Tuomas Sandholm: Poker and Game Theory | Lex Fridman Podcast #12

The following is a conversation with Thomas Sandholm. He's a professor at Samuel and co creator of labradas, 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 New Reps, 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 on Artificial General Intelligence and the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes or simply connect with me on Twitter lexfriedman spelled F R I D. And now here's my conversation with Thomas Sanholm. 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.

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. 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 Go in that sense, but in 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.

So in Texas hold' Em there's two cards that you only see, they belong to you and there is they gradually lay out some cards that add up overall to five cards that everybody can see. Yeah, the Imperfect nature of the information is the two cards that you're holding 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.

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, Libratus. How do you pronounce it?

Libratos.

Libratus. Libratus beats little Latin there, a little bit of Latin. Libratus 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?

Yeah. So the event was that we invited four of the top 10 players. These are specialist players in Heads up, no Limit Texas Holden, 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 20 days. We wanted to get 120,000 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. 20 days, they were playing basically morning to evening, and I raised 200,000 as a little incentive for them to play. And the setting was so that they didn't all get 50,000. We actually paid them out based on how they did against 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.

And you don't make any money, unfortunately, 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.

Nevertheless, you were keeping track of the money and Lebratus won close to $2 million 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? Were they behind a computer? What was the interface like?

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. Yes, and they did the same thing here. So there was this layout you could imagine there's a table on a screen, there's 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.

Is there a reason they were given access to the betting history for.

Well, we just, it didn't really matter that 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 the human forgetting and the AI somehow trying to get advantage of better memory.

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?

Yeah, that's great, 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. So this time around, it was only 18 months later and I knew that this new AI Libratus 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 5050 shot and the international betting sites put us as a 4 to 1 or 5 to 1 underdog. So it's kind of interesting that people really believe in people over AI. People don't just over believing themselves, but they have overconfidence in other people as well compared to the performance of AI. And yeah, so we were a 4 to 1 or 5 to 1 underdog. And even after three, three days of beating the humans in a row, we were still 5050 on the international betting sites.

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 that 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. They're only looking at betting patterns and nothing else. The betting patterns 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?

Yeah. So I'll split it into two parts. So one is, why do humans trust humans more than AI and have overconfidence in humans? I think 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. So I think that's where that comes from. And that's actually maybe a more general lesson about AI that until you've seen it over perform 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 going to be a huge thing. You can read other people and if you're a good reader, you'll read them like an open book. But at the top levels of poker now, the tells become much smaller and smaller aspect of the game. As you go to the top levels.

The amount of strategies, the amounts of possible actions is very large, 10 to the power of 100 plus. So there has to be some. I've read a few of the papers related. It has to form some abstractions of various hands and actions. So what kind of abstractions are effective for the game of poker?

Yeah, so you're exactly right. So when you go from a game tree, that's 10 to the 161, especially in an imperfect information game, it's way too large to solve directly, even with our fastest equilibrium finding algorithms. So you want to 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.

Now the kinds of abstractions, just to zoom out, we're talking about there's the hands abstractions and then there is betting strategies.

Betting actions.

Yeah, betting actions.

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.

Yourself and the other players.

Yes, yourself and other players. And for information abstraction we were completely automated. So 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 into 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 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.

So what's more important, the strength of the hand. So the information abstraction or 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.

Yeah. So that's why you have to play a lot of hands, so that the role of luck gets smaller. So you could otherwise get lucky and get some good hands and then you're gonna 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 variants. So there are these massive swings in no limit decks as holder. So that's why you have to play not just thousands, but over 100,000 hands to get statistical significance.

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.

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.

Okay, so the hands do have some role to play.

Yes.

So Libratus 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 function estimator. What are your thoughts on this? Maybe as compared to another algorithm which I'm not that familiar with, Deep Stack, 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 Libratus plays the game of poker?

Yeah, so as you said, Libratus 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. Excellent. 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 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 perfect information games. And I don't want to say it's simple there either. It's of course very complicated computationally there too, 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 in imperfect information games, which is very difficult. And you were asking about Deep Stack, 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.

When you talk about lookahead or deep Stack or with Libratus, does it mean considering every possibility that the game can evolve? Are we talking about extremely sort of this exponentially growth of a tree?

Yes. So we're talking about exactly that, much like you do in alpha beta search or Monte crawl 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 Libratus 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 the payout is. It didn't do these depth limited lookaheads. 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 lookahead. 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. So in Libratus we were just doing it for the end, so.

And then the other side, this belief distribution. So is it explicitly modeled, what kind of beliefs that the opponent might have?

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 and uniformly from the deck. So I know that every pair of cards that you might have is equally likely. Yes, 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. Yes, you have to take that into account. That's called card removal. And that's very important.

Is, is the dealing always coming from a single deck in heads up.

So you can assume single deck. You know that if somebody, if, if I have the ace of spades, I know you don't have an ace of space.

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?

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 and defines beliefs for each state. So each state called 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 1 moves first and then Player 2 moves. But of course, it's important that Player 2 doesn't know what Player 1 moved, otherwise Player 2 would win every time. So we can draw that as an information set where Player 1 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 1/3 rock, 1/3 paper, one third scissors. From that I can derive my beliefs on the information set that they're 1/3, 1/3, 1/3.

So Bayes gives you that. But is that specific to a particular player or is it, is that something you quickly update with the specific.

No, the game theory isn't really player specific. So that's also why we don't need any data. 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 am rational. And that's the idea of game theory. So it's really a data free, opponent free approach.

So it comes from the design of the game as opposed to the design of the player.

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 Libratos 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's leading experts in counter exploitation. So I decided that we're not going to turn that stuff on.

Actually, I saw a few of your papers. Exploiting opponents sound very interesting to explore. Do you think there's room for exploitation generally outside of Labradors? Is, 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?

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 players, 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, but it doesn't maximally beat the other opponents. 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.

How, 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 Prisoner's Dilemma or.

Well, it hasn't that, that's a repeated game kind of repeated games, simple Prisoner's dilemma repeated games. But, but even there there's no proof that's that that's the best thing. But experimentally it actually does well.

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. And then there's 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 in.

So let me start from the, 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 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 going to 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. 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 over next games where I might be going to. So, so that's the stochastic game, but it's like matrix games, repeated stochastic games, extensive form games, I.e. 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 being benchmarked on with this heads upon all the text as holder.

Can you describe extensive form games? What's the motto here?

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 versus a matrix 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. 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. 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, even without cooperation. Just even without cooperation. So there's a big gap from 2 player 0 sum to 2 player general sum or even to 3 player 0 sum. That's a big gap, at least in theory.

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.

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, 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. 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.

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, yeah.

So I've done a lot of work, collisional games, and we actually have a paper here with my other student Gabriele Farina and some other collaborators at NIPS on that actually just came back from the poster session where we presented this. So when you have a collusion, it's a different problem and it typically gets even harder then even the game representations, some of the game representations don't really allow good computation. So we actually introduced a new game representation for that.

Is that kind of cooperation part of the model? Do you have information about the fact that other players are cooperating or is it just this chaos that where nothing.

Is known so there's something's unknown.

Can you give an example of a collusion type game or is it usually 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 when I get my cards, I can't whisper to you what my cards are. That would not be allowed. So 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. 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, it's not really built in, but it still can be very helpful for the colluders.

I've read you write somewhere with negotiations, you come to the table with prior like a strategy 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, real world domains that you've worked on?

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 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 military security, cyber security and intelligence.

Applications, I think you worked a little bit in, how do you put it? Advertisement, sort of suggesting ads kind of thing. Auction.

That's another company. Optimized markets, but that's much more about a combinatorial market and optimization based technology that's not using these game theoretic reasoning technologies.

I see. Okay, so what sort of high level do you think about our ability to use game theoretic concepts to model human behavior? 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?

I'm not sure the goal really is modeling humans. Like for example, if I'm playing a zero sum game, 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.

Right. 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. You've done this amazing work with mechanism design, designing games that have certain outcomes. But so I'll tell you an example from my world of autonomous vehicles, right? We're studying pedestrians and, and pedestrians and cars negotiate in this nonverbal 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 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? How do you start to attack a problem like that when you don't even know how the game design the game to describe the situation in order to solve it?

Okay, so I haven't really thought about jaywalking. But one thing that I think could be a good application in autonomous vehicles is the following. So let's say that you have fleets of autonomous cars operating 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, 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, this is how we do it, and it's all going to be faster. But there are way too many situations to negotiate manually. So you could 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 going to always let me go first. Maybe you said, okay, well, in these situations I'll let you go first, but in exchange you're going to give me two hours, you're going to let me go first in this situation. So it's this huge combinatorial negotiation.

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.

No, that's a good question. Yeah, that's a good question.

Do you pay the price of assuming that you don't know everything?

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.

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 the 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.

AI.

In general, for the future of AI development?

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, 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 better, like first order methods, better convergence rates like the CFR based 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, 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 vs. AI Machine poker competition that we organized, 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 cracking the game, for example?

Yeah, it was. A lot of people, 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 all of that. So it was really aggressive. The comments were super aggressive. I got everything. Just short of death threats.

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's AI systems now that outperform humans and they still enjoy the game. It's still a beautiful game.

That's what I think. And I think the same thing happens in poker. And so I didn't think of myself as somebody was going to 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 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 going to steer humans away from it entirely.

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, what 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, and to see if this thing actually works against human. Top human players and so on.

Yeah, 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, to make something big and to organize an event and stuff like that.

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 17 without doing this.

That's right, yes.

And so.

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 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 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.

Yeah. So a little friendly competition can do wonders for progress.

Yes, absolutely.

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 complex like our political system, 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?

Yeah, so I still very much believe in the automated mechanism design direction.

Yes.

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.

Can you describe that again? So, meaning it's impossible to achieve that?

Yeah, unlikely, impossible. So 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 just 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. So one of the famous results in mechanism design is the Myerson Satterthwaite Theorem by Roger Myerson and mark Satterthwaite 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, depending on how you design the game.

Okay, so depending 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.

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 can. Do you think it's applicable to. Yeah, politics or to business, to negotiations, these kinds of things? Designing rules that have certain outcomes?

Yeah, yeah, I do think so.

Have you seen success that successfully done?

They haven't really. Oh, you mean mechanism design or automated mechanism?

Automated mechanism design.

So. So mechanism design itself has had 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 of is the FCC spectrum auctions. So I've also had a small role in that and.