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Speaker 1: Hey, everybody. In this week's Select, it's Josh. By the way.

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Speaker 1: We take a look at the Uncanny Valley, a theoretical

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Speaker 1: phenomenon where robots give us the creeps as they get

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Speaker 1: closer to looking human but still don't seem quite right,

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Speaker 1: and we explore academic research into the creeps, which is

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Speaker 1: pretty cool. So enjoy this episode. Welcome to Stuff You

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Speaker 1: Should Know, a production of iHeartRadio. Hey, and welcome to

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Speaker 1: the podcast. I'm Josh Clark, and there's Charles w Chuck Bryant,

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Speaker 1: and there's Jerry over there doing the robot, which means

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Speaker 1: this is stuff you should Know robot style. I knew

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Speaker 1: i'd get a laugh out you sooner or later. Did

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Speaker 1: you do the robot? Can you do the robot? I

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Speaker 1: think I've seen you do pretty bad robot. Sure, I

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Speaker 1: don't know about pretty bad robot. I can do a

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Speaker 1: pretty great robot, that's what you've seen. I can't do

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Speaker 1: any of that stuff. Yeah, I can't really either. And then, really,

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Speaker 1: if your claim to fame as like a really great

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Speaker 1: robot dance, I don't know, maybe take up some other

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Speaker 1: hobbies as well. Kind of round that out. You don't

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Speaker 1: want that to be the only thing you're good at, right,

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Speaker 1: because if you list that on a dating site, you

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Speaker 1: might turn ladies off. Yeah, according to e Harmony. Yeah

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Speaker 1: that's foreshadowing. I love that one, don't you. Yeah, it's

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Speaker 1: some issues with that whole Oh yeah, bit, Yeah, we'll

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Speaker 1: get to that. This is tad all right. Well, let's

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Speaker 1: start at the at the beginning, almost the beginning, Chuck,

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Speaker 1: Let's go back to nineteen seventy, which was the beginning

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Speaker 1: of the greatest decade in the history of humanity. Yeah,

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Speaker 1: neither one of us are born yet. I can finally

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Speaker 1: even say that I'm still not even born. It must

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Speaker 1: feel good. Yeah, okay, well, welcome to the club. I

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Speaker 1: guess thanks. In nineteen seventy, we're not just going just

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Speaker 1: anywhere in nineteen seven, We're going to Japan in nineteen seventy.

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Speaker 1: I bet Japan was pretty cool in the seventies. Yeah.

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Speaker 1: A lot of bell bottoms, a lot of ninja running around. Still. Yeah,

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Speaker 1: there were calculators being wheelded all over the place. Probably

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Speaker 1: it was a good time, good time for Japan, right,

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Speaker 1: And one of the one of the things that was

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Speaker 1: going on in nineteen seventy I could not for the

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Speaker 1: life of me, find what issue of this journal it

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Speaker 1: came out in what month? But at some point in

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Speaker 1: nineteen seventy there was an obscure journal, a Japanese academic

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Speaker 1: journal called Energy, and at some point during that year

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Speaker 1: it published a article by a Japanese roboticist. And his

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Speaker 1: name is Massa Hiro Morri. Nice, thank you, I have

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Speaker 1: a lot of practice. And Massa Hiro Amori published this

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Speaker 1: article and he named it Bukimi no tani gen show

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Speaker 1: is actually the full name of the whole thing. And

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Speaker 1: as we'll see, it's kind of difficult to translate into English, right,

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Speaker 1: and it took many, many years after he wrote this

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Speaker 1: article for it to be translated into English for anybody

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Speaker 1: even tried to attempt it. So basically more was this

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Speaker 1: roboticist and he wrote this essay, and at the time

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Speaker 1: he just put it out there and went back to work,

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Speaker 1: started teaching more and more roboticists. The whole new generation

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Speaker 1: of roboticists learned under him, and his work just kind

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Speaker 1: of sat there unobserved that article, i should say. And

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Speaker 1: then in two thousand and five a rough translation of

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Speaker 1: it was leaked out wasn't intended for publication, and the

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Speaker 1: world entirely changed, right because massa hiro Omori had in

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Speaker 1: his article put his finger on something that no one

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Speaker 1: had before. In his capacities are roboticists and a human,

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Speaker 1: and that was what we call today the uncanny Valley. Yeah,

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Speaker 1: so that's um the idea that you're making a robot,

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Speaker 1: and we'll see this apply to more than just robots,

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Speaker 1: but in his case, you're making a robot and you

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Speaker 1: want to make it look like a person, which I

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Speaker 1: guess not all roboticists do. Some of them like the

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Speaker 1: clunky jets and style robots like Rosy, But I guess

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Speaker 1: if you're morey, you're you're on the path to designing

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Speaker 1: lifelike robots. And the closer you get to that lifelike look,

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Speaker 1: everything's going great, everything's going great, people are like, this

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Speaker 1: is so cool, this is so cool. Yeah. Then all

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Speaker 1: of a sudden, people go oh, like, right as it

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Speaker 1: approaches it's most or basically, when it reaches it's most

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Speaker 1: lifelike capacity, that this whoever's making it can conjure people

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Speaker 1: are repulsed by it. Yeah, which is something that most

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Speaker 1: people who whoever hear of the Uncanny Valley or like, yeah,

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Speaker 1: you know, that's I've noticed that that's happened to me

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Speaker 1: before too. But the thing is, Chuck, it doesn't it

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Speaker 1: doesn't actually make sense, right, Like we know a robot

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Speaker 1: is a robot. Yeah, so you know, maybe you could

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Speaker 1: be afraid that's gonna like pick you up and break

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Speaker 1: you in two or something like a cartoon. But that's

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Speaker 1: different than being creeped out by Like why would we

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Speaker 1: be creeped out by a robot? And this is what

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Speaker 1: Morey put his finger on, was there's something to this

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Speaker 1: and it doesn't make sense. And he didn't. It wasn't

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Speaker 1: even just um this article that he wrote. He created

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Speaker 1: a graph as well that's become quite famous that um

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Speaker 1: really kind of gets the point across more than anything else. Yeah,

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Speaker 1: And he wasn't even the first person to go over

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Speaker 1: this and to put a put some thought to it.

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Speaker 1: Freud of course, because he'd like to think about everything.

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Speaker 1: He thought about a little bit. And before Freud, there

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Speaker 1: was a Geman name anst Yinched. Oh nice, I did

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Speaker 1: not realize that's how his last name should be pronounced. Yinch.

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Speaker 1: That's good stuff. I think I put a T on

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Speaker 1: the end, but the teas in the middle. Yinch. Yeah,

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Speaker 1: I think that's right. I've been saying Ginch, Well, we'll

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Speaker 1: have to look that up. Then I think you're no.

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Speaker 1: I think of the two of us, you you get

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Speaker 1: the German down. And he had a little term called

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Speaker 1: Umheimlich that he called it so like you know, different

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Speaker 1: languages had different names for it, and you'd go back

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Speaker 1: in time, all the way back to like the seventeenth century,

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Speaker 1: and people were and I guess you know, robots didn't

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Speaker 1: look super lifelike back then, but whatever their version of

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Speaker 1: lifelike was in the sixteen hundreds, people were like, uh,

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Speaker 1: I don't like that. Why is it looking at me? Yeah,

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Speaker 1: it's got a quill and it's writing things. But like

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Speaker 1: you said, Moriy made this graph because he was a roboticist,

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Speaker 1: and he thought, you know, let's look at this on

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Speaker 1: a plotted out so we can stare at it. And

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Speaker 1: on the x axis he had likeness, then on the

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Speaker 1: y axis he had affinity, like whether or not you

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Speaker 1: like the way this thing looks? Right? And just as

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Speaker 1: we're talking about um the graph went up and up

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Speaker 1: as things got more lifelike and people like the way

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Speaker 1: it looked. And then at a certain point there is

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Speaker 1: that valley, there's a big dip that really just kind

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Speaker 1: of says it all right, And again, this all makes

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Speaker 1: sense intuitively, but as we'll see, that's it's been very

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Speaker 1: difficult to prove. And one of the reasons why it's

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Speaker 1: confounded research thus far is because we're not even one

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Speaker 1: hundred percent sure what Morey meant by some of the

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Speaker 1: words he chose, at least as far as translating them

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Speaker 1: to English. Right. For example, bukimi Right, yeah, it was

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Speaker 1: translated in two thousand and five as uncanny, but um

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Speaker 1: again that that original translation was not intended for publication,

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Speaker 1: but it leaked out, and so uncanny Anny Valley became,

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Speaker 1: you know, the way we all think of it here

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Speaker 1: in the West. But boukimi more closely resembles something like eerie.

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Speaker 1: Like I've seen it explain that a word like boukimi

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Speaker 1: means more than Uncanny's just weird or remarkable or noteworthy.

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Speaker 1: It's not necessarily something that gives you the creeps. Boukimi

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Speaker 1: is something that gives you the creeps, like Steve Bukimi's exactly.

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Speaker 1: So boukimi probably more should be should have been translated

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Speaker 1: the Erie Valley, but by the time an actual official

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Speaker 1: translation that m maury signed off on came out in

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Speaker 1: twenty twelve. The cat was out of the bag. Everybody

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Speaker 1: knew of it as the Uncanny Valley, and there's no

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Speaker 1: way anybody who's going to come back and be like, no, no, no,

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Speaker 1: everybody stop calling it that. It's now the Eerie Valley, okay,

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Speaker 1: right right, And it may be one of those things

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Speaker 1: where we're so used to Uncanny Valley now that it's

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Speaker 1: hard to imagine Eerie Valley. But right, I think that

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Speaker 1: was the issue. Yeah, Like, nobody's gonna go along with that.

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Speaker 1: So this graph, like I said, it starts off on

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Speaker 1: that left hand side, and this is where you have

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Speaker 1: things that are super robotic, like you know, a packaging

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Speaker 1: robot in a factory, right that you know, apparently most

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Speaker 1: people don't have fondness for I do because I love

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Speaker 1: mechanical processes, right right, Okay, So there's there's part of

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Speaker 1: the problem. It's like that's not necessarily the kind of

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Speaker 1: feeling that Massa Hiro Morty was talking about. He was like, yeah, yeah,

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Speaker 1: you're interested in robotics and robotic arms and the industrial processes,

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Speaker 1: and you love watching how it's made. Right, what he

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Speaker 1: was talking about was more like how it resembles a

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Speaker 1: human and then how it makes you feel in relation

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Speaker 1: to its resemblance of a human. Right. Well, in that case,

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Speaker 1: it makes me feel nothing because it doesn't look at

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Speaker 1: all like a human, right, Okay, so that would be

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Speaker 1: at about the origin of the graph. It has no

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Speaker 1: resemblance to a human really, and it's not eliciting any

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Speaker 1: real affinity in you at all as far as it

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Speaker 1: looking like a human, right, But lots of affinity as

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Speaker 1: a thing that's just that's called props. So you go

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Speaker 1: a little bit further on the graph and then you

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Speaker 1: have things like um little stuffed animals and I No.

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Speaker 1: C three Po is a common one that's mentioned because

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Speaker 1: C three Pou, you know, is built to look like

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Speaker 1: a human. He does a great robot kind of it,

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Speaker 1: talks like a human and acts like a human. But

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Speaker 1: when it comes to that face, and as we'll see,

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Speaker 1: the face is kind of the key to all this.

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Speaker 1: For the most part, C three PO looks nothing like

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Speaker 1: a human in the face. So everything's still good and

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Speaker 1: people love C three PO. Right, So if you're looking

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Speaker 1: at the graph, C three PO is going up in

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Speaker 1: human likeness because he kind of you know, he's got

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Speaker 1: some some commonality there, and we're feeling affinity for him

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Speaker 1: on that human likeness. So it's he's going up. Yes, Okay,

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Speaker 1: we're going everything's going pretty well so far, right, Chuck,

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Speaker 1: that's right. Okay, So then we're gonna start hitting some

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Speaker 1: areas where things start looking a little more human, a

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Speaker 1: lot more human, I would say than c three po

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Speaker 1: like say, the characters in Moanna or Frozen picks our

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Speaker 1: characters that kind of thing where they look like they're

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Speaker 1: supposed to be human, like they're based on human, but

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Speaker 1: they have very exaggerated features that you would never confuse

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Speaker 1: at first glance for an actual human. Right, So they

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Speaker 1: have like big guys, small noses, things that make them cute, right,

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Speaker 1: And so our affinity for them is going up as

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Speaker 1: the human likeness is going up. Again, things are going

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Speaker 1: really well so far, that's right, because in Mohanna and

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Speaker 1: Frozen they look a little bit more like people, and

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Speaker 1: we like them a lot more for that reason. Right.

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Speaker 1: And then, like you said earlier, out of nowhere, the

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Speaker 1: whole thing, this line that's just been going up very pleasantly,

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Speaker 1: and a nice little slope just drops downward. Right, and

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Speaker 1: it doesn't drop just downward, it goes actually below the

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Speaker 1: X axis into negative territory. And now this is the

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Speaker 1: uncanny valley, that's right, And that's why it has that name,

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Speaker 1: because it's a valley, right, and this is where those

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Speaker 1: things like really really lifelike androids live, or corpses live,

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Speaker 1: or zombies live. Because Maury he had the idea that

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Speaker 1: if something's moving, is even creepier than something similar to

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Speaker 1: it that's not moving. So he actually created two lines

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Speaker 1: on this graph, one for things that are animate and

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Speaker 1: one for things that are inanimate. So if you look

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Speaker 1: at this Uncanny Valley, on the inanimate line, the non

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Speaker 1: moving line, you've got corpses are at the bottom of it.

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Speaker 1: But if you look at the animate line, it's even

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Speaker 1: it dips even further below than the inanimate line, and

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Speaker 1: at the bottom of those are zombies. So dead people

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Speaker 1: up and moving around and saying brains is as creepy

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Speaker 1: as it gets as far as this graph is concerned. Yeah,

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Speaker 1: and Maria wasn't the only one that earns yinch that

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Speaker 1: we talked about the German psychiatrists. He also talked about

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Speaker 1: the fact that if you are looking at something that

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Speaker 1: should not be moving, and it moves. I mean, I

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Speaker 1: think we can all agree that a baby doll that

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Speaker 1: suddenly turns its head and looks at you, right, probably

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Speaker 1: one of the creepier things you can witness, right, you know, Yeah,

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Speaker 1: it's about as creepy as it gets. Or have you

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Speaker 1: ever been to an open casket funeral? A few? I'm

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Speaker 1: not a fan at all. No, it is it makes sense.

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Speaker 1: You know, we've really kind of closed or put a

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Speaker 1: lot of space in between us and death, way more

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Speaker 1: than we used to have in like the nineteenth century. Oh,

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Speaker 1: they either would sit up with the dead, sure, right,

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Speaker 1: so this seems to be like kind of a holdover

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Speaker 1: from that. But if you've ever been to an open

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Speaker 1: casket funeral and have just stared at the corpse long enough,

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Speaker 1: like maybe it's arm or it's fingers or something, your

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Speaker 1: brain is so anticipating that they it's about to start

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Speaker 1: moving that sometimes you can creep yourself out and make

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Speaker 1: yourself think you did actually see it move. You'll also

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Speaker 1: be asked to leave the funeral. Well you shouldn't be

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Speaker 1: like giving a commentary about this out loud, but you can,

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Speaker 1: you know, you can do it to pass the time

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Speaker 1: in the funeral if you're looking to kill some time.

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Speaker 1: So here's the thing with all this. We know this

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Speaker 1: happens because everyone kind of has this feeling, but no

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Speaker 1: one and all this research has been done, and no

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Speaker 1: one is exactly sure why this happens. So, uh, Maury's

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Speaker 1: essay and especially once it was translated, a lot of

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Speaker 1: research started happening in this area. And it's problematic though,

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Speaker 1: because there are a few different problems. One is, well,

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Speaker 1: it's it's subjective. This dependent variable, whether you have an

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Speaker 1: affinity for something is very subjective, so it's hard to

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Speaker 1: kind of nail that down scientifically, right, all right, So

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Speaker 1: then number two is human likeness, right, this is the

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Speaker 1: independent variable. Yeah, and if you have human likeness, like,

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Speaker 1: what does that mean? Like what looks human? What doesn't

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Speaker 1: look human? The like, we haven't pinned that down. So like,

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Speaker 1: if you can't pin the dependent variable down and the

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Speaker 1: independent variable down, it makes it really tough to study correct.

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Speaker 1: And then there's a third one too. I love this one. Yeah.

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Speaker 1: The third one is you know, the original hypothesis. It

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Speaker 1: doesn't have a mathematical model that really specifies the shape

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Speaker 1: of this curve. Right, So it's still hypothetical, I guess, right,

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Speaker 1: which means that so if you look at Mori's graph,

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Speaker 1: it was he just basically made a line, right. It

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Speaker 1: wasn't based on any studies he'd done. The whole thing

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Speaker 1: was really an essay more than anything else. So researchers

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Speaker 1: who are trying to seriously study the scientifically have nothing

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Speaker 1: that they're actually trying to place their findings against, which

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Speaker 1: leads to put it puts them at risk for what's

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Speaker 1: called the Texas sharpshooter fallacy. Yeah, greatest name fallacy around,

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Speaker 1: and it's based on the idea that if you take

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Speaker 1: a sharpshooter out in Texas and have them shoot at

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Speaker 1: the side of a barn a bunch of times, some

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Speaker 1: of them are inevitably going to hit the barn, and

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Speaker 1: then the Texas sharpshooter walks up and then draws the

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Speaker 1: bull's eye around the bullets that he already sunk into

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Speaker 1: the side of the barn. That's the Texas sharpshooter fallacy.

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Speaker 1: It's ignoring data like the ones where he missed the

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Speaker 1: barn in favor of ones that fall into what you're

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Speaker 1: looking for, the bullet holes in the barn. You could

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Speaker 1: do the same thing with the data that you get

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Speaker 1: from testing the Uncanny Valley if you have no model

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Speaker 1: to fit it into already. Yeah, I think they would

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Speaker 1: have done better if they would have just instead of

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Speaker 1: trying to prove something, to maybe just research and call

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Speaker 1: it a thought experiment, right, you know, right, But people

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Speaker 1: are taking it seriously, and we'll we'll talk about some

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Speaker 1: of this research right after this, Chuck, all right, So

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Speaker 1: we're back. And despite the fact that this is really

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Speaker 1: tough to study, it's not even established that it's a

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Speaker 1: real thing in everyone's mind. By the way, there are

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Speaker 1: people out there who are really studying the Uncanny Valley

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Speaker 1: and trying to pin it down. Yeah. One of these

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Speaker 1: people is at Dartmouth College psychologist. And I didn't look

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Speaker 1: up their mascot, the pub Darts, the fighting pub darts

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Speaker 1: at Dartmouth College. We're gonna hear from Dartmouth. But her

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Speaker 1: name is Talia Wheatley, and she's done some research and

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Speaker 1: has found that it's not just like some uniquely Western

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Speaker 1: thing or American thing. It's kind of all over the world.

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Speaker 1: She studied tribes in Cambodia and they have these same

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Speaker 1: sensitivities to these things that look human but aren't human.

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Speaker 1: And they've even found that and I think it kind

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Speaker 1: of all comes down to the eyes. But they found

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Speaker 1: just looking at the eye can be enough. Yeah, somebody

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Speaker 1: can tell whether it's a human or not just looking

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Speaker 1: at a picture of the eye, right. Yeah. And that's

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Speaker 1: where I think people lose credibility in And we'll talk

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Speaker 1: about movies and sculpture and all that stuff, but they

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Speaker 1: just never get you can't get the eyes right, Like

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Speaker 1: you can't put life in lifeless eyes. Yeah, hard, they try.

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Speaker 1: Only God can u. And there was this other experiment

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Speaker 1: where they, you know, like where you can morph af

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Speaker 1: face digitally or whatever like that Michael Jackson Black or

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Speaker 1: White video. Yeah. I think some people were creeped out

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Speaker 1: by that even sure, but they would show this doll

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Speaker 1: image and it would morph into a human face, and

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Speaker 1: basically they would have people mark where they where they

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Speaker 1: thought that it would look more human than doll and

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Speaker 1: it you know, it landed about the sixty five percent

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Speaker 1: mark as far as morphing into human, which I mean

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Speaker 1: you can't really apply that necessarily, but just it's interesting offhand.

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Speaker 1: Sixty five point is about where the Uncanny Valley happened

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Speaker 1: in Maury's mind. Yeah, I would think it would be

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Speaker 1: higher than that. Yeah, but um yeah, it's still super interesting.

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Speaker 1: Um and you were saying that the eyes, and that's

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Speaker 1: what you're betting on, is that it's going to turn

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Speaker 1: out to be the eyes, right. Yeah. So trying to

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Speaker 1: investigate what constitutes human likeness, there's a researcher named Angela

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Speaker 1: Tinwell and she basically says, like, yes, it's all about

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Speaker 1: the upper facial features and that we detect those, we

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Speaker 1: read those. And so if there's any anything that's even

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Speaker 1: just slightly off in like you know, the eyes or

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Speaker 1: the brows or the wrinkles that form, that will lead

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Speaker 1: to the Uncanny Valley. That's the creeping part or the

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Speaker 1: smile too. She also says, well, yeah, and all these

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Speaker 1: things kind of come down to evolution. And her point

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Speaker 1: is like, you can't battle millions of years of evolution

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Speaker 1: that has honed our our dumb, little human brain to

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Speaker 1: detect something that's off about a face, right, it's just

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Speaker 1: too much to overcome, basically, Right. This other researcher named

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Speaker 1: Carl F. McDorman, who's from the University of Indiana, who actually,

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Speaker 1: he's basically like dedicated his career to this. Now. He

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Speaker 1: found that certain kinds of people, if you do like

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Speaker 1: a personality inventory before testing for Uncanny Valley sensitivity, yeah,

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Speaker 1: some types of people are predictably more sensitive to the

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Speaker 1: Uncanny Valley than others. Specifically, he found that very religious

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Speaker 1: people that makes total sense. Yeah, Neurotic people, yeah, and

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Speaker 1: people with high sensitivity to animal reminders. It's basically anything

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Speaker 1: that reminds you that, hey, you're super civilized and you

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Speaker 1: drive a car and you know how to play poker, um,

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Speaker 1: but you're still an animal, just as much as that

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Speaker 1: ape over there is an animal reminder. People who are

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Speaker 1: sensitive to that kind of thing tend to go off

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Speaker 1: on the Uncanny Valley as well. And then people who

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Speaker 1: are anxious are more likely to experience the Uncanny Valley

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Speaker 1: as far as McDorman is concerned. Yeah, that anteresting makes

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Speaker 1: sense too, because they're probably just more prone to be

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Speaker 1: I don't know, just have a reaction to a lot

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Speaker 1: of things like this. Right. But but we should say

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Speaker 1: the science and all this, the fact that the independent

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Speaker 1: and the dependent variable are still not defined. The science

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Speaker 1: is this, This is like the scientific equivalent of that

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Speaker 1: backward over the head half court basketball shot. Yeah, that's

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Speaker 1: the level of science that these people are carrying out

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Speaker 1: at this point, because there they're a lot of them

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Speaker 1: sadly are conducting experiments based on something that again doesn't

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Speaker 1: have a set, dependent or independent variable. So how can

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Speaker 1: you do that? As my question, Well, yeah, I mean

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Speaker 1: because in each experiment, they're going to be using different stimuli,

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Speaker 1: different faces, whether it's a doll or a wax figure

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Speaker 1: or a CGI character, and then they're gonna be doing

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Speaker 1: different things and have different expressions, and each person has

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Speaker 1: their own subjective take, so it is a very tough

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Speaker 1: thing to kind of nail down. Yeah, and I think

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Speaker 1: some of them are actually trying to form the basis

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Speaker 1: of this field of study right now, they're doing the groundwork.

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Speaker 1: But I think some of them also are like just

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Speaker 1: chasing headlines, Like there's no better way to get get

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Speaker 1: into the media cycle with your study than releasing some

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Speaker 1: findings on the uncanny Valley. People just tied up. Sure

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Speaker 1: they love it. One thing I thought was interesting was

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Speaker 1: at Princeton they tried this with monkeys and they found

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Speaker 1: the same thing happen when they had these realistic looking

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Speaker 1: but fake monkey faces. The monkeys were like ah and

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Speaker 1: turned away. It did make me think, though, like all

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Speaker 1: the you've seen these situations where like an orphaned animal

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Speaker 1: has a creepy puppet mother. Yeah, I know exactly what

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Speaker 1: you're talking about, and they seem to like that. But however,

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Speaker 1: and this is a bit of a spoiler, but towards

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Speaker 1: the end of this article, it points out that human

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Speaker 1: babies don't have this reaction at first either, and that

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Speaker 1: it's kind of learned. So maybe that explains it. Maybe

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Speaker 1: with the animals. I know you're talking about that that cage,

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Speaker 1: like why or monkey mother, it's super creepy. It's a

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Speaker 1: black and white photo. Well, no, I mean they do it.

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Speaker 1: There's all kinds of animals. Well, they'll they have like

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Speaker 1: a fake tiger or a fake duck or whatever, just

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Speaker 1: so the animal will feed or I mean it's usually

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Speaker 1: an animal that that milks from the mother. I guess

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Speaker 1: I see. But um, it's a common thing they do

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Speaker 1: for orphaned milk feeding or breastfeed animals. And they're always creepy. Huh. Well,

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Speaker 1: I mean to us, yeah, but to a dumb baby monkey,

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Speaker 1: they're just like sweet, give me the teat yeah, there's

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Speaker 1: a T shirt, maybe even a band name sweet give

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Speaker 1: Me the Teat. Yeah, yeah, this kind of falls into

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Speaker 1: the long band name category. Sure. But here's the thing is,

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Speaker 1: not everyone agrees with this whole thing. Like you said earlier,

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Speaker 1: There's a man named David Hanson and he's a roboticist

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Speaker 1: as well in Plano, Texas, and he did a very

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Speaker 1: very basic study. It was a survey where they showed

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Speaker 1: images of two different robots that were animated to simulate

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Speaker 1: human facial expressions and basically just asked, hey, what do

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Speaker 1: you think of this? And seventy percent said I like them? Yeah,

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Speaker 1: I can you see why people had trouble with this

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Speaker 1: study though? Yeah he said not one person said they

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Speaker 1: were disturbed. Yeah, okay, sounds good for the most part, though,

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Speaker 1: Studies into the uncanny valley or like now we're finding

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Speaker 1: something here. Yeah, although we should be suspicious of ones

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Speaker 1: that basically show the uncanny valley that Morey just graphed

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Speaker 1: out of his like with freehand, right, Like if you

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Speaker 1: if you've come across the study that shows that same thing,

423
00:26:00,240 --> 00:26:02,840
Speaker 1: they're probably cherry picking data. Are you about to say

424
00:26:02,880 --> 00:26:07,800
Speaker 1: out of his butt? Maybe there was another study Edward

425
00:26:07,840 --> 00:26:12,879
Speaker 1: Schneider at Sunny Potsdam in New York. I bet they

426
00:26:12,880 --> 00:26:16,920
Speaker 1: don't even have a mascot. They got together seventy five

427
00:26:17,160 --> 00:26:23,040
Speaker 1: characters from cartoons and video games, everyone from Mickey Mouse

428
00:26:23,119 --> 00:26:28,639
Speaker 1: to Laura Croft and who is very attractive by the way,

429
00:26:29,720 --> 00:26:35,240
Speaker 1: she's a computer character. Yeah. Well no, I'm talking about

430
00:26:35,280 --> 00:26:38,560
Speaker 1: playing tomb Raider. Yeah. I never played. Yeah when it

431
00:26:38,600 --> 00:26:40,520
Speaker 1: first came out, you know, I played tomb Raider and

432
00:26:40,520 --> 00:26:42,919
Speaker 1: I was like, oh, I look at her. Laura Croft

433
00:26:43,040 --> 00:26:46,200
Speaker 1: is kind of hut she well, she's she gets a

434
00:26:46,240 --> 00:26:49,960
Speaker 1: lot of stuff done. That's very attractive. I'm not true

435
00:26:50,000 --> 00:26:53,520
Speaker 1: what that means. Well, she travels a lot. Oh, she's

436
00:26:53,520 --> 00:26:55,600
Speaker 1: an independent person. Yeah, that's what I meant. I was

437
00:26:55,640 --> 00:26:59,040
Speaker 1: attracted to her mind in her adventures. Right. So anyway,

438
00:26:59,040 --> 00:27:02,399
Speaker 1: they ask people in this study, um, how attractive do

439
00:27:02,440 --> 00:27:05,000
Speaker 1: you think these characters are? Or how repulsive do you

440
00:27:05,040 --> 00:27:09,159
Speaker 1: think they are? And again there was um a graph

441
00:27:09,200 --> 00:27:11,920
Speaker 1: with a dip in it at a certain point, as

442
00:27:11,960 --> 00:27:19,240
Speaker 1: you would expect. Yep, careful, careful, everybody. So if you're

443
00:27:19,240 --> 00:27:22,160
Speaker 1: if you're a robot designer, right, One of the things

444
00:27:22,200 --> 00:27:25,360
Speaker 1: like even back in his essay written in nineteen seventy.

445
00:27:25,400 --> 00:27:31,840
Speaker 1: Massa Hiro Morey said, Um, there's there's problems here with movement.

446
00:27:31,960 --> 00:27:34,840
Speaker 1: There's problems here with the smile. It has something to

447
00:27:34,880 --> 00:27:39,440
Speaker 1: do with the face, right yeah, um, And somebody else

448
00:27:39,440 --> 00:27:41,480
Speaker 1: said I don't remember who it was, but there's there

449
00:27:41,520 --> 00:27:44,879
Speaker 1: always seems to be a lag time between how realistic

450
00:27:44,920 --> 00:27:49,879
Speaker 1: a designer can make a robot and how realistic an

451
00:27:49,920 --> 00:27:54,040
Speaker 1: engineer can make that robot look. Yeah, right, and that

452
00:27:54,160 --> 00:27:57,159
Speaker 1: that disconnected Maury's mind was a big part of the

453
00:27:57,320 --> 00:27:59,639
Speaker 1: Uncanny Valley, but he also seemed to focus on the

454
00:27:59,680 --> 00:28:04,160
Speaker 1: smile on the eyes and one of the things that's

455
00:28:04,160 --> 00:28:07,320
Speaker 1: at stake, like besides this just being like an interesting

456
00:28:08,119 --> 00:28:12,639
Speaker 1: topic of discussion, like there are actual real world implications

457
00:28:12,640 --> 00:28:15,760
Speaker 1: for this whole thing, right, Like, if you're a robot designer,

458
00:28:16,320 --> 00:28:19,480
Speaker 1: you want to create something that's not going to freak

459
00:28:19,520 --> 00:28:23,080
Speaker 1: people out because the whole purpose of robots is to

460
00:28:23,160 --> 00:28:26,760
Speaker 1: interact with humans, and you want them to interact with humans.

461
00:28:26,760 --> 00:28:30,840
Speaker 1: I should say life like looking at robots, right, because

462
00:28:30,880 --> 00:28:34,520
Speaker 1: like Ford Motor companies ever gonna buy an android that

463
00:28:34,560 --> 00:28:37,400
Speaker 1: looks human to just work on an assembly line when

464
00:28:37,440 --> 00:28:39,400
Speaker 1: they can get the same thing that does the same

465
00:28:39,520 --> 00:28:42,760
Speaker 1: job cheaper when it just looks like a robotic arm

466
00:28:42,840 --> 00:28:46,760
Speaker 1: or something. Right, the whole purpose of a lifelike looking

467
00:28:46,840 --> 00:28:49,600
Speaker 1: robot is because that robot is being designed to interact

468
00:28:49,600 --> 00:28:52,520
Speaker 1: with humans. And if you are going to run into

469
00:28:52,560 --> 00:28:55,440
Speaker 1: this spot, some people say it's not even a valley.

470
00:28:55,520 --> 00:28:58,240
Speaker 1: Some people think it's insurmountable, a cliff or a wall.

471
00:28:58,760 --> 00:29:01,240
Speaker 1: So if you're going to run up against this, you

472
00:29:01,280 --> 00:29:03,280
Speaker 1: want to figure out how to overcome it, because you

473
00:29:03,280 --> 00:29:06,280
Speaker 1: don't want to creep people out with your creations. Well,

474
00:29:06,320 --> 00:29:08,160
Speaker 1: and you don't want to spend a lot of money

475
00:29:09,360 --> 00:29:16,200
Speaker 1: to develop a robotic Walmart greeter at every store, because

476
00:29:16,280 --> 00:29:19,960
Speaker 1: it's it's happening like this is coming people. Yeah, there's

477
00:29:19,960 --> 00:29:23,960
Speaker 1: a robot called Geminoid F or Actroid F, depending on

478
00:29:24,000 --> 00:29:26,760
Speaker 1: who you ask. I've also seen her called Ellie, and

479
00:29:26,840 --> 00:29:29,040
Speaker 1: she is out of this lab by a guy named

480
00:29:29,120 --> 00:29:34,440
Speaker 1: Hiroshi Ishiguro, and he is probably the world's leading roboticist.

481
00:29:34,520 --> 00:29:38,120
Speaker 1: If you've seen any life like android, it probably came

482
00:29:38,160 --> 00:29:41,719
Speaker 1: out of this guy's lab, right, Yeah, And she is

483
00:29:41,800 --> 00:29:43,960
Speaker 1: starting to get out there in the world. She's been

484
00:29:44,040 --> 00:29:48,560
Speaker 1: a debriefer of soldiers coming back from war with PTSD

485
00:29:49,160 --> 00:29:52,680
Speaker 1: based on the idea that they might share more with

486
00:29:52,720 --> 00:29:55,000
Speaker 1: a robot that they knew was just a robot than

487
00:29:55,040 --> 00:29:58,880
Speaker 1: they would an actual human. Yeah, she's in a play.

488
00:30:00,040 --> 00:30:04,560
Speaker 1: She stars as an android. Good role. Right. And then

489
00:30:04,600 --> 00:30:09,320
Speaker 1: there's Casper. There's a little robot called Casper. Yeah, Casper

490
00:30:09,520 --> 00:30:13,720
Speaker 1: is a robot boy with a great cause, created to

491
00:30:13,720 --> 00:30:18,240
Speaker 1: help children with autism learn to read facial emotions. If

492
00:30:18,280 --> 00:30:21,120
Speaker 1: you look up photos of both of these, geminoid f

493
00:30:22,520 --> 00:30:27,640
Speaker 1: looks great and really like Ishiguro is doing great, great work.

494
00:30:28,760 --> 00:30:33,960
Speaker 1: Casper looks terrifying, right, and so Casper is creepy. But

495
00:30:34,000 --> 00:30:37,040
Speaker 1: that's not his purpose at all, right now, of his

496
00:30:37,160 --> 00:30:42,000
Speaker 1: purpose is to like teach kids with autism how to connect.

497
00:30:42,400 --> 00:30:46,840
Speaker 1: But if he's repelling them through this uncanny valley, he's

498
00:30:46,840 --> 00:30:51,320
Speaker 1: defeating the purpose. Well, they should go to Ishiguro and say, hey,

499
00:30:51,560 --> 00:30:53,840
Speaker 1: we have this great cause, can you make us something

500
00:30:53,880 --> 00:30:57,400
Speaker 1: that doesn't look like the stuff of nightmares? Right exactly?

501
00:30:57,800 --> 00:31:01,720
Speaker 1: I wonder if Casper has been effective, you know, I

502
00:31:01,840 --> 00:31:05,360
Speaker 1: don't know. I don't know. Now I feel bad I

503
00:31:05,440 --> 00:31:08,840
Speaker 1: didn't look into that. Well, I just I don't know.

504
00:31:09,160 --> 00:31:12,600
Speaker 1: He's very creepy looking. I agree wholeheartedly. It's kind of like, no,

505
00:31:12,720 --> 00:31:16,560
Speaker 1: he's not finished, get back to the drawing board. Either

506
00:31:16,640 --> 00:31:19,840
Speaker 1: that or and this is what Moury said, like go

507
00:31:19,960 --> 00:31:24,320
Speaker 1: the other way, Like just make him not human at all,

508
00:31:24,680 --> 00:31:29,400
Speaker 1: just cute or approachable. Right, So the robotists are not

509
00:31:29,440 --> 00:31:32,160
Speaker 1: the only ones who are facing this chuck. There is

510
00:31:32,240 --> 00:31:37,680
Speaker 1: a pretty powerful moneyed contingent of people who are interested

511
00:31:37,760 --> 00:31:41,880
Speaker 1: stakeholders in overcoming the uncanny Valley, or at least figuring

512
00:31:41,920 --> 00:31:46,920
Speaker 1: out if it's totally insurmountable. And that is Hollywood. Yeah,

513
00:31:47,240 --> 00:31:51,280
Speaker 1: Hollywood has a sort of a rich history of getting

514
00:31:51,280 --> 00:31:57,880
Speaker 1: it wrong when it comes to creepy CGI characters. Pixarhad

515
00:31:57,880 --> 00:32:00,240
Speaker 1: their very first short film actually is called Tin Toy.

516
00:32:00,880 --> 00:32:03,120
Speaker 1: It's a little five minutes short, and they showed it

517
00:32:03,160 --> 00:32:06,160
Speaker 1: to this you know, this proceded toy story and everything. Yeah,

518
00:32:06,160 --> 00:32:08,640
Speaker 1: it was actually kind of like the outline of Toy

519
00:32:08,720 --> 00:32:11,880
Speaker 1: stories plot. Yeah, but they showed it to test audiences

520
00:32:11,920 --> 00:32:15,640
Speaker 1: and they made the mistake of making the baby Billy

521
00:32:16,320 --> 00:32:21,800
Speaker 1: look too realistic, and everyone loved Tin Toy and everyone

522
00:32:22,040 --> 00:32:25,800
Speaker 1: hated Billy. Yeah have you seen it? Yeah? Yeah, so

523
00:32:25,840 --> 00:32:28,920
Speaker 1: I hate Billy. Yeah, he's pretty hateable for sure, and

524
00:32:29,000 --> 00:32:32,520
Speaker 1: he has the antagonist, but he struck some chord with

525
00:32:32,640 --> 00:32:35,640
Speaker 1: viewers that that Pixar did not mean to strike, right,

526
00:32:35,960 --> 00:32:40,040
Speaker 1: and they actually, I mean, this is extraordinarily fortunate for Pixar. Sure,

527
00:32:40,200 --> 00:32:44,160
Speaker 1: this is very early on in their history, and they

528
00:32:45,560 --> 00:32:48,520
Speaker 1: learned from it. Actually they're like, Okay, note to self,

529
00:32:48,840 --> 00:32:50,960
Speaker 1: don't try to make any of these characters life like,

530
00:32:51,080 --> 00:32:53,920
Speaker 1: let's go a different direction. And so they came up

531
00:32:53,960 --> 00:32:59,600
Speaker 1: with those um exaggerated features that we've all just come

532
00:32:59,640 --> 00:33:03,200
Speaker 1: to know and love. Yeah, which was a great, great

533
00:33:03,240 --> 00:33:06,240
Speaker 1: direction to go in. Yeah, obviously, because they've had tons

534
00:33:06,280 --> 00:33:08,680
Speaker 1: of success with that model. Right, you can make the

535
00:33:08,720 --> 00:33:12,280
Speaker 1: case that it may have saved the company because other

536
00:33:12,360 --> 00:33:14,720
Speaker 1: companies and other movies, for sure, have not been nearly

537
00:33:14,720 --> 00:33:18,640
Speaker 1: as fortunate. Yeah. One of the first big photo real

538
00:33:19,040 --> 00:33:23,400
Speaker 1: computer animated movies was Final Fantasy Colon the Spirits Within.

539
00:33:23,960 --> 00:33:25,800
Speaker 1: You should never have a colon in your movie title,

540
00:33:25,840 --> 00:33:28,680
Speaker 1: by the way, So that was the first mistake. But

541
00:33:29,080 --> 00:33:32,120
Speaker 1: this one was from two thousand and one and based

542
00:33:32,160 --> 00:33:36,240
Speaker 1: on the video game, and it was off putting to

543
00:33:36,600 --> 00:33:38,280
Speaker 1: a lot of people, and it was a big, big

544
00:33:38,320 --> 00:33:42,600
Speaker 1: bomb for Columbia Pictures. And but this is before Uncanny

545
00:33:42,680 --> 00:33:46,920
Speaker 1: Valley had really been established, Before Maury's essay was translated,

546
00:33:47,000 --> 00:33:50,440
Speaker 1: so reviewers didn't quite know what to say. Now they

547
00:33:50,480 --> 00:33:53,320
Speaker 1: would just say, we've tumbled into the Uncanny Valley again,

548
00:33:54,200 --> 00:33:57,600
Speaker 1: but they would say things like Peter Traver's Great reviewer

549
00:33:57,640 --> 00:34:00,280
Speaker 1: from Rolling Stone said, at first, it's fun to watch

550
00:34:00,320 --> 00:34:06,120
Speaker 1: the characters ellipsis, ellips, ellips. But what's an ellipsis is

551
00:34:06,160 --> 00:34:09,000
Speaker 1: that two of them? A couple of them. But then

552
00:34:09,040 --> 00:34:11,720
Speaker 1: you notice a coldness in the eyes, a mechanical quality

553
00:34:11,719 --> 00:34:14,839
Speaker 1: in the movements, familiar voices emerging from the mouths of

554
00:34:14,880 --> 00:34:19,600
Speaker 1: replicants erect a distance. Yeah, so he's describing the Uncanny Valley.

555
00:34:19,600 --> 00:34:23,239
Speaker 1: He just didn't have the name of it yet. And

556
00:34:23,239 --> 00:34:25,840
Speaker 1: then a couple of years later you had the Polar Express,

557
00:34:26,880 --> 00:34:30,120
Speaker 1: which became I think even more famous than Final Fantasy

558
00:34:30,320 --> 00:34:33,760
Speaker 1: totally as far as the Uncanny Valley goes. But again,

559
00:34:33,800 --> 00:34:36,759
Speaker 1: it's like you said, you know, the reviewers didn't know

560
00:34:36,800 --> 00:34:39,760
Speaker 1: how quite to put their finger on it. And I'm

561
00:34:39,800 --> 00:34:42,520
Speaker 1: not quite sure how Final Fantasy was done, but I

562
00:34:42,560 --> 00:34:48,239
Speaker 1: know that Polar Express used similar software and hardware to

563
00:34:49,239 --> 00:34:54,160
Speaker 1: what roboticists are using now, where it's like motion capture,

564
00:34:54,280 --> 00:34:58,040
Speaker 1: but rather than translating the motion to the robot. It's

565
00:34:58,040 --> 00:35:01,680
Speaker 1: translating the motion into like a digital three D rendering

566
00:35:02,200 --> 00:35:06,720
Speaker 1: of the character. Right. Yeah, So Polar Express was really

567
00:35:07,440 --> 00:35:11,680
Speaker 1: really expressive, but not quite there. So it fell really

568
00:35:11,680 --> 00:35:16,200
Speaker 1: hard in the young Canny Valley. And I think David

569
00:35:16,239 --> 00:35:20,600
Speaker 1: Germaine of the Associated Press compared the kids in this

570
00:35:21,040 --> 00:35:24,799
Speaker 1: heartwarming family Christmas movie to the children from Village of

571
00:35:24,800 --> 00:35:27,919
Speaker 1: the Damned. Yeah, which is not what you want. It's

572
00:35:27,960 --> 00:35:30,200
Speaker 1: not at all what the studio wanted. And I think

573
00:35:30,400 --> 00:35:33,200
Speaker 1: it lost a pretty decent amount of money. Yeah, there

574
00:35:33,239 --> 00:35:35,839
Speaker 1: was another one, and these are all, by the way,

575
00:35:36,080 --> 00:35:40,120
Speaker 1: courtesy of Robert Zemecas he really had his He went

576
00:35:40,160 --> 00:35:44,160
Speaker 1: all in on this technology. I don't know why. I

577
00:35:44,200 --> 00:35:47,759
Speaker 1: think he just I think sometimes as an artist you

578
00:35:47,800 --> 00:35:51,719
Speaker 1: can get so wrapped up in the coolness of wow,

579
00:35:51,760 --> 00:35:54,560
Speaker 1: look what we can do now that you don't step

580
00:35:54,600 --> 00:35:58,040
Speaker 1: back and look at what you're doing. Look should should

581
00:35:58,080 --> 00:36:02,200
Speaker 1: we be doing this? Because he also had a part

582
00:36:02,320 --> 00:36:05,200
Speaker 1: in the Beowulf movie in two thousand and seven that

583
00:36:05,280 --> 00:36:07,919
Speaker 1: was a huge bomb in The New York Times said

584
00:36:07,920 --> 00:36:10,279
Speaker 1: this about that people who are meant to be enraged,

585
00:36:10,640 --> 00:36:12,879
Speaker 1: who are at risk of plummeting to their deaths, just

586
00:36:12,960 --> 00:36:15,040
Speaker 1: look a little out of sorts. When it was over,

587
00:36:15,160 --> 00:36:17,640
Speaker 1: I felt relieved to be back in the company of

588
00:36:17,800 --> 00:36:23,520
Speaker 1: uncreepy flesh and blood humans again sad. And then did

589
00:36:23,520 --> 00:36:26,800
Speaker 1: you see The Adventures of Tintin? Yeah, I really liked Tintin,

590
00:36:26,880 --> 00:36:30,360
Speaker 1: though I did too. I think Spielberg, I mean, there

591
00:36:30,480 --> 00:36:34,480
Speaker 1: is that uncanny valley a little bit, but the story

592
00:36:34,520 --> 00:36:37,600
Speaker 1: in the movie were so good he overcame that. I think.

593
00:36:37,880 --> 00:36:41,239
Speaker 1: I was about to say, I think Spielberg has come

594
00:36:41,280 --> 00:36:47,759
Speaker 1: the closest to overcoming that chasm of anybody. But did

595
00:36:47,760 --> 00:36:50,320
Speaker 1: he do it through good storytelling or through the eyes

596
00:36:50,600 --> 00:36:53,200
Speaker 1: I don't know. I don't know if it I don't know.

597
00:36:53,239 --> 00:36:56,799
Speaker 1: If it was a combination of the two. I don't know.

598
00:36:56,840 --> 00:37:02,040
Speaker 1: But it is extraordinarily, it's an extradit so you know,

599
00:37:02,080 --> 00:37:04,719
Speaker 1: those that that stuff you'll see every once a while,

600
00:37:04,719 --> 00:37:07,359
Speaker 1: wish somebody does like what Beavis and butt Head would

601
00:37:07,360 --> 00:37:10,239
Speaker 1: actually look like in real life, or what Charlie Brown

602
00:37:10,239 --> 00:37:13,400
Speaker 1: would look like in real life. Fantastic, right, So it

603
00:37:13,880 --> 00:37:17,319
Speaker 1: still has kind of got a cartoonish quality to it.

604
00:37:17,400 --> 00:37:20,319
Speaker 1: It's the same thing with the Tintin movie. But it

605
00:37:20,400 --> 00:37:23,200
Speaker 1: was like it was as if you were living in

606
00:37:23,200 --> 00:37:29,120
Speaker 1: a dimension where humans looked somewhat cartoonish. Is that making

607
00:37:29,160 --> 00:37:30,879
Speaker 1: any sense or does that just make the whole thing

608
00:37:30,880 --> 00:37:33,879
Speaker 1: even harder to understand? No? I get that. So so

609
00:37:33,960 --> 00:37:37,920
Speaker 1: he somehow was like, here, I'm not trying to nail

610
00:37:38,080 --> 00:37:40,520
Speaker 1: what humans look like. I'm going to take you to

611
00:37:40,560 --> 00:37:43,319
Speaker 1: another world where these people live. And if you lived

612
00:37:43,360 --> 00:37:45,319
Speaker 1: in this world, you would look like this too. It's

613
00:37:45,520 --> 00:37:48,239
Speaker 1: it's weird. It's like he bridged an uncanny valley that

614
00:37:48,280 --> 00:37:51,360
Speaker 1: doesn't exist in this dimension. Yeah, he built a temporary

615
00:37:51,840 --> 00:37:56,160
Speaker 1: disintegrating bridge across the uncanny valley, right. I think the

616
00:37:56,160 --> 00:37:58,600
Speaker 1: biggest example in recent years was, or the one that

617
00:37:58,640 --> 00:38:01,360
Speaker 1: got the most attention was in Rogue One. Did you

618
00:38:01,360 --> 00:38:03,919
Speaker 1: see that? The Star Wars movie. I haven't seen any

619
00:38:03,920 --> 00:38:06,160
Speaker 1: of the new Star Wars ones except for I've seen

620
00:38:06,280 --> 00:38:10,480
Speaker 1: the first six, I guess, but none of the two

621
00:38:10,640 --> 00:38:15,600
Speaker 1: new new ones. Well, in Rogue one, they completely bring

622
00:38:15,640 --> 00:38:19,759
Speaker 1: back to life Grand Moth Tarkin, who was played by

623
00:38:19,440 --> 00:38:23,960
Speaker 1: the deceased Peter Cushing, and they brought him back as

624
00:38:24,000 --> 00:38:28,239
Speaker 1: a character in this movie and in the theater like

625
00:38:28,320 --> 00:38:31,839
Speaker 1: when he when it first happens, he's got his back

626
00:38:31,880 --> 00:38:33,719
Speaker 1: to you, and it's sort of in the shadows and

627
00:38:33,719 --> 00:38:37,360
Speaker 1: you're like, oh wow, like that's pretty cool. And I

628
00:38:37,440 --> 00:38:40,600
Speaker 1: didn't know that they would do that, but they they

629
00:38:40,640 --> 00:38:45,440
Speaker 1: got too comfortable, I think, and showed too much and

630
00:38:45,719 --> 00:38:48,640
Speaker 1: gave him too many lines and too much light, and

631
00:38:48,800 --> 00:38:52,720
Speaker 1: then it became Uncanny Valley really. Oh yeah, for sure.

632
00:38:52,920 --> 00:38:56,480
Speaker 1: Think about poor Peter Cushing's family having to see that. Yeah,

633
00:38:56,560 --> 00:38:59,360
Speaker 1: I don't know how, but they just weep during that

634
00:38:59,600 --> 00:39:02,040
Speaker 1: that movie. Well, I'm curious about like life rights and

635
00:39:02,080 --> 00:39:03,719
Speaker 1: image rights and stuff like that, if they had to

636
00:39:04,239 --> 00:39:06,279
Speaker 1: get that cleared. I don't even know. I'm sure there's

637
00:39:06,280 --> 00:39:09,920
Speaker 1: a backstory there. Oh. Cushing was famously mellow. That was

638
00:39:09,960 --> 00:39:12,600
Speaker 1: he He would have taken and draw off his dubie

639
00:39:12,600 --> 00:39:15,319
Speaker 1: and been like that's whatever. Man. Yeah. I think he

640
00:39:15,360 --> 00:39:17,640
Speaker 1: spent the last year of his life on his weed

641
00:39:17,719 --> 00:39:22,840
Speaker 1: farm in northern California. Right, what about this Mars Needs Moms.

642
00:39:22,880 --> 00:39:25,080
Speaker 1: I had never ever heard of that movie, and so

643
00:39:25,719 --> 00:39:27,960
Speaker 1: I went and watched the trailer and I still was like,

644
00:39:28,120 --> 00:39:30,560
Speaker 1: I have no idea what this is. Yeah, you know

645
00:39:30,600 --> 00:39:34,319
Speaker 1: that comic strip bloom County. Well, you know, I'm a huge,

646
00:39:34,400 --> 00:39:37,239
Speaker 1: huge life along Bloom County fan. Oh okay, so Burke

647
00:39:37,719 --> 00:39:39,879
Speaker 1: so maybe you know how to say the last name.

648
00:39:40,360 --> 00:39:45,200
Speaker 1: It's Berkeley. Breathed or breathed. They said breathed, but I

649
00:39:45,239 --> 00:39:46,759
Speaker 1: don't know if I've ever heard it said out loud.

650
00:39:47,040 --> 00:39:50,239
Speaker 1: Breathed sounds nice. Let's go with that. So Berkeley breath

651
00:39:50,440 --> 00:39:53,759
Speaker 1: the person, the guy who did Bloom County. Yeah, he

652
00:39:53,800 --> 00:39:57,080
Speaker 1: wrote a book, a children's book called Mars Needs Moms,

653
00:39:57,360 --> 00:40:00,680
Speaker 1: And basically Mars had some sort of shortage of moms.

654
00:40:01,040 --> 00:40:04,160
Speaker 1: So the Martians came and kidnapped human moms and it

655
00:40:04,200 --> 00:40:06,040
Speaker 1: was up to the human kids to go get their

656
00:40:06,040 --> 00:40:09,200
Speaker 1: moms back from Mars. Right, pretty cute little premise, but

657
00:40:09,320 --> 00:40:12,160
Speaker 1: they took it and ran it through Zemeckis's nightmare mill

658
00:40:13,080 --> 00:40:17,840
Speaker 1: u right. Image Movers Digital was the was the trade

659
00:40:17,920 --> 00:40:20,160
Speaker 1: name of it, but everybody knew it's just steer clear

660
00:40:20,160 --> 00:40:22,440
Speaker 1: of this place, right, Yeah. And this was like the

661
00:40:22,560 --> 00:40:27,280
Speaker 1: Apex or the what's the opposite of the Apex? The Valley,

662
00:40:27,840 --> 00:40:31,600
Speaker 1: I guess, so the deepest part of the CGI Valley,

663
00:40:31,920 --> 00:40:36,080
Speaker 1: of the Uncanny Valley, right, it was what the stuff

664
00:40:36,120 --> 00:40:39,640
Speaker 1: that they created. It was so off and just so

665
00:40:39,680 --> 00:40:43,879
Speaker 1: spectacularly and colossally off that when I guess Disney came

666
00:40:43,920 --> 00:40:47,680
Speaker 1: along and bought this company. They came in, looked around

667
00:40:47,760 --> 00:40:51,200
Speaker 1: and said, we're shutting you down. This movie's that we're

668
00:40:51,200 --> 00:40:53,520
Speaker 1: not doing this anymore. What you guys are doing here

669
00:40:53,600 --> 00:40:57,239
Speaker 1: is wrong, and you're all going to jail. Yeah, here's

670
00:40:57,280 --> 00:41:00,120
Speaker 1: my thoughts on that. I watched the trailer and it

671
00:41:00,160 --> 00:41:02,279
Speaker 1: didn't look any worse than any of the other ones

672
00:41:02,320 --> 00:41:05,239
Speaker 1: to me. And in fact, I don't know the character's names,

673
00:41:05,239 --> 00:41:06,920
Speaker 1: but there's a kid and then there's this one kind

674
00:41:06,960 --> 00:41:10,160
Speaker 1: of chubby guy in mars. Yeah, the chubby guy looked

675
00:41:10,160 --> 00:41:13,440
Speaker 1: pretty good. Actually, I thought I think this was a victim.

676
00:41:13,520 --> 00:41:16,719
Speaker 1: I bet the movie sucked really bad. Yeah, and I

677
00:41:16,760 --> 00:41:18,839
Speaker 1: think it was the last straw at the end of

678
00:41:18,880 --> 00:41:22,839
Speaker 1: all these Uncanny Valley failures. Yeah, because this again, this

679
00:41:22,920 --> 00:41:27,759
Speaker 1: is the same company that had created a Polar Express. Yeah,

680
00:41:27,800 --> 00:41:30,799
Speaker 1: the Nightmare Factory and a Christmas Carol did not do

681
00:41:30,960 --> 00:41:34,040
Speaker 1: very well either. So yeah, I think it definitely bore

682
00:41:34,080 --> 00:41:37,480
Speaker 1: the brunt of its predecessors as well. Yeah, but I

683
00:41:37,520 --> 00:41:40,279
Speaker 1: thought this was as bad as it got. If you

684
00:41:40,280 --> 00:41:44,600
Speaker 1: ask me, I totally saw what Disney saw with this one. Yeah.

685
00:41:44,640 --> 00:41:47,960
Speaker 1: Anytime something is marked as the thing that killed the thing, right,

686
00:41:48,160 --> 00:41:52,560
Speaker 1: it's always just the last thing. Yeah, you're right, you know, yeah, anyway,

687
00:41:52,600 --> 00:41:54,440
Speaker 1: but it could have also been the thing that saved

688
00:41:54,440 --> 00:41:58,520
Speaker 1: the thing had they gotten it right. You know, that's true.

689
00:41:59,080 --> 00:42:01,840
Speaker 1: So like that, Maury was like, and every time I

690
00:42:01,920 --> 00:42:04,960
Speaker 1: say more now unless I say it like Mory just

691
00:42:05,040 --> 00:42:08,000
Speaker 1: saying morey Jewish guy, I think I think of the

692
00:42:08,040 --> 00:42:13,880
Speaker 1: Wig Salesman and Goodfellas. Yeahs like give me money and

693
00:42:14,080 --> 00:42:17,160
Speaker 1: ray leotis this in their laughing because More's two payfalls off.

694
00:42:17,920 --> 00:42:20,080
Speaker 1: Imagine that guy is the guy who came up with

695
00:42:20,120 --> 00:42:23,320
Speaker 1: the Uncanny Valley. Okay, he gives a whole different spin

696
00:42:23,400 --> 00:42:28,959
Speaker 1: to it, right, Yeah. So Moury says, um, just don't

697
00:42:29,000 --> 00:42:31,479
Speaker 1: even try. Guys like you're never going to be able

698
00:42:31,480 --> 00:42:34,080
Speaker 1: to do this, even if you can, We're so far

699
00:42:34,160 --> 00:42:36,560
Speaker 1: away from it. And this is in nineteen seventy he

700
00:42:36,600 --> 00:42:39,160
Speaker 1: was saying it, and it still holds true. Now, yeah,

701
00:42:39,320 --> 00:42:42,200
Speaker 1: we're so far away from this that that just maybe

702
00:42:43,040 --> 00:42:46,160
Speaker 1: put your put your emphasis elsewhere. And the example he

703
00:42:46,200 --> 00:42:49,040
Speaker 1: gave was say, like a prosthetic hand, right, yea, rather

704
00:42:49,080 --> 00:42:52,520
Speaker 1: than trying to create a lifelike prosthetic hand that that

705
00:42:52,719 --> 00:42:56,719
Speaker 1: was in danger of creeping people out, which is the

706
00:42:56,719 --> 00:43:00,239
Speaker 1: opposite of what somebody wearing a prosthetic hand wants when

707
00:43:00,239 --> 00:43:02,920
Speaker 1: they're walking around the prosthetic hand. He said, you know,

708
00:43:03,200 --> 00:43:07,920
Speaker 1: maybe choose some like like wood, well sanded, beautifully grained

709
00:43:07,960 --> 00:43:11,080
Speaker 1: wood in the shape of a human hand. It gets

710
00:43:11,080 --> 00:43:13,600
Speaker 1: the point across. This is my hand. I lost my hand.

711
00:43:13,600 --> 00:43:15,440
Speaker 1: I don't have my hand. But there's nothing to be

712
00:43:15,440 --> 00:43:18,279
Speaker 1: creeped out about here. It's kind of beautiful looking, isn't it.

713
00:43:18,760 --> 00:43:21,840
Speaker 1: That was Maury's take, and a lot of people side

714
00:43:21,840 --> 00:43:23,960
Speaker 1: with him as well. As a matter of fact, you know,

715
00:43:24,000 --> 00:43:26,000
Speaker 1: I said, I think at the beginning that he was

716
00:43:26,120 --> 00:43:30,440
Speaker 1: already an established roboticist when he wrote The Uncanny Valley

717
00:43:30,440 --> 00:43:33,319
Speaker 1: in nineteen seventy, and he went on to teach a

718
00:43:33,320 --> 00:43:37,240
Speaker 1: lot of people roboticists, or a lot of roboticists as well.

719
00:43:37,440 --> 00:43:43,360
Speaker 1: And that very famous robot Asimo Osimo, you know, the

720
00:43:43,400 --> 00:43:45,480
Speaker 1: one I'm talking about. He was one of the first

721
00:43:45,520 --> 00:43:48,480
Speaker 1: ones that could jog in place. And he's kind of

722
00:43:48,800 --> 00:43:53,680
Speaker 1: humanoid for sure, but very cute, all white, shiny lacquer plastic. Yeah,

723
00:43:54,120 --> 00:43:58,200
Speaker 1: you've seen him before. He was created by one of

724
00:43:58,400 --> 00:44:03,080
Speaker 1: Morey's students, who clearly subscribe to Maury's theory that you.

725
00:44:03,080 --> 00:44:05,960
Speaker 1: You're not gonna you're not gonna overcome the Uncanny Valley.

726
00:44:06,040 --> 00:44:10,440
Speaker 1: So just make these things exaggerated and non human like. Yeah,

727
00:44:10,480 --> 00:44:13,440
Speaker 1: and you'll you'll have people love your robot. Yeah. I

728
00:44:13,480 --> 00:44:16,200
Speaker 1: think that's a good tech. Yeah. All right, we're gonna

729
00:44:16,200 --> 00:44:19,120
Speaker 1: take another break here and then come back and finish

730
00:44:19,200 --> 00:44:21,680
Speaker 1: up with a little bit. We're gonna take a step

731
00:44:21,680 --> 00:44:51,839
Speaker 1: back and just talk generally about creepiness, all right. So

732
00:44:52,200 --> 00:44:55,960
Speaker 1: I promised that we would talk about creepiness. So that's

733
00:44:55,960 --> 00:44:59,440
Speaker 1: what we'll do. You promised. Chuck the creeps such a

734
00:44:59,440 --> 00:45:02,520
Speaker 1: great phrase. Everyone says it gives me the creeps. It's

735
00:45:02,560 --> 00:45:05,440
Speaker 1: just such a just it's one of those phrases that

736
00:45:05,560 --> 00:45:09,120
Speaker 1: sums things up so perfectly. It's livid as a fresh bruise.

737
00:45:10,160 --> 00:45:14,400
Speaker 1: And we have Charles Dickens to thank for this, evidently,

738
00:45:14,680 --> 00:45:18,000
Speaker 1: because he gets credit for using the creeps. In David

739
00:45:18,040 --> 00:45:22,040
Speaker 1: Copperfield in eighteen forty nine, people had had this feeling before,

740
00:45:22,600 --> 00:45:26,120
Speaker 1: this sort of unpleasant off you know what it feels

741
00:45:26,120 --> 00:45:28,080
Speaker 1: like to get the creeps. But they said things like

742
00:45:28,200 --> 00:45:31,480
Speaker 1: eel like or clammy. Not bad, not bad. But if

743
00:45:31,480 --> 00:45:34,200
Speaker 1: you said that thing makes me feel eel like. Today

744
00:45:34,239 --> 00:45:36,359
Speaker 1: people would be like, what the heck are you talking about? Right?

745
00:45:37,200 --> 00:45:39,840
Speaker 1: I think also you would use that to describe somebody

746
00:45:40,000 --> 00:45:41,920
Speaker 1: who gave you the creeps as well, like that guy's

747
00:45:41,960 --> 00:45:45,640
Speaker 1: really clammy, you know what I mean? Sure, all that

748
00:45:45,680 --> 00:45:48,399
Speaker 1: means you're touching them though, like like Peter Lorie would

749
00:45:48,440 --> 00:45:50,880
Speaker 1: be clammy or eel like in some of his characters.

750
00:45:51,840 --> 00:45:57,560
Speaker 1: You know, Peter Lourie, I do too. So everybody understands

751
00:45:57,560 --> 00:46:00,399
Speaker 1: that there is such thing as the creeps, right, but

752
00:46:01,800 --> 00:46:06,120
Speaker 1: we don't understand why we get the creeps still to

753
00:46:06,200 --> 00:46:08,440
Speaker 1: this day, and again this is important and relates to

754
00:46:08,480 --> 00:46:11,960
Speaker 1: the Uncanny Valley because another way to put the creeps

755
00:46:12,960 --> 00:46:17,440
Speaker 1: is negative affinity. Remember affinity was the x axis, and

756
00:46:17,560 --> 00:46:20,719
Speaker 1: when the valley dropped down below the X axis, you

757
00:46:20,840 --> 00:46:25,439
Speaker 1: dipped into negative affinity, into the creeps. The creeps exactly right.

758
00:46:25,520 --> 00:46:30,520
Speaker 1: So you talked about Ernst Yinch Yeah, yeah, I'd get it.

759
00:46:30,640 --> 00:46:33,879
Speaker 1: Sure it was probably the first person to actually sit

760
00:46:33,960 --> 00:46:37,799
Speaker 1: down and study the creeps or creepiness. I bet he

761
00:46:37,880 --> 00:46:40,480
Speaker 1: was creepy himself. I don't know. I think he was

762
00:46:40,520 --> 00:46:45,480
Speaker 1: just kind of a neat thinking man, right, So yinch Man,

763
00:46:45,560 --> 00:46:48,439
Speaker 1: I like saying his name a lot more now. He

764
00:46:49,120 --> 00:46:52,040
Speaker 1: wrote an essay in nineteen oh six called on the

765
00:46:52,080 --> 00:46:57,200
Speaker 1: Psychology of the Uncanny, and that's the English translation. The

766
00:46:57,280 --> 00:47:00,400
Speaker 1: German word he used, like you said, is high aimlich

767
00:47:00,880 --> 00:47:06,080
Speaker 1: Is that right, M not in unheimlich unheimlich Yeah, okay, nice,

768
00:47:06,239 --> 00:47:10,399
Speaker 1: getting better, thank you? Uh. He he used that word.

769
00:47:10,440 --> 00:47:15,440
Speaker 1: And Unheimlich is a variation of the word heimlich um,

770
00:47:15,760 --> 00:47:18,160
Speaker 1: which is not just to say the maneuver. It means

771
00:47:18,200 --> 00:47:23,560
Speaker 1: something else entirely, which is homie, we're familiar, right, Yeah.

772
00:47:23,840 --> 00:47:27,760
Speaker 1: Unheimlich is the opposite of that. It's something strange and foreign,

773
00:47:28,040 --> 00:47:31,720
Speaker 1: and very frequently is translated into uncanny here in the West,

774
00:47:31,840 --> 00:47:37,920
Speaker 1: here here in in England. Yeah, and he he has. Uh.

775
00:47:37,960 --> 00:47:39,640
Speaker 1: He thought a lot about this, and one of the

776
00:47:39,680 --> 00:47:41,960
Speaker 1: things that he noted, which I think thought was pretty interesting,

777
00:47:42,520 --> 00:47:46,240
Speaker 1: was that people that he thought were more intellectually discriminating

778
00:47:46,960 --> 00:47:51,280
Speaker 1: um are more prone to have these uncanny experiences because

779
00:47:51,320 --> 00:47:55,719
Speaker 1: they're critical thinkers about the world. Right, So uh that

780
00:47:55,800 --> 00:47:58,640
Speaker 1: makes sense, Like just they pay attention maybe a little more, yeah,

781
00:47:58,760 --> 00:48:00,960
Speaker 1: or they're curious, like they're they're like, why am I

782
00:48:01,000 --> 00:48:02,600
Speaker 1: creeped out? Let me get to the bottom of this,

783
00:48:02,719 --> 00:48:04,480
Speaker 1: rather than oh I'm creeped out him and and go

784
00:48:04,880 --> 00:48:07,759
Speaker 1: eat a whole thing at chips ahoy, and hide under

785
00:48:07,760 --> 00:48:12,080
Speaker 1: the covers. Right. He also actually went even further and said,

786
00:48:12,120 --> 00:48:16,640
Speaker 1: it's it's possible that all of humanity's knowledge has been

787
00:48:16,640 --> 00:48:23,000
Speaker 1: accrued over these millions of years from the people investigating

788
00:48:23,040 --> 00:48:26,160
Speaker 1: what's behind this creepiness. Yeah, it's a pretty weird and

789
00:48:26,280 --> 00:48:30,120
Speaker 1: neat theory of knowledge. Well. Yeah, and speaking of theories,

790
00:48:30,120 --> 00:48:33,759
Speaker 1: there are a bunch of theories on creepiness and why

791
00:48:33,760 --> 00:48:36,160
Speaker 1: this happens, and I think they're all pretty interesting. Yeah.

792
00:48:36,520 --> 00:48:39,319
Speaker 1: The first one is called pathogen avoidance theory, and we

793
00:48:39,400 --> 00:48:43,439
Speaker 1: talked earlier about evolution, and this one kind of fits

794
00:48:43,480 --> 00:48:49,400
Speaker 1: into that bucket. Basically, a warning that we have evolved

795
00:48:50,040 --> 00:48:53,200
Speaker 1: to have in our brain says that person is off,

796
00:48:53,360 --> 00:48:57,399
Speaker 1: they are diseased, even you don't want to go near them, right,

797
00:48:57,760 --> 00:49:01,879
Speaker 1: you want to avoid that pathogen makes sense. Yeah, it's

798
00:49:01,920 --> 00:49:05,919
Speaker 1: pretty pretty approachable. Sure. There's another one that I've seen

799
00:49:05,960 --> 00:49:08,920
Speaker 1: that's I think fairly recent, and it's the idea that

800
00:49:09,040 --> 00:49:14,000
Speaker 1: things give us the creeps when when they're trying to

801
00:49:14,920 --> 00:49:20,280
Speaker 1: nonverbally mimic people. Yeah, and so like a robot doesn't

802
00:49:20,320 --> 00:49:24,560
Speaker 1: do it, so we're like, oh, that's unsettling, or somebody

803
00:49:24,600 --> 00:49:27,760
Speaker 1: who you would describe as clammy or eel like maybe

804
00:49:27,880 --> 00:49:30,560
Speaker 1: overdoes it a little bit. Yeah, like they're trying to

805
00:49:30,600 --> 00:49:34,080
Speaker 1: fit in. It's not natural to them. Yeah, and that

806
00:49:34,200 --> 00:49:36,640
Speaker 1: can give you the creeps as well. That makes sense,

807
00:49:36,680 --> 00:49:40,040
Speaker 1: but it doesn't really encompass everything. It's definitely not a

808
00:49:40,600 --> 00:49:43,279
Speaker 1: unified theory of creepiness. It just seems to kind of

809
00:49:43,280 --> 00:49:48,080
Speaker 1: inhabit one corner of the creepy spectrum. Yeah, there's another

810
00:49:48,120 --> 00:49:52,920
Speaker 1: one called violation of expectation. This is like, you know,

811
00:49:53,000 --> 00:49:55,960
Speaker 1: you've shaken hands with thousands of people over your life,

812
00:49:56,280 --> 00:49:58,640
Speaker 1: but if you go and you shake a hand and

813
00:49:59,120 --> 00:50:01,680
Speaker 1: you don't know that you're going to get a prosthetic hand,

814
00:50:02,719 --> 00:50:05,680
Speaker 1: it may give you the creeps, right, And that is

815
00:50:05,719 --> 00:50:08,760
Speaker 1: probably very fleeting, because you might just say, oh, okay,

816
00:50:08,840 --> 00:50:10,839
Speaker 1: well it doesn't give me the creeps now, but it's

817
00:50:10,880 --> 00:50:14,200
Speaker 1: just unexpected for me. And actually you said that was fleeting, right, Chuck.

818
00:50:14,280 --> 00:50:18,040
Speaker 1: So I think it was unch or somebody who said

819
00:50:18,800 --> 00:50:23,399
Speaker 1: that creepiness what gives us the creeps one time might

820
00:50:23,440 --> 00:50:26,000
Speaker 1: not give us the creeps later on, Yeah, which will

821
00:50:26,080 --> 00:50:29,400
Speaker 1: kind of come into play later like Ernst Junch. Basically

822
00:50:29,440 --> 00:50:32,520
Speaker 1: he laid the groundwork for the study of creepiness, and

823
00:50:32,760 --> 00:50:35,000
Speaker 1: it seems to have gotten a lot of it right

824
00:50:35,120 --> 00:50:38,240
Speaker 1: right out of the gate. Yeah, and like you said,

825
00:50:38,320 --> 00:50:40,960
Speaker 1: if it doesn't give you the creeps later, then that

826
00:50:41,000 --> 00:50:46,000
Speaker 1: would fit neatly into the violation of expectation, because then

827
00:50:46,040 --> 00:50:50,000
Speaker 1: you can change your expectation, right exactly, Yes. Yes. Another

828
00:50:50,040 --> 00:50:55,760
Speaker 1: one's mortality salience theory. Yeah, this one Mori and Freud

829
00:50:55,800 --> 00:51:01,759
Speaker 1: both subscribed to, and it basically said that we when

830
00:51:01,800 --> 00:51:06,200
Speaker 1: we encounter like a robot or an automaton in Freud's day,

831
00:51:07,280 --> 00:51:09,719
Speaker 1: they remind us of dead people, which in turn gets

832
00:51:09,719 --> 00:51:12,000
Speaker 1: our mind to thinking about how we're going to die

833
00:51:12,080 --> 00:51:13,879
Speaker 1: one day, And so all of a sudden we find

834
00:51:13,880 --> 00:51:18,040
Speaker 1: ourselves in the uncanny valley, right, which again raises another

835
00:51:18,080 --> 00:51:21,440
Speaker 1: sorry for the sidetrack, but raises another of Unch's points.

836
00:51:22,680 --> 00:51:27,960
Speaker 1: Is uncanniness inherent in the object or is it inside

837
00:51:28,000 --> 00:51:31,919
Speaker 1: the observer who's experiencing the creeps or uncannyness? I think

838
00:51:31,920 --> 00:51:35,160
Speaker 1: it's in the observer. Yeah, I think it is, too,

839
00:51:35,160 --> 00:51:38,239
Speaker 1: which would explain why it can go away you when

840
00:51:38,239 --> 00:51:42,319
Speaker 1: you come to experience it again. Yeah, like this, when

841
00:51:42,360 --> 00:51:44,560
Speaker 1: you go through the when you shake the same prosthetic

842
00:51:44,600 --> 00:51:47,719
Speaker 1: hand again, it's not creepy the second time. It might

843
00:51:47,760 --> 00:51:51,880
Speaker 1: even be interesting, or why some people might not experience

844
00:51:51,920 --> 00:51:54,520
Speaker 1: it at all. Like someone might sit there and see

845
00:51:54,560 --> 00:51:56,640
Speaker 1: a doll and the doll's head turns and looks at them,

846
00:51:56,680 --> 00:51:59,839
Speaker 1: and they're like, neat, right, how much for that doll?

847
00:52:00,160 --> 00:52:03,560
Speaker 1: Which means you've just met a serial killer? Right? And

848
00:52:03,600 --> 00:52:07,560
Speaker 1: then the dolls creeped out after that? This one I

849
00:52:07,600 --> 00:52:10,279
Speaker 1: like them, even though I can never say this word

850
00:52:10,280 --> 00:52:15,400
Speaker 1: for some reason. And thro po morphism, nice job, dehumanization dichotomy,

851
00:52:15,440 --> 00:52:19,080
Speaker 1: which basically as we attribute these human attributes to the

852
00:52:19,160 --> 00:52:24,239
Speaker 1: robot until we realize that they don't have them, right. So,

853
00:52:24,360 --> 00:52:26,680
Speaker 1: like we're looking at this robot that looks like a person.

854
00:52:26,719 --> 00:52:28,880
Speaker 1: We're saying, oh, look, it's just like a human. And

855
00:52:28,920 --> 00:52:30,839
Speaker 1: they're walking and they're talking and they're smiling, and then

856
00:52:30,840 --> 00:52:33,600
Speaker 1: oh god, look at their eyes. Their eyes are dead.

857
00:52:33,680 --> 00:52:35,839
Speaker 1: Look at the eyes. They don't they don't have any

858
00:52:35,920 --> 00:52:39,239
Speaker 1: internal thoughts at all. They're not human. Yeah, And then

859
00:52:39,320 --> 00:52:42,080
Speaker 1: all of a sudden, on Caney Valley, which is a

860
00:52:42,080 --> 00:52:45,360
Speaker 1: little bit about expectation too, I think those are crawls

861
00:52:45,360 --> 00:52:49,799
Speaker 1: over a little I think sure, And so creepiness, I think,

862
00:52:50,480 --> 00:52:55,080
Speaker 1: especially the modern incarnation of creepiness. This is these are

863
00:52:55,120 --> 00:52:59,400
Speaker 1: my thoughts. They seem to be They represent a crossroads

864
00:52:59,480 --> 00:53:07,040
Speaker 1: right where evolutionarily, creepiness I think was probably it alerts us.

865
00:53:07,040 --> 00:53:09,800
Speaker 1: We're on alert when something's creeping us out. We're really

866
00:53:09,840 --> 00:53:13,000
Speaker 1: focused on that thing, right then, Ye, but we're also

867
00:53:13,600 --> 00:53:17,319
Speaker 1: bound by society not to just turn and run from

868
00:53:17,360 --> 00:53:20,560
Speaker 1: anything that could conceivably be a threat. You can also

869
00:53:20,600 --> 00:53:23,800
Speaker 1: take it a little further and say that evolutionarily speaking,

870
00:53:24,120 --> 00:53:25,840
Speaker 1: it would not make sense for us to turn and

871
00:53:25,920 --> 00:53:28,480
Speaker 1: run from every single thing that could conceivably be a

872
00:53:28,520 --> 00:53:31,160
Speaker 1: threat before we've identified it as a threat, because we

873
00:53:31,200 --> 00:53:33,680
Speaker 1: would be using up a lot of calories and energy,

874
00:53:33,840 --> 00:53:35,680
Speaker 1: and we would have to find more food than we do.

875
00:53:35,719 --> 00:53:39,080
Speaker 1: Would be inefficient. Right, So we're kind of bound socially

876
00:53:39,120 --> 00:53:42,560
Speaker 1: to stand in place until we identify something as a

877
00:53:42,600 --> 00:53:46,839
Speaker 1: threat or not, in which case, during this period, that's

878
00:53:46,880 --> 00:53:50,160
Speaker 1: when we experience creepiness. Yeah, And I think everyone has

879
00:53:50,200 --> 00:53:53,080
Speaker 1: experienced this. Like you're in a coffee shop or something

880
00:53:53,719 --> 00:53:58,080
Speaker 1: and like some super creepy dude comes in and if

881
00:53:58,120 --> 00:54:01,600
Speaker 1: you're liking me, you're just like, all right, I'm gonna

882
00:54:01,760 --> 00:54:04,399
Speaker 1: I'm gonna keep my eye on that guy. I'm I'm

883
00:54:04,400 --> 00:54:06,680
Speaker 1: not gonna bolt and run, but it might stay near

884
00:54:06,680 --> 00:54:09,400
Speaker 1: the door. Sure, you know, I might get my car

885
00:54:09,480 --> 00:54:13,719
Speaker 1: keys ready exactly right. Uh, it is it's this weird

886
00:54:13,760 --> 00:54:17,840
Speaker 1: social contract. Um, and you know I feel bad for

887
00:54:17,960 --> 00:54:21,439
Speaker 1: people that just inherently look a little creepy. Well yeah,

888
00:54:21,560 --> 00:54:24,359
Speaker 1: let's talk about that. Yeah. So there was these there

889
00:54:24,360 --> 00:54:27,719
Speaker 1: are these researchers from Knox College who did what they

890
00:54:27,760 --> 00:54:30,640
Speaker 1: build as the first empirical study of creepiness. And this

891
00:54:30,680 --> 00:54:33,720
Speaker 1: is in twenty sixteen. Such a great study and um,

892
00:54:33,760 --> 00:54:37,319
Speaker 1: it was an online survey, very little heavy lifting, but

893
00:54:37,360 --> 00:54:40,440
Speaker 1: it was a pretty pretty cool survey. It was in

894
00:54:40,480 --> 00:54:46,240
Speaker 1: four parts, and um, what they found overall was that, yeah,

895
00:54:46,400 --> 00:54:51,680
Speaker 1: physical characteristics, physical traits that are almost stereotypically linked to

896
00:54:52,000 --> 00:54:57,480
Speaker 1: creepy people do have an effect. They are creepy. Um,

897
00:54:57,520 --> 00:55:01,399
Speaker 1: as far as as the participants in this that are concerned. Yeah.

898
00:55:02,000 --> 00:55:06,160
Speaker 1: So the first section said, hey, you know, what is

899
00:55:06,160 --> 00:55:09,200
Speaker 1: the likelihood that this person is creepy? And there's like,

900
00:55:09,600 --> 00:55:14,200
Speaker 1: you know, descriptions of them. There's forty four different behaviors, right, Yeah,

901
00:55:14,840 --> 00:55:18,480
Speaker 1: And the second part was participants rated the creepiness of

902
00:55:18,480 --> 00:55:21,160
Speaker 1: twenty one different occupations. I love to see that list.

903
00:55:22,280 --> 00:55:26,279
Speaker 1: The third section it said, list two hobbies that you

904
00:55:26,320 --> 00:55:29,919
Speaker 1: think are creepy. They only needed too. It was open

905
00:55:30,040 --> 00:55:34,360
Speaker 1: ended too. And then the last section the participants said

906
00:55:34,600 --> 00:55:37,359
Speaker 1: whether or not they agreed with fifteen statements about the

907
00:55:37,440 --> 00:55:41,680
Speaker 1: nature of creepy people. Yeah, and overall again like they

908
00:55:41,719 --> 00:55:45,320
Speaker 1: found like, yes, if you have physical traits that people

909
00:55:45,360 --> 00:55:48,720
Speaker 1: find creepy, like bulging eyes, or you lick your lips

910
00:55:48,760 --> 00:55:51,720
Speaker 1: a lot, or you know, you you arch your fingers

911
00:55:51,719 --> 00:55:55,280
Speaker 1: and then just kind of tap them together a lot, Okay,

912
00:55:55,719 --> 00:55:59,920
Speaker 1: it's kind of creepy. But the Knox researchers concluded that

913
00:56:00,040 --> 00:56:03,640
Speaker 1: those aren't creepy necessarily in and of themselves. It's when

914
00:56:03,680 --> 00:56:08,600
Speaker 1: it's in conjunction with other creepy behavior that somebody comes

915
00:56:08,600 --> 00:56:13,360
Speaker 1: across as creepy. Right, And of course, the one behavior

916
00:56:13,400 --> 00:56:15,280
Speaker 1: they put in here I think there was probably universally

917
00:56:15,280 --> 00:56:19,640
Speaker 1: creepy was someone who persistently steers the conversation toward a

918
00:56:19,640 --> 00:56:22,640
Speaker 1: sexual topic, right, yeah, you don't, you don't do that.

919
00:56:23,239 --> 00:56:27,440
Speaker 1: They also found no. They also found ninety five percent

920
00:56:27,480 --> 00:56:30,200
Speaker 1: of participants and this is like I think eighteen hundred

921
00:56:30,200 --> 00:56:33,600
Speaker 1: and thirteen hundred and forty one people, ninety five percent

922
00:56:33,680 --> 00:56:36,160
Speaker 1: of them said that men were more likely to be

923
00:56:36,239 --> 00:56:40,600
Speaker 1: creepy than women. Yeah, I think that's generally true. Um,

924
00:56:41,480 --> 00:56:44,319
Speaker 1: I don't remember getting the creeps a lot in my

925
00:56:44,400 --> 00:56:48,960
Speaker 1: life by strictly from the appearance of a woman, right,

926
00:56:49,080 --> 00:56:51,440
Speaker 1: But a lot of dudes on a weekly basis give

927
00:56:51,520 --> 00:56:54,759
Speaker 1: me the creeps. Sure, but we should say so. There's

928
00:56:54,760 --> 00:56:59,000
Speaker 1: a website called girl dot com touurl dot com, and

929
00:56:59,080 --> 00:57:01,160
Speaker 1: they went on to read it and found a thread

930
00:57:01,160 --> 00:57:03,960
Speaker 1: somewhere that they wrote a blog post about and now

931
00:57:03,960 --> 00:57:07,280
Speaker 1: we're reporting on it, so it's really come full circle.

932
00:57:08,200 --> 00:57:11,000
Speaker 1: But it was a threat about how women can be creepy,

933
00:57:11,120 --> 00:57:15,359
Speaker 1: and it was written by dudes. And there are some

934
00:57:15,440 --> 00:57:22,080
Speaker 1: things that apparently are universally creepy among boys with women. Right. Yeah,

935
00:57:22,280 --> 00:57:26,120
Speaker 1: women that are too needy can be creepy. Women who

936
00:57:26,200 --> 00:57:30,000
Speaker 1: use baby talk too much, or who quote never leave

937
00:57:30,040 --> 00:57:33,960
Speaker 1: a guy alone. Yeah, I just I'm just gonna go

938
00:57:34,000 --> 00:57:37,880
Speaker 1: ahead and dump that right into the trash bend. That's

939
00:57:37,920 --> 00:57:42,080
Speaker 1: my only comment on that. Okay, what about e harmony?

940
00:57:42,240 --> 00:57:45,040
Speaker 1: I mean, if you come home and Glenn closes in

941
00:57:45,040 --> 00:57:50,439
Speaker 1: your kitchen boiling your pet bunny. Well that's a threat. Yeah,

942
00:57:50,480 --> 00:57:53,600
Speaker 1: that's not even creepy, that's just a threat. Right. Although

943
00:57:53,600 --> 00:57:58,200
Speaker 1: I will say in Fatal Attraction, the scene where she

944
00:57:58,400 --> 00:58:01,680
Speaker 1: is sitting there clicking the light on and off, listening

945
00:58:01,680 --> 00:58:04,520
Speaker 1: to Madam Butterfly, that was that was kind of creepy.

946
00:58:04,880 --> 00:58:08,760
Speaker 1: I was trying to think of like a creepy woman.

947
00:58:09,080 --> 00:58:11,760
Speaker 1: I really couldn't come up with anybody. Well, these are

948
00:58:11,800 --> 00:58:15,160
Speaker 1: creepy behaviors though, you know, yeah, not like Glencoe's close

949
00:58:15,240 --> 00:58:17,360
Speaker 1: walked into the room and you're like, oh, I don't

950
00:58:17,360 --> 00:58:22,520
Speaker 1: know about that, right, right, right, there's a difference, right,

951
00:58:22,560 --> 00:58:28,160
Speaker 1: There's a difference between genuine creepiness and just doing creepy things. Yeah.

952
00:58:28,200 --> 00:58:29,840
Speaker 1: I think it is much harder for women to be

953
00:58:29,880 --> 00:58:33,960
Speaker 1: creepy than men. Cannot think of a single actual creepy woman, No,

954
00:58:35,960 --> 00:58:40,680
Speaker 1: I'd like to hear from people, though. Yeah. E harmony,

955
00:58:41,280 --> 00:58:44,520
Speaker 1: So we've talked about Reddit, now we're gonna talk about

956
00:58:44,560 --> 00:58:47,520
Speaker 1: e harmony. Right. They had an article where they wrote

957
00:58:47,520 --> 00:58:50,400
Speaker 1: advice to dudes. It was called how to avoid the

958
00:58:50,400 --> 00:58:56,680
Speaker 1: creep Zone, right, and their advice was for your hobbies

959
00:58:56,680 --> 00:58:59,800
Speaker 1: that you list to be just sort of vanilla, all right,

960
00:59:00,120 --> 00:59:04,160
Speaker 1: don't like. And even if you are an amateur taxidermist,

961
00:59:04,520 --> 00:59:08,280
Speaker 1: maybe don't put that down. Right. They said, it can

962
00:59:08,320 --> 00:59:10,520
Speaker 1: be attractive for a guy to have an off the

963
00:59:10,600 --> 00:59:14,200
Speaker 1: beaten path hobby. And one of the examples they gave

964
00:59:14,240 --> 00:59:17,840
Speaker 1: of an off the beaten path hobby was collecting punk records.

965
00:59:19,120 --> 00:59:22,360
Speaker 1: But don't get weirder than that. Yeah, And if you

966
00:59:22,400 --> 00:59:25,080
Speaker 1: know taxidermy in and of itself, some people might say

967
00:59:25,120 --> 00:59:27,120
Speaker 1: a super creepy we did an episode on that. Other

968
00:59:27,160 --> 00:59:30,040
Speaker 1: people might say, no, it's just just beautiful artwork, right,

969
00:59:30,280 --> 00:59:33,240
Speaker 1: But Norman Bates was into taxidermy for a reason. In

970
00:59:33,280 --> 00:59:38,040
Speaker 1: Psycho right, it was unsettling. Yeah, you know. Yeah, And

971
00:59:38,080 --> 00:59:44,240
Speaker 1: so the Knox people who carried out this survey, the

972
00:59:44,320 --> 00:59:48,120
Speaker 1: Knox University researchers, they basically said, here's what we think

973
00:59:48,200 --> 00:59:52,160
Speaker 1: it is. Here's creepiness explained. And what they explained was

974
00:59:52,200 --> 00:59:57,120
Speaker 1: what can be called is the threat ambiguity theory. Yeah,

975
00:59:57,120 --> 00:59:58,840
Speaker 1: this this one, I think they kind of put a

976
00:59:58,920 --> 01:00:01,000
Speaker 1: cherry on top on this one. Yeah, we really did.

977
01:00:01,040 --> 01:00:04,080
Speaker 1: Like it's just basically where you are creeped out by

978
01:00:04,120 --> 01:00:07,919
Speaker 1: something because your hackles are raised right then, and it's

979
01:00:07,960 --> 01:00:11,600
Speaker 1: because you haven't determined whether that thing's a threat or not. Yep. Right.

980
01:00:12,320 --> 01:00:15,800
Speaker 1: There's another one though that I subscribe to. I think

981
01:00:15,840 --> 01:00:19,600
Speaker 1: it is finally the unified theory of creepiness. I think

982
01:00:19,600 --> 01:00:25,160
Speaker 1: it covers everything. And it's called the category ambiguity theory. Yeah.

983
01:00:25,200 --> 01:00:28,960
Speaker 1: That was Now did David Livingstone Smith make this up

984
01:00:29,080 --> 01:00:32,600
Speaker 1: or was he just champion this? I think he made

985
01:00:32,600 --> 01:00:36,600
Speaker 1: it up because he wrote about the Knox researchers and said,

986
01:00:36,720 --> 01:00:40,120
Speaker 1: what they're talking about you can call threat ambiguity category

987
01:00:40,520 --> 01:00:44,480
Speaker 1: or threat ambiguity theory. With category ambiguity theory, he didn't

988
01:00:44,480 --> 01:00:47,880
Speaker 1: cite anybody else, so it seemed to be his own construct. Yeah,

989
01:00:47,880 --> 01:00:49,760
Speaker 1: so this is the idea. It's sort of like the

990
01:00:49,800 --> 01:00:55,320
Speaker 1: threat ambiguity in that there is some confusion, but it's

991
01:00:55,320 --> 01:00:56,840
Speaker 1: not a threat like I think this dude in the

992
01:00:56,840 --> 01:00:59,720
Speaker 1: coffee shop is gonna kill me. It's more like I

993
01:00:59,760 --> 01:01:03,040
Speaker 1: don't know how to categorize that guy, right, and that

994
01:01:03,080 --> 01:01:08,320
Speaker 1: freaks me out right. And it's based on what's called essentialism, right,

995
01:01:08,600 --> 01:01:11,880
Speaker 1: where if you are a member of a species of animal,

996
01:01:11,960 --> 01:01:17,120
Speaker 1: whether human or raccoon or tiger, there's something about you,

997
01:01:17,720 --> 01:01:20,919
Speaker 1: or there's some collection or set of things about you

998
01:01:21,360 --> 01:01:26,360
Speaker 1: that are totally unique to your species. Yeah, it's something

999
01:01:26,440 --> 01:01:30,600
Speaker 1: you possess because you're a member that species. And because

1000
01:01:30,640 --> 01:01:33,400
Speaker 1: you're a member that species, you possess these things and

1001
01:01:33,440 --> 01:01:35,840
Speaker 1: it can be very difficult to put your finger on it.

1002
01:01:35,960 --> 01:01:38,000
Speaker 1: But it's just one of those things that you know

1003
01:01:38,080 --> 01:01:40,560
Speaker 1: when you see it, or no, when you don't see it, right, Yeah,

1004
01:01:40,800 --> 01:01:44,520
Speaker 1: And there are clear borders between these things. You either

1005
01:01:44,640 --> 01:01:47,320
Speaker 1: have this essence fully or you don't have it at all.

1006
01:01:47,360 --> 01:01:50,080
Speaker 1: You're lacking and you're missing it and something's really wrong.

1007
01:01:50,640 --> 01:01:53,560
Speaker 1: So in this article he used the example of a

1008
01:01:53,560 --> 01:01:57,960
Speaker 1: wax dummy. Yeah, have you ever been to like Madame Tussod's, Sure, Yeah,

1009
01:01:58,680 --> 01:02:00,520
Speaker 1: I find that the ones and again with the eyes,

1010
01:02:00,560 --> 01:02:03,120
Speaker 1: the ones that work the best, or the ones where

1011
01:02:03,120 --> 01:02:06,880
Speaker 1: they have sunglasses on. Oh yeah, you know again, Michael Jackson,

1012
01:02:07,400 --> 01:02:10,080
Speaker 1: that's right. But the whole point with these wax dummies

1013
01:02:10,080 --> 01:02:13,640
Speaker 1: with the eyes is they're fixed. They're not moving around.

1014
01:02:13,840 --> 01:02:17,960
Speaker 1: The facial expression is locked in the skin itself. You know,

1015
01:02:18,480 --> 01:02:21,160
Speaker 1: it can only do so much. And Madam Tusso's and

1016
01:02:21,280 --> 01:02:23,960
Speaker 1: museums like that are the best of the best, and

1017
01:02:24,000 --> 01:02:26,560
Speaker 1: they do look pretty good. But that's the whole point

1018
01:02:26,560 --> 01:02:29,840
Speaker 1: with the uncanny Valley is you can't get ninety nine

1019
01:02:30,360 --> 01:02:33,840
Speaker 1: there and say we're fine. It's that one percent that

1020
01:02:34,000 --> 01:02:38,920
Speaker 1: still gives people the creeps exactly. And that's and it

1021
01:02:39,000 --> 01:02:42,200
Speaker 1: sums up everything, like the threat ambiguity could fall into this,

1022
01:02:42,560 --> 01:02:45,680
Speaker 1: whether you're talking about robots, whether you're talking about a

1023
01:02:45,680 --> 01:02:49,760
Speaker 1: half dog half lizard combo, which living Stone sites or

1024
01:02:49,800 --> 01:02:53,800
Speaker 1: living Stone Smith sites the desert. Yeah, a dessert would

1025
01:02:53,800 --> 01:02:57,040
Speaker 1: be creepy when you saw it. Yeah, but so things

1026
01:02:57,080 --> 01:03:00,800
Speaker 1: that are a threat are creepy. But there's also things

1027
01:03:00,800 --> 01:03:03,320
Speaker 1: that are creepy that aren't a threat in this category.

1028
01:03:03,320 --> 01:03:07,080
Speaker 1: Ambiguity theory figured it out. So if that's true, Chuck

1029
01:03:07,520 --> 01:03:10,720
Speaker 1: and David Livingstone Smith figured out what is the basis

1030
01:03:10,760 --> 01:03:16,600
Speaker 1: of creepiness, we finally have the independent variable lickt and

1031
01:03:17,040 --> 01:03:20,600
Speaker 1: Massa hiro Morty's Uncanny Valley graph, and we can get

1032
01:03:20,600 --> 01:03:24,600
Speaker 1: to work. Is he still around? Yeah? Yes, when that's

1033
01:03:24,680 --> 01:03:28,840
Speaker 1: happy about all this, I get the impression that he's

1034
01:03:28,920 --> 01:03:31,919
Speaker 1: kind of like, just whatever, gone off on his own

1035
01:03:31,960 --> 01:03:35,160
Speaker 1: little thing, Okay, and he's fine. He wrote it in

1036
01:03:35,200 --> 01:03:38,000
Speaker 1: nineteen seventy after all, you know, Yeah, I mean most

1037
01:03:38,080 --> 01:03:42,880
Speaker 1: fifty years ago. Yeah, so he's probably up there yeah, uh,

1038
01:03:43,400 --> 01:03:46,000
Speaker 1: are you anything else? I got nothing else? Good ones. Yeah,

1039
01:03:46,240 --> 01:03:48,200
Speaker 1: if you want to know more about the Uncanny Valley,

1040
01:03:48,280 --> 01:03:50,560
Speaker 1: we should say this was based originally on a grabst

1041
01:03:50,760 --> 01:03:53,520
Speaker 1: article too. But if you want to know more about

1042
01:03:53,520 --> 01:03:56,200
Speaker 1: the Uncanny Valley, cum read that Grabster article. You can

1043
01:03:56,240 --> 01:03:59,480
Speaker 1: type Uncanny Valley in the search bar at HowStuffWorks dot com.

1044
01:03:59,520 --> 01:04:04,840
Speaker 1: And since I said search bars, time for listener mail. Well,

1045
01:04:04,880 --> 01:04:07,440
Speaker 1: and today it's a very special listener mail. This is

1046
01:04:07,920 --> 01:04:10,640
Speaker 1: Josh edition because you picked out a very special one.

1047
01:04:10,920 --> 01:04:13,120
Speaker 1: I love this one. I'm going to butcher the dude's name,

1048
01:04:13,160 --> 01:04:15,480
Speaker 1: but that's right, take it away. It's a good one. Okay.

1049
01:04:16,200 --> 01:04:19,760
Speaker 1: I'm going to call this one email from a real

1050
01:04:19,880 --> 01:04:24,520
Speaker 1: Irish historian and it feels pretty good. Chuck, am I

1051
01:04:24,520 --> 01:04:28,000
Speaker 1: out of a job? Yeah? Maybe? Okay. Hi guys, I'm

1052
01:04:28,000 --> 01:04:30,440
Speaker 1: a big fan of the show. It's informative and insightful,

1053
01:04:30,480 --> 01:04:32,560
Speaker 1: and I find myself interested in things that I never

1054
01:04:32,640 --> 01:04:36,360
Speaker 1: looked twice at before. One subject that I'd always found

1055
01:04:36,360 --> 01:04:40,200
Speaker 1: fascinating was the correlation between the Native American Choctaw tribe

1056
01:04:40,400 --> 01:04:42,560
Speaker 1: and the people of Ireland. I didn't realize that was

1057
01:04:42,600 --> 01:04:44,960
Speaker 1: a thing that you not at all. This is a

1058
01:04:45,000 --> 01:04:47,480
Speaker 1: story which isn't well known, Okay, which isn't well known

1059
01:04:47,480 --> 01:04:50,160
Speaker 1: outside of some areas of Ireland and of course within

1060
01:04:50,200 --> 01:04:52,480
Speaker 1: the tribe. But it's a really good story of solidarity

1061
01:04:52,520 --> 01:04:55,560
Speaker 1: between two groups of people, despite being thousands of miles apart.

1062
01:04:56,040 --> 01:04:58,280
Speaker 1: Less than twenty years after the Trail of Tears, which

1063
01:04:58,320 --> 01:05:02,440
Speaker 1: forcibly displaced thousand of natives, the Great Famine hit Ireland.

1064
01:05:02,760 --> 01:05:05,040
Speaker 1: During this time, as you know, Ireland was colonized by

1065
01:05:05,080 --> 01:05:07,720
Speaker 1: the British and the people of Ireland were treated poorly

1066
01:05:07,920 --> 01:05:11,160
Speaker 1: due to the common misconception that Irish Catholics were lower

1067
01:05:11,200 --> 01:05:14,680
Speaker 1: caliber of human. He goes on to give more examples,

1068
01:05:14,680 --> 01:05:17,439
Speaker 1: but just suffice to say it was not good for

1069
01:05:17,560 --> 01:05:21,200
Speaker 1: the Irish people. During the famine, words spread to America

1070
01:05:21,320 --> 01:05:24,560
Speaker 1: and to the Choctaw tribe. They sympathized with the Irish

1071
01:05:24,560 --> 01:05:27,560
Speaker 1: people so much that only fifteen years after the Trail

1072
01:05:27,600 --> 01:05:31,200
Speaker 1: of Tears, they donated seven hundred and ten dollars during

1073
01:05:31,240 --> 01:05:34,480
Speaker 1: eighteen forty five to send to Ireland as part of

1074
01:05:34,480 --> 01:05:37,720
Speaker 1: a relief fund. This is estimated to be roughly sixty

1075
01:05:37,760 --> 01:05:41,280
Speaker 1: eight thousand dollars in today's money. This was greatly appreciated

1076
01:05:41,320 --> 01:05:44,040
Speaker 1: by the Irish people, and after the famine, the bond continued.

1077
01:05:44,440 --> 01:05:47,200
Speaker 1: In Cork, we have a sculpture honoring the tribute of

1078
01:05:47,240 --> 01:05:50,280
Speaker 1: the Choctaw people, and in nineteen ninety members of the

1079
01:05:50,320 --> 01:05:52,800
Speaker 1: tribe came to Ireland and walked the Famine Walk in

1080
01:05:52,880 --> 01:05:56,000
Speaker 1: Mayo to replicate the walk that starving people made to

1081
01:05:56,000 --> 01:05:59,080
Speaker 1: ask the landlord for help. In nineteen ninety two, an

1082
01:05:59,120 --> 01:06:03,320
Speaker 1: Irish commemoration group walked from Oklahoma to Mission to replicate

1083
01:06:03,360 --> 01:06:06,960
Speaker 1: the Trail of Tears and raised seven hundred thousand dollars

1084
01:06:07,280 --> 01:06:10,560
Speaker 1: to help poverty in Africa. These two groups continue to

1085
01:06:10,600 --> 01:06:13,000
Speaker 1: work together and to this day our President has declared

1086
01:06:13,000 --> 01:06:16,040
Speaker 1: an honorary member of the Choctaw tribe. Along with the

1087
01:06:16,120 --> 01:06:18,520
Speaker 1: Quakers who fed Irish people to the point that their

1088
01:06:18,560 --> 01:06:22,320
Speaker 1: members ended starving themselves, the Choctaw tribe remained some of

1089
01:06:22,360 --> 01:06:25,640
Speaker 1: the unsung heroes of the famine story of Ireland. Sorry

1090
01:06:25,640 --> 01:06:27,680
Speaker 1: it went on so long. I'm an Irish historian, so

1091
01:06:27,720 --> 01:06:29,920
Speaker 1: I tend to waffle. Love the show. Best of luck

1092
01:06:29,960 --> 01:06:37,240
Speaker 1: with yourselves, Royson kill roy Bean, fantastic, great story. Thanks

1093
01:06:37,240 --> 01:06:39,400
Speaker 1: a lot, Roisen. I'm quite sure that it's not the

1094
01:06:39,440 --> 01:06:41,720
Speaker 1: actual pronunciation of your name, because there's a lot of

1095
01:06:41,760 --> 01:06:45,880
Speaker 1: accent marks over letters there normally aren't, ye, So I

1096
01:06:45,880 --> 01:06:48,880
Speaker 1: apologize for that, but I nailed your last name. I'm

1097
01:06:48,880 --> 01:06:52,200
Speaker 1: positive of it. And Josh Clark three and a half stars,

1098
01:06:52,200 --> 01:06:54,560
Speaker 1: not bad out of three and a half. Right, I

1099
01:06:54,600 --> 01:06:57,439
Speaker 1: don't remember what was star search the four stars? Oh,

1100
01:06:57,480 --> 01:07:00,560
Speaker 1: I don't remember. I just just now remember there was

1101
01:07:00,600 --> 01:07:04,520
Speaker 1: such thing as Starson. Yeah, well okay, well you take

1102
01:07:04,520 --> 01:07:07,520
Speaker 1: that in part, Chuck, since I took listenermail, Oh geez,

1103
01:07:08,800 --> 01:07:12,120
Speaker 1: thanks for listening. Um. Hey, if you want to get

1104
01:07:12,160 --> 01:07:14,240
Speaker 1: in touch with us, you can go to our official pages.

1105
01:07:14,840 --> 01:07:18,440
Speaker 1: Stuff you Should Know podcasts, what else? Let's see if

1106
01:07:18,440 --> 01:07:20,800
Speaker 1: they want to send us an email? Oh yeah, email

1107
01:07:20,880 --> 01:07:24,920
Speaker 1: us at stuff podcast at HowStuffWorks dot com. And have

1108
01:07:25,000 --> 01:07:27,560
Speaker 1: a good day. Is that what you say? That's good enough?

1109
01:07:27,960 --> 01:07:33,640
Speaker 1: All right? Stuff you Should Know is a production of iHeartRadio.

1110
01:07:34,120 --> 01:07:37,280
Speaker 1: For more podcasts My heart Radio, visit the iHeartRadio app,

1111
01:07:37,480 --> 01:07:40,400
Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.