YouSaid · the spoken record

Matt Botvinick

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2020-07-03
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2020-07-03
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  1. “And so the real world is sort of Saturated with this kind of property. It's endless variety with endless redundancy. And that's the setting in which this kind of meta-learning happens.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “You never do exactly the same thing that you did the day before, but everything that you do is sort of has a family resemblance. It shares structure with something that you did before”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “It was memory augmented neural networks. I think the title was MetaLearning in Memory Augmented Neural Networks. It was the same exact story. 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 reads and writes that occurred on this slot-based memory. This will just happen. And so, but this brings us back to something I was saying earlier about the importance of the environment. 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. And that's something that's very obviously true of the world that humans inhabit. We're constantly, like, if you just kind of think about what you do every day.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “Have to have memory, it actually doesn't have to be a recurrent neural network. One of a paper that I was honored to be involved with even earlier used a kind of slot-based memory.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Yeah, it's something that just happens in a sense. In a sense, you can't avoid this happening if you have a system that has memory and the function of that memory is shaped by reinforcement learning, and this system is trained in a series of interrelated tasks. Is going to happen. You can't stop it. As long as”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “The kind of meta 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 in that sense, it can be as meta as it wants to be, right? There's no limit on how abstract the meta-learning can get. Because it's not reliant on a human engineering a particular meta-learning algorithm to get there. And that's, I also, I don't know, I guess I hope that that's relevant in the brain. I think there's a kind of beauty in the ability of this emergent.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “I mean, there are a lot of forms of meta-learning algorithms that have been proposed since then that are fascinating and effective in their domains of application. But they're engineer. They're things that somebody had to say, well, gee, if we wanted meta-learning to happen, how would we do that? Here's an algorithm that would, but there's something about...”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “One thing that really fascinated me about this mechanism that we were starting to look at, and other groups started talking about very similar things at the same time. And then a kind of explosion of interest in meta-learning happened in the AI community shortly after that. I don't know if we had anything to do with that, but I was gratified to see that a lot of people started talking about meta-learning. One of the things that I like about the kind of flavor of meta-learning that we were studying was that it didn't require anything special. It was just if you took a system that had some form of memory. That the function of which could be shaped by pick your RL algorithm, then this would just happen.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “One of the people in AI who started thinking about meta-learning from very early on J ⁇ rgen Schmidhuber sort of cheekily suggested, I think it may have been in his PhD thesis, that we should think about meta, meta, meta, meta, meta, meta learning. That's really what's going to get us to true intelligence.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Maybe the prefrontal cortex is doing that sort of thing strictly in its activation patterns. It's keeping around a memory, in its activity patterns of what you did, how much reward you got. And it's using that activity-based memory as a basis for updating behavior. But then the question is, well, how did the prefrontal cortex 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 recurrent dynamics of the prefrontal cortex arise? And one answer that became evident in this work was, well, maybe the mechanisms that operate on the synaptic level, which we believe are mediated by dopamine, are responsible for shaping those dynamics.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “Exactly. So the kind of metaphorical transition we made to neuroscience was to think, okay, well, we know that the prefrontal cortex is highly recurrent. We know that it's an important locus for working memory, for activation-based memory. So maybe the prefrontal cortex supports reinforcement learning. In other words, what is reinforcement learning? You take an action, you see how much reward you got, you update your policy of behavior”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “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 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 is kind of why it's good to have people working on AI that know a little bit about neuroscience and vice versa because we started thinking about whether we could apply this principle to neuroscience. And that's where the paper came from.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “Figure out which button is best. And the recurrent neural network will do this just fine. It figures out 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. How is that happening? It's happening because the activity of the activity dynamics of the network have been shaped by this 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. And as we were kind of realizing that this is a thing.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

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

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “As I said before, instead of just giving you one of these tasks, I give you a whole sequence. I give you two buttons and you figure out which one's best and I go, good job. Here's a new box. Two new buttons you have to figure out which one's best. Good job. Here's a new box. And every box has its own probabilities and you have to figure it out. So if you train a recurrent neural network on that kind of sequence of tasks, what happens, it seemed almost magical to us when we first started kind of realizing what was going on. The slow learning algorithm that's adjusting the synaptic weights.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “So, the punchline 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. The activation dynamics will become very interesting. So imagine I give you a task where you have to press one button or another, left button or right button. And there's some probability that I'm going to give you an M&M if you press the left button and there's some probability I'll give you an M&M if you press the other button. And you have to figure out what those probabilities are just by trying things out.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

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

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “Yeah, so the origin of that paper was in AI work that we were doing in my group. We were looking at what happens when you train a recurrent neural network using standard reinforcement learning algorithms, but you train that network not just in one task, but you train it in a bunch of interrelated tasks. And then you ask what happens when you give it yet another task in that sort of line of interrelated tasks. And what we started to realize is that A form of meta learning spontaneously happens in recurrent neural networks. The simplest way to explain it is to say”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “But that's the idea of meta-learning. It relates to the old idea in psychology of learning to learn, situations where 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 novel, but let's say you've learned two foreign languages, the third foreign language obviously is going to be much easier to pick up. And why? Because you've learned how to learn. You know how this goes. You know, okay, I'm going to have to learn how to conjugate. I'm going to have to. That's a simple form of metal learning, right? In the sense that there's some slow learning mechanism that's helping you kind of update your fast learning mechanism.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “Meta learning is by definition A situation in which 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. 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 thin air. I can say more about what I mean by that. I don't mean to be obscure.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “What we know about what neurons deep in the brain are doing. And over and over and over and over, 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 patterns of activity, patterns of response, patterns of neuronal behavior in these artificial models that look hauntingly similar to what you see in the brain. And is that, I mean, is that a coincidence?”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “Oh, heck yes. Look, I wouldn't be a scientist if I didn't think there was any chance we were wrong. But, I mean, 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. But, you know, if you go back to the 1980s, there's sort of an unbroken chain of research in which a particular strategy is taken, which is, hey, let's train a deep learning system. Let's train a multi-layer neural network on this task that we trained our rat on or our monkey on or this human being on. 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”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “Of spike production may be important for communication. But I'm still pretty old school in the sense that I think that the things that we're building in AI research constitute reasonable models of how a brain would work.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  24. “Now, what we do when we build artificial neural networks of the kind that are now popular in the AI 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 we consider people talk about that as the activity of a neuron. And so the activity of units in a deep learning system is broadly analogous to the spike rate of a neuron. There are people who believe that there are other forms of communication in the brain. In fact, I've been involved in some research recently that suggests that the voltage fluctuations that occur in populations of neurons that aren't sort of below the level of”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  25. “This is a classical view. I mean, this is not the only way in which this stance would be controversial in the sense that 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, they wash up on a neuron. The neuron has receptors for those transmitters. 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 release. This is just like neuroscience 101. This is like the way the brain is supposed to work.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  26. “Yeah, is it firing fast or slow? Let's put a number on that. And that number is enough to capture what neurons are doing. Still uncertainty about whether that's an adequate description of how information is transmitted within the brain. 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. Having said that, I feel like we can get somewhere by sticking to this high level of abstraction.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  27. “Well, 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 the mechanisms that carry information in the brain. So the usual way of articulating that is to say, what really matters is a rate code. What matters is how quickly is an individual neuron spiking? What's the frequency at which it's spiking?”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  28. “Information from very different domains. So the history of neuroscience is sort of this oscillation between the two views that you articulated, you know, the kind of modular view and then the big mush view. And I think we're going to end up somewhere in the middle, 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, but something that kind of lies in between is confusing, but we're going to have to get used to it, I think.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  29. “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 cortical way station for processing visual information. Basically, what it should care about is where are the edges in this scene that I'm viewing. 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 behavior the animal is engaged in right now and how much reward is on offer in the task that it's pursuing. So it's clear that even regions whose function is pretty well defined at a coarse grain are nonetheless carrying some information.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  30. “Yeah, oh, yeah, yeah, the prefrontal cortex is made up of a bunch of different subregions. The functions of which are not clearly defined and which the borders of which seem to be quite vague. And then there's another thing that's popping up in very recent research, which Involves application of these new techniques, which are a number of studies that suggest that. Of the brain that we would have previously thought were quite”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  31. “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 differences between regions are graded rather than being discrete. So it doesn't seem like it's easy to divide the brain up into true modules that have clear boundaries and that have like Clear channels of communication between them instead”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  32. “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. This follows immediately from the kinds of studies of brain damage that we were chatting about before. 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 There are two other things to add, which kind of I don't know, maybe tug in the other direction. One is that”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  33. “That experiment may not have worked because you asked a mouse to deal with stimuli and behaviors that were very unnatural for the mouse. If instead you kept the logic of the experiment the same but put it in a presented 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, you know, dealing with physical problems. I have to drag this piece of food back to my lair, but there's something in my way. And how do I get rid of that thing? So I think these are open questions, to put it, to sum that up.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  34. “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, were able to generalize from one stimulus to another in a way that suggests that they have abstractions that guide their generalization. I've had many conversations in which I will start by observing recounting some 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 then a mouse researcher will say to me, well, you know, hold on.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  35. “Yeah. 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 monkeys, let alone humans. And so 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. And to put it briefly, there's disagreement.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  36. “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 stuck out my hand, Want you instead to present your elbow, a patient with frontal damage would have a great deal of trouble with that. Somebody proffering their hand would elicit a handshake. The prefrontal cortex is what allows us to say, hold on. That's the usual thing, but I have the ability to bear in mind even very unusual contexts and to reason about what behavior is appropriate there.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  37. “It's interesting. 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. But just to finish the kind of dissertation here, the role of the prefrontal cortex is generally understood these days sort of in contradistinction to that habitual domain. In other words, the prefrontal cortex is what helps you override those habits. It's what allows you to say, whoa, whoa, 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, right? 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.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  38. “Yeah, what later helped bring this function into better focus was a distinction between controlled and automatic behavior or in other literatures this is referred to as habitual behavior versus gold-directed behavior. So it's 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 automatized so that they don't require you to concentrate too much. So that leaves your cognitive capacity free to do other things. Just think about the difference between Driving when you're learning to drive versus driving after you're fairly expert. There are brain pathways that slowly absorb those frequently performed behaviors so that they can be habits, so that they can be automatic.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  39. “Finger on a syndrome that was associated with frontal damage. Actually, one of them was a Russian neuropsychologist named Luria, who, you know, students of cognitive psychology still read. And what he started to figure out was that the frontal cortex was somehow involved in flexibility 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.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  40. “Yeah, exactly. And in fact, the early history of neuroscientific investigation of what this front part of the brain does is sort of funny to read because It was really World War I 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. One of the frustrations that neuropsychologists faced was they couldn't really identify exactly what the deficit was that arose from damage to these most frontal parts of the brain. It was just a very difficult thing to pin down. There were a couple of neuropsychologists who identified through a large amount of clinical experience and close observation, they started to put their”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  41. “Exactly. 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.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  42. “Well, my area of expertise, if I have one, is prefrontal cortex. It depends on who you ask. The technical definition is anatomical. There are parts of your brain that are responsible for motor behavior, and they're very easy to identify. 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.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  43. “I'm with you. One thing I will say along these lines. Is that I think Think a serious effort to understand human intelligence. And maybe to build human-like intelligence needs to pay just as much attention to the structure of the environment as to the structure of the The cognizing system, whether it's a brain or an AI 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. The structure of cognition is really Only partly a function of the architecture of the brain and the learning algorithms that it implements, what really shapes it is the interaction of those things with the structure of World in which those things are embedded, right?”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  44. “Well, I don't know. I mean, I think there's something to the argument that that kind of strictly bottom-up approach is wrong-headed. In other words, there are basic phenomena, basic aspects of human intelligence that can only be understood in the context of groups. I'm perfectly open to that. I've never been particularly convinced by the notion that we should consider intelligence to inhere at the level of communities. I don't know why. I'm sort of stuck on the notion that the basic unit that we want to understand is individual humans. And if we have to understand in the context of other humans, fine. But for me, intelligence is just, I'm stubbornly, I stubbornly define it as. Something that is an aspect of an individual human. That's just my, I don't know.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  45. “Well, yeah, I can't. I mean, I have the honor of working with a lot of incredibly smart people, and I wouldn't want to take any credit for leading the way on the multi-agent work that's come out of my group or DeepMind lately. But I do find it fascinating. I mean, I think Can't be debated, you know, human behavior arises within communities. That just seems to me self-evident.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  46. “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. It's not going to be enough for me to understand what the computations are that allow people to. Do arithmetic or play chess. I want the whole thing.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  47. “One thing I often think about is that If you take an introductory computer science course and they are introducing you to the notion of Turing machines, one way of articulating what the significance of a Turing machine is, is that it's a machine emulator. It can emulate any other machine. That to me of looking at a Turing machine really sticks with me. I think of humans as maybe sharing in some of that character where capacity limited. We're not turning 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 software.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  48. “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 the context calls for it, that is so characteristic of human behavior.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  49. “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, still do.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source

  50. “Look carefully at the data. If you look at 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 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, 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.”

    2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source