Pod Insights Lex Fridman Podcast Tuomas Sandholm: Poker and Game Theory About this episode Tuomas Sandholm is a professor at CMU and co-creator of Libratus, which is the first AI system to beat top human players at the game of Heads-Up No-Limit Texas Hold’em. He has published over 450 papers on game theory and machine learning, including a best paper in 2017 at NIPS / NeurIPS. His research and companies have had wide-reaching impact in the real world, especially because he and his group not only propose new ideas, but also build systems to prove these ideas work in the real world. Video version is available on YouTube https://www.youtube.com/watch?v=b7bStIQovcY. 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, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations.
Transcript 10,716 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Lex Fridman? The following is a conversation with Thomas Sandholm. He's a professor at Samhoub and co-creator of Lebradas, which is the first AI system to beat top human players in the game of Heads Up No Limit Texas Hold'em. He has published over 450 papers on game theory and machine learning, including a best paper in 2017 at NIPS, now renamed to NeurIPS, which is where I caught up with him for this conversation. His research and companies have had wide-reaching impact in the real world, especially because he and his group not only propose new ideas, but also build systems to prove that these ideas work in the real world. This conversation is part of the MIT course
0:44 Lex Fridman? on artificial general intelligence and the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at LexFriedman, spelled F-R-I-D. And now, here's my conversation with Thomas Sandholm.
1:19 Lex Fridman? Can you describe, at the high level, the game of poker, Texas Hold'em, Heads Up, Texas Hold'em, for people who might not be familiar, this card game?
1:29 Speaker 01 Yeah, happy to. So Heads Up No Limit Texas Hold'em has really emerged in the AI community as a main benchmark for testing these application-independent algorithms for imperfect information game solving. And this is a game that's actually played by humans. You don't see that much on TV or casinos, because, well, for various reasons. But you do see it in some expert-level casinos, and you see it in the best poker movies of all time. It's actually an event in the World Series of Poker, but mostly it's played online, and typically for pretty big sums of money. And this is a game that usually only experts play.
2:11 Speaker 01 So if you go to your home game on a Friday night, it probably is not going to be Heads Up No Limit Texas Hold'em. It might be No Limit Texas Hold'em in some cases, but typically for a big group, and it's not as competitive. While Heads Up means it's two players, so it's really like... Me against you, am I better or are you better, much like chess or or or Go in that sense? But an imperfect information game, which makes it much harder because I have to deal with issues of you knowing things that I don't know and I know things that you don't know, instead of pieces being nicely laid on the board for both of us to see.
2:47 Lex Fridman? So in Texas Hold'em, there's two cards that you only see, that belong to you. And then there is, they gradually lay out some cards that add up overall to five cards that everybody can see.
3:00 Speaker 01 Yeah.
3:00 Lex Fridman? So the imperfect nature of the information is the two cards that you're holding in your hand.
3:05 Speaker 01 Up front, yeah. So as you said, you know, you first get two cards in private each and then you, there's a betting round. Then you get three cards in public on the table, then there's a betting round. Then you get the fourth card in public on the table, there's a betting round. Then you get the fifth card on the table, there's a betting round. So there's a total of four betting rounds and four tranches of information revelation, if you will. Only the first tranche is private and then it's public from there.
3:33 Lex Fridman? And this is probably, probably by far the most popular game in AI and just the general public in terms of imperfect information. So it's probably the most popular spectator game to watch, right? So which is why it's a super exciting game to tackle. So it's it's on the order of chess, I would say, in terms of popularity, in terms of AI setting it as the bar of what is intelligence. So in 2017, Lebradas, how do you pronounce it?
4:06 Speaker 01 Lebrados.
4:07 Lex Fridman? Lebradas. Lebradas beat.
4:09 Speaker 01 A little Latin there.
4:10 Lex Fridman? A little bit Latin. Lebradas beat a few, four expert human players. Can you describe that event? What you learned from it? What was it like? What was the process in general for people who have not read the papers and studied?
4:26 Speaker 01 Yeah, so the event was at, we invited four of the top 10 players, who these are specialist players in Heads Up No Limit Texas Hold'em, which is very important because this game is actually quite different than. The multiplayer version. We brought them in to Pittsburgh to play at the Reverse Casino for twenty days. We wanted to get a hundred and twenty thousand hands in because we wanted to get statistical significance. So it's a lot of hands for humans to play, even for these top pros who play fairly quickly normally. So we couldn't just have one of them play so many hands. Twenty days, they were playing basically morning to evening. And they raised two hundred thousand as a little incentive for them to play.
5:12 Speaker 01 And the setting was so that they didn't all get fifty thousand. We actually paid them out based on how they did against the AI each. So they had an incentive to play as hard as they could, whether they're way ahead or way behind or right at the mark of beating the AI.
5:30 Lex Fridman? And you don't make any money, unfortunately.
5:32 Speaker 01 Right, no, we can't make any money. So originally, a couple of years earlier, I actually explored whether we could actually play for money. Because that would be, of course, interesting as well, to play against the top people for money. But the Pennsylvania Gaming Board said no, so we couldn't. So this is much like an exhibit, like for a musician or a boxer or something like that.
5:56 Lex Fridman? Nevertheless, you were keeping track of the money and brought us one close to two million dollars, I think. So if it was for real money, if you were able to earn money, that was a quite impressive and inspiring achievement. Just a few details, what were the players looking at? I mean, were they behind a computer? What was the interface like?
6:18 Speaker 01 Yes, they were playing much like they normally do. These top players, when they play this game, they play mostly online. So they're used to playing through a UI.
6:27 Lex Fridman? Yes.
6:28 Speaker 01 And they did the same thing here. So there was this layout, you could imagine there's a table on a screen. This, the human sitting there and then there's the AI sitting there. And the screen shows everything that's happening, the cards coming out and shows the bets being made. And we also had the betting history for the human. So if the human forgot what had happened in the hand so far, they could actually reference back and so forth.
6:53 Lex Fridman? Is there a reason they were given access to the betting history for?
6:57 Speaker 01 Well, we just, uh, it's, uh, it didn't really matter. They wouldn't have forgotten anyway. These are top quality people, but we just wanted to put out there. So it's not a question of a human forgetting and the AI somehow trying to get advantage of better memory.
7:13 Lex Fridman? So what was that like? I mean, that was an incredible accomplishment. So what did it feel like before the event? Did you have doubt, hope? Where was your confidence at?
7:24 Speaker 01 Yeah, that's great. So great question. So 18 months earlier, I had organized a similar brains versus AI competition with our previous AI called Cloudical, and we couldn't beat the humans.
7:37 Speaker 01 So this time around, it was only 18 months later, and I knew that this new AI, Libra tus, was way stronger. But it's hard to say how you'll do against the top humans before you try. So I thought we had about a 50 50 shot. And the international betting sites put us as a four to one or five to one underdog. So it's kind of interesting that people really believe in people and over AI. Not just people, people don't just believe, over believe in themselves, but they have overconfidence in other people as well compared to the performance of AI. And yeah, so we were a four to one or five to one underdog. And even after three days of beating the humans in a row, we were still 50 50 on the international betting sites.
8:22 Lex Fridman? Do you think there's something special and magical about poker in the way people think about it? In the sense you have, I mean, even in chess, there's no Hollywood movies. Poker is the star of many movies. And there's this feeling that certain human facial expressions and body language, eye movement, all these tells are critical to poker. Like you can look into somebody's soul and understand their betting strategy and so on. So that's probably why, possibly, do you think that is why people have a confidence that humans will outperform? Because AI systems cannot, in this construct, perceive these kinds of tells.
9:07 Lex Fridman? They're only looking at betting patterns and.
9:11 Lex Fridman? And nothing else, betting plans and and statistics. So what's more important to you if you step back on human players, human versus human? What's the role of these tells of these ideas that we romanticize?
9:29 Speaker 01 Yeah, so I I'll split it into two parts. So one is why do humans trust humans more than AI and have overconfidence in humans?
9:39 Lex Fridman? Yes.
9:40 Speaker 01 I think that's that's not really related to the tell question. It's just that they've seen these top players, how good they are, and they're really fantastic. So it's just hard to believe that an AI could beat them.
9:53 Lex Fridman? Yes.
9:53 Speaker 01 So I think that's where that comes from. And and that's actually maybe a more general lesson about AI that until you've seen it overperform a human, it's hard to believe that it could. But then the tells, a lot of these top players, they're so good at hiding tells that among the top players, it's actually not really worth it for them to invest a lot of effort trying to find tells in each other because they're so good at hiding them. So, yes, at the kind of Friday evening game, tells are gonna be a huge thing. You can read other people, and if you're a good reader, you'll you'll read them like an open book. But at the top levels of poker now, the tells become a less, a much, much smaller and smaller
10:37 Speaker 01 aspect of the game as you go to the top levels.
10:41 Lex Fridman? The the amount of strategies, the amounts of possible actions is is very large, ten to the power of hundred plus. So there has to be some, I've read a few of the papers related, there has, it has to form some abstractions of various hands and actions. So what kind of abstractions are effective for the game of poker?
11:05 Speaker 01 Yeah, so you're exactly right. So when you go from a game tree that's ten to the one hundred and sixty one, especially in an imperfect information game, it's way too large to solve directly, even with our fastest equilibrium finding algorithms. So you wanna abstract it first. And abstraction in games is much trickier than abstraction in MDPs or other single agent settings. Because you have these abstraction pathologies, that if I have a finer-grained abstraction, The strategy that I can get from that for the real game might actually be worse than the strategy I can get from the coarse-grained abstraction. So you have to be very careful.
11:45 Lex Fridman? Now, the kinds of abstractions, just to zoom out, we're talking about, there's the hands abstractions, and then there's betting strategies.
11:53 Speaker 01 Yeah, betting actions, yeah.
11:54 Lex Fridman? Baiting actions?
11:55 Speaker 01 So there's information abstraction, to talk about general games, information abstraction, which is the abstraction of what chance does, and this would be the cards in the case of poker. And then there's action abstraction, which is abstracting the actions of the actual players, which would be bets in the case of poker.
12:16 Lex Fridman? Yourself and the other players?
12:17 Speaker 01 Yes, yourself and other players. And for information abstraction, we were completely automated. So these were, these are algorithms, they do what we call potential-aware abstraction, where we don't just look at the value of the hand, but also how it might materialize in the good or bad hands over time, and it's a certain kind of bottom-up process with integer programming there, and clustering, and various aspects, how do you build this abstraction? And then in the action abstraction, there, it's largely based on how humans and other AIs have played this game in the past, but in the beginning, we actually used an automated
13:02 Speaker 01 action abstraction technology, which is provably convergent, that it finds the optimal combination of bet sizes, but it's not very scalable. So we couldn't use it for the whole game, but we used it for the first couple of betting actions.
13:16 Lex Fridman? So what's more important, the strength of the hand, so the information abstraction, or the how you play them, the actions? Does it, you know, the romanticized notion again is that it doesn't matter what hands you have, that the actions, the betting, may be the way you win, no matter what hands you have?
13:36 Speaker 01 Yeah, so that's why you have to play a lot of hands, so that the role of luck... Get smaller. So you could otherwise get lucky and get some good hands and then you're going to win the match. Even with thousands of hands, you can get lucky. Because there's so much variance in no limit Texas Hold'em. Because if we both go all in, it's a huge stack of variance. So there are these massive swings in no limit Texas Hold'em. So that's why you have to play not just thousands, but over 100,000 hands to get statistical significance.
14:11 Lex Fridman? So let me ask another way this question. If you didn't even look at your hands, but they didn't know that, your opponents didn't know that, how well would you be able to do?
14:22 Speaker 01 That's a good question. There's actually, I heard this story that there's this Norwegian female poker player called Annette Oberstad, who's actually won a tournament by doing exactly that. But that would be extremely rare. So you cannot really play well that way.
14:39 Lex Fridman? Okay. So the hands do have some role to play.
14:43 Speaker 01 Yes.
14:44 Lex Fridman? So Lebradas does not use, as far as I understand, use learning methods, deep learning. Is there room for learning in, you know, there's no reason why Lebradas doesn't, you know, combine with an AlphaGo type approach for estimating the quality for a function estimator. What are your thoughts on this? Maybe as compared to another algorithm which I'm not that familiar with, DeepStack, the engine that does use deep learning, that is unclear how well it does, but nevertheless uses deep learning. So what are your thoughts about learning methods to aid in the way that Lebradas plays in the game of poker?
15:25 Speaker 01 Yeah. So as you said, Lebrados did not use learning methods and played very well without them. Since then, we have actually, actually here, we have a couple of papers on things that do use learning techniques.
15:38 Lex Fridman? Excellent.
15:39 Speaker 01 So, and deep learning in particular. And sort of the way you're talking about, where it's learning an evaluation function. But in imperfect information games, unlike, let's say in Go or now also in chess and Shogi, it's not sufficient to learn an evaluation for a state, because the
16:06 Speaker 01 Value of an information set depends not only on the exact state, but it also depends on both players' beliefs. Like, if I have a bad hand, I'm much better off if the opponent thinks I have a good hand. And vice versa, if I have a good hand, I'm much better off if the opponent believes I have a bad hand. So the value of a state is not just a function of the cards. It depends on, if you will, the path of play, but only to the extent that it's captured in the belief distributions. So that's why it's not as simple as it is in imperfect information games. And I don't want to say it's simple there either. It's, of course, very complicated computationally there too.
16:50 Speaker 01 But at least conceptually, it's very straightforward. There's a state, there's an evaluation function, you can try to learn it. Here, you have to do something more. And what we do is, in one of these papers, we're looking at allowing, where we allow the opponent to actually take different strategies at the leaf of the search tree, if you will. And that is a different way of doing it. And it doesn't assume therefore a particular way that the opponent plays. But it allows the opponent to choose from a set of different continuation strategies. And that forces us to not be too optimistic in a look-ahead search. And that's one way you can do sound look-ahead search
17:35 Speaker 01 in imperfect information games, which is very difficult. And you were asking about DeepStack. What they did was very different than what we do, either in Libratus or in this new work. They were randomly generating various situations in the game. Then they were doing the look-ahead from there to the end of the game, as if that was the start of a different game. And then they were using deep learning to learn those values of those states. But the states were not just the physical states, they include the belief distributions.
18:09 Lex Fridman? When you talk about look-ahead for DeepStack or with Libratus, does it mean considering every possibility that the game can involve? Are we talking about extremely, sort of this exponentially growth of a tree?
18:23 Speaker 01 Yes, so we're talking about exactly that.
18:27 Speaker 01 Much like you do in alpha beta search or Monte-Carlo tree search, but with different techniques. So there's a different search algorithm, and then we have to deal with the leaves differently. So if you think about what LibraDus did, we didn't have to worry about this because we only did it at the end of the game. So we would always terminate into a real situation, and we would know what to pay out this. It didn't do these depth limited look aheads. But now in this new paper, which is called depth limited, I think it's called depth limited search for imperfect information games, we can actually do sound depth limited look ahead. So we can actually start to do the look ahead from the beginning of the game on because that's too complicated to do for this whole long game.
19:11 Speaker 01 So in LibraDus, we were just doing it for the end.
19:14 Lex Fridman? So and then the other side, this belief distribution. So is it explicitly modeled what kind of beliefs that the opponent might have?
19:23 Speaker 01 Yeah, it is explicitly modeled, but it's not assumed. The beliefs are actually output, not input. Of course, the starting beliefs are input, but they just fall from the rules of the game because we know that the dealer deals uniformly from the deck. So I know that every pair of cards that you might have is equally likely. I know that for a fact. That just follows from the rules of the game. Of course, except the two cards that I have. I know you don't have those.
19:53 Lex Fridman? Yes.
19:53 Speaker 01 You have to take that into account. That's called card removal, and that's very important.
19:57 Lex Fridman? Is the dealing always coming from a single deck? In heads up, so you can assume?
20:02 Speaker 01 Single deck. So you know that if I have the ace of spades, I know you don't have an ace of spades.
20:10 Lex Fridman? Great. So in the beginning, your belief is basically the fact that it's a fair dealing of hands. But how do you start to adjust that belief?
20:19 Speaker 01 Well, that's where this beauty of game theory comes. So Nash equilibrium, which John Nash introduced in 1950, introduces what rational play is when you have more than one player. And these are pairs of strategies, where strategies are contingency plans, one for each player, so that neither player wants to deviate to a different strategy, given that the other doesn't deviate. But as a side effect, you get the beliefs from Bayes rule. So Nash equilibrium really isn't just deriving, in these imperfect information games, Nash equilibrium doesn't just define strategies, it also defines beliefs for both of us.
21:01 Speaker 01 And it defines beliefs for each state. So at each state, each, they call information sets. At each information set in the game, there's a set of different states that we might be in, but I don't know which one we're in. Nash equilibrium tells me exactly what is the probability distribution over those real world states in my mind. How does Nash equilibrium give you that distribution? So why? I'll do a simple example. So you know the game rock, paper, scissors? So we can draw it as player one moves first, and then player two moves. But of course, it's important that player two doesn't know what player one moved. Otherwise, player two
21:43 Speaker 01 would win every time. So we can draw that as an information set, where player one makes one or three moves first, and then there's an information set for player two. So player two doesn't know which of those nodes the world is in. But once we know the strategy for player one, Nash equilibrium will say that you play one-third rock, one-third paper, one-third scissors. From that, I can derive my beliefs on the information set that they're one-third, one-third, one-third. So Bayes gives you that? Bayes gives you. But is that specific to a particular player? Or is it, is it something you quickly update with the specific player? No, the game theory isn't really player-specific. So that's also why we don't need any data.
22:28 Speaker 01 We don't need any history, how these particular humans played in the past, or how any AI or human had played before. It's all about rationality. So we just think, the AI just thinks about, what would a rational opponent do? And what would I do if I were, I am rational, and that's the idea of game theory. So it's really a data-free, opponent-free...
22:51 Lex Fridman? So it comes from the design of the game as opposed to the design of the player?
22:56 Speaker 01 Exactly. There's no opponent modeling per se. I mean, we've done some work on combining opponent modeling with game theory so you can exploit weak players even more, but that's another strand and in LibraDus we didn't turn that on because I decided that these players are too good and when you start to exploit an opponent, you typically open yourself up to exploitation. And these guys have so few holes to exploit and they're world-leading experts in counter-exploitation. So I decided that we're not going to turn that stuff on.
23:25 Lex Fridman? Actually, I saw a few of your papers exploiting opponents. It sounds very interesting to explore. Do you think there's room for exploitation generally outside of LibraDus? Is there subject or people differences that could be exploited? Maybe not just in poker, but in general interactions, negotiations, all these other domains that you're considering?
23:50 Speaker 01 Yeah, definitely. We've done some work on that and I really like the work that hybridizes the two. So you figure out what would a rational opponent do? And by the way, that's safe in these zero-sum games, two-player zero-sum games, because if the opponent does something irrational, yes, it might throw off my beliefs, but the amount that the player can gain by throwing off my belief is always less than they lose by playing poorly. So it's safe. But still, if somebody's weak as a player, you might want to play differently to exploit them more. So you can think about it this way: a game theoretic strategy is unbeatable,
24:32 Speaker 01 but it doesn't maximally beat the other opponent. So the winnings per hand might be better with a different strategy. And the hybrid is that you start from a game theoretic approach and then as you gain data about the opponent in certain parts of the game tree, then in those parts of the game tree, you start to tweak your strategy more and more towards exploitation, while still staying fairly close to the game theoretic strategy so as to not open yourself up to exploitation too much.
25:03 Lex Fridman? How do you do that? Do you try to vary up strategies, make it unpredictable? It's like, what is it, tit-for-tat strategies in Prisoners' Dilemma or?
25:17 Speaker 01 Well, that's a repeated game.
25:19 Lex Fridman? Repeated games.
25:20 Speaker 01 Simple prisoner's dilemma, repeated games. But even there, there's no proof that says that that's the best thing. But experimentally, it actually does well.
25:29 Lex Fridman? So what kind of games are there, first of all? I don't know if this is something that you could just summarize. There's perfect information games, where all the information is on the table. There is imperfect information games. There's repeated games that you play over and over. There's zero-sum games. There's non-zero-sum games.
25:50 Speaker 01 Yeah.
25:51 Lex Fridman? And then there's a really important distinction you're making, two-player versus more players. So what are... What other games are there? And what's the difference, for example, with this two-player game versus more players? What are the key differences?
26:08 Speaker 01 So let me start from the basics. So a repeated game is a game where the same exact game is played over and over. In these extensive-form games, where you think about tree form, maybe with these information sets to represent incomplete information, you can have kind of repetitive interactions. Even repeated games are a special case of that, by the way. But the game doesn't have to be exactly the same. It's like in sourcing auctions. Yes, we're gonna see the same supply base year to year, but what I'm buying is a little different every time, and the supply base is a little different every time, and so on. So it's not really repeated.
26:50 Speaker 01 So to find a purely repeated game is actually very rare in the world. So they're really a very coarse model of what's going on. Then, if you move up from just repeated, simple repeated matrix games, not all the way to extensive-form games, but in between, there's stochastic games, where, you know, there's these... You think about it like these little matrix games. And when you take an action, and your opponent takes an action, they determine not which next state I'm going to, next game I'm going to, but the distribution of our next games, where I might be going to. So that's the stochastic game.
27:31 Speaker 01 But it's like matrix games repeated, stochastic games, extensive-form games. That is from less to more general. And poker is an example of the last one. So it's really in the most general setting. Extensive form games, and that's kind of what the AI community has been working on and been benchmarked on with this heads up and all of this Texas Hold'em.
27:54 Lex Fridman? Can you describe extensive form games? What's the motto here?
27:58 Speaker 01 Yeah, so if you're familiar with the tree form, so it's really the tree form, like in chess, there's a search tree.
28:04 Lex Fridman? Versus a matrix.
28:05 Speaker 01 Versus a matrix, yeah. And the matrix is called the matrix form or bi-matrix form or normal form game. And here you have the tree form, so you can actually do certain types of reasoning there that you lose the information when you go to normal form. There's a certain form of equivalence, like if you go from tree form and you say it, every possible contingency plan is a strategy. Then I can actually go back to the normal form, but I lose some information from the lack of sequentiality. Then the multiplayer versus two-player distinction is an important one. So two-player games in zero-sum are conceptually easier and computationally easier.
28:49 Speaker 01 They're still huge, like this one, but they're conceptually easier and computationally easier in that, conceptually, you don't have to worry about which equilibrium is the other guy going to play when there are multiple, because any equilibrium strategy is the best response to any other equilibrium strategy. So I can play a different equilibrium from you, and we'll still get the right values of the game. That falls apart even with two players when you have general sum games.
29:17 Lex Fridman? Even without cooperation, just?
29:19 Speaker 01 Even without cooperation. So there's a big gap from two-player zero-sum to two-player general sum, or even to three-player zero-sum. That's a big gap, at least in theory.
29:30 Lex Fridman? Can you maybe, non-mathematically, provide the intuition why it all falls apart with three or more players? It seems like you should still be able to have a Nash equilibrium that, yeah, that's instructive, that holds.
29:47 Speaker 01 Okay, so it is true that all finite games have a Nash equilibrium. So this is what John Nash actually proved. So they do have a Nash equilibrium. That's not the problem. The problem is that there can be many. And then there's a question of which equilibrium to select. So, and if you select your strategy from a different equilibrium and I select mine.
30:13 Speaker 01 Then what does that mean? And in these non-zero sum games, we may lose some joint benefit by being just simply stupid. We could actually both be better off if we did something else.
30:25 Lex Fridman? Yes.
30:26 Speaker 01 And in three player, you get other problems also like collusion. Like maybe you and I can gang up on a third player and we can do radically better by colluding. So there are lots of issues that come up there.
30:38 Lex Fridman? So No Brown, the student you work with on this, has mentioned, I looked through the AMA on Reddit. He mentioned that the ability of poker players to collaborate would make the game. He was asked the question of how would you make the game of poker, or both of you were asked the question, how would you make the game of poker beyond being solvable by current AI methods? And he said that there's not many ways of making poker more difficult, but collaboration or cooperation between players would make it extremely difficult. So can you provide the intuition behind why that is? If you agree with that idea?
31:21 Speaker 01 Yeah, so I've done a lot of work on coalitional games. And we actually have a paper here with my other student, Caprile Farina, and some other collaborators at NIPS on that. Actually, just came back from the poster session where we presented this. But so when you have collusion, it's a different problem. And it typically gets even harder then.
31:44 Speaker 01 Even the game representations, some of the game representations don't really allow
31:50 Speaker 01 good computation. So we actually introduced a new game representation for that.
31:55 Lex Fridman? Is that kind of cooperation part of the model? Are you, do you have, do you have information about the fact that other players are cooperating? Or is it just this chaos that where nothing is known?
32:06 Speaker 01 So there's some, some things unknown.
32:08 Lex Fridman? Can you give an example of a collusion type game? Or is it usually?
32:13 Speaker 01 So like bridge. So think about bridge. It's like when you and I are on a team. Our payoffs are the same. The problem is that we can't talk. So, so when I get my cards, I can't whisper to you what my cards are. That would not be allowed. So, uh, we have to somehow coordinate our strategies ahead of time. And only ahead of time. And then there are certain signals we can talk about, but they have to be such that the other team also understands them. So, uh, so that's, that's, that's an example where the coordination is already built into the rules of the game. But in many other situations like auctions or negotiations or diplomatic relationships, poker,
32:57 Speaker 01 it's not really built in, but it still can be very helpful for the colliders.
33:01 Lex Fridman? I've, I've read you write somewhere, the, with negotiations, you come to the table with prior, uh, like a strategy that, like, that you're willing to do and not willing to do, those kinds of things. So how do you start to, now moving away from poker, moving beyond poker into other applications like negotiations? How do you start applying this to other, to other domains?
33:26 Speaker 01 Yeah.
33:26 Lex Fridman? Even real world domains that you've worked on?
33:29 Speaker 01 Yeah, I actually have two startup companies doing exactly that. One is called Strategic Machine, and that's for kind of business applications, gaming, sports, all sorts of things like that. Any applications of this to business and to sports and to, uh, gaming, to various types of things in finance, electricity markets and so on. And the other is called Strategy Robot, where we are taking this to, uh, military, security, cybersecurity and intelligence applications.
33:59 Lex Fridman? I think you worked, uh, a little bit in, um, how, how do you put it, advertisement, uh, sort of suggesting, um, ads kind of thing.
34:11 Speaker 01 Yes, that's another company, Optimized Markets. But that's much more about a combinatorial market and optimization based technology. That's not using these, uh, game theoretic reasoning technologies.
34:23 Lex Fridman? I see. Okay, so what high, sort of high level do you think about our ability to use?
34:31 Lex Fridman? Game theoretic concepts to model human behavior. Do you think, do you think human behavior is amenable to this kind of modeling? So outside of the poker games and where have you seen it done successfully in your work?
34:43 Speaker 01 I'm not sure the goal really is modeling humans.
34:50 Speaker 01 Like, for example, if I'm playing a zero-sum game.
34:52 Lex Fridman? Yes.
34:52 Speaker 01 I don't really care that the opponent is actually following my model of rational behavior. Because if they're not, that's even better for me.
35:02 Lex Fridman? Right, so, so see with the opponents in games, there's a, the prerequisite is that you formalize the interaction in some way that can be amenable to analysis. And you've done this amazing work with mechanism design, designing games that have certain outcomes.
35:26 Lex Fridman? But, so I'll tell you an example from my, from my world of autonomous vehicles, right? We're studying pedestrians and pedestrians and cars negotiate in this non-verbal communication. There's this weird game dance of tension where pedestrians are basically saying, I trust that you won't kill me. And so as a jaywalker, I will step onto the road even though I'm breaking the law and there's this tension. And the question is, we really don't know how to model that well in, in trying to model intent. And so people sometimes bring up ideas of game theory and so on. Do you think that aspect of human behavior can use these kinds of imperfect information approaches, modeling?
36:10 Lex Fridman? How do we, how do you start to attack a problem like that? When you don't even know how to gain, to design the game to describe the situation in order to solve it?
36:20 Speaker 01 Okay, so I haven't really thought about jaywalking. But one thing that I, I think could be a good application in, in, in autonomous vehicles is the following. So let's say that you have fleets of autonomous cars operated by different companies. So maybe here's the Waymo fleet and here's the Uber fleet. If you think about the rules of the road. They define certain legal rules, but that still leaves a huge strategy space open. Like as a simple example, when cars merge, you know, how humans merge, you know, they slow down and look at each other and try to try to merge. Wouldn't it be better if these situations would already be pre-negotiated? So we can actually merge at full speed and we know that this is the situation.
37:03 Speaker 01 This is how we do it and it's all gonna be faster. But there are way too many situations to negotiate manually. So you could do use automated negotiation, this is the idea at least. You could use automated negotiation to negotiate all of these situations or many of them in advance. And of course, it might be that, hey, maybe you're not gonna always let me go first. Maybe you said, okay, well, in these situations, I'll let you go first. But in exchange, you're gonna give me to almost you're gonna let me go first in these situations. So it's this huge combinatorial negotiation.
37:37 Lex Fridman? And do you think there's room in that example of merging to model this whole situation as an imperfect information game? Or do you really want to consider it to be a perfect?
37:46 Speaker 01 No, that's a good question. Yeah, that's a good question.
37:49 Lex Fridman? Do you pay the price of assuming that you don't know everything?
37:56 Speaker 01 Yeah, I don't know. It's certainly much easier. Games with perfect information are much easier. So if you can get away with it, you should. But if the real situation is of imperfect information, then you're gonna have to deal with imperfect information.
38:11 Lex Fridman? Great. So what lessons have you learned? The annual computer poker competition? An incredible accomplishment of AI. You know, you look at the history of Deep Blue, AlphaGo, these kind of moments when AI stepped up in an engineering effort and a scientific effort combined to beat the best of human players. So what do you take away from this whole experience? What have you learned about designing AI systems that play these kinds of games? And what does that mean for sort of AI in general, for the future of AI development?
38:47 Speaker 01 Yeah, so that's a good question. There's so much to say about it. I do like this type of performance-oriented research, although in my group we go all the way from like idea to theory to experiments, to big system building, to commercialization. So we span that spectrum. But I think that in a lot of situations in AI, you really have to build the big systems and evaluate them at scale before you know what works and doesn't. And we've seen that in the computational game theory community, that there are a lot of techniques that look good in the small, but then they cease to look good in the large. And we've also seen that there are a lot of techniques that look superior in theory. And I really mean in terms of convergence rates,
39:32 Speaker 01 better, like first order methods, better convergence rates, like the CFR-based algorithms. Yet the CFR-based algorithms are the fastest in practice. So it really tells me that you have to test this in reality. The theory isn't tight enough, if you will, to tell you which algorithms are better than the others. And you have to look at these things in the large, because any sort of projections you do from the small can at least in this domain be very misleading. So that's kind of from a kind of a science and engineering perspective, from a personal perspective. It's been just a wild experience in that with the first poker competition, the first brains versus AI, man-machine poker competition that we organized.
40:16 Speaker 01 There had been, by the way, for other poker games, there had been previous competitions, but this was for Heads Up No Limit, this was the first. And I probably became the most hated person in the world of poker. And I didn't mean to. Why is that? For cracking the game for something? Yeah, it was, a lot of people felt that it was a real threat to the whole game, the whole existence of the game. If AI becomes better than humans... People would be scared to play poker because there are these superhuman AIs running around taking their money and, you know, all of that. So I just, it was really aggressive. The comments were super aggressive. I got everything, just short of death threats.
40:58 Lex Fridman? Do you think the same was true for chess? Because right now, they just completed the world championships in chess, and humans just started ignoring the fact that there is AI systems now that outperform humans and they still enjoy the game. It's still a beautiful game.
41:11 Speaker 01 That's what I think. And I think the same thing happens in poker. And so I didn't think of myself as somebody who was gonna kill the game, and I don't think I did. I've really learned to love this game. I wasn't a poker player before, but learned so many nuances about it from these AIs. And they've really changed how the game is played, by the way. So they have these very Martian ways of playing poker, and the top humans are now incorporating those types of strategies into their own play. So if anything, to me, our work has made poker a richer, more interesting game for humans to play, not something that is gonna steer humans away from it entirely.
41:50 Lex Fridman? Just a quick comment on something you said, which is, if I may say so, in academia is a little bit rare sometimes. It's pretty brave to put your ideas to the test in the way you described, saying that sometimes good ideas don't work when you actually try to apply them at scale. So where does that come from? I mean, if you could do advice for people, what drives you in that sense? Were you always this way? I mean, it takes a brave person, I guess is what I'm saying, to test their ideas and to see if this thing actually works against human, top human players and so on.
42:28 Speaker 01 I don't know about brave, but it takes a lot of work. It takes a lot of work and a lot of time to organize, do make something big and to organize an event and stuff like that.
42:39 Lex Fridman? And what drives you in that effort? Because you could still, I would argue, get a best paper award at NIPS, as you did in seventeen, without doing this.
42:47 Speaker 01 That's right, yes. And so, in general, I believe it's very important to do things in the real world and at scale. And that's really where the... The pudding, if you will, proof is in the pudding. That's where it is. In this particular case, it was kind of a competition between different groups and for many years as to who can be the first one to beat the top humans at Heads Up No Limit Texas Hold'em. So it became, it became kind of a, like a competition who can get there.
43:25 Lex Fridman? Yeah, so a little friendly competition could be, could do wonders for progress.
43:30 Speaker 01 Yes, absolutely.
43:32 Lex Fridman? So the topic of mechanism design, which is really interesting, also kind of new to me, except as an observer of, I don't know, politics and any, I'm an observer of mechanisms. But you write in your paper, an automated mechanism design that I quickly read. So mechanism design is designing the rules of the game. So you get a certain desirable outcome. And you have this work on doing so in an automatic fashion as opposed to fine tuning it. So what have you learned from those efforts? If you look, say, I don't know, at complexes, like our political system.
44:12 Lex Fridman? Can we design our political system to have, in an automated fashion, to have outcomes that we want? Can we design something like traffic lights to be smart where it gets outcomes that we want? So what are the lessons that you draw from that work?
44:31 Speaker 01 Yeah, so I still very much believe in the automated mechanism design direction.
44:35 Lex Fridman? Yes.
44:37 Speaker 01 But it's not a panacea. There are impossibility results in mechanism design, saying that there is no mechanism that accomplishes objective X in class C. So it's not going to, there's no way using any mechanism design tools, manual or automated, to do certain things in mechanism design.
44:59 Lex Fridman? Can you describe that again? So meaning there it's impossible to achieve that?
45:03 Speaker 01 Yeah.
45:04 Lex Fridman? And or it's unlikely?
45:06 Speaker 01 Impossible. So these are, these are not statements about human ingenuity, who might come up with something smart. These are proofs that if you want to accomplish properties X in class C, that is not doable with any mechanism. The good thing about automated mechanism design is that we're not really designing for a class. We're designing for specific settings at a time. So even if there's an impossibility result for the whole class, it doesn't mean that all of the cases in the class are impossible. It just means that some of the cases are impossible. So we can actually carve these islands of possibility within these known impossible classes. And we've actually done that.
45:48 Speaker 01 So one of the famous results in mechanism design is a Meyerson-Satithwaite theorem by Roger Meyerson and Mark Satithwaite from 1983. It's an impossibility of efficient trade under imperfect information. We show that you can in many settings avoid that and get efficient trade anyway.
46:07 Lex Fridman? Depending on how you design the game, okay.
46:10 Speaker 01 Depending on how you design the game. And of course, it doesn't in any way contradict the impossibility result. The impossibility result is still there, but it just finds spots within this impossible class where in those spots you don't have the impossibility.
46:28 Lex Fridman? Sorry if I'm going a bit philosophical, but what lessons do you draw towards, like I mentioned, politics or human interaction and designing mechanisms for outside of just these kinds of trading or auctioning or purely formal games or human interaction, like a political system? What, how, do you think it's applicable to, yeah, politics or to business, to negotiations, these kinds of things? Designing rules that have certain outcomes?
47:05 Speaker 01 Yeah, yeah, I do think so.
47:07 Lex Fridman? Have you seen that successfully done?
47:10 Speaker 01 They haven't really, oh, you mean mechanism design or automated mechanism?
47:13 Lex Fridman? Automated mechanism design.
47:15 Speaker 01 So mechanism design itself has had...
47:20 Speaker 01 Fairly limited success so far. There are certain cases, but most of the real world situations are actually not sound from a mechanism design perspective. Even in those cases where they've been designed by very knowledgeable mechanism design people, the people are typically just taking some insights from the theory and applying those insights into the real world, rather than applying the mechanisms directly. So one famous example is the FCC spectrum auctions. So I've also had a small role in that. And very good economists have been, excellent economists have been working on that with no game theory. Yet the rules that are designed in practice there,
48:03 Speaker 01 they're such that bidding truthfully is not the best strategy. Usually in mechanism design, we try to make things easy for the participants. So telling the truth is the best strategy. But even in those very high stakes auctions where you have tens of billions of dollars worth of spectrum being auctioned, truth telling is not the best strategy. And by the way, nobody knows even a single optimal bidding strategy for those auctions.
48:30 Lex Fridman? What's the challenge of coming up with an optimal? Because there's a lot of players and there's imperfect.
48:34 Speaker 01 It's not a lot of players, but many items for sale. And these mechanisms are such that even with just two items or one item, bidding truthfully wouldn't be the best strategy.
48:47 Lex Fridman? If you look at the history of AI, it's marked by seminal events. And AlphaGo beating a world champion human Go player. I would put Lebradas winning the Heads Up No Limit Hold'em as one of such event.
49:01 Speaker 01 Thank you.
49:02 Lex Fridman? And what do you think is the next such event? Whether it's in your life or in the broadly AI community that you think might be out there that would surprise the world?
49:18 Speaker 01 So that's a great question and I really know the answer. In terms of game solving, Heads Up No Limit, Texas Hold'em really was the.
49:27 Speaker 01 One remaining widely agreed upon benchmark. So that was the big milestone. Now, are there other things? Yeah, certainly there are, but there is not one that the community has kind of focused on. So what could be other things? There are groups working on StarCraft. There are groups working on Dota 2. These are video games.
49:47 Lex Fridman? Yes.
49:48 Speaker 01 Or you could have like Diplomacy or Hanabi, you know, things like that. These are like recreational games, but none of them are really acknowledged as kind of the main next challenge problem, like chess or Go or Heads Up No Limit, Texas Hold'em was. So I don't really know in the game solving space what is or what will be the next benchmark. I kind of hope that there will be a next benchmark because really the different groups working on the same problem really drove these application independent techniques forward very quickly over 10 years.
50:23 Lex Fridman? Do you think there's an open problem that excites you that you start moving away from games into real world games like say the stock market trading?
50:33 Speaker 01 Yeah, so that's kind of how I am. So I am probably not going to work as hard on these recreational benchmarks. I'm doing two startups on game solving technology, Strategic Machine and Strategy Robot, and we're really interested in pushing this stuff into practice.
50:53 Lex Fridman? What do you think would be really, you know, a powerful result that would be surprising? That would be, if you can say, I mean, you know, five years, 10 years from now, something that statistically you would say is not very likely, but if there's a breakthrough, what achieve?
51:17 Speaker 01 Yeah, so I think that overall, we're in a very different situation in game theory than we are in, let's say, machine learning.
51:27 Lex Fridman? Yes.
51:28 Speaker 01 So in machine learning, it's a fairly mature technology and it's very broadly applied and proven success in the real world. In game solving, there are almost no applications yet. We have just become superhuman, which machine learning, you could argue, happened in the 90s, if not earlier, and at least on supervised learning, certain complex supervised learning applications.
51:54 Speaker 01 Now, I think the next challenge problem, I know you're not asking about it this way, you're asking about the technology breakthrough. But I think that the big, big breakthrough is to be able to show that, hey, maybe most of, let's say, military planning or most of business strategy will actually be done strategically using computational game theory. That's what I would like to see as the next five or 10-year goal.
52:14 Lex Fridman? Maybe you can explain to me, again, forgive me if this is an obvious question, but, you know, machine learning methods, neural networks, suffer from not being transparent, not being explainable. Game theoretic methods, you know, Nash equilibrium, do they generally, when you see the different solutions, are they, when you talk about military operations, are they, once you see the strategies, do they make sense, are they explainable, or do they suffer from the same problems as neural networks do?
52:44 Speaker 01 So that's a good question. I would say a little bit yes and no. And what I mean by that is that these game theoretic strategies, let's say, Nash equilibrium, it has provable properties. So it's unlike, let's say, deep learning, where you kind of cross your fingers, hopefully it'll work, and then after the fact, when you have the weights, you're still crossing your fingers, and I hope it will work. Here, you know that the solution quality is there, this provable solution quality guarantees. Now, that doesn't necessarily mean that the strategies are human understandable. That's a whole other problem. So I think that deep learning and computational game theory are in the same boat in that sense, that both are difficult to understand.
53:30 Speaker 01 But at least the game theoretic techniques, they have this... Guarantees of solving quality.
53:36 Lex Fridman? So do you see business operations, strategic operations, even military in the future being at least the the strong candidates being proposed by automated systems? Do you see that?
53:50 Speaker 01 Yeah, I do. I do. But that's more of a belief than a substantiated fact.
53:56 Lex Fridman? Depending on where you land, optimism or pessimism, that's a really, to me, that's an exciting future, especially if there are provable things in terms of optimality. So looking into the future, there's a few folks worried about the, especially you look at the game of poker, which is probably one of the last benchmarks in terms of games being solved. They they worry about the future and the existential threats of artificial intelligence. So the negative impact in whatever form on society. Is that something that concerns you as much? Or are you more optimistic about the positive impacts of AI?
54:38 Speaker 01 Oh, I am much more optimistic about the positive impacts. So just in my own work, what we've done so far, we run the nationwide kidney exchange. Hundreds of people are walking around alive today, who would it be? And it's increased employment. You have you have a lot of people now running kidney exchanges and at transplant centers, interacting with the kidney exchange. You have extra surgeons, nurses, anesthesiologists, hospitals, all of that. So so employment is increasing from that, and the world is becoming a better place. Another example is combinatorial sourcing auctions. We did 800 large scale
55:18 Speaker 01 combinatorial sourcing auctions from 2001 to 2010 in a previous startup of mine called CombineNet. And
55:27 Speaker 01 We increased the supply chain efficiency on that $60 billion of spend by 12.6%. So that's over $6 billion of efficiency improvement in the world. And this is not like shifting value from somebody to somebody else, just efficiency improvement. Like in trucking, less empty driving, so there's less waste, less carbon footprint and so on.
55:50 Lex Fridman? This is a huge positive impact in the near term, but sort of to stay in it for a little longer, because I think game theory has a role to play here.
55:59 Speaker 01 Oh, let me actually come back on that as one thing. I think AI is also going to make the world much safer. So that's another aspect that often gets overlooked.
56:10 Lex Fridman? Well, let me ask this question, maybe you can speak to the safer. So I talked to Max Tegmark and Stuart Russell, who are very concerned about existential threats of AI. And often the concern is about value misalignment. So AI systems basically are working, operating towards goals that are not the same as human civilization, human beings. So it seems like game theory has a role to play there to make sure the values are aligned with human beings. I don't know if that's how you think about it. If not, how do you think AI might help with this problem? How do you think AI might make the world safer?
56:55 Speaker 01 Yeah, I think this value misalignment is a fairly theoretical worry. And I haven't really seen it in it because I do a lot of real applications. I don't see it anywhere. The closest I've seen it was the following type of mental exercise really, where I had this argument in the late 80s when we were building these transportation optimization systems. And somebody had heard that it's a good idea to have high utilization of assets. So they told me that, hey, why don't you put that as objective? And we didn't even put it as an objective because I just showed him that, you know, if you had that as your objective, the solution would be to load your trucks full and
57:37 Speaker 01 Drive in circles, nothing would ever get delivered, you'd have a hundred percent utilization.
57:41 Lex Fridman? That's right.
57:41 Speaker 01 So yeah, I know this phenomenon, I've known this for over thirty years, but I've never seen it actually be a problem in reality. And yes, if you have the wrong objective, the AI will optimize that to the hilt and it's gonna hurt more than some human who's kind of trying to
57:59 Speaker 01 solve it in a half-baked way with some human insight too, but I just haven't seen that materialize in practice.
58:05 Lex Fridman? There's this gap that you've actually put your finger on very clearly just now between theory and reality. That's very difficult to put into words, I think. It's what you can theoretically imagine. The worst possible case or even, yeah, I mean, bad cases and what usually happens in reality. So for example, to me, maybe it's something you can comment on, having grown up, I grew up in the Soviet Union. You know, there's currently ten thousand nuclear weapons in the world. And for many decades, it's theoretically surprising to me that the nuclear war has not broken out.
58:47 Lex Fridman? Do you think about this aspect from a game theoretic perspective in general? Why is that true? Why in theory you could see how things would go terribly wrong and somehow yet they have not?
59:00 Speaker 01 Yeah.
59:01 Lex Fridman? How do you think about it?
59:02 Speaker 01 So I do think about that a lot. I think the biggest two threats that we're facing as mankind, one is climate change and the other is nuclear war. So those are my main two worries that I worry about. And I've tried to do something about climate, thought about trying to do something for climate change twice. Actually, for two of my startups, I've actually commissioned studies of what we could do on those things. And we didn't really find a sweet spot, but I'm still keeping an eye out on that. If there's something where we could actually provide a market solution or optimization solution or some other technology solution to problems. Right now, like for example, pollution credit markets was what we were looking at then. And it was much more the lack of political will by those.
59:47 Speaker 01 Markets were not so successful rather than bad market design. So I could go in and make a better market design. But that wouldn't really move the needle on the world very much. If there's no political will and in the US, you know, the market. Uh, at least the Chicago market was just shut down. Uh, uh, and so on. So then it doesn't really help how great your market design was.
1:00:07 Lex Fridman? And on the nuclear side, it's more so global warming is a more encroaching.
1:00:15 Lex Fridman? Problem, you know, nuclear weapons have been here. It's an obvious problem that's just been sitting there. So how do you think about? What is the mechanism design there that just made everything? Seems stable and are you still extremely worried?
1:00:31 Speaker 01 I am still extremely worried. So you probably know the simple game theory of MAD. So, so, so, uh, this was a mutually assured destruction. And it's like, it doesn't require any computation with small matrices. You can actually convince yourself that the game is such that nobody wants to initiate. Yeah, that's a very coarse grained analysis. And it really works in a situation where you have. Two superpowers or small number of superpowers. Now things are very different. You have a smaller nuke, so the threshold. Of initiating is smaller and you have smaller countries and non, non nation actors. Who may get nukes and so on. So it's, I, I think it's riskier now than it was maybe ever before.
1:01:14 Lex Fridman? And what idea? Application of AI, you've talked about a little bit, but what is the most exciting to you right now? I mean, you, you're here at NIPS, NeurIPS now. You have a few excellent pieces of work, but what are you thinking into the future? With several companies you're doing, what's the most exciting thing or one of the exciting things?
1:01:37 Speaker 01 The number one thing for me right now is. Coming up with these scalable techniques for game solving and applying them into the real world. Uh, I'm still very interested in market design as well, and we're doing that in the optimized markets. But I'm most interested if number one right now is. Strategic machine, strategy robot, getting that technology out there and seeing. As you were in the trenches doing applications, what needs to be actually filled, what technology gaps still need to be filled? So it's so hard to just put your feet on the table and imagine what needs to be done. But when you're actually doing real applications, the applications tell you what needs to be done. And I really enjoy that interaction.
1:02:17 Lex Fridman? Is it a challenging process to apply some of the state-of-the-art techniques you're working on? And having the various players in industry or the military or people who could really benefit from it actually use it? What's that process like of, you know, in autonomous vehicles, we'll work with automotive companies and they're in many ways are a little bit old fashioned. It's difficult. They really want to use this technology. There's clearly will have a significant benefit, but the systems aren't quite in place to easily have them integrated in terms of data, in terms of compute, in terms of all these kinds of things.
1:03:00 Lex Fridman? So is that one of the bigger challenges that you're facing? And how do you tackle that challenge?
1:03:06 Speaker 01 Yeah, I think that's always a challenge. That's kind of slowness and inertia, really.
1:03:12 Speaker 01 Of let's do things the way we've always done it. You just have to find the internal champions at the customer who understand that, hey, things can't be the same way in the future. Otherwise, bad things are going to happen.
1:03:22 Lex Fridman? Right.
1:03:23 Speaker 01 And it's in autonomous vehicles. It's actually very interesting that the carmakers are doing that. Then they're very traditional. But at the same time, you have tech companies who have nothing to do with cars or transportation, like Google and Baidu, really pushing on autonomous cars. I find that fascinating.
1:03:39 Lex Fridman? Clearly, you're super excited about actually these ideas having an impact in the world. In terms of the technology, in terms of ideas and research, are there directions that you're also excited about? Whether that's on the, some of the approaches you talked about for imperfect information games? Whether it's applying deep learning to some of these problems? Is there something that you're excited in the research side of things?
1:04:05 Speaker 01 Yeah, yeah. Lots of different things in the game solving. So solving even bigger games, games where you have more hidden action of the player actions as well. Poker is a game where really chance actions are hidden, or some of them are hidden, but the player actions are public.
1:04:27 Speaker 01 Multiplayer games of various sorts, collusion, opponent exploitation, and even longer games, some games that basically go forever, but they're not repeated. So extensive one games that go forever, what would that even look like? How do you represent that? How do you solve that?
1:04:48 Lex Fridman? What's an example of a game like that? Or this is some of the stochastic games that you mentioned?
1:04:52 Speaker 01 Let's say business strategy. So it's not just modeling like a particular interaction, but thinking about the business from here to eternity.
1:05:00 Lex Fridman? I see.
1:05:01 Speaker 01 Or let's say military strategy. So it's not like war is going to go away. How do you think about military strategy that's going to go forever? How do you even model that? How do you know whether a move was good that somebody made? And so on. So that's kind of one direction. I'm also very interested in learning much more scalable techniques for integer programming. So we had an ICML paper this summer on that, the first automated algorithm configuration paper that has theoretical generalization guarantees. So if I see this many training examples and I tool my algorithm in this way, it's going
1:05:45 Speaker 01 to have good performance on the real distribution, which I've not seen. So which is kind of interesting that, you know, algorithm configuration has been going on now for at least 17 years seriously, and there has not been any generalization theory before.
1:06:01 Lex Fridman? Well, this is really exciting and it's been, it's a huge honor to talk to you. Thank you so much, Tomas. Thank you for bringing Lebradas to the world and all the great work you're doing.
1:06:10 Speaker 01 Well, thank you very much. It's been fun. Good questions.
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