Stuff You Should Know - A List Of Games You Would Surely Lose to a Computer

Episode Date: May 22, 2018

We live in a time where computers can beat the best humans in the world at chess, checkers, poker and video games. But these games are really just demonstrations of how intelligent our machines are growing. They’re growing more intelligent by the hour.  With special guest, Tech Stuff's Jonathan Strickland. Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.

On the podcast, Hey Dude, the 90s called, David Lasher and Christine Taylor, stars of the cult classic show, Hey Dude, bring you back to the days of slip dresses and choker necklaces. We're gonna use Hey Dude as our jumping off point, but we are going to unpack and dive back into the decade of the 90s.

We lived it, and now we're calling on all of our friends to come back and relive it. Listen to Hey Dude, the 90s called on the iHeart radio app, Apple Podcasts, or wherever you get your podcasts. Hey, I'm Lance Bass, host of the new iHeart podcast, Frosted Tips with Lance Bass. Do you ever think to yourself, what advice would Lance Bass

and my favorite boy bands give me in this situation? If you do, you've come to the right place because I'm here to help. And a different hot, sexy teen crush boy bander each week to guide you through life. Tell everybody, ya everybody, about my new podcast and make sure to listen so we'll never, ever have to say. Bye, bye, bye.

Listen to Frosted Tips with Lance Bass on the iHeart radio app, Apple Podcasts, or wherever you listen to podcasts. Welcome to Stuff You Should Know from HowStuffWorks.com. Hey, and welcome to the podcast. I'm Josh Clark. There's Charles W. Chuck Bryant.

There's Jerry over there. I'm just gonna come out and tell everybody, making fun of me for some weird reason. Vaguely, in weird ways, but I'm all right. So Chuck, I have a story for you. Okay. I'm gonna take us back to the 1770s, and they'll swing in town of Vienna, not Virginia,

not Vienna, Georgia, but you know that's how they pronounce it, right? Vienna. Vienna sausages. Right, Vienna, Austria. You ever been there? Vienna, Austria? No, been to Brussels.

That was pretty close. Vienna's lovely. I'm sure. I think it's a lot like Brussels. Very clean, lovely town. I just remembered it being very clean. Yeah, very clean, gorgeous architecture. Weird little angled side streets.

They're very narrow, very pretty town. So we're in Vienna, and there is a dude skulking about going to the Royal Palace in Vienna. His name is Wolfgang von Kempelin, and he's an inventor, he's an engineer. He's a pretty sharp dude, and he's got with him what would come to be known as the Turk, but he called it the mechanical Turk,

or the automaton chess player, and that's what it was. It was a wooden figure that moved mechanically, seated at a cabinet, and on top of the cabinet was a chessboard, and when he brought it out to show to the Royal Court, he would, it was cool kind of, but nothing they hadn't seen before, because automata was kind of a hip thing by then. Yeah, people loved building these,

engineering these automata machines to do various things, and people were just knocked out by the fact that, you know, you hide these gears and levers behind wood or a cloth, and it looks as though there's a real, well, not real, but you know what I mean. That it's like a real machine. Yeah, but they weren't fooled anything like, is that a real man?

It was, but it was, for their time, it was so advanced looking that it's like us seeing Ex Machina in the movie theater. Sure, does that make sense? Yeah, no, it does make sense, but imagine seeing like Ex Machina and being like, I've seen this before, this isn't anything special, okay? Yeah, and this thing, to be clear, looked like a,

is it Zoltar or Zoltan from Big? Zoltar? Zoltan? I don't know, it's one of those two for sure. One of those two, like this guy's wearing a turban, and it's in a glass case, like a bust, like, you know, like a chest-up thing. Yeah, he's seated at this cabinet,

so there's no need for legs or anything like that. Yeah. But the thing, this is what was amazing about the Turk. He could play chess, and he could play chess really well. So, yeah, he was like an automaton, and he moved all herky-jerky or whatever, but he could play you in chess, which was a huge, huge advance at the time.

Like, this is something that wouldn't come up again until the 1990s, more than 200 years later. This thing, this automaton, could play a human being in chess and beat them. Well, yeah, and it looked like when the game started, it would look down at the chessboard and, like, cock his head, like, hmm, what should my first move be? Right.

And if people, I love this part, if people tried to cheat, apparently, Napoleon tried to cheat this thing, because this guy, he debuted at the V&E's court, but then it went on a world tour. Yeah, and he was even, it was taken over by a successor to the guy who toured with it even further. People went nuts for this stuff.

They did, they loved it, because they were like, this is crazy, I can't believe what I'm seeing. Most people, though, were not taken in by it. They're like, there's some trick here. Sure. But Von Kempelin and the guy who came after him, I don't remember his name, they would demonstrate, you could open this cabinet and you could see

all the workings of the mechanical Turk inside. Right, so what I was saying is, if this thing sensed a cheater, like Napoleon supposedly did, it would, you know, Napoleon would move a piece out of Turner illegally or something, this dude, the Turk, Turk 182, would pick up the chess piece, move it back, as if to say like, no, no, Napoleon,

let's see what you're doing. And then if the person attempted to move it again, I don't know how many times, maybe two or three times, eventually it would just go, ah, and wipe his hand across the board and knock off all the pieces game over, which is pretty great, it's a nice little feature. Yeah, but it even showed even more

that this thing was thinking for itself. That's the key here, right? Sure. Chess have been for a very long time viewed as only something that a human would be capable of, because it took a human intellect. And there was actually a guy, an English engineer, I think he was a mechanical engineer.

His name was Robert Willis. He said that chess was in quote, the province of intellect alone. So the idea that there was this automaton playing chess blew people away. But again, people figured out like, okay, there's something going on here. We think that Von Kempelin is controlling this thing

remotely somehow, maybe using magnets or whatever. Other people hit upon the idea that there was a small person inside the cabinet who would hide when the workings were shown, when the cabinet was open to show the workings. And then when the cabinet was closed again and the mechanical turks started playing, the person had crawled back out

and was actually controlling it. This seems to be the case that there was a person controlling it. But the idea that it was a machine that could think and beat humans in chess had like kind of unsettling implications. Yeah, this author, Philip Thickness, great name, British author, Philip Thickness.

Yeah. He said, and people like you said, all those more complicated explanations. And this article he sent astutely points out that he followed Occam's razor and basically said he's got a little kid in there. He's got a little Bobby Fisher in there that's really good at chess.

And that's what's going on. And other people speculated that other little people might be in there, just adults who would fit in there. But then there's the explanation that he would open it up and shine a candle around and say, nothing to see here, everyone. So what's, should we reveal the real deal? Sure.

I think I did already. Well, I don't think you spelled it out as. Oh, we'll spell it out. There was a little person in there. Yeah. Not just one little person, but they would travel around and recruit people. I guess people would get tired of being in there.

Or they'd forget about them and they'd starve and have to replace them. But it really was a trick. There was a little person in there. They did the same thing as like the magic acts, you know, when they saw a person in half, it's the lady just gets into a tiny little ball in one section of that box.

But my thing is this, like this is not a satisfying explanation to me, Chuck. I think it's great. How did the person keep up with the board above? Well, I mean, some, I don't know if they ever proved exactly how it was going. That's what I'm saying. Oh, okay, whether or not,

I think the Zoltar, or I'm sorry, the Turk, was just hollowed out and you would just put your arms through the arm holes. So you would crawl up into the Turk. Yeah, you would become the Turk. Okay. You and the Turk would fuse. That's what some people thought. I think that's what Edgar Allen Poe thought too.

He wrote a treatise on it. He loved to sing, he kid me. Poe? Other people thought that the little person was underneath in the cabinet, operating the Turk with levers and stuff like that. Well, there could have been a mirror or something, you know. I guess it's true. Like a little telescopic mirror.

That's what's getting me is, how would they keep up with the game? Right. You could keep track of the game, but how could you see where the other person moved? You would know where you moved, but you wouldn't be able to see where the other person moved. That's what I don't get.

Just mirrors, smoking mirrors. Maybe so. But the point is, is it was a fake, it was a fraud, but it raised some really big questions about the idea of a machine beating a person at something like chess. Yeah, and it really piqued the mind of one Charles Babbage. He was a kid or young at least at the time,

when he saw the Turk in person. And a few years afterward, he began work on something called the difference engine, which was a machine that he designed to calculate mathematics automatically. So some point to this is kind of maybe the beginnings of humans trying to create AI. Well, yeah.

Babbage is differential machine or difference machine? Yeah, difference engine. But at the very least, what this is, is the first that I know of example of man versus machine, even though it was really man versus man, because it was a man in the machine. Right, it was a fraud. Yeah, but it sparked that idea.

It definitely did. And that's something that like chess in particular has always been like this idea of like, if you can teach a machine to play chess, you have really achieved a milestone. And there's been plenty of programs, most notably Deep Blue, which we'll talk about. But there's been this idea that like part of AI

is chess, teaching it to play chess. But the people who develop AI never set out to make a chess playing AI, just to make a machine that can play chess. That's not the point. Chess has always been this way to demonstrate the progress of artificial intelligence. Yeah, because it's a complex game

that you can't just program it, like it almost has to learn. Well, it depends on how you come at it at first, right? So initially, they did try to program it, okay? There is this, from basically 1950 to about the mid, like about say 1950 to 2010, 60 years, right? That is how they approached AI and chess, is you figured out how to break chess down

and explain it to a computer. Now, if you could, ideally, you would have this computer or this AI, this artificial intelligence, be able to think about the outcome of every possible, or the every possible outcome of a move before making it. Right. That's just not possible. It's still today we don't have computers

that can do that, right? So what you have to do is figure out how to create shortcuts for the machine, give it best practices, that kind of thing. And that was actually laid out in 1950 by a guy named Claude Shannon, who is the father of information theory. And he wrote a paper with a pretty on-the-nose title

called Programming a Computer for Playing Chess. And you have to say it like that when you say the name. Yeah, it's got a question mark at the end. Right, but he laid out two big things. One is creating a function of the different moves. And then another one is called a Minimax. And if those were the two things that Shannon laid out, and they established about 50 or 60 years of development

in teaching an AI to play chess. Yeah, so this evaluation function is just sort of the very basis of it all, kind of where it starts, which is you kind of create a numerical evaluation based on the state of the board at that moment. And assign a real number evaluation to it. So the highest number that you would shoot for

is obviously getting a checkmate, getting a king and checkmate. Right, right. So what you've just done now is by assigning a number to a state like the pieces on a board, what you've done is to say like shoot for this number, the higher the number, like you're gonna give this AI the rule now,

the higher the number, the more desirable that this move that could lead to that higher number function, evaluation function is what you wanna do. Right, like capture the knight or capture the queen, capture the queen would have a higher evaluation number. Right, exactly. So that's the function.

And then there's another one called the Minimax. Yeah, this is pretty great. Where you wanna minimize the maximum. And this is another shortcut that they taught computers. Maximum loss that is. Right, so what they taught computers to do is so no computer can look through an entire game, every possible outcome.

But what you, there are computers that can look pretty far down the line at every possible outcome. And what you can say is, okay, you want to find the evaluation function that is the worst case scenario, the maximum loss, and then find the move that will minimize the possibility for that outcome. Yeah, and this is your only limited by your programming power,

but by looking not only at the state of the board right now, but if I make this move and I move the pawn to this spot, what are the next like three moves possibly that could happen as a result of this move? Right. And you're only limited, like I said, by programming power. So obviously the more juice you have, the more moves ahead that you can look.

Exactly. And then they just shy away from ones with a higher function number. Exactly. Or a lower function number, depending on how you've programmed it. Right. But they're making these decisions based on these rules.

And then there's other things you can do, like little shortcuts to say, if a decision tree leads to the other players, king being and checkmate, don't even think about that move any further. Don't evaluate any longer, just abandon it because we would never want to make that move, right? So there's all these shortcuts you can do.

And that's what they did to teach computers. That's what deep blue did when it beat Gary Kasparov in 1997. It was this huge massive computer that knew a lot of chess, a lot about chess. It had a lot of rules, a lot of incredibly intricate programming that was extremely sharp. And it actually won.

It became the first computer to beat an actual human chess grandmaster in like regulation match play. Yeah. I mean, and I don't think Kasparov gets enough credit for like willing, being willing to do this. Because it was a big deal for him to lose. It was in this community and the AI community.

It sent shockwaves. And everyone that was alive that was alive remembers, even if you didn't know anything about either one, remembers deep blue being all over the news. It was a really big deal. And Kasparov put his name on the line and lost. Yeah. And I was wondering, Chuck,

how like you would get somebody to do that. I'm sure. The mountain of catch. I guess that would probably be part of it. But also, I mean, I bet that's out there. We just, I just didn't look it up. So that's possible. It's also possible that they said,

look man, like this is chess, we're talking about whatever, but really what you're doing is helping advance artificial intelligence. Right. Because we're not really trying ultimately to win chess games. We're trying to cure cancer. Yeah. We're going to take your title

because we're going to beat you or our machine's going to beat you. But even still, you're going to be helping with cancer. Think of the cancer, Kasparov. That's probably what they said. Should we take a break? Yeah, let's. Well, should we tease our special guests first?

Is he okay? I can smell him. I don't think we even said, we're going to have a special guest later in the episode, Mr. Jonathan Strickland of Tech Stuff. Nice. It's been a long time since like years since we had Strikon. The last time we had Strikon was like 2009

with the Necronomicon episode. What is, where has he been besides sitting in between this every day? It's been a Strickland drought is what it's been. Yeah. So Strickland's coming later, but we're going to come back after this and talk a little bit more about Man vs. Machine. On the podcast, Hey Dude, the 90s called David Lasher

and Christine Taylor, stars of the cult classic show, Hey Dude, bring you back to the days of slip dresses and choker necklaces. We're going to use Hey Dude as our jumping off point, but we are going to unpack and dive back into the decade of the 90s. We lived it and now we're calling on all of our friends to come back and relive it.

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to the 90s. Listen to Hey Dude, the 90s called on the iHeart radio app, Apple Podcasts, or wherever you get your podcasts. Hey, I'm Lance Bass, host of the new iHeart podcast, Frosted Tips with Lance Bass. The hardest thing can be knowing who to turn to when questions arise or times get tough or you're at the end of the road.

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Listen to Frosted Tips with Lance Bass on the iHeart radio app, Apple Podcasts, or wherever you listen to podcasts. OK, dude, so what we just described was how AI was taught to play things like chess or to think. Like you take something, you figure out how to break it down into little rules and things that a computer can think of, right? And then follow these kind of rules

to make the best decision. That's how it used to be. The way that it's done now that everybody's doing now is where you are creating a machine that teaches itself. Yeah, that's the jam. That was the breakthrough. You may have noticed back in about 2013, 2014, all of a sudden, things like Siri and Alexa

got way better at what they are doing. They got way less confused. Oh, really? Your navigation app got a lot better. The reason why is because this new type of AI, this new type of machine learning, that can teach itself and learn on its own, just hit the scene. And they just started exploding.

And one of the things that they were first trained on was games. Yeah, and it makes sense. And if you thought chess was complicated and difficult, when it comes to these new AIs that they're teaching to teach themselves game strategy, they said, we might as well dive in to the Chinese strategic game Go, because it has been called the most complex game ever devised by humans.

And this was actually, that was actually a quote from Demi Hassabi, a neuroscientist and the founder of DeepMind, which was DeepMind, they were purchased by Google? Or were they always part of Google? I don't know if they were a spinoff branch or were they were purchased, but it's one of Google's AI outfits. Well, they're one of the teams, yeah,

that are designing these new programs. And to give you an idea of how complex Go is, it deals with a board with different stones. And there are 10, how do you even say that? 10 to the 170th power. So that means 170 zeros. And take that number, and that's the number of possible configurations of a Go board.

Right, so like you say, chess is very complex and complicated, and it's very difficult to master Go, and I've never played Go with you. No. So supposedly it's easy to learn. Right, but very complicated in its simplicity. Right, right, exactly. It's extremely difficult to master.

And there was a guy in the late 90s, and I'm guessing that he was saying this after Deep Blue beat Kasparov. It was an astrophysicist from Princeton. He said that it would probably be 100 years before a computer beats a human at Go. To give you an idea of just how complex Go is, that Deep Blue would just be Kasparov,

and this guy's saying it'll still be 100 years before anyone gets beat at Go by a computer. And he was someone who knew about this stuff. Who was an astrophysicist? He wasn't just some shmo at home and drunk in his recliner. He was making asinine predictions. So, and again, we've said this before, but I want to reiterate the people that I think

AlphaGo is the name of this program, the people that created this at DeepMind. They wanted to stress that this is a problem solving program. We're just teaching it this game at first to make it learn and to see if it can get good at what it does. But they said it is built with the idea that any task that has a lot of data that is unstructured and you want to find patterns in the data

and then decide what to do. And that's kind of like what we were talking about. It crunches down all these possible options, a.k.a. data, to decide what move should I make. And you could apply that. Ideally, they're going to apply this to Alzheimer's and cancer and all sorts of things. Right, that's general purpose thinking, right?

And thinking on the fly, too, when faced with novel stuff. So, one of the reasons why it's good to use games like Chess or Go or whatever, those are called perfect information games where both players or anybody watching has all the information that's available on it. There are definite rules or structure. It's a good proving ground.

But as we'll see, AI makers are getting further and further away from those structured games as their AI becomes more and more sophisticated because the structure and the limitations aren't necessarily needed anymore because these things are starting to be able to think on their own in a very generalized and even creative way. Yeah, it's really, really interesting.

The way that they're, like you said earlier and before the break that we don't have computers that can run all the possibilities. So, what they teach in the case of AlphaGo, this program teaches itself by playing itself in these games and Go, specifically. And the more it plays itself, the more it learns and the more ability it has during a game

to choose a move by narrowing down possibilities. So, instead of like, well, there are 20 million different variations here, by playing itself, it's able to say, well, in this scenario, there are really only 50 different moves that I could or should make. Right, or... That's kind of a simplified way to say it.

Right, no, but it's true, but that's exactly right. And what they're doing is basically the same thing that a human does, it's going back to its memory banks. Yeah, exactly. Its experience and saying, well, I've been faced with something like this before and this is what I used and it was successful 40 out of 50 times, I'll do this one. This is a pretty reasonable move.

That is what humans do. Yeah, not only, I mean, boy, we screwed up the chess episode, but I get the idea that when you're a chess master, you don't just think, what do the numbers say and what does the book say? But, oh man, I did this move that one time and it didn't go as the book said. Right.

So that's now factored into my thinking. Right, except imagine being able to learn from scratch and get to that point in eight days or eight hours. Yeah. So that Go team, the Alpha Go, the first iteration of Alpha Go, I think they started working on it in 2014. And in 2016, at the end of 2016,

they unleashed it secretly onto an Alpha Go website and it started just wiping the floor with everybody. Everybody's like, this thing's pretty good. Oh, it's Alpha Go. What year is this? That was the end of 2016. Okay, so chess had already come and gone. Like by this point, you can download a program

that's like deep blue, right? That's a great point, yeah. Like today, the stuff you play chess with on your laptop is even more advanced than deep blue was in the 90s. And it's just on your laptop. Yeah. But this is, so this is Go, this is the end of 2016. The end of 2017, Alpha Go was replaced with Alpha Go Zero.

It learned what Alpha Go had taken two years or three years to learn in 40 days by teaching itself. And it beat the master. Yeah. Finally, in May of 2017, Alpha Go took on Key G, the highest ranked Go player in the world. Don't know if he or she still is. No, Lisa Dahl is the current or was until Alpha Go beat him.

Oh man. Yeah. Did they get knocked off and Alpha Go is the champion? Yeah. Like that's not fair. If it's match play and the player, the human player is accepted a challenge from the computer. I don't see why it wouldn't be the world champion.

Or do they just now say on websites like human champion and italics with like a sneer? Right, maybe. Yeah. Interesting. What do they call that, wet wear? Like your brain, your neurons and all that? What?

Instead of hardware, it's wet wear. Oh, I don't know about that. I think that's the term for it. What does that mean though? It means like you have a substrate, right? Your intelligence, your intellect is based on your neurons and they're firing all that stuff and it's wet and squishy and meat.

Then there's hardware that you can do the same thing on, you can build intelligence on, but it's hardware, it's not wet wear. Oh, interesting. So that's probably it. It's the wet wear champion versus the hardware champion. But wet wear is italicized. With the sneer.

So where things really got interesting, because you were talking earlier about, what is it with the chess and go, what do they call it, what kind of games? Perfect information games. Right. Then you think, and my first thought, when you said that was, well yeah,

then there's games like poker, like Texas Hold'em, where there are a set of rules, but poker is not about the set of rules. It is about sitting down in front of whatever, five or six people and lying and bluffing and getting away with it. And being bluffed yourself. Being bluffed, like there's so many human emotions

and contextual clues and micro expressions and all these things, like surely you could never ever teach a machine to win at Texas Hold'em poker. Yeah, it'll be a hundred years, at least before that happens, I predict. No. They did it.

And more than one team has done it. Yeah, I read, there was one from Carnegie Mellon called Liberatus AI. Go Mellonheads. Yeah, go the Thornton Mellons. Yeah, I mean, University of Alberta has one called Deep Stack. That was the one I read about. Okay.

And it actually, here's the thing, if you read the release on it, you're like, you don't know how this thing works, do you? Oh, really? Yeah, and I'm pretty sure they don't fully get it, because that's one of the problems. I actually talked about this in the existential risks series.

That is to be released. Right, that there is a type of machine learning where the machine teaches itself, but we don't really understand how it's teaching itself. That's probably the scariest one, right? Or what it's learning, but that's the most prevalent one. That's what a lot of this is, is like these machines, it's like, here's chess.

Go figure it out. And they go, okay, got it. How'd you do that? Wouldn't you like to know? So that's the scariest presentation you will see on AI is when someone says, well, how does all this work? And they go, mm-hmm. But we just know it can be the human at poker.

But the thing about deep stack at the University of Alberta is that it learned somehow some sort of intuition, because that's what's required. It's not just the perfect information where you have all the information on the board. It's with poker, you don't know what the other person's cards are and you don't know if they're lying or bluffing or what they're doing.

So that's an imperfect information game. So that would require intuition. And apparently not one, but two different research groups taught AI to intuit. Yeah, Carnegie Mellon came out in January of 2017 with its Liberatus AI. And they spent 20 days playing 120,000 hands of Texas Holdem with four professional poker players.

And one, and smoked them basically, got up to, they weren't playing with real money obviously, but they would have been great. They were playing with Skittles like me as a kid. Funded their next project. Liberatus was up by 1.7 million. And one of the quotes from one of the poker players that he made to Wired Magazine said,

I felt like I was playing against someone who was cheating. Like it could see my cards. I'm not accusing you of cheating. It was just that good. So that's a really interesting thing, man, that they could teach, self-teach a program or a program could teach itself intuition. That's creepy.

I thought this part was interesting. The Atari stuff, this gets pretty fun. Google DeepMind let its AI wreak havoc on Atari. 49 different Atari 2600 games. See, they could figure out how to win. And apparently the most difficult one was Miss Pac-Man, which is a tough game still, man. Miss Pac-Man, they nailed it.

It's still one of the great games. But their game, or their deep queue network algorithm, beat it. I think it got the highest score, 999,900 points. And no human or machine has ever achieved that high score from what I understand. Amazing, and the way this one does it, the hybrid reward architecture that it uses

is really interesting. It says here, it generates a top agent that's like a senior manager, and then all these other 150 individual agents. So it's almost like they've devised this artificial structural hierarchy of these little worker agents that go out and collect, I guess, data,

and then move it up the chain to this top agent. Right, and then this thing says, okay, you know, I think that you're probably right. You, what these agents are probably doing, and I don't know, this is exactly true, but there are models out there like this, where the agent says, this is, you have a 90% chance of success at getting this pellet

if we take this action. Somebody else says, you got a 82% chance of evading this ghost if we go this way, and then the top agent, the senior manager, can put all this stuff together and say, well, if I listen to this guy and this guy, not only will I evade this ghost, I'll go get this pellet,

and it's based on what confidence level that the lower agents have in success in recommending these moves, and then the top agent weighs these things. Wow, they should give him a little cap. But all this is happening like that. Oh yeah. You know what I'm saying?

This isn't like, well, hold on, hold on, everybody. What is Harvey? Harvey, what do you have to say? Well, let's get some Chinese in here and hash it out, and everybody sits there and orders some Chinese food, and then you wait for it to come, and then you pick up the meeting from that point on, and then finally, Harvey gives his idea,

but he forgot what he was talking about, so he just sits down and eats his egg roll. Well, here's a pretty frightening survey. There was a survey of more than 350 AI researchers, and they had the following things to say, and these are the pros that are doing this for a living. They predicted that within 10 years, AI will drive better than we do.

By 2049, they will be able to write a best-selling novel, AI will generate this, and by 2053, be better at performing surgery than humans are. You know, so again, one of the things that about the field of artificial intelligence, which you know a lot about now. Famous, it is famous for making huge predictions that did not pan out,

but you've also seen it's also famous for beating predictions that have been levied against it, but there is something in there, Chuck, that stands out to me, and that's the idea of an AI writing a novel. Like for a very long time, I thought, well, yeah, okay, you can teach a robot arm to like put a car part

or something somewhere if you wanted to. Just follow these mechanical things, or it can use logic and reason, but to create that's different, right? That was like the new frontier where it used to be chess, and then it was go. The next frontier is creativity, and they're starting to bang on that door big time.

There's a game designing AI called Angelina out of the University of Falmouth, which I always want to say foul mouth, but we'll just call it Falmouth like it's supposed to. And Angelina actually comes up with ideas for new games, not like a different level or something, like you should put a purple loincloth on that player. You know, that'll look kind of cool.

Like new games, but whacked out games that humans would never think of. One example I saw is in a dungeon battle royale game, a player controls like 10 players at once, and some you have to sacrifice to be killed to save the others. Like just stuff that human wouldn't necessarily think of. This AI is coming up with.

Well, I mean, when you think of creatively, especially something like writing a novel or a film, if there are only seven stories, I mean, in that sort of the thinking that they're basically every dramatic story is a variation of one of seven things. Yeah, so I mean, you can look at like AI is scary and in some ways it very much is and can be,

but there's also like definitely a level of excitement of the whole thing and the idea that there are artificial minds that are coming online or that have come online now that are out there that they'll just naturally by definition see things differently than we do. And the idea that they can come up with stuff that we've never even thought of there

is just gonna knock our socks off, hopefully in good ways, that's a really cool thing. And so maybe there's just seven as far as humans know, but there's an unlimited amount as if you put computer minds to thinking about these kinds of things, that's the premise of it. Right, so the robot would be like, you never thought of boy meets girl meets, well.

Trilobite. But see, even that's a variation of probably. Just imagine something that we've never even thought of. Well, do you know how they should do this? If they do do that is not, is just release a book and not tell anyone that it was written by an AI program. Because if they do that, then it's gonna be so under scrutiny.

Oh yeah. They should secretly release this book. And then after it's a New York Times bestseller, say meet the whopper, the author of this, you know. Well, his interests are roller skating, playing tic-tac-toe and global thermal nuclear war. All right, should we take a break and get Strickland in here? Yeah, we're gonna end the Strickland drought

because it is about to rain Strickland in this piece. You're gross. On the podcast, Paydude the 90s called David Lasher and Christine Taylor, stars of the cult classic show, Hey Dude, bring you back to the days of slip dresses and choker necklaces. We're gonna use Hey Dude as our jumping off point,

but we are going to unpack and dive back into the decade of the 90s. We lived it and now we're calling on all of our friends to come back and relive it. It's a podcast packed with interviews, co-stars, friends and non-stop references to the best decade ever. Do you remember going to Blockbuster? Do you remember Nintendo 64?

Do you remember getting Frosted Tips? Was that a cereal? No, it was hair. Do you remember AOL Instant Messenger and the dial-up sound like poltergeist? So leave a code on your best friend's beeper because you'll want to be there when the nostalgia starts flowing. Each episode will rival the feeling

of taking out the cartridge from your Game Boy, blowing on it and popping it back in as we take you back to the 90s. Listen to Hey Dude, the 90s called on the iHeart radio app, Apple Podcasts or wherever you get your podcasts. Hey, I'm Lance Bass, host of the new iHeart podcast, Frosted Tips with Lance Bass.

The hardest thing can be knowing who to turn to when questions arise or times get tough or you're at the end of the road. Ah, okay, I see what you're doing. Do you ever think to yourself, what advice would Lance Bass and my favorite boy bands give me in this situation? If you do, you've come to the right place because I'm here to help.

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Yep, we know that, Michael. And a different hot, sexy teen crush boy bander each week to guide you through life step by step. Oh, not another one. Kids, relationships, life in general can get messy. You may be thinking, this is the story of my life. Just stop now. If so, tell everybody, ya everybody about my new podcast

and make sure to listen so we'll never, ever have to say bye, bye, bye. Listen to Frosted Tips with Lance Bass on the iHeart Radio app, Apple Podcasts, or wherever you listen to podcasts. Hey, hey, hey, hey, hey, hey, hey, hey, hey, hey, hey, hey. OK, we're back and get this. The scent of strict has permeated our place.

And it's a beautiful scent. It smells like a soldering gun and a circuit board. And I'll feel the lavender. And a protein bar. Yeah, that's fair. We're going to say Draco Noir, but that would have been a lie. Is that how you say it? I always call it Dracar.

Dracar? That's fair. No, Dracar. I always pronounced it Benetton colors because that was what I wore. Oh, is that what you wore? Yeah, during what I call the Year of Cologne. I had a couple of years.

87-ish. This is scintillating. Why am I here? So we know that you already know because we talked via email about this, but we'll tell everybody else. We have brought you in here because you're the master of tech and we're talking tech today, which we've talked about without you before, but frankly, Chuck and I and Jerry huddled.

And we said, this is not quite as good with that strict. So let's try something different. Gotcha. And we're talking about games and machine versus man and that whole evolution and how that's gone super crazy over the last few years. Games without frontiers is Peter Gabriel would say. War without fear.

And we've talked, I mean, we've talked a lot about the evolution of machine learning and how now it's starting to take off like a rocket because they can teach themselves. But one thing we haven't really talked about are solved games. I mean, we talked about chess. We talked about go. Would those constitute solved games? Not really.

So a solved game is the concept where if you were to assume perfect play on either sides of the game, you would always know how it was going to end. Which we always assume perfect play, right? Yeah. That's going to work back. That's stuff you should know motto. So perfect play just meaning that no one ever makes a mistake.

So very much the way I do my work, right? Stuff you should know motto. Exactly. So if you were to take a game like tic-tac-toe and you assume perfect play on both sides, it is always going to end in a draw. Which is what's in war games. Yes. The only way to win is not to play.

Right. Yes. So a game like... It's quarter talk. A game like connect four, whoever goes first, is always going to win assuming perfect play on both sides. Yes. I don't think I've played connect four.

That's where you drop, or a long time. That's when you drop the little tokens. Yeah, kind of like checkers. We did an interstitial with playing connect four, remember? I was faking it though. And you had perfect play. So I knew it was useless. I was going to say that I'm so humiliated by all the connect four

games that I've lost. Starting even. Yeah, but I mean, perfect play. That's something that obviously only the best players typically achieve with significantly complex games. Obviously, the simpler the game, the easier it is to play perfectly. Right. Tic-tac-toe, once you've mastered the basics of tic-tac-toe

and the other person has, you're never really going to win unless someone has just made a silly mistake because they weren't paying attention. Like they put a star instead of an x-ray. Right, which doesn't count. Automatically disqualifies you. One thing I found that's very enjoyable is playing with little kids who haven't figured out

that tic-tac-toe is very easy to win. Yes. Yes, smash their face on the board and rub it in. I mean, same reason why I like to join in on little league games because I can really wail that ball out of the park. Yeah, I really miss we feel like a man. That's the most tech stuffy thing you've ever said. You really wail that ball out of the park.

Well, to be fair, I did just do a tech stuff episode about the technology behind baseball bats. So it's still fresh on the mind. Oh, interesting, nice. I'm going to listen to that one, actually. It's a lot of fun. So there have been a lot of games that have been solved. Checkers was one that was recently solved back in,

recently by the early 90s when it was played against a computer called Chinook and CHI in OOK. Yeah, like the helicopter or the winds that blow through Alberta. Exactly. And so there are certain games that are more easily solved than others. You do it through an algorithm.

But other games like chess are more complicated because you can, in chess, you have multiple moves that you can do where you can move a piece back the way you went, right? It's not, you're not committed to going a specific direction with certain pieces. Never thought about that. Like with a knight, you could go right back

to where you started on your next move if you wanted to. And that creates more complexity. So the more complex the game, the more difficult it is to solve. And some games are not solvable simply because you'll never know what the full state of the game is from any given moment. Did you have a chance to talk about the difference between perfect knowledge and imperfect knowledge in a game?

Yeah, yeah, we talked about that some. Yeah, so computers, obviously, they do really well if they understand the exact state of the game all the way through, if they have perfect knowledge. All of the information is there on the board, right? Right, and all players can see all information at all times. But games like poker, which you guys talked about, obviously, you have imperfect information.

You only know part of the state of the game. That's why those games have been more difficult, more challenging for computers to get better than humans until relatively recently. And there have been two major ways of doing that. You either throw more processing power at it, like you get a supercomputer, or you create neural networks, artificial neural networks, and you start teaching computers

to, quote unquote, learn the way people do. So we talked about that. And one of the things that we talked about was how there's this idea that the programmers, especially say the people who are making programs that are playing poker and are getting good at poker, aren't exactly sure how the machines are learning to play poker, or what they're learning, they're just

getting better at poker. Do they know how they're learning poker? They just know that they're learning poker and that they're good at it now. Like, where's the intuition? How is that being learned? An excellent question. The way it typically is learned, especially

with artificial neural networks, is that you set up the computer to play millions of hands of poker that are randomly assigned. So it's truly as random as computers can get. That's a whole philosophical discussion that I don't think we're ready to go into right now. But you have games come up where the computer is playing itself millions upon millions of times,

and learning every single time how the statistics play out, how different betting strategies play out. It's sort of partitioning its own mind to play against itself. And through that process, it's as if you, as a human player, were playing thousands of games with your friends. And you start to figure out, oh, when I have these particular cards, and they're in my hand,

and let's say we're playing Texas Holdem, and the community cards are these, then I know that, generally speaking, maybe three times out of 10, I end up winning. Maybe I shouldn't bet in this. Well, the computer's doing that, but on a scale that far dwarfs what any human can do, and in a fraction of the amount of time. And so it's sort of, well, it's intuition in the sense

of it's just done it so much. Right, but does that mean it's completely ignoring micro-expressions and facial cues, so that doesn't even come into play? I should say Strickland just nodded, yeah. Yeah, I was waiting for Trump to finish. How many years have you been doing this? I still nod when I do a solo show,

and I do a lot of expressive dance. What do you think, Jonathan? I don't know, Jonathan. It gets lonely in here, guys. But yes, what you're saying, all the tells, right? All the tells that you would use as a human player, the computer does not pick up on this. Oh, so it's just data.

Yes, typically what it would do is it would study the outcomes of the games from a purely statistical expression. So most of these poker games tend to be computer-based poker games. So it's not that it's playing. It's not like there's a computer that says, push 10 more chips into the table. I tick.

Right, exactly. It's a little winky face emoticon. I don't have good cards. It's all usually sort of like internet poker, which a lot of the people who play professional poker cut their teeth on, especially in the more recent generations of professional poker players. These kids today.

Yeah, those kids. They don't know what it's like to be in a smoky saloon. Like Money Maker, when Money Maker rose to the top a few years ago, more like a decade ago now, he had come from the world of internet poker. And so he was using those same sort of skills in a real world setting. But obviously there are subtle things

that we humans do in our expressions that our computers do not pick up on. And in fact, that leads us sort of into the realm of games where our computers don't do as well as humans. Yeah, is that list you sent a joke or is it real? No, that's real. It does seem like it's weird. Like one of the games on there is Pictionary, for example.

Or Tag. Or Tag, yeah. But these are, some of these are, they sound silly, but when you start to think about them in terms of computation and robotics, you start to realize how incredibly complex it is from a technical perspective. But incredibly easy it is for your average human being. So with humans, a game of tag,

once you know the basics, it's all an instinct. You know what to do. You run after the person, you try to catch up with them and you tag them. But you also know- Push them in the back as hard as you can. Well, if you're Josh, you push them as hard as you can. But most of us, we tag and we're not trying to cause harm.

Robots, however, robots, not so good on the- That's the second stuffiest thing you said. I'm just saying. Isaac Asimov's rules of robotics aside, robots are not very good at judging how hard they have to hit something in order to make contact, right? They're not as good at even your bipedal robots that walk around like people, even the ones that can run

and do flips and stuff. Have you seen that one the other day, the footage of that thing running and jumping? It's really impressive. And super creepy. Yeah, but even so, that's a clip of the best of. If you ever see the clips where they show all the times the robot's fallen over.

Yeah. We're pouring hot coffee in someone's head. Yes. But they always play those clip shows to yackety sacks. Yes, this is true. So DARPA had its big robotics challenge a few years ago where they had bipedal robots try to go through a scenario that was simulating the Fukushima nuclear disaster.

So the interesting thing was the robot had to complete a series of tasks that would have been mundane to humans. Things like open up a door and walk through it and pick up a power tool and use it against a wall. And you can watch the footage of some of these robots doing things like being unable to open the door because they can't tell if they need to pull or push or they open the door but then immediately fall

over the threshold of the door. And when you see that, you realize as advanced as robotics is, as advanced as machine learning has become and as incredible as our technology has progressed, there are still things that are fundamentally simple to your average human that are incredibly complicated from a technical standpoint. Like a six-year-old can play Jenga better than a robot.

Right, right, okay. But the thing is, is we're talking robots here and as we go more and more and more online and our world becomes more and more web-based rather than reality-based. Doesn't the fact that a robot can't walk through a door matter less and less? And the idea that machines are learning intellect

and creativity and reasoning, you just blew my mind, that that's becoming more and more vital and important and something we should be paying attention to? It absolutely is something we should pay attention to. I mean, we have robotic stock traders. They're trading on thousands of trades per second, right? So fast that we have had stock market booms and crashes that last less than a second long due to that.

So the robot army that will ultimately defeat us is not something from the Terminator, it's invisible. Right. It's online, it will be online. It's what's determining our retirement right now. Yeah, the global economy or our municipal waters apply or whatever. Yeah, now there's, the fascinating thing to me

about this is not just that we're training machine intelligence to learn and to perform at a level better than humans, but that we're putting a lot of trust in those devices and things that have real incredible impact on our lives, significant enough impact where if things were to go south, it would be really bad for us. And not in that Terminator respect. Terminator is a terrifying dystopian science fiction story,

but then when you realize what could really happen behind the scenes, you think, oh, the robots don't have to do any physical harm to us to really mess things up. So there are certainly some cases for us to be very vigilant in the way we deploy this artificial intelligence. We're right from the outset. Exactly. But isn't it too late?

Depends. No, not necessarily. I think it's, I don't think it's too late, but I think it's getting to that point of no return very, very quickly. By December of this year. Yeah. Well, if you're someone like Elon Musk, you'd say,

if we don't do something now, we're totally going to plummet off the edge of the cliff. But now is a window that is rapidly closing. Yes. Yeah. The now is a time where we've got a deadline. We don't know exactly when that deadline is going to be up, but we know that it's not getting further out.

We're just getting closer to that deadline. So, and a lot of this is covered in deep conversations and the artificial intelligence and machine learning fields that has been going on for ages to the point where you even have bodies like the European Union that have debated on concepts like granting personhood to artificial intelligence. So, this is a really fascinating and deep subject

and the games thing is a great entry point into having that conversation. I'm lucky if I can win a game of chess against another human being. Oh yeah. Right? So I- We can't even describe chess.

My big thing is I do that night thing. I call it the night shuffle. I just move them back and forth. I just castle. If I can castle, then I'm so happy. And that's the third tech stuffiest thing to come in threes. Well, Strick, thank you for stopping by.

Thank you so much. I think you should stick around for listener mail. I think you should too. I'd love to. And throw out any funny comments that you have. I'll throw out comments and then Jerry can decide which ones are funny. Okay.

All right, fair enough. All right, so if you want to know more about AI, go listen to tech stuff. Strick does this every week. What days? Monday, Tuesday, Wednesday, Thursday, and Friday. Wow. That's amazing, buddy.

And wherever you find your podcasts. Yep. Okay. And you've been doing it for years. So if you love this, there's a whole big backlog, 900 plus episodes. You're celebrating your 10 year as well, right? Yep, I sure am.

I'll be, we'll be turning 10 and tech stuff on June 11th. Come on, congratulations. Congratulations. Thanks. Well, since I said happy anniversary, it means it's time for listener mail. Guys, I'm going to call this Matt Groening and cultural relativism about that.

Nice. Hey guys, love your podcast so much. The massive archive makes for endless learning and entertainment. My favorite part is your such rad guys, including Strickland, and I could totally imagine, how did they know? I could totally imagine myself getting a beer with you two,

but without Strickland. Your Simpsons episodes were absolutely perfect. I used to live in Portland and drove on Flanders and Lovejoy streets a lot. Wait, is this Matt Groening? No, Matt Groening drew Bart in the sidewalk cement behind Lincoln High School in downtown Portland. You can Google that.

I would like to offer one interesting observation though. I've noticed that on several episodes, you guys have said that you are cultural relativists. Is that pronounced right? Yeah. But then in nearly every episode, I hear you pass moral judgments on all the messed up stuff that people do,

whether it's racism, freak shows, or crematoriums bearing bodies on the slide. You guys are never shy to condemn something that deserves to be condemned. Reminds me of something I read from Yale sociologist, Philip Gorsky, who points out that our own relativism is rarely as radical as our theory requires.

We can't be complete relativists in our daily lives. He then gives the example of how academic social scientists who are diehard relativists get furious and moralistic at the data fudging of other researchers. Anyway, love the show guys. Love tech stuff especially. And we'll forever be indebted to you

for your hilarity and knowledgeability. Cheers, Jesse Lusko. PS Go Tech Stuff. That's sweet. How about that? Yeah, thanks a lot, Jesse. There was an actual episode, and I don't remember which one it was,

where we abandoned our cultural relativism. Do you remember? Yeah, I don't remember that. Because we used to just be like, no judgment, no judgment. We just can't judge, you know? And then finally we were like, you know what? No, that's not true.

We changed our philosophy to include the idea that there are moral absolutes that are universal. Although sometimes we are just judgy, even beyond that. Look at us. Yeah. Well, if you wanna get in touch with us, you can tweet to us. I'm at Josh M. Clark and John Strickland's at John Strickland.

That's correct. On Twitter, Chuck's at Movie Crush. And you have a tech stuff, HSW Twitter, right? That's right. And then we also have SYSK Podcast. We're on facebook.com slash stuff you should know, slash. Tech stuff. And slash.

Movie Crush. Oh, do you have a Movie Crush page? For Facebook? Yeah, that's actually where I spend most of my time. Oh, I didn't know that. I would have said it all this time. And then there's also a slash stuff you should know, Facebook page.

You guys have a lot of Facebook in to do it. So many social meds flying around. You can send us an email to stuffpodcast.howstuffworks.com. You can send John an email to techstuffathowstuffworks.com. Nice. And then hang out with us at our home on the web, stuffyoushouldknow.com and just go to techstuff. Just search it in Google.

I come up all the time. Fair enough. For more on this and thousands of other topics, visit howstuffworks.com. On the podcast, Hey Dude, the 90s called David Lasher and Christine Taylor, stars of the cult classic show, Hey Dude, bring you back to the days of slip dresses and choker necklaces. We're going to use Hey Dude as our jumping off point, but we are going to unpack and dive back into the decade of the 90s.

We lived it, and now we're calling on all of our friends to come back and relive it. Listen to Hey Dude, the 90s called on the iHeart radio app, Apple Podcasts, or wherever you get your podcasts. Listen to Frosted Tips with Lance Bass on the iHeart radio app, Apple Podcasts, or wherever you listen to podcasts.