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Speaker 1: Hey, everybody, it's Josh and Chuck is here in spirit too,

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Speaker 1: and we just wanted to drop a casual reminder that

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Speaker 1: we are going to have a swinging Pacific Northwest Swing

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Speaker 1: this coming February, and tickets are now on sale. February

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Speaker 1: one will be at the More Theater in Seattle, February

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Speaker 1: two will be at Revolution Hall in Portland, and on

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Speaker 1: February three, for s F Sketch Fest, will be at

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Speaker 1: the Sydney Goldstein Theater. Go check out all of our

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Speaker 1: social media's for more information and links to tickets, and

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Speaker 1: we'll see you in February. Welcome to Stuff You Should Know,

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Speaker 1: a production of I Heart Radio. Hey, and welcome to

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Speaker 1: the podcast. I'm Josh, and there's Chuck and Jerry's here too,

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Speaker 1: and this is Stuff you Should Know. The day after

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Speaker 1: Labor Day edition, which Chuck loves. Uh. Yeah, you know,

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Speaker 1: Emily show me that article all that informed part of

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Speaker 1: this episode the other day. Yeah, and we'll get to it.

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Speaker 1: But I started looking at this photographer's doppelganger photo series. Yeah,

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Speaker 1: and boy, some of those are crazy similar. Yeah. What

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Speaker 1: about the two bears with their shirts off? Yeah, those

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Speaker 1: guys struck me as extremely close, the two women with

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Speaker 1: the bangs and the black tank tops looked like identical twins.

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Speaker 1: To me. I could not tell them apart. Yeah, I

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Speaker 1: feel like we shouldn't tease anybody anymore. There's a guy

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Speaker 1: and like you said, we'll get to him, um in

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Speaker 1: a little bit, but or he'll figure in in a

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Speaker 1: little bit, I should say. But he's a Montreal I guess.

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Speaker 1: The quebec Qua photographer Francois Brunel, And for twenty years

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Speaker 1: he's been assembling pictures of doppel gangers, people who already

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Speaker 1: knew they had a doppelganger and wrote in Doppelgangers. He's

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Speaker 1: looked for and found on the street and he brings

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Speaker 1: them together are into a studio and photographs them together.

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Speaker 1: But he does some like really cool stuff with it.

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Speaker 1: It's not just like here you stand here and you

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Speaker 1: stand next to him. The way that he poses them

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Speaker 1: and dresses him and everything like it creates like a

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Speaker 1: sense of intimacy between them. It makes you think you're

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Speaker 1: looking at identical twin photos. Yeah, I mean, you know

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Speaker 1: some of them people favor one another, and then when

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Speaker 1: you have them styled to look alike and postal like that,

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Speaker 1: informs what you're seeing. But a handful of them, it

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Speaker 1: was they look so much like identical twins that it's

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Speaker 1: just it's weird to think that you could walk down

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Speaker 1: the street one day and someone who looks exactly like you. Yeah,

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Speaker 1: and I mean like that is got to be pretty eerie.

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Speaker 1: And luckily one of the great things about social media

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Speaker 1: is that people can post their random encounters. You've got

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Speaker 1: a phone with camera on it, You've got access to

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Speaker 1: social media through that phone. You can just snap a

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Speaker 1: picture of you and your doppelganger, post it to the

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Speaker 1: internet and let everyone go crazy. And that does happen,

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Speaker 1: it seems like from time to time. But this dude's

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Speaker 1: whole jam it's called them. I'm not a look alike

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Speaker 1: the exclamation point. It sounds like a Disney movie from

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Speaker 1: the sixties or something. Um, but that's that's a Francois

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Speaker 1: Brunel's twenty Years series. Yeah, and not just hey, Chuck,

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Speaker 1: you look like every tubby guy with a beard and glasses.

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Speaker 1: It's like, that's that's lazy. I think when you're in

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Speaker 1: the public eye, people tend to do things like that

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Speaker 1: to say, Okay, you look like all of these people Um,

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Speaker 1: we're talking like real lookalikes. Um, which I you know,

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Speaker 1: I think I've gotten a couple here and there over

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Speaker 1: the years that that I was like, Okay, I could

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Speaker 1: see that for sure, But um, doppel gangers are different. Yeah.

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Speaker 1: So doppelganger, if you haven't just surmised already, it's a

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Speaker 1: German word at its origin, and um, it's not one

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Speaker 1: of those really clever German words. It really just means

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Speaker 1: double goer, or a different interpretation is double walker. And

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Speaker 1: it's not some super old like like medieval word. It

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Speaker 1: was actually coined at the end of the eighteenth entry

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Speaker 1: by um an author named Jean Paul, which is an

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Speaker 1: unlikely German name if you ask me. But he wrote

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Speaker 1: a book called The sieben Cuss. Yeah, sieben Cuss, I

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Speaker 1: nailed it, um. And that book was about a man

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Speaker 1: who had a doppel ganger. Right, yeah, I think John

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Speaker 1: Paul's hiding something with that name. I do think he's

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Speaker 1: actually a double I think there's a dousel Doworf at

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Speaker 1: the end or something that he left off, right. Uh. Yeah,

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Speaker 1: so he wrote about it in that novel. But um,

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Speaker 1: as Olivia is keen to point out, Livia helped us

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Speaker 1: with this one. The uh, he sort of mixes up

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Speaker 1: the words when he was actually talking about a look

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Speaker 1: alike human. He used the word with a T doupled ganga,

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Speaker 1: but did use the word doppelganger in there as a

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Speaker 1: as a meal with two courses being served together. So

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Speaker 1: I don't know if things just got a little confused

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Speaker 1: or people thought it was just fine to drop the T,

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Speaker 1: but double ganger became the eventual word that we use for,

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Speaker 1: you know, a non related look alike, and that's I

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Speaker 1: don't know if we pointed that out. The obvious thing

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Speaker 1: here is that you're not not related to these people

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Speaker 1: at all. Yeah. And the other thing about a um

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Speaker 1: doppel gangers again, like you said, it's not like, oh,

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Speaker 1: you bear a passing resemblance or you favor one another,

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Speaker 1: like you're as close to the person's double body double

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Speaker 1: as you can get in. The closer you are, the

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Speaker 1: more of a doppel ganger you are, basically. But um,

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Speaker 1: but John Paul I can't say his name any other way. Um,

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Speaker 1: he coined those terms, so it's funny that he kind

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Speaker 1: of got it wrong. Everybody else is like, no, we're

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Speaker 1: gonna drop the T, like you said. But the fact

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Speaker 1: that he coined the term doppel ganger doesn't mean he

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Speaker 1: invented the concept. It's actually a much older concept that

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Speaker 1: just didn't have the same name that we use now. Um.

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Speaker 1: Apparently there's a longstanding um superstition in England in Germany

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Speaker 1: dating back I think to the medieval age, maybe before Chuck,

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Speaker 1: where if you saw your doppel ganger three times it

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Speaker 1: meant you were about to die. And that's still I

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Speaker 1: think people still kind of believe that one. Yeah, And

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Speaker 1: that's I mean, there are all kinds of old pieces

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Speaker 1: of folklore and just about every country you can explore

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Speaker 1: that has some sort of and it's usually not if

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Speaker 1: you find your doppelganger then you know good luck will

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Speaker 1: come your way. It always seems like it's some kind

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Speaker 1: of uh, spooky negative thing will happen, whether you see

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Speaker 1: them three times or whether. I mean, there was one

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Speaker 1: I think in the North mythology where a spirit appears.

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Speaker 1: This one's just a little weird. It appears just like you,

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Speaker 1: but it it beat to you to wherever you're going.

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Speaker 1: So if you're traveling on a journey, your doppelganger would

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Speaker 1: beat you there, and people think that you would have

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Speaker 1: arrived earlier. I don't know if that's the solves tardiness

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Speaker 1: for the North mythology, but that one's not super evil,

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Speaker 1: but it's usually, especially when you get into literature, Uh,

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Speaker 1: some sort of know evil force or evil twin that's

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Speaker 1: coming along to wreck things for you. Yeah, and that

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Speaker 1: Norse one that arrives, I just want to put a

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Speaker 1: button on that because I did a little research it.

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Speaker 1: Um it's called the bard Dooger. And the reason I

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Speaker 1: know how to pronounce this because I looked up with

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Speaker 1: the oh with the slash is pronounced like and it's

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Speaker 1: the same thing as oh with the oom wount. I

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Speaker 1: didn't know that before, did you. Yeah, well you might

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Speaker 1: be a little more of a metal fan of me.

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Speaker 1: It's cool looking, just like the Umlaut. Super cool. I

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Speaker 1: think it's cooler than the Umlaut. It's got like a

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Speaker 1: slash through it, you know, like take that oh, whereas

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Speaker 1: the Umlauts, Like, I'm adorning this. Oh it's a little

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Speaker 1: different all right. Uh. Edgar Allan Poe wrote a story

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Speaker 1: called William Wilson. Uh. It's sort of like I mentioned

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Speaker 1: before about you know, evil things happening. A narrator meets

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Speaker 1: a boy at school. Uh, and this one had the

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Speaker 1: same name. I'm presuming William Wilson, otherwise it be a

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Speaker 1: weird title. And this boy, though, only speaks in a whisper.

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Speaker 1: So it seems like all through literature it's always some

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Speaker 1: sort of a creepy, shadowy figure. Yeah. There's also Dustoyevsky

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Speaker 1: um put kind of a spin on it where he

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Speaker 1: introduced a doppel ganger to the main character in a

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Speaker 1: novel called or novella I think called The Double, and

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Speaker 1: I love this. In it, the doppel ganger is like

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Speaker 1: a more likable version of the main character and ends

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Speaker 1: up breaking the main character character mentally, which I could

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Speaker 1: totally see that. You know, like if this this impostor

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Speaker 1: comes along or this other version of you comes along

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Speaker 1: and people are like a lot more. Yeah, it reminds me.

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Speaker 1: Have you seen that Paul Rudd show Living with Yourself?

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Speaker 1: I don't know how it escaped, but they did a

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Speaker 1: season of it in two thousand nineteen. Didn't follow up

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Speaker 1: on it, but he plays himself and his doppel ganger

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Speaker 1: too great comedic effect, but it's also like really fascinating

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Speaker 1: and interesting. It's a really great show. It's on Netflix. Well,

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Speaker 1: that's the other thing, and we'll talk about some more

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Speaker 1: in history. But of course with t V, oftentimes it's

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Speaker 1: flipped to a more fun thing and it's played for laughs,

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Speaker 1: whether you know. I don't think Three's Company had a

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Speaker 1: doppelganger episode, but it wouldn't surprise me because it can

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Speaker 1: be played for a sort of mixed identity hijinks as well,

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Speaker 1: totally like a one where Mr Furley was into Jack

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Speaker 1: but it was really his doppelganger. Yeah, and I mean

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Speaker 1: it would probably be like its twin brother who came

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Speaker 1: to visit in that case. But this still counts an

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Speaker 1: unknown twin still in the cannon counts as a doppel ganger,

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Speaker 1: do you think, well, yeah, I think so. Al Right,

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Speaker 1: well he just negated what I said that they're not related.

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Speaker 1: But well, okay, that's true. Once you find out. Here's

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Speaker 1: the thing that this actually proves your point, Chuck. Once

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Speaker 1: the characters find out, oh it's a twin, mystery solved,

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Speaker 1: no longer a doppelganger. It becomes a twin. But up

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Speaker 1: to that point, when they're like crossing paths with one

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Speaker 1: another and never in the same room at the same time,

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Speaker 1: doppel ganger, Yes, exactly. But for really doesn't have a

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Speaker 1: brother or does why is he acting so weird? Uh?

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Speaker 1: There have been some true historical figures over the years

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Speaker 1: that bought into this stuff. Um Abraham Lincoln. Uh, he

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Speaker 1: and his wife, they had all kinds of kind of

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Speaker 1: interesting beliefs. It was the age of spiritualism, Yeah, for sure,

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Speaker 1: So you have to take that consideration, I think. But um,

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Speaker 1: Lincoln supposedly saw his image three times I think in

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Speaker 1: the mirror in eighteen sixty which, uh, what was his

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Speaker 1: wife's name, Mary Todd? Mary Todd. Mary Todd said, uh,

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Speaker 1: you know you're gonna serve two terms as president and

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Speaker 1: that's what that means, but you're gonna die before the

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Speaker 1: end of the second term. And he said, well good

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Speaker 1: night here. But see, I take issue with some with

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Speaker 1: some of these listed historically because they're more ghostly specters

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Speaker 1: than real human doppelganger. So John Dunn's um story is

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Speaker 1: strictly by location. I would not call it a doppelganger

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Speaker 1: one where he saw his wife a few times while

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Speaker 1: he was in Paris and she was back in England

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Speaker 1: and she was actually ill and their child had died.

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Speaker 1: By location doesn't count. But Percy by Shelley, he saw

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Speaker 1: his doppelganger and died a few weeks later in a

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Speaker 1: sailing accident. That I would say that one counts in

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Speaker 1: the lore. And then Catherine the Greates was pretty good too, right, Yeah,

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Speaker 1: And I think Percy By Shelley's doppelganger had a T

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Speaker 1: shirt on and said I'd rather be sailing made it

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Speaker 1: even weirder. No, he had his T shirt said no

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Speaker 1: plates because that was his third third choice. Who was

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Speaker 1: the other one? Catherine the Great? Yeah, this was a

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Speaker 1: real one to actually um Supposedly there was a servant

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Speaker 1: right who discovered a double like that somehow got in there,

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Speaker 1: fake their way in there and was sitting on the throne.

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Speaker 1: So this was a case where someone was I don't

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Speaker 1: think trying to, like, you know, realistically take over as queen,

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Speaker 1: but at least got in there and it's like I'm

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Speaker 1: gonna sit on the throne and I got past the

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Speaker 1: guards because I look like you. Yeah. I also saw

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Speaker 1: it interpreted as like phantasma gork too, like it didn't happen,

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Speaker 1: know that it was like a phantom or adopt like

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Speaker 1: a supernatural adopted doppelganger, rather than like a double or

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Speaker 1: something like that. Again, not a real doppelganger to me.

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Speaker 1: And then my favorite one of all time. I've known

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Speaker 1: this one since I was a kid, back when I

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Speaker 1: was into the Time Life mysteries books, because I think

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Speaker 1: that's where I first read it. But the German poet

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Speaker 1: and philosopher good To. One of the reasons I like

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Speaker 1: this anecdote is because I love saying Gertz's name. He

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Speaker 1: um was writing one time from Alsace to I want

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Speaker 1: to say Dusseldorf, but I think that's wrong, but it works, right,

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Speaker 1: I mean, sure, it's the name of that author, and

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Speaker 1: he passed himself on the road and the other his

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Speaker 1: doppelganger was wearing some gray outfit or whatever. And then

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Speaker 1: like eight years later he was on his way from

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Speaker 1: Dusseldorf to Alsace going the other way, and he realized

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Speaker 1: he was wearing that same outfit that his doppelganger have

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Speaker 1: been wearing eight years before, and he was like, this

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Speaker 1: is pretty awesome. And that was a Time Life books thing. Yeah,

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Speaker 1: I remember it very well. Yeah, that sounds about right.

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Speaker 1: I didn't know how to say good It's his name

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Speaker 1: at the time, though, so I was like, this really

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Speaker 1: weird thing happened to goth all right, maybe we should

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Speaker 1: take a little break now that we've sort of given

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Speaker 1: some past examples of what may or may not be doppelgangers,

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Speaker 1: and we'll talk about real doppelgangers, real life humans walking

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Speaker 1: around that looked like you right after this. All right,

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Speaker 1: so we mentioned, uh, the photographer um France Wis Brunell,

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Speaker 1: who's been doing this project. It's called I'm Not a

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Speaker 1: look Alike. Where they where he takes pictures of these people.

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Speaker 1: I'm sorry, there's an exclamation point at the end of that. Yeah,

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Speaker 1: I'm not a look alike. I encourage people to go

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Speaker 1: check this out when they're in a safe space and

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Speaker 1: you're like not driving or whatever, to look at some

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Speaker 1: of these photos because it's it's amazing. I mean some

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Speaker 1: of them, like I said, look to my eye, like

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Speaker 1: identical twins or the other two sort of um gentlemen

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Speaker 1: probably in their sixties with a little mustache. Did you

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Speaker 1: see those guys, the shirtless ones. No, the picture I saw,

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Speaker 1: they had on like little uh, sort of like newsboy caps.

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Speaker 1: I don't know if I saw those guys. They look

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Speaker 1: I mean, they look like identical twins and um those

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Speaker 1: are the ones that This is the article that Emily

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Speaker 1: sent me where it talked about that project, and then

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Speaker 1: it got into a really interesting study because if you think, well,

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Speaker 1: you know, what is a doppelganger? I've always since I

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Speaker 1: was a kid, I've always sort of um realize that

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Speaker 1: there are only so many facial combinations you can have.

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Speaker 1: And with the amount of we talked about this over

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Speaker 1: the years in the show too, like over the years

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Speaker 1: and through history especially, seems to happen. If you're looking

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Speaker 1: through like yearbooks from like the forties and fifties and sixties,

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Speaker 1: you'll like see people that look like people you know,

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Speaker 1: are like yourself. They're just only so many ways you

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Speaker 1: can rearrange a face until you're bound at some point

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Speaker 1: to get someone who maybe favor somebody a little bit

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Speaker 1: or even more, or to the point where they look

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Speaker 1: just alike. Uh. And so you might think, well, what

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Speaker 1: does that have to say about what's going on inside?

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Speaker 1: Is there any anything to d n A And they

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Speaker 1: looked into that, right, Yeah, I guess Brunel's work caught

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Speaker 1: the attention of UM A researcher named Manel Estellor from

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Speaker 1: Spain from the Careers Leukemia Research Institute who said, I

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Speaker 1: want to try to figure this out, and these people

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Speaker 1: are like a perfect population sample. So he took I

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Speaker 1: think thirty two pairs of doppelgangers, ran them through three

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Speaker 1: different facial recognition programs, and half of them I guess

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Speaker 1: sixteen of the pairs. As far as the a I

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Speaker 1: was concerned in the programs, they were identical twins. That's

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Speaker 1: what the AI took them for and all three programs, right.

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Speaker 1: So they took those people and they analyzed their genomes

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Speaker 1: and they found actually, these people have a lot of

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Speaker 1: similar genes and even though they're not related. That was

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Speaker 1: another part of the study. They went through and made

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Speaker 1: sure that these people weren't like there was some unknown

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Speaker 1: relation that they had to one another. They didn't. They

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Speaker 1: were as genetically unrelated as two random strangers would be

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Speaker 1: on the street. But they shared some some very similar

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Speaker 1: genes and some key places, especially like facial construction. Yeah,

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Speaker 1: and they were also uh. And again this doesn't like

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Speaker 1: less than the study any to me, but they were

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Speaker 1: generally from the same country or the same ethnic background. Um.

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Speaker 1: Although I did see UH gender varied because there was

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Speaker 1: one where it was a man and a woman who

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Speaker 1: looked a lot alike uh in those photos that they

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Speaker 1: put together there, which is pretty interesting. But they looked

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Speaker 1: into how they matched up, uh, I think with their

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Speaker 1: microbiome epigenetically, which is basically how your behaviors in your

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Speaker 1: environment is going to affect your genes, and then just

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Speaker 1: your straight up genome. And what they found out was

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Speaker 1: they did share a lot of what are called SNPs

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Speaker 1: single nucleotide polymorphisms, which are the variations in our DNA

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Speaker 1: that create most of the genetic genetic variation that people

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Speaker 1: have with one another. So they shared, uh a lot

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Speaker 1: of them. And I wish we would have gotten like

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Speaker 1: the raw numbers, because all it says is like, you know,

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Speaker 1: they shared many s and p s, whereas I'd like

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Speaker 1: to see sort of the actual data, which I couldn't

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Speaker 1: get to. Um. But what they did find was that

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Speaker 1: epigenetically and with their microbiomes, they were basically random, so

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Speaker 1: it was only genetically that they shared something. Well yeah,

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Speaker 1: and they said, well, actually, I think this is probably

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Speaker 1: what accounts for a lot of the differences in identical twins.

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Speaker 1: Like people look at identical twins and they're like, oh,

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Speaker 1: you're identical, But if you really start to look at

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Speaker 1: them closely. You're like, actually, there's a lot more differences

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Speaker 1: than you'd think between them. And even though they share

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Speaker 1: the same genes, I think of the same d n A.

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Speaker 1: What makes identical twins slightly different is in their epigenome.

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Speaker 1: And this this study basically found the same thing with applegangers.

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Speaker 1: They can share a lot of similar genes that that

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Speaker 1: give them the same facial traits or builds or um.

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Speaker 1: I think even habits. They found a lot of them,

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Speaker 1: Like if one smoke, the other was likely to smoke.

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Speaker 1: You know, they carried their weight about the same way. Um.

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Speaker 1: But they found that that, Like you said that the

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Speaker 1: differences were found in their epigenome, and I don't know

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Speaker 1: if I know they tested it, but I didn't see

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Speaker 1: that their microbio might have provided some differences to It's

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Speaker 1: basically nature versus nurture, or more to the point, it's

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Speaker 1: like nature and nurture working together to make us slightly different,

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Speaker 1: even when we're basically bad owned to look the same genetically. Yeah,

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Speaker 1: I think the microbiome was the same as the epigenetic finding,

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Speaker 1: which was I think the quote was that generally wasn't

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Speaker 1: any closer than two randos. That's what they said in

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Speaker 1: the paper. Randos they did, they did. I'm trying to

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Speaker 1: find real quick the data the number of s MPs um.

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Speaker 1: I can't find it that quick. There's a lot of

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Speaker 1: numbers and weird like like capital C, lower case P,

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Speaker 1: capital G. I'm like, I don't know what that is,

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Speaker 1: but I will tell you this. I looked up those

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Speaker 1: those older guys in the news caps. They are spitting image.

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Speaker 1: And not only are they spinning image of one another,

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Speaker 1: they're spinning image of the Lennox air conditioning man look

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Speaker 1: exactly like that guy down to the hat. Well. I

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Speaker 1: guess if that guy ever, you know, leaves the earth

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Speaker 1: and they got two backups, I mean, what's it called

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Speaker 1: when you get three doppel gangers together? A tropic ganger?

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Speaker 1: I guess. So what I'd be curious to see is

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Speaker 1: if and maybe I'll look more just for my own

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Speaker 1: funzies into uh, the photographer's project, because what I'm curious

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Speaker 1: about is if these like, how these people get along,

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Speaker 1: and if they like formed friendships, if they liked each other,

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Speaker 1: if they had similar interests. You know, that's that's pretty

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Speaker 1: fascinating to me. I think so too, and again like

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Speaker 1: that's that's the kind of stuff They can take a

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Speaker 1: really a real turn. I guess depending on how you

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Speaker 1: were raised or even like you know, or you're raising

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Speaker 1: your power lines. Great, that means you're into baseball cards.

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Speaker 1: That kind of thing, um that we just haven't put

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Speaker 1: our finger on yet, but it does seem like, you know,

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Speaker 1: it's becoming clear and clear. It's not just genetics, it's

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Speaker 1: not just epigenetics. It's it's all that stuff put together.

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Speaker 1: And it seems like so wildly unattainable that information, um,

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Speaker 1: because it's so complicated. There's so many variables and so

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Speaker 1: many factors. But if you step back and think about it,

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Speaker 1: there's a finite number of variables and factors, as many

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Speaker 1: as it seems to us, it's a finite number. It's

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Speaker 1: not infinite, which means someday we could conceivably get a

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Speaker 1: grasp on exactly what makes a person them, which I

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Speaker 1: find really exciting. You know, it would be funny as

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Speaker 1: if they really got along and they were like, oh

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Speaker 1: my god, we have so many similarities. We look just alike.

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Speaker 1: This is such a weird thing. And they were having

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Speaker 1: lunch and one of them was like boy, what a

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Speaker 1: shame that Hilary lost their right and the other one

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Speaker 1: by unveils and make America Great again t shirt or

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Speaker 1: some chops there. Fork. They just get up in part

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Speaker 1: ways silently. Yeah, that would be the best case scenario,

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Speaker 1: I think. Yeah. Uh so this study, um, this next

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Speaker 1: one kind of irritates me a little bit. It was

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Speaker 1: from and I think was well intentioned. But there was

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Speaker 1: an Australian biologists name Tegan Lucas who looked at got

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Speaker 1: off to a great start, looked at four thousand faces

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Speaker 1: from the u US and throw uh Anthropometric Survey database,

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Speaker 1: which is something the U. S Army puts together. And

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Speaker 1: Ta Tigan Lucas is listening now, She's like, oh, he

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Speaker 1: got off to a great start on the US Anthropometric Survey. Chuck. Well,

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Speaker 1: I think Teagan Lucas overachieved because what they did was

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Speaker 1: looked across eight facial features and calculated the like, the

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Speaker 1: chances that you look just like someone else's one in

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Speaker 1: a trillion. I see these pictures of these doppelgangers. I'm like,

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Speaker 1: there's just no way because I'm looking at the evidence

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Speaker 1: and it turns out that Teagan really got into like

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Speaker 1: measuring the millimeter of a nostril and stuff like that,

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Speaker 1: and the comparison it was just too identical. Like I

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Speaker 1: think with identical twins you wouldn't get that close. No,

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Speaker 1: But I think that's what she was going for, is like,

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Speaker 1: you know, would it be possible for two identical people

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Speaker 1: to be walking around? And one of the applications of

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Speaker 1: her research is if we're creating facial recognition systems, which

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Speaker 1: we done an episode on before. It was pretty eerie

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Speaker 1: if I remember correctly, But if we're creating these things,

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Speaker 1: if we're building cute computers to recognize people's faces, are

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Speaker 1: there going to be any kind of like false positives

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Speaker 1: out there? Because there's two people out there who are

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Speaker 1: that similar, and the chances are really low unless we

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Speaker 1: just keep going population wise, and even with the number

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Speaker 1: of people out today, I frankly don't think it's that

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Speaker 1: low of a chance that there are two people who

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Speaker 1: are identical across eight measures of your face. And she

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Speaker 1: wouldn't she wouldn't reveal what the eight measures were because

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Speaker 1: apparently her work was that like groundbreaking that they She's like,

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Speaker 1: I can't let anybody know what to what to hide

422
00:23:42,880 --> 00:23:48,480
Speaker 1: or disguise. But those. The point is it's eight different dimensions,

423
00:23:48,680 --> 00:23:51,359
Speaker 1: and like you said, I think like nostril with ear length,

424
00:23:51,520 --> 00:23:54,639
Speaker 1: that kind of stuff down to the millimeter, and she

425
00:23:54,720 --> 00:23:56,840
Speaker 1: found that it's like a one in a trillion chance

426
00:23:57,280 --> 00:24:00,199
Speaker 1: that there are two people walking around like that. The

427
00:24:00,240 --> 00:24:03,399
Speaker 1: thing is, there's eight billion people on the planet, and

428
00:24:03,440 --> 00:24:06,840
Speaker 1: that's means there's no doppelgangers. It's not well, it's not

429
00:24:06,920 --> 00:24:10,160
Speaker 1: close to a trillion, but it's not as far off

430
00:24:10,280 --> 00:24:12,440
Speaker 1: from a trillion. Is like if there were two people

431
00:24:12,480 --> 00:24:14,560
Speaker 1: on the planet. So if you have two people on

432
00:24:14,600 --> 00:24:16,760
Speaker 1: the planet and a one into trillion chance that they're

433
00:24:16,760 --> 00:24:19,040
Speaker 1: gonna look alike, the chance that they're going to look

434
00:24:19,080 --> 00:24:21,280
Speaker 1: like is basically nothing. But as you get closer to

435
00:24:21,320 --> 00:24:24,600
Speaker 1: a trillion, there's a really good chance that there are

436
00:24:24,600 --> 00:24:27,160
Speaker 1: going to be two people walking around that are identical.

437
00:24:27,480 --> 00:24:29,200
Speaker 1: And we're at eight millions, so we're at I think

438
00:24:29,280 --> 00:24:32,080
Speaker 1: a one in a hundred and twenty five chance that

439
00:24:32,160 --> 00:24:35,320
Speaker 1: today on Earth there are two people who look exactly

440
00:24:35,400 --> 00:24:39,320
Speaker 1: alike across those eight measures. The point is for you

441
00:24:39,400 --> 00:24:42,520
Speaker 1: and me to just be looking at stuff. We don't

442
00:24:42,520 --> 00:24:44,960
Speaker 1: need those eight measures to be exactly right. If they're

443
00:24:45,000 --> 00:24:48,000
Speaker 1: close enough, that's when people look like doppelgangers to you

444
00:24:48,080 --> 00:24:53,240
Speaker 1: and me, but we're not artificial intelligence. We're sentient beings. Well, yeah,

445
00:24:53,400 --> 00:24:58,600
Speaker 1: we're artificial, and I think we're intelligent, but we're not

446
00:24:58,600 --> 00:25:02,560
Speaker 1: those two things together. Are we artificial? Sure? I just

447
00:25:02,560 --> 00:25:04,880
Speaker 1: want to druin freak people out a little bit. Okay.

448
00:25:05,000 --> 00:25:06,880
Speaker 1: The Josh bott in the Chuck butter Hard at work

449
00:25:06,880 --> 00:25:10,760
Speaker 1: here does not compute. Well, I think we What we're

450
00:25:10,880 --> 00:25:13,400
Speaker 1: arriving at is a point that we've failed to make earlier,

451
00:25:13,480 --> 00:25:17,000
Speaker 1: which is there is no agreed upon definition of what

452
00:25:17,080 --> 00:25:20,480
Speaker 1: constitutes adoppel ganger. Like how I like, do you have

453
00:25:20,600 --> 00:25:24,160
Speaker 1: to look to someone to say you're a doppel ganger? Right?

454
00:25:24,600 --> 00:25:26,920
Speaker 1: You know, like I look like the guy I look

455
00:25:27,000 --> 00:25:31,040
Speaker 1: like the bass player for Jason Isabel in the four Unit,

456
00:25:31,840 --> 00:25:36,120
Speaker 1: But is he my doppel ganger? Probably not? Okay. So

457
00:25:36,560 --> 00:25:38,600
Speaker 1: one of the reasons why I think people would kind

458
00:25:38,640 --> 00:25:41,040
Speaker 1: of look at you and then the bass player for

459
00:25:42,480 --> 00:25:46,600
Speaker 1: Jason Isabel and the four Unit sure, um, and put

460
00:25:46,680 --> 00:25:48,840
Speaker 1: you guys together and said, oh, they really look alike

461
00:25:48,920 --> 00:25:52,560
Speaker 1: is because we use like shortcuts when we're looking at

462
00:25:52,920 --> 00:25:56,280
Speaker 1: at people's faces or even just people in general. So

463
00:25:56,359 --> 00:25:58,439
Speaker 1: let's say you're both wearing the same hat and the

464
00:25:58,520 --> 00:26:01,600
Speaker 1: same you both have a similar beard, I'm guessing. Yeah,

465
00:26:01,920 --> 00:26:03,360
Speaker 1: I don't want to name him because I don't want

466
00:26:03,359 --> 00:26:05,040
Speaker 1: to just keep calling that because I love that group.

467
00:26:06,000 --> 00:26:08,600
Speaker 1: His name is Jimbo Heart. And when you look at

468
00:26:08,600 --> 00:26:11,520
Speaker 1: a picture of Jimbo Heart, he knows a great name

469
00:26:11,640 --> 00:26:13,960
Speaker 1: to like, look him up. He's got the beard. He's

470
00:26:14,119 --> 00:26:15,520
Speaker 1: you know, when I'm reading, I got on my black

471
00:26:15,600 --> 00:26:18,359
Speaker 1: room glasses. I like to wear my hats these days,

472
00:26:18,400 --> 00:26:24,080
Speaker 1: my you know, various sort of cowboy hats and Fedora's sure. Yeah,

473
00:26:24,119 --> 00:26:27,360
Speaker 1: I mean the guy looks like me. Okay, so if

474
00:26:27,400 --> 00:26:29,439
Speaker 1: you put you guys together, you both want the same glasses,

475
00:26:29,480 --> 00:26:31,840
Speaker 1: You've got a beard, got hats on, and all that stuff.

476
00:26:31,840 --> 00:26:34,760
Speaker 1: People would be like spitting image. Take the glasses off,

477
00:26:35,160 --> 00:26:38,160
Speaker 1: shave the beard, take the hat off, and then put

478
00:26:38,200 --> 00:26:40,879
Speaker 1: you guys together. How close would you really be? And

479
00:26:40,960 --> 00:26:43,560
Speaker 1: probably not that close. I can tell you I can

480
00:26:43,560 --> 00:26:47,200
Speaker 1: see him without a beard, and he's not nearly as close. Okay, Right,

481
00:26:47,480 --> 00:26:50,159
Speaker 1: so there you go, Like we make these shortcuts with

482
00:26:50,200 --> 00:26:53,360
Speaker 1: our face with our minds um to just kind of

483
00:26:53,400 --> 00:26:55,720
Speaker 1: as like kind of a shorthand for like, oh that's Chuck,

484
00:26:56,000 --> 00:26:58,440
Speaker 1: I don't have to analyze them anymore because that uses

485
00:26:58,520 --> 00:27:00,720
Speaker 1: up a lot of brain power and energy, and I'm

486
00:27:00,760 --> 00:27:03,520
Speaker 1: kind of low on carbs today, so I just know

487
00:27:03,600 --> 00:27:05,679
Speaker 1: that that's Chuck where it really turned out to be

488
00:27:05,760 --> 00:27:12,480
Speaker 1: Jimbo Heart the basis for the unit that's right, Uh, right,

489
00:27:12,560 --> 00:27:14,960
Speaker 1: And you know you've seen this and here's a good

490
00:27:14,960 --> 00:27:18,280
Speaker 1: example of how this can play tricks on people. Uh,

491
00:27:18,280 --> 00:27:20,000
Speaker 1: And this is just one example. A lot of people

492
00:27:20,000 --> 00:27:22,399
Speaker 1: have done that kind of fun thing where you switch

493
00:27:22,440 --> 00:27:26,840
Speaker 1: out a face on someone and put it on someone else,

494
00:27:27,240 --> 00:27:30,080
Speaker 1: and it's like it should be obvious, but it's like

495
00:27:30,640 --> 00:27:34,119
Speaker 1: it just looks like a weird, uncanny version of that person. Uh.

496
00:27:34,160 --> 00:27:36,080
Speaker 1: The one that Livia picked out was from M I

497
00:27:36,160 --> 00:27:40,520
Speaker 1: T when they put George Bush's face on Dick Cheney's

498
00:27:40,800 --> 00:27:45,840
Speaker 1: head and Bill Clinton's face on al. Gore said that

499
00:27:46,400 --> 00:27:51,440
Speaker 1: dates the creation of that particular meme or whatever it was. Uh,

500
00:27:51,560 --> 00:27:54,399
Speaker 1: the Dick Cheney one looks like you gotta look a

501
00:27:54,400 --> 00:27:57,280
Speaker 1: bunch of times before you realize that's George Bush's face.

502
00:27:58,119 --> 00:28:01,240
Speaker 1: Um the al or when I mean it's so clearly

503
00:28:01,800 --> 00:28:05,119
Speaker 1: that's it's just kind of funny looking. But the Bush

504
00:28:05,200 --> 00:28:08,119
Speaker 1: Cheney swap actually kind of worked in that I guess

505
00:28:08,160 --> 00:28:10,680
Speaker 1: goes to sort of prove that you look at things

506
00:28:10,720 --> 00:28:14,720
Speaker 1: like hair and uh, stuff like that as you know

507
00:28:14,800 --> 00:28:17,400
Speaker 1: that big Clinton mop for that big Al Gore mop

508
00:28:17,480 --> 00:28:19,879
Speaker 1: that he had, or Cheny's bald head and that's a

509
00:28:19,920 --> 00:28:23,520
Speaker 1: big clue. Yeah. And also like the I think one

510
00:28:23,520 --> 00:28:26,600
Speaker 1: of the foundations of the swap is that it can't

511
00:28:26,600 --> 00:28:30,400
Speaker 1: be like an egregious change. Like if you remember back

512
00:28:30,480 --> 00:28:32,600
Speaker 1: to I think it was Conan that used to put

513
00:28:32,640 --> 00:28:35,679
Speaker 1: Steve Bushemy eyes on different celebrities and see what they

514
00:28:35,720 --> 00:28:38,400
Speaker 1: look like. That's an agreed just change, it's not going

515
00:28:38,440 --> 00:28:42,120
Speaker 1: to work. But it was Dick Cheney and George Bush's

516
00:28:42,120 --> 00:28:44,720
Speaker 1: eyes look similar enough that if you put George Bush's

517
00:28:44,720 --> 00:28:47,880
Speaker 1: eyes on Dick Cheney's face, You're not really going to

518
00:28:48,000 --> 00:28:50,240
Speaker 1: notice because it's not that agreed just have a changed.

519
00:28:50,480 --> 00:28:54,920
Speaker 1: And there was a study i think from seven Swinging

520
00:28:55,040 --> 00:28:58,440
Speaker 1: Groovy study and it basically tried to figure out how

521
00:28:58,600 --> 00:29:01,360
Speaker 1: people register face is in what order and they found

522
00:29:01,360 --> 00:29:03,520
Speaker 1: that first we look at the eyes, then we look

523
00:29:03,520 --> 00:29:05,760
Speaker 1: at the mouth, and then we look at the nose,

524
00:29:06,320 --> 00:29:08,160
Speaker 1: and those are the things we kind of take in.

525
00:29:08,800 --> 00:29:11,600
Speaker 1: But there's other research and I think This is also

526
00:29:11,720 --> 00:29:15,160
Speaker 1: kind of like supported by the same study, Like when

527
00:29:15,160 --> 00:29:16,840
Speaker 1: you look at someone's face, you don't go all right,

528
00:29:16,920 --> 00:29:19,800
Speaker 1: let me see the eyes, got that, let me see

529
00:29:19,840 --> 00:29:25,800
Speaker 1: the mouth, look at that and then the right right, Oh,

530
00:29:25,840 --> 00:29:28,200
Speaker 1: I want to kill that person. No, like we just

531
00:29:28,280 --> 00:29:30,480
Speaker 1: look at the whole thing, look at the whole package.

532
00:29:30,480 --> 00:29:32,880
Speaker 1: Which is why it's so easy for us to overlook

533
00:29:32,960 --> 00:29:37,320
Speaker 1: differences between even identical twins or doppelgangers and make people

534
00:29:37,400 --> 00:29:40,080
Speaker 1: look closer together than they are, because our brains are

535
00:29:40,160 --> 00:29:43,440
Speaker 1: lazy and fat. That's right. And it also depends on

536
00:29:43,680 --> 00:29:45,880
Speaker 1: as far as whether you might look like someone about

537
00:29:46,600 --> 00:29:48,200
Speaker 1: you know, it just sort of makes sense if you

538
00:29:48,240 --> 00:29:50,880
Speaker 1: look and it's hard to say that someone's average looking.

539
00:29:50,960 --> 00:29:53,760
Speaker 1: But what we mean by that is, and Livia points out,

540
00:29:53,800 --> 00:29:56,720
Speaker 1: as you know, you're not six ft nine and have

541
00:29:56,840 --> 00:29:59,719
Speaker 1: flaming red hair and freckles, like those things will make

542
00:29:59,720 --> 00:30:01,680
Speaker 1: you very much stand out. Whereas if you live in

543
00:30:01,680 --> 00:30:06,400
Speaker 1: a population of you know, Middle Georgia and you you know,

544
00:30:06,480 --> 00:30:08,239
Speaker 1: you're a little tubby, you've got a beard, you've got

545
00:30:08,320 --> 00:30:12,160
Speaker 1: dark hair, uh, and you dress a certain way, then

546
00:30:12,600 --> 00:30:16,000
Speaker 1: you might look like a lot of people. Sure, And um,

547
00:30:16,080 --> 00:30:21,480
Speaker 1: that actually raises a recent um internet sensation, a guy

548
00:30:21,520 --> 00:30:24,600
Speaker 1: named Neil Douglas UM was I think flying to Ireland

549
00:30:24,600 --> 00:30:27,880
Speaker 1: on a Ryanair flight and uh went to get in

550
00:30:27,920 --> 00:30:30,080
Speaker 1: his seat and saw that there was somebody sitting in

551
00:30:30,200 --> 00:30:32,520
Speaker 1: his seat, and the person looked up and it was

552
00:30:32,640 --> 00:30:36,320
Speaker 1: his doppel gang. I mean, isn't that even more bizarre?

553
00:30:36,400 --> 00:30:37,880
Speaker 1: Like it wasn't in like the back of the plane,

554
00:30:37,880 --> 00:30:40,240
Speaker 1: like this guy was in his seat, which makes it

555
00:30:40,280 --> 00:30:43,120
Speaker 1: even more eerie. And so they snapped a selfie and

556
00:30:43,200 --> 00:30:46,280
Speaker 1: like posted it and every everyone just went bonkers because

557
00:30:46,320 --> 00:30:48,920
Speaker 1: these guys had no idea who the other one was

558
00:30:48,960 --> 00:30:51,640
Speaker 1: and weren't related, but they looked a lot like each other.

559
00:30:52,040 --> 00:30:54,440
Speaker 1: But there's this BBC article that covered it where they

560
00:30:54,480 --> 00:30:56,719
Speaker 1: basically said, okay, but let's really stop and think about this.

561
00:30:56,800 --> 00:30:58,920
Speaker 1: That's amazing and the fact that he was in his

562
00:30:59,120 --> 00:31:01,240
Speaker 1: in the other guy's seat is even more amazing. But

563
00:31:01,400 --> 00:31:04,520
Speaker 1: how how unlikely is it that these two, you know,

564
00:31:05,120 --> 00:31:07,680
Speaker 1: we're on the same plane. And they figured out that

565
00:31:07,760 --> 00:31:10,920
Speaker 1: based on a number of different factors like the hair,

566
00:31:11,080 --> 00:31:14,640
Speaker 1: the face, the beard, the nose, um I think the

567
00:31:14,680 --> 00:31:18,720
Speaker 1: brown eyes. That there's something like maybe eighty thousand people

568
00:31:18,760 --> 00:31:22,400
Speaker 1: walking around out there that looked just like them essentially. Ye,

569
00:31:23,520 --> 00:31:25,440
Speaker 1: that's a lot of people. It is a lot of

570
00:31:25,440 --> 00:31:29,000
Speaker 1: people that look essentially like those two enough that that

571
00:31:29,120 --> 00:31:30,960
Speaker 1: the internet would go crazy if you put two of

572
00:31:31,000 --> 00:31:33,120
Speaker 1: them together and said these two are strangers and they

573
00:31:33,200 --> 00:31:36,120
Speaker 1: just met, uh and and posted a picture of it.

574
00:31:36,120 --> 00:31:38,720
Speaker 1: It was a pretty fun picture because they're so delighted

575
00:31:39,040 --> 00:31:43,280
Speaker 1: clearly with seeing each other. And then another guy. There's

576
00:31:43,640 --> 00:31:46,760
Speaker 1: a triple picture somewhere too of another guy that was like, hey,

577
00:31:46,800 --> 00:31:49,800
Speaker 1: I'm one of those seventy four thousand, and they put

578
00:31:49,840 --> 00:31:51,640
Speaker 1: all three of them close to each other and they

579
00:31:51,680 --> 00:31:56,080
Speaker 1: looked pretty similar. Um. The one sort of difference here

580
00:31:56,320 --> 00:31:59,840
Speaker 1: is and height is one of the dudes was five

581
00:32:00,000 --> 00:32:03,000
Speaker 1: evan the other was six three. Because there's other pictures

582
00:32:03,000 --> 00:32:05,960
Speaker 1: of them out drinking together in Island, I guess they

583
00:32:06,040 --> 00:32:08,240
Speaker 1: kind of hung out and stuff, which is great. Well,

584
00:32:08,320 --> 00:32:11,120
Speaker 1: I read they kept running into each other and finally, okay,

585
00:32:11,280 --> 00:32:13,760
Speaker 1: let's go get a pint together, Like they were at

586
00:32:13,760 --> 00:32:16,520
Speaker 1: the same hotel and like they kept running into one another,

587
00:32:16,560 --> 00:32:18,480
Speaker 1: so they went with it. See, if you believe in

588
00:32:18,560 --> 00:32:22,200
Speaker 1: like weird fate or intervention, you might wonder if like

589
00:32:22,480 --> 00:32:25,280
Speaker 1: maybe I should give some thought to what This means, like,

590
00:32:25,320 --> 00:32:29,080
Speaker 1: am I supposed to do something special with this person? Right?

591
00:32:29,320 --> 00:32:33,320
Speaker 1: If the movie Serendipity with John Cusack really spoke to you,

592
00:32:34,280 --> 00:32:36,040
Speaker 1: I don't. I never even saw that. You mentioned that

593
00:32:36,080 --> 00:32:38,680
Speaker 1: movie a lot. What happened in the movie great. I

594
00:32:38,720 --> 00:32:42,920
Speaker 1: don't want to spoil it from anybody. Was there a doubelganger? No, okay,

595
00:32:42,960 --> 00:32:45,040
Speaker 1: but it was. It was they kept running into one

596
00:32:45,080 --> 00:32:47,480
Speaker 1: another and like it just kind of was, Oh, it's

597
00:32:47,520 --> 00:32:50,400
Speaker 1: just the chance of true love, over and over. It

598
00:32:50,480 --> 00:32:53,000
Speaker 1: was a good movie. Okay. Then they go to some

599
00:32:53,240 --> 00:32:55,320
Speaker 1: ice cream shop in New York, famous ice cream shop.

600
00:32:55,720 --> 00:33:00,240
Speaker 1: I don't remember that. I think you're talking about Midnight Cowboy. Yeah,

601
00:33:00,280 --> 00:33:04,680
Speaker 1: that's right. Um, let's go get some ice creams. That

602
00:33:04,800 --> 00:33:08,400
Speaker 1: was a great John voch Um there was. There was

603
00:33:08,440 --> 00:33:11,440
Speaker 1: another one that came along recently too, that I think

604
00:33:11,520 --> 00:33:13,840
Speaker 1: is even better than than the other one. There's a

605
00:33:13,880 --> 00:33:17,080
Speaker 1: dude named Sean mccartel who was hanging around a pool

606
00:33:17,080 --> 00:33:21,040
Speaker 1: in Vegas and his double, Chuck. Just look it up,

607
00:33:21,360 --> 00:33:25,440
Speaker 1: Sean mccartel doppelganger. His double happened to be floating along

608
00:33:25,440 --> 00:33:28,320
Speaker 1: in the same pool wearing the exact same glasses. Apparently

609
00:33:28,320 --> 00:33:30,840
Speaker 1: they were the only two people at the pool wearing glasses,

610
00:33:30,840 --> 00:33:34,600
Speaker 1: not sunglasses, but glasses. Glasses, the same glasses, same hat.

611
00:33:35,120 --> 00:33:37,720
Speaker 1: They actually do have like the same build and same

612
00:33:37,720 --> 00:33:41,520
Speaker 1: height and everything. They are the spinning one another and

613
00:33:41,600 --> 00:33:44,280
Speaker 1: they were just randomly in the same pool in Las

614
00:33:44,360 --> 00:33:47,560
Speaker 1: Vegas at the same time. So funny. Yeah, I think

615
00:33:47,680 --> 00:33:50,440
Speaker 1: my first thought would be, like, what kind of crimes

616
00:33:50,440 --> 00:33:57,360
Speaker 1: can we commit? Well, yeah, the bank mm hmm, that's

617
00:33:57,400 --> 00:34:00,560
Speaker 1: my first thought. That's terrible. We're just kidding. Um, are

618
00:34:00,600 --> 00:34:02,960
Speaker 1: we do for another break? Yeah? Or should we wait? Think? So, yeah,

619
00:34:03,040 --> 00:34:04,720
Speaker 1: let's take a break. All right, We'll take a break

620
00:34:04,760 --> 00:34:26,200
Speaker 1: and come back and sort this all out. So one

621
00:34:26,200 --> 00:34:29,560
Speaker 1: of the reasons they think, chuck that um, we have

622
00:34:30,480 --> 00:34:33,480
Speaker 1: like a propensity to notice doppelgangers and also just be

623
00:34:33,840 --> 00:34:37,800
Speaker 1: just crazed by them is that we are really really

624
00:34:37,840 --> 00:34:41,839
Speaker 1: good at picking out faces, so good in fact, that

625
00:34:41,880 --> 00:34:44,480
Speaker 1: we have a region of our brain that it's starting

626
00:34:44,480 --> 00:34:51,160
Speaker 1: to become clear is is intended exclusively for seeing faces.

627
00:34:51,880 --> 00:34:54,920
Speaker 1: And that actually bucks the current trend of understanding with

628
00:34:54,960 --> 00:34:57,800
Speaker 1: neuroscience where different parts of the brain get recruited to

629
00:34:57,880 --> 00:35:00,520
Speaker 1: kind of specialize in different things from since re input

630
00:35:00,680 --> 00:35:04,239
Speaker 1: over time you put a lot of sound in one

631
00:35:04,400 --> 00:35:05,879
Speaker 1: part of the brain, it's gonna be like, all right, fine,

632
00:35:05,920 --> 00:35:09,120
Speaker 1: I'll handle sound from now on. Um. This it seems

633
00:35:09,120 --> 00:35:13,239
Speaker 1: you're born with this ability to recognize faces. Uh. The

634
00:35:13,280 --> 00:35:16,240
Speaker 1: regions called the fusiform gyrus, and they've done some really

635
00:35:16,239 --> 00:35:18,480
Speaker 1: interesting studies about it. And I think we should actually

636
00:35:18,520 --> 00:35:22,320
Speaker 1: do an entire episode on the fusiform gyrus and face

637
00:35:22,440 --> 00:35:29,080
Speaker 1: face blindness, Hubba, and can you tell them hot? I'm

638
00:35:29,080 --> 00:35:31,840
Speaker 1: all about it because this one experiment is something I

639
00:35:31,840 --> 00:35:34,880
Speaker 1: would actually like to do. Um. It's pretty freaky. They

640
00:35:34,920 --> 00:35:38,880
Speaker 1: got people in a room, they gave them a mild

641
00:35:38,920 --> 00:35:43,399
Speaker 1: electric current straight to the fusiform gyrus. Isn't that right?

642
00:35:44,680 --> 00:35:48,040
Speaker 1: And they said, you know, we're gonna do this and

643
00:35:48,080 --> 00:35:49,640
Speaker 1: just look at me and tell me what you see.

644
00:35:50,200 --> 00:35:54,680
Speaker 1: And basically they shape shifted in front of their very

645
00:35:54,719 --> 00:35:58,840
Speaker 1: face when they gave them this electrical stimulation, so it

646
00:35:59,080 --> 00:36:02,920
Speaker 1: changed their person reception of what they look like. It

647
00:36:02,960 --> 00:36:06,080
Speaker 1: didn't change their height or weight, or their clothing or

648
00:36:06,080 --> 00:36:09,760
Speaker 1: their skin color or anything like that, but it sounds

649
00:36:09,800 --> 00:36:13,520
Speaker 1: like it literally in their own perception at least transformed

650
00:36:13,640 --> 00:36:15,480
Speaker 1: what their face looked liked, which has got to be

651
00:36:15,880 --> 00:36:19,520
Speaker 1: really weird. Yeah, oh man, I can't imagine seeing that,

652
00:36:20,120 --> 00:36:23,600
Speaker 1: especially you're seeing while you're undergoing open brain surgery and

653
00:36:23,640 --> 00:36:27,000
Speaker 1: you're conscious. And then they started messing with parts of it. Right.

654
00:36:27,640 --> 00:36:29,919
Speaker 1: There was a follow up study too that I find

655
00:36:30,040 --> 00:36:33,759
Speaker 1: just even more fascinating. They recruited some participants who were

656
00:36:33,800 --> 00:36:38,040
Speaker 1: blind from birth, so they've never seen human face ever

657
00:36:38,200 --> 00:36:41,439
Speaker 1: with their eyes, and they gave them, um, they put

658
00:36:41,480 --> 00:36:43,920
Speaker 1: them in an m r I and they gave them

659
00:36:44,040 --> 00:36:47,480
Speaker 1: models of like three D models of faces and had

660
00:36:47,520 --> 00:36:50,440
Speaker 1: them rubbed the faces, and the fusiform gyrus lit up,

661
00:36:50,800 --> 00:36:54,160
Speaker 1: so like it is strictly for faces. They also they

662
00:36:54,200 --> 00:36:56,160
Speaker 1: were like, okay, well maybe it's just you know, it's

663
00:36:56,400 --> 00:36:58,440
Speaker 1: it's it's for other things too. They gave them three

664
00:36:58,520 --> 00:37:02,480
Speaker 1: D models of cubes and other stuffs that that weren't faces.

665
00:37:03,000 --> 00:37:05,680
Speaker 1: Nothing happened in the fusiform gyros. So we have a

666
00:37:05,680 --> 00:37:08,880
Speaker 1: place in the brain that's strictly for recognizing faces. And

667
00:37:08,920 --> 00:37:12,840
Speaker 1: I think that's probably why we can pick out doppelgangers

668
00:37:12,840 --> 00:37:15,480
Speaker 1: so easily, because our our brains are kind of a

669
00:37:15,560 --> 00:37:18,719
Speaker 1: tune to look for faces everywhere. Yeah, and you know,

670
00:37:18,760 --> 00:37:21,080
Speaker 1: if you're thinking this might be like I mentioned, committing

671
00:37:21,080 --> 00:37:23,919
Speaker 1: a crime sort of been passing. But if you think

672
00:37:23,960 --> 00:37:26,600
Speaker 1: this could be a big problem as far as the

673
00:37:26,680 --> 00:37:31,400
Speaker 1: advent uh and more widespread use of facial recognition AI,

674
00:37:31,520 --> 00:37:35,080
Speaker 1: especially with CCTV and trying to catch bad people doing

675
00:37:35,120 --> 00:37:37,520
Speaker 1: bad things. Uh. And we've talked about this in the

676
00:37:37,560 --> 00:37:40,200
Speaker 1: past on I think a couple of different episodes. But

677
00:37:40,760 --> 00:37:45,040
Speaker 1: that stuff works okay, but it doesn't work great with

678
00:37:45,200 --> 00:37:48,920
Speaker 1: dark skin, it doesn't work great for people of color, um,

679
00:37:49,200 --> 00:37:52,759
Speaker 1: black people, Asian people. It gets confused a lot. And

680
00:37:52,840 --> 00:37:56,680
Speaker 1: this is already Uh, you know, people have been wrongly

681
00:37:56,760 --> 00:38:02,280
Speaker 1: imprisoned because facial recognition AI has a mistake. Yeah, there's

682
00:38:02,320 --> 00:38:05,120
Speaker 1: a one guy is recently's two thousand nineteen from New Jersey,

683
00:38:05,480 --> 00:38:09,560
Speaker 1: UM named Nigier Parks, and he had to end up

684
00:38:09,600 --> 00:38:12,960
Speaker 1: spending five grand to defend himself, spent I think, uh,

685
00:38:13,000 --> 00:38:17,640
Speaker 1: twenty days in jail, possibly because he was accused or

686
00:38:18,000 --> 00:38:21,960
Speaker 1: I guess AI facial recognition system accused him of shoplifting

687
00:38:22,040 --> 00:38:23,960
Speaker 1: and then trying to hit a police officer with his

688
00:38:24,040 --> 00:38:28,240
Speaker 1: car and um, it turns out he'd been thirty miles away,

689
00:38:28,280 --> 00:38:30,960
Speaker 1: but he had been misidentified by this And I think

690
00:38:31,040 --> 00:38:33,640
Speaker 1: in our episode we talked about this at length, that

691
00:38:33,719 --> 00:38:38,720
Speaker 1: facial recognition systems are really good at recognizing white men's faces,

692
00:38:39,760 --> 00:38:41,880
Speaker 1: but other people, it just starts to go down and

693
00:38:41,920 --> 00:38:43,919
Speaker 1: down until you get to the point where a black

694
00:38:43,960 --> 00:38:48,560
Speaker 1: woman's faces forty times likelier to come up with a

695
00:38:48,600 --> 00:38:53,120
Speaker 1: false positive than a light skinned man. And apparently it's

696
00:38:53,120 --> 00:38:55,520
Speaker 1: just the data that they're trained on. They just said,

697
00:38:55,840 --> 00:38:58,000
Speaker 1: let's just give them photos of white men and I'm

698
00:38:58,040 --> 00:39:00,719
Speaker 1: sure it'll be fine, and it just hasn't been fine. Well,

699
00:39:00,719 --> 00:39:02,279
Speaker 1: I mean, that's one of the problems with it all.

700
00:39:02,280 --> 00:39:04,319
Speaker 1: This is just it's just working from a database. So

701
00:39:04,360 --> 00:39:07,640
Speaker 1: it's not like it can take a face and find

702
00:39:07,680 --> 00:39:10,560
Speaker 1: someone just somewhere on planet Earth, like you gotta be

703
00:39:10,600 --> 00:39:14,440
Speaker 1: on a database somehow, right. But I'm saying the AI

704
00:39:14,640 --> 00:39:18,440
Speaker 1: is trained on photos, and it comes to understand a

705
00:39:18,520 --> 00:39:20,560
Speaker 1: face is a face based on the photos that it's

706
00:39:20,719 --> 00:39:24,759
Speaker 1: trained on. And I guess they didn't select enough, um,

707
00:39:24,960 --> 00:39:27,600
Speaker 1: people of photos of people of color, photos of women.

708
00:39:27,880 --> 00:39:30,279
Speaker 1: I don't know why, but it seems like they got

709
00:39:30,320 --> 00:39:32,680
Speaker 1: really good at white men because they were given more

710
00:39:32,680 --> 00:39:35,279
Speaker 1: photos of white men. That's that's how I take it. Yeah, No,

711
00:39:35,360 --> 00:39:38,080
Speaker 1: that's how I took it to So Chuck. If let's

712
00:39:38,120 --> 00:39:41,480
Speaker 1: say that you are a doppelganger of somebody, somebody famous

713
00:39:41,520 --> 00:39:46,760
Speaker 1: like Madonna or Johnny Depp or Rizzo. Let's say Rizzo

714
00:39:46,880 --> 00:39:50,480
Speaker 1: to just to get the kids involved. Um no, Lizzo.

715
00:39:52,719 --> 00:39:56,480
Speaker 1: I was like, uh good, I was thinking back to

716
00:39:56,640 --> 00:40:01,240
Speaker 1: Ratso Rizzo. I think, Um, so, if you're a doppel

717
00:40:01,239 --> 00:40:05,120
Speaker 1: ganger of Lizzas, what would you do with that talent? Well, uh,

718
00:40:05,360 --> 00:40:07,879
Speaker 1: you could go hang out on Hollywood Boulevard and your

719
00:40:07,880 --> 00:40:11,560
Speaker 1: man's Chinese theater makes some money walking around charging five

720
00:40:11,600 --> 00:40:14,719
Speaker 1: dollars a picture. You could get employed full time with

721
00:40:14,760 --> 00:40:18,799
Speaker 1: an agency, um to you know, get sent out. If

722
00:40:18,800 --> 00:40:22,560
Speaker 1: someone wants uh Bill Gates at their party and you

723
00:40:22,640 --> 00:40:24,960
Speaker 1: are a look alike, then you can go to their

724
00:40:24,960 --> 00:40:30,240
Speaker 1: party and make a couple of hundred bucks probably probably. Um.

725
00:40:30,320 --> 00:40:33,200
Speaker 1: You could also work on movies. Apparently there's lots of

726
00:40:33,280 --> 00:40:38,160
Speaker 1: jobs for you. Yeah, I mean this one is Um

727
00:40:38,200 --> 00:40:42,279
Speaker 1: it's less face like when your body doubling someone or

728
00:40:42,320 --> 00:40:45,680
Speaker 1: photo doubling or standing in kind of in that order,

729
00:40:46,120 --> 00:40:48,640
Speaker 1: or did I get that wrong? Stand in is least

730
00:40:48,760 --> 00:40:51,720
Speaker 1: important that you actually look like someone. But you should

731
00:40:51,760 --> 00:40:55,760
Speaker 1: be the same size as somebody because they're they're lighting

732
00:40:55,760 --> 00:40:58,759
Speaker 1: you for camera and stuff like that, like you can't

733
00:40:58,840 --> 00:41:02,320
Speaker 1: you know, um, the rock can't have a body double

734
00:41:02,440 --> 00:41:05,799
Speaker 1: or or a standing that looks like me. You know.

735
00:41:07,160 --> 00:41:08,560
Speaker 1: I don't know if you were one of those like

736
00:41:08,640 --> 00:41:12,960
Speaker 1: ripped T shirts all right and get away with it. Uh.

737
00:41:13,000 --> 00:41:16,920
Speaker 1: But you know, if you do look like a celebrity

738
00:41:17,000 --> 00:41:18,880
Speaker 1: and you want to get work as a photo double,

739
00:41:19,360 --> 00:41:21,680
Speaker 1: then that certainly doesn't hurt if you look like them

740
00:41:21,760 --> 00:41:24,520
Speaker 1: in the face, but it's more body size and skin

741
00:41:24,560 --> 00:41:28,080
Speaker 1: tone and stuff like that is more important. But the

742
00:41:28,120 --> 00:41:31,080
Speaker 1: places where it really kind of comes in handy if

743
00:41:31,120 --> 00:41:33,839
Speaker 1: you really look like the person in the face and

744
00:41:33,880 --> 00:41:36,120
Speaker 1: the build and the hair and the mustache and all

745
00:41:36,160 --> 00:41:39,360
Speaker 1: that is to um serve as like a decoy, a

746
00:41:39,360 --> 00:41:44,319
Speaker 1: political decoy, because throughout history, the more hated you are,

747
00:41:44,480 --> 00:41:48,680
Speaker 1: either within your country or internationally or both, the more

748
00:41:48,719 --> 00:41:52,279
Speaker 1: you need more than probably one political decoys, which is

749
00:41:52,640 --> 00:41:54,520
Speaker 1: essentially somebody is saying like, yeah, I look like you,

750
00:41:54,560 --> 00:41:57,239
Speaker 1: all go do the boring stuff, but also risk my

751
00:41:57,360 --> 00:42:00,480
Speaker 1: life because somebody might assassinate you at some point. Yeah,

752
00:42:00,560 --> 00:42:01,960
Speaker 1: And I get the sense in some of these that

753
00:42:02,080 --> 00:42:04,120
Speaker 1: it's a little less like I look like you would

754
00:42:04,160 --> 00:42:07,799
Speaker 1: like to do this then Kim Jong Un says, hey,

755
00:42:07,920 --> 00:42:11,120
Speaker 1: you guys, come with me. You're gonna double me. Now,

756
00:42:11,160 --> 00:42:13,959
Speaker 1: this is your new job, because he is of course,

757
00:42:14,000 --> 00:42:17,360
Speaker 1: has been listed as having body doubles, UM to the

758
00:42:17,360 --> 00:42:20,520
Speaker 1: point where there's been speculation fueled about whether or not

759
00:42:20,960 --> 00:42:24,120
Speaker 1: he was sick in the hospital or actually dead. Um.

760
00:42:24,360 --> 00:42:27,759
Speaker 1: Sodom Hussein had a bunch of decoys. They made a

761
00:42:27,760 --> 00:42:32,319
Speaker 1: movie called The Devil's Double about a man that was

762
00:42:32,560 --> 00:42:38,799
Speaker 1: his son's double. Um day, and they made a I

763
00:42:38,880 --> 00:42:41,040
Speaker 1: meant to see it actually look kind of good, but

764
00:42:41,640 --> 00:42:43,760
Speaker 1: that was out inn It was called the Double's Double,

765
00:42:44,200 --> 00:42:48,760
Speaker 1: and Latif yahia Um basically spoke out a lot afterwards

766
00:42:48,800 --> 00:42:51,360
Speaker 1: that like, I was Udi Hussein's double, and he was

767
00:42:51,400 --> 00:42:54,160
Speaker 1: a very bad guy, so it was not probably the

768
00:42:54,160 --> 00:43:01,000
Speaker 1: best job. Um. Apparently Stalin had at least four doubles.

769
00:43:01,920 --> 00:43:04,880
Speaker 1: And then this one is amazing to me. There was

770
00:43:04,920 --> 00:43:08,880
Speaker 1: a British general named Bernard Montgomery and he hired a

771
00:43:08,960 --> 00:43:12,680
Speaker 1: double by the name of Emmy Clifton James great name.

772
00:43:13,320 --> 00:43:17,560
Speaker 1: He was Australian, and he posed as Bernard Montgomery and

773
00:43:17,600 --> 00:43:21,920
Speaker 1: went around I would guess Europe um talking about Allied

774
00:43:21,960 --> 00:43:24,919
Speaker 1: plans in the hopes that like his his plans would

775
00:43:24,960 --> 00:43:28,000
Speaker 1: be or that the talks would be overheard by German spies.

776
00:43:28,440 --> 00:43:30,879
Speaker 1: And the plans, of course were fake Um and they

777
00:43:30,880 --> 00:43:34,400
Speaker 1: were meant to serve as a decoy er distraction for

778
00:43:34,440 --> 00:43:37,160
Speaker 1: the Nazis, and they think that possibly had an influence

779
00:43:37,200 --> 00:43:40,799
Speaker 1: on the storming of Normandy. That's pretty cool. There's there's

780
00:43:40,840 --> 00:43:42,760
Speaker 1: a movie in there somewhere, or at least a scene.

781
00:43:43,520 --> 00:43:46,400
Speaker 1: It's surely easily they could have thrown it in his

782
00:43:46,440 --> 00:43:49,960
Speaker 1: bonus material in the Operation Mincemeat movie. You know, did

783
00:43:49,960 --> 00:43:54,400
Speaker 1: you see that? Yeah? Was it good? I liked our

784
00:43:54,440 --> 00:43:57,239
Speaker 1: episode better, but yes, it was. It was. It was

785
00:43:57,280 --> 00:44:00,520
Speaker 1: a fine It was a fine movie. And I need

786
00:44:00,520 --> 00:44:02,960
Speaker 1: to check that out. I saw one recently. I just

787
00:44:03,000 --> 00:44:04,840
Speaker 1: want It has nothing to do with anything but Um.

788
00:44:04,880 --> 00:44:09,360
Speaker 1: It's a five part series in Swedish about the assassination

789
00:44:09,400 --> 00:44:14,520
Speaker 1: of Um, their prime minister in Olaf Palma, and it

790
00:44:14,719 --> 00:44:19,040
Speaker 1: is really fascinating because they think that one of the

791
00:44:19,080 --> 00:44:23,319
Speaker 1: main witnesses was actually the assassin and they kind of

792
00:44:23,360 --> 00:44:25,600
Speaker 1: recreate how it would have happened and they dive into

793
00:44:25,600 --> 00:44:27,960
Speaker 1: his life. It's really really good. It's worth watching. I

794
00:44:27,960 --> 00:44:30,239
Speaker 1: think that's on Netflix too. By the way, I don't

795
00:44:30,280 --> 00:44:34,520
Speaker 1: work for Netflix. Um, do you got anything else? I

796
00:44:34,560 --> 00:44:39,480
Speaker 1: got nothing else that's doppelgangers, then, everybody, I hope you

797
00:44:39,600 --> 00:44:42,080
Speaker 1: enjoyed this one. I sure did. I know chucked it.

798
00:44:42,080 --> 00:44:43,960
Speaker 1: And if you want to know more about doppelgangers, you

799
00:44:44,000 --> 00:44:46,000
Speaker 1: can start searching on the internet and there's a lot

800
00:44:46,040 --> 00:44:50,080
Speaker 1: of cool posts from social media people meeting their doppelgangers

801
00:44:50,080 --> 00:44:52,520
Speaker 1: that will just knock your socks off. And since I

802
00:44:52,560 --> 00:44:57,080
Speaker 1: said that, of course it's time for listener mail. Uh.

803
00:44:57,080 --> 00:45:00,360
Speaker 1: This is in response to the Kissing Cousins episod zode

804
00:45:01,239 --> 00:45:05,839
Speaker 1: from a friend from the Ashkenazi Jewish community. I've got

805
00:45:05,920 --> 00:45:10,280
Speaker 1: point two percent Ashkenazi and me. Oh yeah, I also

806
00:45:10,320 --> 00:45:14,000
Speaker 1: have some and Neanderthal too. All right, hey, guys, just

807
00:45:14,080 --> 00:45:17,080
Speaker 1: listen to the episode Kissing Cousins. I am from an

808
00:45:17,080 --> 00:45:21,040
Speaker 1: insular Ashkenazi Jewish community. I'm sure you understand this. Being

809
00:45:21,080 --> 00:45:24,439
Speaker 1: insular makes the gene pool that much smaller. We also

810
00:45:24,480 --> 00:45:26,799
Speaker 1: tend to marry younger than the rest of the Western world. Thus,

811
00:45:26,840 --> 00:45:29,200
Speaker 1: and pretty much every high school in our community we

812
00:45:29,239 --> 00:45:32,840
Speaker 1: do genetic testing for recessive genes for defects or serious

813
00:45:32,880 --> 00:45:37,760
Speaker 1: illnesses common in Ashkenazi Jews. We don't get the results

814
00:45:37,800 --> 00:45:39,879
Speaker 1: to the tests, only an I D number. A big

815
00:45:39,920 --> 00:45:42,160
Speaker 1: part of our culture is being set up by matchmakers

816
00:45:43,040 --> 00:45:45,319
Speaker 1: before meeting for the first time. Both families will call

817
00:45:45,719 --> 00:45:48,080
Speaker 1: the screening center with our I D number and be

818
00:45:48,120 --> 00:45:50,279
Speaker 1: told of it's safe for them to go ahead with

819
00:45:50,360 --> 00:45:53,840
Speaker 1: the matchmaking. It's amazing it is. I met my current

820
00:45:53,840 --> 00:45:55,799
Speaker 1: partner on my own and we decided we were ready

821
00:45:55,800 --> 00:45:58,600
Speaker 1: to settle down. It was a little scarier to call

822
00:45:58,640 --> 00:46:00,319
Speaker 1: and see if we were compatible, but because we had

823
00:46:00,320 --> 00:46:04,839
Speaker 1: already decided we wanted to be together. Can you imagine that? Yeah? No,

824
00:46:04,920 --> 00:46:07,040
Speaker 1: I mean, I bet that was pretty nerve racking. Uh.

825
00:46:07,080 --> 00:46:08,360
Speaker 1: The call was more to see if we need to

826
00:46:08,360 --> 00:46:11,040
Speaker 1: speak to a geneticist before having children than to see

827
00:46:11,080 --> 00:46:12,799
Speaker 1: if we should get together for the first time at all.

828
00:46:13,200 --> 00:46:16,360
Speaker 1: But thankfully we were good to go. Uh. Separately, I

829
00:46:16,400 --> 00:46:18,279
Speaker 1: had a close friend whose husband passed away a few

830
00:46:18,360 --> 00:46:20,880
Speaker 1: years ago. She had the option of marrying her brother

831
00:46:20,920 --> 00:46:23,719
Speaker 1: in law, remember we talked about that in the episode,

832
00:46:24,640 --> 00:46:27,480
Speaker 1: which she had no interest in doing. She did to

833
00:46:27,520 --> 00:46:29,759
Speaker 1: take a pair of his shoes though, and spit on

834
00:46:29,840 --> 00:46:34,520
Speaker 1: them rather than him, so that part of the old

835
00:46:34,520 --> 00:46:37,600
Speaker 1: test is kind of still kept in some places. Uh.

836
00:46:37,640 --> 00:46:39,640
Speaker 1: And of course I'm not speaking for all Oshka Nazi,

837
00:46:39,719 --> 00:46:41,680
Speaker 1: choose only what I see in my community. I just

838
00:46:41,719 --> 00:46:43,600
Speaker 1: thought you might be interested to know how a group

839
00:46:44,160 --> 00:46:47,040
Speaker 1: is trying to keep tradition while staying safe enough to

840
00:46:47,120 --> 00:46:50,600
Speaker 1: date with science in the twenty one century. Uh. And

841
00:46:50,640 --> 00:46:52,840
Speaker 1: this is a lovely sign off here, wishing you the

842
00:46:52,880 --> 00:46:57,080
Speaker 1: strength to continue for a long time coming nice. And

843
00:46:57,120 --> 00:47:00,040
Speaker 1: that's from Elkie. Thanks a lot, Elkie. That was a

844
00:47:00,040 --> 00:47:02,880
Speaker 1: great email. I appreciate that big time. And if you

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Speaker 1: want to be like Elki and send us a super

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00:47:04,880 --> 00:47:07,880
Speaker 1: fascinating email, you can do that too. All you have

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00:47:07,960 --> 00:47:12,520
Speaker 1: to do is right to Stuff podcast at iHeart radio

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Speaker 1: dot com, Press send and see what happens, Right, Chuck,

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Speaker 1: that's right. Stuff You should know is a production of

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Speaker 1: I Heart Radio. For more podcasts my heart Radio, visit

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Speaker 1: the iHeart Radio app, Apple Podcasts, or wherever you listen

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Speaker 1: to your favorite shows. H