Daniel Kahneman: Thinking Fast and Slow, Deep Learning, and AI | Lex Fridman Podcast #65

The following is a conversation with Daniel Conorman, winner of the Nobel Prize in Economics for his integration of economic science with the psychology of human behavior, judgment, and decision-making. He's the author of the popular book thinking fast and slow that summarizes in an accessible way his research of several decades, often in collaboration with Amos Diverski on cognitive biases, prospect theory, and happiness. The central thesis of this work is the dichotomy between two modes of thought. What he calls system one is fast, instinctive and emotional. System two is slower, more deliberative and more logical. The book delineates cognitive biases associated with each of these two types of thinking. His study of the human mind and its peculiar and fascinating limitations are both instructive and inspiring for those of us seeking to engineer intelligence systems. This is the artificial intelligence podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcast, follow on Spotify, support it on Patreon, or simply connect with me on Twitter, Lex Freedman, spelled F R I D M. I recently started doing ads at the end of the introduction. I'll do one or two minutes after introducing the episode and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the app store. I personally use Cash App to send money to friends, but you can also use it to buy, sell, and deposit Bitcoin in just seconds. Cash App also has a new investing feature. You can buy fractions of a stock, say $1 worth, no matter what the stock price is. Broker services are provided by Cash App Investing, a subsidiary of Square and member SIPC. I'm excited to be working with Cash App to support one of my favorite organizations called FIRST. 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What do you think World War II taught us about human psychology broadly? Well, I think the the only big surprise is the extermination policy genocide by the German people. That's when you look back on it and you know I think that's a major surprise. It's a surprise because it's a surprise that they could do it. It's a surprise that they that enough people willingly participated in that. This is this is a surprise. Now it's no longer a surprise but it's changed many people's views I think about about human beings. Uh certainly for me the Akman trial in that teaches you something because it's very clear that if it could happen in Germany it could happen anywhere. It's not that the Germans were special. This could happen anywhere. So what do you think that is? Do you think we're all capable of evil? We're all capable of cruelty. I don't think in those terms. I think that what is certainly possible is you can dehumanize people so that they you treat them not as people anymore but as animals and and the same way that you can slaughter animals without feeling much of anything. uh it can the same and when you feel that the I think the combination of dehumanizing the other side and and having uncontrolled power over other people I think that doesn't bring out the most generous aspect of human nature. So, uh, that Nazi soldier, uh, you know, he he was a good man. I mean, you know, he and he was perfectly capable of killing a lot of people, and I'm sure he did. But what what did the Jewish people mean to Nazis? So what the dismissal of Jewish as well worthy of again this is surprising that it was so extreme but it's not one thing in human nature I don't want to call it evil but the distinction between the in-roup and the outroup that is very basic so that's built in the the loyalty and affection towards inroup and the willingness to dehumanize the group that's that is inhuman nature and that's that's what I think uh probably didn't need the Holocaust to teach us that but the Holocaust is is a very sharp lesson of you know what can happen to people and what what people can do so the effect of the ingroup and the out groupoup you know that it's clear that those were people you know you could you could shoot them. You could, you know, they were not human. They were not, there was no empathy or very very little empathy left. So occasionally you know there might have been and and very quickly by the way uh the empathy disappeared if there was initially and the fact that everybody around you was doing it that that completely the group doing it and everybody shooting Jews I think that that uh makes it permissible. Now, how much, you know, whether it would it could happen in every culture or whether the Germans were just particularly efficient and and disciplined so they could get away with it? That's a question. It's an interesting question. Are these artifacts of history or is it human nature? I think that's really human nature. You know, you put some people in a position of power relative to other people and and then they become less human. They become different. But in general in war, outside of concentration camps in World War II, it seems that war brings out darker sides of human nature, but also the beautiful things about human nature. Well, you know, I mean, what it what it brings out is the the loyalty among soldiers. I mean, it brings out the bonding, male bonding, I think, is a very real thing that that happens. And so, and and there is a certain thrill to friendship and there is certainly a certain thrill to friendship under risk and to shared risk. And so people have very profound emotions up to the point where it gets so traumatic that uh that little is left. But so let's talk about psychology a little bit. Uh in your book thinking fast and slow you describe two modes of thought. system one, the fast, instinctive and emotional one, and system two, the slower, deliberate, logical one. At the risk of asking Darwin to discuss theory of evolution, uh, can you describe distinguishing characteristics for people who have not read your book of the two systems? Well, I mean, the word system is a bit misleading, but it's at the same time it's misleading. It's also very useful. But what I call system one, it's easier to think of it as as a family of activities. And primarily the way I describe it is there are different ways for ideas to come to mind. And some ideas come to mind automatically. And the example standard example is 2 plus two. And then something happens to you. and and in other cases you've got to do something you've got to work in order to produce the idea and my example I always give the same pair of numbers is 27* 14 I think you have to perform some algorithm in your head some steps and and it takes time it's a very different nothing comes to mind except something comes to mind which is the algorithm I mean that you've got to perform and then it's work and it engages es short-term memory and it engages executive function and it makes you incapable of doing other things at the same time. So uh the the main characteristic of system two is that there is mental effort involved and there is a limited capacity for mental effort whereas system one is effortless essentially that's the major distinction. So you talk about there, you know, it's really convenient to talk about two systems, but you also mentioned just now and in general that there's no distinct two systems in the brain from a neurobiological even from psychology perspective. But why does it seem to uh from the experiments you've conducted there does seem to be kind of emerging two modes of thinking. So at some point these kinds of systems came into a brain architecture. Maybe mammals share it. But or do you not think of it at all in those terms that it's all a mush and these two things just emerge? you know, evolutionary theorizing about this is cheap and and easy. So, it's the way I think about it is that it's very clear that animals uh have have a perceptual system and that includes an ability to understand the world at least to the extent that they can predict. They can't explain anything but they can anticipate what's going to happen. And that's a key form of understanding the world. And my crude idea is that we what I call system two uh well system two grew out of this and you know there is language and there is the capacity of manipulating ideas and the capacity of imagining futures and of imagining counterfactual things that haven't happened and and to do conditional thinking and there are really a lot of abilities that without language and without the the very large brain that we have compared to others would be impossible. Uh now system one is more like what the animals have but system one uh also can talk. I mean it has language it understands language indeed it speaks for us. I mean you know I'm not choosing every word as a deliberate process. the words I have some idea and then the words come out and that's automatic and effortless and uh many of the experiments you've done is to show that listen system one exists and it does speak for us and we should be careful about it the voice it provides because I mean you know we have to trust it u because it's the speed at which it acts at system two if we if We're dependent on system two for survival. We wouldn't survive very long because it's very slow. Yeah. Crossing the street. Crossing the street. I mean many things depend on there being automatic. One very important aspect of system one is that it's not instinctive. You use the word instinctive. It contains skills that clearly have been learned. So that skilled behavior like driving a car or or speaking in fact uh skilled behavior has to be learned and so it doesn't you know you don't come equipped with with driving. You have to learn how to drive and and you have to go through a period where driving is not automatic before it becomes automatic. So yeah, you construct I mean this is where you talk about heristic and biases is you uh to make it automatic, you create a pattern and then uh system one essentially matches a new experience against a previously seen pattern. And when that match is not a good one, that's when the cogn all the all the mess happens. But it's most of the time it works. And so it's pretty most of the time the anticipation of what's going to happen next is correct and and most of the time uh the plan about what you have to do is correct and so most of the time everything works just fine. What's interesting actually is that in some sense system one is much better as at what it does than system two is at what it does. that is there is that quality of effortlessly solving enormously complicated problems which clearly uh exists so that a chess player a very good chess player uh all the moves that come to their mind are strong moves. So all the selection of strong moves happens unconsciously and automatically and very very fast and and all that is in system one. So system two verifies. So along this line of thinking really what we are are machines that construct pretty effective system one. You could think of it that way. So, so we're now talking about humans, but if we think about building artificial intelligence systems, robots, do you think all the features and bugs that you have highlighted in human beings are useful for constructing AI systems? So, both systems are useful for perhaps instilling in robots. What is happening these days is that actually what is happening in deep learning is is more like a system one product than like a system two product. I mean deep learning matches patterns and anticipate what's going to happen. So it's highly predictive. uh what that's right what deep learning doesn't have and you know many people think that this is a critical it it doesn't have the ability to reason so it it does there is no system to there but I think very importantly it doesn't have any causality or any way to represent meaning and to represent real interaction so uh until that is solved uh the you know what can be accomplished is marvelous and very exciting but limited. That's actually really nice to think of uh current advances in machine learning is essentially system one advances. So how far can we get with just system one if we think of deep learning and artificial intelligence systems in you know it's very clear that deep mind has already gone way way beyond what people thought was possible. I think I think the thing that has impressed me most about the developments in AI is the speed. It's that things at least in the context of deep learning and maybe this is about to slow down but things moved a lot faster than anticipated. The transition from solving solving chess to solving go uh was I mean that's bewildering how quickly it went. The move from alpha go to alpha zero is sort of bewildering the speed at which they accomplish that. Now clearly uh there there so there are many problem that you can solve that way but there are some problems for which you need something else something like reasoning. well reasoning and also you know the one of the real mysteries uh psychologist Gary Marcus who is also a critic of AI um I mean he what he points out and I think he has a point is that uh humans learn quickly uh children don't need a million examples they need two or three examples So clearly there is a fundamental difference and what enables uh what enables a machine to to learn quickly what you have to build into the machine because it's clear that you have to build some expectations or something in the machine to make it ready to learn quickly. Uh that's that at the moment seems to be unsolved. I'm pretty sure that Deep Mind is working on it, but um yeah, they're if they have solved it, I I haven't heard yet. They're trying to actually them and OpenAI are trying to to start to get to use neural networks to reason. So, assemble knowledge. Uh of course, causality is temporal causality is out of reach to most everybody. You mentioned the benefits of system one is essentially that it's fast allows us to function in the world fast and skilled you know it's skill and it has a model of the world you know in a sense I mean there was the early phase of of uh AI attempted to model reasoning and they were moderately successful but you know reasoning by itself doesn't get you much uh deep learning has been much more successful in terms of you know what they can do but now it's an interesting question whether it's approaching its limits what do you think I think absolutely so I I just talked to Gian Lun he mentioned you know I know him so he thinks that uh the limits we're not going to hit the limits with neol networks that ultimately this kind of system one pattern matching will start to start to look like system too with without significant transformation of the architecture. So I'm more with the with the majority of the people who think that yes neural networks will hit a limit in their capability. He on the one hand I have heard him tell the Misabies essentially that you know what they have accomplished is not a big deal that they have just touched that basically you know they can't do unsupervised learning in in an effective way and but you're telling me that he thinks that the current within the current architecture you can do causality and reasoning. So he's very much a pragmatist in a sense that's saying that we're very far away that there's still Yeah. I think uh there's this idea that he says is uh we can only see one or two mountain peaks ahead and there might be either a few more after or thousands more after. Yeah. So that kind of idea. I heard that metaphor. Yeah. Right. But nevertheless, it doesn't see a the final answer not fundamentally looking like one that we currently have. So neural networks being a huge part of that. Yeah, I mean that's very likely because because pattern matching is so much of what's going on. But and you can think of neural networks as processing information sequentially. Yeah. I mean, you know, there is there is an important aspect to, for example, you get systems that translate and they do a very good job, but they really don't know what they're talking about. Uh and and and for that I'm really quite surprised for that you would need you would need an AI that has sensation an AI that is in touch with the world. Yes. uh self awareness and maybe even something resembles consciousness kind of ideas. Certainly awareness of you know awareness of what's going on so that the the words have meaning or can get are in touch with some perception or some action. Yeah. So uh that's a big thing for Yan is uh what he refers to as grounding to the physical space. So So that's what we're talking about the same. Yeah. So, but so how how you ground I mean the grounding without grounding then you get you get a machine that doesn't know what it's talking about because it is talking about the world ultimately the question the open question is what it means to ground I mean we're very uh humanentric in our thinking but what does it mean for a machine to understand what it means to be in this world does it need to have a body does it need to have a finite tightness like we humans have all of these elements. It's it's a very it's um you know I'm not sure about having a body but having a perceptual system having a body would be very helpful too. I mean if if you think about human mimicking human or but having a perception that seems to be essential uh so that you can build you can accumulate knowledge about the world. So if a you can im you can imagine a human completely paralyzed and there is a lot that the human brain could learn you know with a paralyzed body. So uh if we got a machine that could do that that would be a big deal. And then the flip side of that something you see in children and something in machine learning world is called active learning. Maybe it is also is uh being able to play with the world. Uh how important for developing system one or or system two do you think it is to play with the world to be able to interact with? Certainly a lot a lot of what you learn as you learn to anticipate uh the outcomes of your actions. I mean you can see that how babies learn it you know with their hands. they how they learn uh you know to connect uh you know the movements of their hands with something that clearly is something that happens in the brain and and and the ability of the brain to learn new patterns. So, you know, it's the kind of thing that you get with artificial limbs that you connect it and then people learn to operate the artificial limb, you know, really impressively quickly at least from from what I hear. Uh, so we have a system that is ready to learn the world through action. At the risk of going into way too mysterious of land, what do you think it takes to build a system like that? Obviously, we're very far from understanding how the the brain works, but how difficult is it to build this mind of ours? You know, I mean, I think that Yandun's answer that we don't know how many mountains there are. I think that's a very good answer. I think that, you know, if you if you look at what Ray Kotzwell is saying, that strikes me as offthe-wall, but uh but I think people are much more realistic than that were actually Demis is and Jan is and so the people who are actually doing the work fairly realistic. I think to maybe phrase it another way from a perspective not of building it but from understanding it. How complicated are human beings in the in the following sense. You know I work with autonomous vehicles and pedestrians. So we tried to model pedestrians. How difficult is it to model a human being, their perception of the world, the two systems they operate under sufficiently to be able to predict whether the pedestrian is going to cross the road or not? I'm, you know, I'm fairly optimistic about that actually because what we're talking about is uh a huge amount of information that every vehicle has and that feeds into one system into one gigantic system. And so anything that any vehicle learns becomes part of what the whole system knows. And with with a system multiplier like that uh there is a lot that you can do. So human beings are very complicated but and and you know system is going to make mistakes but human makes mistakes. I think that they'll be able to I think they are able to anticipate pedestrians otherwise a lot would happen. they're able to, you know, they're able to get into a roundabout and into the into traffic. So, they must know both to expect or to anticipate how people will react when they're sneaking in. And there's a lot of learning that's involved in that. Currently, the pedestrians are treated as things that cannot be hit and they're not treated as agents with whom you interact in a game theoretic way. So, I mean, it's not it's a totally open problem and every time somebody tries to solve it, it seems to be harder than we think. and nobody's really tried to seriously solve the problem of that dance because uh I'm not sure if you've thought about the problem of pedestrians but you're really putting your life in the hands of the driver. You know there is a dance there is part of the dance that would be quite complicated but for example when I cross the street and there is a vehicle approaching I look the driver in the eye and I think many people do that and you know that's a signal uh that that I'm sending and I would be sending that machine to an autonomous vehicle and it had better understand it because it means I'm crossing. So, and there's another thing you do that actually. So, I'll tell you what you do because we watched I've watched hundreds of hours of video on this is when you step in the street, you do that before you step in the street. And when you step in the street, you actually look away. Look away. Yeah. Uh now, what what is that? What that's saying is mean you're trusting that the car who hasn't slowed down yet will slow down. Yeah. And you're telling him, Yeah. I'm committed. I mean this is like in a game of chicken. So I'm committed and if I'm committed I'm looking away. So there is you you just have to stop. So the question is whether a machine that observes that needs to understand mortality here. I'm not sure that it's got to understand so much as it's got to anticipate. So, and here, but you know, you're surprising me because here I would think that maybe you can anticipate without understanding because I think this is clearly what's happening in playing go or in playing chess. There's a lot of anticipation and there is zero understanding. Exactly. So, uh I thought that you didn't need a model of the human. Yes. and a model of the human mind to avoid hitting pedestrians. But you are suggesting that actually we do. Yeah, you do. And then it's then it's a lot harder. So this is and I have a followup question to see where your intuition lies is it seems that almost every robot human collaboration system is a lot harder than people realize. So, do you think it's possible for robots and humans to collaborate successfully? We we talked a little bit about semi-autonomous vehicles like in the Tesla autopilot, but just in tasks in general, if you think we talked about current neural networks being kind of system one, do you think uh those same systems can borrow humans for system two type tasks and collaborate successfully? Well, I think that in any system where humans and and the machine interact that the human will be superfluous within a fairly short time and that is if if the machine is advanced enough so that it can really help the human then it may not need the human for a long time. Now it would be very interesting if if there are problems that for some reason the machine doesn't cannot solve but that people could solve then you would have to build into the machine an ability to recognize that it is in that kind of problematic situation and and to call the human that that cannot be easy without understanding that is it's it must be very difficult to to program a recognition that you are in a problematic situation without understanding the problem. But that's very true. In order to understand the full scope of situations that are problematic, you almost need to be smart enough to solve all those problems. It's not clear to me how much the machine will need the human. I think the example of chess is very instructive. I mean there was a time at which Kasparov was saying that human machine combinations will beat everybody. Uh even Stockfish doesn't need people. Yeah. And Alpha Zero certainly doesn't need people. The question is just like you said, how many problems are like chess? And how many problems are the ones where are not like chess? Where well every problem in the end is like chess. The question is how long is that transition period? I mean, you know, that's that's a question I would ask you in terms of I mean, autonomous vehicle just driving is probably a lot more complicated than go to solve that. Yes. And that's surprising because it's open. No, I mean, you know, it wouldn't that's not surprising to me because the because that there is a hierarchical aspect to this which is recognizing a situation and then within the situation bringing bringing up the relevant knowledge and uh and for that hierarchical type of system to work uh you need a more complicated system than we currently have. A lot of people think because as human beings, this is probably the the cognitive biases, they think of driving as pretty simple because they think of their own experience. This is actually a a big problem for a AI researchers or people thinking about AI because they evaluate how hard a particular problem is based on very limited knowledge based on how hard it is for them to do the task. Yeah. and then they take for granted. Maybe you can speak to that because most people tell me driving is trivial and and humans in fact are terrible at driving is what people tell me and I see humans and humans are actually incredible at driving and driving is really terribly difficult. Yeah. Uh so is that just another element of the effects that you've described in your work on the psychology side? No, I mean I haven't really, you know, I would say that my research has contributed nothing to understanding the ecology and to understanding the structure of situations and the complexity of problems. Uh so all all we know is very clear that that goal it's endlessly complicated but it's very constrained. So uh and and in the real world there are far fewer constraints and and many more potential surprises. So uh so that's obvious because it's not always obvious to people right? So when you think about well I mean you know people thought that reasoning was hard and perceiving was easy but you know they quickly learned that actually modeling vision was tremendously complicated and modeling even proving theorems was relatively straightforward. to push back on that a little bit on the quickly part. They haven't it took several decades to learn that and most people still haven't learned that. I mean our intuition of course AI researchers have but you you drift a little bit outside the specific AI field. The intuition is still perception to sol. That's true. I mean the intuitions the intuitions of the public haven't changed radically and they are they are as you said they're evaluating the complexity of problems by how difficult it is for for them to solve the problems and that's got very little to do with the complexities of solving them in AI. How do you think from the perspective of AI researcher do we deal with the intuitions of the public? So in trying to think I mean arguably the combination of uh hype investment and the public intuition is what led to the AI winters. I'm sure that same can be applied to tech or that the intuition of the public leads to media hype leads to companies investing in the tech and then the tech doesn't make the company's money and then there's a crash. Is there a way to educate people to fight the let's call it system one thinking in general? No. You know, I think that's a simple answer. Um, and it's going to take a long time before the understanding of what those systems can do becomes, you know, part becomes public knowledge. I and and then and the expectations, you know, there are several aspects that are going to be very complicated that are the the fact that you have a device that cannot explain itself is a major major difficulty and uh and we're already seeing that. I mean this is this is really something that is happening. So it's happening in the judicial system. So you have uh you have system that are clearly better at predicting parole violations than uh than judges but uh but they can't explain their reasoning and so uh people don't want to trust them. We uh seem to in system one even use cues to make judgments about our environment. So this explanability point do you think humans can explain stuff? No. But themselves uh I mean there is a very interesting uh aspect of that. Humans think they can explain themselves right? So when you say something and I ask you why do you believe that then reasons will occur to you and you will but actually my own belief is that in most cases the reasons have very little to do with why you believe what you believe. So that the reasons are a story that that comes to your mind when you need to explain yourself. But um but but people traffic in those explanations. I mean the human interaction depends on those shared fictions and and the stories that people tell themselves. You just made me actually realize and we'll talk about stories in a second that not to be cynical about it but perhaps there's a whole movement of people trying to do explainable AI and really we don't necessarily need to explain AI doesn't need to explain itself. It just needs to tell a convincing story. Yeah, absolutely. It doesn't necess the story doesn't necessarily need to uh reflect the truth as it might it just needs to be convincing. There's something to that. It can you can say exactly the same thing in a way that's sounds cynical or doesn't sound cynical. I mean, so but but the objective brilliant of having an explanation is is to tell a story that will be acceptable to people and and and for it to be acceptable and to be robustly acceptable, it has to have some element of truth. But but the objective is for people to accept it. It's quite brilliant actually. Um but so on the uh on the stories that we tell, sorry to ask me ask you the question that most people know the answer to, but uh you talk about two selves in terms of how life is lived, the experience self and the remembering self. Can you describe the distinction between the two? Well, sure. I mean the there is an aspect of uh of life that occasionally you know most of the time we just live and we have experiences and they're better and they are worse and it goes on over time and mostly we forget everything that happens or we forget most of what happens. Then occasionally you when something ends or at different points uh you evaluate the past and you form a memory and the memory is schematic. It's not that you can roll a film of an interaction. You construct in effect the elements of a story about an about an episode. So there is the experience and there is the story that is created about the experience and that's what I call the remembering. So I I had the image of two selves. So there is a self that lives and there is a self that evaluates life. Now the paradox and the deep paradox in that is that um we have one system or one self that does the living but the other system uh the remembering self is all we get to keep and basically decision making and and everything that we do is governed by our memories not by what actually happened. It's it's governed by by the story that we told ourselves or by the story that we're keeping. So that's that's the distinction. I mean there's a lot of brilliant ideas about the pursuit of happiness that come out of that. What are the properties of happiness which emerge from self? There are there are properties of how we construct stories that are really important. So uh that I studied a few but but a couple are really very striking and one is that in stories time doesn't matter. M there's a sequence of events or there are highlight or not and and how long it took you know they lived happily ever after or and three years later something it time really doesn't matter and in stories events matter but time doesn't that that leads to a very interesting set of problems because time is all we not to live. I mean, you know, time is the currency of life. Uh, and yet time is not represented basically in evaluated memories. So, that that creates a lot of uh paradoxes that I've thought about. Yeah. That are fascinating. But if you were to give uh advice on how one lives a happy life based on such properties, what's the optimal? Well, you know, I gave up I abandoned happiness research because I couldn't solve that problem. I couldn't I couldn't see. Uh and in the first place, it's very clear that if you do talk in terms of those two selves, then that what makes the remembering self happy and what makes the experiencing self happy are different things. And I I asked the question uh of suppose you're planning a vacation and you're just told that at the end of the vacation you'll get an amnesic drug so you remember nothing and they'll also destroy all your photos. So there'll be nothing. Would you still go to the same vacation? And and it's it turns out we go to vacations in large part to construct memories not to have experiences but to construct memories. And it turns out that the vacation that you would want for yourself if you knew you will not remember is probably not the same vacation that you will want for yourself if you will remember. So uh I have no solution to these problems but clearly those are big issues and you've talked about difficult issues. You've talked about sort of how many minutes or hours you spend about the vacation. It's an interesting way to think about it because that's how you really experience the vacation outside the being in it. But there's also a modern I don't know if you think about this or interact with it. There's a modern way to uh magnify the remembering self which is by posting on Instagram, on Twitter, on social networks. A lot of people live life for the picture that you take that you post somewhere and now thousands of people share and it potentially potentially millions and then you can relive it even much more than just those minutes. Do you think about that magnification much? You know, I'm too old for social networks. I, you know, I've never seen Instagram, so I cannot really speak intelligently about those things. I'm just too old. But it's interesting to watch the exact effects you describe. It will make a very big difference. I mean, and it will make it will also make a difference. And that I don't know whether uh it's clear that in some ways the devices that serve us uh supplant function. So you don't have to remember phone numbers. You don't have you you really don't have to know facts. I mean the number of conversations I'm involved with where somebody says well let's look it up. Uh so it's it's in a way it's made conversations well it's it means that it's much less important to know things. You know it used to be very important to know things. This is changing. So the requirements of that that we have for ourselves and for other people are changing because of all those supports and because and I have no idea what Instagram does but it's uh well I'll tell you wish I knew I mean I I wish I could just have the my remembering self could enjoy this conversation but I'll get to enjoy it even more by having watch by watching it and and talking to others it'll be about 100,000 people as scary as this to to say will listen or watch this right it changes things it changes the experience of the world that you seek out experiences which could be shared in that way it's and I haven't seen it's it's the same effects that you described and I don't think the psychology of that magnification has been described yet because it's a new world you know the sharing There was a per there was a time when people read books and uh and and you could assume that your friends had read the same books that you read. So there was kind of invisible sharing. There was a lot of sharing going on and there was a lot of assumed common knowledge and you know that was built in. I mean it was obvious that you had read the New York Times. It was obvious that you had read the reviews. I mean uh so a lot was taken for granted that was shared. Uh and you know when there were when there were three television channels it was obvious that you'd seen one of them probably the same. Uh so sharing sharing always was always there. It was just different. At the risk of uh inviting mockery from you, let me say that that I'm also a fan of Sartra and Kimu and existentialist philosophers. And um I'm joking of course about mockery, but from the perspective of the two selves, what do you think of the existentialist philosophy of life? So trying to really emphasize the experiencing self as the proper way to or the best way to live life. I don't know enough philosophy to answer that. But it's not uh you know the emphasis on on experience is also the emphasis in Buddhism. Yeah. Right. That's right. So uh that's you just have got to to experience things and and and not to evaluate and not to pass judgment and not to score not to keep score. So, uh, if when you look at the the grand picture of experience, you think there's something to that that one one of the ways to achieve contentment and maybe even happiness is letting go of any of the things any of the procedures of the remembering self. Well, yeah. I mean, I think, you know, if one could imagine a life in which people don't score themselves. Mhm. uh it it feels as if that would be a better life as if the self-scoring and you know how am I doing uh kind of question uh is not is not a very happy thing to have but I got out of that field because I couldn't solve that problem and and that was because my intuition was that the experiencing self that's reality But then it turns out that what people want for themselves is not experiences. They want memories and they want a good story about their life. And so you cannot have a theory of happiness that doesn't correspond to what people want for themselves. And when I when I realized that this this was where things were going, I really sort of left the field of research. Do you think there's something instructive about this emphasis of reliving memories in building AI systems? So, currently artificial intelligence systems are more like experiencing cells in that they react to the environment. there's some pattern formation like uh learning so on but you really don't construct memories uh except in reinforcement learning every once in a while that you replay over and over. Yeah, but but you know that would in principle would not be Do you think that's useful? Do you think it's a feature or a bug of human beings that we uh that we look back? Oh, I think that's definitely a feature. It's not a bug. I mean you you have to look back in order to look forward. So uh without without looking back you couldn't you couldn't really intelligently look forward. You're looking for the echoes of the same kind of experience in order to predict how what the future holds. Yeah. though Victor Frankle in his book Man's Search for Meaning, I'm not sure if you've read, but describes his experience at the concentr concentration camps during World War II as a way to describe that finding identifying a purpose in life, a positive purpose in life can save one from suffering. First of all, do you connect with the philosophy that he describes there? M not really. I mean the So I can I can really see that somebody who has that feeling of purpose and meaning and so on that that could sustain you. Uh I in general don't have that feeling and I'm pretty sure that if I were in a concentration camp I'd give up and die, you know. So he talks he is he is a survivor. Yeah. And you know he survived with that and um and I'm not sure how essential to survival this sense is but I do know when I think about myself that I would have given up at oh this isn't going anywhere. uh and there is there is a sort of character that that that manages to survive in conditions like that and then because they survive they tell stories and it sounds as if they survived because of what they were doing we have no idea they survived because of the kind of people that they are and they are the kind of people who survives and who tell themselves stories of a particular kind so I'm not uh so you don't think seeking purpose is a significant driver in our I mean it's it's a very interesting question because when you ask people whether it's very important to have meaning in their life they say oh yes that's the most important thing but when you ask people what kind of a day did you have and and you know what were the experiences that you remember you don't get much meaning you get social experiences then uh and and some people say that for example in in in child you know in taking care of children the fact that they are your children and you're taking care of them uh makes a very big difference. I think that's entirely true. uh but it's more because of a story that we're telling ourselves which is a very different story when we're taking care of our children or when we're taking care of other things. Jumping around a little bit in doing a lot of experiments. Let me ask a question. Most of the work I do for example is in in the in the real world but most of the clean good science that you can do is in the lab. So that distinction do you think we can understand the fundamentals of human behavior through controlled experiments in the lab? If we talk about pupil diameter for example, it's much easier to do when you can control lighting conditions. Yeah. Right. Both. Uh so when we look at driving lighting variation destroys almost completely your ability to use pupil diameter but in the lab for as I mentioned semi-autonomous or autonomous vehicles in driving simulators we can't we don't capture true honest uh human behavior in that particular domain. So in your what's your intuition? How much of human behavior can we study in this controlled environment of the lab? A lot. But you'd have to verify it. You know that you your conclusions are basically limited to the situation to the experimental situation. Then you have to jump that a big inductive leap to the real world. Uh so and and that's the flare. That's where the difference I think between the good psychologists and others that are mediocre is in the sense that your experiment capture something that's important, right? And something that's real and others are just running experiments. So what is that like the birth of an idea to development in your mind to something that leads to an experiment? Is that similar to maybe like what Einstein or a good physicist do is your intuition. You basically use your intuition to build up. Yeah. But I mean, you know, it's it's very skilled intuition. I mean, I I just had that experience actually. I had an idea that turns out to be very good idea uh a couple of days ago and and you and you have a sense of that building up. So, I'm working with a collaborator and he he essentially was saying, you know, what what are you doing? You know, what's what's going on? And I was I really I couldn't exactly explain it, but I knew this is going somewhere. But, you know, I've been around that game for a very long time. And so, I can you you develop that anticipation that yes, this this is worth following up. That's part of the skill. Is that something you can reduce to words in describing a process in in the form of advice to others? No. Follow your heart essentially. I mean, you know, it's it's like trying to explain what it's like to drive. It's not you've got to break it apart and it's not and then you lose and then you lose the experience. You mentioned collaboration. And you've written about your collaboration with Amos Diverski that this is you writing the 12 or 13 years in which most of our work was joint or years of interpersonal and intellectual bliss. Everything was interesting. Almost everything was funny. And there was a current joy of seeing an idea take shape. So many times in those years we shared the magical experience of one of us saying something which the other one would understand more deeply than the speaker had done. Contrary to the old laws of information theory, it was common for us to find that more information was received than had been sent. I have almost never had the experience with anyone else. If you have not had it, you don't know how marvelous collaboration can be. So let me ask a perhaps a silly question. How does one find and create such a collaboration? That may be asking like how does one find love? But yeah, you have to be you have to be lucky and and and I think you have to have the character for that because I've had many collaborations. I mean, none were as exciting as with Elmos, but I've had and I'm having just very So, it's a skill. I think I'm good at it. Uh, not everybody is good at it. And then it's the luck of finding people who are also good at it. Is there advice in a form for for a young scientist who also seeks to violate this law of information theory? I really think it's so much luck is involved and you know in in those really serious collaborations at least in my experience are a very personal experience and and I have to like the person I'm working with otherwise you know I mean there is that kind a collaboration which is like an exchange, a commercial exchange of I'm giving this, you give me that. But the the real ones are interpersonal. They're between people who like each other and and who like making each other think and who like the way that the other person responds to your thoughts. Uh you have to be lucky. Yeah. I mean, but I already noticed the p even just me showing up here. You you've quickly started to digging in on a particular problem I'm working on and already new information started to emerge. If is that a process just a process of curiosity of talking to people about problems and seeing I'm curious about anything to do with AI and robotics and you know and uh so and I knew you were dealing with that so I was curious. just follow your curiosity. Jumping around on on the psychology front, the a dramatic sounding terminology of replication crisis, but really just the at times this this effect that at times studies do not are not fully generalizable. They don't if you're being polite. It's worse than that. But is it? So I'm actually not fully familiar to the degree how bad it is, right? So what do you think is the source? Where do you think I think I know what's going on actually. I mean I have a theory about what's going on and what's going on is that there is first of all a very important distinction between two types of experiments and one type is within subject. So it's the same person has two experimental conditions and the other type is between subjects where some people have this condition other people have that condition. They're different worlds and between subject experiments are much harder to predict and much harder to anticipate. And the reason uh and they're also more expensive because you need more people and it's it's just so between subject experiments is where the problem is. Uh it's not so much and within subject experiments, it's really between and there is a very good reason why the intuitions of researchers about between subject experiments are wrong. And that's because when you are a researcher, you're in a within subject situation. That is you are imagining the two conditions and you see the causality and you feel it and but in the between subjects condition they don't they see they live in one condition and the other one is just nowhere. So our intuitions are very weak about between subject experiments and that I think is something that people haven't realized and and in addition because of that we have no idea about the power of uh manipulations of experimental manipulations because the same manipulation is much more powerful when when you are in the two conditions than when you live in only one condition. And so the experimenters have very poor intuitions about between subject experiments. And and there is something else which is very important I think which is that almost all psychological hypotheses are true. That is in the sense that you know directionally if you have a hypothesis that A really causes B that that it's not true that A causes the opposite of B maybe A just has very little effect but hypotheses are true mostly except mostly they're very weak they're much weaker than you think when you are having images of so the reason I'm excited about that is that I recently heard about uh some some friends of mine who uh they essentially funded 53 studies of behavioral change by 20 different teams of people with a very precise objective of changing the number of time that people go to the gym. But you know and and the success rate was zero. Not one of the 53 studies worked. Now what's interesting about that is those are the best people in the field and they have no idea what's going on. So they're not calibrated. They think that it's going to be powerful because they can imagine it. But actually it's just weak because the you are focusing on on your manipulation and it feels powerful to you. There's a thing that I've written about that's called the focusing illusion. That is that when you think about something it looks very important more important than it really is more important than it really is. But if you don't see that effect the 53 studies doesn't that mean you just report that? So what what's I guess the solution to that? Well, I mean the the solution is for people to trust their intuitions less or to try out their intuitions before I mean experiments have to be pre-registered and by the time you run an experiment you have to be committed to it and you have to run the experiment seriously enough and uh in a public and so this is happening. The interesting thing is uh what what happens before and how do people prepare themselves and how they run pilot experiments. It's going to train the way psychology is done and it's already happening. Do you have a hope for this might connect to uh the this study sample size? Yeah. Uh do you have a hope for the internet or I mean you know this is really happening murk. Yeah. Uh, everybody is running experiments on MTOK and and it's very cheap and very effective. So, do you think that changes psychology essentially because you're think you can now run 10,000 subjects eventually it will. Yeah, I mean I you know I can't put my finger on how exactly but it's that's been true in psychology with whenever an important new method came in it changes the feel so and and murk is really a method because it it makes it very much easier to do some to do some things. Is there uh undergrad students will ask me you know how big a neural network should be for a particular problem. So let me ask you an equivalent equivalent question. Uh how big how many subjects a study have for it to have a conclusive result? Well it depends on the strength of the effect. So if you're studying visual perception or the perception of color, many of the the classic results in in visual in color perception were done on three or four people and I think one of them was color blind but or partly color blind but on vision you know you it's highly related many uh people don't need a lot of replications for some type of of neurological experiment. Neuro uh when you're studying weaker phenomena and especially when you're studying them between subjects then you need a lot more subjects than people have been running and that is that's one of the thing that are happening in psychology now is that the power the statistical power of the experiments is is increasing rapidly. Does the between subject as the number of subjects goes to infinity approach? Well, I mean you know goes to infinity is exaggerated but people the standard number of subjects for an experiment psychology with 30 or 40 and for a weak effect that's simply not enough and you may need a couple of hundred. I mean it's that that sort of uh order of magnitude. What are the major disagreements and theories and effects that you've observed throughout your career that still stand today? You've worked on several fields. Yeah. What still is out there as as major disagreement that pops into your mind? And I've had one extreme experience of, you know, controversy with somebody who really doesn't like the work that Amoski and I did and and he's been after us for 30 years or more at least. Do you want to talk about it? Well, I mean, his name is Good Giganzer. He's a well-known German psychologist. And that's the one controversy I have which I it's been unpleasant and and no I don't particularly want to talk about it but is there is there open questions even in your own mind every once in a while you know uh we talked about semi-autonomous vehicles in my own mind I see what the data says but I also constantly torn do you have things where you your studies have found something but you're also intellectual torn about what it means and there's maybe been maybe disagreements without your within your own mind about particular things. I mean it's you know one of the things that are interesting is how difficult it is for people to change their mind essentially uh you know once they are committed people just don't change their mind about anything that matters and that is surprisingly but it's true about scientists. So the controversy that I described uh you know that's been going on like 30 years and it's never going to be resolved uh and you build a system and you live within that system and other other systems of ideas look foreign to you and and and there is very little contact and very little mutual influence that happens a fair amount. Do you have a hopeful advice or message on that? We thinking about science, thinking about politics, thinking about things that have impact on this world. How can we change our mind? I think that I mean on things that matter you know which are political or rel political or religious and people just don't don't change their mind and by and large and there's very little that you can do about it. Uh the what does happen is that if leaders change their minds. So for example, the public, the American public doesn't really believe in climate change, doesn't take it very seriously, but if some religious leaders decided this is a major threat to humanity, that would have a big effect. So that we we have the opinions that we have not because we know why we have them, but because we trust some people and we don't trust other people. And uh so it's much less about evidence than it is about stories. So the way one way to change your mind isn't at the individual level is that the leaders of the communities you look up with the stories change and therefore your mind changes with them. So there's a guy named Alan Touring came up with a touring test. Yeah. Uh what what do you think is a good test of intelligence? Perhaps we're drifting in a topic that we're um maybe philosophizing about, but what do you think is a good test for intelligence for an artificial intelligence system? Well, the standard definition of you know of artificial general intelligence that it can do anything that people can do and it can do them better. Yes. What what we are seeing is that in many domains you have domain specific uh and you know devices or programs or software and they beat people easily in specified way. What we are very far from is that general ability a general purpose intelligence. So we we in in machine learning people are approaching something more general. I mean for Alpha Zero was was much more general than than Alpha Go. And but it's still extraordinarily narrow and specific in what it can do. So, so we're quite far from from something that can in every domain think like a human except better. What aspect? So, the the touring test has been criticized. It's natural language conversation. Yeah. That is too simplistic. Uh it's easy to quote unquote pass under under constraints specified. What aspect of conversation would impress you if you heard it? Is it humor? Is it what what would impress the heck out of you if uh if you saw it in conversation? Yeah, I mean certainly wit would you know wit would be impressive uh and and and humor would be more impressive than just factual conversation which I think is is easy and illusions would be interesting and metaphors would be interesting. I mean but new metaphors not practiced metaphors. So there is a lot that you know would be sort of impressive if that is completely natural in conversation but that you really wouldn't expect. Does the possibility of creating an a human level intelligence or superhuman level intelligence system excite you? Scare you? Well, I mean how does it make you feel? Uh I find the whole thing fascinating. Absolutely fascinating. exciting I think and exciting. It's also terrifying, you know, but but I'm not going to be around to see it. And uh so I'm curious about what is happening now, but I also know that that predictions about it are silly. Uh we really have no idea what it will look like 30 years from now. No idea. Speaking of silly, bordering on the profound, they may ask the question of in your view, what is the meaning of it all? The meaning of life? He's a descendant of great apes that we are. Why? What drives us as a civilization, as a human being, as a force behind everything that you've observed and studied? Is there any answer or is it all just a beautiful mess? There is no answer that that I can understand. Uh and I'm not and I'm not actively looking for one. Um do you think an answer exists? No, there is no answer that we can understand. I'm not qualified to speak about what we cannot understand. But there is I know that we cannot understand reality you know and I mean there are a lot of thing that we can do I mean you know gravity waves I mean that's that's a big moment for humanity and when you imagine that ape you know being able to to go back to the big bang that's that's but but the why yeah the why bigger than us the boy is hopeless. Really, Danny, thank you so much. It was an honor. Thank you for speaking today. Thank you. Thanks for listening to this conversation and thank you to our presenting sponsor, Cash App. Download it. Use code Lex Podcast. You'll get $10 and $10 will go to First, a STEM education nonprofit that inspires hundreds of thousands of young minds to become future leaders and innovators. If you enjoy this podcast, subscribe on YouTube. Give it five stars on Apple Podcast, follow on Spotify, support it on Patreon, or simply connect with me on Twitter. And now, let me leave you with some words of wisdom from Daniel Conorman. Intelligence is not only the ability to reason, it is also the ability to find relevant material in memory and to deploy attention when needed. Thank you for listening and hope to see you next time.