Selects: A List Of Games You Would Surely Lose to a Computer

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With no fees or minimums, banking with Capital One is the easiest decision in the history of decisions. Even easier than deciding to listen to another episode of your favorite podcast. And with no overdraft fees, is it even a decision? That's banking reimagined. What's in your wallet? Terms apply. See CapitalOne.com slash bank. Capital One N-A, member FDIC. Hi everyone, it's Josh. And for this week's select, I've chosen our 2018 episode, some games you would surely lose to a computer. It's a philosophical discussion about AI that's disguised as an episode on computer games. Honestly, we didn't plan it to be like that. It just turned out that way. We're pretty happy that it did. And in light of the recent advances with machine learning like chat GPT, a few of the things we say seem naively quaint now. Plus it has a dollop of our tech stuff colleague Jonathan Strickland at the end. So that's a bonus. I hope you enjoy. SPEAKER_08: Welcome to Stuff You Should Know, a production of iHeartRadio. SPEAKER_02: Hey, and welcome to the podcast. I'm Josh Clark. There's Charles W. Chuck Bryant. There's Jerry over there. I'm just going to 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. SPEAKER_02: I'm going to take us back to the 1770s and the swinging town of Vienna, not Virginia, not Vienna, Georgia, which 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 is lovely. SPEAKER_07: I'm sure. SPEAKER_02: I think it's a lot like Brussels. SPEAKER_07: Very clean, lovely town. I just remembered it being very clean. SPEAKER_02: Yeah, very clean, gorgeous architecture, weird little angled side streets. They're very narrow, very pretty town. SPEAKER_02: So we're in Vienna and there is a dude skulking about going to the Royal Palace in Vienna. SPEAKER_02: His name is Wolfgang von Kempelen 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. SPEAKER_02: And that's what it was. SPEAKER_02: It was a wooden figure that moved mechanically seated at a cabinet and on top of the cabinet was a chess board. 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. SPEAKER_07: Yeah, people loved building these, engineering these automata machines to do various things SPEAKER_07: and people were just knocked out by the fact that 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. SPEAKER_02: That it's like a real machine. SPEAKER_07: Yeah, but they weren't fooled into thinking 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. SPEAKER_02: Sure. SPEAKER_07: Does that make sense? SPEAKER_02: 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? SPEAKER_07: Yeah, and this thing, to be clear, looked like a, is it Zoltar or Zoltan from Big? SPEAKER_02: 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 SPEAKER_07: bust, like you know, like a chest up thing. SPEAKER_02: 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 SPEAKER_02: 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. SPEAKER_07: 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? SPEAKER_07: And if people, I love this part, if people tried to cheat, apparently Napoleon tried to cheat this thing, because this guy, he debuted it at the Viennese court, but then it went on a world tour. SPEAKER_02: Yeah, and he was even, it was taken over by his 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. But von Kempelen 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 SPEAKER_07: did, it would, you know, Napoleon would move a piece out of Turner illegally or something. SPEAKER_07: 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. SPEAKER_07: 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 ahh and wipe his hand across the board and knock off all the pieces game over. SPEAKER_07: Which is pretty great. It's a nice little feature. SPEAKER_02: Yeah, it is. But it even showed even more that this thing was thinking for itself. SPEAKER_02: That's the key here, right? Chess had 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 is this automaton playing chess blew people away. But again, people figured out like, okay, there's something going on here. We think that von Kempelen 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 cab, when the workings were shown, when the cabinet was open to show the workings. SPEAKER_02: And then when the cabinet was closed again and the mechanical Turk 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. SPEAKER_07: SPEAKER_07: He said, and people like you said, all those more complicated explanations in this article you sent, astutely points out that he followed Occam's razor and basically said, he's got a little kid in there. SPEAKER_03: He's got a little Bobby Fisher in there that's really good at chess. SPEAKER_07: 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. SPEAKER_07: So what's should we reveal the real deal? SPEAKER_02: Sure. I think I did already. SPEAKER_07: Well, I don't think you spelled it out as. Oh, well spell it out. SPEAKER_07: 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. SPEAKER_02: Or they'd forget about them and they'd starve and have to replace them. SPEAKER_07: But it really was a trick. There was a little person in there. They did the same thing as like the magic ax. When they saw a person in half, the lady just gets into a tiny little ball in one section of that box. SPEAKER_02: But my thing is this, 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, I don't know if they ever proved exactly how it was going. SPEAKER_07: That's what I'm saying. Oh, okay. I think the Zoltar, or I'm sorry, the Turk, was just hollowed out and you would just put SPEAKER_07: your arms through the arm holes in your head. So you would crawl up into the Turk. SPEAKER_02: Yeah, you would become the Turk. SPEAKER_07: You and the Turk would fuse. SPEAKER_02: That's what some people thought. I think that's what Edgar Allen Poe thought too. He wrote a treatise on it. He loved this thing. SPEAKER_07: Are you kidding me? SPEAKER_02: Right. 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. SPEAKER_07: I guess that's true. Like a little telescopic mirror. SPEAKER_02: That's what's getting me is how would they keep up with the game? SPEAKER_07: Right. SPEAKER_02: 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, smoke and 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. SPEAKER_07: 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 as kind of maybe the beginnings of humans trying to create AI. SPEAKER_07: SPEAKER_02: Well, yeah, with Babbage's differential machine or difference machine? Yeah, difference engine. SPEAKER_07: 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 and a machine. SPEAKER_02: Right. It was a fraud. SPEAKER_07: Yeah, but it sparked that idea. SPEAKER_02: It definitely did. And that's something that chess in particular has always been this idea of 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. SPEAKER_03: SPEAKER_02: But there's been this idea that part of AI is chess, teaching it to play chess. SPEAKER_02: 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. SPEAKER_02: 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 SPEAKER_07: learn. SPEAKER_02: Well, it depends on how you come at it at first, right? So initially, they did try to program it. Okay. From basically 1950 to about the mid, like about say 1950 to 2010, 60 years, right? SPEAKER_02: That is how they approached AI and chess, is you figured out how to break chess down SPEAKER_02: and explain it to a computer. If you could, ideally, you would have this computer or this AI, this artificial intelligence, SPEAKER_02: be able to think about the outcome of every possible outcome of a move before making it. SPEAKER_02: 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. SPEAKER_02: And that was actually laid out in 1950 by a guy named Claude Shannon, who is the father of information theory. 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. SPEAKER_02: Right. But he laid out two big things. One is creating a function of the different moves. And then another one is called a mini max. And those were the two things that Shannon laid out. They established about 50 or 60 years of development in teaching an AI to play chess. SPEAKER_07: 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 give a number, create a numerical evaluation based SPEAKER_07: 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. SPEAKER_02: 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 going to 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 want to do. SPEAKER_07: Right, like capture the knight or capture the queen, capture the queen would have a higher evaluation number. Right, exactly. SPEAKER_02: So that's the function. Then there's another one called the mini max. Yeah, this is pretty great. SPEAKER_02: Where you want to 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. 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 SPEAKER_02: case scenario, the maximum loss, and then find the move that will minimize the possibility for that outcome. Yeah, by, and this is you're only limited by your programming power, but by looking SPEAKER_07: 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? SPEAKER_05: Right. SPEAKER_07: 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. SPEAKER_02: 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. 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 player's king being in 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 Garry Kasparov in 1997. It was this huge, massive computer that knew 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. SPEAKER_07: I don't think Kasparov gets enough credit for being willing to do this because it was SPEAKER_07: a big deal for him to lose. It was in this community and the AI community, it sent shockwaves. And everyone that was alive remembers, even if you didn't know anything about either one, SPEAKER_07: remembers Deep Blue being all over the news. It was a really big deal. SPEAKER_07: And Kasparov put his name on the line and lost. Yeah. SPEAKER_02: I'm wondering, Chuck, how you would get somebody to do that. I'm sure. A mountain of catch. SPEAKER_07: I guess that would probably be part of it. SPEAKER_02: But I also think... Do you think you'd probably get paid? SPEAKER_07: I don't know. I don't know. I bet that's out there. I just didn't look it up. SPEAKER_02: So that's possible. It's also possible that they said, look, man, this is chess we're talking about or whatever. But really what you're doing is helping advance artificial intelligence. SPEAKER_07: Right. Because we're not really trying ultimately to win chess games. We're trying to cure cancer. SPEAKER_02: I mean, yeah, we're going to take your title because we're going to beat you or our machine's SPEAKER_02: 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. SPEAKER_07: Should we take a break? SPEAKER_02: Yeah, let's. SPEAKER_07: Well, should we tease our special guest first? SPEAKER_02: Is he... OK. I can smell him. SPEAKER_07: 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. SPEAKER_02: Nice. SPEAKER_07: It's been a long time since, like years since we had Strick on. SPEAKER_02: The last time we had Strickland was like 2009 with the Necronomicon episode. SPEAKER_07: What is, where's he been besides sitting in between this every day? It's been a Strickland drought is what it's been. SPEAKER_07: Yeah, so Strickland's coming later, but we're going to come back after this and talk a little bit more about man versus machine. SPEAKER_18: As the number one audio company, I heart media gives you access to all every audience, live conversations, trusted influencers and the insights and data you need to grow. I heart media is your access company. Go to I heart results dot com for more. SPEAKER_17: What's up, everybody? I'm Dwayne Wade, and I've been blessed to have so many titles so far in my life. SPEAKER_17: And now I'm adding podcast hosts with my new podcast called The Why with Dwayne Wade. How did you feel about me in 2006? Well, there wasn't a lot of love there, I'd say. SPEAKER_17: So there was definitely yeah, there was definitely some some cold times. SPEAKER_17: As I step into a new phase of my life after basketball, I find myself with new inspirations, new motivations, and new whys. On this show, I will have intimate conversations with some of the biggest names in sports and music entertainment and fashion, and we will discuss the whys in their lives. Everybody welcome Rick Ross to the podcast. SPEAKER_11: My God, my brother Melo, Lindsey Bond, Paul Gasol, Pat Riley, Dirk. SPEAKER_17: SPEAKER_17: Listen to the why we're doing way on the I heart radio app, Apple podcast, or whatever you can get your podcast. SPEAKER_12: Hey, everyone, it's Sophia Bush, host of podcast Work in Progress. I am thrilled to tell you that Work in Progress is back for a third season. My friends, it has never been more important than right now for us to have all of these big conversations together, we are going to get educated a little bit enlightened, and we will definitely be entertained. I started Work in Progress because I'm a curious person. And I realized there are so many people I get to speak to that are fascinating and rare. And so I thought, why not take these conversations out into the world. I'm going to be having deep chats with thought leaders, newsmakers, celebrities, entertainers, authors, elected officials, and more. You can join us and listen to Work in Progress on the I heart radio app, Apple podcasts, or wherever you listen to podcasts. SPEAKER_02: Okay, 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. SPEAKER_02: 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. SPEAKER_02: That was the breakthrough. You may have noticed back in about 2013, 2014, all of a sudden, things like Siri and Alexa SPEAKER_02: got way better at what they are doing. They got way less confused. Your navigation app got a lot better. The reason why is because this new type of AI, this new type of machine learning that SPEAKER_04: SPEAKER_02: 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. SPEAKER_07: 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 SPEAKER_07: dive in to the Chinese strategic game Go, because it has been called the most complex game ever devised by humans. SPEAKER_07: That was actually a quote from Demi Hassabi, a neuroscientist and the founder of DeepMind, SPEAKER_07: which was DeepMind, they were purchased by Google or were they always part of Google? I don't know if they were a spun off branch or they were purchased, but it's one of Google's SPEAKER_02: AI outfits. Well, they're one of the teams that are designing these new programs. SPEAKER_07: 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. SPEAKER_07: So that means 170 zeros. And take that number and that's the number of possible configurations of a Go board. SPEAKER_02: Right. So like you say, chess is very complex and complicated and it's very difficult to master Go. I've never played Go with you. No. So it's supposedly it's easy to learn. Right. SPEAKER_07: But very complicated in its simplicity. SPEAKER_02: 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 DeepBlue beat Kasparov. There's 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, DeepBlue would just beat Kasparov and SPEAKER_02: this guy's saying it'll still be 100 years before anyone gets beat at Go by a computer. SPEAKER_07: And he was someone who knew about this stuff. Who's an astrophysicist? SPEAKER_07: He wasn't just some schmo at home and drunk in his recliner. Right. SPEAKER_02: Just making asinine predictions. SPEAKER_07: So 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 SPEAKER_07: solving program. We're just teaching it this game at first just 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. SPEAKER_03: Right. SPEAKER_07: And that's kind of like what we were talking about. It crunches down all these possible options, aka data, to decide what move should I make. SPEAKER_05: Right. SPEAKER_07: And you can apply that. Ideally, they're going to apply this to Alzheimer's and cancer and all sorts of things. SPEAKER_02: Right. It's general purpose thinking, right? Yeah. 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 SPEAKER_02: information that's available on it. There are definite rules, there's structure. It's a good proving ground. SPEAKER_02: But as we'll see, AI makers are getting further and further away from those structure 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. SPEAKER_07: Yeah, it's really, really interesting. The way that they're, like you said earlier before the break, that we don't have computers that can run all the possibilities. SPEAKER_07: So what they teach in the case of AlphaGo, this program teaches itself by playing itself in these games, in Go specifically. SPEAKER_07: And the more it plays itself, the more it learns and the more ability it has during SPEAKER_07: 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, SPEAKER_07: it's able to say, well, in this scenario, there are really only 50 different moves that I could or should make. Right. That's kind of a simplified way to say it. Right. SPEAKER_02: 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. It's 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. SPEAKER_07: Yeah. SPEAKER_02: That is what humans do. SPEAKER_07: 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 are the numbers saying? What does the book say? Right. And you're like, oh, man, I did this move that one time and it didn't go as the book SPEAKER_07: said. SPEAKER_07: Right. So that's now factored into my thinking. SPEAKER_02: Right. Except imagine being able to learn from scratch and get to that point in eight days or eight SPEAKER_02: hours. SPEAKER_02: Yeah. So that Go team, the AlphaGo, the first iteration of AlphaGo, I think they started working on it in 2014. And in 2016, at the end of 2016, they unleashed it secretly onto an AlphaGo website and it started just wiping the floor with everybody. Yeah. Everybody's like, this thing's pretty good. Oh, it's AlphaGo. What year is this? That was the end of 2016. SPEAKER_05: SPEAKER_07: Okay. So chess had already come and gone. Like by this point, you can download a program that's like deep blue, right? SPEAKER_02: That was, that's a great point. Yeah. And today the stuff you play chess with on your laptop is even more advanced than deep blue was in the nineties 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 AlphaGo was replaced with AlphaGo Zero. It learned what AlphaGo had taken two years or three years to learn in 40 days by teaching SPEAKER_02: itself. SPEAKER_07: And it beat the master. SPEAKER_02: Yeah. SPEAKER_07: And finally in May of 2017, AlphaGo took on a Key G, the highest ranked Go player in the SPEAKER_07: world. I don't know if he or she still is. SPEAKER_02: No, Lee Zadol is the current or was until AlphaGo beat him. SPEAKER_02: Oh man. SPEAKER_07: Yeah. Did they get knocked off and AlphaGo is the champion? SPEAKER_02: Yeah. SPEAKER_02: Like that's, that's not fair. SPEAKER_02: If it's match play and the player, the human player has 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? SPEAKER_07: Right. SPEAKER_02: Maybe. Yeah. Interesting. What do they call that? Wetware? Like your brain, your neurons and all that. What? Instead of hardware, it's wetware. Oh, I don't know about that. SPEAKER_02: I think that's the term for it. What does that mean though? SPEAKER_02: It means like you, 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 wetware. SPEAKER_02: Oh, interesting. SPEAKER_02: So that's probably it. It's the wetware champion versus the hardware champion. SPEAKER_07: But wetware is italicized. SPEAKER_02: With the sneer. SPEAKER_07: So where things really got interesting, because you were talking earlier about, what is it with the chess and go, what are they called? What kind of games? SPEAKER_02: Perfect information games. SPEAKER_07: 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 they're a set of rules, but poker is not about SPEAKER_07: the set of rules. It is about sitting down in front of whatever, five or six people and lying and bluffing. SPEAKER_07: And getting away with it. And being bluffed yourself. And your game face. Being bluffed. There's so many human emotions and contextual clues and micro expressions and all these things. Surely you could never, ever teach a machine to win at Texas Hold'em Poker. SPEAKER_02: SPEAKER_07: Yeah, it'll be 100 years at least before that happens, I predict. SPEAKER_07: No. They did it. Man. And more than one team has done it. SPEAKER_02: Yeah, I read, there was one from Carnegie Mellon called Liberatus AI. SPEAKER_07: Go Mellonheads. SPEAKER_02: Yeah. Go the Thornton Mellons. Yeah, I mean, University of Alberta has one called Deep Stack. SPEAKER_02: That was the one I read about. SPEAKER_07: Okay. SPEAKER_02: And it actually, here's the thing, like if you read the release on it, you're like, you don't know how this thing works, do you? Oh, really? SPEAKER_02: Yeah. And I'm pretty sure they don't fully get it. Because that's one of the problems, I actually talk about this in the existential risks series. SPEAKER_07: That's scary, that is to be released. SPEAKER_02: 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? SPEAKER_07: So that's the scariest presentation you will see on AI is when someone says, well, how does all this work? And they go, hmm. SPEAKER_02: But we just know it can beat a human at poker. But the thing about DeepStack at the University of Alberta is that it learned somehow some sort of intuition. Yeah. SPEAKER_02: 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. SPEAKER_02: So that's an imperfect information game. SPEAKER_02: So that would require intuition. And apparently not one, but two different research groups taught AI to intuit. Yeah. SPEAKER_07: Carnegie Mellon came out in January of 2017 with its Liberatus AI. And they said they spent 20 days playing 120,000 hands of Texas Hold'em with four professional poker players and won and smoked them basically. Got up to, they weren't playing with real money obviously, but they would have been great. SPEAKER_02: They were playing with Skittles like me as a kid. SPEAKER_07: 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. SPEAKER_07: So that's a really interesting thing, man, that they could teach, self-teach a program or a program could teach itself intuition. SPEAKER_07: Right. SPEAKER_07: It's creepy. SPEAKER_07: Yeah. I thought this part was interesting, the Atari stuff. SPEAKER_07: This gets pretty fun. Google DeepMind let its AI wreak havoc on Atari. Forty-nine different Atari 2600 games. See if it could figure out how to win. SPEAKER_07: And apparently the most difficult one was Ms. Pac-Man, which is a tough game still, man. Ms. Pac-Man, they nailed it. It's still one of the great games. SPEAKER_02: But their game or their deep Q 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. SPEAKER_07: Amazing. And 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 SPEAKER_07: agents that go out and collect, I guess, data and then move it up the chain to this top agent. SPEAKER_02: Right. And then this thing says, okay, you know, I think that you're probably right. What these agents are probably doing, and I don't know if this is exactly true, but there are models out there like this where the agent says, you have a 90% chance of success at getting this pellet if we take this action. Somebody else says, you've got an 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. SPEAKER_07: They should give them a little cap. SPEAKER_02: But all this is happening like that. Oh, yeah. SPEAKER_02: You know what I'm saying? 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. SPEAKER_07: 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. SPEAKER_07: 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. SPEAKER_02: You know, so again, one of the things about the field of artificial intelligence, it is SPEAKER_07: Which you know a lot about now. SPEAKER_02: Famous. Yeah. It's famous for making huge predictions that did not pan out. Sure. 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 SPEAKER_02: 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. SPEAKER_02: 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. Yeah. SPEAKER_02: 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 the stuff that a human wouldn't necessarily think of, this AI is coming up with. SPEAKER_07: 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, isn't that sort of the thinking that they're basically every dramatic story is a variation of one of seven things. Yeah. SPEAKER_02: You can look at like AI as 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 SPEAKER_02: now that are out there that are, 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 that is just going to 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 is if you put computer minds to thinking about these kinds of things, that's the premise of it. SPEAKER_07: 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 a... SPEAKER_02: Right. Just imagine something that we've never even thought of. Well, do you know how they should do this? SPEAKER_07: If they do do that 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 going to be so under scrutiny. They should secretly release this book. And then after it's a New York Times bestseller, say, meet the Whopper, the author of this. SPEAKER_02: His interests are roller skating, playing tic tac toe and global thermal nuclear war. All right. SPEAKER_07: Should we take a break and get Strickland in here? SPEAKER_02: Yeah, we're going to end the Strickland drought because it is about to rain Strickland in this piece. SPEAKER_07: Ew, gross. SPEAKER_10: Hey, everyone, it's Sophia Bush, host of the podcast Work in Progress. SPEAKER_12: I am thrilled to tell you that Work in Progress is back for a third season. My friends, it has never been more important than right now for us to have all of these big conversations. Together, we are going to get educated, a little bit enlightened, and we will definitely be entertained. I started Work in Progress because I'm a curious person. And I realized there are so many people I get to speak to that are fascinating and rare. And so I thought, why not take these conversations out into the world? I'm going to be having deep chats with thought leaders, newsmakers, celebrities, entertainers, authors, elected officials, and more. You can join us and listen to Work in Progress on the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. SPEAKER_17: What's up, everybody? I'm Dwayne Wade, and I've been blessed to have so many titles so far in my life. But now I'm adding podcast hosts with my new podcast called The Why with Dwayne Wade. How did you feel about me in 2006? Well, there wasn't a lot of love there, I'd say. SPEAKER_17: So there was definitely some cold times. SPEAKER_17: As I step into a new phase of my life after basketball, I find myself with new inspirations, new motivations, and new whys. On this show, I will have intimate conversations with some of the biggest names in sports, in music, in entertainment, in fashion. And we will discuss the whys in their lives. Everybody welcome Rick Ross to the podcast. My God. SPEAKER_17: My brother Melo, Lindsey Vonn, Paul Gasol, Pat Riley. Welcome. SPEAKER_03: Listen to The Why with Dwayne Wade on the iHeartRadio app, Apple Podcasts, or wherever SPEAKER_17: you can get your podcasts. SPEAKER_00: Hi, I'm Sarah Jakes Roberts, host of Woman Evolved Podcast. You may also know me as a pastor, author, wife, mother, business woman, and leader. Women are shattering glass ceilings that once limited their ability to dream, grow, and change the world. Well, as the definition of womanhood continues to advance, so does the woman's need to connect and assess where she fits in this ever-changing world around her. No longer do women have to choose between family or career since you can have it all. You're already superwoman on your own, but imagine how transformative things could be if you allowed yourself the opportunity to embrace sisterhood. Well, over here, we encourage each other. We hold each other accountable. We teach each other and we cast out the spirit of shame. Through honest conversations, sermons, and interviews with other dynamic women, my goal is to empower women around the world to elevate to the best versions of themselves. So girl, get up and listen to the Woman Evolved Podcast every Wednesday on the Black Effect Podcast Network, iHeartRadio app, Apple Podcast, or wherever you get your podcasts. SPEAKER_02: Okay, we're back and get this. The scent of Strik has permeated our place. That's a beautiful scent. SPEAKER_07: It smells like a soldering gun and a circuit board. SPEAKER_02: And a fuel lavender. And a protein bar. SPEAKER_06: That's fair. I was going to say Drakonois, but that would have been a lie. SPEAKER_02: Is that how you say it? I always call it Drakkar. SPEAKER_06: Drakkar? That's fair. SPEAKER_02: Drakkar. SPEAKER_07: I always pronounced it Benetton colors. That was what I wore. Oh, is that what you wore? Yeah, during my, what I call the year of cologne. SPEAKER_02: I had a couple of years. SPEAKER_06: 87-ish. This is scintillating. Why am I here? SPEAKER_02: 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 were 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 without Strik. So let's try something different. SPEAKER_06: 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. SPEAKER_07: War without frontiers, as Peter Gabriel would say. War without fear. SPEAKER_02: 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. SPEAKER_02: We talked about Go. SPEAKER_02: Would those constitute solved games? SPEAKER_06: 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. SPEAKER_07: Which we always assume perfect play, right? Yeah. That's kind of our bag. That's the stuff you should know motto. SPEAKER_06: So perfect play just means that no one ever makes a mistake. So very much the way I do my work. 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. SPEAKER_07: Which is what's in war games. SPEAKER_06: Yes. SPEAKER_06: The only way to win is not to play. SPEAKER_06: Right. Yes. So a game like connect four, whoever goes first is always going to win assuming perfect SPEAKER_06: play on both sides. Really? SPEAKER_06: Yes. SPEAKER_07: I don't think I've played connect four. That's where you drop, or in a long time, that's the one where you drop the little tokens. Yeah, kind of like checkers. SPEAKER_02: We did an interstitial playing connect four, remember? SPEAKER_07: I was faking it though. And then you had perfect play, so I knew it was useless. No, I was going to say that I'm so humiliated by all the connect four games that I've lost. SPEAKER_02: Starting even. SPEAKER_06: 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. 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. SPEAKER_07: Right, which doesn't count. SPEAKER_02: Automatically disqualifies you. One thing I've found that's very enjoyable is playing with little kids who haven't figured out that tic tac toe is very easy to play. Yes. They smash their face on the board and rub it in. SPEAKER_07: Yeah. SPEAKER_06: Same reason why I like to join in on little league games, because I can really wail that ball out of the park. SPEAKER_07: I really miss me feel like a man. That's the most tech stuffy thing you've ever said. You really wail that ball out of the park. SPEAKER_06: Well to be fair, I did just do a tech stuff episode about the technology behind baseball bats. Oh, interesting. Nice. SPEAKER_07: You'll have to listen to that one actually. SPEAKER_06: 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, well recently by the early 90s when it was played against a computer called Chinook and C-H-I-N-O-O-K. SPEAKER_06: 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. SPEAKER_02: SPEAKER_06: 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? You're not committed to going a specific direction with certain pieces. Oh, never thought about that. SPEAKER_06: So 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? SPEAKER_07: Yeah, yeah, we talked about that some. SPEAKER_06: 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? SPEAKER_06: 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. SPEAKER_06: 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. SPEAKER_02: 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 SPEAKER_02: learning. They're just getting better at poker. SPEAKER_02: Do they know how they're learning poker? They just know that they're learning poker and that they're good at it now. SPEAKER_06: Like where's the intuition? SPEAKER_06: 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 SPEAKER_06: 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 SPEAKER_06: and they're in my hand and let's say we're playing Texas Hold'em 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. Well, the computer is 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. SPEAKER_07: But does that mean it's completely ignoring micro expressions and facial cues? So that doesn't even come into play. Yeah, it doesn't. SPEAKER_02: I should say Strickland just nodded yes. SPEAKER_06: Yeah, I was waiting for Chuck to finish. How many years have you been doing this? I still nod. I do a solo show and I do a lot of expressive dance. What do you think Jonathan? I don't know, Jonathan. SPEAKER_06: It gets lonely in here guys. But yes, what you're saying, all the tells, right? The tells that you would use as a human player, the computer does not pick up on this. SPEAKER_06: Oh, so it's just data. SPEAKER_06: Yes. Typically what it would do is it would study the outcomes of the games from a purely statistical expression. All right, well that makes more sense. 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 SPEAKER_06: chips into the table. Eye tick. Right, exactly. It's a little winky face emoticon. I don't have good cards. It's all usually over 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. SPEAKER_07: Kids today. SPEAKER_06: Yeah. SPEAKER_07: I don't know what it's like to be in a smoky saloon. SPEAKER_06: Like Money Maker. When Money Maker rose to the top a few years ago, well 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 computers do not pick up on. And in fact, that leads us sort of into the realm of games where computers don't do as well as humans. Yeah, is that list you sent a joke or is it real? SPEAKER_07: No, that's real. SPEAKER_06: It does seem like it's weird. Like one of the games on there is Pictionary, for example. Or Tag. Or Tag, yeah. 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. SPEAKER_06: 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. SPEAKER_06: But you also know... You 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. SPEAKER_07: I'm just saying. SPEAKER_06: 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. SPEAKER_07: Have you seen that one the other day, the footage of that thing running and jumping? It's really impressive. And super creepy. SPEAKER_06: 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... SPEAKER_07: Or pouring hot coffee in someone's head. Yes. SPEAKER_02: But they always play those clip shows to Yakety Sax. SPEAKER_06: 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. SPEAKER_06: 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. SPEAKER_06: 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. SPEAKER_07: Like a six year old can play Jenga better than a robot. SPEAKER_06: Right. SPEAKER_02: Right, right. Okay, but the thing is we're talking robots here and as we go more and more and more online and our world becomes more and more like 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. The robot is the door. You just blew my mind. That that's becoming more and more vital and important and something we should be paying SPEAKER_06: attention to. It absolutely is something we should pay attention to. I mean we have robotic stock traders. They're trading 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. SPEAKER_07: So the robot army that will ultimately defeat us is not something from the Terminator. It's invisible. SPEAKER_06: Right. It's online. It will be online. It's what's determining our retirement right now. SPEAKER_06: Yeah, the global economy or our municipal water supply or whatever. SPEAKER_06: Yeah, no 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. The 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 artificial intelligence. Do it right from the outset. Exactly. But isn't it too late? SPEAKER_06: Depends. Not necessarily. SPEAKER_06: I think, 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. SPEAKER_07: Yeah. SPEAKER_06: Well, if you're, if you're someone like, if you're someone like Elon Musk, you'd say, if we don't do something now, we're, we're totally going to plummet off the edge of the cliff. SPEAKER_02: But now is a window that is rapidly closing. Yes. SPEAKER_06: Yeah. So 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 in 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. SPEAKER_06: So this is a really fascinating and deep subject that and the games thing is a great entry point into having that conversation. You know, I'm lucky if I can win a game of chess against another human being. Oh, yeah. SPEAKER_02: Right. So I can't even describe chess. SPEAKER_07: My big thing is I do that night thing. I call it the night shuffle. I just move them back and forth. SPEAKER_06: Right. I just castle. If I can castle, then I'm, I'm, I'm so happy. SPEAKER_06: And that's the third tech stuffiest thing. SPEAKER_07: They come in threes. SPEAKER_07: Well, Strik, thank you for stopping by. SPEAKER_02: Thank you so much. I think you should stick around for listener mail. SPEAKER_07: I think you should too. SPEAKER_02: I'd love to. And throw out any funny comments that you have. SPEAKER_06: I'll throw out comments and then Jerry can decide which ones are funny. SPEAKER_02: OK. All right. Fair enough. All right. So if you want to know more about AI, go listen to tech stuff. Strik does this every week. What days? Monday, Tuesday, Wednesday, Thursday, and Friday. Wow. That's amazing, buddy. Yeah. And wherever you find your podcasts. Yep. OK. 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, you're celebrating your 10 year as well, right? SPEAKER_07: Yep. SPEAKER_06: I sure am. I'll be, we'll be turning 10 and tech stuff on June 11th. Oh man. Congratulations. SPEAKER_02: Happy anniversary, man. SPEAKER_02: Thanks. Well, since I said happy anniversary, it means it's time for listener mail. SPEAKER_07: Guys, I'm going to call this Matt Groening and cultural relativism. About that. Nice. SPEAKER_07: Hey guys, love your podcast so much. The massive archive makes for endless learning and entertainment. My favorite part is you are 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, no. OK. I'm reading Drew Bart and 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. 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 sly. You guys are never shy to condemn something that deserves to be condemned. Reminds me of something I read from Yale sociologist, Philip Gorski, 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 or diehard relativists get furious and moralistic at the data fudging of other researchers. SPEAKER_02: Anyway, love the show guys. SPEAKER_07: Love Tech Stuff especially, and we'll forever be indebted to you for your hilarity and knowledge ability. Cheers, Jesse Lusko. SPEAKER_02: P.S. 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 remember that. Because we used to just be like, no judgment, no judgment. Right. 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. SPEAKER_02: Look at us. Yeah. Well, if you want to get in touch with us, you can send us an email to stuffpodcast.howstuffworks.com. You can send John an email to techstuff.howstuffworks.com. Nice. And then hang out with us at our home on the web, stuffyoushouldknow.com. And? Just go to techstuff. SPEAKER_06: Just search it in Google. I come up all the time. Fair enough. SPEAKER_09: Stuff You Should Know is a production of iHeartRadio. For more podcasts to my heart radio, visit the iHeartRadio app, Apple podcasts, or wherever you listen to your favorite shows. SPEAKER_17: I'm Duane Way, and I've been blessed to have so many titles so far in my life. SPEAKER_17: But now I'm adding podcast hosts with my new podcast called The Why with Duane Way. On this show, I will have intimate conversations with some of the biggest names in sports, in music, in entertainment, in fashion, and we will discuss the whys in their lives. Listen to The Why with Duane Way on the iHeartRadio app, Apple podcasts, or wherever you can get your podcasts. SPEAKER_01: Merry Christmas! It's a Wonderful Life is one of the most popular movies ever, but it has more to offer you than you ever thought. You know how long it takes a working man to save $5,000? In this world where there's a lot of hopelessness, people need this movie. George Bailey was never born. Join the many partaking in this one-of-a-kind podcast experience. Listen to all 10 episodes available now on the iHeartRadio app, Apple podcasts, or wherever you get your podcasts. SaveGeorgeBailey.com. Subscribe now. SPEAKER_15: Brothers and daughters, it's always a complicated relationship. At that moment, I fell in love with heroin. Like I spent so much of my life trying so hard not to be like you. Oh my God, I don't want to do this right now. Join me for some raw and honest conversations with my mom. This is Crumbs. It's a show about the things we settle for and the bits of ourselves that make us who we are. Listen to Crumbs season two on the iHeartRadio app, Apple podcasts, or wherever you get your podcasts.