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	WEBVTT

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	The following is a conversation with Thomas Sanholm.

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	He's a professor at CMU and co creator of Labratus,

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	which is the first AI system to beat top human players

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	in the game of Heads Up No Limit Texas Holdem.

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	He has published over 450 papers

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	on game theory and machine learning,

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	including a best paper in 2017 at NIPS,

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	now renamed to Newrips,

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	which is where I caught up with him for this conversation.

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	His research and companies have had wide reaching impact

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	in the real world,

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	especially because he and his group

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	not only propose new ideas,

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	but also build systems to prove that these ideas work

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	in the real world.

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	This conversation is part of the MIT course

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	on artificial general intelligence

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	and the artificial intelligence podcast.

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	If you enjoy it, subscribe on YouTube, iTunes,

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	or simply connect with me on Twitter

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	at Lex Friedman, spelled F R I D.

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	And now here's my conversation with Thomas Sanholm.

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	Can you describe at the high level

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	the game of poker, Texas Holdem, Heads Up Texas Holdem

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	for people who might not be familiar with this card game?

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	Yeah, happy to.

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	So Heads Up No Limit Texas Holdem

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	has really emerged in the AI community

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	as a main benchmark for testing these

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	application independent algorithms

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	for imperfect information game solving.

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	And this is a game that's actually played by humans.

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	You don't see that much on TV or casinos

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	because well, for various reasons,

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	but you do see it in some expert level casinos

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	and you see it in the best poker movies of all time.

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	It's actually an event in the World Series of Poker,

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	but mostly it's played online

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	and typically for pretty big sums of money.

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	And this is a game that usually only experts play.

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	So if you go to your home game on a Friday night,

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	it probably is not gonna be Heads Up No Limit Texas Holdem.

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	It might be No Limit Texas Holdem in some cases,

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	but typically for a big group and it's not as competitive.

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	While Heads Up means it's two players.

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	So it's really like me against you.

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	Am I better or are you better?

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	Much like chess or go in that sense,

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	but an imperfect information game,

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	which makes it much harder because I have to deal

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	with issues of you knowing things that I don't know

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	and I know things that you don't know

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	instead of pieces being nicely laid on the board

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	for both of us to see.

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	So in Texas Holdem, there's two cards

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	that you only see that belong to you.

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	Yeah. And there is,

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	they gradually lay out some cards

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	that add up overall to five cards that everybody can see.

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	Yeah. So the imperfect nature

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	of the information is the two cards

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	that you're holding in your hand.

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	Up front, yeah.

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	So as you said, you first get two cards

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	in private each and then there's a betting round.

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	Then you get three cards in public on the table.

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	Then there's a betting round.

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	Then you get the fourth card in public on the table.

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	There's a betting round.

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	Then you get the 5th card on the table.

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	There's a betting round.

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	So there's a total of four betting rounds

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	and four tranches of information revelation if you will.

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	The only the first tranche is private

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	and then it's public from there.

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	And this is probably by far the most popular game in AI

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	and just the general public

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	in terms of imperfect information.

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	So that's probably the most popular spectator game

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	to watch, right?

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	So, which is why it's a super exciting game to tackle.

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	So it's on the order of chess, I would say,

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	in terms of popularity, in terms of AI setting it

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	as the bar of what is intelligence.

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	So in 2017, Labratus, how do you pronounce it?

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	Labratus.

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	Labratus.

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	Labratus beats.

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	A little Latin there.

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	A little bit of Latin.

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	Labratus beats a few, four expert human players.

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	Can you describe that event?

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	What you learned from it?

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	What was it like?

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	What was the process in general

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	for people who have not read the papers and the study?

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	Yeah, so the event was that we invited

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	four of the top 10 players,

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	with these specialist players in Heads Up No Limit,

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	Texas Holden, which is very important

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	because this game is actually quite different

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	than the multiplayer version.

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	We brought them in to Pittsburgh

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	to play at the Reverse Casino for 20 days.

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	We wanted to get 120,000 hands in

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	because we wanted to get statistical significance.

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	So it's a lot of hands for humans to play,

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	even for these top pros who play fairly quickly normally.

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	So we couldn't just have one of them play so many hands.

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	20 days, they were playing basically morning to evening.

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	And I raised 200,000 as a little incentive for them to play.

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	And the setting was so that they didn't all get 50,000.

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	We actually paid them out

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	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,

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	whether they're way ahead or way behind

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	or right at the mark of beating the AI.

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	And you don't make any money, unfortunately.

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	Right, no, we can't make any money.

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	So originally, a couple of years earlier,

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	I actually explored whether we could actually play for money

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	because that would be, of course, interesting as well,

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	to play against the top people for money.

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	But the Pennsylvania Gaming Board said no, so we couldn't.

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	So this is much like an exhibit,

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	like for a musician or a boxer or something like that.

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	Nevertheless, they were keeping track of the money

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	and brought us close to $2 million, I think.

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	So if it was for real money, if you were able to earn money,

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	that was a quite impressive and inspiring achievement.

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	Just a few details, what were the players looking at?

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	Were they behind a computer?

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	What was the interface like?

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	Yes, they were playing much like they normally do.

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	These top players, when they play this game,

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	they play mostly online.

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	So they're used to playing through a UI.

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	And they did the same thing here.

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	So there was this layout.

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	You could imagine there's a table on a screen.

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	There's the human sitting there,

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	and then there's the AI sitting there.

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	And the screen shows everything that's happening.

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	The cards coming out and shows the bets being made.

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	And we also had the betting history for the human.

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	So if the human forgot what had happened in the hand so far,

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	they could actually reference back and so forth.

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	Is there a reason they were given access

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	to the betting history for?

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	Well, we just, it didn't really matter.

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	They wouldn't have forgotten anyway.

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	These are top quality people.

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	But we just wanted to put out there

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	so it's not a question of the human forgetting

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	and the AI somehow trying to get advantage

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	of better memory.

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	So what was that like?

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	I mean, that was an incredible accomplishment.

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	So what did it feel like before the event?

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	Did you have doubt, hope?

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	Where was your confidence at?

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	Yeah, that's great.

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	So great question.

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	So 18 months earlier, I had organized a similar brains

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	versus AI competition with a previous AI called Cloudyco

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	and we couldn't beat the humans.

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	So this time around, it was only 18 months later.

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	And I knew that this new AI, Libratus, was way stronger,

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	but it's hard to say how you'll do against the top humans

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	before you try.

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	So I thought we had about a 50, 50 shot.

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	And the international betting sites put us

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	as a four to one or five to one underdog.

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	So it's kind of interesting that people really believe

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	in people and over AI, not just people.

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	People don't just over believe in themselves,

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	but they have overconfidence in other people as well

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	compared to the performance of AI.

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	And yeah, so we were a four to one or five to one underdog.

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	And even after three days of beating the humans in a row,

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	we were still 50, 50 on the international betting sites.

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	Do you think there's something special and magical

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	about poker and the way people think about it,

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	in the sense you have,

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	I mean, even in chess, there's no Hollywood movies.

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	Poker is the star of many movies.

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	And there's this feeling that certain human facial

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	expressions and body language, eye movement,

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	all these tells are critical to poker.

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	Like you can look into somebody's soul

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	and understand their betting strategy and so on.

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	So that's probably why, possibly,

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	do you think that is why people have a confidence

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	that humans will outperform?

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	Because AI systems cannot, in this construct,

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	perceive these kinds of tells.

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	They're only looking at betting patterns

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	and nothing else, betting patterns and statistics.

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	So what's more important to you

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	if you step back on human players, human versus human?

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	What's the role of these tells,

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	of these ideas that we romanticize?

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	Yeah, so I'll split it into two parts.

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	So one is why do humans trust humans more than AI

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	and have overconfidence in humans?

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	I think that's not really related to the tell question.

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	It's just that they've seen these top players,

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	how good they are, and they're really fantastic.

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	So it's just hard to believe that an AI could beat them.

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	So I think that's where that comes from.

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	And that's actually maybe a more general lesson about AI.

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	That until you've seen it overperform a human,

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	it's hard to believe that it could.

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	But then the tells, a lot of these top players,

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	they're so good at hiding tells

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	that among the top players,

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	it's actually not really worth it

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	for them to invest a lot of effort

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	trying to find tells in each other

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	because they're so good at hiding them.

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	So yes, at the kind of Friday evening game,

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	tells are gonna be a huge thing.

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	You can read other people.

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	And if you're a good reader,

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	you'll read them like an open book.

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	But at the top levels of poker now,

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	the tells become a much smaller and smaller aspect

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	of the game as you go to the top levels.

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	The amount of strategies, the amount of possible actions

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	is very large, 10 to the power of 100 plus.

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	So there has to be some, I've read a few of the papers

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	related, it has to form some abstractions

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	of various hands and actions.

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	So what kind of abstractions are effective

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	for the game of poker?

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	Yeah, so you're exactly right.

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	So when you go from a game tree that's 10 to the 161,

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	especially in an imperfect information game,

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	it's way too large to solve directly,

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	even with our fastest equilibrium finding algorithms.

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	So you wanna abstract it first.

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	And abstraction in games is much trickier

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	than abstraction in MDPs or other single agent settings.

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	Because you have these abstraction pathologies

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	that if I have a finer grained abstraction,

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	the strategy that I can get from that for the real game

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	might actually be worse than the strategy

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	I can get from the coarse grained abstraction.

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	So you have to be very careful.

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	Now the kinds of abstractions, just to zoom out,

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	we're talking about, there's the hands abstractions

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	and then there's betting strategies.

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	Yeah, betting actions, yeah.

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	Baiting actions.

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	So there's information abstraction,

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	don't talk about general games, information abstraction,

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	which is the abstraction of what chance does.

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	And this would be the cards in the case of poker.

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	And then there's action abstraction,

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	which is abstracting the actions of the actual players,

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	which would be bets in the case of poker.

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	Yourself and the other players?

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	Yes, yourself and other players.

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	And for information abstraction,

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	we were completely automated.

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	So these are algorithms,

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	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,

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	but also how it might materialize

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	into good or bad hands over time.

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	And it's a certain kind of bottom up process

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	with integer programming there and clustering

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	and various aspects, how do you build this abstraction?

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	And then in the action abstraction,

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	there it's largely based on how humans and other AIs

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	have played this game in the past.

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	But in the beginning,

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	we actually used an automated action abstraction technology,

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	which is provably convergent

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	that it finds the optimal combination of bet sizes,

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	but it's not very scalable.

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	So we couldn't use it for the whole game,

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	but we use it for the first couple of betting actions.

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	So what's more important, the strength of the hand,

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	so the information abstraction or the how you play them,

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	the actions, does it, you know,

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	the romanticized notion again,

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	is that it doesn't matter what hands you have,

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	that the actions, the betting may be the way you win

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	no matter what hands you have.

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	Yeah, so that's why you have to play a lot of hands

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	so that the role of luck gets smaller.

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	So you could otherwise get lucky and get some good hands

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	and then you're gonna win the match.

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	Even with thousands of hands, you can get lucky

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	because there's so much variance

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	in No Limit Texas Holden because if we both go all in,

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	it's a huge stack of variance, so there are these

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	massive swings in No Limit Texas Holden.

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	So that's why you have to play not just thousands,

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	but over 100,000 hands to get statistical significance.

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	So let me ask another way this question.

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	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,

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	how well would you be able to do?

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	Oh, that's a good question.

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	There's actually, I heard this story

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	that there's this Norwegian female poker player

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	called Annette Oberstad who's actually won a tournament

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	by doing exactly that, but that would be extremely rare.

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	So you cannot really play well that way.

	14:23.440 --> 14:27.840
	Okay, so the hands do have some role to play, okay.

	14:27.840 --> 14:32.840
	So Labradus does not use, as far as I understand,

	14:33.120 --> 14:35.320
	they use learning methods, deep learning.

	14:35.320 --> 14:40.320
	Is there room for learning in,

	14:40.600 --> 14:44.120
	there's no reason why Labradus doesn't combine

	14:44.120 --> 14:46.400
	with an AlphaGo type approach for estimating

	14:46.400 --> 14:49.200
	the quality for function estimator.

	14:49.200 --> 14:52.040
	What are your thoughts on this,

	14:52.040 --> 14:54.760
	maybe as compared to another algorithm

	14:54.760 --> 14:56.720
	which I'm not that familiar with, DeepStack,

	14:56.720 --> 14:59.280
	the engine that does use deep learning,

	14:59.280 --> 15:01.560
	that it's unclear how well it does,

	15:01.560 --> 15:03.480
	but nevertheless uses deep learning.

	15:03.480 --> 15:05.400
	So what are your thoughts about learning methods

	15:05.400 --> 15:09.280
	to aid in the way that Labradus plays in the game of poker?

	15:09.280 --> 15:10.640
	Yeah, so as you said,

	15:10.640 --> 15:13.080
	Labradus did not use learning methods

	15:13.080 --> 15:15.680
	and played very well without them.

	15:15.680 --> 15:17.840
	Since then, we have actually, actually here,

	15:17.840 --> 15:20.000
	we have a couple of papers on things

	15:20.000 --> 15:22.360
	that do use learning techniques.

	15:22.360 --> 15:23.200
	Excellent.

	15:24.440 --> 15:26.360
	And deep learning in particular.

	15:26.360 --> 15:29.920
	And sort of the way you're talking about

	15:29.920 --> 15:33.360
	where it's learning an evaluation function,

	15:33.360 --> 15:37.400
	but in imperfect information games,

	15:37.400 --> 15:42.400
	unlike let's say in Go or now also in chess and shogi,

	15:42.440 --> 15:47.400
	it's not sufficient to learn an evaluation for a state

	15:47.400 --> 15:52.400
	because the value of an information set

	15:52.920 --> 15:55.400
	depends not only on the exact state,

	15:55.400 --> 15:59.200
	but it also depends on both players beliefs.

	15:59.200 --> 16:01.240
	Like if I have a bad hand,

	16:01.240 --> 16:04.720
	I'm much better off if the opponent thinks I have a good hand

	16:04.720 --> 16:05.560
	and vice versa.

	16:05.560 --> 16:06.480
	If I have a good hand,

	16:06.480 --> 16:09.360
	I'm much better off if the opponent believes

	16:09.360 --> 16:10.280
	I have a bad hand.

	16:11.360 --> 16:15.640
	So the value of a state is not just a function of the cards.

	16:15.640 --> 16:19.600
	It depends on, if you will, the path of play,

	16:19.600 --> 16:22.040
	but only to the extent that it's captured

	16:22.040 --> 16:23.720
	in the belief distributions.

	16:23.720 --> 16:26.240
	So that's why it's not as simple

	16:26.240 --> 16:29.320
	as it is in perfect information games.

	16:29.320 --> 16:31.080
	And I don't wanna say it's simple there either.

	16:31.080 --> 16:34.200
	It's of course very complicated computationally there too,

	16:34.200 --> 16:36.520
	but at least conceptually, it's very straightforward.

	16:36.520 --> 16:38.760
	There's a state, there's an evaluation function.

	16:38.760 --> 16:39.800
	You can try to learn it.

	16:39.800 --> 16:43.280
	Here, you have to do something more.

	16:43.280 --> 16:47.160
	And what we do is in one of these papers,

	16:47.160 --> 16:50.800
	we're looking at where we allow the opponent

	16:50.800 --> 16:53.000
	to actually take different strategies

	16:53.000 --> 16:56.440
	at the leaf of the search tree, if you will.

	16:56.440 --> 16:59.840
	And that is a different way of doing it.

	16:59.840 --> 17:02.560
	And it doesn't assume therefore a particular way

	17:02.560 --> 17:04.040
	that the opponent plays,

	17:04.040 --> 17:05.840
	but it allows the opponent to choose

	17:05.840 --> 17:09.800
	from a set of different continuation strategies.

	17:09.800 --> 17:13.400
	And that forces us to not be too optimistic

	17:13.400 --> 17:15.520
	in a look ahead search.

	17:15.520 --> 17:19.040
	And that's one way you can do sound look ahead search

	17:19.040 --> 17:21.480
	in imperfect information games,

	17:21.480 --> 17:23.360
	which is very difficult.

	17:23.360 --> 17:26.080
	And you were asking about DeepStack.

	17:26.080 --> 17:29.280
	What they did, it was very different than what we do,

	17:29.280 --> 17:32.000
	either in Libratus or in this new work.

	17:32.000 --> 17:35.440
	They were randomly generating various situations

	17:35.440 --> 17:36.440
	in the game.

	17:36.440 --> 17:38.080
	Then they were doing the look ahead

	17:38.080 --> 17:39.840
	from there to the end of the game,

	17:39.840 --> 17:42.960
	as if that was the start of a different game.

	17:42.960 --> 17:44.920
	And then they were using deep learning

	17:44.920 --> 17:47.960
	to learn those values of those states,

	17:47.960 --> 17:50.280
	but the states were not just the physical states.

	17:50.280 --> 17:52.560
	They include belief distributions.

	17:52.560 --> 17:56.800
	When you talk about look ahead for DeepStack

	17:56.800 --> 17:59.480
	or with Libratus, does it mean,

	17:59.480 --> 18:02.680
	considering every possibility that the game can evolve,

	18:02.680 --> 18:04.280
	are we talking about extremely,

	18:04.280 --> 18:06.880
	sort of this exponentially growth of a tree?

	18:06.880 --> 18:09.720
	Yes, so we're talking about exactly that.

	18:11.280 --> 18:14.280
	Much like you do in alpha beta search

	18:14.280 --> 18:17.480
	or Monte Carlo tree search, but with different techniques.

	18:17.480 --> 18:19.280
	So there's a different search algorithm.

	18:19.280 --> 18:21.920
	And then we have to deal with the leaves differently.

	18:21.920 --> 18:24.000
	So if you think about what Libratus did,

	18:24.000 --> 18:25.520
	we didn't have to worry about this

	18:25.520 --> 18:28.560
	because we only did it at the end of the game.

	18:28.560 --> 18:32.280
	So we would always terminate into a real situation

	18:32.280 --> 18:34.000
	and we would know what the payout is.

	18:34.000 --> 18:36.880
	It didn't do these depth limited lookaheads,

	18:36.880 --> 18:40.680
	but now in this new paper, which is called depth limited,

	18:40.680 --> 18:42.120
	I think it's called depth limited search

	18:42.120 --> 18:43.880
	for imperfect information games,

	18:43.880 --> 18:47.040
	we can actually do sound depth limited lookahead.

	18:47.040 --> 18:49.240
	So we can actually start to do the look ahead

	18:49.240 --> 18:51.080
	from the beginning of the game on,

	18:51.080 --> 18:53.400
	because that's too complicated to do

	18:53.400 --> 18:54.920
	for this whole long game.

	18:54.920 --> 18:57.680
	So in Libratus, we were just doing it for the end.

	18:57.680 --> 19:00.720
	So, and then the other side, this belief distribution,

	19:00.720 --> 19:05.320
	so is it explicitly modeled what kind of beliefs

	19:05.320 --> 19:07.400
	that the opponent might have?

	19:07.400 --> 19:11.840
	Yeah, it is explicitly modeled, but it's not assumed.

	19:11.840 --> 19:15.400
	The beliefs are actually output, not input.

	19:15.400 --> 19:18.840
	Of course, the starting beliefs are input,

	19:18.840 --> 19:20.640
	but they just fall from the rules of the game

	19:20.640 --> 19:23.520
	because we know that the dealer deals uniformly

	19:23.520 --> 19:27.720
	from the deck, so I know that every pair of cards

	19:27.720 --> 19:30.440
	that you might have is equally likely.

	19:30.440 --> 19:32.200
	I know that for a fact, that just follows

	19:32.200 --> 19:33.160
	from the rules of the game.

	19:33.160 --> 19:35.200
	Of course, except the two cards that I have,

	19:35.200 --> 19:36.560
	I know you don't have those.

	19:36.560 --> 19:37.560
	Yeah.

	19:37.560 --> 19:38.720
	You have to take that into account.

	19:38.720 --> 19:40.920
	That's called card removal and that's very important.

	19:40.920 --> 19:43.760
	Is the dealing always coming from a single deck

	19:43.760 --> 19:45.880
	in Heads Up, so you can assume.

	19:45.880 --> 19:50.880
	Single deck, so you know that if I have the ace of spades,

	19:50.880 --> 19:53.560
	I know you don't have an ace of spades.

	19:53.560 --> 19:56.880
	Great, so in the beginning, your belief is basically

	19:56.880 --> 19:59.320
	the fact that it's a fair dealing of hands,

	19:59.320 --> 20:02.800
	but how do you start to adjust that belief?

	20:02.800 --> 20:06.800
	Well, that's where this beauty of game theory comes.

	20:06.800 --> 20:10.920
	So Nash equilibrium, which John Nash introduced in 1950,

	20:10.920 --> 20:13.800
	introduces what rational play is

	20:13.800 --> 20:16.040
	when you have more than one player.

	20:16.040 --> 20:18.440
	And these are pairs of strategies

	20:18.440 --> 20:20.360
	where strategies are contingency plans,

	20:20.360 --> 20:21.600
	one for each player.

	20:22.880 --> 20:25.720
	So that neither player wants to deviate

	20:25.720 --> 20:26.960
	to a different strategy,

	20:26.960 --> 20:29.160
	given that the other doesn't deviate.

	20:29.160 --> 20:33.840
	But as a side effect, you get the beliefs from base roll.

	20:33.840 --> 20:36.440
	So Nash equilibrium really isn't just deriving

	20:36.440 --> 20:38.360
	in these imperfect information games,

	20:38.360 --> 20:41.920
	Nash equilibrium, it doesn't just define strategies.

	20:41.920 --> 20:44.960
	It also defines beliefs for both of us

	20:44.960 --> 20:48.840
	and defines beliefs for each state.

	20:48.840 --> 20:53.280
	So at each state, it's called information sets.

	20:53.280 --> 20:55.560
	At each information set in the game,

	20:55.560 --> 20:59.000
	there's a set of different states that we might be in,

	20:59.000 --> 21:00.880
	but I don't know which one we're in.

	21:01.760 --> 21:03.400
	Nash equilibrium tells me exactly

	21:03.400 --> 21:05.000
	what is the probability distribution

	21:05.000 --> 21:08.280
	over those real world states in my mind.

	21:08.280 --> 21:11.440
	How does Nash equilibrium give you that distribution?

	21:11.440 --> 21:12.280
	So why?

	21:12.280 --> 21:13.320
	I'll do a simple example.

	21:13.320 --> 21:16.760
	So you know the game Rock, Paper, Scissors?

	21:16.760 --> 21:20.000
	So we can draw it as player one moves first

	21:20.000 --> 21:21.600
	and then player two moves.

	21:21.600 --> 21:24.520
	But of course, it's important that player two

	21:24.520 --> 21:26.400
	doesn't know what player one moved,

	21:26.400 --> 21:28.600
	otherwise player two would win every time.

	21:28.600 --> 21:30.480
	So we can draw that as an information set

	21:30.480 --> 21:33.280
	where player one makes one of three moves first,

	21:33.280 --> 21:36.200
	and then there's an information set for player two.

	21:36.200 --> 21:39.920
	So player two doesn't know which of those nodes

	21:39.920 --> 21:41.800
	the world is in.

	21:41.800 --> 21:44.920
	But once we know the strategy for player one,

	21:44.920 --> 21:47.320
	Nash equilibrium will say that you play 1 3rd Rock,

	21:47.320 --> 21:49.400
	1 3rd Paper, 1 3rd Scissors.

	21:49.400 --> 21:52.600
	From that, I can derive my beliefs on the information set

	21:52.600 --> 21:54.480
	that they're 1 3rd, 1 3rd, 1 3rd.

	21:54.480 --> 21:56.280
	So Bayes gives you that.

	21:56.280 --> 21:57.560
	Bayes gives you.

	21:57.560 --> 21:59.760
	But is that specific to a particular player,

	21:59.760 --> 22:03.960
	or is it something you quickly update

	22:03.960 --> 22:05.040
	with the specific player?

	22:05.040 --> 22:08.800
	No, the game theory isn't really player specific.

	22:08.800 --> 22:11.720
	So that's also why we don't need any data.

	22:11.720 --> 22:12.760
	We don't need any history

	22:12.760 --> 22:14.800
	how these particular humans played in the past

	22:14.800 --> 22:17.400
	or how any AI or human had played before.

	22:17.400 --> 22:20.240
	It's all about rationality.

	22:20.240 --> 22:22.720
	So the AI just thinks about

	22:22.720 --> 22:24.880
	what would a rational opponent do?

	22:24.880 --> 22:28.000
	And what would I do if I am rational?

	22:28.000 --> 22:31.080
	And that's the idea of game theory.

	22:31.080 --> 22:35.560
	So it's really a data free, opponent free approach.

	22:35.560 --> 22:37.680
	So it comes from the design of the game

	22:37.680 --> 22:40.040
	as opposed to the design of the player.

	22:40.040 --> 22:43.080
	Exactly, there's no opponent modeling per se.

	22:43.080 --> 22:45.600
	I mean, we've done some work on combining opponent modeling

	22:45.600 --> 22:48.840
	with game theory so you can exploit weak players even more,

	22:48.840 --> 22:50.280
	but that's another strand.

	22:50.280 --> 22:52.320
	And in Librarus, we didn't turn that on.

	22:52.320 --> 22:55.000
	So I decided that these players are too good.

	22:55.000 --> 22:58.080
	And when you start to exploit an opponent,

	22:58.080 --> 23:01.800
	you typically open yourself up to exploitation.

	23:01.800 --> 23:04.000
	And these guys have so few holes to exploit

	23:04.000 --> 23:06.760
	and they're world's leading experts in counter exploitation.

	23:06.760 --> 23:09.200
	So I decided that we're not gonna turn that stuff on.

	23:09.200 --> 23:12.160
	Actually, I saw a few of your papers exploiting opponents.

	23:12.160 --> 23:14.800
	It sounded very interesting to explore.

	23:15.720 --> 23:17.880
	Do you think there's room for exploitation

	23:17.880 --> 23:19.920
	generally outside of Librarus?

	23:19.920 --> 23:24.080
	Is there a subject or people differences

	23:24.080 --> 23:27.920
	that could be exploited, maybe not just in poker,

	23:27.920 --> 23:30.440
	but in general interactions and negotiations,

	23:30.440 --> 23:33.480
	all these other domains that you're considering?

	23:33.480 --> 23:34.680
	Yeah, definitely.

	23:34.680 --> 23:35.920
	We've done some work on that.

	23:35.920 --> 23:39.880
	And I really like the work at hybrid digested too.

	23:39.880 --> 23:43.440
	So you figure out what would a rational opponent do.

	23:43.440 --> 23:46.280
	And by the way, that's safe in these zero sum games,

	23:46.280 --> 23:47.480
	two player zero sum games,

	23:47.480 --> 23:49.560
	because if the opponent does something irrational,

	23:49.560 --> 23:52.200
	yes, it might throw off my beliefs,

	23:53.080 --> 23:55.760
	but the amount that the player can gain

	23:55.760 --> 23:59.160
	by throwing off my belief is always less

	23:59.160 --> 24:01.800
	than they lose by playing poorly.

	24:01.800 --> 24:03.080
	So it's safe.

	24:03.080 --> 24:06.720
	But still, if somebody's weak as a player,

	24:06.720 --> 24:10.240
	you might wanna play differently to exploit them more.

	24:10.240 --> 24:12.040
	So you can think about it this way,

	24:12.040 --> 24:15.600
	a game theoretic strategy is unbeatable,

	24:15.600 --> 24:19.600
	but it doesn't maximally beat the other opponent.

	24:19.600 --> 24:22.800
	So the winnings per hand might be better

	24:22.800 --> 24:24.240
	with a different strategy.

	24:24.240 --> 24:25.720
	And the hybrid is that you start

	24:25.720 --> 24:27.080
	from a game theoretic approach.

	24:27.080 --> 24:30.840
	And then as you gain data about the opponent

	24:30.840 --> 24:32.600
	in certain parts of the game tree,

	24:32.600 --> 24:34.360
	then in those parts of the game tree,

	24:34.360 --> 24:37.800
	you start to tweak your strategy more and more

	24:37.800 --> 24:40.960
	towards exploitation while still staying fairly close

	24:40.960 --> 24:42.160
	to the game theoretic strategy

	24:42.160 --> 24:46.840
	so as to not open yourself up to exploitation too much.

	24:46.840 --> 24:48.320
	How do you do that?

	24:48.320 --> 24:53.320
	Do you try to vary up strategies, make it unpredictable?

	24:53.640 --> 24:57.520
	It's like, what is it, tit for tat strategies

	24:57.520 --> 25:00.720
	in Prisoner's Dilemma or?

	25:00.720 --> 25:03.240
	Well, that's a repeated game.

	25:03.240 --> 25:04.080
	Repeated games.

	25:04.080 --> 25:06.520
	Simple Prisoner's Dilemma, repeated games.

	25:06.520 --> 25:08.760
	But even there, there's no proof that says

	25:08.760 --> 25:10.080
	that that's the best thing.

	25:10.080 --> 25:13.280
	But experimentally, it actually does well.

	25:13.280 --> 25:15.320
	So what kind of games are there, first of all?

	25:15.320 --> 25:17.040
	I don't know if this is something

	25:17.040 --> 25:18.600
	that you could just summarize.

	25:18.600 --> 25:20.360
	There's perfect information games

	25:20.360 --> 25:22.400
	where all the information's on the table.

	25:22.400 --> 25:25.480
	There is imperfect information games.

	25:25.480 --> 25:28.560
	There's repeated games that you play over and over.

	25:28.560 --> 25:31.320
	There's zero sum games.

	25:31.320 --> 25:34.440
	There's non zero sum games.

	25:34.440 --> 25:37.520
	And then there's a really important distinction

	25:37.520 --> 25:40.720
	you're making, two player versus more players.

	25:40.720 --> 25:44.760
	So what are, what other games are there?

	25:44.760 --> 25:46.160
	And what's the difference, for example,

	25:46.160 --> 25:50.040
	with this two player game versus more players?

	25:50.040 --> 25:51.680
	What are the key differences in your view?

	25:51.680 --> 25:54.600
	So let me start from the basics.

	25:54.600 --> 25:59.600
	So a repeated game is a game where the same exact game

	25:59.600 --> 26:01.800
	is played over and over.

	26:01.800 --> 26:05.800
	In these extensive form games, where it's,

	26:05.800 --> 26:08.480
	think about three form, maybe with these information sets

	26:08.480 --> 26:11.400
	to represent incomplete information,

	26:11.400 --> 26:14.840
	you can have kind of repetitive interactions.

	26:14.840 --> 26:17.760
	Even repeated games are a special case of that, by the way.

	26:17.760 --> 26:21.520
	But the game doesn't have to be exactly the same.

	26:21.520 --> 26:23.040
	It's like in sourcing auctions.

	26:23.040 --> 26:26.320
	Yes, we're gonna see the same supply base year to year,

	26:26.320 --> 26:28.800
	but what I'm buying is a little different every time.

	26:28.800 --> 26:31.000
	And the supply base is a little different every time

	26:31.000 --> 26:31.840
	and so on.

	26:31.840 --> 26:33.400
	So it's not really repeated.

	26:33.400 --> 26:35.680
	So to find a purely repeated game

	26:35.680 --> 26:37.840
	is actually very rare in the world.

	26:37.840 --> 26:42.840
	So they're really a very course model of what's going on.

	26:42.840 --> 26:46.360
	Then if you move up from just repeated,

	26:46.360 --> 26:49.040
	simple repeated matrix games,

	26:49.040 --> 26:50.800
	not all the way to extensive form games,

	26:50.800 --> 26:53.600
	but in between, they're stochastic games,

	26:53.600 --> 26:57.000
	where, you know, there's these,

	26:57.000 --> 27:00.520
	you think about it like these little matrix games.

	27:00.520 --> 27:04.200
	And when you take an action and your opponent takes an action,

	27:04.200 --> 27:07.680
	they determine not which next state I'm going to,

	27:07.680 --> 27:09.120
	next game I'm going to,

	27:09.120 --> 27:11.440
	but the distribution over next games

	27:11.440 --> 27:13.360
	where I might be going to.

	27:13.360 --> 27:15.360
	So that's the stochastic game.

	27:15.360 --> 27:19.000
	But it's like matrix games, repeated stochastic games,

	27:19.000 --> 27:20.400
	extensive form games.

	27:20.400 --> 27:23.040
	That is from less to more general.

	27:23.040 --> 27:26.280
	And poker is an example of the last one.

	27:26.280 --> 27:28.400
	So it's really in the most general setting.

	27:29.560 --> 27:30.640
	Extensive form games.

	27:30.640 --> 27:34.520
	And that's kind of what the AI community has been working on

	27:34.520 --> 27:36.280
	and being benchmarked on

	27:36.280 --> 27:38.040
	with this Heads Up No Limit Texas Holdem.

	27:38.040 --> 27:39.760
	Can you describe extensive form games?

	27:39.760 --> 27:41.560
	What's the model here?

	27:41.560 --> 27:44.320
	Yeah, so if you're familiar with the tree form,

	27:44.320 --> 27:45.760
	so it's really the tree form.

	27:45.760 --> 27:47.560
	Like in chess, there's a search tree.

	27:47.560 --> 27:48.720
	Versus a matrix.

	27:48.720 --> 27:50.080
	Versus a matrix, yeah.

	27:50.080 --> 27:53.000
	And the matrix is called the matrix form

	27:53.000 --> 27:55.320
	or bi matrix form or normal form game.

	27:55.320 --> 27:57.080
	And here you have the tree form.

	27:57.080 --> 28:00.000
	So you can actually do certain types of reasoning there

	28:00.000 --> 28:04.680
	that you lose the information when you go to normal form.

	28:04.680 --> 28:07.000
	There's a certain form of equivalence.

	28:07.000 --> 28:08.880
	Like if you go from tree form and you say it,

	28:08.880 --> 28:12.720
	every possible contingency plan is a strategy.

	28:12.720 --> 28:15.080
	Then I can actually go back to the normal form,

	28:15.080 --> 28:18.600
	but I lose some information from the lack of sequentiality.

	28:18.600 --> 28:21.280
	Then the multiplayer versus two player distinction

	28:21.280 --> 28:22.880
	is an important one.

	28:22.880 --> 28:27.320
	So two player games in zero sum

	28:27.320 --> 28:32.320
	are conceptually easier and computationally easier.

	28:32.840 --> 28:36.000
	They're still huge like this one,

	28:36.000 --> 28:39.680
	but they're conceptually easier and computationally easier

	28:39.680 --> 28:42.920
	in that conceptually, you don't have to worry about

	28:42.920 --> 28:45.360
	which equilibrium is the other guy going to play

	28:45.360 --> 28:46.640
	when there are multiple,

	28:46.640 --> 28:49.920
	because any equilibrium strategy is a best response

	28:49.920 --> 28:52.000
	to any other equilibrium strategy.

	28:52.000 --> 28:54.360
	So I can play a different equilibrium from you

	28:54.360 --> 28:57.320
	and we'll still get the right values of the game.

	28:57.320 --> 28:59.240
	That falls apart even with two players

	28:59.240 --> 29:01.360
	when you have general sum games.

	29:01.360 --> 29:03.120
	Even without cooperation just in general.

	29:03.120 --> 29:04.800
	Even without cooperation.

	29:04.800 --> 29:07.640
	So there's a big gap from two player zero sum

	29:07.640 --> 29:11.160
	to two player general sum or even to three player zero sum.

	29:11.160 --> 29:14.280
	That's a big gap, at least in theory.

	29:14.280 --> 29:18.920
	Can you maybe non mathematically provide the intuition

	29:18.920 --> 29:22.120
	why it all falls apart with three or more players?

	29:22.120 --> 29:24.400
	It seems like you should still be able to have

	29:24.400 --> 29:29.400
	a Nash equilibrium that's instructive, that holds.

	29:31.280 --> 29:36.000
	Okay, so it is true that all finite games

	29:36.000 --> 29:38.200
	have a Nash equilibrium.

	29:38.200 --> 29:41.080
	So this is what John Nash actually proved.

	29:41.080 --> 29:42.920
	So they do have a Nash equilibrium.

	29:42.920 --> 29:43.840
	That's not the problem.

	29:43.840 --> 29:46.600
	The problem is that there can be many.

	29:46.600 --> 29:50.400
	And then there's a question of which equilibrium to select.

	29:50.400 --> 29:52.200
	So, and if you select your strategy

	29:52.200 --> 29:54.640
	from a different equilibrium and I select mine,

	29:57.920 --> 29:59.920
	then what does that mean?

	29:59.920 --> 30:02.080
	And in these non zero sum games,

	30:02.080 --> 30:05.720
	we may lose some joint benefit

	30:05.720 --> 30:07.040
	by being just simply stupid.

	30:07.040 --> 30:08.400
	We could actually both be better off

	30:08.400 --> 30:09.920
	if we did something else.

	30:09.920 --> 30:11.760
	And in three player, you get other problems

	30:11.760 --> 30:13.200
	also like collusion.

	30:13.200 --> 30:16.560
	Like maybe you and I can gang up on a third player

	30:16.560 --> 30:19.800
	and we can do radically better by colluding.

	30:19.800 --> 30:22.200
	So there are lots of issues that come up there.

	30:22.200 --> 30:25.640
	So Noah Brown, the student you work with on this

	30:25.640 --> 30:29.360
	has mentioned, I looked through the AMA on Reddit.

	30:29.360 --> 30:31.280
	He mentioned that the ability of poker players

	30:31.280 --> 30:33.800
	to collaborate will make the game.

	30:33.800 --> 30:35.200
	He was asked the question of,

	30:35.200 --> 30:37.920
	how would you make the game of poker,

	30:37.920 --> 30:39.280
	or both of you were asked the question,

	30:39.280 --> 30:41.560
	how would you make the game of poker

	30:41.560 --> 30:46.560
	beyond being solvable by current AI methods?

	30:47.000 --> 30:50.560
	And he said that there's not many ways

	30:50.560 --> 30:53.120
	of making poker more difficult,

	30:53.120 --> 30:57.760
	but a collaboration or cooperation between players

	30:57.760 --> 30:59.760
	would make it extremely difficult.

	30:59.760 --> 31:03.320
	So can you provide the intuition behind why that is,

	31:03.320 --> 31:05.280
	if you agree with that idea?

	31:05.280 --> 31:10.200
	Yeah, so I've done a lot of work on coalitional games

	31:10.200 --> 31:11.680
	and we actually have a paper here

	31:11.680 --> 31:13.680
	with my other student Gabriele Farina

	31:13.680 --> 31:16.640
	and some other collaborators at NIPS on that.

	31:16.640 --> 31:18.520
	Actually just came back from the poster session

	31:18.520 --> 31:19.760
	where we presented this.

	31:19.760 --> 31:23.800
	But so when you have a collusion, it's a different problem.

	31:23.800 --> 31:26.120
	And it typically gets even harder then.

	31:27.520 --> 31:29.600
	Even the game representations,

	31:29.600 --> 31:32.320
	some of the game representations don't really allow

	31:33.600 --> 31:34.480
	good computation.

	31:34.480 --> 31:37.600
	So we actually introduced a new game representation

	31:37.600 --> 31:38.720
	for that.

	31:38.720 --> 31:42.040
	Is that kind of cooperation part of the model?

	31:42.040 --> 31:44.560
	Are you, do you have, do you have information

	31:44.560 --> 31:47.040
	about the fact that other players are cooperating

	31:47.040 --> 31:50.000
	or is it just this chaos that where nothing is known?

	31:50.000 --> 31:52.360
	So there's some things unknown.

	31:52.360 --> 31:55.840
	Can you give an example of a collusion type game

	31:55.840 --> 31:56.680
	or is it usually?

	31:56.680 --> 31:58.360
	So like bridge.

	31:58.360 --> 31:59.640
	So think about bridge.

	31:59.640 --> 32:02.320
	It's like when you and I are on a team,

	32:02.320 --> 32:04.480
	our payoffs are the same.

	32:04.480 --> 32:06.400
	The problem is that we can't talk.

	32:06.400 --> 32:09.000
	So when I get my cards, I can't whisper to you

	32:09.000 --> 32:10.320
	what my cards are.

	32:10.320 --> 32:12.480
	That would not be allowed.

	32:12.480 --> 32:16.080
	So we have to somehow coordinate our strategies

	32:16.080 --> 32:19.920
	ahead of time and only ahead of time.

	32:19.920 --> 32:22.760
	And then there's certain signals we can talk about,

	32:22.760 --> 32:25.240
	but they have to be such that the other team

	32:25.240 --> 32:26.840
	also understands them.

	32:26.840 --> 32:30.440
	So that's an example where the coordination

	32:30.440 --> 32:33.000
	is already built into the rules of the game.

	32:33.000 --> 32:35.640
	But in many other situations like auctions

	32:35.640 --> 32:40.640
	or negotiations or diplomatic relationships, poker,

	32:40.880 --> 32:44.160
	it's not really built in, but it still can be very helpful

	32:44.160 --> 32:45.280
	for the colluders.

	32:45.280 --> 32:48.240
	I've read you write somewhere,

	32:48.240 --> 32:52.800
	the negotiations you come to the table with prior,

	32:52.800 --> 32:56.080
	like a strategy that you're willing to do

	32:56.080 --> 32:58.320
	and not willing to do those kinds of things.

	32:58.320 --> 33:01.960
	So how do you start to now moving away from poker,

	33:01.960 --> 33:04.520
	moving beyond poker into other applications

	33:04.520 --> 33:07.000
	like negotiations, how do you start applying this

	33:07.000 --> 33:11.640
	to other domains, even real world domains

	33:11.640 --> 33:12.520
	that you've worked on?

	33:12.520 --> 33:14.440
	Yeah, I actually have two startup companies

	33:14.440 --> 33:15.480
	doing exactly that.

	33:15.480 --> 33:17.800
	One is called Strategic Machine,

	33:17.800 --> 33:20.000
	and that's for kind of business applications,

	33:20.000 --> 33:22.880
	gaming, sports, all sorts of things like that.

	33:22.880 --> 33:27.200
	Any applications of this to business and to sports

	33:27.200 --> 33:32.120
	and to gaming, to various types of things

	33:32.120 --> 33:34.240
	in finance, electricity markets and so on.

	33:34.240 --> 33:36.600
	And the other is called Strategy Robot,

	33:36.600 --> 33:40.640
	where we are taking these to military security,

	33:40.640 --> 33:43.520
	cyber security and intelligence applications.

	33:43.520 --> 33:46.240
	I think you worked a little bit in,

	33:48.000 --> 33:51.000
	how do you put it, advertisement,

	33:51.000 --> 33:55.360
	sort of suggesting ads kind of thing, auction.

	33:55.360 --> 33:57.800
	That's another company, optimized markets.

	33:57.800 --> 34:00.880
	But that's much more about a combinatorial market

	34:00.880 --> 34:02.840
	and optimization based technology.

	34:02.840 --> 34:06.840
	That's not using these game theoretic reasoning technologies.

	34:06.840 --> 34:11.600
	I see, okay, so what sort of high level

	34:11.600 --> 34:15.280
	do you think about our ability to use

	34:15.280 --> 34:18.040
	game theoretic concepts to model human behavior?

	34:18.040 --> 34:21.640
	Do you think human behavior is amenable

	34:21.640 --> 34:24.720
	to this kind of modeling outside of the poker games,

	34:24.720 --> 34:27.520
	and where have you seen it done successfully in your work?

	34:27.520 --> 34:32.520
	I'm not sure the goal really is modeling humans.

	34:33.640 --> 34:36.480
	Like for example, if I'm playing a zero sum game,

	34:36.480 --> 34:39.840
	I don't really care that the opponent

	34:39.840 --> 34:42.960
	is actually following my model of rational behavior,

	34:42.960 --> 34:46.400
	because if they're not, that's even better for me.

	34:46.400 --> 34:50.200
	Right, so see with the opponents in games,

	34:51.120 --> 34:56.120
	the prerequisite is that you formalize

	34:56.120 --> 34:57.800
	the interaction in some way

	34:57.800 --> 35:01.000
	that can be amenable to analysis.

	35:01.000 --> 35:04.160
	And you've done this amazing work with mechanism design,

	35:04.160 --> 35:08.160
	designing games that have certain outcomes.

	35:10.040 --> 35:12.320
	But, so I'll tell you an example

	35:12.320 --> 35:15.460
	from my world of autonomous vehicles, right?

	35:15.460 --> 35:17.040
	We're studying pedestrians,

	35:17.040 --> 35:20.200
	and pedestrians and cars negotiate

	35:20.200 --> 35:22.160
	in this nonverbal communication.

	35:22.160 --> 35:25.040
	There's this weird game dance of tension

	35:25.040 --> 35:27.280
	where pedestrians are basically saying,

	35:27.280 --> 35:28.800
	I trust that you won't kill me,

	35:28.800 --> 35:31.840
	and so as a jaywalker, I will step onto the road

	35:31.840 --> 35:34.720
	even though I'm breaking the law, and there's this tension.

	35:34.720 --> 35:36.640
	And the question is, we really don't know

	35:36.640 --> 35:40.720
	how to model that well in trying to model intent.

	35:40.720 --> 35:43.080
	And so people sometimes bring up ideas

	35:43.080 --> 35:44.880
	of game theory and so on.

	35:44.880 --> 35:49.120
	Do you think that aspect of human behavior

	35:49.120 --> 35:53.080
	can use these kinds of imperfect information approaches,

	35:53.080 --> 35:57.860
	modeling, how do you start to attack a problem like that

	35:57.860 --> 36:00.940
	when you don't even know how to design the game

	36:00.940 --> 36:04.280
	to describe the situation in order to solve it?

	36:04.280 --> 36:06.800
	Okay, so I haven't really thought about jaywalking,

	36:06.800 --> 36:10.120
	but one thing that I think could be a good application

	36:10.120 --> 36:13.000
	in autonomous vehicles is the following.

	36:13.000 --> 36:16.320
	So let's say that you have fleets of autonomous cars

	36:16.320 --> 36:18.340
	operating by different companies.

	36:18.340 --> 36:22.120
	So maybe here's the Waymo fleet and here's the Uber fleet.

	36:22.120 --> 36:24.320
	If you think about the rules of the road,

	36:24.320 --> 36:26.560
	they define certain legal rules,

	36:26.560 --> 36:30.080
	but that still leaves a huge strategy space open.

	36:30.080 --> 36:32.840
	Like as a simple example, when cars merge,

	36:32.840 --> 36:36.000
	how humans merge, they slow down and look at each other

	36:36.000 --> 36:39.240
	and try to merge.

	36:39.240 --> 36:40.920
	Wouldn't it be better if these situations

	36:40.920 --> 36:43.480
	would already be prenegotiated

	36:43.480 --> 36:45.200
	so we can actually merge at full speed

	36:45.200 --> 36:47.440
	and we know that this is the situation,

	36:47.440 --> 36:50.540
	this is how we do it, and it's all gonna be faster.

	36:50.540 --> 36:54.120
	But there are way too many situations to negotiate manually.

	36:54.120 --> 36:56.400
	So you could use automated negotiation,

	36:56.400 --> 36:57.780
	this is the idea at least,

	36:57.780 --> 36:59.840
	you could use automated negotiation

	36:59.840 --> 37:02.060
	to negotiate all of these situations

	37:02.060 --> 37:04.320
	or many of them in advance.

	37:04.320 --> 37:05.460
	And of course it might be that,

	37:05.460 --> 37:09.180
	hey, maybe you're not gonna always let me go first.

	37:09.180 --> 37:11.280
	Maybe you said, okay, well, in these situations,

	37:11.280 --> 37:13.560
	I'll let you go first, but in exchange,

	37:13.560 --> 37:14.520
	you're gonna give me too much,

	37:14.520 --> 37:17.260
	you're gonna let me go first in this situation.

	37:17.260 --> 37:20.680
	So it's this huge combinatorial negotiation.

	37:20.680 --> 37:24.080
	And do you think there's room in that example of merging

	37:24.080 --> 37:25.600
	to model this whole situation

	37:25.600 --> 37:27.160
	as an imperfect information game

	37:27.160 --> 37:30.120
	or do you really want to consider it to be a perfect?

	37:30.120 --> 37:32.240
	No, that's a good question, yeah.

	37:32.240 --> 37:33.080
	That's a good question.

	37:33.080 --> 37:37.080
	Do you pay the price of assuming

	37:37.080 --> 37:38.640
	that you don't know everything?

	37:39.800 --> 37:40.760
	Yeah, I don't know.

	37:40.760 --> 37:42.120
	It's certainly much easier.

	37:42.120 --> 37:45.060
	Games with perfect information are much easier.

	37:45.060 --> 37:49.280
	So if you can't get away with it, you should.

	37:49.280 --> 37:52.640
	But if the real situation is of imperfect information,

	37:52.640 --> 37:55.160
	then you're gonna have to deal with imperfect information.

	37:55.160 --> 37:58.080
	Great, so what lessons have you learned

	37:58.080 --> 38:00.680
	the Annual Computer Poker Competition?

	38:00.680 --> 38:03.440
	An incredible accomplishment of AI.

	38:03.440 --> 38:07.000
	You look at the history of Deep Blue, AlphaGo,

	38:07.000 --> 38:10.400
	these kind of moments when AI stepped up

	38:10.400 --> 38:13.960
	in an engineering effort and a scientific effort combined

	38:13.960 --> 38:16.400
	to beat the best of human players.

	38:16.400 --> 38:19.480
	So what do you take away from this whole experience?

	38:19.480 --> 38:22.440
	What have you learned about designing AI systems

	38:22.440 --> 38:23.960
	that play these kinds of games?

	38:23.960 --> 38:28.280
	And what does that mean for AI in general,

	38:28.280 --> 38:30.760
	for the future of AI development?

	38:30.760 --> 38:32.800
	Yeah, so that's a good question.

	38:32.800 --> 38:34.560
	So there's so much to say about it.

	38:35.440 --> 38:39.120
	I do like this type of performance oriented research.

	38:39.120 --> 38:42.000
	Although in my group, we go all the way from like idea

	38:42.000 --> 38:44.880
	to theory, to experiments, to big system building,

	38:44.880 --> 38:47.960
	to commercialization, so we span that spectrum.

	38:47.960 --> 38:51.080
	But I think that in a lot of situations in AI,

	38:51.080 --> 38:53.440
	you really have to build the big systems

	38:53.440 --> 38:55.640
	and evaluate them at scale

	38:55.640 --> 38:57.520
	before you know what works and doesn't.

	38:57.520 --> 39:00.080
	And we've seen that in the computational

	39:00.080 --> 39:02.880
	game theory community, that there are a lot of techniques

	39:02.880 --> 39:04.280
	that look good in the small,

	39:05.200 --> 39:07.120
	but then they cease to look good in the large.

	39:07.120 --> 39:10.160
	And we've also seen that there are a lot of techniques

	39:10.160 --> 39:13.280
	that look superior in theory.

	39:13.280 --> 39:16.200
	And I really mean in terms of convergence rates,

	39:16.200 --> 39:18.440
	like first order methods, better convergence rates,

	39:18.440 --> 39:20.880
	like the CFR based algorithms,

	39:20.880 --> 39:24.880
	yet the CFR based algorithms are the fastest in practice.

	39:24.880 --> 39:28.240
	So it really tells me that you have to test this in reality.

	39:28.240 --> 39:30.880
	The theory isn't tight enough, if you will,

	39:30.880 --> 39:34.360
	to tell you which algorithms are better than the others.

	39:34.360 --> 39:38.600
	And you have to look at these things in the large,

	39:38.600 --> 39:41.480
	because any sort of projections you do from the small

	39:41.480 --> 39:43.800
	can at least in this domain be very misleading.

	39:43.800 --> 39:46.240
	So that's kind of from a kind of a science

	39:46.240 --> 39:49.120
	and engineering perspective, from a personal perspective,

	39:49.120 --> 39:51.280
	it's been just a wild experience

	39:51.280 --> 39:54.160
	in that with the first poker competition,

	39:54.160 --> 39:57.200
	the first brains versus AI,

	39:57.200 --> 39:59.840
	man machine poker competition that we organized.

	39:59.840 --> 40:01.760
	There had been, by the way, for other poker games,

	40:01.760 --> 40:03.240
	there had been previous competitions,

	40:03.240 --> 40:06.360
	but this was for Heads Up No Limit, this was the first.

	40:06.360 --> 40:09.560
	And I probably became the most hated person

	40:09.560 --> 40:10.880
	in the world of poker.

	40:10.880 --> 40:12.880
	And I didn't mean to, I just saw.

	40:12.880 --> 40:13.720
	Why is that?

	40:13.720 --> 40:15.840
	For cracking the game, for something.

	40:15.840 --> 40:20.000
	Yeah, a lot of people felt that it was a real threat

	40:20.000 --> 40:22.760
	to the whole game, the whole existence of the game.

	40:22.760 --> 40:26.080
	If AI becomes better than humans,

	40:26.080 --> 40:28.520
	people would be scared to play poker

	40:28.520 --> 40:30.680
	because there are these superhuman AIs running around

	40:30.680 --> 40:32.760
	taking their money and all of that.

	40:32.760 --> 40:36.200
	So I just, it's just really aggressive.

	40:36.200 --> 40:37.880
	The comments were super aggressive.

	40:37.880 --> 40:40.920
	I got everything just short of death threats.

	40:40.920 --> 40:44.000
	Do you think the same was true for chess?

	40:44.000 --> 40:45.760
	Because right now they just completed

	40:45.760 --> 40:47.720
	the world championships in chess,

	40:47.720 --> 40:49.560
	and humans just started ignoring the fact

	40:49.560 --> 40:52.920
	that there's AI systems now that outperform humans

	40:52.920 --> 40:55.520
	and they still enjoy the game, it's still a beautiful game.

	40:55.520 --> 40:56.360
	That's what I think.

	40:56.360 --> 40:58.800
	And I think the same thing happens in poker.

	40:58.800 --> 41:01.040
	And so I didn't think of myself

	41:01.040 --> 41:02.360
	as somebody who was gonna kill the game,

	41:02.360 --> 41:03.800
	and I don't think I did.

	41:03.800 --> 41:05.600
	I've really learned to love this game.

	41:05.600 --> 41:06.960
	I wasn't a poker player before,

	41:06.960 --> 41:10.520
	but learned so many nuances about it from these AIs,

	41:10.520 --> 41:12.480
	and they've really changed how the game is played,

	41:12.480 --> 41:13.320
	by the way.

	41:13.320 --> 41:16.240
	So they have these very Martian ways of playing poker,

	41:16.240 --> 41:18.400
	and the top humans are now incorporating

	41:18.400 --> 41:21.400
	those types of strategies into their own play.

	41:21.400 --> 41:26.400
	So if anything, to me, our work has made poker

	41:26.560 --> 41:29.800
	a richer, more interesting game for humans to play,

	41:29.800 --> 41:32.160
	not something that is gonna steer humans

	41:32.160 --> 41:34.200
	away from it entirely.

	41:34.200 --> 41:35.960
	Just a quick comment on something you said,

	41:35.960 --> 41:39.400
	which is, if I may say so,

	41:39.400 --> 41:42.400
	in academia is a little bit rare sometimes.

	41:42.400 --> 41:45.520
	It's pretty brave to put your ideas to the test

	41:45.520 --> 41:47.200
	in the way you described,

	41:47.200 --> 41:49.360
	saying that sometimes good ideas don't work

	41:49.360 --> 41:52.760
	when you actually try to apply them at scale.

	41:52.760 --> 41:54.200
	So where does that come from?

	41:54.200 --> 41:58.880
	I mean, if you could do advice for people,

	41:58.880 --> 42:00.760
	what drives you in that sense?

	42:00.760 --> 42:02.360
	Were you always this way?

	42:02.360 --> 42:04.080
	I mean, it takes a brave person.

	42:04.080 --> 42:06.760
	I guess is what I'm saying, to test their ideas

	42:06.760 --> 42:08.640
	and to see if this thing actually works

	42:08.640 --> 42:11.680
	against human top human players and so on.

	42:11.680 --> 42:12.960
	Yeah, I don't know about brave,

	42:12.960 --> 42:15.000
	but it takes a lot of work.

	42:15.000 --> 42:17.320
	It takes a lot of work and a lot of time

	42:18.400 --> 42:20.360
	to organize, to make something big

	42:20.360 --> 42:22.920
	and to organize an event and stuff like that.

	42:22.920 --> 42:24.760
	And what drives you in that effort?

	42:24.760 --> 42:26.880
	Because you could still, I would argue,

	42:26.880 --> 42:30.280
	get a best paper award at NIPS as you did in 17

	42:30.280 --> 42:31.440
	without doing this.

	42:31.440 --> 42:32.960
	That's right, yes.

	42:32.960 --> 42:37.640
	And so in general, I believe it's very important

	42:37.640 --> 42:41.480
	to do things in the real world and at scale.

	42:41.480 --> 42:46.160
	And that's really where the pudding, if you will,

	42:46.160 --> 42:48.400
	proof is in the pudding, that's where it is.

	42:48.400 --> 42:50.080
	In this particular case,

	42:50.080 --> 42:55.080
	it was kind of a competition between different groups

	42:55.160 --> 42:59.080
	and for many years as to who can be the first one

	42:59.080 --> 43:02.040
	to beat the top humans at Heads Up No Limit, Texas Holdem.

	43:02.040 --> 43:07.040
	So it became kind of like a competition who can get there.

	43:09.560 --> 43:11.800
	Yeah, so a little friendly competition

	43:11.800 --> 43:14.040
	could do wonders for progress.

	43:14.040 --> 43:15.040
	Yes, absolutely.

	43:16.400 --> 43:19.040
	So the topic of mechanism design,

	43:19.040 --> 43:22.280
	which is really interesting, also kind of new to me,

	43:22.280 --> 43:25.680
	except as an observer of, I don't know, politics and any,

	43:25.680 --> 43:27.600
	I'm an observer of mechanisms,

	43:27.600 --> 43:31.440
	but you write in your paper an automated mechanism design

	43:31.440 --> 43:34.000
	that I quickly read.

	43:34.000 --> 43:37.880
	So mechanism design is designing the rules of the game

	43:37.880 --> 43:40.200
	so you get a certain desirable outcome.

	43:40.200 --> 43:44.920
	And you have this work on doing so in an automatic fashion

	43:44.920 --> 43:46.720
	as opposed to fine tuning it.

	43:46.720 --> 43:50.680
	So what have you learned from those efforts?

	43:50.680 --> 43:52.280
	If you look, say, I don't know,

	43:52.280 --> 43:56.200
	at complexes like our political system,

	43:56.200 --> 43:58.560
	can we design our political system

	43:58.560 --> 44:01.800
	to have, in an automated fashion,

	44:01.800 --> 44:03.360
	to have outcomes that we want?

	44:03.360 --> 44:08.360
	Can we design something like traffic lights to be smart

	44:09.000 --> 44:11.800
	where it gets outcomes that we want?

	44:11.800 --> 44:14.840
	So what are the lessons that you draw from that work?

	44:14.840 --> 44:17.240
	Yeah, so I still very much believe

	44:17.240 --> 44:19.400
	in the automated mechanism design direction.

	44:19.400 --> 44:20.840
	Yes.

	44:20.840 --> 44:23.000
	But it's not a panacea.

	44:23.000 --> 44:26.520
	There are impossibility results in mechanism design

	44:26.520 --> 44:30.240
	saying that there is no mechanism that accomplishes

	44:30.240 --> 44:33.920
	objective X in class C.

	44:33.920 --> 44:36.120
	So it's not going to,

	44:36.120 --> 44:39.000
	there's no way using any mechanism design tools,

	44:39.000 --> 44:41.000
	manual or automated,

	44:41.000 --> 44:42.800
	to do certain things in mechanism design.

	44:42.800 --> 44:43.800
	Can you describe that again?

	44:43.800 --> 44:47.480
	So meaning it's impossible to achieve that?

	44:47.480 --> 44:48.320
	Yeah, yeah.

	44:48.320 --> 44:50.440
	And it's unlikely.

	44:50.440 --> 44:51.280
	Impossible.

	44:51.280 --> 44:52.120
	Impossible.

	44:52.120 --> 44:55.240
	So these are not statements about human ingenuity

	44:55.240 --> 44:57.120
	who might come up with something smart.

	44:57.120 --> 44:59.880
	These are proofs that if you want to accomplish

	44:59.880 --> 45:02.480
	properties X in class C,

	45:02.480 --> 45:04.880
	that is not doable with any mechanism.

	45:04.880 --> 45:07.080
	The good thing about automated mechanism design

	45:07.080 --> 45:10.840
	is that we're not really designing for a class.

	45:10.840 --> 45:14.160
	We're designing for specific settings at a time.

	45:14.160 --> 45:16.720
	So even if there's an impossibility result

	45:16.720 --> 45:18.240
	for the whole class,

	45:18.240 --> 45:21.360
	it just doesn't mean that all of the cases

	45:21.360 --> 45:22.560
	in the class are impossible.

	45:22.560 --> 45:25.080
	It just means that some of the cases are impossible.

	45:25.080 --> 45:28.240
	So we can actually carve these islands of possibility

	45:28.240 --> 45:30.920
	within these known impossible classes.

	45:30.920 --> 45:31.960
	And we've actually done that.

	45:31.960 --> 45:35.160
	So one of the famous results in mechanism design

	45:35.160 --> 45:37.360
	is the Meyerson Satethweight theorem

	45:37.360 --> 45:41.000
	by Roger Meyerson and Mark Satethweight from 1983.

	45:41.000 --> 45:43.480
	It's an impossibility of efficient trade

	45:43.480 --> 45:45.200
	under imperfect information.

	45:45.200 --> 45:48.560
	We show that you can, in many settings,

	45:48.560 --> 45:51.480
	avoid that and get efficient trade anyway.

	45:51.480 --> 45:54.160
	Depending on how they design the game, okay.

	45:54.160 --> 45:55.880
	Depending how you design the game.

	45:55.880 --> 46:00.240
	And of course, it doesn't in any way

	46:00.240 --> 46:01.800
	contradict the impossibility result.

	46:01.800 --> 46:03.920
	The impossibility result is still there,

	46:03.920 --> 46:08.000
	but it just finds spots within this impossible class

	46:08.920 --> 46:12.440
	where in those spots, you don't have the impossibility.

	46:12.440 --> 46:14.760
	Sorry if I'm going a bit philosophical,

	46:14.760 --> 46:17.480
	but what lessons do you draw towards,

	46:17.480 --> 46:20.160
	like I mentioned, politics or human interaction

	46:20.160 --> 46:24.880
	and designing mechanisms for outside of just

	46:24.880 --> 46:26.960
	these kinds of trading or auctioning

	46:26.960 --> 46:31.960
	or purely formal games or human interaction,

	46:33.480 --> 46:34.920
	like a political system?

	46:34.920 --> 46:39.160
	How, do you think it's applicable to, yeah, politics

	46:39.160 --> 46:44.160
	or to business, to negotiations, these kinds of things,

	46:46.280 --> 46:49.040
	designing rules that have certain outcomes?

	46:49.040 --> 46:51.360
	Yeah, yeah, I do think so.

	46:51.360 --> 46:54.200
	Have you seen that successfully done?

	46:54.200 --> 46:56.440
	They haven't really, oh, you mean mechanism design

	46:56.440 --> 46:57.280
	or automated mechanism design?

	46:57.280 --> 46:59.000
	Automated mechanism design.

	46:59.000 --> 47:01.520
	So mechanism design itself

	47:01.520 --> 47:06.440
	has had fairly limited success so far.

	47:06.440 --> 47:07.600
	There are certain cases,

	47:07.600 --> 47:10.200
	but most of the real world situations

	47:10.200 --> 47:14.680
	are actually not sound from a mechanism design perspective,

	47:14.680 --> 47:16.920
	even in those cases where they've been designed

	47:16.920 --> 47:20.000
	by very knowledgeable mechanism design people,

	47:20.000 --> 47:22.760
	the people are typically just taking some insights

	47:22.760 --> 47:25.040
	from the theory and applying those insights

	47:25.040 --> 47:26.280
	into the real world,

	47:26.280 --> 47:29.280
	rather than applying the mechanisms directly.

	47:29.280 --> 47:33.520
	So one famous example of is the FCC spectrum auctions.

	47:33.520 --> 47:36.880
	So I've also had a small role in that

	47:36.880 --> 47:40.600
	and very good economists have been working,

	47:40.600 --> 47:42.560
	excellent economists have been working on that

	47:42.560 --> 47:44.040
	with no game theory,

	47:44.040 --> 47:47.440
	yet the rules that are designed in practice there,

	47:47.440 --> 47:49.840
	they're such that bidding truthfully

	47:49.840 --> 47:51.800
	is not the best strategy.

	47:51.800 --> 47:52.960
	Usually mechanism design,

	47:52.960 --> 47:56.160
	we try to make things easy for the participants.

	47:56.160 --> 47:58.560
	So telling the truth is the best strategy,

	47:58.560 --> 48:01.480
	but even in those very high stakes auctions

	48:01.480 --> 48:03.080
	where you have tens of billions of dollars

	48:03.080 --> 48:05.200
	worth of spectrum being auctioned,

	48:06.360 --> 48:08.280
	truth telling is not the best strategy.

	48:08.280 --> 48:10.040
	And by the way,

	48:10.040 --> 48:12.920
	nobody knows even a single optimal bidding strategy

	48:12.920 --> 48:14.120
	for those auctions.

	48:14.120 --> 48:15.960
	What's the challenge of coming up with an optimal,

	48:15.960 --> 48:18.160
	because there's a lot of players and there's imperfect.

	48:18.160 --> 48:20.040
	It's not so much that a lot of players,

	48:20.040 --> 48:22.320
	but many items for sale,

	48:22.320 --> 48:26.000
	and these mechanisms are such that even with just two items

	48:26.000 --> 48:28.400
	or one item, bidding truthfully

	48:28.400 --> 48:30.400
	wouldn't be the best strategy.

	48:31.400 --> 48:34.560
	If you look at the history of AI,

	48:34.560 --> 48:37.160
	it's marked by seminal events.

	48:37.160 --> 48:40.160
	AlphaGo beating a world champion human Go player,

	48:40.160 --> 48:43.680
	I would put Liberatus winning the Heads Up No Limit Holdem

	48:43.680 --> 48:45.000
	as one of such event.

	48:45.000 --> 48:46.040
	Thank you.

	48:46.040 --> 48:51.040
	And what do you think is the next such event,

	48:52.560 --> 48:56.640
	whether it's in your life or in the broadly AI community

	48:56.640 --> 48:59.040
	that you think might be out there

	48:59.040 --> 49:01.640
	that would surprise the world?

	49:01.640 --> 49:02.800
	So that's a great question,

	49:02.800 --> 49:04.520
	and I don't really know the answer.

	49:04.520 --> 49:06.160
	In terms of game solving,

	49:07.360 --> 49:08.920
	Heads Up No Limit Texas Holdem

	49:08.920 --> 49:13.920
	really was the one remaining widely agreed upon benchmark.

	49:14.400 --> 49:15.880
	So that was the big milestone.

	49:15.880 --> 49:17.800
	Now, are there other things?

	49:17.800 --> 49:18.920
	Yeah, certainly there are,

	49:18.920 --> 49:21.080
	but there's not one that the community

	49:21.080 --> 49:22.920
	has kind of focused on.

	49:22.920 --> 49:25.240
	So what could be other things?

	49:25.240 --> 49:27.640
	There are groups working on StarCraft.

	49:27.640 --> 49:29.840
	There are groups working on Dota 2.

	49:29.840 --> 49:31.560
	These are video games.

	49:31.560 --> 49:36.240
	Or you could have like Diplomacy or Hanabi,

	49:36.240 --> 49:37.080
	things like that.

	49:37.080 --> 49:38.640
	These are like recreational games,

	49:38.640 --> 49:42.040
	but none of them are really acknowledged

	49:42.040 --> 49:45.840
	as kind of the main next challenge problem,

	49:45.840 --> 49:50.000
	like chess or Go or Heads Up No Limit Texas Holdem was.

	49:50.000 --> 49:52.360
	So I don't really know in the game solving space

	49:52.360 --> 49:55.400
	what is or what will be the next benchmark.

	49:55.400 --> 49:57.840
	I kind of hope that there will be a next benchmark

	49:57.840 --> 49:59.560
	because really the different groups

	49:59.560 --> 50:01.120
	working on the same problem

	50:01.120 --> 50:05.120
	really drove these application independent techniques

	50:05.120 --> 50:07.480
	forward very quickly over 10 years.

	50:07.480 --> 50:09.120
	Do you think there's an open problem

	50:09.120 --> 50:11.480
	that excites you that you start moving away

	50:11.480 --> 50:15.000
	from games into real world games,

	50:15.000 --> 50:17.200
	like say the stock market trading?

	50:17.200 --> 50:19.320
	Yeah, so that's kind of how I am.

	50:19.320 --> 50:23.120
	So I am probably not going to work

	50:23.120 --> 50:27.640
	as hard on these recreational benchmarks.

	50:27.640 --> 50:30.200
	I'm doing two startups on game solving technology,

	50:30.200 --> 50:32.320
	Strategic Machine and Strategy Robot,

	50:32.320 --> 50:34.160
	and we're really interested

	50:34.160 --> 50:36.560
	in pushing this stuff into practice.

	50:36.560 --> 50:40.080
	What do you think would be really

	50:43.160 --> 50:45.920
	a powerful result that would be surprising

	50:45.920 --> 50:49.960
	that would be, if you can say,

	50:49.960 --> 50:53.280
	I mean, five years, 10 years from now,

	50:53.280 --> 50:56.480
	something that statistically you would say

	50:56.480 --> 50:57.920
	is not very likely,

	50:57.920 --> 51:01.480
	but if there's a breakthrough, would achieve?

	51:01.480 --> 51:03.800
	Yeah, so I think that overall,

	51:03.800 --> 51:08.800
	we're in a very different situation in game theory

	51:09.000 --> 51:11.760
	than we are in, let's say, machine learning.

	51:11.760 --> 51:14.360
	So in machine learning, it's a fairly mature technology

	51:14.360 --> 51:16.480
	and it's very broadly applied

	51:16.480 --> 51:19.680
	and proven success in the real world.

	51:19.680 --> 51:22.840
	In game solving, there are almost no applications yet.

	51:24.320 --> 51:26.680
	We have just become superhuman,

	51:26.680 --> 51:29.600
	which machine learning you could argue happened in the 90s,

	51:29.600 --> 51:30.640
	if not earlier,

	51:30.640 --> 51:32.960
	and at least on supervised learning,

	51:32.960 --> 51:35.400
	certain complex supervised learning applications.

	51:36.960 --> 51:39.000
	Now, I think the next challenge problem,

	51:39.000 --> 51:40.560
	I know you're not asking about it this way,

	51:40.560 --> 51:42.640
	you're asking about the technology breakthrough,

	51:42.640 --> 51:44.240
	but I think that big, big breakthrough

	51:44.240 --> 51:46.120
	is to be able to show that, hey,

	51:46.120 --> 51:48.280
	maybe most of, let's say, military planning

	51:48.280 --> 51:50.080
	or most of business strategy

	51:50.080 --> 51:52.200
	will actually be done strategically

	51:52.200 --> 51:54.120
	using computational game theory.

	51:54.120 --> 51:55.800
	That's what I would like to see

	51:55.800 --> 51:57.640
	as the next five or 10 year goal.

	51:57.640 --> 51:59.520
	Maybe you can explain to me again,

	51:59.520 --> 52:01.920
	forgive me if this is an obvious question,

	52:01.920 --> 52:04.000
	but machine learning methods,

	52:04.000 --> 52:07.840
	neural networks suffer from not being transparent,

	52:07.840 --> 52:09.280
	not being explainable.

	52:09.280 --> 52:12.400
	Game theoretic methods, Nash equilibria,

	52:12.400 --> 52:15.280
	do they generally, when you see the different solutions,

	52:15.280 --> 52:19.640
	are they, when you talk about military operations,

	52:19.640 --> 52:21.800
	are they, once you see the strategies,

	52:21.800 --> 52:23.880
	do they make sense, are they explainable,

	52:23.880 --> 52:25.840
	or do they suffer from the same problems

	52:25.840 --> 52:27.120
	as neural networks do?

	52:27.120 --> 52:28.720
	So that's a good question.

	52:28.720 --> 52:31.240
	I would say a little bit yes and no.

	52:31.240 --> 52:34.560
	And what I mean by that is that

	52:34.560 --> 52:36.160
	these game theoretic strategies,

	52:36.160 --> 52:38.520
	let's say, Nash equilibrium,

	52:38.520 --> 52:40.320
	it has provable properties.

	52:40.320 --> 52:42.360
	So it's unlike, let's say, deep learning

	52:42.360 --> 52:44.440
	where you kind of cross your fingers,

	52:44.440 --> 52:45.680
	hopefully it'll work.

	52:45.680 --> 52:47.880
	And then after the fact, when you have the weights,

	52:47.880 --> 52:48.920
	you're still crossing your fingers,

	52:48.920 --> 52:50.160
	and I hope it will work.

	52:51.160 --> 52:55.400
	Here, you know that the solution quality is there.

	52:55.400 --> 52:58.040
	There's provable solution quality guarantees.

	52:58.040 --> 53:00.920
	Now, that doesn't necessarily mean

	53:00.920 --> 53:03.480
	that the strategies are human understandable.

	53:03.480 --> 53:04.720
	That's a whole other problem.

	53:04.720 --> 53:08.680
	So I think that deep learning and computational game theory

	53:08.680 --> 53:10.720
	are in the same boat in that sense,

	53:10.720 --> 53:12.680
	that both are difficult to understand.

	53:13.760 --> 53:15.680
	But at least the game theoretic techniques,

	53:15.680 --> 53:19.840
	they have these guarantees of solution quality.

	53:19.840 --> 53:22.880
	So do you see business operations, strategic operations,

	53:22.880 --> 53:26.040
	or even military in the future being

	53:26.040 --> 53:28.320
	at least the strong candidates

	53:28.320 --> 53:32.760
	being proposed by automated systems?

	53:32.760 --> 53:34.000
	Do you see that?

	53:34.000 --> 53:35.040
	Yeah, I do, I do.

	53:35.040 --> 53:39.640
	But that's more of a belief than a substantiated fact.

	53:39.640 --> 53:42.320
	Depending on where you land in optimism or pessimism,

	53:42.320 --> 53:45.720
	that's a really, to me, that's an exciting future,

	53:45.720 --> 53:48.760
	especially if there's provable things

	53:48.760 --> 53:50.560
	in terms of optimality.

	53:50.560 --> 53:54.040
	So looking into the future,

	53:54.040 --> 53:58.760
	there's a few folks worried about the,

	53:58.760 --> 54:01.200
	especially you look at the game of poker,

	54:01.200 --> 54:03.360
	which is probably one of the last benchmarks

	54:03.360 --> 54:05.480
	in terms of games being solved.

	54:05.480 --> 54:07.520
	They worry about the future

	54:07.520 --> 54:10.520
	and the existential threats of artificial intelligence.

	54:10.520 --> 54:13.840
	So the negative impact in whatever form on society.

	54:13.840 --> 54:17.440
	Is that something that concerns you as much,

	54:17.440 --> 54:21.600
	or are you more optimistic about the positive impacts of AI?

	54:21.600 --> 54:24.720
	Oh, I am much more optimistic about the positive impacts.

	54:24.720 --> 54:27.560
	So just in my own work, what we've done so far,

	54:27.560 --> 54:29.920
	we run the nationwide kidney exchange.

	54:29.920 --> 54:32.960
	Hundreds of people are walking around alive today,

	54:32.960 --> 54:34.080
	who would it be?

	54:34.080 --> 54:36.120
	And it's increased employment.

	54:36.120 --> 54:39.920
	You have a lot of people now running kidney exchanges

	54:39.920 --> 54:42.200
	and at the transplant centers,

	54:42.200 --> 54:45.560
	interacting with the kidney exchange.

	54:45.560 --> 54:49.440
	You have extra surgeons, nurses, anesthesiologists,

	54:49.440 --> 54:51.400
	hospitals, all of that.

	54:51.400 --> 54:53.560
	So employment is increasing from that

	54:53.560 --> 54:55.320
	and the world is becoming a better place.

	54:55.320 --> 54:59.040
	Another example is combinatorial sourcing auctions.

	54:59.040 --> 55:04.040
	We did 800 large scale combinatorial sourcing auctions

	55:04.040 --> 55:08.240
	from 2001 to 2010 in a previous startup of mine

	55:08.240 --> 55:09.400
	called CombineNet.

	55:09.400 --> 55:13.080
	And we increased the supply chain efficiency

	55:13.080 --> 55:18.080
	on that $60 billion of spend by 12.6%.

	55:18.080 --> 55:21.440
	So that's over $6 billion of efficiency improvement

	55:21.440 --> 55:22.240
	in the world.

	55:22.240 --> 55:23.760
	And this is not like shifting value

	55:23.760 --> 55:25.240
	from somebody to somebody else,

	55:25.240 --> 55:28.200
	just efficiency improvement, like in trucking,

	55:28.200 --> 55:31.120
	less empty driving, so there's less waste,

	55:31.120 --> 55:33.440
	less carbon footprint and so on.

	55:33.440 --> 55:36.720
	So a huge positive impact in the near term,

	55:36.720 --> 55:40.680
	but sort of to stay in it for a little longer,

	55:40.680 --> 55:43.080
	because I think game theory has a role to play here.

	55:43.080 --> 55:45.320
	Oh, let me actually come back on that as one thing.

	55:45.320 --> 55:49.400
	I think AI is also going to make the world much safer.

	55:49.400 --> 55:53.760
	So that's another aspect that often gets overlooked.

	55:53.760 --> 55:54.920
	Well, let me ask this question.

	55:54.920 --> 55:56.960
	Maybe you can speak to the safer.

	55:56.960 --> 55:59.960
	So I talked to Max Tegmark and Stuart Russell,

	55:59.960 --> 56:02.680
	who are very concerned about existential threats of AI.

	56:02.680 --> 56:06.240
	And often the concern is about value misalignment.

	56:06.240 --> 56:10.240
	So AI systems basically working,

	56:11.880 --> 56:14.680
	operating towards goals that are not the same

	56:14.680 --> 56:17.920
	as human civilization, human beings.

	56:17.920 --> 56:21.160
	So it seems like game theory has a role to play there

	56:24.200 --> 56:27.880
	to make sure the values are aligned with human beings.

	56:27.880 --> 56:29.960
	I don't know if that's how you think about it.

	56:29.960 --> 56:34.960
	If not, how do you think AI might help with this problem?

	56:35.280 --> 56:39.240
	How do you think AI might make the world safer?

	56:39.240 --> 56:43.000
	Yeah, I think this value misalignment

	56:43.000 --> 56:46.480
	is a fairly theoretical worry.

	56:46.480 --> 56:49.960
	And I haven't really seen it in,

	56:49.960 --> 56:51.840
	because I do a lot of real applications.

	56:51.840 --> 56:53.920
	I don't see it anywhere.

	56:53.920 --> 56:55.240
	The closest I've seen it

	56:55.240 --> 56:57.920
	was the following type of mental exercise really,

	56:57.920 --> 57:00.720
	where I had this argument in the late eighties

	57:00.720 --> 57:01.560
	when we were building

	57:01.560 --> 57:03.560
	these transportation optimization systems.

	57:03.560 --> 57:05.360
	And somebody had heard that it's a good idea

	57:05.360 --> 57:08.160
	to have high utilization of assets.

	57:08.160 --> 57:11.400
	So they told me, hey, why don't you put that as objective?

	57:11.400 --> 57:14.720
	And we didn't even put it as an objective

	57:14.720 --> 57:16.480
	because I just showed him that,

	57:16.480 --> 57:18.480
	if you had that as your objective,

	57:18.480 --> 57:20.320
	the solution would be to load your trucks full

	57:20.320 --> 57:21.840
	and drive in circles.

	57:21.840 --> 57:23.000
	Nothing would ever get delivered.

	57:23.000 --> 57:25.120
	You'd have a hundred percent utilization.

	57:25.120 --> 57:27.240
	So yeah, I know this phenomenon.

	57:27.240 --> 57:29.680
	I've known this for over 30 years,

	57:29.680 --> 57:33.360
	but I've never seen it actually be a problem in reality.

	57:33.360 --> 57:35.240
	And yes, if you have the wrong objective,

	57:35.240 --> 57:37.800
	the AI will optimize that to the hilt

	57:37.800 --> 57:39.800
	and it's gonna hurt more than some human

	57:39.800 --> 57:43.800
	who's kind of trying to solve it in a half baked way

	57:43.800 --> 57:45.480
	with some human insight too.

	57:45.480 --> 57:49.160
	But I just haven't seen that materialize in practice.

	57:49.160 --> 57:52.720
	There's this gap that you've actually put your finger on

	57:52.720 --> 57:57.080
	very clearly just now between theory and reality.

	57:57.080 --> 57:59.680
	That's very difficult to put into words, I think.

	57:59.680 --> 58:02.240
	It's what you can theoretically imagine,

	58:03.240 --> 58:08.000
	the worst possible case or even, yeah, I mean bad cases.

	58:08.000 --> 58:10.520
	And what usually happens in reality.

	58:10.520 --> 58:11.960
	So for example, to me,

	58:11.960 --> 58:15.720
	maybe it's something you can comment on having grown up

	58:15.720 --> 58:17.680
	and I grew up in the Soviet Union.

	58:19.120 --> 58:22.120
	There's currently 10,000 nuclear weapons in the world.

	58:22.120 --> 58:24.200
	And for many decades,

	58:24.200 --> 58:28.360
	it's theoretically surprising to me

	58:28.360 --> 58:30.880
	that the nuclear war is not broken out.

	58:30.880 --> 58:33.760
	Do you think about this aspect

	58:33.760 --> 58:36.080
	from a game theoretic perspective in general,

	58:36.080 --> 58:38.440
	why is that true?

	58:38.440 --> 58:40.720
	Why in theory you could see

	58:40.720 --> 58:42.600
	how things would go terribly wrong

	58:42.600 --> 58:44.280
	and somehow yet they have not?

	58:44.280 --> 58:45.600
	Yeah, how do you think about it?

	58:45.600 --> 58:47.240
	So I do think about that a lot.

	58:47.240 --> 58:50.320
	I think the biggest two threats that we're facing as mankind,

	58:50.320 --> 58:53.320
	one is climate change and the other is nuclear war.

	58:53.320 --> 58:57.200
	So those are my main two worries that I worry about.

	58:57.200 --> 58:59.920
	And I've tried to do something about climate,

	58:59.920 --> 59:01.320
	thought about trying to do something

	59:01.320 --> 59:02.880
	for climate change twice.

	59:02.880 --> 59:05.040
	Actually, for two of my startups,

	59:05.040 --> 59:06.760
	I've actually commissioned studies

	59:06.760 --> 59:09.480
	of what we could do on those things.

	59:09.480 --> 59:11.040
	And we didn't really find a sweet spot,

	59:11.040 --> 59:12.680
	but I'm still keeping an eye out on that.

	59:12.680 --> 59:15.160
	If there's something where we could actually

	59:15.160 --> 59:17.800
	provide a market solution or optimizations solution

	59:17.800 --> 59:20.960
	or some other technology solution to problems.

	59:20.960 --> 59:23.360
	Right now, like for example,

	59:23.360 --> 59:26.760
	pollution critic markets was what we were looking at then.

	59:26.760 --> 59:30.040
	And it was much more the lack of political will

	59:30.040 --> 59:32.840
	by those markets were not so successful

	59:32.840 --> 59:34.640
	rather than bad market design.

	59:34.640 --> 59:37.080
	So I could go in and make a better market design,

	59:37.080 --> 59:38.600
	but that wouldn't really move the needle

	59:38.600 --> 59:41.160
	on the world very much if there's no political will.

	59:41.160 --> 59:43.600
	And in the US, the market,

	59:43.600 --> 59:47.520
	at least the Chicago market was just shut down and so on.

	59:47.520 --> 59:48.760
	So then it doesn't really help

	59:48.760 --> 59:51.040
	how great your market design was.

	59:51.040 --> 59:53.560
	And then the nuclear side, it's more,

	59:53.560 --> 59:57.560
	so global warming is a more encroaching problem.

	1:00:00.840 --> 1:00:03.280
	Nuclear weapons have been here.

	1:00:03.280 --> 1:00:05.720
	It's an obvious problem that's just been sitting there.

	1:00:05.720 --> 1:00:07.480
	So how do you think about,

	1:00:07.480 --> 1:00:09.240
	what is the mechanism design there

	1:00:09.240 --> 1:00:12.280
	that just made everything seem stable?

	1:00:12.280 --> 1:00:14.800
	And are you still extremely worried?

	1:00:14.800 --> 1:00:16.640
	I am still extremely worried.

	1:00:16.640 --> 1:00:20.040
	So you probably know the simple game theory of mad.

	1:00:20.040 --> 1:00:23.760
	So this was a mutually assured destruction

	1:00:23.760 --> 1:00:27.360
	and it doesn't require any computation with small matrices.

	1:00:27.360 --> 1:00:28.600
	You can actually convince yourself

	1:00:28.600 --> 1:00:31.480
	that the game is such that nobody wants to initiate.

	1:00:31.480 --> 1:00:34.600
	Yeah, that's a very coarse grained analysis.

	1:00:34.600 --> 1:00:36.880
	And it really works in a situational way.

	1:00:36.880 --> 1:00:40.400
	You have two superpowers or small number of superpowers.

	1:00:40.400 --> 1:00:41.960
	Now things are very different.

	1:00:41.960 --> 1:00:43.080
	You have a smaller nuke.

	1:00:43.080 --> 1:00:47.240
	So the threshold of initiating is smaller

	1:00:47.240 --> 1:00:51.520
	and you have smaller countries and non nation actors

	1:00:51.520 --> 1:00:53.760
	who may get a nuke and so on.

	1:00:53.760 --> 1:00:58.320
	So I think it's riskier now than it was maybe ever before.

	1:00:58.320 --> 1:01:03.320
	And what idea, application of AI,

	1:01:03.640 --> 1:01:04.640
	you've talked about a little bit,

	1:01:04.640 --> 1:01:07.560
	but what is the most exciting to you right now?

	1:01:07.560 --> 1:01:10.160
	I mean, you're here at NIPS, NeurIPS.

	1:01:10.160 --> 1:01:14.920
	Now you have a few excellent pieces of work,

	1:01:14.920 --> 1:01:16.680
	but what are you thinking into the future

	1:01:16.680 --> 1:01:17.840
	with several companies you're doing?

	1:01:17.840 --> 1:01:21.120
	What's the most exciting thing or one of the exciting things?

	1:01:21.120 --> 1:01:23.160
	The number one thing for me right now

	1:01:23.160 --> 1:01:26.360
	is coming up with these scalable techniques

	1:01:26.360 --> 1:01:30.440
	for game solving and applying them into the real world.

	1:01:30.440 --> 1:01:33.160
	I'm still very interested in market design as well.

	1:01:33.160 --> 1:01:35.400
	And we're doing that in the optimized markets,

	1:01:35.400 --> 1:01:37.560
	but I'm most interested if number one right now

	1:01:37.560 --> 1:01:40.000
	is strategic machine strategy robot,

	1:01:40.000 --> 1:01:41.440
	getting that technology out there

	1:01:41.440 --> 1:01:45.560
	and seeing as you were in the trenches doing applications,

	1:01:45.560 --> 1:01:47.120
	what needs to be actually filled,

	1:01:47.120 --> 1:01:49.800
	what technology gaps still need to be filled.

	1:01:49.800 --> 1:01:52.040
	So it's so hard to just put your feet on the table

	1:01:52.040 --> 1:01:53.800
	and imagine what needs to be done.

	1:01:53.800 --> 1:01:56.280
	But when you're actually doing real applications,

	1:01:56.280 --> 1:01:59.120
	the applications tell you what needs to be done.

	1:01:59.120 --> 1:02:00.840
	And I really enjoy that interaction.

	1:02:00.840 --> 1:02:04.480
	Is it a challenging process to apply

	1:02:04.480 --> 1:02:07.760
	some of the state of the art techniques you're working on

	1:02:07.760 --> 1:02:12.760
	and having the various players in industry

	1:02:14.080 --> 1:02:17.720
	or the military or people who could really benefit from it

	1:02:17.720 --> 1:02:19.040
	actually use it?

	1:02:19.040 --> 1:02:21.400
	What's that process like of,

	1:02:21.400 --> 1:02:23.680
	autonomous vehicles work with automotive companies

	1:02:23.680 --> 1:02:28.200
	and they're in many ways are a little bit old fashioned.

	1:02:28.200 --> 1:02:29.240
	It's difficult.

	1:02:29.240 --> 1:02:31.840
	They really want to use this technology.

	1:02:31.840 --> 1:02:34.640
	There's clearly will have a significant benefit,

	1:02:34.640 --> 1:02:37.480
	but the systems aren't quite in place

	1:02:37.480 --> 1:02:41.080
	to easily have them integrated in terms of data,

	1:02:41.080 --> 1:02:43.760
	in terms of compute, in terms of all these kinds of things.

	1:02:43.760 --> 1:02:48.680
	So is that one of the bigger challenges that you're facing

	1:02:48.680 --> 1:02:50.000
	and how do you tackle that challenge?

	1:02:50.000 --> 1:02:52.360
	Yeah, I think that's always a challenge.

	1:02:52.360 --> 1:02:54.520
	That's kind of slowness and inertia really

	1:02:55.560 --> 1:02:57.920
	of let's do things the way we've always done it.

	1:02:57.920 --> 1:03:00.120
	You just have to find the internal champions

	1:03:00.120 --> 1:03:02.120
	at the customer who understand that,

	1:03:02.120 --> 1:03:04.680
	hey, things can't be the same way in the future.

	1:03:04.680 --> 1:03:06.960
	Otherwise bad things are going to happen.

	1:03:06.960 --> 1:03:08.600
	And it's in autonomous vehicles.

	1:03:08.600 --> 1:03:09.680
	It's actually very interesting

	1:03:09.680 --> 1:03:11.120
	that the car makers are doing that

	1:03:11.120 --> 1:03:12.440
	and they're very traditional,

	1:03:12.440 --> 1:03:14.360
	but at the same time you have tech companies

	1:03:14.360 --> 1:03:17.120
	who have nothing to do with cars or transportation

	1:03:17.120 --> 1:03:21.880
	like Google and Baidu really pushing on autonomous cars.

	1:03:21.880 --> 1:03:23.240
	I find that fascinating.

	1:03:23.240 --> 1:03:25.160
	Clearly you're super excited

	1:03:25.160 --> 1:03:29.320
	about actually these ideas having an impact in the world.

	1:03:29.320 --> 1:03:32.680
	In terms of the technology, in terms of ideas and research,

	1:03:32.680 --> 1:03:36.600
	are there directions that you're also excited about?

	1:03:36.600 --> 1:03:40.840
	Whether that's on some of the approaches you talked about

	1:03:40.840 --> 1:03:42.760
	for the imperfect information games,

	1:03:42.760 --> 1:03:44.000
	whether it's applying deep learning

	1:03:44.000 --> 1:03:45.120
	to some of these problems,

	1:03:45.120 --> 1:03:46.520
	is there something that you're excited

	1:03:46.520 --> 1:03:48.840
	in the research side of things?

	1:03:48.840 --> 1:03:51.120
	Yeah, yeah, lots of different things

	1:03:51.120 --> 1:03:53.240
	in the game solving.

	1:03:53.240 --> 1:03:56.400
	So solving even bigger games,

	1:03:56.400 --> 1:03:59.760
	games where you have more hidden action

	1:03:59.760 --> 1:04:02.040
	of the player actions as well.

	1:04:02.040 --> 1:04:05.880
	Poker is a game where really the chance actions are hidden

	1:04:05.880 --> 1:04:07.080
	or some of them are hidden,

	1:04:07.080 --> 1:04:08.720
	but the player actions are public.

	1:04:11.440 --> 1:04:14.000
	Multiplayer games of various sorts,

	1:04:14.000 --> 1:04:18.080
	collusion, opponent exploitation,

	1:04:18.080 --> 1:04:21.280
	all and even longer games.

	1:04:21.280 --> 1:04:23.160
	So games that basically go forever,

	1:04:23.160 --> 1:04:24.680
	but they're not repeated.

	1:04:24.680 --> 1:04:27.880
	So see extensive fun games that go forever.

	1:04:27.880 --> 1:04:30.080
	What would that even look like?

	1:04:30.080 --> 1:04:31.040
	How do you represent that?

	1:04:31.040 --> 1:04:32.040
	How do you solve that?

	1:04:32.040 --> 1:04:33.440
	What's an example of a game like that?

	1:04:33.440 --> 1:04:35.600
	Or is this some of the stochastic games

	1:04:35.600 --> 1:04:36.440
	that you mentioned?

	1:04:36.440 --> 1:04:37.320
	Let's say business strategy.

	1:04:37.320 --> 1:04:40.840
	So it's not just modeling like a particular interaction,

	1:04:40.840 --> 1:04:44.440
	but thinking about the business from here to eternity.

	1:04:44.440 --> 1:04:49.040
	Or let's say military strategy.

	1:04:49.040 --> 1:04:51.000
	So it's not like war is gonna go away.

	1:04:51.000 --> 1:04:54.280
	How do you think about military strategy

	1:04:54.280 --> 1:04:55.520
	that's gonna go forever?

	1:04:56.680 --> 1:04:58.080
	How do you even model that?

	1:04:58.080 --> 1:05:01.000
	How do you know whether a move was good

	1:05:01.000 --> 1:05:05.200
	that somebody made and so on?

	1:05:05.200 --> 1:05:06.960
	So that's kind of one direction.

	1:05:06.960 --> 1:05:09.800
	I'm also very interested in learning

	1:05:09.800 --> 1:05:13.440
	much more scalable techniques for integer programming.

	1:05:13.440 --> 1:05:16.560
	So we had an ICML paper this summer on that.

	1:05:16.560 --> 1:05:20.280
	The first automated algorithm configuration paper

	1:05:20.280 --> 1:05:23.560
	that has theoretical generalization guarantees.

	1:05:23.560 --> 1:05:26.200
	So if I see this many training examples

	1:05:26.200 --> 1:05:28.560
	and I told my algorithm in this way,

	1:05:28.560 --> 1:05:30.560
	it's going to have good performance

	1:05:30.560 --> 1:05:33.200
	on the real distribution, which I've not seen.

	1:05:33.200 --> 1:05:34.840
	So, which is kind of interesting

	1:05:34.840 --> 1:05:37.680
	that algorithm configuration has been going on now

	1:05:37.680 --> 1:05:41.200
	for at least 17 years seriously.

	1:05:41.200 --> 1:05:45.000
	And there has not been any generalization theory before.

	1:05:45.960 --> 1:05:47.200
	Well, this is really exciting

	1:05:47.200 --> 1:05:49.840
	and it's a huge honor to talk to you.

	1:05:49.840 --> 1:05:51.160
	Thank you so much, Tomas.

	1:05:51.160 --> 1:05:52.880
	Thank you for bringing Labradus to the world

	1:05:52.880 --> 1:05:54.160
	and all the great work you're doing.

	1:05:54.160 --> 1:05:55.000
	Well, thank you very much.

	1:05:55.000 --> 1:05:55.840
	It's been fun.

	1:05:55.840 --> 1:06:16.840
	No more questions.