Winning Money with AI – with Matt Ginsberg

Can artificial intelligence predict the future? On this episode, Neil deGrasse Tyson and co-hosts Chuck Nice & Gary O’Reilly explore algorithms, computing, and how to win Warren Buffett’s March Madness money and beyond with AI 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 of course, I've got Gary O'Reilly.

• 0:19Gary O'Reilly?

  Gary. Hi Neil.

• 0:22Neil deGrasse Tyson?

  Gary is the only street cred we have for this programming, having been a professional athlete himself and a professional sportscaster over in the UK in the soccer realm. Yes. And Chuck Nice, always good to have you here, man.

• 0:36Chuck Nice?

  Always a pleasure.

• 0:37Neil deGrasse Tyson?

  Yeah, the one person who has done no organized sports in his entire life.

• 0:41Chuck Nice?

  I mean, actually, you could have just said, "has done nothing."

• 0:45Neil deGrasse Tyson?

  Nothing.

• 0:46Chuck Nice?

  And just left it there. And you would have been spot on.

• 0:51Neil deGrasse Tyson?

  Spot. So Gary, March is looming.

• 0:58Gary O'Reilly?

  Yes.

• 0:59Neil deGrasse Tyson?

  And so what do you have in store for us today?

• 1:00Gary O'Reilly?

  All right. Perfect. A little bit of March Madness. The NCAA's March Madness, college basketball is approaching. And if you work, and I'll say this in an English way, and then again in American, if you work for Berkshire Hathaway, or Berkshire Hathaway, right, you nail a bracket, because it's all about brackets, brackets, brackets. You get some of Warren's money.

• 1:23Neil deGrasse Tyson?

  Really?

• 1:24Gary O'Reilly?

  Yes.

• 1:25Neil deGrasse Tyson?

  Who? He said this?

• 1:27Gary O'Reilly?

  Mm-hmm. So you have to be a company employee. Sometimes they open it up to the public, but generally, it's a closed shop for Berkshire Hathaway employees only. You can get up, you can get a hundred thousand if you do a decent job. Or if you nail a perfect bracket, you get a million dollars a year for life.

• 1:45Chuck Nice?

  Right. In other words, in other words, play the lottery, people. Just go with your state lottery.

• 1:53Neil deGrasse Tyson?

  Chuck, Chuck, I think, I think.

• 1:58Neil deGrasse Tyson?

  I think he knows nobody's gonna win that. So he's letting you do it and he ends up with more money at the end.

• 2:03Chuck Nice?

  Yeah, that's my point. It's like you got a better chance of winning your state lottery than you do of picking a perfect bracket.

• 2:10Gary O'Reilly?

  So that's just it. How do you pick a perfect bracket? Now, if it's beyond our human brains to achieve that, and we know Warren's a very, very wealthy man because he doesn't give his money away. So he's holding on to his money, but is there a way? Is there a way to break the system, break the bank and get Warren's dough? Possibly, but for that, we need an AI expert.

• 2:33Neil deGrasse Tyson?

  I know of only one person for that, and he's a friend of StarTalk, has appeared before. And that's one and only Dr. Phil.

• 2:47Neil deGrasse Tyson?

  We have to explain that in a moment. Matt Ginsberg, Matt, welcome back to StarTalk, dude.

• 2:51Speaker 03

  Thank you. It's always a blast to be here.

• 2:53Neil deGrasse Tyson?

  Yeah, so we call you Dr. Phil because those in the know know that you programmed a computer to solve crossword puzzles and you named that program Dr. Phil. That was just brilliant. Phil, F I L L, brilliant. And so.

• 3:09Chuck Nice?

  How's that working out for you?

• 3:12Chuck Nice?

  I see you making crossword puzzles.

• 3:19Speaker 03

  So Dr. Phil, beat all humans and it and I retired from crossword.

• 3:27Chuck Nice?

  Oh.

• 3:27Neil deGrasse Tyson?

  Yeah, well, once you've beaten every single human, it's time to go on to something else, right?

• 3:32Speaker 03

  Well.

• 3:32Neil deGrasse Tyson?

  So you got to. I need to get a pedigree out here. Let me finish the man's pedigree, please. Okay, he's got a PhD in mathematics from none other than Oxford University.

• 3:43Chuck Nice?

  Yay.

• 3:43Neil deGrasse Tyson?

  And I don't think that's Oxford, Mississippi. I think it's Oxford.

• 3:46Chuck Nice?

  Oh, no.

• 3:47Neil deGrasse Tyson?

  UK, a scientist, entrepreneur, author, and you're specialist in AI, especially where it comes to solving hard problems.

• 3:59Chuck Nice?

  Wow.

• 3:59Neil deGrasse Tyson?

  Rather than just sort of taking over the world. And now gainfully employed at Google. So what did they have you doing at Google? By the way, of course, you're working at Google.

• 4:08Chuck Nice?

  Yeah.

• 4:08Neil deGrasse Tyson?

  Of course. Right, we could have guessed that.

• 4:10Chuck Nice?

  Well, you know, I'm building Chromebooks.

• 4:14Chuck Nice?

  That's a terrible misallocation, terrible misallocation of resources. That's all I can tell you. They brought me in, I'm an AI specialist, and they were like, all right, we need you to build some Chromebooks.

• 4:25Neil deGrasse Tyson?

  Yeah, because everyone at Google is brilliant, and he's at the low end.

• 4:28Speaker 03

  So I actually, I repair Chromebooks. I don't get to build Chromebooks. I'm aspiring so that a few years from now, if I do a good job repairing Chromebooks, they'll move me up to actually building Chromebooks. And in my spare time at Google, I work for Google X, which is a moonshot factory. We basically try and solve incredibly hard problems, any one of which is likely to fail. But between them all, we expect to deliver a reasonable amount of useful technologies to the real world.

• 4:59Neil deGrasse Tyson?

  So that's a name inspired presumably by the X Prize, which is money given to impossible tasks solved by brilliant people.

• 5:07Speaker 03

  I, I, they haven't told me where it came from. I think it's been around. It may have been around for a long. I do, I do want to say.

• 5:16Neil deGrasse Tyson?

  I'm betting, no, trust me on that one. I think I got that one.

• 5:19Speaker 03

  I think you probably did. And I, I just want to say that all of this disblaming, you know, Chuck saying, I have never played a sport, I, he sounded like he's never even watched a sport. When I was at Oxford, I was the captain of the bridge team. And we actually tried to get bridge recognized as a real sport so that we could get Oxford Blues and put them on our shirts and be official athletes. They were having none of it. So Chuck, you and I are in the same boat here as far as.

• 5:51Chuck Nice?

  Okay. Well, then I'm in very good company. I, I feel better.

• 5:56Neil deGrasse Tyson?

  All right, so tell me then about game theory broadly, AI specifically invoked to predict brackets in athletic events. And the most famous of all brackets, as Gary said, is the March Madness with college basketball. You know, when I think of game theory, I think of elements of chance and how you can sort of maximize those to your favor. But how much do we think of athletic contests as exercises in chance?

• 6:29Speaker 03

  I think, so I was thinking, as Gary introduced this topic, and he said, you know, can we use AI to build a perfect bracket? And I can see, I can save a lot of time here. No. That's it. No. And

• 6:46Neil deGrasse Tyson?

  Okay, that's the end of the show. Thank you.

• 6:48Speaker 03

  That's great. This has been a real blast. So I think, I think what you have to understand, when you're betting, whether you're betting against Berkshire Hathaway, or you're betting against Vegas, or you're betting against your buddy who lives down the street, you have an opponent. And AI can make you better. But when you try and make a perfect bracket, your opponent at some level is like, God. You're actually trying to predict the future, not better than the next guy, but perfectly. AI can't make you better than God, or as good as God

• 7:27Speaker 03

  or anything. But what it can do is it can make you potentially better than the guy who lives down the street, so that you rate to make money. It can make you maybe better than the guys in Vegas who are quoting me up. So you go to Vegas and you rate to make money. And I think that's where we can potentially add value.

• 7:48Neil deGrasse Tyson?

  Okay, very important point to distinguish there. Just being better than your opponent versus having all knowledge of all of creation.

• 7:57Neil deGrasse Tyson?

  You just want to make some money tomorrow, that's all. So could you explain just in, just in as few words as possible? What?

• 8:07Neil deGrasse Tyson?

  What do brackets mean? Just, I don't know why someone who would be listening to this program would not know about brackets, but just, I'm an educator, I just want to make sure we're all on the same page. Describe the concept of brackets.

• 8:19Speaker 03

  So the March Madness tournament, they're roughly speaking, they're sixty fourteen. And it's a single elimination, so every game, one team is eliminated. If you want to get down to a winner, you gotta play sixty-three games to eliminate sixty-three of the sixty-four teams. So they divide it and they try and set it up so that better teams are playing worse teams, at least initially. And that way, the better teams sort of have an easier path and they get to the end and the better teams keep playing. And if you can predict who's gonna win each of these sixty-three games, you nailed the bracket, you guys.

• 8:58Speaker 03

  If you get one wrong, a team that you thought was gonna be eliminated plays on and you didn't get the bracket right.

• 9:06Speaker 03

  So if every game were a coin toss, they're asking you to predict sixty-three coin tosses in a row correctly. And your chances of doing that are, you know, two coin tosses, it's one in four, three, it's one in eight. Sixty-three, it's one in some giant number that I'm not even gonna try and compute.

• 9:28Neil deGrasse Tyson?

  You can say it, it's astronomical, say it.

• 9:31Speaker 03

  It's astronomical, Neil, it's just astronomical.

• 9:36Neil deGrasse Tyson?

  Thank you. So, so if, so if they have sixty-four teams, you know, normally we see it like as a diagram, there's like thirty-two teams on one half, thirty-two on the other half. And then they whittle down and then you have sixteen and sixteen, eight and eight, four and four, two and two, one and one. So these are, this is the bracketing, is that how we can think about it?

• 9:58Speaker 03

  It is, and people, people think of it as the first, you're gonna play sixty-three games, the first thirty-two games are the first round. It sort of happens simultaneously. After those thirty-two games, thirty-two teams have been eliminated. You've gone from sixty-four teams to thirty-two. The second round, you can play sixteen games simultaneously. That brings you down to sixteen teams. The Sweet Sixteen, then the Elite Eight, and then the Final Four, and then the Finals, and then the

• 10:28Neil deGrasse Tyson?

  But if you know that one team is gonna win, you don't care about who else loses, right? So why do you have to predict all 64 games correctly?

• 10:34Speaker 03

  Because that's the only way, that's the only way Warren will give you his money, is if you get them all right. So picking who's gonna win, that's not so bad. I mean, at worst, that's one in 64. It's random, you have a one in 64 chance. But picking who's gonna win every game, that's 63 coin tosses, which is, I think it's called astronomical.

• 11:00Neil deGrasse Tyson?

  Okay, I just calculated the odds if all each were a coin flip, what it would take to predict 64 outcomes. So I get basically two times 10 to the 19th power. So that's A lot, a lot. The answer is that's a lot. That's, that's, that's, sorry. So that's 20, let me get it, 20 quintillion is what that is. One in 20 quintillion.

• 11:33Speaker 03

  Yeah, that sounds right.

• 11:35Neil deGrasse Tyson?

  All right, so I'm on the right page, I'm on the

• 11:37Speaker 03

  You're on a good page.

• 11:38Neil deGrasse Tyson?

  Chuck, Gary, you take it over. Yes.

• 11:40Gary O'Reilly?

  All right. So if we say that 20 quintillion is just too much for us to really grapple with, what will we need, Matt, to understand a copper hen, to build a system to do really well at brackets? Is there, are there some fundamentals that you have to bake in to an algorithm to be able to bring forward something that would have some success?

• 12:09Speaker 03

  So my question is, what does really well mean, right? And this is why your opponent, Matt. If really, really well means getting it all right and getting buckets money.

• 12:20Speaker 03

  The answer is pick another project. It's just too hard.

• 12:23Gary O'Reilly?

  Yeah.

• 12:24Speaker 03

  If doing really well means getting enough of them right that you win money in Vegas, now we can talk. Because now there's all sorts of stuff I can tell you about. So in terms of why it's so hard, imagine you've got two reasonably evenly matched teams playing. And you can really predict it. You can look at the individual matchups and you can say, oh, this is actually going to go really well for these guys. You look at who's injured. But what you don't know is that halfway through the second quarter, somebody is going to slip on a little bit of moisture on the floor and twist their ankle and be out.

• 13:05Speaker 03

  You can't predict that. But the game can easily hinge up. So there is just stuff that makes this more like a coin flip that you just can't figure out in advance. Now, the good news is both teams are probably equally likely to slip on the coin.

• 13:23Speaker 03

  So if you conclude team one is 80% to win the game, then team one probably is about 80% to win. But if you have to get 63 right in a row, 80% isn't good enough.

• 13:35Speaker 03

  Because you just need to be sure.

• 13:37Gary O'Reilly?

  Is there no way, Matt, to factor in? I mean, in a minute, I'm going to ask you what an algorithm is. Because I think we all, me, if I look at it from my own personal point of view, I can say algorithm. I know they're used, they're in my smartphone, they can dictate trends on a YouTube channel, all those things. But what is an algorithm? We talk about it, but I talk about it, but I don't know exactly what one is. What you would bake into an algorithm? Do you have to have the outcome set in your mind in advance and then build it backwards from there? So if we've dismantled it like a car engine, what do we get with an algorithm?

• 14:16Speaker 03

  So an algorithm is simple, right? You're just telling an idiot, a computer, but fundamentally an idiot, telling an idiot a sequence of steps to take to get the answer to some problem.

• 14:33Neil deGrasse Tyson?

  A really competent idiot, yes.

• 14:35Speaker 03

  Well, it's a, it's, I don't know the competent, they're not, humans aren't really actually that competent. You can't ask him anything sort of coherent, but they're really fat, but they really have going for them.

• 14:46Neil deGrasse Tyson?

  My favorite, my favorite line for computers from decades ago was, a computer doesn't do what you want it to do. It does what you tell it to do. Yeah, what you program it to do, and that's not always what you want it to do. Yeah.

• 14:57Speaker 03

  So John McCarthy was one of the people who invented AI. And he said AI was the attempt to get machines to do badly what people do well.

• 15:08Chuck Nice?

  That's brilliant.

• 15:12Neil deGrasse Tyson?

  Guys, we're going to take a quick break, but when we come back, we'll pick up that conversation on algorithms. But we also want to know in what detailed way is AI operating to make these decisions? When StarTalk Sports Edition returns.

• 15:38Neil deGrasse Tyson?

  We're back, StarTalk Sports Edition. We're talking about bracketology, if there is even such a word. We have a friend of StarTalk, Matt Ginsberg, who's all about AI and betting and winning bets, not against God, apparently. This was first disclosed in this episode, but against your opponents. So if you beat God, that's a whole other kind of, what else you got cooking in your, in your basement there? So let's pick up the algorithm question. Gary, where were you? Where did we leave off?

• 16:06Gary O'Reilly?

  Well, from my own point of view, I mean, I look at it, I say, I'm familiar with algorithms, but I don't actually know what one does, how it works, why it works. So I just wondered if we had that. Seeing as how it's difficult to play God with an, as an algorithm and produce perfect brackets. Maybe we could get Matt to just break down a little bit of what an algorithm can do and what, why it does it and how it does it.

• 16:33Chuck Nice?

  And, and how can it be predictive? It's one thing to have the machine do what you tell it to do. But how do you tell it to predict something that maybe has not happened?

• 16:47Speaker 03

  So the thing, the thing to remember here is that, like I said, algorithms are incredibly simple. They're just doing what you tell them to do. I could say, I want you to predict the webinar. And if I lived in the Sahara Desert, I could write an algorithm. It would say sunny and hot. Every day, just say sunny and hot, you'll be fine. But if I live in Pittsburgh, where it's notoriously difficult.

• 17:21Speaker 03

  You need much more sophistication. And somebody else decides, I want to use this algorithm to predict the weather. The algorithm is just a series of steps. The fact that it happens to predict the weather is sort of magical. It's what makes one algorithm better than the other. If you decide, I want to predict who's going to win the Super Bowl.

• 17:46Speaker 03

  You need an algorithm. You're going to put in all these steps. And at the end of the day, it's going to generate an answer. If the algorithm is good, it'll tend to predict the Super Bowl. If the algorithm is bad, it'll predict randomly. If it's incredibly bad.

• 18:05Neil deGrasse Tyson?

  The two elements of this, one of them is, are you doing the right calculations with the available data? Another one is, do you have all the data you need in order to predict with accuracy? So, do you know at any given time, if you have all the proper information, you're just not smart enough to stitch it together to make the prediction?

• 18:22Speaker 03

  You always know that you don't have all of the information. So if you go back to my example.

• 18:30Neil deGrasse Tyson?

  That's very humble of us all, yes.

• 18:32Speaker 03

  You don't know that there's moisture on the floor. You'd love to know that there's moisture on the floor. You'd love to know where it is. But you don't know that. Because depending upon where it is on the court, one team is going to be significantly more likely to get hurt than the other. I would love to know that. I would love to know that so and so was out partying last night and didn't tell the coach. I'm not going to know that. So there's always a ton of information that is inaccessible. And the question is, how good a job can you do with the information that you do have? And as AI has gotten more sophisticated, it has become, the algorithms that the AI scientists produce

• 19:12Speaker 03

  have become more capable at drawing effective conclusions from the data to which they had access. But they're still just doing a bunch of specific steps. They have no idea what's actually going on. They're just add register five to memory location thirty seven and put the result in memory location eighty four. Then and they have a huge

• 19:39Neil deGrasse Tyson?

  Don't forget to carry the two.

• 19:41Speaker 03

  And they have an astronomical number of steps like this that they're executing sequentially. And at the end of the day, one of the steps says, report the answer. So they report the answer. And then the next step probably says, wait until something happens. Question. But they're just running steps. Now, one of the interesting questions is whether we're actually doing the same. Can you take the view that our brains are actually just neurons firing? And the neurons don't know what's going on. So it's very curious. You know, we could just be running.

• 20:14Neil deGrasse Tyson?

  Okay, but Matt, I I'm distinguishing what you just said for an algorithm duly answering Gary's question from what I think of, at least in my romanticized view of AI, where the AI writes its own damn algorithm and doesn't even need you.

• 20:28Chuck Nice?

  Uh oh, talking about sentience now.

• 20:31Speaker 03

  So

• 20:32Chuck Nice?

  This is when they take over.

• 20:33Speaker 03

  So a division of Google called Deep Love has just produced code that will write code. And on the internet, there are these programming competitions where people go and they they write some code to solve a problem that's described in English. And these guys at Deep Mind have written a program that actually reads the English description and produces code. And it's about average in terms of how good it is. So the people entering these programming competitions, it's about average. I don't, again, it doesn't know what it's doing. Does that matter? Maybe.

• 21:12Neil deGrasse Tyson?

  Well, then it's not intelligence. It's just a fast human brain doing calculations.

• 21:16Gary O'Reilly?

  So if. Code can write code now and we call them machine learning, right? At some point, it will evolve to write better code, just like we've evolved to do better things with ourselves. Let's go back to Warren Buffett's money. If we're not getting our hands on Warren, this is all about the dough, all right? If we don't get our hands on Warren's money, what if we take a trip to Vegas with our nicely polished algorithm? Are we going to fare any better? Rather than try and predict the sixty-three coin tosses, why don't we just go in there and have a little bit of a smart move on that part of the world?

• 21:58Chuck Nice?

  Because they'll break your knees.

• 22:02Gary O'Reilly?

  You know what, Chuck? Here's my thought. If I, my algorithm, right? I've invented my algorithm in the garage and I'm really proud of it and I'm going to take it to Vegas and I'm going to be really clever. Do you think the guys in Vegas don't have their own garages and their own algorithms? And so here we go, Matt. If I started to use an algorithm to win against the bookies, will, do they have their own Sentinels, their own AI Sentinels, waiting for guys like me to turn up?

• 22:34Speaker 03

  They certainly, Chuck is sort of right. If you start winning money regularly and in large quantities, yes, they will notice. They absolutely will notice and they will be very concerned. I'm not saying they'll break anybody's knees, or that they won't, but they certainly will notice and they'll threaten about it.

• 23:00Neil deGrasse Tyson?

  I'm going with Chuck on this one. They're going to break your knees.

• 23:02Gary O'Reilly?

  Yeah, I'm pretty sure my knees are going to be in a bad place.

• 23:06Chuck Nice?

  Not even right now, because now they're using auto shufflers to make it almost impossible for you to count cards, because they shuffle after every hand with eight decks in the shoe. But before that... Even card counters, they knew you were counting cards because the eye in the sky was like, "That guy right there keeps winning. All right, now let's go and focus on him even tighter, and let's give him more scrutiny." And so you would have to be kind of a manipulative genius to win enough and then lose enough, and then win enough, and then over a period of time, you walk out with the money. And who can really do that?

• 23:49Speaker 03

  So there's a distinction here that I think is incredibly important that we're not making. And one is, and it's the difference between beating blackjack, where you are taking money from the house, and going in and playing poker, where the house is just taking, they're raking, you know, they're taking a small rake off every hand. And if you win money, you're winning it not from the house, but from the other player. And sports is-

• 24:14Neil deGrasse Tyson?

  OPM, other people's money.

• 24:15Speaker 03

  Other people's money. And sports is in this weird limbo. Because let's say that for some reason, everybody, and I literally mean everybody, wants to bet on the Bengals in the Super Bowl. The odds are gonna change because the house doesn't wanna, they wanna just, they wanna make their rake. They don't wanna be taking a strong position on who's gonna win the Super Bowl. So if you, at some level, to win in sports betting, you don't have to be so much smarter than the house. You have to be smarter than the other people who are betting on sports betting. They're never gonna find out you're winning. They're not gonna come and break your knees. So in sports, there is an element of both. Vegas does set the line. And if they get the line wrong, and you come running in and you're constantly beating them

• 25:00Speaker 03

  because they set the line and they're making a mistake, they'll notice. But it's some, but they are also setting the line based on everybody else's bet. And at some point, you're betting against the other players as well, and it becomes a little safer. Wow.

• 25:13Neil deGrasse Tyson?

  Okay, but in March Madness and in, you know, football as well, but any of these sports betting, you know, the common ones that you find in the office place- It is a redistribution of wealth. Uh, right. I bet and I lose, but that goes to Chuck, right? And Chuck and I bet and we both lose and it goes to Gary.

• 25:33Chuck Nice?

  Yeah.

• 25:33Neil deGrasse Tyson?

  So, so I guess there's, there's no bad will there because everyone is participating in this with this understanding. But if I learned that you use AI and the rest of us are just sort of reading the paper, yeah, we're gonna break your knees. Alright, so Matt, what parameters are people putting in other than hunches to beat their opponent in bracketology? What do you, what are you programming into your computer?

• 25:59Speaker 03

  See, you obviously put in, well fundamentally, what you put in is all of the play records from all of the previous games. And that's basically all the information that's available. And maybe you can put in that somebody got hurt, but mostly it's just all of the play records for all the previous games. So then you actually can find out if these two people are on the floor together, surprising things happen. Well, then you know that going forward. You can find out this guy, he hasn't gotten any rest for the last two weeks. And historically, if he doesn't get any rest, he plays terribly. So now you know that. So you're looking mostly for patterns in data that you, that you sort of,

• 26:42Speaker 03

  the community has agreed is sort of the, the starting point, which is all of the play records from all the previous games.

• 26:48Neil deGrasse Tyson?

  And that would include what teams, not just simply whether they won, but what teams they beat.

• 26:53Speaker 03

  No, it includes, it includes everything. It includes that this guy made a three-point shot at two minutes and 22 seconds left in the second quarter. It includes absolutely everything. And these are the other players.

• 27:07Neil deGrasse Tyson?

  Secondhand.

• 27:08Speaker 03

  And these are the other players who were on the floor at the time. And it includes every, you know, this guy committed a foul at this point. This is who else was on the court. This guy was absolutely everything. And this data is available. So you, you can actually get every, everything about every play and so forth and so on. Now, one of the things that typically people work with play records, these days there's also video. So you can potentially look at the video and see where does somebody shoot from and you so there's a ton of information out.

• 27:46Neil deGrasse Tyson?

  There's a difference. There's a difference between data and knowledge. So what you do with that information might be different from what someone else does. I guess that's the magic of who whose algorithm.

• 27:57Speaker 03

  That's the magic. So the algorithms process the data to make predictions. Roughly speaking, we're all working with the same data, but you'll have some people with better algorithms, some people with worse algorithms. If people are using vastly different data, that's when people start getting upset because they feel like they've been cheated because I didn't know so and so twisted his ankle and you did. That's not fair. But when you work with the same data, it somehow sort of seems fair. And now it's really just a matter of who's smart enough to create the best algorithms, who has the computational horsepower to run those algorithms because they're becoming very computationally intensive and thereby making better predictions.

• 28:39Gary O'Reilly?

  So the raw material for this, Neil, to my mind, is the quality of data. Because if you've got access to some of the random things that can happen, like for instance, someone was partying the night before when they shouldn't have been, someone is carrying an injury that you don't know about, that that hasn't come out into the public domain, that quality of knowledge is what will enable an algorithm and a builder to to make a better algorithm. Am I right there, Matt?

• 29:08Speaker 03

  I think there there are two ways you can win. One is you can have better data.

• 29:14Gary O'Reilly?

  Hmm.

• 29:15Speaker 03

  That sort of feels a little unethical. The other is have a better algorithm that does a better job of processing the shared data. A better algorithm means you're now maybe I'm a scientist, so maybe I just like it when smart people win. A better algorithm means you were smarter than the guy with the worst algorithm. Better data means, you know, you followed the basketball players the night before to see what they were actually up to, and that somehow just doesn't seem right. It seems like that's an unlevel playing field in this, you know, but. I like it when better algorithms work because I like making good outcomes.

• 29:55Neil deGrasse Tyson?

  We're gonna take a quick break. When we come back for our third and final segment, we'll finish up on that topic, but then we're gonna, you know, chew the fat and just get into some of the more philosophical dimensions of this topic, applying AI to predicting sports brackets on StarTalk Sports Edition.

• 30:22Neil deGrasse Tyson?

  We're back. StarTalk Sports Edition. We have a friend of StarTalk, AI expert, mathematics, all-around math guy, Matt Ginsberg. Matt, great to have you back on StarTalk. How do we find out? Do you hang out anywhere on social media?

• 30:37Speaker 03

  I have a website, mattginsberg.com, and that is practically it. I have a Twitter account that I never use. I don't belong to Facebook. I'm really as quiet as possible.

• 30:50Neil deGrasse Tyson?

  Wow, that means you probably read and do things with your spare time.

• 30:55Speaker 03

  I do. I do.

• 30:57Neil deGrasse Tyson?

  What a concept. Okay.

• 30:59Chuck Nice?

  Yeah. That must be torturous.

• 31:06Neil deGrasse Tyson?

  Transitioning out of the last segment, let me just ask Matt before we get into our more philosophical questions. Isn't there this force operating on who wins that doesn't show up in the data, such as how badly do they want to win?

• 31:23Chuck Nice?

  I wanted it more.

• 31:25Neil deGrasse Tyson?

  Exactly. And for equal talent, that could elevate someone's performance. Why is it that, you can do this best in track and field, that in big events, many people have their personal best, whether or not it's a world record, they just perform better in that moment, under those circumstances than they ever have in their entire life. And so something was going on within them that didn't show up in their previous data.

• 31:50Chuck Nice?

  The last NBA Finals, the Milwaukee Bucks, Giannis, whatever his name is, because I can't say it. But it's widely held that he just rose above his game and above his team's performance to carry them almost single-handedly to a championship.

• 32:14Neil deGrasse Tyson?

  And you look at Bob Beaman's world record-setting long jump in the 1968 Mexico Olympics, beating the previous record by over a foot. And, and no one came near that for, for decades. So, so I don't see how that shows up in your AI algorithm.

• 32:32Speaker 03

  It is totally in the data.

• 32:36Chuck Nice?

  Oh, this is fascinating.

• 32:37Speaker 03

  So.

• 32:38Neil deGrasse Tyson?

  Ooh, throw down, throw down, here you go, go on.

• 32:41Speaker 03

  You can look at the previous performance of athletes as a function of whether or not it was a clutch situation. And if there are athletes who reliably perform better when the pressure is on, it's totally in the data. And if there are athletes who reliably perform worse when the pressure is on because they choke, that's in the data. And if there are people who just perform the same independent of pressure, that's in the data. So it's, now, it is the case that being in the Olympics is special, being in the Super Bowl is special.

• 33:18Speaker 03

  But you can also look at, for somebody, for a generic player who performs 5% better in a clutch situation, how does he perform, how much does he perform better in his first Super Bowl? That's in the data. Not this guy who's about to play in his first Super Bowl, but I know how this guy responds to pressure, and I have data telling me how much pressure the Super Bowl really is. Put it all together, and I'll know how this guy's likely to play in the Super Bowl. Some people are gonna have good days, some people are gonna have bad days. But, but all the stuff you've been talking about, it's all in the data.

• 33:54Neil deGrasse Tyson?

  So how about the fact that some coaches give better pep talks at halftime than others do?

• 34:00Speaker 03

  That's in the data.

• 34:01Chuck Nice?

  Alright guys, now listen, we're down 212 to nothing. But I'm telling you right now, you gotta reach inside yourself, find something.

• 34:13Speaker 03

  One point at a time.

• 34:14Neil deGrasse Tyson?

  This is the value of the coach.

• 34:15Speaker 03

  But the value of the coach.

• 34:16Neil deGrasse Tyson?

  That's in your data too, I guess.

• 34:18Speaker 03

  Because you know who coached the team.

• 34:19Neil deGrasse Tyson?

  Is in the data.

• 34:21Speaker 03

  So, what's not in the data are, there's a, there's a wet spot on the floor, or this bit of astroturf was replaced badly. That's not in the data.

• 34:34Speaker 03

  But.

• 34:36Neil deGrasse Tyson?

  All right, Chuck, you had an interesting philosophical question back a couple of segments ago. What was that?

• 34:40Chuck Nice?

  I'm saying like if you if if you look at the question of whether or not a algorithm or computer is doing pretty much the same thing we do, I'm trying to figure out how it's not when if you look at semantic memory, episodic memory, aren't we just taking the collection of all of our knowledge, making associations, coming to a conclusion, then making a decision?

• 35:10Neil deGrasse Tyson?

  That's exactly what Matt's been saying. So why so Matt, why are you better at this? Is it because you can handle more variables? But so are we, right? I can think of, you know, what is Tom Brady thinking when he he's hiked the ball? He's got the whole field in his head, the history and the he's got it all. And no one is telling there's no adding machine that you can point to. It's almost at an intuitive level going on in his head.

• 35:35Chuck Nice?

  And the same way he's he's looking for patterns. He knows how to read defenses. He's seeing that certain guys going in motion or being out of position means certain things.

• 35:45Neil deGrasse Tyson?

  Or moving left or moving right. So why is that any different, Matt? And why should we trust you and not Chuck's intuition?

• 35:53Speaker 03

  So I don't that's not the I don't think it is different. And I think, you know, one of the things you said, Neil, earlier on, you said, well, then why isn't it help? And I don't know. I don't I don't know that intelligence has meaning other than sort of a performance. If you can do smart stuff, you're intelligent. And I don't care how you got there. If you can always throw to the right place, you're a good quarterback. And I don't care what the processing that happened inside your brain to make it happen is. Now, there are some people, I think probably the best known is Roger Penrose, who actually

• 36:33Speaker 03

  believe that there is stuff going on inside of us that is not computable, that it depends on quantum mechanics, that it depends on this magical relationship between our minds and the world of mathematics that a machine can never duplicate. He could be right.

• 36:50Neil deGrasse Tyson?

  But just a quick bio, a quick bio on Roger Penrose. He's a physicist at the University of Oxford, who in fact just got the Nobel Prize a couple of years ago for his early work showing that Einstein's general theory of relativity has to give you black holes. Like it's a necessary prediction from the foundational physics that's there. And he's long respected in physics and in astrophysics. Then he stepped into the world of consciousness and into a world where we really don't have good explanations. So you want to, those are the kind of places that attract smart people. And he was just imagining that, you know, we can think of the brain as electrochemical, but let's go a step deeper. Is it also quantum mechanical?

• 37:33Neil deGrasse Tyson?

  And if that's the case, then you're, then all bets are off because you're not predict, the quantum physics creates a, an unpredictable randomness to it that may be the source of all of our wisdom. Is that a fair way to think about his contributions to consciousness?

• 37:49Speaker 03

  It is. I mean, he was my thesis advisor. And he and I have argued about this. And you have described his position fairly.

• 37:58Speaker 03

  And what's interesting is, I mean, when we argued about this, it didn't take long for us to realize that we had this fundamental disagreement. Is what's going on in your head quantum mechanics or is it sort of classical mechanics that a machine can duplicate? And we realized we were on different sides. We realized neither would convince the other. I believe Chuck appears to believe that what's going on in your head, you could capture as a process. If somebody eventually makes a real AI, an actually intelligent artifact, then Roger will be wrong. If we keep trying and never get there, maybe he's going to turn out to be right. But right now, nobody, nobody knows. You know, we side on.

• 38:41Neil deGrasse Tyson?

  And just to be clear, for Chuck, Matt mentioned classical physics. So what he means there is if everything that's happening in your head is classical, then it means you can point to all causes of all effects.

• 38:54Speaker 03

  Exactly.

• 38:55Neil deGrasse Tyson?

  And trace it back and how they interact with each other. If there's a quantum domain going on, then the cause and effect gets disrupted. And then you can't then classically describe it. But so Matt, then what about, I mean, you're at Google. What about the future? I'm pie in the sky here. What about quantum computing? Will that be able to mimic what might be quantum influence in our ability to predict the future?

• 39:21Speaker 03

  So I don't know. And the right person to ask is Roger, because I actually think we don't need quantum computing, because I think we are classical artifacts. What's going on in our head is classical. I don't know where he is on that question. I know, you know, Google does quantum computing. Everybody's doing quantum computing these days. It's exciting. It can break encryption. It's an amazing process.

• 39:47Neil deGrasse Tyson?

  Maybe they're afraid to put you on that topic because then you might take over the world.

• 39:53Neil deGrasse Tyson?

  Or at least take over, at least take over Google.

• 39:56Speaker 03

  It's.

• 39:57Neil deGrasse Tyson?

  They've got you in the Chromebook assembly line.

• 39:59Speaker 03

  I know.

• 40:00Neil deGrasse Tyson?

  Just to keep you out of trouble.

• 40:02Speaker 03

  The Chromebook repair line. I aspire to do that.

• 40:07Gary O'Reilly?

  If it's data driven, this whole thing about, and if we don't know it, we don't know it. Do we then not point AI in the opposite direction and say, go and find what we don't know? Bring it back and then we'll build even better. Is, am I, am I seeing this in the wrong way completely?

• 40:24Speaker 03

  I think that's a great question. And I think that.

• 40:28Neil deGrasse Tyson?

  And by the way, let me interject here. What we do when we send robots to other planets, they're, they're often have a serendipity mode where it looks around and if there's something it doesn't recognize that we've programmed into it, then that is targeted for extra curiosity. And so, but you have to be able to notice something that you don't recognize. That's an important step and not, and not otherwise just look right past it. So just pick up Matt. I just want to put that out there.

• 40:58Speaker 03

  I think that the consensus these days is that there is a huge amount of scope from proving our ability to draw conclusions from the data we have. So when you look at all the stuff about Big Brother and tracking terrorists with data, and Amazon predicting which book you're gonna wanna buy next if you read books, Netflix predicting which TV show you're gonna wanna watch next, it's amazing how well these systems perform. It was a huge surprise. All they're doing is they're doing a better job of exploiting the data we already have, and the consensus is we should be focusing on improving our ability to use the data we've got,

• 41:42Speaker 03

  rather than looking for more data when we're not even using our existing data as well as we could. By and large, acquiring more data is expensive, it's hard, it's intrusive, and I think it's the consensus of the AI community that there's still so much scope for doing more than we know, than we have, that that's where we should be focused.

• 42:05Chuck Nice?

  Hmm, interesting. That's very interesting. And so when you say, is it just the amount of work that it takes to get more data? Is that why it's just... No, it just gets more invasive, you know? You have to go deeper and deeper. What was your dentist appointment like? Yeah, yeah. It's like the way Netflix always keeps trying to get me to say, "Please rate this so that we can better understand how you..." And I'm like, "Screw you." I'm not giving you any more information about me than you already have, because you have everything about me to... Rate this so they can sell something better.

• 42:39Speaker 03

  Chuck, do you have an Alexa?

• 42:41Chuck Nice?

  I do, I have an Alexa, I have a Google Home, and I have an Amazon, or whatever the other thing is. I don't use any of them, because the first time I used it... So you think, so you think. Oh no, they're unplugged. So you think.

• 42:59Chuck Nice?

  Well played, sir, well played.

• 43:03Chuck Nice?

  They're tapping into the electromagnetic energies of the universe. Yeah, but the first time I used one, it scared the hell out of me, because it immediately started to, as they say, "learn."

• 43:16Chuck Nice?

  And that was frightening to me.

• 43:18Speaker 03

  So that is Amazon or Google or whomever. Do you use your cell phone, Chuck?

• 43:24Chuck Nice?

  Unfortunately, yes. And I see it every time I use it. I see the results. I see it come up with the ads that I never looked for. And then all of a sudden, I'm like, hmm, I should buy diapers. I don't even have a baby.

• 43:40Chuck Nice?

  But then a diaper ad shows up.

• 43:42Neil deGrasse Tyson?

  No, Chuck, you should move your bowels about now. You have done so in the last three days at this time.

• 43:48Chuck Nice?

  And by the way, here's a great product to help you do that.

• 43:52Speaker 03

  This is the lengths that these companies and I would now work for will go to get more data.

• 43:58Chuck Nice?

  Got you.

• 43:59Speaker 03

  And

• 44:00Chuck Nice?

  That's the more data part.

• 44:01Speaker 03

  That's the more data part.

• 44:02Neil deGrasse Tyson?

  So Matt, from March Madness, are you entering that contest? Because somebody's going to have, out of 20 quintillion possibilities, somebody will come in ahead of everybody else. Because that's how it works. But they're not going to get all 20 quintillion.

• 44:20Chuck Nice?

  They'll at least get the 100 grand.

• 44:22Neil deGrasse Tyson?

  But there's going to be someone who does better than everybody else. And then they want to say, well, how did they do it? And then you get a peek at their AI. Will everyone focus around them? Or we're going to say it's random dumb luck?

• 44:32Speaker 03

  Hard to know, right? If they win by a lot, they're like way better than whoever's second. People are going to pay attention. If they just edge out the guy who was right behind them, then it was going to have enough of a luck element. There's probably not something to know. But I think it is the case that if somebody fails to get Warren's money because they only predict 62 of the 63 games correctly.

• 44:56Chuck Nice?

  Right. If it's marginal, then it's just like Emily down in shipping, who wins the pool because she was like, I like the teams with the bright colors. And then it's like, and she beat everybody.

• 45:09Gary O'Reilly?

  You're getting an email from Emily. You know that.

• 45:12Neil deGrasse Tyson?

  Emily's going to be all on your case. So I think this came up in a previous episode, Matt, with you as a guest, where if everything you say, and if you're as good as you say you are, why isn't this podcast filming you at your villa?

• 45:33Chuck Nice?

  You know what Matt's gonna say right now? Because this is an actual screensaver that I have.

• 45:43Neil deGrasse Tyson?

  Why aren't we on your yacht? Why, why, why are we in some corner? Why, what?

• 45:51Speaker 03

  I have made my life about solving hard problems. And that is a choice that I have absolutely no problems about.

• 45:58Chuck Nice?

  I regret immensely.

• 45:59Speaker 03

  No, I have.

• 46:01Neil deGrasse Tyson?

  That is a choice.

• 46:02Speaker 03

  I have absolutely no problems about.

• 46:03Chuck Nice?

  That is a choice, that is a choice that makes my wife extremely upset with me.

• 46:08Speaker 03

  I have, I have had a better life and I have healthier children because, so.

• 46:13Chuck Nice?

  Yeah, well, no.

• 46:14Neil deGrasse Tyson?

  That's probably true, for sure.

• 46:15Chuck Nice?

  Definitely, yeah.

• 46:17Speaker 03

  So.

• 46:17Gary O'Reilly?

  That's pretty special.

• 46:18Chuck Nice?

  Yeah, you know what, though? I will say, as, as a point of admiration, Matt, that,

• 46:26Chuck Nice?

  that, that is philosophically a much higher position to take.

• 46:33Neil deGrasse Tyson?

  Nobler, it's a nobler.

• 46:34Chuck Nice?

  Nobler is what I'm saying.

• 46:36Gary O'Reilly?

  That is way higher level.

• 46:37Chuck Nice?

  Yeah.

• 46:38Gary O'Reilly?

  You like to solve the unsolvable, you know, the hard problems. Are we pointing enough AI at the problems this planet has right now?

• 46:47Neil deGrasse Tyson?

  Good question, Gary.

• 46:48Gary O'Reilly?

  To find solutions.

• 46:49Neil deGrasse Tyson?

  Why are we finding brackets to basketball?

• 46:51Gary O'Reilly?

  Yeah, I mean, we can.

• 46:52Neil deGrasse Tyson?

  We should be finding solutions to cancer.

• 46:54Gary O'Reilly?

  Let's go make some money, or let's go solve some really big gigantic problems.

• 46:58Speaker 03

  So, the first, the first project I worked on at X was to use AI to help decarbonize the electricity. Generate electricity, distribute electricity without burning fossil.

• 47:14Speaker 03

  That's as important as it gets.

• 47:16Chuck Nice?

  That's really important.

• 47:17Speaker 03

  And I think that.

• 47:19Neil deGrasse Tyson?

  And you failed at that, and so then you went to basketball, okay?

• 47:25Speaker 03

  I think that, I think that there is a significant amount of work spent on these, on these incredibly important problems. It is also the case that if you look at getting a computer to play chess. That doesn't save anybody's life. But the work on computer chess moved AI forward an enormous amount and allowed people to take what they learned from chess and apply it to problems that actually matter. So a lot of the, a lot of the magic, I think is probably the right word, of being a scientist is about finding problems

• 48:08Speaker 03

  that are hard, but still just barely tractable and have the property that when you solve them, you will learn something that you can take elsewhere to make the world a better place.

• 48:19Chuck Nice?

  Yeah. Yeah, I mean, you look at the medical industry, which is now utilizing-

• 48:24Neil deGrasse Tyson?

  Wait, Chuck, that was a, he just, he just, he just composed a beautiful ending sentence. And now you gotta keep talking?

• 48:30Chuck Nice?

  Yeah, okay, all right. I was just agreeing with Matt, but that's okay.

• 48:35Neil deGrasse Tyson?

  His sentence was like, you could, belongs on a statue somewhere. Okay, Chuck, go on.

• 48:41Chuck Nice?

  No, no, I was just, it's just as it just, I was just agreeing with him, like, you know, the fact that it is, it's becoming increasingly expansive and it's making life better for so many people.

• 48:51Speaker 03

  It is.

• 48:52Neil deGrasse Tyson?

  And, and, and many other branches beyond, and this is, this is what mathematicians have known forever, that they're, they're an engine of so much advance in culture and civilization in ways that people don't really appreciate because the advance is somebody else's invention based on what they did. And the other person gets the credit, not Matt. But we love you, Matt.

• 49:13Speaker 03

  We got you. Thank you. I'm delighted.

• 49:16Chuck Nice?

  We're gonna start a GoFundMe, Matt.

• 49:19Speaker 03

  Okay.

• 49:20Gary O'Reilly?

  So something you said about AI playing chess, right? And that, that then inspired people to think in a certain way. Is this the legacy that we're finding AI is leaving us? It's, it's making us think better, think differently.

• 49:36Chuck Nice?

  And is it actually teaching us?

• 49:39Speaker 03

  There's so many things I could say. I mean, one of the things that people have often joked about in AI is that as soon as anything actually becomes successful, it leaves AI and it becomes part of autonomous driving or some other discipline. AI is teaching us about ourselves. Certainly, we are learning about what we can do, what we can't do. My belief has always been that computers solve problems differently than we do, and they always will. And that's going to mean that we can solve more problems with machines at our side than we could solve by ourselves or they could solve by themselves. And it's tremendously positive, tremendously optimistic. They're tools. They are tools.

• 50:22Speaker 03

  And we will be able to do more with them than we could ever have done without them. And we should all be ecstatic about that. It's not going to be like the Terminator. It's going to be like figuring out how to get carbon out of the electricity. And making the world.

• 50:37Neil deGrasse Tyson?

  On that note, we got to land this plane. We got to land this plane.

• 50:40Chuck Nice?

  Oh, I had, oh, I just wanted one more thing to know. Oh.

• 50:44Neil deGrasse Tyson?

  Who's going to win the Super Bowl?

• 50:46Chuck Nice?

  Please.

• 50:47Neil deGrasse Tyson?

  Okay, Chuck.

• 50:48Chuck Nice?

  It's not, no.

• 50:49Neil deGrasse Tyson?

  Okay, Chuck, Chuck, go. Make it fast.

• 50:51Chuck Nice?

  Really fast. Back to it's in the data. Is it possible with all the data of every war ever fought on this planet to be able to predict when we might be going to war and if that war will lead to our ultimate destruction?

• 51:06Speaker 03

  Probably.

• 51:07Neil deGrasse Tyson?

  Okay. There we go.

• 51:09Chuck Nice?

  And with that, we say good evening to you all.

• 51:13Neil deGrasse Tyson?

  Okay. Thank you, Matt, for that succinct reply to Chuck's doomsday question. We got to call it quits there. Matt, always good to have you. Chuck, Gary. Pleasure. Making this work. Thank you, Matt. This has been StarTalk Sports Edition. What do we call this? We call it Bracketology, if there's even such a word. There it is now. You've got it. I've been Neil deGrasse Tyson, your personal astrophysicist. Keep looking up.