to build a deep neural network and trained it on this data and see how it ends up representing all of this richness.

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So the way that deep learning was deployed in cognitive psychology was that was the spirit of it. It was about that richness. And that's something that I always found very, very compelling.

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Still do is is there something especially interesting and profound to in terms of our current deep learning neural network, artificial neural network approaches and the whatever we do understand about the biological neural networks and our brain is there. There's some there's quite a few differences. Are some of them to you either interesting or perhaps profound in terms of in terms of the gap?

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We might want to try to close in trying to create a human level intelligence?

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What I would say here is something that a lot of people are saying, which is that. One seeming limitation of the systems that we're building now is that they lack the kind of flexibility, the readiness to turn on a dime when when the context calls for it. That is so characteristic of human behavior.

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So is that connected to you, to the language aspect of the neural networks that are in our brain? Is that connected to is that closer to the cognitive science level of.

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Now, again, see, like my natural inclination is to separate into three disciplines of of neuroscience, cognitive science and psychology. And you've already kind of shut that down by saying you're kind of see them as separate. But just to look at those layers, I guess, where is there something about the lowest layer of the way the neural neurons interact that is profound to you in terms of this difference to the artificial neural networks? Or is all the the key differences at a higher level of abstraction?

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One thing I often think about is that. You know, if you take an introductory computer science course and they are introducing you to the notion of turning machines, one way of articulating what the significance of a Turing machine is is that it's a machine emulator. It it can emulate any other machine. And that that to me, you know, that that kind of that way of looking at a Turing machine, you know, really sticks with me, I think of humans as maybe sharing in some of that character or capacity limited.

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We're not talking machines, obviously, but we have the ability to adapt behaviors that are very much unlike anything we've done before. But there's some basic mechanism that's implemented in our brain that allows us to run run software.

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But you just in that point, you mentioned it to a machine, but nevertheless, it's fundamentally our brains are just computational devices. In your view, is that what you're getting like is I was a little bit unclear to this line. You Drew, is is there any magic in there or is it just basic computation? I'm happy to think of it as just basic computation, but mind you, I won't be satisfied until somebody explains to me how what the basic computations are that are leading to the full richness of human cognition.

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Yes. I mean, it's not going to be enough for for me to, you know, understand what the computations are that allow people to do arithmetic or play chess.

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I want I want the whole the whole, you know, the whole thing.

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And a small tangent, because you kind of mentioned coronavirus, this group behavior.

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Is that is there something interesting to your search of understanding the human mind where the behavior of large groups of just behavior of groups is interesting? You know, seeing that as a collective mind, as a collective intelligence, perhaps seeing the groups of people as a single intelligent organism, especially looking at the reinforcement learning work you've done recently.

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Well, yeah, I can't. I can't. I mean, I, I have the I have the the honor of working with a lot of incredibly smart people. And I wouldn't want to take any credit for for leading the way on the the multi agent work that's come out of out of my group or mine lately. But I do find it fascinating. And I mean, I think they are you know, I think it can't be debated.

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You know, human behavior arises within communities. That just seems to me self-evident.

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But to me. So it is self-evident, but that seems to be a profound aspects of something that created that was like if you look at like 2001, A Space Odyssey when the monkeys touched. Yeah. Like that's the magical moment.

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I think you've Ahari argues that the ability of our large numbers of humans to hold an idea, to converge towards idea together, like you said, shaking and bumping elbows somehow converge like without even like like without you know, without being in a room all together.

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Just kind of this distributed convergence towards an idea over a particular period of time seems to be fundamental to to just every aspect of our cognition, of our intelligence, because humans will talk about reward. But it seems like we don't really have a clear objective function under which we operate, but we all kind of converge towards one somehow. And that that to me, is always been a mystery that I think is somehow productive for also understanding A.I. systems. But I guess I guess that's the next step.

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The first step is try to understand the mind. Well, I don't know.

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I mean, I think there's something to the argument that that kind of botnet, like strictly bottom up approach is wrongheaded. In other words, you know, there are there are basic phenomena that, you know, you know, basic aspects of human intelligence that, you know, can only be understood in in the context of groups. I'm perfectly open to that. I've never been particularly convinced by the notion that we should be we should consider intelligence to in here at the level of communities.

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I, I don't know why. I just I'm sort of stuck on the notion that the basic unit that we want to understand is individual humans. And if if we have to understand that in the context of other humans, fine.

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But for me, intelligence is just I'm stubbornly I stubbornly define it as something that is, you know, an aspect of an individual human. That's just my I don't know, I'm with you. But that could be the reductionist dream of a scientist because you can understand a single human. It also is very possible that intelligence can only arise when there's multiple intelligences, when there's multiple sort of it's a sad thing if that's true, because it's very difficult to study.

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But if if it's just one human, that one human will not be homosapien would not become that intelligent. That's a. There's a possibility I'm with you. One thing I will say along these lines is that I think. I think a serious effort to understand human intelligence. And maybe to build a human like intelligence needs to pay just as much attention to the structure of the environment as to the structure of the. You know, the the cognizant system, whether it's a brain or an eye system, that's one thing I took away actually from my early studies with the pioneers of neural network research, people like Jay McClelland and John Cohen.

[00:38:49]

You know, the the structure of cognition is really. It's only only partly a function of the the the the architecture of the brain and the learning algorithms that it implements, what it's really a function, what it what really shapes it is the interaction of those things with the structure of the world in which those things are embedded.

[00:39:13]

Right. And that's especially important for the main, most clear and reinforcement learning where a simulated environment is. You can only learn as much as you can simulate. And that's what made with deep mind, made very clear with the other aspect of the environment, which is the soft play mechanism of the other agent, of the competitive behavior, which the other agent becomes the environment essentially. Yeah. And that's I mean, one of the most exciting ideas in Asia is the self play mechanism that's able to learn successfully.

[00:39:45]

So there you go. There's a there's a thing where competition is essential for learning. Yeah. At least in that context. So if we can step back into another sort of beautiful world, which is the actual mechanics, the dirty mess of it, of the human brain, is is there something for people who might not know? Is there something you can comment on or describe the key parts of the brain that are important for intelligence or just in general, what are the different parts of the brain that you're curious about that you've studied and that are just good to know about when you're thinking about cognition?

[00:40:23]

Well, my area of expertise, if I have one, is prefrontal cortex, so units that will do it depends on who you ask.

[00:40:38]

The technical definition is, is anatomical.

[00:40:43]

There are there are parts of your brain that are responsible for motor behavior and they're very easy to identify.

[00:40:52]

And the region of your cerebral cortex, the sort of outer crust of your brain that lies in front of those is defined as the prefrontal cortex.

[00:41:06]

And when you say anatomical, sorry to interrupt. So that's referring to sort of the geographic region as opposed to some kind of functional definition.

[00:41:17]

Exactly.

[00:41:17]

So this is kind of the coward's way out. I'm telling you what the prefrontal cortex is just in terms of like what part of the real estate it occupies. The thing in the front of the. Yeah, exactly.

[00:41:28]

And and in fact, the early history of, you know, the neuroscientific investigation of what this front part of the brain does is sort of funny to read because, you know.

[00:41:47]

It was really it was really World War One that started people down this road of trying to figure out what different parts of the brain, the human brain do in the sense that there were a lot of people with brain damage who came back from the war with brain damage. And that provided as tragic as that was, it provided an opportunity for scientists to try to identify the functions of different brain regions. And that was actually incredibly productive. But one of the frustrations that neuropsychologist faced was they couldn't really identify exactly what the deficit was that arose from damage to this these most, you know, kind of frontal parts of the brain.

[00:42:25]

It was just a very difficult thing to, you know, to, you know, to pin down.

[00:42:30]

There were a couple of neuropsychologists who identified through through a large amount of clinical experience and close observation.

[00:42:40]

They started to put their finger on a syndrome that was associated with frontal damage.

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Actually, one of them was a Russian neuropsychologist named Luria, who, you know, students of cognitive psychology still read.

[00:42:53]

And and what he started to figure out was that the frontal cortex was somehow involved in flexibility, the in in in guiding behaviors that required someone to override a habit or to do something unusual or to change what they were doing in a very flexible way from one moment to another.

[00:43:19]

So focused on the new experiences. And so so the way your brain processes and acts in new experiences. Yeah.

[00:43:28]

What later helped bring this function into better focus was a distinction between controlled and automatic behavior or to in other literatures. This is referred to as habitual behavior versus goal directed behavior. So it's very, very clear that the human brain has pathways that are dedicated to habits, to things that you do all the time, and they need to be automated so that they don't require you to concentrate too much. So that leaves your cognitive capacity for you to do other things.

[00:44:07]

Just think about the difference between driving when you're learning to drive versus driving after you're fairly expert.

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There are brain pathways that slowly absorb those frequently performed behaviors so that they can be habits so that they can be automatic.

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So that's kind of like the purest form of learning, I guess, is happening there, which is why, I mean, this is kind of jumping ahead, which is why that perhaps is the most useful for us to focusing on and trying to see how artificial intelligence systems can learn. Is that the way it's interesting?

[00:44:45]

I do think about this distinction between controlled and automatic or goal directed and habitual behavior a lot in thinking about where we are in AI research.

[00:44:58]

But. But just to finish finish the the kind of dissertation here, the the role of the front of the prefrontal cortex is generally understood these days, sort of in contradistinction to that habitual domain.

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In other words, the prefrontal cortex is what helps you override those habits. It is what allows you to say, well, what I usually do in this situation is X, but given the context, I probably should do Y. I mean, the elbow bump is a great example.

[00:45:34]

If, you know, reaching out and shaking hands is probably a habitual behavior. And it's the prefrontal cortex that allows us to bear in mind that there's something unusual going on right now. And in this situation, I need to not do the usual thing.

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The kind of behaviors that Luria reported and he built tests for detecting these kinds of things were exactly like this. So in other words, when I stick out my hand. I want you instead to present your elbow, a patient with frontal damage would have a great deal of trouble with that. You know, somebody proffering their hand would elicit, you know, a handshake.

[00:46:16]

The prefrontal cortex is what allows us to say, hold on, hold on. That's the usual thing.

[00:46:21]

But I'm I have the ability to bear in mind even very unusual contexts and to reason about what behavior is appropriate there just to get a sense is, are us humans special in the presence of the prefrontal cortex?

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Do mice have a prefrontal cortex? Do other mammals that we can study? If if no, then how do they integrate new experiences?

[00:46:47]

Yeah, that's a that's a really tricky question and a very timely question because we have. Revolutionary new technologies for monitoring, measuring and also causally influencing neural behavior in mice and fruit flies. And these techniques are not fully available even for studying brain function in in monkeys, let alone humans. And so it's a it's a very sort of for me at least, a very urgent question whether the kinds of things that we want to understand about human intelligence can be pursued in these other organisms.

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And, you know, to put it briefly, there's disagreement.

[00:47:45]

You know, people who study fruit flies will often tell you, hey, fruit flies are smarter than you think. And they'll point to experiments where fruit flies were able to learn new behaviors. We're able to generalize from one stimulus to another in a way that suggests that they have abstractions that guide their generalisation. I've had many conversations in which I will start by observing, you know, recounting some.

[00:48:18]

Some observation about mouse behavior where it seemed like mice were taking an awfully long time to learn a task that for a human would be profoundly trivial, and I will conclude from that that mice really don't have the cognitive flexibility that we want to explain and that a mouse researcher will say to me, well, you know, hold on.

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That experiment may not have worked because you asked a mouse to deal with stimuli and behaviors that were very unnatural for the mouse.

[00:48:51]

If instead you kept the logic of the experiment the same, but put, you know, kind of put it in a you know, presented it the information in a way that aligns with what mice are used to dealing with in their natural habitats, you might find that a mouse actually has more intelligence than you think. And then they'll go on to show you videos of mice doing things in their natural habitat, which seem strikingly intelligent, dealing with physical problems.

[00:49:19]

You know, I have to drag this piece of food back to my, you know, back to my lair, but there's something in my way and how do I get rid of that thing?

[00:49:27]

So I think I think these are open questions to put it, you know, to some that up and then taken a small step back related to that is you kind of mentioned we're taking a little shortcut by saying it's a geographic geographic part of the the prefrontal cortex is a region of the brain. But if we what's your sense in a bigger philosophical view, prefrontal cortex in the brain in general, they have a sense that it's a set of subsystems in the way we've kind of implied that they're pretty distinct or to what degree is it that or to what degree is it a giant interconnected mess where everything kind of does everything and is impossible to disentangle them?

[00:50:12]

I think there's overwhelming evidence that there's functional differentiation, that it's clearly not the case that all parts of the brain are doing the same thing.

[00:50:24]

This follows immediately from the kinds of studies of brain damage that we were chatting about before. It it's obvious from what you see, if you stick an electrode in the brain and measure what's going on at the level of neural activity. Having said that.

[00:50:45]

There are two other things to add, which kind of. I don't know, maybe tug in the other direction, one is that. It's when you look carefully at functional differentiation in the brain, what you usually end up concluding, at least this is my observation of the literature, is that the the differences between regions are graded rather than being discrete.

[00:51:12]

So it doesn't seem like it's easy to divide the brain up into true modules where you know, that are, you know, that have clear boundaries and that have, you know, like I didn't like clear channels of communication between them instead lies to the prefrontal cortex.

[00:51:35]

Yeah, yeah, yeah.

[00:51:36]

The prefrontal cortex is made up of a bunch of different subregions, the, you know, the the functions of which are not clearly defined and which the borders of which seem to be quite vague.

[00:51:49]

And then then there's another thing that's popping up in very recent research, which, you know, which involves application of these new techniques, which there are a number of studies that suggest that parts of the brain that we would have previously thought were quite.

[00:52:11]

Focused in their function are actually carrying signals that we wouldn't have thought would be there, for example, looking in the primary visual cortex, which is classically thought of as basically the first critical weigh station for processing visual information. Basically, what it should care about is, you know, where are the edges in this scene that I'm viewing?

[00:52:34]

It turns out that if you have enough data, you can recover information from primary visual cortex about all sorts of things like, you know, what what behavior the animal is engaged in right now and what what how much reward is on offer in the task that it's pursuing.

[00:52:48]

So it's clear that even even regions whose function is pretty well defined at a core screen are nonetheless carrying some information about information from very different domains.

[00:53:04]

So, you know, the history of neuroscience is sort of this oscillation between the two views that you articulated, the kind of modular view and then the big, you know, mush view. And, you know, I think I guess we're going to end up somewhere in the middle, which is which is unfortunate for our understanding, because there's something about our conceptual system that finds it's easy to think about a modularized system and easy to think about a completely undifferentiated system.

[00:53:32]

But something that kind of lies in between is confusing.

[00:53:37]

But we're going to have to get used to it, I think, unless we can understand deeply the lower level mechanism of neuronal communication. So, yeah. So on that on that topic, you kind of mentioned information just to get a sense. I imagine something that there's still mystery and disagreement on is how does the brain carry information and signal? Like what in your sense is the basic mechanism of communication in the brain?

[00:54:03]

Well, I, I, I guess I'm old fashioned in that I consider the networks that we use in deep learning research to be a reasonable approximation to, you know, the the mechanisms that carry information in the brain.

[00:54:19]

So the usual way of articulating that is to say what really matters is a rate code. What matters is how, how how quickly is an individual neuron spiking, how, you know, what's the frequency at which it's spiking? Is the timing of the spike. Yeah. Is it is it firing fast or slow? Let's let's put a number on that. And that number is enough to capture what what neurons are doing.

[00:54:43]

There's you know, there's. Still, uncertainty about whether that's an adequate description of how information is is transmitted within the brain there.

[00:54:57]

You know, there there are studies that suggest that the precise timing of spikes matters. There are studies that suggest that there are computations that go on within the dendritic tree, within a neuron that are quite rich and structured, and that really don't equate to anything that we're doing in our artificial neural networks.

[00:55:20]

Having said that, I feel like we can get. I feel like I feel like we're getting somewhere by sticking to this high level of abstraction, just the rate. And by the way, we're talking about the electrical signal that I remember reading some vague paper somewhere recently where the mechanical signal, like the vibrations or something of the of the neurons also communicate.

[00:55:45]

And I haven't seen that. But there's somebody was arguing that the the electrical signal this is in nature paper, something like that, where the electrical signal is actually a side effect of the mechanical signal. But I don't think that changes the story. But it's almost the interesting idea that there could be a deeper it's like it's always like in physics with quantum mechanics. There's always a deeper story that could be underlying the whole thing. But you think it's basically the rate of spiking that gets us that's like the lowest hanging fruit that can get us really far.

[00:56:21]

This is a this is a classical view. I mean, this is this is this is not the only way in which this stance would be controversial is, you know, in the sense that there are there are members of the neuroscience community who are interested in alternatives. But this is really a very mainstream view. The way that neurons communicate is that neurotransmitters arrive, you know, at at a at you know, they wash up on a neuron.

[00:56:50]

The neuron has receptors for those transmitters. The the the the meeting of the transmitter with these receptors changes the voltage of the neuron. And if enough voltage change occurs, then a spike occurs. Right. One of these discrete events and it's that spike that is conducted down the axon and leads to neurotransmitter released. This is just this is just like neuroscience 101. This is like the way the brain is supposed to work.

[00:57:16]

Now, what we do when we build artificial neural networks of the kind that are now popular in the A.I. community, is that we don't worry about those individual spikes. We just worry about the frequency at which those spikes are being generated. And the you know, we consider you know, people talk about that as the activity of a neuron.

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And so the the activity of units in a deep learning system is, you know, broadly analogous to the spike rate of a neuron there.

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There are people who who believe that there are other forms of communication in the brain.

[00:57:56]

In fact, I've been involved in some research recently that suggests that the voltage, the voltage fluctuations that occur in populations of neurons that aren't, you know, that are sort of below the level of of spike production may be important for for communication, but I'm still pretty old school in the sense that I think that the things that we're building in A.I. research constitute reasonable models of how a brain would work.

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Let me ask just for fun, a crazy question, because I can. Do you think it's possible we're completely wrong about the way this basic mechanism of neuronal communication, that the information stored is some very different kind of way in the brain?

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Oh, heck, yes. I look, I wouldn't be a scientist if I didn't think there was any chance we were wrong.

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But but I mean, if you look if you look at the history of deep learning research as it's been applied to neuroscience, of course, the vast majority of deep learning research these days isn't about neuroscience.

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But, you know, if you go back to the 1980s, there's, you know, sort of an unbroken chain of research in which a particular strategy is taken, which is, hey, let's train a deep a deep learning system.

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Let's train a multilayer neural network on this task that we trained our rat on or our monkey on or this human being on.

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And then let's look at what the units deep in the system are doing and let's ask whether what they're doing resembles what we know about what neurons deep in the brain are doing and over and over and over and over.

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That strategy works in the sense that the learning algorithms that we have access to, which typically center on back propagation, they give rise to, you know, patterns of activity, patterns of response, patterns of like neuronal behavior in these in these artificial models that look hauntingly, hauntingly similar to what you see in the brain.

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And, you know, is that I mean, that's a coincidence. Like at a certain point, it starts looking like such coincidence is unlikely to not be deeply meaningful.

[01:00:20]

Yeah, yeah. That's yeah.

[01:00:22]

The circumstantial evidence is overwhelming, but it could be always open to a total of flipping a table.

[01:00:27]

Yeah, of course. So you have co-authored several recent papers that sort of weave beautifully between the world of neuroscience and artificial intelligence. And this maybe if we could just try to dance around and talk about some of them, maybe try to pick up interesting ideas that jump to your mind from memory. So maybe looking at who we're talking about, the prefrontal cortex, the twenty eighteen, I believe, paper called the prefrontal cortex is a matter of reinforcement learning system.

[01:00:59]

What is there a key idea that you can speak to from that paper? Yeah, I mean, the key idea is about metal learning, so what is metal learning? Metal learning is by definition. A situation in which. You have a learning algorithm. And the learning algorithm operates in such a way that it gives rise to another learning algorithm in the earliest applications of this idea, you had one learning algorithm sort of adjusting the parameters on another learning algorithm.

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But the case that we're interested in this paper is one where you start with just one learning algorithm and then another learning algorithm kind of emerges out of out of thin air.

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I can say more about what I mean by that. I don't mean to be different, but that's the idea of metal learning. It relates to the old idea in psychology of learning to learn situations where you you you have experiences that make you better at learning something new. Like a familiar example would be learning a foreign language the first time you learn a foreign language. It may be quite laborious and disorienting and and novel, but if let's say you've learned to do foreign languages, the third foreign language obviously is going to be much easier to pick up.

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And why?

[01:02:33]

Because you've learned how to learn. You know, how this goes. You know, OK, I'm going to have to learn how to conjugate. I'm going to.

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But that's a that's a simple form of metal learning in the sense that there's some slow learning mechanism that's giving that's helping you kind of update your fast learning mechanism that makes.

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So how from from our understanding, from the psychology world, from neuroscience, our understanding how learning works might work in the human brain. What what lessons can draw from that that we can bring into the artificial intelligence world?

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Well, yeah. So we the origin of that paper was in I work that that we were doing in my group. We were we were looking at what happens when you train a recurrent neural network using standard reinforcement learning algorithms.

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But you train that network not just in one task, but you train it in a bunch of interrelated tasks.

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And then you ask what happens when you give it yet another task in that sort of line of interrelated tasks?

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And and what we started to realize is that. A form of metal learning spontaneously happens in recurrent neural networks and in the simplest way to explain it is to say. A recurrent a recurrent neural network has a kind of memory in its activation patterns, its recurrent by definition, in the sense that you have units that connect to other units that connect to other units. So you have sort of loops of connectivity which allows activity to stick around and be updated over time.

[01:04:14]

In psychology, we call in neuroscience, we call this working memory. It's like actively holding something in mind and. And so that memory gives the recurrent neural network dynamics, the way that the activity pattern evolves over time is inherent to the connectivity of the neural network. So that's that's idea number one. Now, the dynamics of that network are shaped by the connectivity, by the synaptic weights in those synaptic weights are being shaped by this reinforcement learning algorithm that you're training the network with.

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So the punch line is if you train a recurrent neural network with a reinforcement learning algorithm that's adjusting its weights and you do that for long enough.

[01:05:04]

The activation dynamics will become very interesting. Right, so imagine imagine I give you a task where you have to press one button or another left button, a right button and some time, and there's some probability that I'm going to give you an Eminem. If you press the left button and there's some probability, I'll give you an Eminem if you press the other button and you have to figure out what those probabilities are just by trying things out. But as I said before, instead of just giving you one of these tasks, I give you a whole sequence, you know, I give you two buttons and you figure out which one is best.

[01:05:37]

And I go, good job. Here's here's a new box to new buttons. You have to figure out which one's best. Good job. Here's a new box, and every box has its own probability and you have to figure. So if you train a neural net, a recurrent neural network on that kind of sequence of tasks, what happens?

[01:05:52]

It seemed almost magical to us when we first started kind of realizing what was going on, the slow learning algorithm that adjusting the the synaptic weights.

[01:06:04]

Those slow synaptic changes give rise to a network dynamics that the cell that the dynamics themselves turn into a learning algorithm. So in other words, you can you can tell this is happening by just freezing the synaptic of saying, OK, no more learning, you're done. Here's a new box.

[01:06:22]

Figure out which button's best and the recurring on that work will do this just fine. There's no like it figures out which which button is best. It kind of transitions from exploring the two buttons to just pressing the one that it likes best in a very rational way.

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How is that happening? It's happening because the activity of the activity dynamics of the network have been shaped by the slow learning process that's occurred over many, many boxes. And so what's happened is that this slow learning algorithm that's slowly adjusting the weights is changing the dynamics of the network, the activity dynamics into its own learning algorithm.

[01:07:00]

And as we were as we were kind of realizing that this is the thing, it just so happened that the group that was working on this included a bunch of neuroscientists and it started kind of ringing a bell for us, which is to say that we thought this sounds a lot like the distinction between synaptic learning and activity, synaptic memory and activity based memory in the brain. And it also reminded us of recurrent connectivity that's very characteristic of prefrontal function. So this this is kind of why it's good to have people working on A.I. that know a little bit about neuroscience and vice versa, because we started thinking about whether we could apply this principle to to neuroscience.

[01:07:49]

And that's where the paper came from. So the kind of principle of the occurrence they can see in the prefrontal cortex, then you start to realize that it's possible to look for something like an idea of a learning to learn emerging from this learning process as long as you keep varying the environment sufficient. Exactly.

[01:08:12]

So so the kind of metaphorical transition we made to neuroscience was to think, OK, well, we know that the prefrontal cortex is highly recurrent. We know that it's an important locus for working memory, for active activation based memory. So maybe the prefrontal cortex supports reinforcement learning.

[01:08:32]

In other words, you know what is reinforcement learning? You take an action and you see how much reward you got. You update your policy of behavior. Maybe the prefrontal cortex is doing that sort of thing strictly in its activation patterns, its keeping around a memory in its activity patterns of what you did, how much reward you got, and it's using that that activity based memory as a basis for updating behavior.

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But then the question is, well, how did the prefrontal cortex get get so smart? In other words, how did it where did these activity dynamics come from? How did that program that's implemented in the current dynamics of the prefrontal cortex arise?

[01:09:11]

And one answer that became evident in this work was, well, maybe maybe the mechanisms that operate on the synaptic level, which we believe are mediated by dopamine, are responsible for shaping those dynamics.

[01:09:27]

So this may be a silly question, but because this kind of several temporal sort of classes of learning are happening and the learning to learn as it emerges, can you just can you keep.

[01:09:44]

Building stacks of learning to learn, to learn, learning, to learn, to learn, to learn, to learn, because it keeps. I mean, basically abstractions of more powerful abilities to generalize, of learning complex rules. Yeah. Or is this as overstretching the this kind of mechanism?

[01:10:03]

Well, one one of the one of the people in AEI who started thinking about meta learning from very early on, Juergen and Schmidt Hubber sort of cheekily suggested, I think it is it may have been in his his PhD thesis that we should think about Medda meta, meta, meta, Matamata learning, you know, that that's really that's really what's going to get us to true intelligence.

[01:10:30]

Certainly there's a poetic aspect to it, and it seems interesting and correct that that kind of level of abstraction would be powerful. But is that something you see in the brain? This kind of is it useful to think of learning in these Metamora melt away, or is it just a matter of learning?

[01:10:49]

Well, one thing that really fascinated me about this mechanism that we were starting to look at and, you know, other groups started talking about very similar things at the same time. And then a kind of explosion of interest in metal learning happened in the A.I. community shortly after that. I don't know if we had anything to do with that, but but I was gratified to see that a lot of people started talking about metal learning. One of the things that I like about the kind of flavor of metal learning that we were studying was that it didn't require anything special.

[01:11:23]

It was just if you took a system that had some form of memory.

[01:11:28]

That the function of which could be shaped by. Pick your RL algorithm, then this would just happen. Yes, I mean, there are a lot of forms of there are a lot of metal learning algorithms that have been proposed since then that are fascinating and effective in in their in their domains of application. But they're you know, they're engineered. They're things that we had to say, well, gee, if we wanted metal learning to happen, how would we do that?

[01:11:53]

Here's an algorithm that would.

[01:11:54]

But there's something about the kind of metal learning that we were studying that seemed to me special in the sense that it wasn't an algorithm. It was just something that automatically happened. If you had a system that had memory and it was trained with a reinforcement learning algorithm and. And in that sense, it can be as meta as it wants to be. There's no limit on how abstract the metal learning can get because it's not reliant on the human engineering, a particular metal learning algorithm to get there.

[01:12:28]

And that's I also I don't know, I guess I hope that that's relevant in the brain.

[01:12:35]

I think there's a kind of beauty in in in the ability of this emergent the emergent aspect of it.

[01:12:41]

Yeah, it's something engineered. Exactly. It's something that just it just happens in a sense. In a sense. You can't avoid this happening if you have a system that has memory.

[01:12:53]

And the function of that memory is shaped by reinforcement learning and this system is trained in a series of interrelated tasks. This is going to happen, you can't stop it, you know, as long as you have certain properties, maybe like a current structure to you have to have, it actually doesn't have to be recurrent neural network one.

[01:13:12]

A paper that I was honored to be involved with even earlier used to kind of slot based memory.

[01:13:19]

You remember the title just it was memory augmented neural networks, I think.

[01:13:25]

I think the title is Meta Learning in Memory, Augmented Neural Networks in.

[01:13:32]

And, you know, it was the same exact story.

[01:13:34]

You know, if you have a system with memory here, it was a different kind of memory. But the function of that memory is. Shaped by reinforcement learning here, it was the you know, the reads and writes that occurred on this slot based memory, this this will just happen.

[01:13:55]

And and so this but this brings us back to something I was saying earlier about the importance of the environment. This this will happen if the system is being trained in a setting where there's like a sequence of tasks that all share some abstract structure. Sometimes talk about task distributions.

[01:14:16]

And that's something that's very obviously true of the world that humans inhabit.

[01:14:23]

We're we're constantly like, if you just kind of think about what you do every day, you never you never do exactly the same thing that you did the day before.

[01:14:34]

But everything that you do is sort of has a family resemblance at share structure with something that you did before. And so, you know, the real world is sort of.

[01:14:45]

You know, saturated with this kind of this property, it's, you know, endless variety with endless redundancy, and that's the setting in which this kind of learning happens and does seem like we're just so good at finding just like in this emergent phenomenon you describe, we're really good at finding that redundancy, finding those similarities, the family resemblance. Some people call it sort of what is it? Billy Mitchell is talking about analogies. So we're able to connect concepts together in this kind of way in in this same kind of automated, emergent way, which is there's so many echoes here of psychology and neuroscience and obviously now with reinforcement learning, with recurrent real networks at the core.

[01:15:35]

If we could talk a little bit about dopamine, you have really you're a part of coauthoring really exciting recent paper, very recent in terms of release on dopamine, a temporal difference learning. Can you describe the key ideas of that paper?

[01:15:52]

Sure, yeah. I mean, one thing I want to pause to do is acknowledge my co-authors on actually both of the papers we're talking about. So the I'll just I'll certainly post all their names. OK, wonderful. Yeah.

[01:16:03]

Because I you know, I I'm I'm sort of abashed to be the spokesperson for these papers when I had such amazing collaborators on both. So it's it's a comfort to me to know that you'll you'll acknowledge that this is an incredible team nevertheless.

[01:16:17]

Oh yeah. It's such a it's so much fun. And and in the case of the the dopamine paper, we also collaborated with now cheat at Harvard, who, you know, obviously a paper simply wouldn't have happened without him.

[01:16:29]

But so so you were asking for like a thumbnail sketch of.

[01:16:34]

Yes. A thumbnail sketch or ideas or, you know, things the insights that continue continuing our kind of discussion here between neuroscience and I.

[01:16:44]

Yeah, I mean, this was another a lot of the work that we've done so far is taking ideas that have bubbled up in A.I. and, you know, asking the question of whether the brain might be doing something related, which I think on the surface sounds like something that's really mainly of use to neuroscience.

[01:17:06]

We see it also as a way of validating what we're doing on the side.

[01:17:12]

If we can gain some evidence that the brain is using some technique that we've been trying out in our eye work, that gives us confidence that, you know, it may be a good idea that it'll, you know, scale to rich, complex tasks, that it'll interface well with other mechanisms.

[01:17:32]

So you see is a two way road. Yeah, for sure. Because a particular paper is a little bit focused on from one to the from another, from your networks to neuroscience. Ultimately, the discussion, the thinking, the productive long term aspect of it is the two way road nature of the whole thing.

[01:17:50]

Yeah. I mean we've talked about the notion of a virtuous circle between A.I. and neuroscience and, you know, the way I see it.

[01:17:59]

That's always been there since the two fields, you know, jointly existed.

[01:18:07]

There have been some phases in that history when I was sort of a head. There are some phases when neuroscience was sort of ahead. I feel like given the burst of.

[01:18:17]

Innovation that's happened recently on the side eye is kind of ahead in the sense that there are all of these ideas that we you know, we, you know, for which it's exciting to consider that there might be neural analogs and neuroscience, you know, in a sense, has been focusing on approaches to studying behavior that come from, you know, are kind of derive from this earlier era of cognitive psychology.

[01:18:46]

And so in some ways fail to connect with some of the issues that we're grappling with in a I like how do we deal with large, complex environments?

[01:18:58]

But I you know, I think it's inevitable that this circle will keep turning and there will be a moment in the not too distant distant future when neuroscience is pelting A.I. researchers with insights that may change the direction of our work.

[01:19:15]

Just as just a quick human question is that you have these parts of your brain is very matter, but they're able to both think about neuroscience. And I you know, I don't often meet people like that. Do you think the amount of plasticity question, do you think a human being can be both good at AI and neuroscience is like what on the team, a deep mind? What kind of human can occupy these two realms? And is that something you see everybody should be doing, can be doing?

[01:19:51]

Or is it a very special few can kind of jump just simply to our history? I would think it's a special person that can major in art history and also consider being a surgeon otherwise known as a dilettante doti.

[01:20:06]

Yeah, easily distracted.

[01:20:09]

No, I, I. I think it does take a special kind of person to be truly world class at both A.I. and neuroscience, and I am not on that list.

[01:20:23]

I happen to be someone who who's interest in neuroscience and psychology involved using the kinds of modeling techniques that are now very central and A.I. and that sort of, I guess, bought me a ticket to be involved in all of the amazing things that are going on in my research right now.

[01:20:46]

I do know a few people who I would consider pretty expert on both fronts, and I won't embarrass them by naming them.

[01:20:53]

But there are there are like exceptional people out there who are like this.

[01:20:58]

The one the one thing that I find is a is a barrier to being truly world class on both fronts is is the just the complexity of the technology that's involved in both disciplines now.

[01:21:15]

So the the engineering expertise that it takes to to do, you know, truly front line hands on A.I. research is really, really considerable.

[01:21:27]

The learning curve of the tools, just like the specifics of just what the programming of the kind of tools necessary to collect the data, to manage the data, to distribute, to compute all that kind of stuff. Yeah. And on the neuroscience, I guess there would be all different sorts of tools. Exactly.

[01:21:42]

Especially with the recent explosion in, you know, in neuroscience methods.

[01:21:46]

So but but, you know, so having said all that, I think. I think the I think the best scenario for both neuroscience and A.I. is to have people who are interacting, who live at every point on the spectrum from exclusively focused on neuroscience to exclusively focused on the engineering side of A.I. But but to have those people, you know, inhabiting a community where they're talking to people who live elsewhere on the on the spectrum.

[01:22:22]

And I may be someone who's very close to the center in the sense that I have one foot in the neuroscience world and one foot in the world. And that central position, I will admit, prevents me at least someone with my limited cognitive capacity from being a truly, you know, having true technical expertise and in either domain.

[01:22:45]

But at the same time, I I at least hope that it's worthwhile having people around who can kind of see the connections, the community, the.

[01:22:55]

Yeah. The emergent intelligence of the community. Yeah. Yeah. It's nicely distributed is useful. OK. Exactly. Yeah.

[01:23:02]

So hopefully that I mean I've seen that work, I've seen that work out well at the mind there are, there are people who I mean even if you just focus on the A.I. work that happens at the mine, it's been a good thing to have some people around doing that kind of work whose PhDs are in neuroscience or psychology. Every every academic discipline has its.

[01:23:26]

Kind of blind spots and kind of unfortunate obsessions and it's metaphors and it's reference points and having some intellectual diversity is is really healthy.

[01:23:41]

People get each other unstuck. I think I see it all the time at Deep Mind. And, you know, I like to think that the people who bring some neuroscience background to the table or are helping with that.

[01:23:54]

So one of the one of them, I think probably the deepest passion for me. What I would say maybe we kind of spoke off Mike a little bit about it, but that that, I think is a blind spot for at least robotics and AI folks is human robot interaction, human agent interaction. And maybe do you have thoughts about how we reduce the size of that blind spot? Do you also share the feeling that not enough folks are studying this aspect of interaction?

[01:24:27]

Well, I I'm I'm actually pretty intensively interested in this issue now.

[01:24:33]

And there are people in my group who've actually pivoted pretty hard over the last few years from doing more traditional cognitive psychology and cognitive neuroscience to doing experimental work on human agent interaction.

[01:24:47]

And there are a couple of reasons that I'm pretty passionately interested in this. One is. It's kind of the outcome of having thought for a few years now about. What we're up to, what you what are we doing, like what is this what is this age I research for? So what does it mean to make the world a better place? I think I'm pretty sure that means making life better for humans. Yeah.

[01:25:19]

And so how do you make life better for humans? That's that's a proposition that when you look at it carefully and honestly is. Rather horrendously complicated, especially when the systems that you're. That you're building are learning systems, they're not, you're not. You know, programming something that you then introduced to the world and it just works as programmed like Google Maps or something, we're building systems that that learn from experience. So that typically leads to a safety questions.

[01:26:00]

How do we keep these things from getting out of control? How do we keep them from doing things that harm humans? And I mean, I hasten to say, I consider those hugely important issues.

[01:26:11]

And there are large sectors of the research community, a deep mind and of course, elsewhere, who are dedicated to thinking hard all day, every day about that.

[01:26:22]

But there's a there's I guess I guess I would say a positive side to this, too, which is to say, well. What would it mean to make human life better and how how can we imagine learning systems doing that? And and in talking to my colleagues about that, we reached the initial conclusion that. It's not sufficient to philosophize about that, you actually have to take into account how humans actually work and what humans want and the difficulties of knowing what humans want and the difficulties that arise when humans want different things.

[01:27:04]

And so human agent interaction has become quite a quite intensive focus of my group lately.

[01:27:13]

If for no other reason, that. In order to really address that that issue in an adequate way, you have to I mean, psychology becomes part of the picture and so there's a few elements there.

[01:27:27]

So if you focus on solving like the if you focus on the robotics problem, say ajai without humans in the picture is you're missing fundamentally the final step. When you do want to help human civilization, you eventually have to interact with humans. And when you create a learning system, just as you said, that will eventually have to interact with humans, the interaction itself has to be become has to become part of the learning process. Right. So you can't just watch.

[01:27:59]

Well, my sense is it sounds like your sense is you can't just watch humans to learn about humans. Yeah. You have to also be part of the human world. You have to interact with humans.

[01:28:08]

Yeah, exactly. And I mean, then questions arise that start imperceptibly but inevitably to slip beyond the realm of engineering. So questions like.

[01:28:21]

If you have an agent that can do something that you can't do. Under what conditions do you want that Egypt to do it so, you know, if you know, if I if I have a if I have a robot that can play.

[01:28:39]

Beethoven sonatas better than any human in the sense that the you know, the sensitivity, that the expressing the expression is just beyond what any human do I do.

[01:28:52]

I want to listen to that. Do I want to go to a concert and hear a robot play? These are these are these aren't engineering questions. These are questions about human preference and human culture and psychology bordering on philosophy. Yeah.

[01:29:06]

And then and then you start asking, well, well, even if we knew the answer to that, is it our place as AI engineers to build that into these agents?

[01:29:16]

Probably the agents should interact with humans.

[01:29:20]

Beyond the population of A.I. engineers and figure out what those humans want, and then, you know, when you start I referred this a moment ago, but even that becomes complicated because what if what if two what if two humans want different things and you have only one agent that's able to interact with them and try to satisfy their preferences, then you're into the realm of of of like economics and social choice theory and and even politics.

[01:29:50]

So there's a sense in which if you if you kind of follow what we're doing to its logical conclusion, then it goes beyond questions of engineering and technology and starts to shade imperceptibly into questions about what kind of society do you want? And actually that. Once once that dawned on me, I actually felt. I don't know what the right word is quite refreshed in my in my involvement in A.I. research is almost like this building.

[01:30:22]

This kind of stuff is going to lead us back to asking really fundamental questions about what's, you know, what is this like, what what's the good life and and who gets to decide.

[01:30:33]

And and, you know, you know, bringing in viewpoints from multiple subcommunities to help us shape the way that we live this. It's it's there's something.

[01:30:45]

It started making me feel like doing a A.I. research in in a fully responsible way would, you know, could potentially lead to a kind of like cultural renewal.

[01:31:00]

Yeah, it's the way to is the is the way to understand human beings that the individual at the societal level and maybe come away to answer all the silly human questions of the meaning of life and all the all those kinds of things.

[01:31:13]

Even if it doesn't even if it doesn't give us a way of answering those questions, it may force us back to thinking about thinking about, you know, and it might bring it might bring it might restore a certain, I don't know, a certain depth to or even, dare I say, spirituality to the way that, you know, to to to the world.

[01:31:34]

I don't know. Maybe that's too grandiose, but. Well, I don't I I'm with you. I think it's it's I will be the philosophy of the 21st century, the the way which will open the door.

[01:31:46]

I think a lot of er researchers are afraid to open that door of exploring the view, beautiful richness of the human agent interaction, human interaction. I'm really happy that somebody like you have opened that door.

[01:32:00]

And one thing one thing I often think about is, you know, the the usual the usual schema for thinking about human human interaction is this kind of dystopian, you know, oh, you know, our robot overlords.

[01:32:16]

And and again, I hasten to say, safety is usually and you know, I'm not saying we shouldn't be thinking about those risks totally on board for that.

[01:32:26]

But there's. Having said that, there's there's a I would often follows for me is the thought that, you know, there's another there's another kind of narrative that might be relevant, which is when we think of when we think of humans gaining more and more information about, you know, like human life, the narrative there is usually that they gain more and more wisdom and more, you know, they get closer to enlightenment and, you know, and they become more benevolent.

[01:33:00]

And, you know, like the Buddha is like the like that's that's a totally different narrative. And why isn't it the case that we imagine that the A.I. systems that we're creating are going to like they're going to figure out more and more about the way the world works and the way that humans interact and they'll they'll become beneficent? I'm not saying that will happen.

[01:33:17]

I'm not I'm not you know, I don't honestly expect that to happen without some careful setting things up very carefully. But it's another way things could go right. And yeah. And I would even push back on that. I personally believe that the the most trajectories, natural human trajectories will lead us towards progress. So for me, there is a kind of sense that most trajectories and the AI development will lead us into trouble to me and we overfocus on the worst case.

[01:33:54]

It's like in computer science. Theoretical computer science has been this focus on worst case analysis. There's something appealing to our human mind. At some lowest level. It's big. We don't want to be eaten by the tiger, I guess. So we want to do the worst case analysis. But the reality is that shouldn't stop us from actually building out all the other trajectories which are potentially leading to all the positive worlds, all the all the Enlightenment, this book, Enlightenment Now with Steven Pinker and so on.

[01:34:24]

This is looking generally at human progress. And there's so many ways that human progress can happen with AIDS. And I think you have to do that research. You have to do that work. You have to do the not just the safety work of the one worst case analysis. How do we prevent that?

[01:34:40]

But the the actual tools and the glue and the mechanisms of human interaction that would lead to all the positive actions and go, yes, super exciting area.

[01:34:53]

Yeah.

[01:34:54]

You know, we should be spending we should be spending a lot of our time saying what can go wrong.

[01:35:00]

I think it's harder to see that there's work to be done to bring into focus the question of what what it would look like for things to go right. Yeah, it's you know, that's not obvious.

[01:35:14]

And we wouldn't be doing this if we didn't have the sense there was huge potential. Right.

[01:35:19]

We're not doing this, you know. You know, for no reason. We we have a sense that ajai would be a major boon to humanity. But I think I think it's worth starting now, even when our technology is quite primitive, asking, well, exactly what would that mean?

[01:35:36]

We can start now with applications that are already going make the world a better place, like, you know, solving protein folding. You know, I think this deep mine has gotten heavy into science applications lately, which I think is, you know, you know, a wonderful, wonderful move for us to to be making.

[01:35:53]

But when we think about Adjei, when we think about building, you know, fully intelligent agents that are going to be able to, in a sense, do whatever they want, you know, we should start thinking about what do we want them to want.

[01:36:06]

But what what kind of world do we want to live in?

[01:36:09]

That's not an easy question.

[01:36:11]

And I think we just need to start working on it and even on the path to sort of it doesn't have to be a guy who was just intelligent agents that interact with us and help us enrich our own existence. On social networks, for example, recommender systems are very intelligent. There's so much interesting interaction that's yet to be understood and studied. And, you know, how how do you create I mean, Twitter's is struggling with this very idea. How do you create A.I. systems that increase the quality in the health of a conversation?

[01:36:41]

For sure. Yeah, it's a beautiful, beautiful human psychology question.

[01:36:45]

And how do you do that without without deception being involved, without manipulation, being involved, you know, maximizing human autonomy?

[01:37:00]

And how do you how do you make these choices in a democratic way? How do you how how do we how do we face the how do we begin?

[01:37:08]

I'm speaking for myself here.

[01:37:10]

How do we face the fact that it's a small group of people who have the skill set to build these kinds of systems.

[01:37:20]

But the you know, what it means to make the world a better place is something that we all have to be talking about.

[01:37:28]

Yeah, the. The world that we're trying to make a better place includes a huge variety of different kinds of people.

[01:37:37]

Yeah, how do we cope with that? This is this is a problem that has been discussed, you know, in in gorry, extensive detail in social choice theory.

[01:37:47]

You know, one thing I'm really enjoying about the recent direction work has taken some parts of my team is that, yeah, we're reading the literature, we're reading the neuroscience literature, but we've also started reading like economics and as I mentioned, social choice theory, even some political theory, because it turns out that it's you know, it all becomes relevant.

[01:38:07]

It all becomes relevant and.

[01:38:10]

But at the same time, we've been trying not to write. Philosophy papers, right, we've been trying not to write position papers, we're trying to figure out ways of doing actual empirical research that kind of take the first small steps to thinking about what it really means for humans with all of their complexity and contradiction and paradox, you know, to be to be brought into contact with these systems in a way that that really makes the world a better place.

[01:38:42]

As often reinforcement learning frameworks actually kind of allow you to do that machine learning. And so that's the exciting thing about it, is allows you to reduce the unsolvable problem, philosophical problem into something more concrete that you can get a hold of. Yeah.

[01:38:59]

And it allows you to kind of define the problem in some way that. Allows for. Growth in the system, that's sort of you know, you're not responsible for the details, right? You say this is generally what I want you to do and then learning takes care of the rest.

[01:39:16]

Of course, the safety issues are, you know, arise in that context. But I think also some of these positive issues arise in that context. What would it mean for an AI system to really come to understand what humans want?

[01:39:31]

And, you know, in with all of the subtleties of that. Right. You know, humans humans want help with certain things, but they don't want everything done for them. Right. There is part of part of the satisfaction that humans get from life is in accomplishing things.

[01:39:50]

So if there were devices around that did everything for me know, I often think of the movie Wally that's like dystopian in a totally different way as like the machines are doing everything for us. That's that's not what we wanted, you know.

[01:40:01]

Anyway, I just I find this, you know, this kind of opens up a whole landscape of research that feels affirmative. Yeah. And to me, it's one of the most exciting and it's wide open. Yeah. We have to because it's a cool paper. Talk about dopamine.

[01:40:16]

Oh yeah. OK, so I thought we were going to we were going to I was going to give you a quick summary is a quick summary of what's the title of the paper.

[01:40:27]

I think we called it a distributional distributional code for value in dopamine based reinforcement learning.

[01:40:36]

Yes. So that's another project that grew out of pure AI research.

[01:40:43]

A number of people that Deep Mind and a few other places had started working on a new version of reinforcement learning, the which was defined by taking something in traditional reinforcement learning and just tweaking it.

[01:40:59]

So the thing that they took from traditional reinforcement learning was a value signal. So at the at the center of reinforcement learning, at least most algorithms, is some representation of how well things are going, your expected cumulative future reward. And that's usually represented as a single number. So if you imagine a gambler in a casino and the gamblers thinking, well, I have this probability of winning such and such an amount of money and I have this probability of losing such and such an amount of money, the that situation would be represented as a single number, which is like the expected weighted average of all those outcomes.

[01:41:38]

And this new form of reinforcement learning said, well, what if we what if we generalize that to distributional representation? So now we think of the gambler as literally thinking, well, there's this probability that I'll win this amount of money and there's this probability that I'll lose that amount of money. And we don't reduce that to a single number.

[01:41:56]

And it had been observed through experiments, through just trying this out, that that that kind of distributional representation really accelerated reinforcement learning and led to better policies.

[01:42:11]

What's your intuition about? So we're talking about rewards. Yeah. So what's your intuition? Why that is? What is it?

[01:42:17]

Well, it's it's kind of a surprising historical note, at least surprised me when I learned it that this had been tried out in a kind of heuristic way. People thought, well, gee, what would happen if we tried? And then it had this empirically, it had this striking effect. And it was only then that people started thinking, well, gee, what?

[01:42:37]

Why, why, why? Why is this working?

[01:42:40]

And that's led to a series of studies just trying to figure out why it works, which is ongoing. But one thing that's already clear from that research is that one reason that it helps is that it drives richer representation learning. So if you imagine imagine two situations that have the same expected value, the same kind of weighted average value standard deep reinforcement learning algorithms are going to take those two situations.

[01:43:10]

And kind of in terms of the way they're represented internally, then squeeze them together, because the the thing that you're trying to represent, which is their expected value, is the same. So all the way through the system, things are going to be mushed together. But what if what if what if those two situations actually have different value distributions? They have the same average value, but they have different distributions of value in that situation. Distributional learning will will maintain the distinction between these two things.

[01:43:42]

So to make a long story short, distributional learning can keep things separate in the internal representation that might otherwise be conflated or squished together.

[01:43:52]

And maintaining those distinctions can be useful in in when the system is now faced with some other task where the distinction is important, if we look at the optimistic and pessimistic dopamine neurons. So first of all. What is dopamine and why is this why is this at all useful to the. To to think about in the artificial intelligence sense, but what do we know about dopamine in the human brain? What is what is it wise to use for? Why is an interesting what does it have to do with the prefrontal cortex and learning in general?

[01:44:27]

Yeah, so. Well, this this is also a case where there's a huge amount of detail and debate, but one one one currently prevailing idea is that the function of this neurotransmitter dopamine resembles a particular component of standard reinforcement learning algorithms, which is called the reward prediction error. So I was talking a moment ago about these value representations. How do you learn them? How do you update them based on experience?

[01:45:04]

Well, if you if you made some prediction about a future reward and then you get more reward than you were expecting, then probably retrospectively you want to go back and increase the the the the value representation that you attached to the that earlier situation.

[01:45:21]

If you got less reward than you were expecting, you should probably decrement that estimate.

[01:45:25]

And that's a process of temporal difference. Exactly. This is the central mechanism of temporal difference learning, which is one of several kind of, you know, kind of back the sort of the backbone of our armamentarium in our P&L.

[01:45:37]

And it was this connection between the reward prediction error and dopamine was was made in the in the 1990s.

[01:45:49]

And there's been a huge amount of research that, you know, seems to back it up, dopamine made to be doing other things.

[01:45:54]

But this is clearly at least roughly one of the things that it's doing.

[01:45:59]

But the usual idea was that dopamine was representing these reward prediction errors, again, in this kind of single.

[01:46:07]

No way that representing your surprise, you know, with a single number and in distributional reinforcement learning this this kind of new elaboration of the standard approach.

[01:46:20]

It's not only the value of the value function that's represented as a single number, it's also the reward prediction error. Mm hmm. And so.

[01:46:31]

What happened was that Will Dabney, one of my collaborators who was one of the first people to work on distributional temporal distance learning, talked to a guy in my group will escort Nelson, who's a computational neuroscientist, and said, gee, you know, is it possible that dopamine might be doing something like this distributional coding thing?

[01:46:50]

And they started looking at what was in the literature and then they brought me in.

[01:46:54]

We started talking to Now Okita, and we came up with some specific predictions about, you know, if the brain is using this kind of distributional coding, then in the tasks that now has studied, you should see this, this, this and this. And that's where the paper came from.

[01:47:07]

We kind of enumerated a set of predictions, all of which ended up being fairly clearly confirmed, and all of which leads to at least some initial indication that the brain might be doing something like this, distributional coding, that dopamine might be representing surprise signals in a way that is not just collapsing everything to a single number, but instead it's kind of respecting the the variety of future outcomes, if that makes sense.

[01:47:33]

So, yeah, so that's showing suggesting possibly that dopamine has a really interesting representation scheme for for in the human brain for its reward signal. Exactly. As fascinating as just that's another beautiful example of a revealing something that's about neuroscience potentially suggesting possibilities. Well, you never know.

[01:47:54]

So the minute you published a paper like that, the next thing you think is I hope that replicates like I hope I hope we see that same thing in other data sets.

[01:48:02]

But of course, several labs now are doing the follow up experiments, so we'll know soon. But it has been it has been a lot of fun for us to, you know, to take these ideas from A.I. and kind of bring them into neuroscience and see how far we can get.

[01:48:16]

So we kind of talked about it a little bit. But where do you see the field of neuroscience and artificial intelligence heading broadly? Like what are the possible exciting areas that you can see breakthroughs in the next let's get crazy, not just three or five years, but next 10, 20, 30 years. That would make you excited and perhaps you'll be part of. On the neuroscience side. There's a great deal of interest now in what's going on in AI and.

[01:48:56]

And at the same time. I feel like so neuroscience, especially the part of neuroscience that's focused on circuits and and systems, you know, kind of really mechanism focused, there's been this explosion in new technology and.

[01:49:20]

Up until recently, the the experiments that have exploited this technology have. Have not involved a lot of interesting behavior, and this is for a variety of reasons, you know, one of which is in order to employ some of these technologies, you actually have to if you're if you're studying a mouse, you have to head fix the mouse. In other words, you know, you have to immobilize the mouse. And so it's been it's been tricky to come up with ways of eliciting interesting behavior from a mouse that's that's restrained in this way.

[01:49:50]

But people have begun to create very interesting solutions to this, like virtual reality environments where the animal can kind of move a trackball and and and and as people have kind of begun to explore what you can do with these technologies, I feel like more and more people are asking, well, let's try to bring behavior into the picture. Let's try to like reintroduce behavior, which was supposed to be what this whole thing was about.

[01:50:19]

And I'm hoping that those two trends, the the kind of growing interest in behavior and the widespread widespread interest in what's going on in, I will come together to kind of open a new chapter in neuroscience research where there's a kind of a re a rebirth of interest in the structure of behavior and its underlying substrates, but that that research is being informed by computational mechanisms that we're coming to understand.

[01:50:52]

And I you know, if we can do that, then we might be taking a step closer to this utopian future that we were talking about earlier, where there's really no distinction between psychology and neuroscience. Neuroscience is about studying the mechanisms that underlie whatever it is the brain is for. And, you know, what is the brain for?

[01:51:14]

It's for behavior. I feel like we could I feel like we could maybe take a step toward that now if people are motivated in the right way.

[01:51:24]

We also asked I saw the neuroscience, cause you said neuroscience, that's right, and especially a like the mind are interested in both branches. So what about the engineering of intelligence systems? I think I think the one of the key challenges that a lot of people are seeing now in EHI is to build systems that have the kind of flexibility and the the kind of flexibility that humans have in two senses. One is that humans can be good at many things.

[01:51:59]

They're not just expert at one thing. And they're also flexible in the sense that they can switch between things very easily and they can pick up new things very quickly because they they very they very ably see what a new task has in common with other things that they've done.

[01:52:18]

And and that's something that our systems to, you know, blatantly do not have.

[01:52:26]

There are some people who like to argue that deep learning and DeParle are simply wrong for getting that kind of flexibility. I don't share that belief, but. The simple fact of the matter is we're not building things yet that do have that kind of flexibility and and I think the the the attention of a large part of the A.I. community is starting to pivot to that question. How do we get that?

[01:52:50]

That's going to lead to a focus on abstraction. It's going to lead to a focus on what in psychology we call cognitive control, which is the ability to switch between tasks, the ability to quickly put together a program of behavior that you've never executed before.

[01:53:08]

But, you know, makes sense for a particular set of demands. It's very closely related to what the prefrontal cortex does on the neuroscience side. So I think it's going to be an interesting an interesting new chapter.

[01:53:22]

So that's the reasoning side and cognition side. But let me ask the overromanticized question. Do you think we'll ever engineer and ajai system that we humans would be able to love and then would love us back? So have that level and depth of connection.

[01:53:43]

I love that question, and it it it relates closely to things that I've been thinking about a lot lately, you know, in the context of this human eye research, there's social psychology research in particular by Susan Fiske at Princeton.

[01:54:02]

In the department I used to wear I used to work where she she dissects human attitudes toward other humans into a sort of two dimensional, you know, a two dimensional, two dimensional scheme.

[01:54:17]

And one dimension is about ability.

[01:54:20]

You know, how how able how capable is is this other person. And but the other dimension is warmth. So you can imagine another person who's very skilled and capable but is very cold.

[01:54:36]

Right.

[01:54:37]

And you wouldn't you wouldn't really like highly you might have some reservations about that other person.

[01:54:43]

Right.

[01:54:44]

But there's also a kind of reservation that we might have about another person who who elicits in us or displays a lot of human warmth, but is not good at getting things done right, that the greatest esteem that we we reserve our greatest esteem really for people who are both highly capable and also quite warm.

[01:55:08]

Right. That's that's like the best of the best. I mean, I'm just this isn't a normative statement I'm making.

[01:55:14]

This is just an empirical is an empirical statement. This is what humans seem. This these are the two dimensions that people seem to kind of like along which people size other people up. And and in my research, we really focus on this capability thing. Like we want our agents to be able to do stuff. You know, this thing can go at a super human level. That's awesome.

[01:55:32]

And but that's only one dimension. What's what about the other dimension?

[01:55:36]

What would it mean for any system to be warm?

[01:55:41]

And, you know, I don't know, maybe there are easy solutions here. Like, we can put him put a face on our system. It's cute. Has big ears. I mean, that's probably part of it. But I think it also has to do with a pattern of behavior, a pattern of, you know, what would it mean for an A.I. system to display caring, compassionate behavior in a way that actually made us feel like it was for real?

[01:56:04]

Yeah, we didn't feel like it was simulated. We didn't feel like we were being duped to me that, you know, people talk about the Turing test or some some descendent of it. I feel like that's the ultimate Turing test. You know, is there a is there an AI system that can not only convince us that it knows how to reason and it knows how to interpret language, but that we're comfortable saying, yeah, that A.I. system is a good guy?

[01:56:32]

You know, I mean, on the warmth scale. Yeah, whatever warmth is, we kind of intuitively understand it. But we also want to be able to. Yeah, we don't understand it explicitly enough yet to be able to engineer it. Exactly. And that's and that's an open scientific question you kind of alluded to several times in the human interaction. That's the question that should be studied and probably one of the most important questions. And as humans, we humans are are so good at it.

[01:57:03]

Yeah. You know, it's not just weird. It's not just that we're born warm. You know, I suppose some people are are warmer than others given, you know, whatever genes they managed to inherit.

[01:57:12]

But there's also there's also there are also learned skills involved. Right?

[01:57:18]

I mean, there are ways of communicating to other people that you care, that they matter to you, that you're enjoying interacting with them. Right. And we learn these skills from one another.

[01:57:31]

And it's not out of the question that we could build engineered systems. I think it's hopeless, as you say, that we could somehow hand design these sorts of these sorts of behaviors.

[01:57:43]

But it's not out of the question that we could build systems that kind of. We we we instill in them something that sets them out in the right direction so that they they end up learning what it is to interact with humans in a way that's gratifying to humans. I mean, honestly, if that's not where we're headed. I want out. I think it's exciting as a scientific problem, just as you described. I honestly don't see a better way to end it than talking about warmth and love.

[01:58:18]

And I don't think I've ever had such a wonderful conversation where my questions were so bad and your answers was so beautiful. So I deeply appreciate. I really enjoyed it. That's very fun. I know that, as you can probably tell, I, I really you know, I there's something I like about kind of thinking outside the box like. Yeah.

[01:58:38]

So it's going to have an opportunity to do that. Awesome. Thanks so much for doing it. Thanks for listening to this conversation with Matt Gopnik. Thank you to our sponsors, the Jordan Harbage, a show and magic spoon, low carb, Quito's cereal. Please consider supporting this podcast by going to Jordan Harbage of dotcom leks and also going to magic spoon dot com slash. And using code leks at checkout. Click the links, buy all the stuff.

[01:59:08]

It's the best way to support this podcast and the journey I'm on in my research and the startup. Enjoy this thing, subscribe on YouTube, review it with the five stars and have podcast report on Patrón, follow on Spotify or connect with me on Twitter. Allex Friedman again spelled miraculously without the E, just F.R. Eyed Man. And now let me leave you with some words from neurologist V.S. Chandran. How can a three pound mass of jelly they can hold in your palm?

[01:59:42]

Imagine Angell's contemplate the meaning of an infinity, even question its own place and cosmos, especially on spiring is the fact that any single brain, including yours, is made up of atoms that were forged in the hearts of countless far flung stars billions of years ago. These particles drifted for eons and light years until gravity and change brought them together here. Now these atoms now form a conglomerate, your brain that can not only ponder the very stars that gave it birth, but can also think about its own ability to think and wonder about its own ability to wander with the arrival of humans.

[02:00:26]

It has been said the universe has suddenly become conscious of itself. This truly is the greatest mystery of all. Thank you for listening and hope to see you next time.