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| WEBVTT | |
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| The following is a conversation with Thomas Sanholm. He's a professor at SameU and cocreator of | |
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| Labratus, which is the first AI system to beat top human players in the game of Heads Up No Limit | |
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| Texas Holdem. He has published over 450 papers on game theory and machine learning, including a | |
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| best paper in 2017 at NIPS, now renamed to New Rips, which is where I caught up with him for this | |
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| conversation. His research and companies have had wide reaching impact in the real world, | |
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| especially because he and his group not only propose new ideas, but also build systems to prove | |
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| that these ideas work in the real world. This conversation is part of the MIT course on | |
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| artificial journal intelligence and the artificial intelligence podcast. If you enjoy it, subscribe | |
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| on YouTube, iTunes, or simply connect with me on Twitter at Lex Friedman, spelled FRID. And now | |
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| here's my conversation with Thomas Sanholm. Can you describe at the high level the game of poker, | |
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| Texas Holdem, Heads Up Texas Holdem, for people who might not be familiar with this card game? | |
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| Yeah, happy to. So Heads Up No Limit Texas Holdem has really emerged in the AI community | |
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| as a main benchmark for testing these application independent algorithms for | |
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| imperfect information game solving. And this is a game that's actually played by humans. You don't | |
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| see that much on TV or casinos because, well, for various reasons, but you do see it in some | |
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| expert level casinos and you see it in the best poker movies of all time. It's actually an event | |
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| in the world series of poker, but mostly it's played online and typically for pretty big sums | |
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| of money. And this is a game that usually only experts play. So if you go to your home game | |
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| on a Friday night, it probably is not going to be Heads Up No Limit Texas Holdem. It might be | |
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| No Limit Texas Holdem in some cases, but typically for a big group and it's not as competitive. | |
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| Well, Heads Up means it's two players, so it's really like me against you. Am I better or are you | |
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| better, much like chess or go in that sense, but an imperfect information game which makes it much | |
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| harder because I have to deal with issues of you knowing things that I don't know and I know | |
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| things that you don't know instead of pieces being nicely laid on the board for both of us to see. | |
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| So in Texas Holdem, there's two cards that you only see that belong to you and there is | |
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| they gradually lay out some cards that add up overall to five cards that everybody can see. | |
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| Yeah, the imperfect nature of the information is the two cards that you're holding up front. | |
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| Yeah. So as you said, you know, you first get two cards in private each, and then you there's a | |
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| betting round, then you get three cards in public on the table, then there's a betting round, then | |
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| you get the fourth card in public on the table, there's a betting round, then you get the fifth | |
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| card on the table, there's a betting round. So there's a total of four betting rounds and four | |
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| tranches of information revelation, if you will. The only the first tranche is private and then | |
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| it's public from there. And this is probably by far the most popular game in AI and just | |
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| the general public in terms of imperfect information. So it's probably the most popular | |
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| spectator game to watch, right? So, which is why it's a super exciting game tackle. So it's on | |
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| the order of chess, I would say in terms of popularity, in terms of AI setting it as the bar | |
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| of what is intelligence. So in 2017, Libratus beats a few four expert human players. Can you | |
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| describe that event? What you learned from it? What was it like? What was the process in general | |
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| for people who have not read the papers in the study? Yeah. So the event was that we invited | |
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| four of the top 10 players with these specialist players in Heads Up No Limit Texas Holden, | |
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| which is very important because this game is actually quite different than the multiplayer | |
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| version. We brought them in to Pittsburgh to play at the reverse casino for 20 days. We wanted to | |
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| get 120,000 hands in because we wanted to get statistical significance. So it's a lot of hands | |
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| for humans to play, even for these top pros who play fairly quickly normally. So we couldn't just | |
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| have one of them play so many hands. 20 days, they were playing basically morning to evening. | |
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| And you raised 200,000 as a little incentive for them to play. And the setting was so that | |
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| they didn't all get 50,000. We actually paid them out based on how they did against the AI each. | |
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| So they had an incentive to play as hard as they could, whether they're way ahead or way behind | |
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| or right at the mark of beating the AI. And you don't make any money, unfortunately. Right. No, | |
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| we can't make any money. So originally, a couple of years earlier, I actually explored whether we | |
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| could actually play for money because that would be, of course, interesting as well to play against | |
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| the top people for money. But the Pennsylvania Gaming Board said no. So we couldn't. So this | |
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| is much like an exhibit, like for a musician or a boxer or something like that. Nevertheless, | |
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| they were keeping track of the money and brought us one close to $2 million, I think. So if it was | |
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| for real money, if you were able to earn money, that was a quite impressive and inspiring achievement. | |
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| Just a few details. What were the players looking at? Were they behind a computer? What | |
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| was the interface like? Yes, they were playing much like they normally do. These top players, | |
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| when they play this game, they play mostly online. So they used to playing through a UI. | |
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| And they did the same thing here. So there was this layout, you could imagine. There's a table | |
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| on a screen. There's the human sitting there. And then there's the AI sitting there. And the | |
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| screen shows everything that's happening. The cards coming out and shows the bets being made. | |
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| And we also had the betting history for the human. So if the human forgot what had happened in the | |
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| hand so far, they could actually reference back and so forth. Is there a reason they were given | |
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| access to the betting history? Well, it didn't really matter. They wouldn't have forgotten | |
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| anywhere. These are top quality people. But we just wanted to put out there so it's not the question | |
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| of the human forgetting and the AI somehow trying to get that advantage of better memory. | |
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| So what was that like? I mean, that was an incredible accomplishment. So what did it feel like | |
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| before the event? Did you have doubt, hope? Where was your confidence at? | |
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| Yeah, that's great. Great question. So 18 months earlier, I had organized a similar | |
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| brains versus AI competition with a previous AI called Cloudical. And we couldn't beat the humans. | |
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| So this time around, it was only 18 months later. And I knew that this new AI, Libratus, | |
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| was way stronger. But it's hard to say how you'll do against the top humans before you try. | |
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| So I thought we had about a 50 50 shot. And the international betting sites put us as a | |
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| four to one or five to one underdog. So it's kind of interesting that people really believe in people | |
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| and over AI, not just people, people don't just believe over believing themselves, | |
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| but they have overconfidence in other people as well, compared to the performance of AI. | |
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| And yeah, so we were a four to one or five to one underdog. And even after three days of beating | |
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| the humans in a row, we were still 50 50 on the international betting sites. | |
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| Do you think there's something special and magical about poker in the way people think about it? | |
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| In the sense you have, I mean, even in chess, there's no Hollywood movies. Poker is the star | |
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| of many movies. And there's this feeling that certain human facial expressions and body language, | |
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| eye movement, all these tells are critical to poker. Like you can look into somebody's soul | |
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| and understand their betting strategy and so on. So that's probably why the possibly do you think | |
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| that is why people have a confidence that humans will outperform because AI systems cannot in this | |
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| construct perceive these kinds of tells, they're only looking at betting patterns and | |
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| nothing else, the betting patterns and statistics. So what's more important to you if you step back | |
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| on human players, human versus human? What's the role of these tells of these ideas that we romanticize? | |
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| Yeah, so I'll split it into two parts. So one is why do humans trust humans more than AI and | |
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| have overconfidence in humans? I think that's not really related to the tell question. It's | |
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| just that they've seen these top players how good they are and they're really fantastic. So it's just | |
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| hard to believe that the AI could beat them. So I think that's where that comes from. And that's | |
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| actually maybe a more general lesson about AI that until you've seen it overperform a human, | |
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| it's hard to believe that it could. But then the tells, a lot of these top players, they're so good | |
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| at hiding tells that among the top players, it's actually not really worth it for them to invest a | |
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| lot of effort trying to find tells in each other because they're so good at hiding them. So yes, | |
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| at the kind of Friday evening game, tells are going to be a huge thing. You can read other people | |
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| and if you're a good reader, you'll read them like an open book. But at the top levels of poker, | |
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| no, the tells become a less much smaller and smaller aspect of the game as you go to the top | |
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| levels. The amount of strategies, the amounts of possible actions is very large, 10 to the power | |
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| of 100 plus. So there has to be some, I've read a few of the papers related that has, | |
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| it has to form some abstractions of various hands and actions. So what kind of abstractions are | |
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| effective for the game of poker? Yeah, so you're exactly right. So when you go from a game tree | |
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| that's 10 to the 161, especially in an imperfect information game, it's way too large to solve | |
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| directly. Even with our fastest equilibrium finding algorithms. So you want to abstract it | |
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| first. And abstraction in games is much trickier than abstraction in MDPs or other single agent | |
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| settings. Because you have these abstraction pathologies. But if I have a finer grained abstraction, | |
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| the strategy that I can get from that for the real game might actually be worse than the strategy | |
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| I can get from the coarse grained abstraction. So you have to be very careful. Now, the kinds | |
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| of abstractions just to zoom out, we're talking about there's the hands abstractions and then there | |
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| is betting strategies. Yeah, betting actions. So there's information abstraction to talk about | |
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| general games, information abstraction, which is the abstraction of what chance does. And this | |
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| would be the cards in the case of poker. And then there's action abstraction, which is abstracting | |
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| the actions of the actual players, which would be bets in the case of poker, yourself, any other | |
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| players, yes, yourself and other players. And for information abstraction, we were completely | |
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| automated. So these were these are algorithms, but they do what we call potential aware abstraction, | |
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| where we don't just look at the value of the hand, but also how it might materialize in the | |
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| good or bad hands over time. And it's a certain kind of bottom up process with integer programming | |
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| there and clustering and various aspects, how do you build this abstraction. And then in the | |
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| action abstraction, there, it's largely based on how humans other and other AI's have played | |
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| this game in the past. But in the beginning, we actually use an automated action abstraction | |
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| technology, which is provably convergent, that it finds the optimal combination of bet sizes, | |
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| but it's not very scalable. So we couldn't use it for the whole game, but we use it for the first | |
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| couple of betting actions. So what's more important, the strength of the hand, so the | |
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| information abstraction or the how you play them, the actions, does it, you know, the romanticized | |
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| notion again, is that it doesn't matter what hands you have, that the actions, the betting, | |
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| maybe the way you win, no matter what hands you have. Yeah, so that's why you have to play a lot | |
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| of hands, so that the role of luck gets smaller. So you could otherwise get lucky and get some good | |
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| hands, and then you're going to win the match. Even with thousands of hands, you can get lucky. | |
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| Because there's so much variance in no limit, Texas hold them, because if we both go all in, | |
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| it's a huge stack of variance. So there are these massive swings in no limit Texas hold them. So | |
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| that's why you have to play not just thousands, but over a hundred thousand hands to get statistical | |
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| significance. So let me ask another way this question. If you didn't even look at your hands, | |
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| but they didn't know that the opponents didn't know that, how well would you be able to do? | |
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| That's a good question. There's actually, I heard the story that there's this Norwegian female poker | |
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| player called Annette Oberstad, who's actually won a tournament by doing exactly that. But that | |
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| would be extremely rare. So you cannot really play well that way. Okay, so the hands do have | |
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| some role to play. So Lebrados does not use, as far as I understand, use learning methods, | |
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| deep learning. Is there room for learning in, you know, there's no reason why Lebrados doesn't, | |
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| you know, combine with an alpha go type approach for estimating the quality for function estimator. | |
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| What are your thoughts on this? Maybe as compared to another algorithm, which I'm not | |
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| that familiar with deep stack, the engine that does use deep learning that is unclear how well | |
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| it does, but nevertheless uses deep learning. So what are your thoughts about learning methods to | |
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| aid in the way that Lebrados plays the game of poker? Yeah, so as you said, Lebrados did not | |
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| use learning methods and played very well without them. Since then, we have actually actually here, | |
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| we have a couple of papers on things that do use learning techniques. Excellent. So and deep learning | |
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| in particular, and sort of the way you're talking about where it's learning an evaluation function. | |
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| But in imperfect information games, unlike, let's say in go war, now also in chess and shogi, | |
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| it's not sufficient to learn an evaluation for a state because the value of an information set | |
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| depends not only on the exact state, but it also depends on both players beliefs. Like if I have | |
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| a bad hand, I'm much better off if the opponent thinks I have a good hand. And vice versa, | |
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| if I have a good hand, I'm much better off if the opponent believes I have a bad hand. So the value | |
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| of a state is not just a function of the cards. It depends on if you will, the path of play, | |
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| but only to the extent that it's captured in the belief distributions. So that's why it's not as | |
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| simple as it is in perfect information games. And I don't want to say it's simple there either. | |
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| It's of course, very complicated computationally there too. But at least conceptually, it's very | |
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| straightforward. There's a state, there's an evaluation function, you can try to learn it. | |
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| Here, you have to do something more. And what we do is in one of these papers, we're looking at | |
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| allowing where we allow the opponent to actually take different strategies at the leaf of the | |
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| search tree, if you will. And that is a different way of doing it. And it doesn't assume therefore | |
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| a particular way that the opponent plays. But it allows the opponent to choose from a set of | |
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| different continuation strategies. And that forces us to not be too optimistic in a lookahead | |
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| search. And that's that's one way you can do sound lookahead search in imperfect information | |
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| games, which is very different, difficult. And in US, you were asking about deep stack, what they | |
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| did, it was very different than what we do, either in Libertadores or in this new work. | |
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| They were generally randomly generating various situations in the game. Then they were doing | |
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| the lookahead from there to the end of the game, as if that was the start of a different game. And | |
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| then they were using deep learning to learn those values of those states. But the states were not | |
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| just the physical states, they include the belief distributions. When you talk about lookahead, | |
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| or deep stack, or with Libertadores, does it mean considering every possibility that the game can | |
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| evolve? Are we talking about extremely sort of this exponential growth of a tree? Yes. So we're | |
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| talking about exactly that. Much like you doing alpha beta search or Monte Carlo tree search, | |
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| but with different techniques. So there's a different search algorithm. And then we have to | |
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| deal with the leaves differently. So if you think about what Libertadores did, we didn't have to | |
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| worry about this, because we only did it at the end of the game. So we would always terminate into a | |
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| real situation. And we would know what the payout is. It didn't do these depth limited lookaheads. | |
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| But now in this new paper, which is called depth limited, I think it's called depth limited search | |
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| for imperfect information games, we can actually do sound depth limited lookaheads. So we can | |
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| actually start to do the lookahead from the beginning of the game on, because that's too | |
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| complicated to do for this whole long game. So in Libertadores, we were just doing it for the end. | |
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| So and then the other side, this belief distribution. So is it explicitly modeled | |
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| what kind of beliefs that the opponent might have? Yeah, it is explicitly modeled, but it's not | |
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| assumed that beliefs are actually output, not input. Of course, the starting beliefs are input, | |
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| but they just fall from the rules of the game, because we know that the dealer deals uniformly | |
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| from the deck. So I know that every pair of cards that you might have is equally likely. | |
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| I know that for a fact, that just follows from the rules of the game. Of course, | |
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| except the two cards that I have, I know you don't have those. You have to take that into | |
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| account. That's called card removal, and that's very important. Is the dealing always coming | |
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| from a single deck in heads up? Yes. So you can assume single deck. So you know that if I have | |
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| the ace of spades, I know you don't have an ace of spades. So in the beginning, your belief is | |
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| basically the fact that it's a fair dealing of hands. But how do you start to adjust that belief? | |
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| Well, that's where this beauty of game theory comes. So Nash equilibrium, which John Nash | |
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| introduced in 1950, introduces what rational play is when you have more than one player. | |
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| And these are pairs of strategies where strategies are contingency plans, one for each player. | |
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| So that neither player wants to deviate to a different strategy, given that the other | |
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| doesn't deviate. But as a side effect, you get the beliefs from base rule. So Nash equilibrium | |
| 20:34.800 --> 20:39.760 | |
| really isn't just deriving in these imperfect information games. Nash equilibrium doesn't | |
| 20:39.760 --> 20:47.920 | |
| just define strategies. It also defines beliefs for both of us, and it defines beliefs for each state. | |
| 20:47.920 --> 20:54.720 | |
| So at each state, each, if they call information sets, at each information set in the game, | |
| 20:54.720 --> 20:59.840 | |
| there's a set of different states that we might be in, but I don't know which one we're in. | |
| 21:00.960 --> 21:05.440 | |
| Nash equilibrium tells me exactly what is the probability distribution over those real | |
| 21:05.440 --> 21:11.360 | |
| wall states in my mind. How does Nash equilibrium give you that distribution? So why? | |
| 21:11.360 --> 21:17.280 | |
| I'll do a simple example. So you know the game Rock Paper Scissors? So we can draw it | |
| 21:17.280 --> 21:23.600 | |
| as player one moves first, and then player two moves. But of course, it's important that player | |
| 21:23.600 --> 21:28.560 | |
| two doesn't know what player one moved. Otherwise player two would win every time. So we can draw | |
| 21:28.560 --> 21:33.440 | |
| that as an information set where player one makes one or three moves first. And then there's an | |
| 21:33.440 --> 21:40.480 | |
| information set for player two. So player two doesn't know which of those nodes the world is in. | |
| 21:40.480 --> 21:46.400 | |
| But once we know the strategy for player one, Nash equilibrium will say that you play one third | |
| 21:46.400 --> 21:52.160 | |
| rock, one third paper, one third scissors. From that I can derive my beliefs on the information | |
| 21:52.160 --> 21:58.480 | |
| set that they're one third, one third, one third. So Bayes gives you that. But is that specific | |
| 21:58.480 --> 22:06.800 | |
| to a particular player? Or is it something you quickly update with those? No, the game theory | |
| 22:06.800 --> 22:12.560 | |
| isn't really player specific. So that's also why we don't need any data. We don't need any history | |
| 22:12.560 --> 22:17.840 | |
| how these particular humans played in the past or how any AI or even had played before. It's all | |
| 22:17.840 --> 22:24.160 | |
| about rationality. So we just think the AI just thinks about what would a rational opponent do? | |
| 22:24.720 --> 22:30.960 | |
| And what would I do if I were I am rational and what that that's that's the idea of game theory. | |
| 22:30.960 --> 22:38.080 | |
| So it's really a data free opponent free approach. So it comes from the design of the game as opposed | |
| 22:38.080 --> 22:43.600 | |
| to the design of the player. Exactly. There's no opponent modeling per se. I mean, we've done | |
| 22:43.600 --> 22:47.760 | |
| some work on combining opponent modeling with game theory. So you can exploit weak players | |
| 22:47.760 --> 22:53.440 | |
| even more. But that's another strand and in Libra, there's wouldn't turn that on. So I decided that | |
| 22:53.440 --> 22:59.520 | |
| these players are too good. And when you start to exploit an opponent, you typically open yourself | |
| 22:59.520 --> 23:04.720 | |
| up self up to exploitation. And these guys have so few holes to exploit and they're world's leading | |
| 23:04.720 --> 23:09.120 | |
| experts in counter exploitation. So I decided that we're not going to turn that stuff on. | |
| 23:09.120 --> 23:14.400 | |
| Actually, I saw a few your papers exploiting opponents sound very interesting to explore. | |
| 23:15.600 --> 23:19.120 | |
| Do you think there's room for exploitation, generally outside of the broadest? | |
| 23:19.840 --> 23:27.840 | |
| Is there a subject or people differences that could be exploited? Maybe not just in poker, | |
| 23:27.840 --> 23:33.360 | |
| but in general interactions and negotiations all these other domains that you're considering? | |
| 23:33.360 --> 23:39.760 | |
| Yeah, definitely. We've done some work on that. And I really like the work that hybridizes the two. | |
| 23:39.760 --> 23:45.200 | |
| So you figure out what would a rational opponent do. And by the way, that's safe in these zero | |
| 23:45.200 --> 23:49.440 | |
| sum games to players, zero sum games, because if the opponent does something irrational, | |
| 23:49.440 --> 23:56.320 | |
| yes, it might show a throw of my beliefs. But the amount that the player can gain by throwing | |
| 23:56.320 --> 24:04.240 | |
| of my belief is always less than they lose by playing poorly. So it's safe. But still, | |
| 24:04.240 --> 24:09.280 | |
| if somebody's weak as a player, you might want to play differently to exploit them more. | |
| 24:10.160 --> 24:14.560 | |
| So you can think about it this way, a game theoretic strategy is unbeatable, | |
| 24:15.600 --> 24:22.720 | |
| but it doesn't maximally beat the other opponent. So the winnings per hand might be better | |
| 24:22.720 --> 24:27.120 | |
| with a different strategy. And the hybrid is that you start from a game theoretic approach, | |
| 24:27.120 --> 24:33.040 | |
| and then as you gain data about the opponent in certain parts of the game tree, then in those | |
| 24:33.040 --> 24:39.280 | |
| parts of the game tree, you start to tweak your strategy more and more towards exploitation, | |
| 24:39.280 --> 24:44.160 | |
| while still staying fairly close to the game theoretic strategy so as to not open yourself | |
| 24:44.160 --> 24:53.600 | |
| up to exploitation too much. How do you do that? Do you try to vary up strategies, make it unpredictable? | |
| 24:53.600 --> 24:59.520 | |
| It's like, what is it, tit for tat strategies in Prisoner's Dilemma or? | |
| 25:00.560 --> 25:07.440 | |
| Well, that's a repeated game, simple Prisoner's Dilemma, repeated games. But even there, | |
| 25:07.440 --> 25:13.120 | |
| there's no proof that says that that's the best thing. But experimentally, it actually does well. | |
| 25:13.120 --> 25:17.360 | |
| So what kind of games are there, first of all? I don't know if this is something that you could | |
| 25:17.360 --> 25:21.760 | |
| just summarize. There's perfect information games with all the information on the table. | |
| 25:22.320 --> 25:27.680 | |
| There is imperfect information games. There's repeated games that you play over and over. | |
| 25:28.480 --> 25:36.960 | |
| There's zero sum games. There's non zero sum games. And then there's a really important | |
| 25:36.960 --> 25:44.640 | |
| distinction you're making to player versus more players. So what are what other games are there? | |
| 25:44.640 --> 25:49.920 | |
| And what's the difference, for example, with this two player game versus more players? Yeah, | |
| 25:49.920 --> 25:54.720 | |
| what are the key differences? Right. So let me start from the the basics. So | |
| 25:56.320 --> 26:02.160 | |
| a repeated game is a game where the same exact game is played over and over. | |
| 26:02.160 --> 26:08.160 | |
| In these extensive form games, where you think about three form, maybe with these | |
| 26:08.160 --> 26:14.000 | |
| information sets to represent incomplete information, you can have kind of repetitive | |
| 26:14.000 --> 26:18.480 | |
| interactions and even repeated games are a special case of that, by the way. But | |
| 26:19.680 --> 26:24.160 | |
| the game doesn't have to be exactly the same. It's like in sourcing options. Yes, we're going to | |
| 26:24.160 --> 26:29.040 | |
| see the same supply base year to year. But what I'm buying is a little different every time. | |
| 26:29.040 --> 26:34.080 | |
| And the supply base is a little different every time and so on. So it's not really repeated. | |
| 26:34.080 --> 26:39.680 | |
| So to find a purely repeated game is actually very rare in the world. So they're really a very | |
| 26:40.960 --> 26:47.360 | |
| coarse model of what's going on. Then if you move up from just repeated, simple, | |
| 26:47.360 --> 26:52.560 | |
| repeated matrix games, not all the way to extensive form games, but in between, | |
| 26:52.560 --> 26:59.360 | |
| there's stochastic games, where you know, there's these, you think about it like these little | |
| 26:59.360 --> 27:06.080 | |
| matrix games. And when you take an action and your own takes an action, they determine not which | |
| 27:06.080 --> 27:11.280 | |
| next state I'm going to next game, I'm going to, but the distribution over next games, | |
| 27:11.280 --> 27:15.760 | |
| where I might be going to. So that's the stochastic game. But it's like | |
| 27:15.760 --> 27:22.160 | |
| like matrix games, repeated stochastic games, extensive form games, that is from less to more | |
| 27:22.160 --> 27:28.080 | |
| general. And poker is an example of the last one. So it's really in the most general setting, | |
| 27:29.440 --> 27:34.720 | |
| extensive form games. And that's kind of what the AI community has been working on and being | |
| 27:34.720 --> 27:39.680 | |
| benchmarked on with this heads up no limit, Texas Holden. Can you describe extensive form games? | |
| 27:39.680 --> 27:45.600 | |
| What's the model here? So if you're familiar with the tree form, so it's really the tree form, | |
| 27:45.600 --> 27:51.280 | |
| like in chess, there's a search tree versus a matrix versus a matrix. Yeah. And that's the | |
| 27:51.280 --> 27:56.640 | |
| matrix is called the matrix form or by matrix form or normal form game. And here you have the tree | |
| 27:56.640 --> 28:01.680 | |
| form. So you can actually do certain types of reasoning there, that you lose the information | |
| 28:02.320 --> 28:07.840 | |
| when you go to normal form. There's a certain form of equivalence, like if you go from three | |
| 28:07.840 --> 28:13.840 | |
| form and you say it every possible contingency plan is a strategy, then I can actually go back | |
| 28:13.840 --> 28:19.600 | |
| to the normal form, but I lose some information from the lack of sequentiality. Then the multiplayer | |
| 28:19.600 --> 28:29.520 | |
| versus two player distinction is an important one. So two player games in zero sum are conceptually | |
| 28:29.520 --> 28:37.040 | |
| easier and computationally easier. They're still huge like this one, this one. But they're conceptually | |
| 28:37.040 --> 28:42.160 | |
| easier and computationally easier. In that conceptually, you don't have to worry about | |
| 28:42.160 --> 28:47.440 | |
| which equilibrium is the other guy going to play when there are multiple, because any equilibrium | |
| 28:47.440 --> 28:52.400 | |
| strategy is the best response to any other equilibrium strategy. So I can play a different | |
| 28:52.400 --> 28:57.600 | |
| equilibrium from you and we'll still get the right values of the game. That falls apart even | |
| 28:57.600 --> 29:02.960 | |
| with two players when you have general sum games. Even without cooperation, just even without | |
| 29:02.960 --> 29:09.040 | |
| cooperation. So there's a big gap from two player zero sum to two player general sum, or even to | |
| 29:09.040 --> 29:17.600 | |
| three players zero sum. That's a big gap, at least in theory. Can you maybe non mathematically | |
| 29:17.600 --> 29:23.040 | |
| provide the intuition why it all falls apart with three or more players? It seems like you should | |
| 29:23.040 --> 29:32.640 | |
| still be able to have a Nash equilibrium that's instructive, that holds. Okay, so it is true | |
| 29:32.640 --> 29:39.840 | |
| that all finite games have a Nash equilibrium. So this is what your Nash actually proved. | |
| 29:40.880 --> 29:45.600 | |
| So they do have a Nash equilibrium. That's not the problem. The problem is that there can be many. | |
| 29:46.480 --> 29:52.000 | |
| And then there's a question of which equilibrium to select. So and if you select your strategy | |
| 29:52.000 --> 30:00.960 | |
| from a different equilibrium and I select mine, then what does that mean? And in these non zero | |
| 30:00.960 --> 30:07.760 | |
| sum games, we may lose some joint benefit by being just simply stupid, we could actually both be | |
| 30:07.760 --> 30:12.960 | |
| better off if we did something else. And in three player, you get other problems also like collusion. | |
| 30:12.960 --> 30:19.600 | |
| Like maybe you and I can gang up on a third player, and we can do radically better by colluding. | |
| 30:19.600 --> 30:25.440 | |
| So there are lots of issues that come up there. So No Brown, the student you work with on this, | |
| 30:25.440 --> 30:31.120 | |
| has mentioned, I looked through the AMA on Reddit, he mentioned that the ability of poker players | |
| 30:31.120 --> 30:36.320 | |
| to collaborate would make the game. He was asked the question of, how would you make the game of | |
| 30:36.320 --> 30:42.320 | |
| poker? Or both of you were asked the question, how would you make the game of poker beyond | |
| 30:43.440 --> 30:51.440 | |
| being solvable by current AI methods? And he said that there's not many ways of making poker more | |
| 30:51.440 --> 30:59.600 | |
| difficult, but a collaboration or cooperation between players would make it extremely difficult. | |
| 30:59.600 --> 31:05.200 | |
| So can you provide the intuition behind why that is, if you agree with that idea? | |
| 31:05.200 --> 31:11.840 | |
| Yeah, so we've done a lot of work on coalitional games. And we actually have a paper here with | |
| 31:11.840 --> 31:17.040 | |
| my other student Gabriella Farina and some other collaborators on at NIPPS on that actually just | |
| 31:17.040 --> 31:22.080 | |
| came back from the poster session where we presented this. So when you have a collusion, | |
| 31:22.080 --> 31:29.440 | |
| it's a different problem. And it typically gets even harder then. Even the game representations, | |
| 31:29.440 --> 31:35.840 | |
| some of the game representations don't really allow good computation. So we actually introduced a new | |
| 31:35.840 --> 31:43.840 | |
| game representation for that. Is that kind of cooperation part of the model? Do you have | |
| 31:43.840 --> 31:48.720 | |
| information about the fact that other players are cooperating? Or is it just this chaos that | |
| 31:48.720 --> 31:53.760 | |
| where nothing is known? So there's some some things unknown. Can you give an example of a | |
| 31:53.760 --> 31:59.920 | |
| collusion type game? Or is it usually? So like bridge. Yeah, so think about bridge. It's like | |
| 31:59.920 --> 32:06.960 | |
| when you and I are on a team, our payoffs are the same. The problem is that we can't talk. So when | |
| 32:06.960 --> 32:13.360 | |
| I get my cards, I can't whisper to you what my cards are. That would not be allowed. So we have | |
| 32:13.360 --> 32:20.480 | |
| to somehow coordinate our strategies ahead of time. And only ahead of time. And then there's | |
| 32:20.480 --> 32:25.920 | |
| certain signals we can talk about. But they have to be such that the other team also understands | |
| 32:25.920 --> 32:31.920 | |
| that. So so that that's that's an example where the coordination is already built into the rules | |
| 32:31.920 --> 32:38.400 | |
| of the game. But in many other situations, like auctions or negotiations or diplomatic | |
| 32:38.400 --> 32:44.320 | |
| relationships, poker, it's not really built in. But it still can be very helpful for the | |
| 32:44.320 --> 32:51.440 | |
| colliders. I've read you write somewhere, the negotiations, you come to the table with prior | |
| 32:52.640 --> 32:58.160 | |
| like a strategy that, like that you're willing to do and not willing to do those kinds of things. | |
| 32:58.160 --> 33:04.320 | |
| So how do you start to now moving away from poker, moving beyond poker into other applications | |
| 33:04.320 --> 33:10.960 | |
| like negotiations? How do you start applying this to other to other domains, even real world | |
| 33:10.960 --> 33:15.360 | |
| domains that you've worked on? Yeah, I actually have two startup companies doing exactly that. | |
| 33:15.360 --> 33:21.520 | |
| One is called Strategic Machine. And that's for kind of business applications, gaming, sports, | |
| 33:21.520 --> 33:29.040 | |
| all sorts of things like that. Any applications of this to business and to sports and to gaming, | |
| 33:29.040 --> 33:34.800 | |
| to various types of things for in finance, electricity markets and so on. And the other | |
| 33:34.800 --> 33:41.360 | |
| is called Strategy Robot, where we are taking these to military security, cybersecurity, | |
| 33:41.360 --> 33:45.760 | |
| and intelligence applications. I think you worked a little bit in | |
| 33:47.920 --> 33:54.560 | |
| how do you put it, advertisement, sort of suggesting ads kind of thing. | |
| 33:54.560 --> 33:59.040 | |
| Yeah, that's another company, Optimized Markets. But that's much more about a | |
| 33:59.040 --> 34:03.840 | |
| combinatorial market and optimization based technology. That's not using these | |
| 34:04.720 --> 34:12.400 | |
| game theory decreasing technologies. I see. Okay, so what sort of high level do you think about | |
| 34:13.040 --> 34:20.320 | |
| our ability to use game theoretic concepts to model human behavior? Do you think human behavior is | |
| 34:20.320 --> 34:25.680 | |
| amenable to this kind of modeling? So outside of the poker games and where have you seen it | |
| 34:25.680 --> 34:32.000 | |
| done successfully in your work? I'm not sure. The goal really is modeling humans. | |
| 34:33.520 --> 34:40.240 | |
| Like for example, if I'm playing a zero sum game, I don't really care that the opponent is actually | |
| 34:40.240 --> 34:45.680 | |
| following my model of rational behavior. Because if they're not, that's even better for me. | |
| 34:45.680 --> 34:55.440 | |
| All right, so see with the opponents in games, the prerequisite is that you've formalized | |
| 34:56.240 --> 35:02.720 | |
| the interaction in some way that can be amenable to analysis. I mean, you've done this amazing work | |
| 35:02.720 --> 35:12.160 | |
| with mechanism design, designing games that have certain outcomes. But so I'll tell you an example | |
| 35:12.160 --> 35:19.360 | |
| for my world of autonomous vehicles. We're studying pedestrians and pedestrians and cars | |
| 35:19.360 --> 35:25.040 | |
| negotiate in this nonverbal communication. There's this weird game dance of tension where | |
| 35:25.760 --> 35:30.400 | |
| pedestrians are basically saying, I trust that you won't kill me. And so as a J Walker, | |
| 35:30.400 --> 35:34.560 | |
| I will step onto the road even though I'm breaking the law and there's this tension. | |
| 35:34.560 --> 35:40.480 | |
| And the question is, we really don't know how to model that well in trying to model intent. | |
| 35:40.480 --> 35:46.240 | |
| And so people sometimes bring up ideas of game theory and so on. Do you think that aspect | |
| 35:47.120 --> 35:53.520 | |
| of human behavior can use these kinds of imperfect information approaches, modeling? | |
| 35:54.800 --> 36:00.800 | |
| How do you start to attack a problem like that when you don't even know how to design the game | |
| 36:00.800 --> 36:06.640 | |
| to describe the situation in order to solve it? Okay, so I haven't really thought about J walking. | |
| 36:06.640 --> 36:12.080 | |
| But one thing that I think could be a good application in autonomous vehicles is the | |
| 36:12.080 --> 36:18.240 | |
| following. So let's say that you have fleets of autonomous cars operating by different companies. | |
| 36:18.240 --> 36:23.520 | |
| So maybe here's the Waymo fleet and here's the Uber fleet. If you think about the rules of the road, | |
| 36:24.160 --> 36:29.920 | |
| they define certain legal rules, but that still leaves a huge strategy space open. | |
| 36:29.920 --> 36:33.760 | |
| Like as a simple example, when cars merge, you know, how humans merge, you know, | |
| 36:33.760 --> 36:40.800 | |
| they slow down and look at each other and try to merge. Wouldn't it be better if these situations | |
| 36:40.800 --> 36:46.240 | |
| would already be prenegotiated so we can actually merge at full speed and we know that this is the | |
| 36:46.240 --> 36:51.680 | |
| situation, this is how we do it and it's all going to be faster. But there are way too many | |
| 36:51.680 --> 36:57.600 | |
| situations to negotiate manually. So you could use automated negotiation. This is the idea at least. | |
| 36:57.600 --> 37:04.160 | |
| You could use automated negotiation to negotiate all of these situations or many of them in advance. | |
| 37:04.160 --> 37:09.040 | |
| And of course, it might be that, hey, maybe you're not going to always let me go first. | |
| 37:09.040 --> 37:13.520 | |
| Maybe you said, okay, well, in these situations, I'll let you go first. But in exchange, you're | |
| 37:13.520 --> 37:18.240 | |
| going to give me two hours, you're going to let me go first in these situations. So it's this huge | |
| 37:18.240 --> 37:24.240 | |
| combinatorial negotiation. And do you think there's room in that example of merging to | |
| 37:24.240 --> 37:28.080 | |
| model this whole situation as an imperfect information game? Or do you really want to | |
| 37:28.080 --> 37:33.520 | |
| consider it to be a perfect? No, that's a good question. Yeah. That's a good question. Do you | |
| 37:33.520 --> 37:41.120 | |
| pay the price of assuming that you don't know everything? Yeah, I don't know. It's certainly | |
| 37:41.120 --> 37:47.040 | |
| much easier. Games with perfect information are much easier. So if you can get away with it, | |
| 37:48.240 --> 37:52.960 | |
| you should. But if the real situation is of imperfect information, then you're going to | |
| 37:52.960 --> 37:58.480 | |
| have to deal with imperfect information. Great. So what lessons have you learned the annual | |
| 37:58.480 --> 38:04.560 | |
| computer poker competition? An incredible accomplishment of AI. You look at the history | |
| 38:04.560 --> 38:12.240 | |
| of the blue AlphaGo, these kind of moments when AI stepped up in an engineering effort and a | |
| 38:12.240 --> 38:18.240 | |
| scientific effort combined to beat the best human player. So what do you take away from | |
| 38:18.240 --> 38:23.200 | |
| this whole experience? What have you learned about designing AI systems that play these kinds | |
| 38:23.200 --> 38:30.000 | |
| of games? And what does that mean for AI in general, for the future of AI development? | |
| 38:30.720 --> 38:34.160 | |
| Yeah, so that's a good question. So there's so much to say about it. | |
| 38:35.280 --> 38:40.320 | |
| I do like this type of performance oriented research, although in my group, we go all the | |
| 38:40.320 --> 38:46.160 | |
| way from like idea to theory to experiments to big system building to commercialization. So we | |
| 38:46.160 --> 38:52.560 | |
| span that spectrum. But I think that in a lot of situations in AI, you really have to build the | |
| 38:52.560 --> 38:58.400 | |
| big systems and evaluate them at scale before you know what works and doesn't. And we've seen | |
| 38:58.400 --> 39:03.280 | |
| that in the computational game theory community, that there are a lot of techniques that look good | |
| 39:03.280 --> 39:08.640 | |
| in the small, but then they cease to look good in the large. And we've also seen that there are a | |
| 39:08.640 --> 39:15.600 | |
| lot of techniques that look superior in theory. And I really mean in terms of convergence rates, | |
| 39:15.600 --> 39:20.720 | |
| better like first order methods, better convergence rates like the CFR based algorithms, | |
| 39:20.720 --> 39:26.080 | |
| yet the CFR based algorithms are the first fastest in practice. So it really tells me that you have | |
| 39:26.080 --> 39:32.400 | |
| to test these in reality, the theory isn't tight enough, if you will, to tell you which | |
| 39:32.400 --> 39:38.480 | |
| algorithms are better than the others. And you have to look at these things that in the large, | |
| 39:38.480 --> 39:43.600 | |
| because any sort of projections you do from the small can at least in this domain be very misleading. | |
| 39:43.600 --> 39:49.040 | |
| So that's kind of from a kind of science and engineering perspective, from a personal perspective, | |
| 39:49.040 --> 39:55.200 | |
| it's been just a wild experience in that with the first poker competition, the first | |
| 39:56.160 --> 40:00.640 | |
| brains versus AI man machine poker competition that we organized. There had been, by the way, | |
| 40:00.640 --> 40:04.880 | |
| for other poker games, there had been previous competitions, but this was for heads up no limit, | |
| 40:04.880 --> 40:11.200 | |
| this was the first. And I probably became the most hated person in the world of poker. And I | |
| 40:11.200 --> 40:18.400 | |
| didn't mean to sigh. Why is that for cracking the game for? Yeah, it was a lot of people | |
| 40:18.400 --> 40:24.160 | |
| felt that it was a real threat to the whole game, the whole existence of the game. If AI becomes | |
| 40:24.160 --> 40:29.680 | |
| better than humans, people would be scared to play poker, because there are these superhuman | |
| 40:29.680 --> 40:35.120 | |
| AIs running around taking their money and you know, all of that. So I just it was really aggressive. | |
| 40:35.120 --> 40:40.640 | |
| Interesting. The comments were super aggressive. I got everything just short of death threats. | |
| 40:42.000 --> 40:45.760 | |
| Do you think the same was true for chess? Because right now, they just completed the | |
| 40:45.760 --> 40:50.720 | |
| world championships in chess, and humans just started ignoring the fact that there's AI systems | |
| 40:50.720 --> 40:55.360 | |
| now that outperform humans and they still enjoy the game is still a beautiful game. | |
| 40:55.360 --> 41:00.160 | |
| That's what I think. And I think the same thing happened in poker. And so I didn't | |
| 41:00.160 --> 41:03.760 | |
| think of myself as somebody was going to kill the game. And I don't think I did. | |
| 41:03.760 --> 41:07.360 | |
| I've really learned to love this game. I wasn't a poker player before, but | |
| 41:07.360 --> 41:12.400 | |
| learn so many nuances about it from these AIs. And they've really changed how the game is played, | |
| 41:12.400 --> 41:17.600 | |
| by the way. So they have these very Martian ways of playing poker. And the top humans are now | |
| 41:17.600 --> 41:24.880 | |
| incorporating those types of strategies into their own play. So if anything, to me, our work has made | |
| 41:25.600 --> 41:31.760 | |
| poker a richer, more interesting game for humans to play, not something that is going to steer | |
| 41:31.760 --> 41:36.240 | |
| humans away from it entirely. Just a quick comment on something you said, which is, | |
| 41:37.440 --> 41:44.800 | |
| if I may say so, in academia is a little bit rare sometimes. It's pretty brave to put your ideas | |
| 41:44.800 --> 41:50.000 | |
| to the test in the way you described, saying that sometimes good ideas don't work when you actually | |
| 41:50.560 --> 41:57.600 | |
| try to apply them at scale. So where does that come from? I mean, if you could do advice for | |
| 41:57.600 --> 42:04.000 | |
| people, what drives you in that sense? Were you always this way? I mean, it takes a brave person, | |
| 42:04.000 --> 42:09.280 | |
| I guess is what I'm saying, to test their ideas and to see if this thing actually works against human | |
| 42:09.840 --> 42:14.000 | |
| top human players and so on. I don't know about brave, but it takes a lot of work. | |
| 42:14.800 --> 42:20.960 | |
| It takes a lot of work and a lot of time to organize, to make something big and to organize | |
| 42:20.960 --> 42:26.080 | |
| an event and stuff like that. And what drives you in that effort? Because you could still, | |
| 42:26.080 --> 42:31.280 | |
| I would argue, get a Best Paper Award at NIPS as you did in 17 without doing this. | |
| 42:31.280 --> 42:32.160 | |
| That's right, yes. | |
| 42:34.400 --> 42:40.960 | |
| So in general, I believe it's very important to do things in the real world and at scale. | |
| 42:41.600 --> 42:48.320 | |
| And that's really where the pudding, if you will, proves in the pudding. That's where it is. | |
| 42:48.320 --> 42:54.720 | |
| In this particular case, it was kind of a competition between different groups. | |
| 42:54.720 --> 43:00.400 | |
| And for many years, as to who can be the first one to beat the top humans at heads up, | |
| 43:00.400 --> 43:09.440 | |
| no limit takes us hold them. So it became kind of like a competition who can get there. | |
| 43:09.440 --> 43:13.840 | |
| Yeah, so a little friendly competition could do wonders for progress. | |
| 43:13.840 --> 43:14.960 | |
| Yes, absolutely. | |
| 43:16.240 --> 43:21.440 | |
| So the topic of mechanism design, which is really interesting, also kind of new to me, | |
| 43:21.440 --> 43:27.440 | |
| except as an observer of, I don't know, politics and any, I'm an observer of mechanisms, | |
| 43:27.440 --> 43:32.960 | |
| but you write in your paper, an automated mechanism design that I quickly read. | |
| 43:33.840 --> 43:36.960 | |
| So mechanism design is designing the rules of the game, | |
| 43:37.760 --> 43:44.400 | |
| so you get a certain desirable outcome. And you have this work on doing so in an automatic | |
| 43:44.400 --> 43:49.440 | |
| fashion as opposed to fine tuning it. So what have you learned from those efforts? | |
| 43:49.440 --> 43:57.040 | |
| If you look, say, I don't know, at complex, it's like our political system. Can we design our | |
| 43:57.040 --> 44:04.480 | |
| political system to have in an automated fashion, to have outcomes that we want? Can we design | |
| 44:04.480 --> 44:11.680 | |
| something like traffic lights to be smart, where it gets outcomes that we want? | |
| 44:11.680 --> 44:14.880 | |
| So what are the lessons that you draw from that work? | |
| 44:14.880 --> 44:19.280 | |
| Yeah, so I still very much believe in the automated mechanism design direction. | |
| 44:19.280 --> 44:19.520 | |
| Yes. | |
| 44:20.640 --> 44:26.400 | |
| But it's not a panacea. There are impossibility results in mechanism design, | |
| 44:26.400 --> 44:32.800 | |
| saying that there is no mechanism that accomplishes objective X in class C. | |
| 44:33.840 --> 44:40.000 | |
| So it's not going up, there's no way, using any mechanism design tools, manual or automated, | |
| 44:40.800 --> 44:42.720 | |
| to do certain things in mechanism design. | |
| 44:42.720 --> 44:46.960 | |
| Can you describe that again? So meaning there, it's impossible to achieve that? | |
| 44:46.960 --> 44:55.120 | |
| Yeah, there's also an impossible. So these are not statements about human ingenuity, | |
| 44:55.120 --> 45:00.320 | |
| who might come up with something smart. These are proofs that if you want to accomplish properties | |
| 45:00.320 --> 45:06.080 | |
| X in class C, that is not doable with any mechanism. The good thing about automated | |
| 45:06.080 --> 45:12.800 | |
| mechanism design is that we're not really designing for a class, we're designing for specific settings | |
| 45:12.800 --> 45:18.800 | |
| at a time. So even if there's an impossibility result for the whole class, it just doesn't | |
| 45:18.800 --> 45:23.520 | |
| mean that all of the cases in the class are impossible, it just means that some of the | |
| 45:23.520 --> 45:28.800 | |
| cases are impossible. So we can actually carve these islands of possibility within these | |
| 45:28.800 --> 45:34.640 | |
| non impossible classes. And we've actually done that. So one of the famous results in mechanism | |
| 45:34.640 --> 45:40.640 | |
| design is a Meyers and Settled Weight theorem by Roger Meyers and Mark Settled Weight from 1983. | |
| 45:40.640 --> 45:45.760 | |
| So it's an impossibility of efficient trade under imperfect information. We show that | |
| 45:46.880 --> 45:50.480 | |
| you can in many settings avoid that and get efficient trade anyway. | |
| 45:51.360 --> 45:56.640 | |
| Depending on how you design the game. Depending how you design the game. And of course, | |
| 45:56.640 --> 46:03.760 | |
| it doesn't in any way contradict the impossibility result. The impossibility result is still there, | |
| 46:03.760 --> 46:11.200 | |
| but it just finds spots within this impossible class where in those spots you don't have the | |
| 46:11.200 --> 46:17.600 | |
| impossibility. Sorry if I'm going a bit philosophical, but what lessons do you draw towards like I | |
| 46:17.600 --> 46:23.920 | |
| mentioned politics or the human interaction and designing mechanisms for outside of just | |
| 46:24.800 --> 46:27.120 | |
| these kinds of trading or auctioning or | |
| 46:27.120 --> 46:37.200 | |
| purely formal games or human interaction like a political system. Do you think it's applicable | |
| 46:37.200 --> 46:47.920 | |
| to politics or to business, to negotiations, these kinds of things, designing rules that have | |
| 46:47.920 --> 46:53.360 | |
| certain outcomes? Yeah, yeah, I do think so. Have you seen success that successfully done? | |
| 46:53.360 --> 46:58.880 | |
| There hasn't really. Oh, you mean mechanism design or automated mechanism? Automated mechanism design. | |
| 46:58.880 --> 47:07.440 | |
| So mechanism design itself has had fairly limited success so far. There are certain cases, | |
| 47:07.440 --> 47:13.600 | |
| but most of the real world situations are actually not sound from a mechanism design | |
| 47:13.600 --> 47:18.640 | |
| perspective. Even in those cases where they've been designed by very knowledgeable mechanism | |
| 47:18.640 --> 47:24.080 | |
| design people, the people are typically just taking some insights from the theory and applying | |
| 47:24.080 --> 47:29.920 | |
| those insights into the real world rather than applying the mechanisms directly. So one famous | |
| 47:29.920 --> 47:36.880 | |
| example of is the FCC spectrum auctions. So I've also had a small role in that and | |
| 47:38.560 --> 47:45.040 | |
| very good economists have been working on that with no game theory. Yet the rules that are | |
| 47:45.040 --> 47:50.960 | |
| designed in practice there, they're such that bidding truthfully is not the best strategy. | |
| 47:51.680 --> 47:57.040 | |
| Usually mechanism design, we try to make things easy for the participants. So telling the truth | |
| 47:57.040 --> 48:02.480 | |
| is the best strategy. But even in those very high stakes auctions where you have tens of billions | |
| 48:02.480 --> 48:07.840 | |
| of dollars worth of spectrum being auctioned, truth telling is not the best strategy. | |
| 48:09.200 --> 48:14.000 | |
| And by the way, nobody knows even a single optimal bidding strategy for those auctions. | |
| 48:14.000 --> 48:17.600 | |
| What's the challenge of coming up with an optimal bid? Because there's a lot of players and there's | |
| 48:17.600 --> 48:23.440 | |
| imperfections. It's not so much there, a lot of players, but many items for sale. And these | |
| 48:23.440 --> 48:29.200 | |
| mechanisms are such that even with just two items or one item, bidding truthfully wouldn't be | |
| 48:29.200 --> 48:37.040 | |
| the best strategy. If you look at the history of AI, it's marked by seminal events and an | |
| 48:37.040 --> 48:42.160 | |
| AlphaGo beating a world champion, human go player, I would put Lebrados winning the heads | |
| 48:42.160 --> 48:51.280 | |
| up no limit hold them as one of such event. Thank you. And what do you think is the next such event? | |
| 48:52.400 --> 48:58.880 | |
| Whether it's in your life or in the broadly AI community that you think might be out there | |
| 48:58.880 --> 49:04.800 | |
| that would surprise the world. So that's a great question and I really know the answer. In terms | |
| 49:04.800 --> 49:12.880 | |
| of game solving heads up no limit takes us all and really was the one remaining widely agreed | |
| 49:12.880 --> 49:18.400 | |
| upon benchmark. So that was the big milestone. Now, are there other things? Yeah, certainly | |
| 49:18.400 --> 49:23.440 | |
| there are, but there there is not one that the community has kind of focused on. So what could | |
| 49:23.440 --> 49:29.680 | |
| be other things? There are groups working on Starcraft. There are groups working on Dota 2. | |
| 49:29.680 --> 49:36.560 | |
| These are video games. Yes, or you could have like diplomacy or Hanabi, you know, things like | |
| 49:36.560 --> 49:42.640 | |
| that. These are like recreational games, but none of them are really acknowledged as kind of the | |
| 49:42.640 --> 49:49.920 | |
| main next challenge problem. Like chess or go or heads up no limit takes us hold them was. | |
| 49:49.920 --> 49:55.600 | |
| So I don't really know in the game solving space what is or what will be the next benchmark. I | |
| 49:55.600 --> 49:59.920 | |
| kind of hope that there will be a next benchmark because really the different groups working on | |
| 49:59.920 --> 50:06.160 | |
| the same problem really drove these application independent techniques forward very quickly | |
| 50:06.160 --> 50:11.120 | |
| over 10 years. Do you think there's an open problem that excites you that you start moving | |
| 50:11.120 --> 50:18.240 | |
| away from games into real world games like say the stock market trading? Yeah, so that's kind | |
| 50:18.240 --> 50:27.120 | |
| of how I am. So I am probably not going to work as hard on these recreational benchmarks. | |
| 50:27.760 --> 50:32.960 | |
| I'm doing two startups on game solving technology strategic machine and strategy robot and we're | |
| 50:32.960 --> 50:39.680 | |
| really interested in pushing this stuff into practice. What do you think would be really, | |
| 50:39.680 --> 50:50.400 | |
| you know, a powerful result that would be surprising that would be if you can say, I mean, | |
| 50:50.400 --> 50:56.880 | |
| it's, you know, five years, 10 years from now, something that statistically would say is not | |
| 50:56.880 --> 51:03.200 | |
| very likely, but if there's a breakthrough would achieve. Yeah, so I think that overall, | |
| 51:03.200 --> 51:11.600 | |
| we're in a very different situation in game theory than we are in, let's say, machine learning. Yes. | |
| 51:11.600 --> 51:16.720 | |
| So in machine learning, it's a fairly mature technology and it's very broadly applied and | |
| 51:17.280 --> 51:22.480 | |
| proven success in the real world. In game solving, there are almost no applications yet. | |
| 51:24.320 --> 51:29.440 | |
| We have just become superhuman, which machine learning you could argue happened in the 90s, | |
| 51:29.440 --> 51:35.120 | |
| if not earlier, and at least on supervised learning, certain complex supervised learning | |
| 51:35.120 --> 51:40.960 | |
| applications. Now, I think the next challenge problem, I know you're not asking about it this | |
| 51:40.960 --> 51:45.360 | |
| way, you're asking about technology breakthrough. But I think that big breakthrough is to be able | |
| 51:45.360 --> 51:50.720 | |
| to show that, hey, maybe most of, let's say, military planning or most of business strategy | |
| 51:50.720 --> 51:55.600 | |
| will actually be done strategically using computational game theory. That's what I would | |
| 51:55.600 --> 52:01.120 | |
| like to see as a next five or 10 year goal. Maybe you can explain to me again, forgive me if this | |
| 52:01.120 --> 52:07.360 | |
| is an obvious question, but machine learning methods and neural networks suffer from not | |
| 52:07.360 --> 52:12.800 | |
| being transparent, not being explainable. Game theoretic methods, Nash Equilibria, | |
| 52:12.800 --> 52:18.960 | |
| do they generally, when you see the different solutions, are they, when you talk about military | |
| 52:18.960 --> 52:24.640 | |
| operations, are they, once you see the strategies, do they make sense? Are they explainable or do | |
| 52:24.640 --> 52:29.680 | |
| they suffer from the same problems as neural networks do? So that's a good question. I would say | |
| 52:30.400 --> 52:36.560 | |
| a little bit yes and no. And what I mean by that is that these game theoretic strategies, | |
| 52:36.560 --> 52:42.320 | |
| let's say, Nash Equilibrium, it has provable properties. So it's unlike, let's say, deep | |
| 52:42.320 --> 52:47.040 | |
| learning where you kind of cross your fingers, hopefully it'll work. And then after the fact, | |
| 52:47.040 --> 52:52.560 | |
| when you have the weights, you're still crossing your fingers, and I hope it will work. Here, | |
| 52:52.560 --> 52:57.840 | |
| you know that the solution quality is there. There's provable solution quality guarantees. | |
| 52:58.480 --> 53:03.360 | |
| Now, that doesn't necessarily mean that the strategies are human understandable. | |
| 53:03.360 --> 53:08.560 | |
| That's a whole other problem. So I think that deep learning and computational game theory | |
| 53:08.560 --> 53:12.320 | |
| are in the same boat in that sense, that both are difficult to understand. | |
| 53:13.680 --> 53:16.160 | |
| But at least the game theoretic techniques, they have this | |
| 53:16.160 --> 53:22.800 | |
| guarantees of solution quality. So do you see business operations, strategic operations, | |
| 53:22.800 --> 53:31.440 | |
| even military in the future being at least the strong candidates being proposed by automated | |
| 53:31.440 --> 53:39.520 | |
| systems? Do you see that? Yeah, I do. I do. But that's more of a belief than a substantiated fact. | |
| 53:39.520 --> 53:43.840 | |
| Depending on where you land, an optimism or pessimism, that's a really, to me, that's an | |
| 53:43.840 --> 53:52.560 | |
| exciting future, especially if there's provable things in terms of optimality. So looking into | |
| 53:52.560 --> 54:01.120 | |
| the future, there's a few folks worried about the, especially you look at the game of poker, | |
| 54:01.120 --> 54:06.800 | |
| which is probably one of the last benchmarks in terms of games being solved. They worry about | |
| 54:06.800 --> 54:11.760 | |
| the future and the existential threats of artificial intelligence. So the negative impact | |
| 54:11.760 --> 54:18.800 | |
| in whatever form on society, is that something that concerns you as much? Or are you more optimistic | |
| 54:18.800 --> 54:24.640 | |
| about the positive impacts of AI? I am much more optimistic about the positive impacts. | |
| 54:24.640 --> 54:29.920 | |
| So just in my own work, what we've done so far, we run the nationwide kidney exchange. | |
| 54:29.920 --> 54:36.000 | |
| Hundreds of people are walking around alive today, who would it be? And it's increased employment. | |
| 54:36.000 --> 54:42.720 | |
| You have a lot of people now running kidney exchanges and at transplant centers, interacting | |
| 54:42.720 --> 54:50.240 | |
| with the kidney exchange. You have some extra surgeons, nurses, anesthesiologists, hospitals, | |
| 54:50.240 --> 54:55.200 | |
| all of that. So employment is increasing from that and the world is becoming a better place. | |
| 54:55.200 --> 55:03.120 | |
| Another example is combinatorial sourcing auctions. We did 800 large scale combinatorial | |
| 55:03.120 --> 55:09.280 | |
| sourcing auctions from 2001 to 2010 in a previous startup of mine called Combinet. | |
| 55:09.280 --> 55:18.480 | |
| And we increased the supply chain efficiency on that $60 billion of spend by 12.6%. So that's | |
| 55:18.480 --> 55:24.000 | |
| over $6 billion of efficiency improvement in the world. And this is not like shifting value from | |
| 55:24.000 --> 55:28.960 | |
| somebody to somebody else, just efficiency improvement, like in trucking, less empty | |
| 55:28.960 --> 55:35.040 | |
| driving. So there's less waste, less carbon footprint and so on. This is a huge positive | |
| 55:35.040 --> 55:42.080 | |
| impact in the near term. But sort of to stay in it for a little longer, because I think game theory | |
| 55:42.080 --> 55:46.720 | |
| is a role to play here. Let me actually come back on that. That's one thing. I think AI is also going | |
| 55:46.720 --> 55:52.960 | |
| to make the world much safer. So that's another aspect that often gets overlooked. | |
| 55:53.920 --> 55:58.560 | |
| Well, let me ask this question. Maybe you can speak to the safer. So I talked to Max Tagmark | |
| 55:58.560 --> 56:03.200 | |
| and Stuart Russell, who are very concerned about existential threats of AI. And often, | |
| 56:03.200 --> 56:12.960 | |
| the concern is about value misalignment. So AI systems basically working, operating towards | |
| 56:12.960 --> 56:19.680 | |
| goals that are not the same as human civilization, human beings. So it seems like game theory has | |
| 56:19.680 --> 56:28.800 | |
| a role to play there to make sure the values are aligned with human beings. I don't know if that's | |
| 56:28.800 --> 56:36.160 | |
| how you think about it. If not, how do you think AI might help with this problem? How do you think | |
| 56:36.160 --> 56:44.800 | |
| AI might make the world safer? Yeah, I think this value misalignment is a fairly theoretical | |
| 56:44.800 --> 56:52.880 | |
| worry. And I haven't really seen it in because I do a lot of real applications. I don't see it | |
| 56:52.880 --> 56:58.080 | |
| anywhere. The closest I've seen it was the following type of mental exercise, really, | |
| 56:58.080 --> 57:03.200 | |
| where I had this argument in the late 80s when we were building these transportation optimization | |
| 57:03.200 --> 57:08.160 | |
| systems. And somebody had heard that it's a good idea to have high utilization of assets. | |
| 57:08.160 --> 57:13.840 | |
| So they told me that, hey, why don't you put that as objective? And we didn't even put it as an | |
| 57:13.840 --> 57:19.360 | |
| objective, because I just showed him that if you had that as your objective, the solution would be | |
| 57:19.360 --> 57:23.360 | |
| to load your trucks full and drive in circles. Nothing would ever get delivered. You'd have | |
| 57:23.360 --> 57:30.480 | |
| 100% utilization. So yeah, I know this phenomenon. I've known this for over 30 years. But I've never | |
| 57:30.480 --> 57:35.920 | |
| seen it actually be a problem in reality. And yes, if you have the wrong objective, the AI will | |
| 57:35.920 --> 57:40.720 | |
| optimize that to the hilt. And it's going to hurt more than some human who's kind of trying to | |
| 57:40.720 --> 57:47.200 | |
| solve it in a half baked way with some human insight, too. But I just haven't seen that | |
| 57:47.200 --> 57:51.600 | |
| materialize in practice. There's this gap that you've actually put your finger on | |
| 57:52.880 --> 57:59.760 | |
| very clearly just now between theory and reality that's very difficult to put into words, I think. | |
| 57:59.760 --> 58:06.720 | |
| It's what you can theoretically imagine, the worst possible case or even, yeah, I mean, | |
| 58:06.720 --> 58:12.720 | |
| bad cases. And what usually happens in reality. So for example, to me, maybe it's something you | |
| 58:12.720 --> 58:19.680 | |
| can comment on, having grown up and I grew up in the Soviet Union. You know, there's currently | |
| 58:19.680 --> 58:28.320 | |
| 10,000 nuclear weapons in the world. And for many decades, it's theoretically surprising to me | |
| 58:28.320 --> 58:34.240 | |
| that the nuclear war is not broken out. Do you think about this aspect from a game | |
| 58:34.240 --> 58:41.520 | |
| theoretic perspective in general? Why is that true? Why? In theory, you could see how things | |
| 58:41.520 --> 58:46.480 | |
| go terribly wrong. And somehow yet they have not. Yeah, how do you think so? So I do think that | |
| 58:46.480 --> 58:51.120 | |
| about that a lot. I think the biggest two threats that we're facing as mankind, one is climate | |
| 58:51.120 --> 58:57.200 | |
| change, and the other is nuclear war. So so those are my main two worries that they worry about. | |
| 58:57.200 --> 59:01.840 | |
| And I've tried to do something about climate thought about trying to do something for climate | |
| 59:01.840 --> 59:07.920 | |
| change twice. Actually, for two of my startups, I've actually commissioned studies of what we | |
| 59:07.920 --> 59:12.320 | |
| could do on those things. And we didn't really find a sweet spot, but I'm still keeping an eye out | |
| 59:12.320 --> 59:17.280 | |
| on that if there's something where we could actually provide a market solution or optimization | |
| 59:17.280 --> 59:24.080 | |
| solution or some other technology solution to problems. Right now, like for example, pollution | |
| 59:24.080 --> 59:30.000 | |
| critic markets was what we were looking at then. And it was much more the lack of political will | |
| 59:30.000 --> 59:35.520 | |
| by those markets were not so successful, rather than bad market design. So I could go in and | |
| 59:35.520 --> 59:39.920 | |
| make a better market design. But that wouldn't really move the needle on the world very much | |
| 59:39.920 --> 59:44.800 | |
| if there's no political will and in the US, you know, the market, at least the Chicago market | |
| 59:44.800 --> 59:50.560 | |
| was just shut down, and so on. So then it doesn't really help how great your market design was. | |
| 59:50.560 --> 59:59.920 | |
| And on the nuclear side, it's more so global warming is a more encroaching problem. | |
| 1:00:00.560 --> 1:00:05.680 | |
| You know, nuclear weapons have been here. It's an obvious problem has just been sitting there. | |
| 1:00:05.680 --> 1:00:12.240 | |
| So how do you think about what is the mechanism design there that just made everything seem stable? | |
| 1:00:12.240 --> 1:00:18.560 | |
| And are you still extremely worried? I am still extremely worried. So you probably know the simple | |
| 1:00:18.560 --> 1:00:25.280 | |
| game theory of mad. So this was a mutually assured destruction. And it doesn't require any | |
| 1:00:25.280 --> 1:00:29.600 | |
| computation with small matrices, you can actually convince yourself that the game is such that | |
| 1:00:29.600 --> 1:00:36.000 | |
| nobody wants to initiate. Yeah, that's a very coarse grained analysis. And it really works in | |
| 1:00:36.000 --> 1:00:40.960 | |
| a situation where you have two superpowers or small numbers of superpowers. Now things are | |
| 1:00:40.960 --> 1:00:47.920 | |
| very different. You have a smaller nuke. So the threshold of initiating is smaller. And you have | |
| 1:00:47.920 --> 1:00:54.800 | |
| smaller countries and non non nation actors who may get nukes and so on. So it's I think it's | |
| 1:00:54.800 --> 1:01:04.240 | |
| riskier now than it was maybe ever before. And what idea application of AI, you've talked about | |
| 1:01:04.240 --> 1:01:09.440 | |
| a little bit, but what is the most exciting to you right now? I mean, you're here at NIPS, | |
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| NewRips. Now, you have a few excellent pieces of work. But what are you thinking into the future | |
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| with several companies you're doing? What's the most exciting thing or one of the exciting things? | |
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| The number one thing for me right now is coming up with these scalable techniques for | |
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| game solving and applying them into the real world. I'm still very interested in market design | |
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| as well. And we're doing that in the optimized markets. But I'm most interested if number one | |
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| right now is strategic machine strategy robot getting that technology out there and seeing | |
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| as you're in the trenches doing applications, what needs to be actually filled, what technology | |
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| gaps still need to be filled. So it's so hard to just put your feet on the table and imagine what | |
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| needs to be done. But when you're actually doing real applications, the applications tell you | |
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| what needs to be done. And I really enjoy that interaction. Is it a challenging process to | |
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| apply some of the state of the art techniques you're working on, and having the various players in | |
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| industry or the military, or people who could really benefit from it actually use it? What's | |
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| that process like of, you know, in autonomous vehicles, we work with automotive companies and | |
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| they're in many ways are a little bit old fashioned, it's difficult. They really want to use this | |
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| technology, there's clearly will have a significant benefit. But the systems aren't quite in place | |
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| to easily have them integrated in terms of data in terms of compute in terms of all these kinds | |
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| of things. So do you, is that one of the bigger challenges that you're facing? And how do you | |
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| tackle that challenge? Yeah, I think that's always a challenge that that's kind of slowness and inertia | |
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| really of let's do things the way we've always done it. You just have to find the internal | |
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| champions that the customer who understand that hey, things can't be the same way in the future, | |
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| otherwise bad things are going to happen. And it's in autonomous vehicles, it's actually very | |
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| interesting that the car makers are doing that, and they're very traditional. But at the same time, | |
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| you have tech companies who have nothing to do with cars or transportation, like Google and Baidu, | |
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| really pushing on autonomous cars. I find that fascinating. Clearly, you're super excited about | |
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| how actually these ideas have an impact in the world. In terms of the technology, in terms of | |
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| ideas and research, are there directions that you're also excited about, whether that's on the | |
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| some of the approaches you talked about for imperfect information games, whether it's applying | |
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| deep learning to some of these problems? Is there something that you're excited in | |
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| in the research side of things? Yeah, yeah, lots of different things in the game solving. | |
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| So solving even bigger games, games where you have more hidden action of the player | |
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| actions as well. poker is a game where really chance actions are hidden, or some of them are | |
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| hidden, but the player actions are public. multiplayer games or various sorts, collusion, | |
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| opponent exploitation, all and even longer games. So games that basically go forever, | |
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| but they're not repeated. So see extensive fun games that go forever. What would that even look | |
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| like? How do you represent that? How do you solve that? What's an example of a game like that? | |
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| This is some of the stochastic games that you mentioned. Let's say business strategy. So it's | |
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| and not just modeling like a particular interaction, but thinking about the business from here to | |
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| eternity. Or I see, or let's let's say military strategy. So it's not like war is going to go away. | |
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| How do you think about military strategy that's going to go forever? How do you even model that? | |
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| How do you know whether a move was good that you use somebody made? And so on. So that that's | |
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| kind of one direction. I'm also very interested in learning much more scalable techniques for | |
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| integer programming. So we had an ICML paper this summer on that, the first automated algorithm | |
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| configuration paper that has theoretical generalization guarantees. So if I see these many | |
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| training examples, and I tool my algorithm in this way, it's going to have good performance | |
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| on the real distribution, which I've not seen. So which is kind of interesting that, you know, | |
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| algorithm configuration has been going on now for at least 17 years seriously. And there has not | |
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| been any generalization theory before. Well, this is really exciting. And it's been, it's a huge | |
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| honor to talk to you. Thank you so much, Tomas. Thank you for bringing Lebrates to the world | |
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| and all the great work you're doing. Well, thank you very much. It's been fun. Good questions. | |