YouSaid · the spoken record

Matt Botvinick

lines on the record: 126
first: 2020-07-03
most recent: 2020-07-03
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“How can a three pound mass of jelly that you can hold in your palm imagine angels contemplate the meaning of an infinity and even question its own place in cosmos, especially awe inspiring 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 they 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 it has been said the universe has suddenly become”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Thanks for listening to this conversation with Matt Botvinik, and thank you to our sponsors, the Jordan Harbinger Show and Magic Spoon Low Carb Keto Cereal. Please consider supporting this podcast by going to JordanHarbinger dot com slash Lex and also going to magic spoon dot com slash lex and using code lex at checkout. Click the links, buy all the stuff. It's the best way to support this podcast and the journey I'm on in my research and the startup. If you enjoy this thing, subscribe on YouTube, review it with five stars and apple podcasts, support it on Patreon, follow on Spotify, or connect with me on Twitter at Lexfriedman again spelled miraculously without the E just FRIDMAN. And now let me leave you with some words from urologist VS Sermachandran”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“It's been very fun. As you can probably tell, I really, you know, there's something I like about kind of thinking outside the box. So it's good opportunity to do that.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“And hum We humans are so good at it. It's not just that we're born warm. I suppose some people are warmer than others, given whatever genes they manage to inherit. There are also learned skills involved, right? 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. 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 behaviors. But it's not out of the question that we could build systems that kind of instill in them something that sets them out in the right direction so that they end up”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“That we didn't feel like it was simulated. We didn't feel like we were being duped. To me, people talk about the Turing test or some descendant of it. I feel like that's the ultimate Turing test. 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 we're comfortable saying, yeah, that AI system's a good guy.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“We really focus on this capability thing. We want our agents to be able to do stuff. This thing can play Go at a superhuman level. That's awesome. But that's only one dimension. What about the other dimension? What would it mean for an AI system to be warm? And, you know, I don't know. Maybe there are easy solutions here like we can put a face on our AI systems. It's cute. It 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 AI system to display caring, compassionate behavior in a way that actually made us feel like it was for real?”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“So you can imagine another person who's very skilled and capable, but is very cold. And you wouldn't really highly, you might have some reservations about that other person, right? But there's also a kind of reservation that we might have about another person who elicits in us or displays a lot of human warmth, but is not good at getting things done. The greatest esteem that we reserve our greatest esteem, really, for people who are both highly capable and also quite warm, right? That's like the best of the best. This isn't a normative statement I'm making. This is just an empirical statement. This is what humans seem, these are the two dimensions that people seem to kind of like, along which people size other people up. And in AI research.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“I love that question. And it relates closely to things that I've been thinking about a lot lately in the context of this human AI research. There's social psychology research, in particular by Susan Fisk at Princeton in the department I used to, where I used to work, where she dissects human attitudes toward other humans into a sort of two-dimensional scheme. And one dimension is about ability, you know, how able, how capable is this other person? But the other dimension is warmth.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Simpler fact of the matter is we're not building things yet that do have that kind of flexibility. And I think the attention of a large part of the AI community is starting to pivot to that question. How do we get that? 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 Executed before, 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 new chapter.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“I think one of the key challenges that a lot of people are seeing now in AI is to build systems that have the kind of flexibility and the kind of flexibility that humans have in two senses. One is that humans can be good at many things. 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 very ably see what a new task has in common with other things that they've done. And that's something that our AI systems just blatantly do not have. There are some people who like to argue that deep learning and deep RL are simply wrong for getting that kind of flexibility. I don't share that belief.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“And I'm hoping that those two trends, the kind of growing interest in behavior and the widespread interest in what's going on in AI will come together to kind of open a new chapter in neuroscience research where there's a kind of 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 in AI. 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 what is the brain for it. For behavior. I feel like we could maybe take a step toward that now if people are motivated in the right way.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Have not involved a lot of interesting behavior. And this is for a variety of reasons, one of which is in order to employ some of these technologies, you actually have to, if you're studying a mouse, you have to head fix the mouse. In other words, you have to immobilize the mouse. And so it's been tricky to come up with ways of eliciting interesting behavior from a mouse that's restrained in this way. 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 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 reintroduce behavior, which was supposed to be what this whole thing was about.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“The neuroscience side There's a great deal of interest now in what's going on in AI. And And at the same time, I feel like so neuroscience, especially the part of neuroscience that's focused on circuits and systems, kind of really mechanism focused, there's been this explosion in new technology. Up until recently, the experiments that have exploited this technology have.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Possibilities. Well, you never know. So the minute you publish a paper like that, the next thing you think is. That replicates that same thing in other data sets. But of course several labs now are doing the follow-up experiments. So we'll know soon. But it has been a lot of fun for us to take these ideas from AI and kind of bring them into neuroscience and see how far we can get.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Collapsing everything to a single number, but instead is kind of respecting the variety of future outcomes, if that makes sense”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“What happened was that Will Davney, one of my collaborators, who was one of the first people to work on distributional temporal difference learning, talked to a guy in my group, Zeb Kurth-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? And they started looking at what was in the literature, and then they brought me in, and we started talking to Nawachita, and we came up with some specific predictions about 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. 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 distribution encoding, that dopamine might be representing surprise signals in a way that is not just”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. This is the central mechanism of temporal difference. And it was this connection between the Royal Prediction Error and dopamine was made in the 1990s. And there's been a huge amount of research that seems to back it up. Dopamine may be doing other things, but this is clearly, at least roughly, one of the things that it's doing. But the usual idea was that dopamine was representing these reward prediction errors again in this like kind of single number way representing your surprise with a single number. And in distributional reinforcement learning, this kind of new elaboration of the standard approach. It's not only the value function that's represented as a single number, it's also the reward prediction error.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. So, well, this is also a case where there's a huge amount of detail and debate. But 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? Well, 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 value representation that you attached to that earlier situation. If you got less reward than You were expecting, you should probably decrement that estimate.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Represented internally squeeze them together because 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 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 maintain the distinction between these two things. 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. And maintaining those distinctions can be useful when the system is now faced with some other task where the distinction is important.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, 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, wait, why? Wait, why? Why is this working? 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 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 and kind of, in terms of the way they're”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Represented as a single number, which is like the expected average of all those outcomes. And this new form of reinforcement learning said, well, what if we generalize that to a 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. And it had been observed through experiments, through just trying this out, that kind of distributional representation really accelerated reinforcement learning and led to better policies.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's another project that grew out of pure AI research. A number of people at DeepMind and a few other places had started working on a new version of reinforcement learning, which was defined by taking something in traditional reinforcement learning and just tweaking it. So the thing that they took from traditional reinforcement learning was a value signal. So 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 gambler's 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, that situation would be”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“That's like dystopian in a totally different way. It's like the machines are doing everything for us. That's not what we wanted. Anyway, I find this kind of, this opens up a whole landscape of research that feels affirmative and exciting.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, 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. Of course, the safety issues 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? And with all of the subtleties of that, right, humans want help with certain things, but they don't want everything done for them, right? Part of the satisfaction that humans get from life is in accomplishing things. So if there were devices around that did everything for, you know, I often think of the movie Wallie.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“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 to be brought into contact with these AI systems in a way that really makes the world a better place.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“How do we cope with that? This is a problem that has been discussed in gory extensive detail in social choice theory. One thing I'm really enjoying about the recent direction work has taken in some parts of my team is that, yeah, we're reading the AI 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 all becomes relevant. It all becomes relevant. But at the same time, we've been trying not to write.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“And how do you do that without deception being involved, without manipulation being involved, maximizing human autonomy? And how do you make these choices in a democratic way? How do you face for myself here? 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, but what it means to make the world a better place is something that we all have to be talking about.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“That's not obvious. And we wouldn't be doing this if we didn't have the sense there was huge potential, right? We're not doing this for no reason. We have a sense that AGI would be a major boom to humanity. But I think it's worth starting now, even when our technology is quite primitive, asking, well, exactly what would that mean? We can start now with applications that are already going to make the world a better place. Like, you know, solving protein folding, you know, I think DeepMind has gotten heavy into science applications lately, which I think is a wonderful, wonderful move for us to be making. But when we think about AGI, when we think about building fully intelligent agents that are going to be able to, in a sense, do whatever they want, we should start thinking about what do we want them to want?”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, we should be spending a lot of our time saying what can go wrong. I think it's harder to see that there's work to be done. Bring into focus the question of what it would look like for things to go right.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Having said that, what often follows for me is the thought that there's another kind of narrative that might be relevant, which is when we think of humans gaining more and more information about human life, the narrative there is usually that they gain more and more wisdom. They get closer to enlightenment and they become more benevolent. And, you know, like the Buddha is like that's a totally different narrative. And why isn't it the case that we imagine that the AI systems that we're creating are just 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 become beneficent. I'm not saying that will happen. I'm not, you know, I'm. I don't honestly expect that to happen without some careful. Things up very carefully, but it's another way things could go, right?”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“One thing I often think about is the usual schema for thinking about human agent interaction is this kind of dystopian, you know, oh, you know, our robot overlords. And again, I hasten to say AI safety is hugely important. And I'm not. Saying we shouldn't be thinking about those risks. Totally on board for that. But there's a.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“And it might bring it. It might restore a certain, I don't know, a certain depth to or even dare I say, spirituality to the way that the world. I don't know. Maybe that's too grandiose.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know what the right word is quite refreshed in my involvement in AI research. It's almost like this building this kind of stuff is going to lead us back to asking really. Fundamental questions about what is And bringing in viewpoints from multiple subcommunities to help us shape the way that we live. There's something, it started making me feel like doing AI research in a fully responsible way. Could potentially lead to a kind of Cultural renewal.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“And then you start asking if we knew the answer to that. It is our place as AI engineers to build that into these agents? Probably the agents should interact with humans. Beyond the population of AI engineers and figure out what those humans want. And then, you know, when you start, I referred this the moment ago, but even that becomes complicated. 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 economics and social choice theory and even politics. 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 that dawned on me, I actually felt”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. And I mean, then questions arise that start imperceptibly, but inevitably to slip beyond the realm of engineering. So questions like If you have an agent that can do something that you can't do Under what conditions do you want that agent to do it? So, you know, if I have a robot that can play Beethoven sonas Better than any human in the sense that the sensitivity, the expression is just beyond what any human, do I want to listen to that? Do I want to go to a concert and hear a robot play? These aren't engineering questions. These are questions about human preference and human culture.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“What would it mean to make human life better? And how can we imagine learning systems doing that? And in talking to my colleagues about that, we reached the initial conclusion that 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. And so human agent interaction has become quite intensive focus of my group lately. No other reason In order to really address that That issue in an adequate way, you have to, I mean, psychology becomes part of the picture.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“That you're building are learning systems. They're not, you're not. Programming something that you then introduce to the world and it just works as programmed, like Google Maps or something. We're building systems that learn from experience. So that typically leads to AI safety questions. 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 Consider those hugely important issues. And there are large sectors of the research community at DeepMind and, of course, elsewhere who are dedicated to thinking hard all day, every day about that. But I guess I would say a positive side to this too, which is to say, well,”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I'm actually pretty intensively interested in this issue now. 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. 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 What we're up to? What are we doing? What is this AI research 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. And so, how do you make life better for humans? That's a proposition that when you look at it carefully and honestly is Rather horrendously complicated, especially when the AI systems that you're”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“So hopefully that, I mean, I've seen that work, I've seen that work out well at DeepMind. There are, There are people who, I mean, even if you just focus on the AI work that happens at DeepMind, it's been a good thing to have some people around doing that kind of work whose PhDs are in neuroscience or psychology. Every academic discipline has its Blind spots and kind of unfortunate obsessions and it's metaphors and its reference points and having some intellectual diversity is really healthy. People get each other unstuck, I think. I see it all the time at DeepMind. And I like to think that the people who bring some neuroscience background to the table are helping with that.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, inhabiting a community where they're talking to people who live elsewhere on the spectrum. 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 AI world. And that central position, I will admit, prevents me, at least someone with my limited cognitive capacity, from being a truly having true technical expertise in either domain. But at the same time, I at least hope that it's worthwhile having people around who can kind of see the connections. Between”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly, especially with the recent explosion in neuroscience methods. But having said all that, I think I think the best scenario for both neuroscience and AI is to have people who interacting who live at every point on this spectrum from exclusively focused on neuroscience to exclusively focused on the engineering side of AI. But to have those people”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Just the complexity of the technology that's involved in both disciplines now. So the engineering expertise that it takes to do truly frontline hands-on AI research is really, really considerable.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it does take a special kind of person to be truly world class at both AI and neuroscience, and I am not on that list. I happen to be someone whose interest in neuroscience and psychology involved using the kinds of modeling techniques that are now very central in AI. And that sort of, I guess, bought me a ticket to be involved in all of the amazing things that are going on in AI research right now. I do know a few people who I would consider pretty expert on both fronts, and I won't embarrass them by naming them. But there are exceptional people out there who are like this. The one thing that I find is a barrier to being truly world class on both fronts.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Innovation that's happened recently on the AI side, AI is kind of ahead in the sense that they're all of these ideas that for which it's exciting to consider that there might be neural analogs. And neuroscience, in a sense, has been focusing on approaches to studying behavior that come from, you know, that are kind of derived from this earlier era of cognitive psychology. And so in some ways, fail to connect with some of the issues that we're grappling with in AI. Like, how do we deal with large complex environments? But I think it's inevitable that this circle will keep turning and there will be a moment in the not too different future when neuroscience is pelting AI researchers with Insights that may change the direction of our work.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, we've talked about the notion of a virtuous circle between AI and neuroscience. And, you know, the way I see it. That's always been there since the two fields jointly existed. There have been some phases in that history when AI was sort of ahead. There are some phases when neuroscience was sort of ahead. I feel like given the burst of”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“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 AI and 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, we see it also as a way of validating what we're doing on the AI side. If we can gain some evidence that the brain is using some technique that we've been trying out in our AI work that gives us confidence that it may be a good idea that it'll scale to rich complex tasks, that it'll interface well with other mechanisms.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it's such a much fun. And in the case of the dopamine paper, We also collaborate with Nowichita Harvard, who obviously a paper simply wouldn't have happened without him. So you were asking for a thumbnail sketch”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, wonderful. Yeah. Because I'm sort of abashed to be the spokesperson for these papers when I had such amazing collaborators on both. So it's a comfort to me to know that you'll acknowledge them.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source
“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 stop meat paper.”
2020-07-03 · Lex Fridman Podcast · #106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind · IDENTIFIED FROM THE TRANSCRIPT · source