Artificial Intelligence, Real Competition with Liv Boeree and Matt Ginsberg

Can AI have a poker face? On this episode, Neil deGrasse Tyson and co-hosts Gary O’Reilly and Chuck Nice discuss poker and playing against...the machines with former poker player Liv Boeree and artificial intelligence expert Matt Ginsberg.

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

• 0:01Neil deGrasse Tyson?

  Welcome to StarTalk. Your place in the universe where science and pop culture collide. StarTalk begins right now. This is StarTalk Sports Edition. And today, we're titling this one, "The House of Cards." I got with me, of course, Chuck Nice, Jet. Hey Neil. Yeah, yeah, so though you are a stand-up comedian, you have tremendous sports fluency. So that's a good thing. Just want to put it out there, give you some props for that. It's probably the only fluency I might have. Okay. Too much information. Or because you're still working on that comedy thing, right? Yeah, exactly.

• 0:45Neil deGrasse Tyson?

  And I got Gary O'Reilly. Gary, former soccer pro from the UK. Thank, always good to have you here. Good to be here. Yeah, yeah, and I'm your host, Neil deGrasse Tyson, your personal astrophysicist. And let me just give a brief overview of where we're gonna go on this show. I'd like to say the stakes are high because the theme is poker. Poker. And... Red hot. Who's the famous poker? I guess James Bond would like his chips stirred, not whatever. No, he played baccarat. Shaken mustard. Oh, baccarat, that's right. He played baccarat. That's right. And of course, craps and maybe sometimes the roulette. But yeah, less on the poker table, I think. Yeah, yeah.

• 1:29Neil deGrasse Tyson?

  And so, we'll also find out what state-space complexity is. I don't know what that is. We're gonna find out. And whether Nash's theory of equilibrium, this is the famous mathematician from Princeton, for which he got a Nobel Prize, whether it's a key component in a winning hand. And we'll also wonder whether probabilities, and thinking about them, can be an asset in life. Are they just confusing and you should ignore them? And so, all this is gonna happen in this program. And we've got several experts. One of them in particular is Liv Boeree. Liv, welcome to StarTalk.

• 2:04Speaker 01

  Thanks for having me.

• 2:05Neil deGrasse Tyson?

  Yeah, yeah, you're our first guest today. And you're a TV presenter. I get your resume here. Writer, science communicator, love science communicators. We need more of them. Let's get more of them and then I could go to the Bahamas, okay? And leave you all. That's what I want here. You're a former championship professional poker player.

• 2:24Neil deGrasse Tyson?

  And an advocate of effective altruism. That's good. That's good. And also, of course, when you have that much stuff going for you, they get you on TED. You get a TED Talk here. So you had a first-class honors degree in physics and astrophysics. Love it. There it is. My people. My people going out to the world, the University of Manchester. And what else I hear? World Series of Poker and European Poker Tour champion? Have I seen you playing poker on TV? I must have.

• 2:52Speaker 01

  You might have done. I don't know. If you've watched, if you've watched a bit of it, then you might have seen me, yeah.

• 2:56Neil deGrasse Tyson?

  I have to see if you have shifty eyes here, just so I'll know.

• 2:59Speaker 01

  Definitely, well.

• 3:01Chuck Nice?

  Do you have, do you have a poker face, Liv? Is there, is there an actual poker face that you dawn while you're at the table?

• 3:10Speaker 01

  I mean, you're looking at it. Um, everyone always thinks that a poker face is about being completely stoic and robotic. Which, to be fair, like that, if you're starting out, that's a good way to conduct yourself at the table because it's just easier to maintain. But really, what a good poker face is, is just being relaxed and natural and showing that you're comfortable even when you're not.

• 3:35Neil deGrasse Tyson?

  Why not, why, why can't a poker face be the opposite of what kind of hands you got? If you've got a two, four, six and seven, right, all different suits, you look at it. And then you're like, that'll just freak everybody out, right?

• 3:50Chuck Nice?

  You just look around a table and be like, you gonna lose.

• 3:53Speaker 01

  Well, sure, you can do that, but the thing is, is that then, if, I'll very quickly catch on. It'll be quite obvious to your opponents that you're doing that when you have bad hands and you're doing a scared face when you have good hands. So we'll just, we'll just adapt accordingly.

• 4:06Neil deGrasse Tyson?

  No, you just have, you just have a portfolio of faces you draw from.

• 4:10Speaker 01

  Well, that's the thing. If you can, if you can randomize well, uh, between, you know, okay, this, in this situation, I'm gonna be fifty, I'm gonna be fifty fifty doing, or seventy percent of the time doing my scared face and thirty percent of the time doing my happy face. Um, and you can perfectly randomize that. Then, yes, you're being unpredictable. But in reality, it's very hard for humans to, to be.

• 4:30Chuck Nice?

  To randomize.

• 4:31Neil deGrasse Tyson?

  What'll happen is you'll get baseball coaches, given the probabilities of what you're doing per hand. And then they'll talk about, you know, because they have people who shift places on the field. If you always pull the ball, they're all on the left side of the field, right? And so I, I keep thinking, why don't you just punch it the other way? And you get a base hit every time, but then they would learn that and then compensate. So there, there you have it.

• 4:52Speaker 01

  There's probably an Ash equilibrium for that.

• 4:54Neil deGrasse Tyson?

  Yeah, we'll get to that in a minute, but tell me first, what is state space complexity?

• 4:59Speaker 01

  Oh, state space.

• 5:00Neil deGrasse Tyson?

  I don't know. I don't know what it is.

• 5:01Speaker 01

  Uh, state space complexity is, uh, one of many different measures of the complexity of a of a situation. Specifically, it's it's used to describe the complexity of games. Um, it's it's one measure of that. So what it technically means is like the number of possible, uh, states that a game can legally be in from start to finish. So, you know, you're playing tic-tac-toe. Um, it's a grid of three by three. Um, that corresponds to around, I think the somewhere around seven hundred or so possible moves. So that's its state, state space complexity is seven hundred. And then a game like chess, um, with its, you know, it's a eight by eight

• 5:42Speaker 01

  board, that has a state space complexity of about ten to the forty. So as you can imagine, because of combinatorics, the numbers scale up really fast, depending on sort of the number of possible positions and the different types of pieces and so on.

• 5:55Neil deGrasse Tyson?

  So that's why four-year-olds can play tic-tac-toe and and not chess?

• 5:59Speaker 01

  Right. Yeah, it it generally corresponds to sort of, and not always to the difficulty of the game, but certainly for the difficulty of the game, um, when it comes to building an AI to play it. Um, and if you sort of look at the history of of AI, um, over time, where it's been pitted against humans in different types of games, um, it's it's the the more simple the game, the earlier it was either solved or sort of, you know, the best humans were defeated. So like tic-tac-toe, um, was technically solved by a computer back in like nineteen fifty-two, I think. Um, Connect Four was somewhere in the nineties, and then chess, uh, Deep Blue, uh, beat Garry Kasparov in like ninety-seven. And then

• 6:42Speaker 01

  there was a big gap, and then you might have seen the the AlphaGo documentary. But that that one was a big deal, because Go, its state space complexity is ten to the one seventy. So just-

• 6:52Neil deGrasse Tyson?

  That is crazy, huh?

• 6:54Speaker 01

  Astronomical.

• 6:54Chuck Nice?

  Alright, so now what would poker be?

• 6:56Neil deGrasse Tyson?

  Wait, wait, just I have to put in, wait, wait, wait.

• 6:58Chuck Nice?

  Go ahead.

• 6:58Neil deGrasse Tyson?

  I have to put in.

• 7:00Chuck Nice?

  Go ahead.

• 7:00Neil deGrasse Tyson?

  The, the name Google, when spelled correctly, is the number, you know, ten to the hundredth power.

• 7:07Speaker 01

  Yes.

• 7:08Neil deGrasse Tyson?

  So if, if anybody's gonna solve something that's got at least ten to the hundredth things going on in it, it's gonna be the company Google.

• 7:15Speaker 01

  Good point, yeah.

• 7:15Neil deGrasse Tyson?

  And Google, G O O G O L.

• 7:17Chuck Nice?

  I was gonna say, but don't they spell it wrong?

• 7:19Neil deGrasse Tyson?

  Yeah, they spell it wrong.

• 7:20Chuck Nice?

  They can't even spell it.

• 7:22Neil deGrasse Tyson?

  That was early, when they didn't know how to spell it. Now they don't, now that is how you spell it.

• 7:27Chuck Nice?

  There you go. Oh my god, that is so true.

• 7:30Neil deGrasse Tyson?

  That's how you do that.

• 7:31Chuck Nice?

  That is how you spell it now. Oh my god, that's hilarious.

• 7:35Neil deGrasse Tyson?

  So tell me about the Nash theory of equilibrium. Uh, when I was at Princeton, spent some years there. So I just see him walking, not talking to anybody, just kind of with his head down, sort of bobbing in, in contemplation.

• 7:46Speaker 01

  Solving the mysteries of the universe.

• 7:47Neil deGrasse Tyson?

  Yeah, yeah, yeah. So, so tell me, how does that, how does Nash theory of equal, Nash equilibrium? Cause, alright, it's one thing to know the state complexity of a game, but so what?

• 7:57Speaker 01

  Right.

• 7:58Neil deGrasse Tyson?

  Okay, AI needs to know about it, fine. But now you wanna strategize, given that fact. So, tell us about the Nash theory of equilibrium.

• 8:06Speaker 01

  Yeah, so, a Nash equilibrium is basically, the, the way you would describe, like, um, you know, say you and I are playing poker against one another, just in a one-on-one game. In theory, there's-

• 8:19Neil deGrasse Tyson?

  I will, I would lose, clearly I would lose, just put that up.

• 8:22Speaker 01

  Well, maybe, maybe over the, maybe over the, the long term, but you could win in the short term. Cause there's a bit of luck in there, which we'll discuss later, but, um, so if we're playing, um, technically, there's a strategy, a sort of, a strategy that I could employ, where it's so, i- it's so sort of perfect, that, um, your only option is to adopt a similar strategy against me. And when we're both doing that, um, we're basically breaking even against one another over, over the long run. And- It means, and the reason why it's an equilibrium is because it is not, there is no other strategy that either one of us could sort of try and do in order to improve our situation.

• 9:07Speaker 01

  So it's basically a sort of stalemate effectively. And what it means is basically we're unable to exploit one another any further and we're going to be breaking.

• 9:16Neil deGrasse Tyson?

  Is Nash equilibrium applied only to people interacting in games and other sort of people-based systems? Or does, because then just call it the Nash stalemate, right? But equilibrium, equilibrium has a lot of use, that word means a lot in physics, you know? And so to hear that physics term get used at a poker table, I find a little disturbing.

• 9:36Speaker 01

  Well, it's, it's, it doesn't have to be between people. It can be between, technically it's between agents. So an agent could be an AI. It's anything that is.

• 9:44Chuck Nice?

  Right. It can be two computers playing one another.

• 9:46Neil deGrasse Tyson?

  Okay.

• 9:47Chuck Nice?

  And if they're programmed the same way, then they've reached that stalemate position, right?

• 9:53Speaker 01

  Right, exactly. And so what it is, it's just basically a mathematical solution. I don't know if it's technically like a local minima, I guess, and possibly a maxima. But the point is, is that it, all it means is that neither one of us could try and do something differently to what we're doing and expect to make more money. In fact, if we did that, the other one would now be able to start exploiting us because they're technically playing this perfect strategy.

• 10:16Neil deGrasse Tyson?

  No, I've been hogging you this whole time, Chuck.

• 10:19Chuck Nice?

  No, I, I just wanted, no, right. No, I was just intrigued by that conversation you guys were having because with, with respect to poker, there is always an X factor. So every hand has a built-in probability because there's only a certain number of cards, right? So if, let's look at the equilibrium. You and I are exactly matched. The X factor is how I'm able to manipulate you psychologically.

• 10:48Speaker 01

  Nope.

• 10:49Chuck Nice?

  How do you account for that?

• 10:50Speaker 01

  Nope.

• 10:51Neil deGrasse Tyson?

  Wait, Chuck, did you just invent the X factor here in this conversation? Or is this a known?

• 10:54Chuck Nice?

  No, I'm just inventing it.

• 10:55Speaker 01

  He's just making it up.

• 10:58Chuck Nice?

  I'm making this up right now.

• 11:00Neil deGrasse Tyson?

  The X Factor, okay, check.

• 11:02Speaker 01

  No, it's by definition, it's already encompassing any possible X factor you could think of. Otherwise, if, if, if, if I hadn't encompassed it, if there was something that you could do, some X factor, you could suddenly pull out, um, to exploit me, then I'm not playing a perfect, I'm not playing what's called game theory optimal. Um, I forgot to mention that before. So it's called a game theory optimal strategy, um, where you're unexploitable by your opponent. And the only thing you can do.

• 11:27Chuck Nice?

  So this works in a laboratory because in real life, I could never know you so well as a human being that I could account for every single thing that you might do. However, for the most part, it, you know, you, you can get really, really close. But could I, are you saying that human beings, just like machines, can be predictable right down to the thought that you were going to have?

• 11:59Speaker 01

  Well, it's not so much about, it's, it's not, if you're playing game theory optimal, or an AI is playing game theory optimal poker, it's, it's not saying that it can perfectly predict what the opponent's, uh, what their opponent's gonna do. It's not making a statement about that. All it's saying is that it's playing a strategy that has, without getting too much into the weeds, it's like a, it's, it's perfectly randomized, uh, between like bluffing and not bluffing, depending on the, each little sort of minutious, uh, decision point, um, whereby the opponent cannot take advantage of that. Um, so it's not, it's not that it's perfectly, um, and, and, and it works, it works, it's, it's actually independent of the type of player you're playing against. But the interesting thing is, just because you're playing game theory optimal,

• 12:43Speaker 01

  uh, and so therefore you're not, um, you're not, your opponents aren't able to exploit you. What it also means is that you could be missing out on certain things that they are doing. Uh, like, say we're playing rock, paper, scissors, and you don't know anything about me. The, the best, you know, your, your, your best option is to just perfectly randomize between the three, because that way, you're not, I, I can't predict anything that you're going to do. You just use a random number generator and, and, and thirty-three percent each different thing. Um, but if we're doing that for a while, and then you notice that actually I, I throw rock every time. Well, now you'd be stupid to carry on randomizing because you're missing out on this opportunity to throw paper every time and exploit my dumb play. So that's sort of this difference.

• 13:28Speaker 01

  And that's a situation where you'd want to deviate from your past sort of game theory optimal strategy in order to sort of capture all this this value that I'm losing by being an idiot.

• 13:37Gary O'Reilly?

  So that's the strategy.

• 13:38Neil deGrasse Tyson?

  But it refers to two perfect players, then. I mean, that's the thing. That's what I was saying.

• 13:42Speaker 01

  Yes, if to be in a pure equilibrium, then yes, that's what you need.

• 13:45Neil deGrasse Tyson?

  Yeah, yeah.

• 13:46Gary O'Reilly?

  So that that's the framework of your strategy. The real world is on the clock. So how long does it take you to put this into effect successfully?

• 13:56Speaker 01

  Well, I mean, for me, I mean, I'm retired now, so I am far if I went to sat down at a poker table, I'm I'd be far behind the curve of what the, you know, the latest, you know, the the people who are getting the closest to playing a game theory optimal strategy. But even interestingly, no human can actually perfectly play it. In fact, no computer can even perfectly play it. You can only sort of asymptotically approach it, kind of like, you know, the speed of light. And so in terms of like, how long would it take me to get to the level these days of a world, you know, a true world-class player who's who's still sort of playing and studying all the game theory optimal solutions? I mean, I don't even know if I could, to be honest, these days. I've been out the game too long.

• 14:37Speaker 01

  But, I mean, it would take it would take probably a few years of intense, intense studying. And I think the interesting thing about poker is that since we discovered that there are these game theory, game game theory optimal solutions, you sort of mathematical solutions, the the whole sort of style of the game has very much changed. And when I got into it back in two thousand and five, it was still very much a sort of a black box. No one really understood what the mechanics of the game really were. And it was just very much more a sort of people-reading intuitive game where people with sort of the the most sort of street smarts and and human experience were often the best, just because they could pick up on weird quirks of human behavior better than their opponents. But

• 15:21Speaker 01

  Then, since the lid has been lifted and we've seen the mathematical workings of the game, largely due to sort of improvements in computation and software. Now, the best players are the ones who just mimic computers, basically, and play in this very mathematical, semi-robotic style. And there is less of this intuitive people-reading. Although, there is still some.

• 15:43Neil deGrasse Tyson?

  That is a weird fact that I think requires a pause and even perhaps a moment of silence. Okay, we play poker, we invent a computer that's better at poker than we are, and then we imitate the computer. That's just, just let us give a moment of silence for the human mind there. Because we are no longer the best thinkers on this. What we invented as a thinker is the best thinker.

• 16:12Chuck Nice?

  Right. And what Neil is really saying is, let us have a moment of silence for the death of the human race.

• 16:21Chuck Nice?

  Because it's over for you, it's over.

• 16:24Neil deGrasse Tyson?

  That's the beginning of the overlord.

• 16:26Chuck Nice?

  There you go.

• 16:27Speaker 01

  We're moving into the Nova scene, as Jaywalking calls it, yeah.

• 16:32Gary O'Reilly?

  So, if you're playing, everybody seems to play poker online. So therefore, you don't need a poker face, right? Because you're not looking at people, or are you? And then, basically, you've got no chance of physical tells. There's no, oh, have a twitch, they scratch their nose, whatever. Is that why it's all disappearing, because poker's played online so much now?

• 16:51Speaker 01

  Um.

• 16:52Neil deGrasse Tyson?

  Oh, good one.

• 16:53Speaker 01

  Yeah, great question. Um, to an extent, yes. Um, as you correctly said, you can't physically see your opponent. So there is none of that type of information. And so that forces you to rely on the more mechanical information, such as like the amount that they bet, the types of cards that they bet on. Um, the only sort of... Read you can make in terms of sort of something physical is how long they take to click the button. You know, you can see how long they think and that can be information like, oh, they really thought for a long time on this one, whereas they normally act really quickly. That can be informative to an extent.

• 17:26Neil deGrasse Tyson?

  Could you give us, we're about to bring in an AI expert on in-game theory. What has been your experience with AI before we make that transition to our next guest?

• 17:36Speaker 01

  Um, my, my experience with AI is just one of silent obsession. Um, I've just been fascinated with the concept of particularly superintelligence. Um, sort of for the last 10 years, really, um, following, uh, I mean, I was introduced to it through the effective altruism community who are.

• 17:59Neil deGrasse Tyson?

  Tell me about that. What, how does that relate to you? This is a, a, a not-for-profit that you started? Is that correct? Do I understand that correctly?

• 18:06Speaker 01

  Yeah, so I mean, a few poker players and I started a, a not-for-profit that sort of operates under the effective altruism principles. So those principles basically are, you know, we, we, there's limited resources, both time and money, that both any individual or any society or group could ever donate to philanthropic causes. And because these resources are limited, um, it's crucially important that we sort of take a really, we take a step back and, and sort of take stock to figure out what are not only the biggest problems, but the most urgent and the most neglected, uh, the most comparatively neglected, to ensure that the money is, is donated to actually the best

• 18:47Speaker 01

  place where, you know, there's either the strongest evidence or the highest probability of having, you know, the, the optimum impact. Because unfortunately, um, most philanthrop, by far, the majority of philanthropy has at least historically been very, um, Sort of emotion driven and reactionary and hasn't had

• 19:09Neil deGrasse Tyson?

  And a little bit vanity driven, right? It's a person's pet project and they want to solve it and they they're not thinking about math when they do this.

• 19:16Speaker 01

  Exactly. It hasn't, it deeply needed a scientific approach. Um, and fortunately, in the last 10 years, uh, sort of combination of scientists and business people, um, actually, it was a couple of hedge fund managers, really, who got it going.

• 19:32Neil deGrasse Tyson?

  Cause they got the money. Yeah, yeah.

• 19:33Speaker 01

  Well, sure, but they've got the money, but they've also got the, they're statisticians at heart and they care about data and they care about evidence. And, you know, no one bats an eyelid at a business trying to ensure that it, you know, gets the biggest bang for its buck. So why is it sort of, why do people find it strange to think that charities should also try and get the biggest impact for buck, um, particularly when, you know, when we give to one place, that means we're not giving to somewhere else. And if that other place you would have given to was actually going to save 10 times as many lives for the same donation, it's kind of a tragedy, if you ask me, if we don't give to the right place. So based on this sort of general concept, um, there are, there are a number of charities that have been

• 20:15Speaker 01

  identified as being by far the most cost effective in terms of like, they will say, you know, your money will just go so much further if your goal is to improve people's lives.

• 20:23Neil deGrasse Tyson?

  This makes so much sense. I mean, it's embarrassing. We all should be embarrassed that it wasn't around long before this. And, you know, and, and this is, everyone out there, study your math, okay?

• 20:36Speaker 01

  Amen.

• 20:37Neil deGrasse Tyson?

  Don't ever say, I'll never need this again. Well, maybe you will, not that you'll need it, but you'll want it to do something innovative, like Liv Worry has done.

• 20:46Chuck Nice?

  And the other thing you could do, Liv, is just figure out a way to, uh, Robin Hood, uh, Jeff Bezos. Just steal from him on an annual basis, an amount, an amount of money that he will not realize is missing.

• 21:00Neil deGrasse Tyson?

  Yeah, just a few billion, that's all.

• 21:02Chuck Nice?

  And give it to people who need it.

• 21:03Speaker 01

  I mean, if, well, if, but specifically if, if he with his philanthropy, um, not only, you know, it's one thing for him to do more, but even more crucially is for him to make sure that it goes to the most uh, cost effective places.

• 21:18Chuck Nice?

  That is the most, right.

• 21:19Speaker 01

  And that's the key thing. And a lot of times, because, um, You know, so much pressure is put on like, no, do something now, give away this now. And it's like, well, it would surely be better for someone to take to take a year to figure out, okay, where do I get 100x improvements as opposed to give it all now just because of like social pressure. So, you know, it's not as simple as give it all away now, Mr. Bezos.

• 21:40Neil deGrasse Tyson?

  Okay, so Chuck, this is how I'd rather use the X factor. She said 100x factor, 100x improvement? That's an X factor.

• 21:48Neil deGrasse Tyson?

  In fact, you can rename it the X factor is mysterious.

• 21:51Speaker 01

  Yes.

• 21:52Neil deGrasse Tyson?

  Yes. Quantified X factors. There you go. Guys, we got to take a short break. And so we have to say goodbye. Oh, we don't want to say goodbye, not to Olivia. Okay, stay with us because AI expert and friend of StarTalk, Matt Ginsberg, will join us on the other side. And we'll be talking about competitive AI when StarTalk Sports Edition continues.

• 22:34Neil deGrasse Tyson?

  We're back. StarTalk Sports Edition, House of Cards is the title with Chuck Nice and Gary Riley. Guys. Hey. Chuck, Chuck, you're tweeting at Chuck Nice comment. Thank you. Yes, that is correct, sir. Yes. And, and, and Gary, my three left feet, we're still sticking with that no matter what. We are, no matter what. All right. In this segment, we're picking up AI and trying to see what role that plays in, in, in gaming, in, in, in probability, in making decisions. And we're going to juxtapose that with all that we learned from Liv Boeree's.

• 23:17Neil deGrasse Tyson?

  Liborri's commentary about playing poker. And I wonder if she and Matt Ginsberg are like the nemesis of each other. Right? Because they both come at the same problems, but they could be competitors because there's gaming is a big part of this. Well, it's bringing Matt Ginsberg, Matt. Welcome back to StarTalk.

• 23:37Speaker 04

  Hey, Neil. Great to be back.

• 23:39Neil deGrasse Tyson?

  Yeah, and you've got this this resume where you like a young PhD at Oxford. That's kind of cool, although I think we have an over fascination with young precocious kids. Because no one, when you're older, no one says, boy, that was amazing. You did that before you were 40, you know, that doesn't, you know. I mean, why we have such a fascination, I'll never know, but it's there. And we had it with you at age 24, getting a PhD from Oxford in mathematics. Very cool. And you studied computer science at Oxford, of course, in the UK, and you're scientist.

• 24:13Speaker 04

  I studied, I was a mathematician and a physicist.

• 24:17Neil deGrasse Tyson?

  Mathematical physicist.

• 24:19Speaker 04

  I am so old that when I was a student, you couldn't study computer science. It was not yet a discipline.

• 24:25Neil deGrasse Tyson?

  Man, that's old.

• 24:27Speaker 04

  That is really old, older than dirt, as my kids tell me.

• 24:31Neil deGrasse Tyson?

  So the abacus, you would grease the abacus.

• 24:34Chuck Nice?

  So you had to invent computer science so other people could study it?

• 24:39Speaker 04

  I, the first computer and I date to about the same.

• 24:44Chuck Nice?

  Really? Oh.

• 24:46Speaker 04

  Yeah, the first, the first commercial.

• 24:48Neil deGrasse Tyson?

  That commercial, I mean, your birth.

• 24:50Speaker 04

  I'm just too old, yeah.

• 24:52Neil deGrasse Tyson?

  Actually, my wife's PhD is in mathematical physics, so.

• 24:55Speaker 04

  Cool.

• 24:56Neil deGrasse Tyson?

  I get my dose of that when she, when she wants to think about the world with that as a lens. That's, that works real good. So you've provided statistical support for sports teams, something we covered in a earlier visit that you've granted us. And we loved your name of your crossword puzzle algorithm. Please tell us the name.

• 25:21Speaker 04

  Dr. Phil.

• 25:22Neil deGrasse Tyson?

  That is the best name ever. Ever F I L L.

• 25:28Speaker 04

  That was my second choice. And my first choice, I asked a bunch of crossword constructors what I should call this program. And my first choice was actually Deep Clue.

• 25:37Chuck Nice?

  Oh.

• 25:39Neil deGrasse Tyson?

  Good. So what happened to that?

• 25:40Speaker 04

  IBM wouldn't let me do it.

• 25:41Chuck Nice?

  I was gonna say.

• 25:42Neil deGrasse Tyson?

  Because of Deep Blue?

• 25:44Speaker 04

  Yes, of Deep Blue.

• 25:46Neil deGrasse Tyson?

  No, but that would be, that would create resonance. The idiots there, but, but, you know.

• 25:51Speaker 04

  Anyway, so Dr. Phil was, Dr. Phil was a very close second, and that's what we wound up calling.

• 25:56Neil deGrasse Tyson?

  Okay, all right. So you're a specialist in AI?

• 26:00Speaker 04

  I am.

• 26:00Neil deGrasse Tyson?

  And general solver of hard problems, and you're now gainfully employed at Google. So tell me about how you think about the Nash equilibrium when you either program AI to do what you need to do, or when you're thinking about gaming in general?

• 26:18Speaker 04

  For me, even Nash equilibrium stuff, that's just a tool. So when you look at the work I do on statistical support for selecting a play in the NFL, you're really solving a fairly simple game with a relatively simple payoff matrix. You know, if I do a passing play and they call a blitz, what do I expect will happen? So you have this little payoff matrix and you're just, and you know.

• 26:43Neil deGrasse Tyson?

  Wow, so it's much simpler than, than games. It's much simpler than full up games that have, you know, 10 to the 100 possible states of existence, right?

• 26:53Speaker 04

  I think it's fair to say that if you're trying to invert a matrix 10 to the 100 on a side, you're, you're done.

• 27:02Speaker 04

  So you have to, whatever problem you're solving, you have to reduce it to something that's computationally tractable.

• 27:08Neil deGrasse Tyson?

  All right, so, so when we think of poker, there's a, you know, bluffing is a fundamental part of it. So if AI plays poker, does AI bluff?

• 27:20Speaker 04

  Absolutely.

• 27:21Neil deGrasse Tyson?

  Or does it have to? Or does it have to?

• 27:23Speaker 04

  Of, of course it has to. If you don't bluff a poker, you, you suck. So.

• 27:29Neil deGrasse Tyson?

  Yeah, but part of how I bluff is I try to read my opponent. I'm not playing the cards, I'm playing my opponent. Can AI do that?

• 27:36Speaker 04

  Potentially.

• 27:37Neil deGrasse Tyson?

  Or is it just calculating things?

• 27:38Speaker 04

  Potentially it can. I think, I mean, there's been a lot of work on poker and AI. A lot of it, a lot of the foundational work involved translating the poker problem into this linear optimization problem with, I believe, billions of variables, and then just solving it because you can. It's not 10 to the 100, 10 to the 9 is big enough. So you solve this giant.

• 28:04Speaker 04

  Optimization problem and it says, okay, if you're in this situation in terms of betting and you hold these cards, this is how you should bet and it doesn't know that it's a bluff. But it'll say, even though, you know, you've got unsuited two, four, you should still bet a lot with this fraction, you know, this fraction of the time. So there's your bluff. And it's coming out of the fact that you're looking for a, you know, an optimal strategy.

• 28:31Neil deGrasse Tyson?

  Oh, wow.

• 28:32Speaker 04

  According to the Nash definition.

• 28:34Neil deGrasse Tyson?

  Right. So an optimal strategy could include a bluff is the point.

• 28:37Speaker 04

  It has to, right? If you, if you never bluff, then your opponent who, I mean, one of the nice things about the Nash optimum is that if you tell your opponent, I am playing by the Nash optimum, he can't exploit you. So he can play against you for days and see, oh, he's just playing the Nash optimum. Well, I can't exploit that. But if you're not playing the optimal strategy, he will play against you and wow, it's like this guy never bluffs.

• 29:02Neil deGrasse Tyson?

  And that's the, there's the point of exploitation, right?

• 29:05Speaker 04

  Correct.

• 29:06Neil deGrasse Tyson?

  Interesting, interesting. Wow. That is. Interesting. I, I, I don't want to hog Matt's questions. Chuck, Gary, what do you have for him?

• 29:13Gary O'Reilly?

  Okay. So we build an AI program to play poker. Simple enough. Are we cheating?

• 29:17Neil deGrasse Tyson?

  Wait, wait. So we're asking here, if you were assisted by AI in a competition, in a poker competition, does that count as cheating? And, and more broadly, how does AI play out in whether, in the rules of games about whether people will declare someone's cheating or not? Interesting. What's the arc of that, of that, of that line of thinking?

• 29:35Speaker 04

  So if you're using an AI to help you.

• 29:38Neil deGrasse Tyson?

  Yeah.

• 29:39Speaker 04

  And you don't disclose it, you're, yes, you're cheating.

• 29:42Neil deGrasse Tyson?

  Okay.

• 29:43Speaker 04

  Cause you're not supposed to. It's like, it's like, you know, going into a high jump with rockets on your shoes. You can't do that. I think the interesting question.

• 29:50Neil deGrasse Tyson?

  With hidden rockets.

• 29:51Speaker 04

  With hidden rockets on your shoes. I think the interesting question is whether. The organizers should allow it. So you go in and you do have a computer assistant and you say, hey, I'm being assisted. Or conceivably, I just am a computer. What should the organizers do? Should they let you play or should they not let you play?

• 30:09Neil deGrasse Tyson?

  Yeah, but but put one interlocutor in there and you have coaches attached to computers on the sideline, giving instructions to active players who themselves are not using the AI, but the coaches are. Well, we do that in football. Same thing. Doing a football and basketball. That's my point. Isn't that the same thing as you're describing?

• 30:25Speaker 04

  That's using the computer and not telling anybody. Just because you have some guy in the middle. I mean, it's like there's this, um, there's a scene in Galaxy Quest, where Sigourney Weaver, her job is to, when the computer says something, she repeats it to the, to the crew.

• 30:41Neil deGrasse Tyson?

  Right.

• 30:41Speaker 04

  And she says, it's a stupid job, but somebody's got to do it. And, and if you're doing, you're still, they're still using the computer. And the fact that Sigourney Weaver's in the middle doesn't, that doesn't matter. So if you have a computer that's telling you something and you've got a guy with a walkie talkie passing the information in, you're still using a computer.

• 30:59Neil deGrasse Tyson?

  Yeah, but that's allowed, apparently. I don't think anyone's prevented that from the beginning. They suppose they could have. No computer help. No one ever said that. They just, and, and, and by the way, even without the computer, they'd have somebody doing analytics. Baseball is rich with that as a history. So, so what if a computer now does it? And yeah, it's AI, but so what?

• 31:18Speaker 04

  Well, there's a difference between doing something offline and doing something in the game. So in a chess game, for example, you adjourn a chess game and now everybody's going to run to their computer and do.

• 31:29Neil deGrasse Tyson?

  Is that allowed? Is that allowed?

• 31:31Speaker 04

  It's allowed. Um, but what you can't do is you can't have an earbud and somebody's telling you what a computer thinks you should do. That's not allowed.

• 31:39Neil deGrasse Tyson?

  This seems artif, that seems like an artificial, um, uh, rule boundary on this. If I can, on a break, I say I gotta go pee and I go, no, that would be cheating. But if there's an official break and, because I saw this in the Queen's Gambit, right? They all went and analyzed the games. All right, that's before computers, but you get other experts there. And that's the same thing. That's the same thing. And, and what happens? The boxer between rings sits down and the coach tells him, hit him here, not there. That, and, and so, is an AI just not a fundamentally different thing? It's just a matter of degree?

• 32:12Speaker 04

  I think. The answer is, is sort of, but I think the distinction between as the game is going on and during a break, I think is an important one because the bottom line is you can't police the breaks. You can't tell somebody, we're going to adjourn this chess game, come back tomorrow and don't use a computer. How do you enforce that? How do you know? Whereas it's relatively easy to say, we don't want you using your computer while you are playing. That's, that's much easier to, then you can say, you know, now take that thing out of your ear. We don't like it. But we, you can't say, you know, don't go to bed tonight. Don't go home. We're just have a camera on you. It's too much. So I think that's a rational way to do it.

• 32:55Gary O'Reilly?

  I'm just thinking, do the casinos have their own AI programs? So when I, if I played poker online with AI, that they can work out, I'm cheating.

• 33:04Neil deGrasse Tyson?

  Actually, Gary, I, I love that, but I want to pick that up in the next segment because we just ran out of time for this.

• 33:08Gary O'Reilly?

  With pleasure.

• 33:09Neil deGrasse Tyson?

  Yeah. So, so when we come back, we're going to talk about whether the house can use AI so that you don't, people like Matt don't walk in there and exploit their ignorance because of his smarts. When StarTalk continues.

• 33:37Chuck Nice?

  Time now for a Patreon shout out to the following Patreon patrons. Ricardo Torres, Mason Dixon, and Alireza Safat. Without you, what would we do? Well, we probably wouldn't make as good a show. So thank you. And for those of you listening who would like your very own Patreon shout out, please go to patreon.com slash StarTalk Radio and support us.

• 34:10Neil deGrasse Tyson?

  We're back, StarTalk. We've got Chuck Nice, I've got Gary O'Reilly, and Matt Ginsberg. Matt Ginsberg is our resident, well, he's not in residence, but he's in arm's reach of StarTalk. He's our AI expert with his new gig at Google. But we've got him on here to just sort of unpack gaming and what role AI might play in the present and future of gaming. And Gary, I cut off one of your questions. Why don't you put that back on the map?

• 34:35Gary O'Reilly?

  So, do the big casinos, when you play online, have their own AI program to patrol to make sure guys like me don't use my own AI program to win?

• 34:45Neil deGrasse Tyson?

  So, it's the AI police for the AI?

• 34:47Gary O'Reilly?

  Yeah.

• 34:48Neil deGrasse Tyson?

  What's to prevent them from using AI? Right? To make sure you never win?

• 34:53Gary O'Reilly?

  And how long before we're in the weeds?

• 34:55Speaker 04

  So, let me tell you what I know, and I don't know the answer here. I do know that they worry about this. So, there was a guy who was just mopping up on one of the online poker sites, making money at a phenomenal clip, and they went crazy trying to figure out what he was doing. And there was an assumption that he might be a computer or using a computer. And it turned out it was just a guy who was really good at poker, and he won for a while, and then didn't go so well. But they're very concerned about that. On the chess sites, it is a substantial problem where somebody comes along and they're using a computer, and you can't tell. They just have a computer running in the background, and they're playing chess online, and they're screaming everybody. And there is a lot of interest in protecting

• 35:40Speaker 04

  the other players from that. So, computers have a particular style of play, and you look for that. One of the things computers do in chess, for example, is they almost never play really fast. Even if they have just one move, they somehow, for some reason, the software is arranged, so it's going to take them a little while to get out of check the only way they can. And if you see somebody who just never plays fast, that's sort of a tip-off that he may be using a computer. So, people try and figure out...

• 36:11Speaker 04

  I mean, they're aware it's a problem and they try and figure out what they can do about it. It's a problem in computer bridge, in, in online bridge. There, you're not looking at computers, you're looking at people just cheating by calling each other up. Hey, I have the queen of spades.

• 36:24Neil deGrasse Tyson?

  Yeah.

• 36:26Speaker 04

  And, you know, and you gotta figure out if that makes sense. So, so in all of these cases, whether you're using an AI and not disclosing it, whether you're talking to your partner, you're cheating and finding cheaters has always been hard and I think always will be. But people want to do it.

• 36:40Neil deGrasse Tyson?

  So, so one, let me push back a little bit with you on the chess setups. Because as I understand it, when you play a game, you, you are presumably honest about what your chess rating is, and then you play other people that have approximately that rating. And if you wipe the floor with everyone because you're assisted by a computer, your rating goes up, you can no longer play those people and it keeps going up until they will force you into a place where other people will have an equal chance of beating you because you're playing people with an equal rating. Because the rating tracks your success. So isn't that a self-limiting fact that prevents AI from running rampant in chess sites?

• 37:20Speaker 04

  Well, computers are way better than we are at chess. So if I wanted to do this, I would go to chess.com and I'd start an account and it would rate me at 1300. And let's say I can't play chess at all. And I would, on the side, I'd have a computer and I'd say to the computer, hey, play at 1500.

• 37:35Neil deGrasse Tyson?

  Right.

• 37:36Speaker 04

  And then I would do what the computer says and my rating would go up to 1500. And then I'd tell the computer, hey, play at 1700. And I would always be a little bit better than my opponents.

• 37:46Neil deGrasse Tyson?

  And there you are eating lunch while all this is happening.

• 37:49Speaker 04

  Yeah.

• 37:49Neil deGrasse Tyson?

  As you ascend. Okay, yeah.

• 37:52Speaker 04

  And I would just march up the ranks.

• 37:54Neil deGrasse Tyson?

  So, okay, so Matt, you've thought way too much about cheating. I'm a little worried about you. I worry. We'll, we'll come visit you in prison, okay?

• 38:01Speaker 04

  Okay, that would be good because I'm gonna be bored.

• 38:03Neil deGrasse Tyson?

  Yeah.

• 38:07Chuck Nice?

  And he was like, and by prison, I hope you mean Fiji, because that's where I'll be.

• 38:14Gary O'Reilly?

  Kind of like an AI version of Andy Dufresne. Sitting there, just plotting and doing the governor's books.

• 38:22Neil deGrasse Tyson?

  Wait, just to be clear, Andy Dufresne from

• 38:25Speaker 04

  Shawshank Redemption.

• 38:27Neil deGrasse Tyson?

  The novel written by Stephen King, and the movie, which most people saw.

• 38:32Speaker 04

  And I just wanted to be equally clear that I have no desire to be in a prison somewhere where the warden is a horrible sadist and all I can do is think about artificial intelligence while I wait for the guard to beat me up. That is not a future that I really look forward to.

• 38:51Neil deGrasse Tyson?

  But one way the guard doesn't beat you up, that one will be okay.

• 38:55Speaker 04

  It's better.

• 38:56Neil deGrasse Tyson?

  That was a list of seven offenses there. All right, so Gary, did we come through for you?

• 39:02Gary O'Reilly?

  Yeah, absolutely. I mean, if AI, I mean, AI will be better at poker than humans. I mean, I kind of think we understand that now. So when we watch poker tournaments, are people going to care if AI is playing?

• 39:18Speaker 04

  So the World Chess Championship remains incredibly popular. People care how good Magnus Carlsen is. And

• 39:26Neil deGrasse Tyson?

  Because they're not giving a ticker tape parade to an AI algorithm.

• 39:29Speaker 04

  But I will tell you, I don't get it. So my son just started playing chess and he's having a great time playing chess and he's playing chess all the time online. And I'm like, but computers are so much better than, and he says, Dad, I don't want to be the best. I just like playing chess. It's fun. And I like watching other people play chess. I think, you know, watching Usain Bolt run was amazing. Not, and it was just amazing. And there are, there are things that move faster than Usain Bolt, but watching him was just, it was like almost a privilege just to watch.

• 40:06Neil deGrasse Tyson?

  You know, Matt, I have to deeply agree with you there. And not that this is the first time I have agreed with you. Because when you think about the vicarious participation in something, you want another human being to do the act because you're a human. And my analogy here is the space program, right? It matters that a human being takes a step on the moon. Did you know that we had landers on the moon before that? But did anyone celebrate that? We knew it in the science community and the space community, but we were watching for the humans because they can come back and tell a story. You could touch them and you could, like I said, you could put them in a parade.

• 40:47Neil deGrasse Tyson?

  So I gotta go with you on that, that there's real value to knowing it's one of your own, your own species in a high-performance act.

• 40:57Chuck Nice?

  But what about the fact that now we have computers that can experience and describe and tell that same story in a very human fashion, the same way we do?

• 41:10Neil deGrasse Tyson?

  I'm still not giving it a ticker-tape parade, damn it.

• 41:19Speaker 04

  I think the thing to realize is how different computers are than we are. So when a computer plays chess, it's not playing like we do. When a computer plays poker, solves a crossword, plays bridge, they're just not solving things like we do. And I think Neil is right. That makes it harder for us to relate to them because they're aliens. And I think there's also a wider message, which I think is much more important. And that is, there are things we're good at and there are things machines are good at, and they're going to be different things. And we're going to be able to solve problems working together that we can't solve by ourselves. I think that's incredibly important going forward.

• 42:02Neil deGrasse Tyson?

  We are the world, we are the children. Let's solve the problem together.

• 42:08Speaker 04

  Well, I'm sorry to sound like that, but I think that's how it is.

• 42:12Neil deGrasse Tyson?

  You totally sound like that.

• 42:13Chuck Nice?

  All right, well, what do you think about this then? So what do you think about this then, in light of what you just said? The grafting of that same technology into human beings so that our intuition, our method of thinking...

• 42:31Chuck Nice?

  is enhanced and augmented by the sheer computational power of an artificial intelligence. And

• 42:41Neil deGrasse Tyson?

  we'll just make our mistakes that much faster.

• 42:42Chuck Nice?

  Oh, my God. That is horrifying what she just said.

• 42:49Chuck Nice?

  Oh, my God, that is so scary what you just said.

• 42:53Speaker 04

  So this is this is this is sort of interesting, and I think it's certainly worth trying. But if somebody said to me, I have

• 43:03Speaker 04

  a horse and it has all these amazing properties. And I have a dolphin and it has all these other amazing properties. So let's make a horse dolphin. That might not be the right thing to do.

• 43:18Chuck Nice?

  Oh, but I want you to do it.

• 43:20Speaker 04

  You want to use a horse and for the dolphin stuff, you want to use a dolphin.

• 43:23Neil deGrasse Tyson?

  Well, they did that with the camel. The camel, that's a hybrid. It's an idea hybrid, right?

• 43:28Speaker 04

  Sometimes, sometimes, yes. But sometimes you really want to, you know, we're good at what we're good at. And I have no idea, you know, Elon Musk wants to put microprocessors in our in our heads. And I, I think I would, I'm going to be very interested to see what happens when you put a microprocessor in someone else's head. But I don't want to be early here.

• 43:52Neil deGrasse Tyson?

  You're not a first adopter.

• 43:53Speaker 04

  No, I'm not down for that.

• 43:54Gary O'Reilly?

  Okay, Matt, your program, Dr. Phil, crossword solver. But language is organic. It's always developing, right? So how do you program something to develop with something that incorporates slang and just goes in random different directions?

• 44:13Neil deGrasse Tyson?

  But just to be clear, not everywhere does language develop. I mean, France has a has a has an official board of language that restricts the entry of some words and protects the presence of others. So you talk about a place where free where communication channels are free to utter any syllables you want to mean whatever anyone else thinks it means. Right? So AI would not do this at all. So there, that's why we're better than AI because we can make stuff up.

• 44:43Neil deGrasse Tyson?

  Trying to find out how we can still hold on to our dignity here.

• 44:46Gary O'Reilly?

  Well, just think, Neil, words that were slang words, street words from the 50s, 60s, weren't in the lexicon maybe a few years afterwards, but now it's almost like a common part of a sentence. So we are involved, our language evolves through street slang and it becomes incorporated.

• 45:04Neil deGrasse Tyson?

  I see what you're saying. Right, it elevates its way up. Yeah.

• 45:06Gary O'Reilly?

  Yeah.

• 45:07Neil deGrasse Tyson?

  So in a crossword puzzle, if I write a puzzle and I have a clever word that's recently invented, but not yet in all the formal dictionaries, but everyone conversationally knows what I'm talking about, your Dr. Phil is gonna miss it, isn't it?

• 45:20Speaker 04

  That is correct, and it'll be a little bit behind. So there was a puzzle recently where that included the fill GPSs. I think it was in the crossword tournament this year, and GPSs wasn't GPSes, that's how you spell it. And that wasn't in Dr. Phil's dictionary, and he couldn't get it.

• 45:37Gary O'Reilly?

  Right.

• 45:37Speaker 04

  And it viewed it as this random sequence of letters. Now, it can say, well, I'm so sure about the crossing words that I'm, it's gotta be a G, it's gotta be a P, it's gotta be an S, it's gotta be an E and an S. So I don't know what GPSs is, but I'm sticking it in because it just has to be.

• 45:54Neil deGrasse Tyson?

  Everything else works.

• 45:54Speaker 04

  Everything else works. So it's also the case that Dr. Phil's, you know, one of the things that happened this year with Dr. Phil is I started working with the natural language group at Berkeley. And they brought in this, it's a machine learning, natural language processing tool to do sort of question answering. That is constantly getting trained on new crosswords, new data, new books, new everything. So you do see AI evolving, and you see tools like this evolving.

• 46:26Neil deGrasse Tyson?

  So it's not a static dictionary.

• 46:27Speaker 04

  And it's not, nothing, nothing is static.

• 46:30Chuck Nice?

  And, and by the way, it would, the same way that we become exposed to colloquialisms, the AI would be in that same position too. It might be a little behind, but at some point, it's gonna have an exposure as well.

• 46:45Speaker 04

  And if you, yeah, and if you look at the work that, you know, Apple does with Siri, when you ask Siri a question, that Siri, that question goes to Apple. And it's part of their database of uttered language. So when you use slang, off it goes. And the first time somebody asks Siri a question that has some weird word in it, Siri's gonna have no idea, but then it just keeps getting, keeps showing up. This word keeps showing up.

• 47:09Chuck Nice?

  Right.

• 47:09Speaker 04

  So Siri eventually figures out what it means.

• 47:11Chuck Nice?

  And is it true, is it true that like, well, all the search engines who use the natural language, that when they ask you back, did you mean, like when you put something in, it's because they're actually learning if that's how people ask for stuff?

• 47:27Speaker 04

  A little bit, mostly when they say, did you mean, it's because they don't know.

• 47:31Chuck Nice?

  They don't know?

• 47:31Speaker 04

  And it's, and their natural language module said, oh, he might be mean this, and he might mean that, and I don't know which one.

• 47:38Chuck Nice?

  Okay.

• 47:38Speaker 04

  And I'm just gonna ask him. And then they're, they're just trying to do it.

• 47:42Gary O'Reilly?

  Okay, Matt, let me ask you, because I think I heard something, what you just said in an answer, AI doesn't have the capacity to guess, it has to be accurate?

• 47:53Speaker 04

  So I don't, so I think, one of the Berkeley guys actually put this incredibly well, machine learning systems don't know what they don't know.

• 48:04Speaker 04

  So I remember, I, so in the crossword tournament, the, um, the humans solve the puzzles and then they get passed into a room where they get, they get scored. And I typically do some of the scoring as well as running Dr. Phil. And I remember once, I got a puzzle back, and it was done incredibly quickly, because you'd grade it on speed. And I got this puzzle to grade, and the guy had solved it in like a minute and a half. And then it had one corner that was just all random and wrong. And I remember grading it and thinking, this guy's an idiot. He took a minute and a half to solve the puzzle and he surely knew that this corner was totally wrong. And I looked at it and it was Dr. Phil's puzzle. It was my puzzle.

• 48:48Speaker 04

  Oh my gosh, I'm the idiot. But then the problem was that Dr. Phil had solved this corner completely incorrectly and had no idea it was wrong. It doesn't know it's guessing. It just says, well, this is the best answer I could come up with, so I'm gonna go with it. And I don't know if it's right, but it's, I think it's right. It's probably right.

• 49:07Neil deGrasse Tyson?

  One of my favorite clues that I've ever seen, it was four letters and the clue was to come in second.

• 49:15Speaker 04

  Lose.

• 49:15Neil deGrasse Tyson?

  Yeah, it's lose. Lose. But that wasn't where my first urges were. Come and say, is it win, place and show? Is it this, this? And we don't think of the person coming in second as losing. Because I guess it's the, we want everyone to get a trophy or a medal or something. I think in ancient Greece, there was no second or third. You either won and you didn't win in the original Olympics.

• 49:36Speaker 04

  You're not, second place is the first loser. I mean, that's really, that's all there is to it. And that's how I was.

• 49:44Neil deGrasse Tyson?

  Tough crap. That's the, Jerry Seinfeld has a whole bit on that. You know, of all the losers, you came in first.

• 49:56Neil deGrasse Tyson?

  So Matt, tell me about random numbers and their role in this. Do we have a perfect random number generator yet, or are they just good enough?

• 50:06Speaker 04

  We certainly have perfect random number generators, right?

• 50:08Neil deGrasse Tyson?

  No, I thought that was not possible.

• 50:10Speaker 04

  Well, cosmic ray detectors are perfect.

• 50:13Neil deGrasse Tyson?

  Oh, fine, fine. Okay. Fine. But not in a computer.

• 50:17Speaker 04

  We will never have a perfect synthetic random number generator.

• 50:20Neil deGrasse Tyson?

  That's what I was asking. Okay.

• 50:21Speaker 04

  But, but you can come very close. So for example, you can have a clock that measures time in billions of a second. And you can look at the last three digits of that clock. To start your random number generator. And that's, that's almost random. It's hard to imagine it being meaningfully correlated with anything else.

• 50:41Neil deGrasse Tyson?

  Right, right. And so this matters when you're calculating probabilities in your gaming software. Isn't that correct?

• 50:48Speaker 04

  Yes, because there are times when you need a random number generator. Going back to the very, the example we started with Nash equilibrium. You know, what you want is you want to be, say, you want to run a passing play 30% of the time and a rushing play in 70% of the time. You need to flip a coin and you need to decide, do I go with the 30 or do I go with the 70? And you have to generate a random number. So anytime you're, you know, in a poker player, obviously you can't bluff all the time. You can't bluff none of the time. You need a random number generator. But I think we, we have random number generators that appear to be good enough.

• 51:21Neil deGrasse Tyson?

  Okay. So that's one, I just wanted, that's, that's an update for me on the state of that, of that effort. But guys, I think we're like actually ran out of time on this subject. Speaking of random numbers. Which is tragic. Yeah. Which is tragic. Run out of random numbers. We just put in the last random number ever. Anyway, Matt, are you active on social media? Can you remind us?

• 51:42Speaker 04

  Um, I, I don't understand social media. I, I mean, I wrote a book, Factor Man, that I try and promote on social media. I will certainly tweet about the fact that we did this today. And that's, that's about as, as clever as I get. I don't have Facebook.

• 51:57Neil deGrasse Tyson?

  And how do we find you? Is it just Matt Ginsberg?

• 51:59Speaker 04

  It's mattginsberg.com.

• 52:00Neil deGrasse Tyson?

  And dot com. And, and your Twitter handle? What do you have?

• 52:05Speaker 04

  It's Matt L Ginsberg. And.

• 52:07Neil deGrasse Tyson?

  Matt Ginsberg, okay.

• 52:09Speaker 04

  And I think Matt Ginsberg was taken. And that's.

• 52:12Neil deGrasse Tyson?

  Okay.

• 52:13Speaker 04

  That's all I know.

• 52:14Neil deGrasse Tyson?

  All right. And so, uh, good luck at Google. Sometimes you need a little bit of that too.

• 52:17Speaker 04

  Thank you. I, yes, much appreciated.

• 52:20Neil deGrasse Tyson?

  All right, guys. Gary, always good to have you. Chuck. And, and, and Matt, thanks for being a good support with us.

• 52:25Speaker 04

  It's always fun.

• 52:26Neil deGrasse Tyson?

  Literally and figuratively. And you know, we'll come back to you because this is a hot topic.

• 52:29Speaker 04

  Cool.

• 52:30Neil deGrasse Tyson?

  All right. This has been StarTalk Sports Edition. Neil deGrasse Tyson, keep looking.