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Speaker 1: Welcome to stuff you should know from how Stuff Works

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Speaker 1: dot com. Hey, and welcome to the podcast. I'm Josh Clark.

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Speaker 1: There's Charles w Chuck Bryant, there's Jerry over there. I'm

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Speaker 1: just gonna come out and tell everybody making fun of

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Speaker 1: me for some weird reason, vaguely weird ways. But I'm

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Speaker 1: all right. So, Chuck, I have a story for you.

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Speaker 1: I'm gonna take us back to the seventeen seventies and

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Speaker 1: the swinging town of Vienna, not Virginia, not Vanna Georgia,

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Speaker 1: but you know that's how they pronounce it, right, Vanna

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Speaker 1: Yanna sausages, right, Vienna, Austria. You ever been there? Vienna, Austria. No,

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Speaker 1: I've been to Brussels. That was pretty close. Vienna's lovely,

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Speaker 1: I'm sure. I think it's a lot like Brussels. Very clean,

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Speaker 1: lovely town. I just remember to be very clean, Yeah,

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Speaker 1: very clean, gorgeous architecture, weird little angled side streets. They're

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Speaker 1: very narrow, very pretty tone. So we're in Vienna and

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Speaker 1: there is a dude skulking about going to the royal

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Speaker 1: Palace in Vienna. His name is Wolfgang von Kimberlin, and

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Speaker 1: he's an inventor. He's an engineer. He's a pretty sharp dude,

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Speaker 1: and he's got with him what would come to be

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Speaker 1: known as the Turk, but he called it the mechanical

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Speaker 1: Turk or the automaton chess player, and that's what it was.

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Speaker 1: It was a It was a wooden figure that moved mechanically,

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Speaker 1: seated at a cabinet and on top of the cabinet

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Speaker 1: was a chessboard. And when he brought it out to

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Speaker 1: show to the Royal Court, he would um, it was cool,

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Speaker 1: kind of but nothing they hadn't seen before, because automata

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Speaker 1: was kind of a hip thing by that. Yeah, people

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Speaker 1: loved building these engineering these automatum machines to do various things.

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Speaker 1: And people are just knocked out by the fact that,

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Speaker 1: you know, you hide these gears and levers behind wood

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Speaker 1: or a cloth and it looks as though there's a

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Speaker 1: real well I'm not real, but you know what I

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Speaker 1: mean is that it's like a real machine. Yeah, but not.

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Speaker 1: They weren't fooled anything like is that a real man?

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Speaker 1: It was, but it was for their time. It was

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Speaker 1: so advanced looking that it's like us seeing ex machina

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Speaker 1: in the movie theater that makes sense. Yeah, no, it

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Speaker 1: does make sense. But imagine seeing like x Makina and

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Speaker 1: being like I've seen this before. This isn't anything special, okay. Yeah,

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Speaker 1: and this thing, to be clear, look like a is

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Speaker 1: it Zoltar or Zoltan from Big Zoltar Sultan? I don't know,

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Speaker 1: it's one of those two, one of those two. Like this,

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Speaker 1: this guy's wearing a turban and it's in a glass

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Speaker 1: case like bust like you know, like a chest up thing. Yeah,

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Speaker 1: he's seated at this this cabinet, so there's no need

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Speaker 1: for legs or anything like that. Yeah. But the thing,

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Speaker 1: this is what was amazing about the Turk. He could

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Speaker 1: play chess, and he could play chess really well. So yeah,

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Speaker 1: he was like an automaton and he moved all herky

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Speaker 1: jerky or whatever. But he could play you in chess,

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Speaker 1: which was a huge, huge advance at the time. Like,

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Speaker 1: this is something that wouldn't come up again until the nineties,

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Speaker 1: more than two hundred years later. This thing, this automaton

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Speaker 1: could play a human being in chess and beat them. Well, yeah,

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Speaker 1: and it looked like when the game started, it would

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Speaker 1: look down at the chessboard and like cock his head

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Speaker 1: like m what should my first move be? And if

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Speaker 1: people I love this part, if people tried to cheat.

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Speaker 1: Apparently Napoleon tried to cheat this thing because this guy

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Speaker 1: he debuted at the Being's court, but then it, you know,

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Speaker 1: it went on a world tour. Yeah, and he was

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Speaker 1: even it was taken over by his successor to the

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Speaker 1: guy who toured with it. Even further, people went nuts

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Speaker 1: for this stuff. They did. They loved it because they

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Speaker 1: were like, this is crazy. I can't believe what I'm seeing.

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Speaker 1: Most people, though, were not taken in by it. There

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Speaker 1: like there's some trick here. But von kempel In and

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Speaker 1: the guy who came after him, I don't remember his name. Um,

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Speaker 1: they would they would demonstrate. You could open this cabinet

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Speaker 1: and you could see all the workings of the mechanical

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Speaker 1: turk inside. Right. So what I was saying is, if

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Speaker 1: this thing's since a cheater like Napoleon supposedly did it,

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Speaker 1: would you know Napoleon would move a piece out of

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Speaker 1: turner illegally or something. This dude, the turk Turk one

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Speaker 1: two would pick up the chess piece move it back

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Speaker 1: as if to say like, no, no, Napoleon, I see

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Speaker 1: what you're doing. And then if the person attempted to

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Speaker 1: move it again, I don't know how many times on

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Speaker 1: you two or three times, eventually it would just go

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Speaker 1: ah and wipe his hand across the board and knock

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Speaker 1: off all the pieces. Game over, which is pretty great,

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Speaker 1: A nice little feature, yeah it is. But it even

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Speaker 1: showed even more that this thing was thinking for itself.

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Speaker 1: That's the key here, right. Chess had been for a

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Speaker 1: very long time viewed as only something that a human

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Speaker 1: would be capable of because it took a human intellect.

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Speaker 1: And there was actually a guy in English um engineer,

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Speaker 1: I think he was a mechanical engineer. His name was

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Speaker 1: Robert Willis. He said that um chess was in quote

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Speaker 1: the province of intellect alone. So the idea that there

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Speaker 1: was this automaton playing chess blew people away. But again

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Speaker 1: people figured out, like, okay, there's something going on here.

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Speaker 1: We think that um von Kempelin is controlling this thing

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Speaker 1: remotely somehow, maybe using magnets or whatever. Other people hit

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Speaker 1: upon the idea that there was a small person inside

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Speaker 1: the cabinet who would hide when the cab when the

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Speaker 1: workings were shown, when the cabinet was open to show

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Speaker 1: the workings, and then when the cabin was closed again

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Speaker 1: and the mechanical turk started playing, the person had crawled

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Speaker 1: back out and it was actually controlling it. This seems

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Speaker 1: to be the case that there was a person controlling it,

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Speaker 1: but the idea that it was it was a machine

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Speaker 1: that could think and beat humans in chess, had like

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Speaker 1: kind of unsettling implications. Yeah, this author Philip Thickness, great name,

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Speaker 1: British author for sure, Philip Thickness. He uh, he said,

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Speaker 1: and you know, people like you said all those more

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Speaker 1: complicated explanations in this article you sent. Astuteley points out

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Speaker 1: that he followed Occam's razor and basically said, he's got

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Speaker 1: a little kid in there. He's got a little a

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Speaker 1: little Bobby Fisher in there that's really good at chess,

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Speaker 1: and that's what's going on. And other people speculated that other,

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Speaker 1: you know, little people might be in there, Um, just

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Speaker 1: adults who would fit in there. But then you know,

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Speaker 1: there's the explanation that he would open it up and

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Speaker 1: shine a candle around and say, you know nothing, just

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Speaker 1: see here everyone. Um, so what should we reveal the

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Speaker 1: real deal? Sure? I think I did already. Well, I

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Speaker 1: don't think you spelled it out is we'll spell it out.

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Speaker 1: There was a little person in there, Yeah, not just

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Speaker 1: one little person, but they would travel around and recruit people.

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Speaker 1: I guess people would get tired of being in there,

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Speaker 1: or they forget about them and they'd starve and have

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Speaker 1: to replace them. But it really was a trick. There

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Speaker 1: was a little person in there. They did the same

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Speaker 1: thing as like the magic acts. You know, when they

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Speaker 1: saw a person in half. It's that the lady just

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Speaker 1: gets into a tiny little ball in one section of

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Speaker 1: that box. But my thing is this, like, this is

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Speaker 1: not a satisfying explanation to me, Chuck, I think it's great.

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Speaker 1: How did the person keep up with the board above? Well?

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Speaker 1: I mean some I don't know if they ever proved

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Speaker 1: exactly how it was going. That's what I'm saying, Okay,

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Speaker 1: whether or not I think they that the zoltar or

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Speaker 1: I'm sorry, the turk was just hollowed out and you

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Speaker 1: you would just put your arms through the arm so

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Speaker 1: you would call up into the church. Yeah, you would

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Speaker 1: become the churk. When the churk would fuse, That's what

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Speaker 1: some people thought. I think that's what Edgar Allen Poe

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Speaker 1: thought too. He would Other people thought that there were

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Speaker 1: the the the little person was underneath in the cabinet

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Speaker 1: operating the turk with levers and stuff like that. Whether

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Speaker 1: it could have been a mirror or something, you know,

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Speaker 1: I guess a little telescopic mirror. That's what's getting me

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Speaker 1: is how would they keep up with the game. You

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Speaker 1: could keep track of the game, but how could you

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Speaker 1: see where the other person moved? You would know where

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Speaker 1: you moved, but you wouldn't be able to see where

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Speaker 1: the other person. That's what I don't get. Just mirrors

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Speaker 1: smoking mirrors maybe so. But the point is is it

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Speaker 1: was a fake. It was a fraud, but it raised

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Speaker 1: some really big questions about the idea of a machine

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Speaker 1: beating a person at something like chess. Yeah, and it

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Speaker 1: really peaked the mind of one Charles Babbage. He was

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Speaker 1: he was a kid or or young at least at

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Speaker 1: the time when he saw the turk in person, and

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Speaker 1: a few years afterward he began work on something called

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Speaker 1: the Difference Engine, which was a machine that he designed

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Speaker 1: too to calculate mathematics automatically. So at some point to

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Speaker 1: this is kind of maybe the beginnings of humans trying

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Speaker 1: to create a I well, yeah, with Babbage's differential machine

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Speaker 1: or difference machine, yeah, difference engine. But at the very least,

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Speaker 1: what this is is the first that I know of

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Speaker 1: example of man versus machine, even though it was really

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Speaker 1: man versus man because it was a man in the machine.

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Speaker 1: It was a fraud, yeah, but it was it. It

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Speaker 1: sparked that idea, It definitely did. And that's something that UM,

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Speaker 1: like chess in particular, has always been like this idea

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Speaker 1: of like, if you can teach a machine to play chess,

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Speaker 1: you really achieved a milestone. And there's been you know,

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Speaker 1: plenty of programs, was most notably Deep Blue, which we'll

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Speaker 1: talk about, but there's there's been this idea that like

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Speaker 1: part of AI is chess, teaching it to play chess.

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Speaker 1: But they, the people who develop a I never set

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Speaker 1: out to make a chess playing AI, just to make

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Speaker 1: a machine that can play chess. That's not the point.

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Speaker 1: Chess has always been this way to demonstrate the progress

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Speaker 1: of artificial intelligence because it's a complex game that you

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Speaker 1: can't just program it, like it almost has to learn. Uh, well,

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Speaker 1: it depends on how you come at it at first, right,

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Speaker 1: So initially they did try to program it Okay, there's

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Speaker 1: this for for from basically nineteen fifty two the about

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Speaker 1: the mid like about say sixty years right. That is

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Speaker 1: how they approached AI and chess is you figured out

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Speaker 1: how to break chess down and explain it to a computer. Now,

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Speaker 1: what if you could, ideally you would have this this

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Speaker 1: computer or this AI, this artificial intelligence UM be able

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Speaker 1: to think about the outcome of every possible every possible

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Speaker 1: outcome of a move before making it. That's just not possible.

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Speaker 1: It's still today we don't have computers that can do that, right,

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Speaker 1: So what you have to do is figure out how

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Speaker 1: to create shortcuts for the machine, give it best practices,

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Speaker 1: that kind of thing. And that was actually laid out

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Speaker 1: in by a guy named Claude Shannon, who is the

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Speaker 1: father of information theory. And he wrote a paper with

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Speaker 1: a pretty on the nose title called programming a Computer

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Speaker 1: for Playing Chess. And you have to say it like

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Speaker 1: that when you say the name, it's got a question

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Speaker 1: market in the end, right. But he laid out two

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Speaker 1: big things, um. One is creating a function of the

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Speaker 1: different moves, and then another one is called a mini max.

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Speaker 1: And if those were the two things that Shannon laid

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Speaker 1: out and they established about fifty or sixty years of

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Speaker 1: development in teaching an AI to play chess. Yeah, so

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Speaker 1: this evaluation function is just sort of the base, the

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Speaker 1: very basis of it all kind of where it starts,

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Speaker 1: which is you ev kind of give a number to

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Speaker 1: create a numerical evaluation based on the state of the

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Speaker 1: board at that moment, and assign a real number evaluation

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Speaker 1: to it. So, um, the highest number that you would

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Speaker 1: shoot for is obviously getting checkmate, getting a king and

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Speaker 1: checkmate right, right, So what you've just done now is

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Speaker 1: by assigning a number to a state like the pieces

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Speaker 1: on a board. Um, what you what you've done is

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Speaker 1: to say, like shoot for this number. The higher the number,

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Speaker 1: like you're going to give this a either rule. Now,

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Speaker 1: the higher the number, the more desirable that this move

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Speaker 1: that could lead to that higher number. Function. Evaluation function

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Speaker 1: is what you want to do, right, like capture the

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Speaker 1: night or capture the queen. Kept of the queen would

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Speaker 1: have a higher evaluation number, right exactly. So that's the function.

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Speaker 1: And there's another one called the mini max. Yeah, this

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Speaker 1: is pretty great where you want to minimize the maximum.

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Speaker 1: And this is another shortcut that they taught computers the

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Speaker 1: maximum loss, that is right. So what they what they

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Speaker 1: talk computers do is so you know, computer can look

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Speaker 1: through an entire game every possible outcome. But what you

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Speaker 1: there are computers that can look pretty far down the

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Speaker 1: line at every possible outcome. And what you can say is, okay, um,

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Speaker 1: you want to find the evaluation function that is the

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Speaker 1: worst case scenario, the maximum loss, and then find the

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Speaker 1: move that will minimize the possibility for that outcome. Yeah.

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Speaker 1: By and this is your only limited by your programming power,

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Speaker 1: but by looking not only at the state of the

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Speaker 1: board right now, but if I make this move and

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Speaker 1: I move the the pond to this spot, what are

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Speaker 1: the next like three moves possibly that could happen as

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Speaker 1: a result of this move. And you're only limited, like

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Speaker 1: I said, by programming power. So obviously, the more juice

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Speaker 1: you have, the more moves ahead that you can look exactly.

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Speaker 1: And then they just shy away from ones with the

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Speaker 1: higher function number or lower function number, depending on how

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Speaker 1: you've programmed it. But they they they're making these decisions

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Speaker 1: based on these rules, um. And then there's other things

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Speaker 1: you can do, like little shortcuts to say, if if A,

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Speaker 1: if A decision tree leads to a the other players

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Speaker 1: King being in checkmate. Don't even think about that move

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Speaker 1: any further, don't evaluate any longer, just abandon it because

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Speaker 1: we would never want to make that move, right. So

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Speaker 1: there's all the shortcuts you can do. And that's what

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Speaker 1: they did to teach computers. That's what Deep Blue did

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Speaker 1: when it beat Gary Kasparo. It was this huge, massive

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Speaker 1: computer that knew a lot of chess, a lot about chess.

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Speaker 1: It had a lot of rules, a lot of incredibly

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Speaker 1: intricate programming that was extremely sharp, and it actually wanted

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Speaker 1: became the first computer to beat an actual human chess

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Speaker 1: grand master in like regulation match play. Yeah. I mean,

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Speaker 1: and I don't think caspar Off gets enough credit for

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Speaker 1: like willing being willing to do this, because it was

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Speaker 1: a big deal for him to lose. It was in

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Speaker 1: this community and the AI community. It was sent shock waves,

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Speaker 1: and everyone that was alive remembers, even if you didn't

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Speaker 1: know anything about either, one remembers Deep Blue being all

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Speaker 1: over the news. It was a really big deal. And

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Speaker 1: Casperrof put his name on the line and lost. Yeah,

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Speaker 1: And I was wondering, Chuck, how how like you would

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Speaker 1: get somebody to do that. I'm sure the Mountain of Catch.

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Speaker 1: I guess that would probably be part of it. But

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Speaker 1: I mean, I don't know. I bet, I bet that's

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Speaker 1: out there. We just I just didn't look it up.

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Speaker 1: So um, that's possible. It's also possible that they said, look, man,

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Speaker 1: like this is chess, we're talking about what ever, But

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Speaker 1: really what you're doing is helping advance artificial intelligence because

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Speaker 1: we're not at we're not really trying ultimately to win

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Speaker 1: chess games. We're trying to cure cancer. Yeah, we're gonna

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Speaker 1: take your title because we're gonna beat you, or our

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Speaker 1: machine is gonna beat you. But even still, you're gonna

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Speaker 1: be helping with cancer. Think of the cancer Casparrow. That's

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Speaker 1: probably what they said. Should we take a break? Yeah? Uh, what?

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Speaker 1: Well should we tease our special guests? First? Is he okay?

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Speaker 1: I can smell him. I don't think we even said

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Speaker 1: we're gonna have a special guest. Later in the episode,

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Speaker 1: Mr Jonathan Strickling of tech stuff nice. It's just been

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Speaker 1: a long time since, like years since we had stricken.

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Speaker 1: The last time we had stricken was like two thousand

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Speaker 1: nine with the Necronomicon episode what is what where has

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Speaker 1: he been besides sitting in between this every day? It's

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Speaker 1: been a strickling drought is It's yes, the stricklands coming later.

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Speaker 1: But we're gonna come back after this and talk a

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Speaker 1: little bit more about man versus machine. Okay, dude, So

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Speaker 1: what we just described was how AI was taught to

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Speaker 1: play things like chess or to think like you take something,

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Speaker 1: you figure out how to break it down into little

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Speaker 1: rules and and and things that a computer can think of, right,

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Speaker 1: and then follow these kind of rules to to make

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Speaker 1: the best decision. That's how it used to be. The

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Speaker 1: way that it's done now that everybody's doing now is

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Speaker 1: where you are creating a machine that teaches itself. That

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Speaker 1: was the breakthrough. You may have noticed back in about

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Speaker 1: two all of a sudden, things like Siri and alexa

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Speaker 1: um got way better at what they are doing. They

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Speaker 1: got way less confused. You're a navigation app got a

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Speaker 1: lot better. And the reason why is because these these

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Speaker 1: this type, this new type of AI, this new type

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Speaker 1: of machine learning that can teach itself and learn on

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Speaker 1: its own, just hit the scene and they just started exploding.

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Speaker 1: And one of the things that they were first trained

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Speaker 1: on was games. Yeah, and it makes sense. Um. And

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Speaker 1: if you thought chess was complicated and difficult, when it

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Speaker 1: comes to these new AI s that they're teaching to

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Speaker 1: teach themselves game strategy, they said, we might as well

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Speaker 1: dive in to the Chinese strategic game Go because it

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Speaker 1: has been called the most complex game ever devised by humans.

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Speaker 1: And this was actually that was actually quote from Demi Hassabi,

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Speaker 1: neuroscientists and the founder of deep Mind, which was deep Mind.

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Speaker 1: They were purchased by Google or where they are always

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Speaker 1: part of Google. I don't know if they were a

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Speaker 1: spun off branch or where they were purchased, but it's

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Speaker 1: one of Google's AI outfits. Well, they're they're one of

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Speaker 1: the teams, yeah, that they are designing these new programs.

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Speaker 1: And to give you an idea of how complex Go is, uh,

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Speaker 1: it deals with a board with with different stones and

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Speaker 1: there are uh ten how do you even say that

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Speaker 1: tend to the hundred and seventy power So that means

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Speaker 1: a hundred and seventies zeros uh and take that number

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Speaker 1: and that's the number of possible configurations of a Go board. Right, So,

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Speaker 1: like you say, chess is very complex and complicated and

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Speaker 1: it's very difficult to master Go. And I've never played

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Speaker 1: Go of you now, so it's supposedly it's easy to learn, right,

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Speaker 1: but very complicated in its simplicity, right right, exactly, it's

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Speaker 1: extremely difficult to master. And there was a guy in

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Speaker 1: the late nineties, and I'm guessing that that he was

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Speaker 1: saying this after Deep Blue beat casper Off. Um. There

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Speaker 1: was an astrophysicists from Princeton. He said that it would

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Speaker 1: probably be a hundred years before a computer beats the

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Speaker 1: human at Go. To give you an idea of just

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Speaker 1: how complex Go is, that deeply would just be cast

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Speaker 1: prov And this guy is saying it will still be

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Speaker 1: a hundred years before any anyone gets beat at Go

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Speaker 1: by a computer. And he was someone who knew about

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Speaker 1: this stuff, who was an astrophysicist. He wasn't just some

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Speaker 1: schmoe at home and drunk in his recliner just making

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Speaker 1: asinine predictions. Um. So, And again we've we've said this before,

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Speaker 1: but I want to reiterate the people that, uh, I

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Speaker 1: think Alpha Go is the name of this program. The

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Speaker 1: people that created this a deep mind. They wanted to

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Speaker 1: stress that this is a problem solving program. We're just

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Speaker 1: teaching it this game at first, to to make it

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Speaker 1: learn and to see if it can get good at

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Speaker 1: what it does. But they said it is built with

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Speaker 1: the idea that any task that has a lot of

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Speaker 1: data that is unstructured, and you want to find patterns

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Speaker 1: in the data and then decide what to do right,

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Speaker 1: And that's kind of like what we're talking about. It's

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Speaker 1: it crunches down all these possible options aka data to

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Speaker 1: decide what move should I make right, and you can

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Speaker 1: apply that. Ideally, they're gonna apply this to Alzheimer's and

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Speaker 1: cancer and all sorts of things. Right. That's general general

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Speaker 1: purpose thinking, right, yeah, and thinking on the fly to

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Speaker 1: faced with novel stuff. So one of the reasons why

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Speaker 1: it's good to use games like chess or go or whatever.

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Speaker 1: Those are called perfect information games where both players or

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Speaker 1: anybody watching has all the information that's available on it,

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Speaker 1: there are definite rules their structure. It's a good proving ground.

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Speaker 1: But as we'll see, AI makers are getting further and

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Speaker 1: further away from those structure games as their AI becomes

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Speaker 1: more and more sophisticated, because the structure and the limitations

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Speaker 1: aren't necessarily needed anymore. Because these things are starting to

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Speaker 1: be able to think on their own in a very

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Speaker 1: generalized and even creative way. Yeah, it's really really interesting

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Speaker 1: the way that they're like you said um earlier and

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Speaker 1: before the break that we we don't have computers that

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Speaker 1: can run all the possibilities. So what they teach in

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Speaker 1: the case of AlphaGo, this program teaches itself by playing

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Speaker 1: itself in these in these games and go specifically, and

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Speaker 1: the more it plays itself, the more it learns, and

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Speaker 1: the more ability it has during a game to choose

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Speaker 1: a move by narrowing down possibilities. So instead of like, well,

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Speaker 1: there are twenty million different variations here, by playing itself,

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Speaker 1: it's able to say, well, in this scenario, there really

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Speaker 1: only fifty different moves that I could or should make.

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Speaker 1: Right or that's kind of a simplified way to say it.

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Speaker 1: But right, No, but it's true. But that's that's exactly right.

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Speaker 1: And what they're doing is basically the same thing that

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Speaker 1: a human does. It's and it's going back to its

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Speaker 1: memory banks, yeah, exact experience and saying well, I've I've

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Speaker 1: been faced with something like this before, and this is

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Speaker 1: what I used and it was successful. Forty out of

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Speaker 1: fifty times. I'll do this one. This is a pretty

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Speaker 1: reasonable move. That is what humans do. Yeah, not only

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Speaker 1: I mean, boy, we screwed up the chess episode, but

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Speaker 1: I get the idea that when you're a chess master,

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Speaker 1: you don't just think what do the numbers say and

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Speaker 1: what does the book say? But oh man, I did

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Speaker 1: this move that one time and it didn't go as

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Speaker 1: the book said. So that's now factored into my thinking, right,

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Speaker 1: except imagine being able to learn from scratch and get

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Speaker 1: to that point in eight days or eight hours. So

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Speaker 1: that Go team, the Alpha Go the first the first

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Speaker 1: iteration of Alpha Go. I think they started working on

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Speaker 1: it in two thousand fourteen and in two thousands sixteen,

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Speaker 1: at the end of two thousand and sixteen, they unleashed

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Speaker 1: it um secretly onto uh an Alpha Go website and

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Speaker 1: it started just wiping the floor with everybody. Everybody's like,

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Speaker 1: this thing is pretty good. Oh it's Alpha Go. Well,

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Speaker 1: here's this it. That was the end of two thousand

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Speaker 1: and sixteen. Okay, so chess had already come and gone.

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Speaker 1: Like by this point, you can download a program that's

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Speaker 1: like deep Blue, right, that was that's a great point. Yeah,

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Speaker 1: Like today the stuff you played chess with on your

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Speaker 1: laptop is even more advanced than Deep Blue was in

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Speaker 1: the nineties. And it's just on your laptop. Um, but

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Speaker 1: this is so, this is Go. This is the end

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Speaker 1: of two thousand and sixteen. The end of two thousand seventeen,

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Speaker 1: um Alpha Go was replaced with Alpha Go zero. It

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Speaker 1: learned what Alpha Go had taken two years or three

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Speaker 1: years to learn in forty days by teaching itself, and

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Speaker 1: it beat the master. And finally, in May of two

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Speaker 1: thousand seventeen, Alpha Go took on Key G, the highest

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Speaker 1: ranked Go player in the world. I don't know if

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Speaker 1: he or she still is. No Lisa A. Doll is

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Speaker 1: the current or was until Alpha alpha Go beat him. Man, yeah,

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Speaker 1: do they get knocked off in Alpha Go is the champion? Like,

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Speaker 1: that's that's not fair. I if it's match play and

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Speaker 1: the player, the human players accepted a challenge from the computer.

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Speaker 1: I don't see why it wouldn't be the world champions.

426
00:25:18,440 --> 00:25:21,879
Speaker 1: Or do they just now say on websites like human

427
00:25:22,040 --> 00:25:28,679
Speaker 1: champions maybe in italics with like a sneer maybe yeah? Interesting?

428
00:25:28,760 --> 00:25:31,639
Speaker 1: What do they call that wet wear? Like your brain,

429
00:25:32,000 --> 00:25:35,040
Speaker 1: your neurons and all that what instead of hardware, it's

430
00:25:35,040 --> 00:25:37,120
Speaker 1: wet wear. Oh, I don't know about that. I think

431
00:25:37,160 --> 00:25:39,360
Speaker 1: that's the term for it. What does that mean? Though?

432
00:25:39,480 --> 00:25:43,480
Speaker 1: It means like you're you have a substrate, right, you're intelligence.

433
00:25:43,520 --> 00:25:47,560
Speaker 1: You're intellect is based on your neurons and they're firing

434
00:25:47,600 --> 00:25:50,000
Speaker 1: all that stuff, and it's wet and squishy and meat.

435
00:25:50,640 --> 00:25:53,080
Speaker 1: And there's hardware that you can do the same thing on,

436
00:25:53,200 --> 00:25:56,320
Speaker 1: you can build intelligence on, but it's hardware, it's not

437
00:25:56,480 --> 00:25:59,800
Speaker 1: wet wear. Interesting, so it's probably it. It's the wet

438
00:26:00,240 --> 00:26:05,000
Speaker 1: champion versus the hardware champion. But wet wears italicized what

439
00:26:05,119 --> 00:26:08,200
Speaker 1: the sneer? Uh So where things really got interesting because

440
00:26:08,200 --> 00:26:12,280
Speaker 1: you were talking earlier about um, what what is it

441
00:26:12,320 --> 00:26:13,760
Speaker 1: with the chess and go? What are they called? What

442
00:26:13,840 --> 00:26:19,000
Speaker 1: kind of games? Perfect information games? Right? Then you think

443
00:26:19,320 --> 00:26:21,960
Speaker 1: And my first thought when you said that was well, yeah,

444
00:26:22,119 --> 00:26:26,240
Speaker 1: then then there's there's games like poker like Texas Hold Him,

445
00:26:26,280 --> 00:26:29,159
Speaker 1: where there are a set of rules. But poker is

446
00:26:29,160 --> 00:26:31,119
Speaker 1: not about the set of rules. It is about sitting

447
00:26:31,119 --> 00:26:36,120
Speaker 1: down in front of whatever five or six people and lying,

448
00:26:35,800 --> 00:26:39,359
Speaker 1: bluffing and getting away with it in your game face

449
00:26:39,720 --> 00:26:44,440
Speaker 1: being bluff Like there's so many human emotions and contextual

450
00:26:44,880 --> 00:26:49,080
Speaker 1: clues and and uh micro expressions and all these things,

451
00:26:49,119 --> 00:26:53,359
Speaker 1: like surely you could never ever teach a machine to

452
00:26:53,440 --> 00:26:56,199
Speaker 1: win at Texas Holding poker. Yeah, it'll be a hundred

453
00:26:56,320 --> 00:27:00,760
Speaker 1: years at least before that happens, I predict. No, they

454
00:27:00,800 --> 00:27:04,240
Speaker 1: did it, and more than one team has done it. Yeah.

455
00:27:04,280 --> 00:27:07,399
Speaker 1: I read um there was one from Carnegie Melon called

456
00:27:08,200 --> 00:27:13,840
Speaker 1: Liberatus Ai go melon heads, Yeah, go the Thornton Melons.

457
00:27:15,000 --> 00:27:19,080
Speaker 1: Uh yeah, I mean that's was. The University of Alberta

458
00:27:19,119 --> 00:27:21,199
Speaker 1: has one called deep Stack. That was the one I

459
00:27:21,240 --> 00:27:24,679
Speaker 1: read about. And it actually here's the thing, like if

460
00:27:24,720 --> 00:27:28,560
Speaker 1: you read the release on it, you're like, you don't

461
00:27:28,560 --> 00:27:31,160
Speaker 1: know how this thing works to you? Yeah, And I'm

462
00:27:31,160 --> 00:27:33,639
Speaker 1: pretty sure they don't fully get it because that's one

463
00:27:33,640 --> 00:27:36,120
Speaker 1: of the problems. Actually talked about this in the Existential

464
00:27:36,200 --> 00:27:40,040
Speaker 1: Risks series that's Scared that is to be released there, right,

465
00:27:40,280 --> 00:27:43,600
Speaker 1: that there is a there is a type of machine

466
00:27:43,680 --> 00:27:46,679
Speaker 1: learning where the machine teaches itself, but we don't really

467
00:27:46,800 --> 00:27:50,040
Speaker 1: understand how it's teaching probably the scariest one, right, or

468
00:27:50,040 --> 00:27:52,480
Speaker 1: what it's learning, But that's the most prevalent one. That's

469
00:27:52,520 --> 00:27:54,840
Speaker 1: what a lot of this is is like these machines.

470
00:27:54,880 --> 00:27:59,800
Speaker 1: It's like, here's here's chess, go figure it out and

471
00:27:59,840 --> 00:28:02,680
Speaker 1: they go, okay, got it. How did you do that?

472
00:28:02,880 --> 00:28:06,119
Speaker 1: Wouldn't you like to know? So that's the scariest presentation

473
00:28:06,160 --> 00:28:09,159
Speaker 1: you will see on ai as when someone says, well,

474
00:28:09,160 --> 00:28:12,040
Speaker 1: how does all this work? And they go, but we

475
00:28:12,160 --> 00:28:14,520
Speaker 1: just know it can beat the human at poker. But

476
00:28:14,600 --> 00:28:17,560
Speaker 1: the thing about Deep Stack at the University of Alberta

477
00:28:17,640 --> 00:28:22,600
Speaker 1: is that it learned somehow some sort of intuition, because

478
00:28:22,640 --> 00:28:26,359
Speaker 1: that's what's required is not just the perfect information where

479
00:28:26,440 --> 00:28:30,120
Speaker 1: you have all the information on the board. It's with poker,

480
00:28:30,200 --> 00:28:32,280
Speaker 1: you don't know what the other person's cards are and

481
00:28:32,320 --> 00:28:34,399
Speaker 1: you don't know if they're lying or bluffing or what

482
00:28:34,440 --> 00:28:39,440
Speaker 1: they're doing. Um, so that's an imperfect information game. So

483
00:28:39,520 --> 00:28:42,880
Speaker 1: that would require intuition. And apparently not one, but two

484
00:28:42,960 --> 00:28:47,000
Speaker 1: different research groups taught AI too into it. Yeah, Carnegie

485
00:28:47,000 --> 00:28:50,240
Speaker 1: Mellon came out in uh January of two thousand seventeen

486
00:28:50,760 --> 00:28:54,640
Speaker 1: with its Liberatus AI and they said they spent twenty

487
00:28:54,680 --> 00:28:58,520
Speaker 1: days playing a hundred and twenty thou hands of Texas

488
00:28:58,520 --> 00:29:04,360
Speaker 1: hold them with four professional poker players and one and

489
00:29:04,520 --> 00:29:07,240
Speaker 1: smoked him. Basically got up to They weren't playing with

490
00:29:07,280 --> 00:29:12,760
Speaker 1: real money obviously, but they they would have been great funded.

491
00:29:12,800 --> 00:29:16,479
Speaker 1: Their next project, Liberatis, was up by one point seven million,

492
00:29:17,200 --> 00:29:19,000
Speaker 1: and one of the quotes from one of the poker

493
00:29:19,000 --> 00:29:22,120
Speaker 1: players that he made two Wired magazine said, I felt

494
00:29:22,160 --> 00:29:24,440
Speaker 1: like I was playing against someone who was cheating, like

495
00:29:24,560 --> 00:29:27,000
Speaker 1: it could see my cards. I'm not accusing it of cheating,

496
00:29:27,200 --> 00:29:32,080
Speaker 1: it was just that good. So that's a really interesting thing. Man,

497
00:29:32,120 --> 00:29:37,520
Speaker 1: that they could teach self teach program, or a program

498
00:29:37,520 --> 00:29:42,160
Speaker 1: could teach itself intuition. That's creepy. I thought this part

499
00:29:42,240 --> 00:29:46,240
Speaker 1: was interesting. The Atari stuff, Um, this gets pretty fun.

500
00:29:46,280 --> 00:29:52,640
Speaker 1: Google deep mind. Let it's a i um reek havoc

501
00:29:52,680 --> 00:29:56,880
Speaker 1: on Atari forty nine different at twenty games, see they

502
00:29:56,880 --> 00:29:59,880
Speaker 1: could figure out how to win, and apparently the most

503
00:30:00,280 --> 00:30:04,640
Speaker 1: difficult one was Miss Pacman, which is a tough game.

504
00:30:04,720 --> 00:30:07,720
Speaker 1: Still man, Miss pac Man. They nailed it. It's still

505
00:30:07,760 --> 00:30:11,040
Speaker 1: one of the great games. But but there um their

506
00:30:11,120 --> 00:30:17,040
Speaker 1: game or their Q deep Q network algorithm beat it. Yeah.

507
00:30:17,080 --> 00:30:21,520
Speaker 1: I think it got the highest score nine hundred points,

508
00:30:21,560 --> 00:30:24,200
Speaker 1: and no human or machine has ever achieved that high

509
00:30:24,240 --> 00:30:26,719
Speaker 1: score from what I understand. Amazing and the way this

510
00:30:26,760 --> 00:30:30,760
Speaker 1: one does it. The hybrid reward architecture that it uses

511
00:30:31,480 --> 00:30:34,080
Speaker 1: is really interesting. It's it says here, it generates a

512
00:30:34,120 --> 00:30:38,240
Speaker 1: top agent that's like a senior manager, and then all

513
00:30:38,280 --> 00:30:41,800
Speaker 1: these other hundred and fifty individual agents. So it's almost

514
00:30:41,840 --> 00:30:46,960
Speaker 1: like they've devised this artificial structural hierarchy of these little

515
00:30:47,000 --> 00:30:50,720
Speaker 1: worker agents to go out and collect I guess data

516
00:30:51,720 --> 00:30:56,400
Speaker 1: and then move it up the chain to this top agent. Right,

517
00:30:56,520 --> 00:31:00,840
Speaker 1: and then this thing says, um, Okay, you know, I

518
00:31:00,880 --> 00:31:05,760
Speaker 1: think that you're probably right what these agents are probably doing.

519
00:31:05,760 --> 00:31:08,320
Speaker 1: And I don't know this is exactly true, but there's

520
00:31:08,440 --> 00:31:11,240
Speaker 1: there are models out there like this where the agent

521
00:31:11,360 --> 00:31:14,920
Speaker 1: says this is um, you have a nine chance of

522
00:31:14,960 --> 00:31:18,840
Speaker 1: success at getting this pellet um if we take this action.

523
00:31:19,200 --> 00:31:21,800
Speaker 1: Somebody else says, you've got a two percent chance of

524
00:31:21,800 --> 00:31:24,360
Speaker 1: evading this ghost if we go this way. And then

525
00:31:24,560 --> 00:31:28,440
Speaker 1: the the top agent, the senior manager, can put all

526
00:31:28,440 --> 00:31:30,080
Speaker 1: this stuff together and say, well, if I listen to

527
00:31:30,120 --> 00:31:32,160
Speaker 1: this guy and this guy done, only will I evade

528
00:31:32,160 --> 00:31:35,120
Speaker 1: this ghost. I'll go get this pellet um. And it's

529
00:31:35,160 --> 00:31:40,040
Speaker 1: based on what what confidence level that the lower agents

530
00:31:40,080 --> 00:31:44,600
Speaker 1: have in success in recommending these moves, and then the

531
00:31:44,640 --> 00:31:47,880
Speaker 1: top agent ways these things. They should give him a

532
00:31:47,920 --> 00:31:51,080
Speaker 1: little a little cap But all this is happening like that,

533
00:31:51,760 --> 00:31:53,440
Speaker 1: you know what I'm saying. This isn't like well, hold on,

534
00:31:53,520 --> 00:31:55,720
Speaker 1: hold on, everybody, what is Harvey? Harvey? What do you

535
00:31:55,760 --> 00:31:58,200
Speaker 1: have to say? Well, let's get some Chinese in here

536
00:31:58,240 --> 00:32:00,960
Speaker 1: and and hash it out. And everybody sits there in

537
00:32:01,080 --> 00:32:03,400
Speaker 1: order some Chinese food, and then you wait for it

538
00:32:03,440 --> 00:32:05,640
Speaker 1: to come and then you pick up the meeting from

539
00:32:05,680 --> 00:32:09,200
Speaker 1: that point on. And then finally Harvey gives his idea,

540
00:32:09,240 --> 00:32:11,240
Speaker 1: but he forgot what he was talking about, so he

541
00:32:11,360 --> 00:32:14,080
Speaker 1: just sits down and eats his agrol. Well here's a

542
00:32:14,120 --> 00:32:17,720
Speaker 1: pretty frightening. Uh. Survey. Uh. There was a survey of

543
00:32:17,720 --> 00:32:20,880
Speaker 1: more than three fifty AI researchers and they had the

544
00:32:20,920 --> 00:32:23,080
Speaker 1: following things to say. And these are the pros that

545
00:32:23,120 --> 00:32:25,920
Speaker 1: are doing this for a living. They predicted that within

546
00:32:26,160 --> 00:32:29,120
Speaker 1: ten years, ay, I will drive better than we do,

547
00:32:30,880 --> 00:32:32,959
Speaker 1: they will be able to write a best selling novel,

548
00:32:33,240 --> 00:32:37,880
Speaker 1: A I will generate this, and bye be better at

549
00:32:37,920 --> 00:32:42,640
Speaker 1: performing surgery than humans are. You know. So again, one

550
00:32:42,640 --> 00:32:46,640
Speaker 1: of the things that about the field of artificial intelligence

551
00:32:47,120 --> 00:32:50,200
Speaker 1: a you know a lot about now famous. It is

552
00:32:50,400 --> 00:32:54,000
Speaker 1: famous for making huge predictions that did not pan out.

553
00:32:54,880 --> 00:32:58,040
Speaker 1: But you've also seen it's it's also famous for beating

554
00:32:58,080 --> 00:33:02,560
Speaker 1: predictions that you know been levied against it um. But

555
00:33:02,680 --> 00:33:05,720
Speaker 1: there is something in there, Chuck, that stands out to me,

556
00:33:06,160 --> 00:33:08,440
Speaker 1: and that's the idea of an AI writing a novel.

557
00:33:09,000 --> 00:33:12,160
Speaker 1: Like for a very long time, I thought, well, yeah, okay,

558
00:33:12,320 --> 00:33:15,040
Speaker 1: you can teach a robot arm to like put a

559
00:33:15,040 --> 00:33:17,959
Speaker 1: car part or something somewhere if you wanted to just

560
00:33:18,000 --> 00:33:21,000
Speaker 1: follow these these mechanical things, or it can use in

561
00:33:21,040 --> 00:33:23,920
Speaker 1: a wish, or it can use logic and reason. But

562
00:33:24,000 --> 00:33:27,240
Speaker 1: to create that's different, right. That was like the new frontier.

563
00:33:27,560 --> 00:33:29,320
Speaker 1: It used to be chess and then it used there

564
00:33:29,440 --> 00:33:33,120
Speaker 1: was go. The next frontier is creativity, and they're starting

565
00:33:33,160 --> 00:33:36,280
Speaker 1: to bang on that door big time. There's a game

566
00:33:36,360 --> 00:33:40,240
Speaker 1: designing AI called Angelina out of the University of foul Myth,

567
00:33:40,600 --> 00:33:43,320
Speaker 1: which I always want to say foul Mouth, but we'll

568
00:33:43,360 --> 00:33:46,760
Speaker 1: just call it Foulmouth like it's supposed to. And Angelina

569
00:33:46,880 --> 00:33:52,960
Speaker 1: actually comes up with ideas for new games, not um,

570
00:33:53,000 --> 00:33:56,760
Speaker 1: like a different level or something like like you should

571
00:33:56,800 --> 00:33:59,440
Speaker 1: put a purple loincloth on that player. You know that'll

572
00:33:59,440 --> 00:34:03,080
Speaker 1: look kind of like new games, but whacked out games

573
00:34:03,080 --> 00:34:06,200
Speaker 1: that humans would never think of. One example I saw

574
00:34:06,320 --> 00:34:10,280
Speaker 1: is in a Dungeon Battle Royal game, a player controls

575
00:34:10,320 --> 00:34:13,040
Speaker 1: like ten players at once, and some you have to

576
00:34:13,120 --> 00:34:16,080
Speaker 1: sacrifice to be killed to save the others. Like the

577
00:34:16,120 --> 00:34:18,960
Speaker 1: stuff that human wouldn't necessarily think of, this AI is

578
00:34:19,000 --> 00:34:22,000
Speaker 1: coming up with. Well, I mean, when you think of creatively,

579
00:34:22,080 --> 00:34:24,759
Speaker 1: especially something like writing a novel or a film, if

580
00:34:24,800 --> 00:34:28,160
Speaker 1: there are only seven stories, I mean, and that sort

581
00:34:28,200 --> 00:34:32,719
Speaker 1: of thinking that they're basically every every dramatic story is

582
00:34:32,760 --> 00:34:35,360
Speaker 1: a variation of one of seven things. Yeah. So I

583
00:34:35,360 --> 00:34:39,320
Speaker 1: mean you can look at like um AI is scary,

584
00:34:39,440 --> 00:34:43,000
Speaker 1: and in some ways very much is and can be,

585
00:34:43,560 --> 00:34:46,600
Speaker 1: but there's also like definitely a level of excitement of

586
00:34:46,680 --> 00:34:50,080
Speaker 1: the whole thing and the idea that there are artificial

587
00:34:50,200 --> 00:34:53,359
Speaker 1: minds that are coming online or that have come online now,

588
00:34:53,400 --> 00:34:56,440
Speaker 1: that are out there that are they'll they'll just naturally

589
00:34:56,480 --> 00:34:59,840
Speaker 1: by definition, see things differently than we do, and the

590
00:35:00,000 --> 00:35:02,000
Speaker 1: idea that they can come up with stuff that we've

591
00:35:02,000 --> 00:35:03,839
Speaker 1: never even thought of that is just gonna knock our

592
00:35:03,880 --> 00:35:07,920
Speaker 1: socks off, hopefully in good ways. Um that's that's a

593
00:35:07,960 --> 00:35:11,279
Speaker 1: really cool thing. And so maybe there's just seven as

594
00:35:11,320 --> 00:35:14,560
Speaker 1: far as humans know, but there's an unlimited amount if

595
00:35:14,640 --> 00:35:17,120
Speaker 1: if you put computer minds to thinking about these kind

596
00:35:17,160 --> 00:35:19,640
Speaker 1: of things, that's the premise of it. Right, So the

597
00:35:19,719 --> 00:35:22,760
Speaker 1: robot would be like you never thought of boy meets

598
00:35:22,880 --> 00:35:28,719
Speaker 1: girl meets well trilo bite. But see even that's a

599
00:35:28,800 --> 00:35:33,640
Speaker 1: variation of right, just imagine something that just we've never

600
00:35:33,680 --> 00:35:35,920
Speaker 1: even thought. Well, Gina, how they should do this? If

601
00:35:35,920 --> 00:35:39,680
Speaker 1: they do do that is uh is not is just

602
00:35:39,800 --> 00:35:43,120
Speaker 1: release a book and not tell anyone that was written

603
00:35:43,560 --> 00:35:46,520
Speaker 1: by an AI program, because if they do that, then

604
00:35:46,560 --> 00:35:49,600
Speaker 1: it's going to be so under scrutiny. They should secretly

605
00:35:49,600 --> 00:35:52,000
Speaker 1: release this book and then after it's a New York

606
00:35:52,000 --> 00:35:57,400
Speaker 1: Times bestseller, Say, meet the Whopper, the author of this.

607
00:35:58,160 --> 00:36:01,839
Speaker 1: You know his interests are rule skating, playing tic tac toe,

608
00:36:01,920 --> 00:36:05,200
Speaker 1: and global thermal nuclear war. All right, should we take

609
00:36:05,239 --> 00:36:07,080
Speaker 1: a break and get strickling in here. Yeah, we're gonna

610
00:36:07,160 --> 00:36:09,720
Speaker 1: end the strickling drought because it is about to reign

611
00:36:09,840 --> 00:36:36,759
Speaker 1: strickling in this piece. Gross. Okay, we're back and get this.

612
00:36:37,880 --> 00:36:41,600
Speaker 1: The scent of strick has permeated our place, and that's

613
00:36:41,600 --> 00:36:46,000
Speaker 1: a beautiful scent. It smells like a soldering gun and

614
00:36:46,080 --> 00:36:48,880
Speaker 1: a circuit board. You know, feel the lavender in a

615
00:36:48,920 --> 00:36:52,759
Speaker 1: protein bar. That's fair. I'm gonna say, Drac o' noir

616
00:36:52,920 --> 00:36:54,600
Speaker 1: that that would have been a lie. Is that how

617
00:36:54,680 --> 00:36:58,520
Speaker 1: you said? I always called it Drecar, drac Car. That's

618
00:36:58,800 --> 00:37:02,320
Speaker 1: that's fair, d Car. I always pronounced it Benetton colors.

619
00:37:03,200 --> 00:37:05,680
Speaker 1: I think that was what I wore. Oh is that

620
00:37:05,719 --> 00:37:07,640
Speaker 1: what you wore? Yeah? During my what I call the

621
00:37:07,719 --> 00:37:13,200
Speaker 1: Year of Cologne, I had a couple of seven. Uh,

622
00:37:13,880 --> 00:37:16,399
Speaker 1: this is scintillating. Why are you? Why am I here?

623
00:37:17,600 --> 00:37:20,120
Speaker 1: So we know that you already know because we talked

624
00:37:20,239 --> 00:37:23,239
Speaker 1: via email about this, but we'll tell everybody else. We

625
00:37:23,560 --> 00:37:26,080
Speaker 1: have brought you in here because you're the master of tech.

626
00:37:26,200 --> 00:37:29,160
Speaker 1: And we were talking tech today, which we've talked about

627
00:37:29,200 --> 00:37:31,319
Speaker 1: without you before. But frankly, Chuck and I and Jerry

628
00:37:31,400 --> 00:37:33,640
Speaker 1: huddled and we said this is not quite as good

629
00:37:33,719 --> 00:37:37,080
Speaker 1: without strict so let's try something different. Got you and

630
00:37:37,080 --> 00:37:42,120
Speaker 1: and we're talking about games and machine versus man and

631
00:37:42,440 --> 00:37:46,120
Speaker 1: that whole whole evolution and how that's gone super crazy

632
00:37:46,120 --> 00:37:49,160
Speaker 1: over the last few years. Games without frontiers, Peter Gabriel

633
00:37:49,200 --> 00:37:51,799
Speaker 1: would say, war without fear. And we've talked, I mean,

634
00:37:51,800 --> 00:37:55,360
Speaker 1: we've talked a lot about the evolution of machine learning

635
00:37:55,440 --> 00:37:57,480
Speaker 1: and how now it's starting to take off like a

636
00:37:57,560 --> 00:38:00,880
Speaker 1: rocket because they can teach themselves. But one thing we

637
00:38:00,920 --> 00:38:04,560
Speaker 1: haven't really talked about are solved games. And we talked

638
00:38:04,560 --> 00:38:08,799
Speaker 1: about chess. Yeah, we talked about go right with those

639
00:38:08,800 --> 00:38:12,400
Speaker 1: constitutes solved games, not really so so a solved game

640
00:38:12,640 --> 00:38:15,680
Speaker 1: is the concept where if you were to assume perfect

641
00:38:15,719 --> 00:38:19,080
Speaker 1: play on either sides of the game, you would always

642
00:38:19,080 --> 00:38:21,160
Speaker 1: know how it was going to end, which we always

643
00:38:21,160 --> 00:38:24,480
Speaker 1: assume perfect play, right, that's kind of work bags stuff

644
00:38:24,520 --> 00:38:27,200
Speaker 1: should know motto. So perfect play just means that no

645
00:38:27,239 --> 00:38:29,480
Speaker 1: one ever makes a mistake, so very much the way

646
00:38:29,480 --> 00:38:33,640
Speaker 1: I do my work right stuff exactly. So, if you

647
00:38:33,880 --> 00:38:36,040
Speaker 1: were to take a game like Tic tac toe and

648
00:38:36,120 --> 00:38:38,840
Speaker 1: you assume perfect play on both sides, it is always

649
00:38:38,840 --> 00:38:41,200
Speaker 1: going to end in a draw, which is what's in

650
00:38:41,239 --> 00:38:43,880
Speaker 1: war games. Yes, right, the only way to win is

651
00:38:43,920 --> 00:38:48,560
Speaker 1: not to play. Yes, so a game, it's a game

652
00:38:48,640 --> 00:38:52,200
Speaker 1: like Connect four, whoever goes first is always going to win,

653
00:38:52,600 --> 00:38:57,239
Speaker 1: assuming perfect play sides. Yes, what, I don't think I've

654
00:38:57,239 --> 00:38:59,480
Speaker 1: played Connect for it. That's where you dropped for a

655
00:38:59,480 --> 00:39:03,240
Speaker 1: long time, when where you drop the little uh tokens

656
00:39:04,120 --> 00:39:07,320
Speaker 1: kind of checkers. We did an interstitial playing Connect for Remember,

657
00:39:07,600 --> 00:39:09,520
Speaker 1: I was speaking it though, and you had perfect place,

658
00:39:09,560 --> 00:39:11,600
Speaker 1: so I knew it was useless. I was gonna say

659
00:39:11,640 --> 00:39:14,720
Speaker 1: that I'm so humiliated by all the Connect four games

660
00:39:14,719 --> 00:39:18,759
Speaker 1: that I've lost starting even Yeah, but but I mean

661
00:39:18,800 --> 00:39:22,719
Speaker 1: perfect play. That's something that that obviously only the the

662
00:39:22,760 --> 00:39:27,680
Speaker 1: best players typically achieve with with significantly complex games. Obviously,

663
00:39:27,719 --> 00:39:30,759
Speaker 1: the simpler the game, the easier it is to play perfectly.

664
00:39:31,000 --> 00:39:34,000
Speaker 1: Right tic tac toe? If you know, once you've mastered

665
00:39:34,040 --> 00:39:36,719
Speaker 1: the basics of tic tac toe and the other person has,

666
00:39:36,960 --> 00:39:39,680
Speaker 1: you're never really going to win unless someone has just

667
00:39:39,760 --> 00:39:43,040
Speaker 1: made a silly mistake because they weren't paying attention like

668
00:39:43,440 --> 00:39:46,440
Speaker 1: a star instead of an ex right, which doesn't count

669
00:39:46,880 --> 00:39:50,320
Speaker 1: automatically disqualified you. One thing I found this very enjoyable

670
00:39:50,400 --> 00:39:52,480
Speaker 1: is playing with little kids who haven't figured out that

671
00:39:52,560 --> 00:39:55,480
Speaker 1: tic tac toe is very easy to bring. Yes, smashed

672
00:39:55,480 --> 00:39:58,239
Speaker 1: their face on the board, rub it in. Yeah, the

673
00:39:58,280 --> 00:40:00,479
Speaker 1: same reason why I like to join in on little

674
00:40:00,520 --> 00:40:02,799
Speaker 1: league games, because I can really whale that ball out

675
00:40:02,800 --> 00:40:05,160
Speaker 1: of the park. Yeah, it really missed me, feel like

676
00:40:05,160 --> 00:40:07,480
Speaker 1: a man. That's the most tech stuffy thing you've ever said.

677
00:40:07,960 --> 00:40:09,959
Speaker 1: You really wail that ball out of the park. Well,

678
00:40:10,000 --> 00:40:12,320
Speaker 1: to be fair, I did just do a tech stuff

679
00:40:12,320 --> 00:40:17,160
Speaker 1: episode about the technology behind baseball bats so fresh. Actually

680
00:40:17,200 --> 00:40:19,359
Speaker 1: it's a lot of fun. So there have been a

681
00:40:19,360 --> 00:40:22,040
Speaker 1: lot of games that have been solved, but Checkers was

682
00:40:22,120 --> 00:40:25,160
Speaker 1: one that was recently solved back in recently by the

683
00:40:25,360 --> 00:40:29,680
Speaker 1: early nineties when it was played against a computer called

684
00:40:29,840 --> 00:40:33,759
Speaker 1: Chinook and uh h I in O okay, yeah, like

685
00:40:33,800 --> 00:40:37,200
Speaker 1: the helicopter or the winds that blow through Alberta exactly.

686
00:40:37,680 --> 00:40:42,400
Speaker 1: And so there are certain games that are more easily

687
00:40:42,440 --> 00:40:45,359
Speaker 1: solved than others. You do it through an algorithm, but

688
00:40:45,520 --> 00:40:48,319
Speaker 1: other games, like chess, are more complicated because you can

689
00:40:48,719 --> 00:40:51,160
Speaker 1: in chess you have multiple moves that you can do

690
00:40:51,320 --> 00:40:53,719
Speaker 1: where you can you can move a piece back the

691
00:40:53,719 --> 00:40:56,319
Speaker 1: way you win, right, it's not you're not committed to

692
00:40:56,360 --> 00:40:59,239
Speaker 1: going a specific direction with certain pieces, like with a

693
00:40:59,360 --> 00:41:01,560
Speaker 1: night you know you can you could go right back

694
00:41:01,560 --> 00:41:03,759
Speaker 1: to where you started on the next move if you

695
00:41:03,800 --> 00:41:07,960
Speaker 1: wanted to, and that creates more complexity. So the more

696
00:41:08,000 --> 00:41:10,840
Speaker 1: complex the game, the more difficult it is to solve.

697
00:41:10,920 --> 00:41:14,319
Speaker 1: And some games are not solvable simply because you'll never

698
00:41:14,480 --> 00:41:18,240
Speaker 1: know what the full state of the game is from

699
00:41:18,280 --> 00:41:21,879
Speaker 1: any given moment. Did you have a chance to talk

700
00:41:21,920 --> 00:41:25,840
Speaker 1: about the difference between perfect knowledge and imperfect knowledge and

701
00:41:25,880 --> 00:41:28,800
Speaker 1: a game. Yeah, yeah, we talked about that. Yeah, so

702
00:41:28,800 --> 00:41:32,880
Speaker 1: so computers obviously, they do really well if they understand

703
00:41:33,320 --> 00:41:35,439
Speaker 1: the exact state of the game all the way through.

704
00:41:35,440 --> 00:41:37,400
Speaker 1: If they if they have perfect knowledge, all of the

705
00:41:37,440 --> 00:41:40,480
Speaker 1: information is there on the board right and and all

706
00:41:40,520 --> 00:41:43,360
Speaker 1: players can see all information at all times. But games

707
00:41:43,400 --> 00:41:45,960
Speaker 1: like poker what you guys talked about, obviously you have

708
00:41:46,080 --> 00:41:49,640
Speaker 1: imperfect information. You only know part of the state of

709
00:41:49,680 --> 00:41:52,880
Speaker 1: the game. That's why those games have been more difficult,

710
00:41:52,920 --> 00:41:57,680
Speaker 1: more challenging for computers to get better than humans until

711
00:41:57,840 --> 00:42:00,719
Speaker 1: relatively recently. And there've been two major ways of doing that.

712
00:42:00,760 --> 00:42:03,279
Speaker 1: You either throw more processing power at it, like you

713
00:42:03,360 --> 00:42:07,600
Speaker 1: get a supercomputer, or you create neural networks, artificial neural networks,

714
00:42:07,600 --> 00:42:11,840
Speaker 1: and you start teaching computers to quote unquote learn the

715
00:42:11,920 --> 00:42:15,160
Speaker 1: white people do. So we talked about that, and one

716
00:42:15,200 --> 00:42:17,799
Speaker 1: of the things that we talked about was how there's

717
00:42:17,800 --> 00:42:22,399
Speaker 1: this idea that they the programmers, especially, say, the people

718
00:42:22,440 --> 00:42:24,680
Speaker 1: who are making programs that are playing poker and are

719
00:42:24,680 --> 00:42:28,800
Speaker 1: getting good at poker, aren't exactly sure how the machines

720
00:42:28,840 --> 00:42:31,520
Speaker 1: are learning to play poker or what they're learning. They're

721
00:42:31,560 --> 00:42:35,600
Speaker 1: just getting better at poker. Do they know how they're

722
00:42:35,680 --> 00:42:38,120
Speaker 1: learning poker? They just know that they're learning poker and

723
00:42:38,120 --> 00:42:41,719
Speaker 1: that they're good at it. Like, where's the intuition? How

724
00:42:41,840 --> 00:42:44,319
Speaker 1: is that being learned? An excellent question. The way it

725
00:42:44,360 --> 00:42:47,560
Speaker 1: typically is learned, especially with artificial neural networks, is that

726
00:42:47,640 --> 00:42:52,080
Speaker 1: you you set up the computer to play millions of

727
00:42:52,320 --> 00:42:56,200
Speaker 1: hands of poker that are are randomly assigned, so it's

728
00:42:56,239 --> 00:42:59,759
Speaker 1: it's truly as random as computers can get. That's the

729
00:42:59,760 --> 00:43:02,160
Speaker 1: whole philosophical discussion that I don't think we're ready to

730
00:43:02,200 --> 00:43:06,799
Speaker 1: go into right now. But you have games come up

731
00:43:06,800 --> 00:43:10,799
Speaker 1: where the computer is playing itself millions upon millions of

732
00:43:10,880 --> 00:43:15,560
Speaker 1: times and learning every single time how the statistics play out,

733
00:43:15,920 --> 00:43:19,319
Speaker 1: how different betting strategies play out. It's it's sort of

734
00:43:19,360 --> 00:43:23,800
Speaker 1: partitioning its own mind to play against itself. And through

735
00:43:23,840 --> 00:43:26,680
Speaker 1: that process, it's as if you, as a human player,

736
00:43:27,160 --> 00:43:30,279
Speaker 1: we're playing thousands of games with your friends and you

737
00:43:30,320 --> 00:43:33,520
Speaker 1: start to figure out, oh, when I have these particular

738
00:43:33,560 --> 00:43:35,960
Speaker 1: cards and they're in my hand, and let's say we're

739
00:43:35,960 --> 00:43:39,160
Speaker 1: playing Texas, hold them and the community cards are are these?

740
00:43:39,320 --> 00:43:43,000
Speaker 1: Then I know that generally speaking, maybe three times out

741
00:43:43,040 --> 00:43:45,680
Speaker 1: of ten I end up winning, maybe I shouldn't bet.

742
00:43:45,840 --> 00:43:47,719
Speaker 1: And well, the computer is doing that, but on a

743
00:43:47,800 --> 00:43:51,120
Speaker 1: scale that far dwarfs what any human can do, and

744
00:43:51,160 --> 00:43:53,040
Speaker 1: in a in a fraction in the amount of time,

745
00:43:53,640 --> 00:43:57,040
Speaker 1: And so it's it's sort of well, it's intuition in

746
00:43:57,080 --> 00:44:00,440
Speaker 1: the sense of it's just done it so much, right,

747
00:44:00,520 --> 00:44:04,920
Speaker 1: But is that? Does that mean it's completely ignoring um

748
00:44:05,040 --> 00:44:08,920
Speaker 1: micro expressions and facial cues, So that didn't even come

749
00:44:08,920 --> 00:44:12,440
Speaker 1: into play? Ye, yeah, I was. I was waiting for

750
00:44:13,360 --> 00:44:15,440
Speaker 1: you've been doing this. I still nod when I do

751
00:44:15,480 --> 00:44:17,960
Speaker 1: a solo show and I do a lot of expressive dance.

752
00:44:18,040 --> 00:44:21,359
Speaker 1: What do you think, Jonathan, I don't know, Jonathan. It

753
00:44:21,360 --> 00:44:24,920
Speaker 1: gets lonely in here, guys. But yes, what you're saying

754
00:44:25,280 --> 00:44:28,120
Speaker 1: all the tells. It tells that you would use as

755
00:44:28,120 --> 00:44:33,280
Speaker 1: a human player. The computer does not pick up typically data. Yes, typically,

756
00:44:33,280 --> 00:44:35,680
Speaker 1: what it would do is it would study the outcomes

757
00:44:35,800 --> 00:44:40,520
Speaker 1: of the games from a purely statistical expressions. Most of

758
00:44:40,560 --> 00:44:43,240
Speaker 1: these poker games tend to be computer based poker games.

759
00:44:43,280 --> 00:44:45,279
Speaker 1: So it's not that it's playing like it's it's not

760
00:44:45,320 --> 00:44:48,440
Speaker 1: like there's a computer that says push ten more chips

761
00:44:48,520 --> 00:44:53,040
Speaker 1: into the table. You know, it's right, exactly, a little

762
00:44:53,080 --> 00:44:56,080
Speaker 1: it's a little winky face emoticon, like I don't have

763
00:44:56,160 --> 00:45:00,200
Speaker 1: good cards. No, it's it's all usually over of like

764
00:45:00,239 --> 00:45:03,120
Speaker 1: internet poker, which a lot of the people who play

765
00:45:03,280 --> 00:45:06,560
Speaker 1: professional poker cut their teeth on, especially you know in

766
00:45:06,640 --> 00:45:11,960
Speaker 1: the more recent generations of professional poker players. Yeah, they

767
00:45:11,960 --> 00:45:14,600
Speaker 1: don't know what it's like being a smokey saloon like Moneymaker.

768
00:45:15,040 --> 00:45:17,800
Speaker 1: In money Maker rose to the top a few years ago,

769
00:45:17,880 --> 00:45:20,239
Speaker 1: more than like a decade ago. Now he had come

770
00:45:20,280 --> 00:45:23,440
Speaker 1: from the world of internet poker, and so he was

771
00:45:23,520 --> 00:45:26,760
Speaker 1: using those same sort of skills in a real world setting.

772
00:45:27,000 --> 00:45:30,920
Speaker 1: But obviously there are subtle things that we humans do

773
00:45:31,080 --> 00:45:33,640
Speaker 1: in our expressions that computers do not pick up on,

774
00:45:33,680 --> 00:45:36,200
Speaker 1: and in fact that leads us sort of into the

775
00:45:36,200 --> 00:45:41,080
Speaker 1: realm of games where computers don't do as well as humans.

776
00:45:41,719 --> 00:45:44,320
Speaker 1: Is that list you sent a joke or is it really? No,

777
00:45:44,520 --> 00:45:46,920
Speaker 1: that's real. It does seem like it's weird, like one

778
00:45:46,920 --> 00:45:50,560
Speaker 1: of the games on there is Pictionary, for example, or tag. Yeah,

779
00:45:50,880 --> 00:45:53,960
Speaker 1: but these are some of these are are They sound silly,

780
00:45:54,000 --> 00:45:55,800
Speaker 1: But when you start to think about them in terms

781
00:45:55,800 --> 00:45:58,920
Speaker 1: of computation and robotics, you start to realize how incredibly

782
00:45:59,000 --> 00:46:03,280
Speaker 1: complex it is from a technical perspective, but incredibly easy

783
00:46:03,320 --> 00:46:07,320
Speaker 1: it is for your average human being. So with humans

784
00:46:07,680 --> 00:46:10,319
Speaker 1: a game of tag, once you know the basics, it's

785
00:46:10,360 --> 00:46:12,560
Speaker 1: it's all an instinct. You know what to do. You

786
00:46:12,600 --> 00:46:14,319
Speaker 1: run after the person you tried to catch up with

787
00:46:14,360 --> 00:46:16,879
Speaker 1: them and tag them. But you also know push him

788
00:46:16,880 --> 00:46:19,080
Speaker 1: in the back as hard as you can. Well, if

789
00:46:19,120 --> 00:46:20,719
Speaker 1: you're Josh, you push him as hard as you can.

790
00:46:20,800 --> 00:46:22,919
Speaker 1: But most of us we tag and we're not trying

791
00:46:22,960 --> 00:46:27,640
Speaker 1: to cause harm. Robots, however, robots not so good on

792
00:46:29,120 --> 00:46:33,520
Speaker 1: you said, and just say Isaac Asimov Isaac Asimov's rules

793
00:46:33,520 --> 00:46:36,880
Speaker 1: of robotics. Aside, robots are not very good at judging

794
00:46:36,880 --> 00:46:39,239
Speaker 1: how hard they have to hit something in order to

795
00:46:39,280 --> 00:46:43,239
Speaker 1: make contact, right. They're not as good at even your

796
00:46:43,239 --> 00:46:46,680
Speaker 1: bipedal robots that walk around like people, even the ones

797
00:46:46,719 --> 00:46:48,440
Speaker 1: that can run and do flips and stuff. Have you

798
00:46:48,520 --> 00:46:50,520
Speaker 1: seen that one the other day that the footage of

799
00:46:50,560 --> 00:46:53,600
Speaker 1: that thing running and jumping, it's really impressive and in

800
00:46:53,800 --> 00:46:58,480
Speaker 1: super creepy. Yeah, but even so, that's that's a clip

801
00:46:58,520 --> 00:47:00,440
Speaker 1: of the best of If you ever if you ever

802
00:47:00,480 --> 00:47:02,360
Speaker 1: see the clips where they show all the times the

803
00:47:02,440 --> 00:47:06,640
Speaker 1: robots fallen over, we'll pouring hot coffee in someone's head. Yes,

804
00:47:07,760 --> 00:47:10,480
Speaker 1: but they always play those clips shows to Yake, yes

805
00:47:10,920 --> 00:47:13,279
Speaker 1: that this this is true. So so, DARPA had its

806
00:47:13,320 --> 00:47:16,640
Speaker 1: big robotics challenge a few years ago where they had

807
00:47:16,960 --> 00:47:21,000
Speaker 1: bipedal robots tried to go through a scenario that was

808
00:47:21,239 --> 00:47:26,400
Speaker 1: simulating the Fukushima nuclear disaster. So the interesting thing was

809
00:47:26,680 --> 00:47:29,400
Speaker 1: the robot had to complete a series of tasks that

810
00:47:29,440 --> 00:47:32,520
Speaker 1: would have been mundane to humans, things like open up

811
00:47:32,520 --> 00:47:35,120
Speaker 1: a door and walk through it, and pick up a

812
00:47:35,160 --> 00:47:38,399
Speaker 1: power power tool and use it against a wall. And

813
00:47:39,000 --> 00:47:42,040
Speaker 1: you can watch the footage of some of these robots

814
00:47:42,080 --> 00:47:46,360
Speaker 1: doing things like being unable to open the door because

815
00:47:46,719 --> 00:47:49,040
Speaker 1: they can't tell if they need to pull or push

816
00:47:49,280 --> 00:47:51,759
Speaker 1: or they open the door, but then immediately fall over

817
00:47:51,800 --> 00:47:54,400
Speaker 1: the threshold of the door. And when you see that,

818
00:47:54,440 --> 00:47:58,120
Speaker 1: you realize, as advanced as robotics is, as advanced as

819
00:47:58,200 --> 00:48:03,680
Speaker 1: machine learning has become, as incredible as our technology has progressed,

820
00:48:04,120 --> 00:48:07,040
Speaker 1: there are still things that are fundamentally simple to your

821
00:48:07,080 --> 00:48:11,760
Speaker 1: average humans that are incredibly complicated from a technical standpoint,

822
00:48:11,800 --> 00:48:13,560
Speaker 1: Like a six year old can play jinga better than

823
00:48:13,560 --> 00:48:16,279
Speaker 1: a robot, right right, right, Okay, But the thing is

824
00:48:16,360 --> 00:48:18,680
Speaker 1: we're talking robots here, and is we go more and

825
00:48:18,719 --> 00:48:21,200
Speaker 1: more and more online in our world, becomes more and

826
00:48:21,239 --> 00:48:27,200
Speaker 1: more like web based rather than reality based. Doesn't the

827
00:48:27,200 --> 00:48:29,799
Speaker 1: the fact that a robot can't walk through a door

828
00:48:29,880 --> 00:48:34,239
Speaker 1: matter less and less. And the idea that that machines

829
00:48:34,280 --> 00:48:39,319
Speaker 1: are learning intellect and the robotivity, you just blew my

830
00:48:39,360 --> 00:48:42,800
Speaker 1: mind that that's becoming more and more vital and important

831
00:48:42,800 --> 00:48:45,239
Speaker 1: and something we should be paying attention. It absolutely is

832
00:48:45,239 --> 00:48:47,200
Speaker 1: something we should pay attention to. I mean, we have

833
00:48:47,640 --> 00:48:53,200
Speaker 1: robotic stock traders there trading on thousands of trades per second,

834
00:48:53,320 --> 00:48:56,640
Speaker 1: right fast, so fast that we have had stock market

835
00:48:56,760 --> 00:49:00,360
Speaker 1: booms and crashes that last less than a second long

836
00:49:00,520 --> 00:49:03,160
Speaker 1: due to that. So the robot army that will ultimately

837
00:49:03,160 --> 00:49:06,680
Speaker 1: defeat us is not something from the terminator. It's invisible.

838
00:49:07,360 --> 00:49:10,399
Speaker 1: It's online, or it will be online. It's it's it's

839
00:49:10,560 --> 00:49:15,440
Speaker 1: it's what's determining our retirement, right, yeah, the global economy

840
00:49:15,600 --> 00:49:19,520
Speaker 1: or um our municipal waters apply or whatever. Yeah. Now

841
00:49:19,560 --> 00:49:23,919
Speaker 1: there's the fascinating thing to me about this is not

842
00:49:24,080 --> 00:49:28,480
Speaker 1: just that we're training machine intelligence to learn and to

843
00:49:28,680 --> 00:49:31,839
Speaker 1: perform at a level better than humans, but that we're

844
00:49:31,840 --> 00:49:36,200
Speaker 1: putting a lot of trust in those devices and things

845
00:49:36,280 --> 00:49:41,120
Speaker 1: that have real incredible impact on our lives, the significant

846
00:49:41,200 --> 00:49:43,560
Speaker 1: enough impact where if things were to go south, it

847
00:49:43,640 --> 00:49:46,480
Speaker 1: would be really bad for us. Uh, and not in

848
00:49:46,520 --> 00:49:53,719
Speaker 1: that Terminator respect. Terminator is a terrifying dystopian science fiction story.

849
00:49:53,760 --> 00:49:56,200
Speaker 1: But then when you realize what could really happen behind

850
00:49:56,239 --> 00:49:59,160
Speaker 1: the scenes, you think the robots don't have to do

851
00:49:59,200 --> 00:50:01,640
Speaker 1: any physical harm to us to really mess things up.

852
00:50:02,200 --> 00:50:06,680
Speaker 1: So there are certainly some cases for us to be

853
00:50:06,800 --> 00:50:11,400
Speaker 1: very vigilant in the way we deploy right from the

854
00:50:11,480 --> 00:50:17,479
Speaker 1: outset exactly, and that it depends not necessarily I think.

855
00:50:17,600 --> 00:50:21,520
Speaker 1: I think it's I don't think it's too late, but

856
00:50:21,640 --> 00:50:25,280
Speaker 1: I think it's getting to that point of no return

857
00:50:25,760 --> 00:50:28,920
Speaker 1: very very quickly. December of this year. Yeah, Well, if

858
00:50:28,960 --> 00:50:31,880
Speaker 1: you're if you're someone like if you're someone like Elon Musk,

859
00:50:31,960 --> 00:50:34,800
Speaker 1: you'd say, if we don't do something now, we're we're

860
00:50:34,840 --> 00:50:37,840
Speaker 1: totally going to plummet off the edge of the cliff.

861
00:50:37,880 --> 00:50:41,560
Speaker 1: But now is a window that is rapidly closing. Yes, yeah, yeah,

862
00:50:41,719 --> 00:50:43,839
Speaker 1: the now is the now is a time where we've

863
00:50:43,840 --> 00:50:46,719
Speaker 1: got a deadline. We don't know exactly when that deadline

864
00:50:47,320 --> 00:50:50,239
Speaker 1: is going to be up, but we know that it's

865
00:50:50,239 --> 00:50:52,880
Speaker 1: not getting further out it. We're just getting closer to

866
00:50:52,960 --> 00:50:56,239
Speaker 1: that deadline. So, and a lot of this is is

867
00:50:56,640 --> 00:51:00,400
Speaker 1: it covered in deep conversations and the artificial and eligience

868
00:51:00,400 --> 00:51:04,600
Speaker 1: and machine learning fields that has been going on for ages,

869
00:51:04,640 --> 00:51:06,960
Speaker 1: to the point where you even have bodies like the

870
00:51:07,000 --> 00:51:11,879
Speaker 1: European Union that have debated on concepts like granting personhood

871
00:51:12,000 --> 00:51:17,000
Speaker 1: to artificial intelligence. So this is a really fascinating and

872
00:51:17,080 --> 00:51:20,680
Speaker 1: deep subject that and the games thing is a great

873
00:51:21,000 --> 00:51:24,960
Speaker 1: entry point into having that conversation. Uh, you know, I'm

874
00:51:25,040 --> 00:51:26,840
Speaker 1: lucky if I can win a game of chess against

875
00:51:26,880 --> 00:51:30,440
Speaker 1: another human being. Right, So, I can't even describe chess,

876
00:51:31,640 --> 00:51:33,360
Speaker 1: did I did. My big thing is I do that

877
00:51:33,520 --> 00:51:35,279
Speaker 1: night thing I called the night shuffle. I just move

878
00:51:35,360 --> 00:51:39,000
Speaker 1: them back and forth. I just castle. If I can castle,

879
00:51:39,239 --> 00:51:43,200
Speaker 1: then I'm I'm I'm so happy. And that's the third

880
00:51:43,239 --> 00:51:46,879
Speaker 1: tech stuffiest thing to come in threes. Well, strict, thank

881
00:51:46,920 --> 00:51:49,480
Speaker 1: you for stopping by. Thank you should stick around for

882
00:51:49,600 --> 00:51:51,840
Speaker 1: a listener mail. I think you should too, love to

883
00:51:52,080 --> 00:51:55,160
Speaker 1: and and throw out any funny comments that you have

884
00:51:55,840 --> 00:51:58,080
Speaker 1: I'll throw out comments and then Jerry can decide which

885
00:51:58,120 --> 00:52:00,759
Speaker 1: ones are funny. Okay, all right, fair, all right, So

886
00:52:01,040 --> 00:52:03,840
Speaker 1: if you want to know more about AI, go listen

887
00:52:03,840 --> 00:52:10,120
Speaker 1: to Tech Stuff. Strict does this every week, what days, Monday, Tuesday, Wednesday, Thursday,

888
00:52:10,200 --> 00:52:14,880
Speaker 1: and Friday. That's amazing, buddy, and wherever you find your

889
00:52:14,920 --> 00:52:17,880
Speaker 1: podcast Okay, and you've been doing it for years, so

890
00:52:18,000 --> 00:52:20,520
Speaker 1: if you love this, there's a whole big backlog, nine

891
00:52:20,880 --> 00:52:23,919
Speaker 1: plus episodes you're celebrating. You're celebrating your ten year as well,

892
00:52:24,000 --> 00:52:26,239
Speaker 1: right yep, by sure, m I'll be uh, we'll be

893
00:52:26,280 --> 00:52:31,399
Speaker 1: turning ten and Tech Stuff on June eleven, congratulating. Well,

894
00:52:31,480 --> 00:52:33,560
Speaker 1: since I said happy anniversary, it means it's time for

895
00:52:33,680 --> 00:52:39,680
Speaker 1: listener mail. Guys. I'm gonna call this, uh Matt graining

896
00:52:39,719 --> 00:52:44,040
Speaker 1: and cultural relativism about that. Hey, guys, love your podcast

897
00:52:44,120 --> 00:52:47,440
Speaker 1: so much. The massive archive makes for endless learning and entertainment.

898
00:52:47,480 --> 00:52:51,160
Speaker 1: My favorite part is you were such rad guys, including Strickland,

899
00:52:51,440 --> 00:52:54,440
Speaker 1: and I could totally imagine how did they know? I

900
00:52:54,440 --> 00:52:56,560
Speaker 1: can totally imagine myself getting a beer with you two,

901
00:52:57,520 --> 00:53:01,480
Speaker 1: But without Strickland, your Simpsons episodes were absolutely perfect. I

902
00:53:01,520 --> 00:53:03,600
Speaker 1: used to live in Portland and drove on Flanders and

903
00:53:03,640 --> 00:53:06,120
Speaker 1: love Joy Streets a lot. Wait, is this Matt Greening,

904
00:53:07,760 --> 00:53:11,040
Speaker 1: Matt Graenning, Drew bart and the sidewalk cement behind Lincoln

905
00:53:11,080 --> 00:53:14,120
Speaker 1: High School in downtown Portland's You can google that. I

906
00:53:14,120 --> 00:53:17,080
Speaker 1: would like to offer one interesting observation though, I've noticed

907
00:53:17,080 --> 00:53:19,000
Speaker 1: that on several episodes, you guys have said that you

908
00:53:19,000 --> 00:53:23,960
Speaker 1: are cultural relativists. Is that pronounced right? Yeah? But then

909
00:53:23,960 --> 00:53:27,040
Speaker 1: in nearly every episode I hear you pass moral judgments

910
00:53:27,080 --> 00:53:29,800
Speaker 1: on all the messed up stuff that people do, whether

911
00:53:29,840 --> 00:53:33,319
Speaker 1: it's racism, freak shows, or crematori ums bearing bodies on

912
00:53:33,320 --> 00:53:36,040
Speaker 1: the sly you guys are never shy to condemn something

913
00:53:36,360 --> 00:53:38,920
Speaker 1: that deserves to be condemned. Reminds me of something I

914
00:53:38,960 --> 00:53:43,120
Speaker 1: read from Yale sociologists who Loop Gorsky, who points out

915
00:53:43,160 --> 00:53:47,120
Speaker 1: that our own relativism is rarely as radical as our

916
00:53:47,160 --> 00:53:51,400
Speaker 1: theory requires. We can't be complete relativists in our daily lives.

917
00:53:52,160 --> 00:53:54,880
Speaker 1: He then gives the example of how academic social scientists,

918
00:53:55,239 --> 00:53:59,400
Speaker 1: where die hard relativists, get furious at uh and moralistic

919
00:53:59,440 --> 00:54:04,360
Speaker 1: at the data fudging of other researchers. Anyway, love the show, guys,

920
00:54:04,480 --> 00:54:07,920
Speaker 1: love tech stuff especially, and will forever be indebted to

921
00:54:07,960 --> 00:54:13,160
Speaker 1: you for your hilarity and knowledgeability. Cheers Jesse Lusco ps

922
00:54:13,239 --> 00:54:17,960
Speaker 1: go tech stuff. That's that's sweet. So yeah, thanks a lot, Jesse. Um.

923
00:54:18,000 --> 00:54:20,640
Speaker 1: There was an actual episode, and I don't remember which

924
00:54:20,680 --> 00:54:23,840
Speaker 1: one it was, where we abandon our cultural relativism, do

925
00:54:23,880 --> 00:54:26,800
Speaker 1: you remember, Because we used to just be like, no judgment,

926
00:54:26,840 --> 00:54:29,680
Speaker 1: no judgment, we just can't judge, you know, and then

927
00:54:29,680 --> 00:54:31,880
Speaker 1: finally we're like, you know what, No, that's not true.

928
00:54:32,080 --> 00:54:35,520
Speaker 1: We changed our the our philosophy to include the idea

929
00:54:35,560 --> 00:54:40,080
Speaker 1: that there are moral absolutes that are universal, although sometimes

930
00:54:40,120 --> 00:54:44,799
Speaker 1: we are just judging even beyond that. Look at us. Yeah, well,

931
00:54:44,840 --> 00:54:46,319
Speaker 1: if you want to get in touch with us, you

932
00:54:46,360 --> 00:54:49,760
Speaker 1: can tweet to us. I'm at josh um Clark and uh,

933
00:54:50,080 --> 00:54:54,000
Speaker 1: John Strickland's at John Strickland. That's correct. On Twitter, Chuck's

934
00:54:54,000 --> 00:54:56,880
Speaker 1: a movie crush and uh, you have a tech stuff

935
00:54:57,000 --> 00:54:59,799
Speaker 1: hsw Twitter, right, that's right. And then we also have

936
00:54:59,920 --> 00:55:02,760
Speaker 1: s y sk podcast. We're on Facebook dot com slash

937
00:55:02,800 --> 00:55:07,880
Speaker 1: stuff you should know, slash tech stuff and slash movie Crush.

938
00:55:07,920 --> 00:55:10,480
Speaker 1: Do you have a movie crush page for Facebook? Yeah? Yeah,

939
00:55:10,480 --> 00:55:12,279
Speaker 1: that's actually where I spend most of my time. Oh,

940
00:55:12,320 --> 00:55:15,160
Speaker 1: I didn't know that, said it's time. And then there's

941
00:55:15,200 --> 00:55:17,640
Speaker 1: also a slash stuff you Should Know Facebook page. You

942
00:55:17,680 --> 00:55:19,120
Speaker 1: guys have a lot of Facebook and to do it

943
00:55:19,280 --> 00:55:22,080
Speaker 1: so many social medias flying around. You can send us

944
00:55:22,080 --> 00:55:25,000
Speaker 1: an email to stuff podcast at how stuff Works dot com.

945
00:55:25,239 --> 00:55:28,040
Speaker 1: You can send John an email to tex stuff at

946
00:55:28,120 --> 00:55:30,440
Speaker 1: how stuff Works dot com. Nice, and then hang out

947
00:55:30,480 --> 00:55:32,320
Speaker 1: with us at our home on the web Stuff you

948
00:55:32,360 --> 00:55:35,960
Speaker 1: Should Know dot com and just go to tech stuff.

949
00:55:36,000 --> 00:55:38,480
Speaker 1: Just search it in Google. I come up all the time.

950
00:55:38,680 --> 00:55:46,640
Speaker 1: Fair enough for more on this and thousands of other topics,

951
00:55:46,920 --> 00:55:58,719
Speaker 1: is it how stuff Works dot com