Daniel Kahneman: Thinking Fast and Slow, Deep Learning, and AI

Daniel Kahneman is winner of the Nobel Prize in economics for his integration of economic science with the psychology of human behavior, judgment and decision-making. He is 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 Tversky, on cognitive biases, prospect theory, and happiness. The central thesis of this work is a dichotomy between two modes of thought: “System 1” is fast, instinctive and emotional; “System 2” is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584, follow on Spotify https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL, or support it on Patreon https://www.patreon.com/lexfridman. This episode is presented by Cash App. Download it (App Store https://apps.apple.com/us/app/cash-app/id711923939, Google Play https://play.google.com/store/apps/details?id=com.squareup.cash&hl=en_US), use code “LexPodcast”. Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 – Introduction 02:36 – Lessons about human behavior from WWII 08:19 – System 1 and system 2: thinking fast and slow 15:17 – Deep learning 30:01 – How hard is autonomous driving? 35:59 – Explainability in AI and humans 40:08 – Experiencing self and the remembering self 51:58 – Man’s Search for Meaning by Viktor Frankl 54:46 – How much of human behavior can we study in the lab? 57:57 – Collaboration 1:01:09 – Replication crisis in psychology 1:09:28 – Disagreements and controversies in psychology 1:13:01 – Test for AGI 1:16:17 – Meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Daniel Kahneman, 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 Tversky, 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.

• 0:40Lex Fridman?

  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 intelligent systems. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcasts, follow on Spotify, support it on Patreon, or simply connect with me on Twitter at LexFriedman, spelled F-R-I-D-M-A-N. 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.

• 1:23Lex Fridman?

  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. Brokerage services are provided by Cash App Investing, a subsidiary of Square and member SIPC.

• 1:54Lex Fridman?

  I'm excited to be working with Cash App to support one of my favorite organizations called FIRST, best known for their first robotics and Lego competitions. They educate and inspire hundreds of thousands of students in over 110 countries and have a perfect rating at Charity Navigator, which means that donated money is used to maximum effectiveness. When you get Cash App from the App Store or Google Play and use code LEXPODCAST, you'll get $10 and Cash App will also donate $10 to FIRST, which again is an organization that I've personally seen inspire girls and boys to dream of engineering a better world. And now here's my conversation with Daniel Kahneman.

• 2:54Lex Fridman?

  You tell a story of an SS soldier early in the war, World War II, in Nazi occupied France in Paris where he grew up. He picked you up and hugged you and showed you a picture of a boy, maybe not realizing that you were Jewish.

• 3:11Speaker 01

  Not maybe, certainly not.

• 3:13Lex Fridman?

  So I told you I'm from the Soviet Union that was significantly impacted by the war as well, and I'm Jewish as well. What do you think World War II taught us about human psychology broadly?

• 3:27Speaker 01

  Well, I think 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.

• 3:44Lex Fridman?

  It's a surprise because?

• 3:45Speaker 01

  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. Certainly for me, the Achman Trial, 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.

• 4:24Lex Fridman?

  So what do you think that is? Do you think we're all capable of evil? We're all capable of cruelty?

• 4:30Speaker 01

  I don't think in those terms. I think that what is certainly possible is you can dehumanize people. So that you treat them not as people anymore, but as animals. And in the same way that you can slaughter animals without feeling much of anything, it can the same. And when you feel that, I think the combination of dehumanizing the other side and having uncontrolled power over other people. I think that doesn't bring out the most generous aspect of human nature.

• 5:12Speaker 01

  So that Nazi soldier, you know, he was a good man. I mean, you know, and he was perfectly capable of killing a lot of people. And I'm sure he did.

• 5:27Lex Fridman?

  But what did the Jewish people mean to Nazis? So what the dismissal of Jewish as worthy of?

• 5:38Speaker 01

  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-group and the out-group, that is very basic. So that's built in. The loyalty and affection towards in-group and the willingness to dehumanize the out-group. That is in human nature. And that's what I think probably didn't need the Holocaust to teach us that. But the Holocaust is a very sharp lesson of, you know, what can happen to people?

• 6:21Speaker 01

  What people can do.

• 6:23Lex Fridman?

  So the effect of the in-group and the out-group?

• 6:26Speaker 01

  You know, it's clear that those were people, you know, 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.

• 6:41Speaker 01

  So occasionally, you know, there might have been, and, and very quickly, by the way, 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 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.

• 7:25Lex Fridman?

  Is a question.

• 7:26Speaker 01

  It's an interesting question.

• 7:27Lex Fridman?

  Are these artifacts of history, or is it human nature?

• 7:31Speaker 01

  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.

• 7:45Lex Fridman?

  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.

• 7:58Speaker 01

  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, that little is left, but...

• 8:35Lex Fridman?

  So, let's talk about psychology a little bit. In your book, Thinking Fast and Slow... You described 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 the theory of evolution, can you describe distinguishing characteristics for people who have not read your book of the two systems?

• 9:07Speaker 01

  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, a standard example is two 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 times

• 9:52Speaker 01

  14, I think.

• 9:54Lex Fridman?

  You have to perform some algorithm in your head, some steps.

• 9:57Speaker 01

  Yes. And, and it takes time. It's a very difference. 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 short-term memory, and it engages executive function, and it makes you incapable of doing other things at the same time. So, the, the main characteristic of system two, 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.

• 10:33Lex Fridman?

  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 is no distinct two systems in the brain, from a neurobiological, even from psychology perspective. But why does it seem to, from the experiments you've conducted, there does seem to be kind of emergent two modes of thinking? So, at some point, these kinds of systems came into

• 11:09Lex Fridman?

  A brain architecture, maybe man will 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?

• 11:19Speaker 01

  You know, evolutionary theorizing about this is cheap and easy. So it's the way I think about it is that it's very clear that animals have have 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 the key form of understanding the world. And my crude idea is that what I call system two. Well, system two grew out of this.

• 12:02Speaker 01

  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 very large brain that we have compared to others would be impossible. Now, system one is more like what the animals are, but system one 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.

• 12:47Speaker 01

  The words, I have some idea and then the words come out and that's automatic and effortless.

• 12:54Lex Fridman?

  And 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.

• 13:05Speaker 01

  Well, I mean, you know. We have to trust it because it's the speed at which it acts. Uh, system two, if we, if we depend on system two for survival, we wouldn't survive very long because it's very slow.

• 13:22Lex Fridman?

  Yeah, crossing the street.

• 13:23Speaker 01

  Crossing the street. I mean, many things depend on their 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, 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.

• 14:04Lex Fridman?

  Yeah, you construct, I mean, this is where you talk about heuristic and biases is you, you make it automatic. You create a pattern and then system one essentially matches a new experience against the 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.

• 14:27Speaker 01

  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. And system two is at what it does. That is, there is this quality of effortlessly solving enormously complicated problems, which clearly, uh, exists so that a chess player, a very good chess player, all the moves that come to their mind are strong moves.

• 15:10Speaker 01

  So all the selection of strong moves happens unconsciously and automatically and very, very fast. And, and all that is in system one. So a system two verifies.

• 15:24Lex Fridman?

  So along this line of thinking, really what we are are machines that construct pretty effective system one.

• 15:33Lex Fridman?

  You could think of it that way. 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?

• 15:56Speaker 01

  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. What

• 16:20Lex Fridman?

  That's right.

• 16:20Speaker 01

  What deep learning doesn't have, and you know, many people think that this is the critical, it doesn't have the ability to reason. So it does, and there is no system two there. But I think very importantly, it doesn't have any causality or any way to represent meaning and to represent real interactions. So until that is solved, the, you know, what can be accomplished is marvelous and very exciting, but limited.

• 16:52Lex Fridman?

  That's actually really nice to think of 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 and

• 17:06Speaker 01

  I mean, 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 was, I mean, that's bewildering how quickly it went.

• 17:43Speaker 01

  The move from Alpha Go to Alpha Zero is sort of bewildering, the speed at which they accomplished that. Now, clearly, there are, so there are many problems that you can solve that way. But there are some problems for which you need something else.

• 18:01Lex Fridman?

  Something like reasoning.

• 18:03Speaker 01

  Well, reasoning and also, you know, one of the real mysteries, psychologist Gary Marcus, who is also a critic of AI. I mean, he, what he points out, and I think he has a point, is that humans learn quickly.

• 18:26Speaker 01

  Children don't need a million examples, they need two or three examples. So clearly, there is a fundamental difference. And what enables, 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. That, that at the moment seems to be unsolved. I'm pretty sure that DeepMind is working on it, but.

• 19:01Lex Fridman?

  Yeah, they're.

• 19:02Speaker 01

  If they have solved it, I, I haven't heard yet.

• 19:05Lex Fridman?

  They're trying to actually, them and OpenAI are trying to, to start to get to use neural networks to reason. So assemble knowledge, of course causality is, temporal causality is out of reach to most everybody. You, you mentioned the benefits of System One is essentially that it's fast, allows us to function in the world.

• 19:28Speaker 01

  Fast and skilled, you know.

• 19:30Lex Fridman?

  It's skill.

• 19:31Speaker 01

  And it has a model of the world. You know, in a sense, I mean, there was the earliest phase of, of AI attempted to model reasoning. And they were moderately successful, but, you know, reasoning by itself doesn't get you much.

• 19:51Speaker 01

  Deep learning has been much more successful in terms of, you know, what they can do. But now.

• 19:58Speaker 01

  It's an interesting question, whether it's approaching its limits. What do you think?

• 20:02Lex Fridman?

  I think absolutely. So I just talked to Gianlacun, he mentioned, you know.

• 20:07Speaker 01

  I know him.

• 20:08Lex Fridman?

  So he thinks that the limits, we're not going to hit the limits with neural networks, that ultimately this kind of system on pattern matching will start to, start to look like system two 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.

• 20:34Speaker 01

  He, on the one hand, I have heard him tell the Mises, a sub is 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. But you're telling me that he thinks that the current, within the current architecture, you can do causality and reasoning?

• 20:59Lex Fridman?

  So he's very much a pragmatist in a sense that's saying that we're very far away, that there's still.

• 21:05Speaker 01

  Yeah.

• 21:05Lex Fridman?

  I think 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.

• 21:19Speaker 01

  I heard that metaphor.

• 21:21Lex Fridman?

  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.

• 21:36Speaker 01

  Yeah. I mean, that's very likely because, because pattern matching is so much of what's going on.

• 21:43Lex Fridman?

  And you can think of neural networks as processing information sequentially.

• 21:47Speaker 01

  Yeah. I mean, you know, there is.

• 21:51Speaker 01

  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. 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.

• 22:15Lex Fridman?

  Yes, self-awareness and maybe even something resembles consciousness kind of ideas.

• 22:22Speaker 01

  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.

• 22:33Lex Fridman?

  Yeah, so that's a big thing for Jan and is what he refers to as grounding to the physical space. So.

• 22:41Speaker 01

  So that's what we're talking about the same.

• 22:43Lex Fridman?

  Yeah. So, but so how, how you ground.

• 22:46Speaker 01

  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.

• 22:57Lex Fridman?

  The question, the open question is what it means to ground. I mean, we're very human centric 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 finiteness like we humans have? All of these elements, it's, it's a very, it's an open question.

• 23:19Speaker 01

  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, 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 if we got a machine that could do that, that would be a big deal.

• 23:56Lex Fridman?

  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 being able to play with the world. How important for developing system one or system two, do you think it is to play with the world, to be able to interact with the world?

• 24:18Speaker 01

  Certainly a lot, a lot of what you learn is you learn to anticipate the outcomes of your actions. I mean, you can see that, how babies learn it, you know, with their hands, how they learn, you know, to connect, 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.

• 25:01Speaker 01

  So, we have a system that is ready to learn the world through action.

• 25:05Lex Fridman?

  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?

• 25:25Speaker 01

  You know, I mean, I think that Jan LeCun'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 Kurzweil is saying, that strikes me as off the wall, but, but I think people are much more realistic than that, where actually Demis Hassabis is and Jan is, and so the people are actually doing the work fairly realistic, I think.

• 25:56Lex Fridman?

  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?

• 26:09Lex Fridman?

  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?

• 26:28Speaker 01

  I'm, you know, I'm fairly optimistic about that, actually, because what we're talking about is 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 a system multiplier like that, 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.

• 27:13Speaker 01

  Otherwise, a lot would happen. They're able to, you know, they're able to get into a roundabout and into, 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.

• 27:36Lex Fridman?

  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 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.

• 28:12Speaker 01

  You know, there is a dance, there's 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 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.

• 28:39Lex Fridman?

  So, and there's another thing you do that actually, so I'll tell you what you do, because we watch, 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.

• 28:54Speaker 01

  Look away.

• 28:54Lex Fridman?

  Yeah.

• 28:55Speaker 01

  Yeah.

• 28:55Lex Fridman?

  Uh, now, what, what is that? What that's saying is, I mean, you're trusting that the car, who hasn't slowed down yet, will slow down.

• 29:05Speaker 01

  Yeah, and you're telling him.

• 29:07Lex Fridman?

  Yeah.

• 29:07Speaker 01

  I'm committed.

• 29:08Lex Fridman?

  Yeah.

• 29:09Speaker 01

  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.

• 29:18Lex Fridman?

  So the question is whether a machine that observes that needs to understand mortality?

• 29:24Speaker 01

  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. So, uh, I thought that you didn't need a model of the human.

• 30:01Lex Fridman?

  Yes.

• 30:01Speaker 01

  And a model of the human mind to avoid hitting pedestrians. But you are suggesting that actually.

• 30:08Lex Fridman?

  That you do, yeah.

• 30:09Speaker 01

  You do.

• 30:10Lex Fridman?

  As, and.

• 30:10Speaker 01

  Then it's, then it's a lot harder, I thought.

• 30:14Lex Fridman?

  And I have a follow-up 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?

• 30:43Lex Fridman?

  If you think, we talked about current neural networks being kind of system one, do you think those same systems can borrow humans for system two type tasks and collaborate successfully?

• 30:59Speaker 01

  Well, I think that in any system where humans and the machine interact, that the human will be superfluous within a fairly short time. That is, 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 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 to call the human.

• 31:40Speaker 01

  That cannot be easy without understanding. That is, it must be very difficult to program a recognition that you are in a problematic situation without understanding the problem. But...

• 31:58Lex Fridman?

  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.

• 32:10Speaker 01

  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. Even Stockfish doesn't need people and AlphaZero certainly doesn't need people.

• 32:33Lex Fridman?

  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 probably in the end is like chess. The question is, how long is that transition period?

• 32:47Speaker 01

  I mean, you know, 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.

• 32:58Lex Fridman?

  Yes, and that's surprising.

• 33:00Speaker 01

  Because it's open. No, I mean, you know, 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 and for that hierarchical type of system to work, you need a more complicated system than we currently have.

• 33:33Lex Fridman?

  A lot of people think because as human beings, this is probably the cognitive biases. They think of driving as pretty simple because they think of their own experience. This is actually a big problem for AI researchers or people thinking about AI because they evaluate how hard a particular problem is based on very limited knowledge, basically on how hard it is for them to do the task.

• 34:02Speaker 01

  Yeah.

• 34:03Lex Fridman?

  And then they take for granted, maybe you can speak to that because most people tell me driving is trivial 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.

• 34:21Speaker 01

  Yeah.

• 34:22Lex Fridman?

  So is that just another element of the effects that you've described in your work on the psychology side?

• 34:32Speaker 01

  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

• 34:43Lex Fridman?

  Yeah.

• 34:44Speaker 01

  and the complexity of problems.

• 34:47Speaker 01

  So all we know is very clear that.

• 34:53Speaker 01

  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.

• 35:08Lex Fridman?

  So that's obvious because it's not always obvious to people, right? So when you think about.

• 35:12Speaker 01

  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.

• 35:33Lex Fridman?

  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 perceptible.

• 35:52Speaker 01

  Oh, yeah. I mean, 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.

• 36:16Lex Fridman?

  How do you think from the perspective of AI researcher? Do we deal with the intuitions of the public? So in trying to think, arguably, the combination of 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 companies money. And then there's a crash. Is there a way to educate people, so there to fight the, let's call it system one thinking?

• 37:01Speaker 01

  In general, no. I mean, I think that's the simple answer.

• 37:08Speaker 01

  And it's going to take a long time before the understanding of what those systems can do becomes, you know, part and becomes public knowledge.

• 37:22Speaker 01

  And

• 37:25Speaker 01

  and then and the expectations, you know, there are several aspects that are going to be very complicated. The

• 37:37Speaker 01

  the fact that you have a device that cannot explain itself is a major, major difficulty. And 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 you have systems that are clearly better at predicting parole violations than than judges, but but they can't explain their reasoning. And so

• 38:10Speaker 01

  people don't want to trust them.

• 38:13Lex Fridman?

  We seem to, in system one even, use cues to make judgments about our environment. So this explainability point, do you think humans can explain stuff?

• 38:28Speaker 01

  No, but

• 38:30Speaker 01

  I mean, there is a very interesting aspect of that. Humans think they can explain themselves. 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 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.

• 39:15Lex Fridman?

  You just made me actually realize, and we'll talk about stories in a second.

• 39:21Lex Fridman?

  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.

• 39:38Speaker 01

  Yeah, absolutely.

• 39:40Lex Fridman?

  It doesn't necessar-- the story doesn't necessarily need to reflect the truth as it might, it just needs to be convincing. There's something to that.

• 39:50Speaker 01

  You can say exactly the same thing in a way that's sound cynical or doesn't sound cynical.

• 39:55Lex Fridman?

  Right.

• 39:56Speaker 01

  But, but the objective 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 elements of truth. But, but the objective is for people to accept it.

• 40:20Lex Fridman?

  Quite brilliant, actually. But, so on the, on the stories that we tell, sorry to ask me, ask you the question that most people know the answer to, but you talk about two selves in terms of how life is lived, the experienced self and remembering self. Can you describe the distinction between the two?

• 40:44Speaker 01

  Well, sure. I mean, the, there is an aspect of, of life that occasionally, you know, most of the time we just live and we have experiences and they're better and they're 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, 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.

• 41:29Speaker 01

  So there is the experience and there is a story that is created about the experience. And that's what I call the remembering. So 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 we have one system or one self that does the living. But the other system, the remembering self, is all we get to keep. And basically, decision making and everything that we do is governed by our memories, not by what actually happened.

• 42:11Speaker 01

  It's governed by the story that we told ourselves or by the story that we're keeping. So that's the distinction.

• 42:21Lex Fridman?

  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 a remembering self?

• 42:31Speaker 01

  There are properties of how we construct stories that are really important. So I studied a few, but

• 42:43Speaker 01

  a couple are really very striking. And one is that in stories, time doesn't matter. There's a sequence of events or there are highlights or not.

• 42:56Speaker 01

  And how long it took, you know, they lived happily ever after. Or three years later, something. Time really doesn't matter. In stories, events matter, but time doesn't. That leads to a very interesting set of problems. Because time is all we got to live. I mean, you know, time is the currency of life. And yet, time is not represented basically in evaluated memories. So that creates a lot of paradoxes that I've thought about.

• 43:35Lex Fridman?

  Yeah, they're fascinating. But if you were to...

• 43:40Lex Fridman?

  Give advice on how one lives a happy life. Based on such properties, what's the optimal?

• 43:50Speaker 01

  You know, I gave up, I abandoned happiness research because I couldn't solve that problem. I couldn't, I couldn't see. 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 experiencing self happy are different things. And I, I asked the question 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?

• 44:34Speaker 01

  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 I have no solution to these problems, but clearly those are big issues.

• 45:04Lex Fridman?

  And you've talked about, 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 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 it and potentially, potentially millions. And then you can relive it even much more than just those minutes.

• 45:47Lex Fridman?

  Do you think about that magnification much?

• 45:51Speaker 01

  You know, I'm too old for social networks. I, you know, I've never seen Instagram, so.

• 45:59Speaker 01

  I cannot really speak intelligently about those things. I'm just too old.

• 46:03Lex Fridman?

  But it's interesting to watch the exact effects you've described.

• 46:06Speaker 01

  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

• 46:21Speaker 01

  the devices that serve us uh supplants function. So you don't have to remember phone numbers. You don't have, 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. 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. No, it used to be very important to know things. This is changing. So the requirements of that, that we

• 47:00Speaker 01

  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

• 47:12Lex Fridman?

  Well, I'll tell you, 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 watched, by watching it and then talking to others. It'll be about a hundred thousand people, scary as this, to say, well, listen or watch this, right?

• 47:32Lex Fridman?

  It changes things. It changes the experience of the world that you seek out experiences which could be shared in that way. It's in, 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.

• 47:50Speaker 01

  You know, the sharing.

• 47:53Speaker 01

  There was a time when people read books. And you could assume that your friends had read the same books that you read. So there was.

• 48:09Lex Fridman?

  Kind of invisible sharing.

• 48:11Speaker 01

  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'd read the reviews. I mean, so a lot was taken for granted that was shared.

• 48:32Speaker 01

  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.

• 48:43Speaker 01

  So sharing.

• 48:44Lex Fridman?

  It's always been there.

• 48:45Speaker 01

  Always, was always there. It was just different.

• 48:49Lex Fridman?

  At the risk of inviting mockery from you, let me say that I'm also a fan of Sartre and Camus and existentialist philosophers. And 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?

• 49:23Speaker 01

  I don't know enough philosophy to answer that. But it's not, you know, the emphasis on experience is also the emphasis in Buddhism.

• 49:34Lex Fridman?

  Yeah, right. That's right.

• 49:35Speaker 01

  So that's, you just have got to, to experience things and, and not to evaluate and not to pass judgment and not to score, not to keep score. So.

• 49:51Lex Fridman?

  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?

• 50:09Speaker 01

  Well. Yeah, I mean, I think, you know, if one could imagine a life in which people don't score themselves,

• 50:18Speaker 01

  it feels as if that would be a better life, as if the self-scoring and, you know, how am I doing, kind of question,

• 50:30Speaker 01

  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.

• 51:12Lex Fridman?

  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 self in that they react to the environment, there's some pattern formation, like a learning, so on. But you really don't construct memories, except in reinforcement learning, where most of the while that you replay over and over.

• 51:42Speaker 01

  Yeah, but, but you know, that would, in principle, would not be.

• 51:47Lex Fridman?

  Do you think that's useful? Do you think it's a feature or a bug of human beings that we, that we look back?

• 51:54Speaker 01

  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 without, without looking back, you couldn't, you couldn't really intelligently look forward.

• 52:10Lex Fridman?

  You're looking for the echoes of the same kind of experience in order to predict what the future holds?

• 52:15Speaker 01

  Yeah.

• 52:16Lex Fridman?

  Though Viktor Frankl, in his book, Man's Search for Meaning, I'm not sure if you've read,

• 52:23Lex Fridman?

  describes his experience at the concentration camps during World War II as a way to describe that.

• 52:31Lex Fridman?

  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?

• 52:45Speaker 01

  Not really. 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. 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.

• 53:13Lex Fridman?

  Yeah.

• 53:14Speaker 01

  And, you know, he survived with that. And I'm, and I'm not sure how essential to survival this sense is.

• 53:22Lex Fridman?

  Purpose, yeah.

• 53:23Speaker 01

  But I do know, when I think about myself, that I would have given up. At, oh, this isn't going anywhere. 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 survive and who tell themselves stories of a particular kind. So I'm not, uh...

• 53:58Lex Fridman?

  So you don't think seeking purpose is a significant driver in our behavior?

• 54:05Speaker 01

  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...

• 54:32Speaker 01

  And some people say that, for example, in

• 54:37Speaker 01

  in child, you know, in taking care of children, the fact that they are your children and you're taking care of them makes a very big difference. I think that's entirely true. 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.

• 55:02Lex Fridman?

  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 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?

• 55:29Lex Fridman?

  If we talk about pupil diameter, for example, it's much easier to do when you can control lighting conditions.

• 55:37Speaker 01

  Yeah, of course.

• 55:38Lex Fridman?

  So when we look at driving, lighting variation destroys

• 55:44Speaker 01

  Yeah.

• 55:44Lex Fridman?

  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 what's your intuition? How much of human behavior can we study in this controlled environment or the lab?

• 56:10Speaker 01

  A lot, but you'd have to verify it, you know, that 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. So, and, and that's the flair, that's where the difference, I think, between the good psychologists and others that are mediocre is in the sense that, that your experiment captures something that's important.

• 56:45Lex Fridman?

  Right.

• 56:45Speaker 01

  And something that's real, and others are just running experiments.

• 56:50Lex Fridman?

  So what is that? Like, the birth of an idea to its 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?

• 57:06Speaker 01

  Yeah, but I mean, you know, it's it's very skilled intuition.

• 57:10Lex Fridman?

  Right.

• 57:10Speaker 01

  I mean, I just had that experience, actually. I had an idea that turns out to be a very good idea 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 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.

• 57:49Lex Fridman?

  There's something here.

• 57:50Speaker 01

  That's part of the skill.

• 57:51Lex Fridman?

  Is that something you can reduce to words in describing a process in in the form of advice to others?

• 57:59Speaker 01

  No.

• 58:01Lex Fridman?

  Follow your heart, essentially?

• 58:03Speaker 01

  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.

• 58:11Lex Fridman?

  And then you lose.

• 58:12Speaker 01

  And then you lose the experience.

• 58:15Lex Fridman?

  You mentioned collaboration. You've written about your collaboration with Amos Tversky. That, this is you writing, "The 12 or 13 years in which most of our work was joint were 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 a 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.

• 59:00Lex Fridman?

  If you have not had it, you don't know how marvelous collaboration can be."

• 59:06Lex Fridman?

  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?

• 59:18Speaker 01

  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 almost, but I've had, and I'm happy. It's very, so it's a skill. I think I'm good at it. Not everybody is good at it. And then it's the luck of finding people who are also good at it.

• 59:49Lex Fridman?

  Is there advice in a form for a young scientist? Who also seeks to violate this law of information theory?

• 1:00:05Speaker 01

  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 of 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

• 1:00:46Speaker 01

  and who like the way that the other person responds to your thoughts. You have to be lucky.

• 1:00:53Lex Fridman?

  Yeah, I mean, but I already noticed the, even just me showing up here. You've, 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 the process of curiosity of talking to people about problems and seeing?

• 1:01:13Speaker 01

  I'm curious about anything to do with AI and robotics and, you know, and so, and I knew you were dealing with that, so I was curious.

• 1:01:22Lex Fridman?

  Just follow your curiosity.

• 1:01:23Speaker 01

  Yeah.

• 1:01:24Lex Fridman?

  Jumping around on the psychology front, the 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.

• 1:01:46Speaker 01

  You are being polite, it's worse than that.

• 1:01:49Lex Fridman?

  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?

• 1:01:58Speaker 01

  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 subjects, so it's the same person has two experimental conditions. And the other type is between subjects, where some people are this condition, other people are that condition. They are different worlds. And between-subject experiments are much harder to predict and much harder to anticipate.

• 1:02:39Speaker 01

  And the reason, 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. It's not so much in 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.

• 1:03:21Speaker 01

  And but in the between-subject condition, they don't, they, 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 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.

• 1:04:04Speaker 01

  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.

• 1:04:44Speaker 01

  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

• 1:04:59Speaker 01

  some, some friends of mine who, they essentially funded fifty-three studies of behavioral change by twenty different teams of people with a very precise objective of changing the number of times that people go to the gym, but, you know, so. And...

• 1:05:22Speaker 01

  And the success rate was zero. Not one of the fifty-three 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 you are focusing 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.

• 1:06:05Lex Fridman?

  More important than it really is. But if you don't see that effect, the fifty-three studies, doesn't that mean you just report that? So what's, I guess, the solution to that?

• 1:06:16Speaker 01

  Well, I mean, 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 in a public. And so this is happening. The interesting thing is 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.

• 1:06:59Lex Fridman?

  Do you have a hope for? This might connect to the study sample size. Do you have a hope for the internet or digitization?

• 1:07:10Speaker 01

  This is really happening. M Turk. Everybody is running experiments on M Turk.

• 1:07:17Speaker 01

  And it's very cheap and very effective, so.

• 1:07:21Lex Fridman?

  Do you think that changes psychology essentially? Because you're think you cannot run ten thousand subjects.

• 1:07:26Speaker 01

  Eventually it will. 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 field. So, and and M Turk is really a method because it it makes it very much easier to do something, to do some things.

• 1:07:52Lex Fridman?

  Is there undergrad students who'll ask me, you know, how big an neural network should be for a particular problem? So let me ask you an equivalent equivalent question.

• 1:08:04Lex Fridman?

  How big? How many subjects does that you have? For it to have a conclusive result?

• 1:08:11Speaker 01

  Well, it depends on the strength of the effect. So if you're studying visual perception or the perception of color, many of the of the classic results in in visual in color perception were done on three or four people. And I think one of them was colorblind, but or partly colorblind. But on vision, you know, you it's highly reliable. Many people don't need a lot of replications for some type of of neurological experiment. You're

• 1:08:49Speaker 01

  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 things that are happening in psychology now is that the power is the statistical power of experiments is is increasing rapidly.

• 1:09:11Lex Fridman?

  Does the between subject as the number of subjects goes to infinity approach?

• 1:09:16Speaker 01

  Well, I mean, you know, goes to infinity is exaggerated, but people, the standard number of subjects for an experiment, psychology, were thirty or forty. 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

• 1:09:42Speaker 01

  order of magnitude.

• 1:09:45Lex Fridman?

  What are the major disagreements in theories and effects that you've observed throughout your career that still stand today? You've worked on several fields.

• 1:09:57Speaker 01

  Yeah.

• 1:09:58Lex Fridman?

  But what still is out there as major disagreements that pops into your mind?

• 1:10:03Speaker 01

  And I've had one extreme experience of, you know, controversy with somebody who really doesn't like the work that Emes Tversky and I did. And he's been after us for 30 years or more, at least.

• 1:10:19Lex Fridman?

  Do you want to talk about it?

• 1:10:21Speaker 01

  Well, I mean, his name is Gerd Gigerenzer. He's a well-known German psychologist. And that's the one controversy I have, which I, it's been unpleasant. And no, I don't particularly want to talk about it.

• 1:10:38Lex Fridman?

  But is there, is there open questions, even in your own mind? Every once in a while, you know, 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 intellectually torn about what it means? And there's maybe disagreements within your own mind about particular things.

• 1:11:04Speaker 01

  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, you know, once they're 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, you know, that's been going on like 30 years and it's never going to be resolved. 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.

• 1:11:48Speaker 01

  That happens a fair amount.

• 1:11:50Lex Fridman?

  Do you have a hopeful advice or message on that? Thinking about science, thinking about politics, thinking about things that have impact on this world, how can we change our mind?

• 1:12:07Speaker 01

  I think that, I mean, on things that matter, which are political or religious, and people just don't change their mind. And by and large, and there is very little that you can do about it. What does happen is that if leaders change their mind, 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,

• 1:12:52Speaker 01

  not because we know why we have them, but because we trust some people and we don't trust other people. And so it's much less about evidence than it is about stories.

• 1:13:06Lex Fridman?

  So the way, one way to change your mind isn't at the individual level, is that the leaders and the communities you look up with, the stories change and therefore your mind changes with them. So there's a guy named Alan Turing, came up with a Turing test.

• 1:13:22Speaker 01

  Yeah.

• 1:13:24Lex Fridman?

  What do you think is a good test of intelligence? Perhaps we're drifting in a topic that we're maybe philosophizing about, but what do you think is a good test for intelligence, for an artificial intelligence system?

• 1:13:41Speaker 01

  Well, the standard definition of, you know, of artificial general intelligence is that it can do anything that people can do and it can do them better.

• 1:13:52Lex Fridman?

  Yes.

• 1:13:52Speaker 01

  And what, what we are seeing is that in many domains, you have domain-specific

• 1:14:00Speaker 01

  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...

• 1:14:19Speaker 01

  In machine learning, people are approaching something more general. I mean, for AlphaZero was much more general than AlphaGo. But it's still extraordinarily narrow and specific in what it can do. So we're quite far from something that can, in every domain, think like a human, except better.

• 1:14:47Lex Fridman?

  What aspect, so the Turing test has been criticized, it's natural language, conversation, that is too simplistic. It's easy to, quote unquote, "pass" under constraints specified. What aspect of conversation would impress you if you heard it? Is it humor? Is it... What would impress the heck out of you if you saw it in conversation?

• 1:15:12Speaker 01

  Yeah, I mean, certainly wit would, you know, wit would be impressive. And humor would be more impressive than just factual conversation, which I think is easy. And allusions 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. That is completely natural in conversation, but that you really wouldn't expect.

• 1:15:51Lex Fridman?

  Does the possibility of creating a human-level intelligence or superhuman-level intelligence system excite you, scare you?

• 1:16:01Speaker 01

  Well, I mean...

• 1:16:02Lex Fridman?

  How does it make you feel?

• 1:16:04Speaker 01

  I find the whole thing fascinating, absolutely fascinating.

• 1:16:09Lex Fridman?

  So exciting.

• 1:16:09Speaker 01

  I think, and exciting. It's also terrifying, you know, but I'm not going to be around to see it. And so I'm curious about what is happening now. But I also know that predictions about it are silly. We really have no idea what it will look like 30 years from now, no idea.

• 1:16:35Lex Fridman?

  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? These 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?

• 1:17:07Speaker 01

  There is no answer that that I can understand. And I'm not, and I'm not actively looking for one.

• 1:17:17Lex Fridman?

  Do you think an answer exists?

• 1:17:19Speaker 01

  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?

• 1:17:34Speaker 01

  I mean, there are a lot of things that we can do. I mean, you know, gravity waves, and 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.

• 1:17:51Lex Fridman?

  But the why.

• 1:17:52Speaker 01

  Yeah, the why.

• 1:17:53Lex Fridman?

  Is bigger than us.

• 1:17:54Speaker 01

  The why is hopeless, really.

• 1:17:58Lex Fridman?

  Danny, thank you so much. It was an honor. Thank you for speaking today.

• 1:18:00Speaker 01

  Thank you.

• 1:18:02Lex Fridman?

  Thanks for listening to this conversation. And thank you to our presenting sponsor, Cash App. Download it, use code LexPodcast, 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 Podcasts, 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 Kahneman. 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.

• 1:18:46Lex Fridman?

  Thank you for listening and hope to see you next time.