Matt Botvinick is the Director of Neuroscience Research at DeepMind. He is a brilliant cross-disciplinary mind navigating effortlessly between cognitive psychology, computational neuroscience, and artificial intelligence.

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OUTLINE:
0:00 - Introduction
3:29 - How much of the brain do we understand?
14:26 - Psychology
22:53 - The paradox of the human brain
32:23 - Cognition is a function of the environment
39:34 - Prefrontal cortex
53:27 - Information processing in the brain
1:00:11 - Meta-reinforcement learning
1:15:18 - Dopamine
1:19:01 - Neuroscience and AI research
1:23:37 - Human side of AI
1:39:56 - Dopamine and reinforcement learning
1:53:07 - Can we create an AI that a human can love?

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The following is a conversation with Matt Botmanick, Director of Neuroscience Research at DeepMind.

He's a brilliant, cross-disciplinary mind navigating effortlessly between cognitive psychology, computational neuroscience, and artificial intelligence.

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And now, here's my conversation with Matt Botvinick.

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 I feel like we understand a lot about the brain at a very high level, but a very coarse level.

When you say high level, what are you thinking?

So in other words, what is the brain for?

You know, what kinds of computation does the brain do?

You know, what kinds of behaviors would we have to, would we have to explain if we were gonna 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 it's almost like we're seeing it through a fog.

We don't really understand what 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.

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, actually literally see in some cases, what's going on at the single unit level, even the dendritic level.

And then there's this yawning gap in between.

So at the high level, so that's almost a cognitive science level?

And then at the neuronal level, that's neurobiology and neuroscience, just studying single neurons, the synaptic connections and all the dopamine, all

1 blanket statement I should probably make is that as I've gotten older, I have become more and more reluctant to make a distinction between psychology and neuroscience?

To me, the point of neuroscience is to study what the brain is for.

If you're a nephrologist and you want to learn about the kidney, you start by saying, what is this thing for?

Well, it seems to be for taking blood on 1 side that has metabolites in it 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.

So the rest of the work is deciding how it does that.

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 behavior.

It's for going from perceptual inputs to behavioral outputs, and the behavioral outputs should be adaptive.

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 mechanistic level.

That's really interesting, But so unlike the kidney, the brain, the gap between the electrical signal and behavior, so you truly see neuroscience as the science that touches behavior, how the brain generates behavior, or how the brain converts raw visual information into understanding, like you basically see cognitive science, psychology, and neuroscience as all 1 science.

Is that a hopeful or a realistic statement?

So certainly you will be correct in your feeling in some number of years, but that number of years could be 200, 300 years from now.

Is that aspirational or is that pragmatic engineering feeling that you have?

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 psychology or neuroscience if that's not the framing.

I don't know what it means to understand the brain 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, right?

And I would argue that at least in the early days, there's 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 you even understand even the basic structural elements of.

But you're saying in the end the goal should be Yeah.

Well, right, and I think, so I thought about this a lot when I was in grad school because a lot of what I studied in grad school was psychology, And I found myself a little bit confused about what it meant to, it seems like what we were talking about a lot of the time were virtual causal mechanisms.

Like, oh, well, you know, attentional selection then selects some object in the environment, and that is then passed on to the motor, you know, information about that is passed on to the motor system.

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 behavior is arising.

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 were supposed to explain the function of the mind.

If you can't ground them in physical mechanisms, then what is the explanatory validity of these explanations?

And 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, but I know enough to say that, Mendelian genetics preceded Watson and Crick.

And so there was a significant period of time during which people were productively investigating the structure of inheritance using what was essentially a metaphor, the notion of a gene.

Oh, genes do this and genes do that, but where are the genes?

They're sort of an explanatory thing that we made up.

And we ascribed to them these causal properties.

Oh, there's a dominant, there's a recessive, and then they recombine it.

And then later, there was a kind of blank there that was filled in with a physical mechanism.

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

And the fundamental metaphor of cognition, you said, is the interaction of neurons.

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

That's not a physical mechanism that I can examine in its own right, but it's still worth having that metaphorical level.

So the higher level abstractions is the metaphor that's most useful.

But what about, so how does that connect to the idea that that arises from interaction of neurons?

Is the interaction of neurons also not a metaphor to you?

Or is it literally, that's no longer a metaphor.

That's already the lowest level of abstractions that could actually be directly studied?

Well, I'm hesitating because I think what I wanna say could end up being controversial.

So what I wanna say is, yes, the interactions of neurons, that's not metaphorical.

That's where the causal interactions actually occur.

Now, I suppose you could say, well, even that is metaphorical relative to the quantum events that underlie, I don't wanna go down that rabbit hole.

You can say these psychological phenomena can be explained through a very different kind of causal mechanism, which has to do with neurotransmitter release.

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 description that is natural for psychology, for me personally, would be disappointing.

I want to understand how mental activity arises from neural activity.

Studying neural activity without any sense of what you're trying to explain, to me feels like at best groping around at random.

Now, you've kind of talked about this bridging of the gap between psychology and neuroscience, but do you think it's possible, like my love is, like I fell in love with psychology and psychiatry in general with Freud and when I was really young,

And for me, understanding the mind, at least at a young age before I discovered AI 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 neuroscience?

Like you kind of mentioned, to you it's appealing to try to understand the mechanisms at the lowest level, 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 am fond of saying that I have learned much more from psychology than I have from neuroscience.

To me, psychology is a hugely important discipline.

And 1 thing that warms my heart is that ways of investigating behavior that have been native to cognitive psychology since its dawn in the 60s are starting to become, they're starting to become interesting to AI researchers for a variety of reasons.

Can you maybe talk a little bit about what you see as beautiful aspects of psychology, maybe limiting aspects of psychology.

I mean, maybe just started off as a science, as a field.

To me, it was when I understood what psychology is, analytical psychology, like the way it's actually carried out, it's really disappointing to see 2 aspects.

1 is how small the N is, how small the number of subject is in the studies.

And 2, it was disappointing to see how controlled the entire, 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 where I became a little bit disillusioned to psychology and then the modern world of the internet is so exciting to me, the Twitter data or YouTube data, data of human behavior on the internet becomes exciting because the N grows and then in the wild grows.

Do you have a optimistic or pessimistic, cynical view of psychology, how do you see the field broadly?

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

1 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 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.

An experiment answered 1 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.

Do you have an example from memory of what kind of aspects of the mind could be studied in this kind of way?

I mean, the very detailed neuropsychological studies of language function, looking at production and reception and the relationship between visual function, reading and auditory and semantic.

And there were these, 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 before about how, you know, language processing is organized in the brain.

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

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

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

They weren't yet really useful for industrial applications.

So you always found neural networks in biological form beautiful.

Oh, neural networks were very concretely the thing that drew me into science.

I was handed, are you familiar with the PDP books from the 80s?

I went to medical school before I went into science.

I also did a graduate degree in art history, so I kind of explored.

That's just a curious, creative mind, but medical school, the dream of what, if we take that slight tangent?

I actually was quite interested in surgery.

I was interested in surgery and psychiatry, and I thought I must be the only person on the planet who was torn between those 2 fields.

And I said exactly that to my advisor in medical school, who turned out, I found out later, to be a famous psychoanalyst.

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 develop these 2 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 United States.

There's sort of a, you know, there's a cliche phrase that people use now on, you know, like in NPR, the secret life of blankety blank, right?

And that was part of the thrill of surgery, was seeing the secret activity that's inside everybody's abdomen and thorax.

That's a very poetic way to connect it to disciplines that are very practically speaking different from each other.

So I was in medical school and 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, 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.

And he pulled down the PDB books from his shelf and they were still shrink-wrapped.

He hadn't read them, but he handed them to me.

And that was, you know, I went back to my dorm room and I just, you know, read them cover to cover.

Parallel distributed processing, which was 1 of the original names for deep learning.

And so, I apologize for the romanticized question, but what idea in the space of neuroscience, in the space of the human brain, is to you the most beautiful, mysterious, surprising?

What had always fascinated me, even when I was a pretty young kid, I think, was the paradox that lies in the fact that the brain is so mysterious and seems so distant, but at the same time, it's responsible for the full transparency of everyday life.

The brain is literally what makes everything obvious and familiar.

And there's always 1 in the room with you.

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, When people think of scientific inspiration, the metaphor is often, well, look to the stars.

The stars will inspire you to wonder at the universe and think about your place in it and how things work.

I'm all for looking at the stars, but I've always been much more inspired, and my sense of wonder comes not from the distant, mysterious stars, but from the extremely intimately close brain.

There's something just endlessly fascinating to

Like Jessica said, the 1 that's 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 2 brains are communicating?

So the, I guess what I mean is the subjective nature of the experience, if we can take a small tangent into the mystical of it, the consciousness, or when you're 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.

Well, I definitely resonate with that latter thought.

And I often find discussions of artificial intelligence to be disappointingly narrow.

Speaking as someone who has always had an interest in art.

Right, I was just gonna go there because it sounds like somebody who has an interest in art.

Yeah, I mean, there are many layers to full bore human experience.

And in some ways, it's not enough to say, oh, well, don't worry, we're talking about cognition, but we'll add emotion.

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

And yes, so that's part of what fascinates me, is that our brains are producing that.

But at the same time, it's so mysterious to us, how?

We literally, our brains are literally in our heads producing this experience.

And yet there's, it's so mysterious to us.

And so, and the scientific challenge of getting at the actual explanation for that is so overwhelming.

Certain people have fixations on particular questions and that's always, that's just always been mine.

Yeah, I would say the poetry of 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, when you try to create a benchmark for the community to gather around, how much of the magic of language is lost when you create that benchmark.

That there's 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 try to do like real good rigorous science, you lose some of that magic.

When you try to study cognition in a rigorous scientific way, it feels like you're losing some of the magic.

Seeing cognition in a mechanistic way that AI at this stage in our history.

I agree with you, but at the same time, 1 thing that I found really exciting about that first wave of deep learning models in cognition was there was the fact that the people who were 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.

1 said, well, the mind encodes certain rules, and it also has a list of exceptions, because of course, you know, the rule is add E D, but that's not always what you do, so you have to have a list of exceptions.

And then there were the connectionists who evolved into the deep learning people, who said, well, if you look carefully at the data, if you actually look at corpora, like language corpora, it turns out to be very rich because yes, there are most verbs that, and you just tack on ed, and then there are exceptions, but there are also rules that, the exceptions aren't just random.

There are certain clues to which verbs should be exceptional.

And then there are exceptions to the exceptions.

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

In other words, there are rules, but it's messy, and there's structure even among the exceptions.

And it would be, yeah, you 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 train it on this data and see how it ends up representing all of this richness.

So the way that deep learning was deployed in cognitive psychology was, that was the spirit of it.

And that's something that I always found very compelling.

Is there something especially interesting and profound to you in terms of our current deep learning neural network, artificial neural network approaches, and whatever we do understand about the biological neural networks in our brain.

Is there, 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 we might want to try to close in trying to create a human level intelligence?

What I would say here is something that a lot of people are saying, which is that 1 seeming limitation of the systems that we're building now is that they lack the kind of flexibility, the readiness to sort of turn on a dime when the context calls for it, that is so characteristic of human behavior.

that connected to you to the, like which aspect of the neural networks in our brain is that connected to?

Is that closer to the cognitive science level of, now again, see like my natural inclination is to separate into 3 disciplines of neuroscience, cognitive science, and psychology.

And you've already kind of shut that down by saying you're trying to 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 neurons interact that is profound to you in terms of its difference to the artificial neuron that works?

Or is all the key differences at a higher level of abstraction?

1 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 Turing machines, 1 way of articulating what the significance of a Turing machine is, is that it's a machine emulator.

And that to me, that way of looking at a Turing machine really sticks with me.

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Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind | Lex Fridman Podcast #106