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Speaker 1: I think I even say in the episode that I

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Speaker 1: remember learning about doppelgangers from those time-life books about the

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Speaker 1: paranormal back when I was a kid. Well, fast forward

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Speaker 1: to when I'm a grown-up, a.k.a. now, I was really

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Speaker 1: glad to learn that they're an actual thing. There are

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Speaker 1: people walking around who have a twin that they're in

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Speaker 1: no way related to, and the stories behind them tend

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Speaker 1: to be very interesting. I'm out. This is the last

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Speaker 1: one for me, so let me say thanks for listening

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Speaker 1: to our playlist.

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Speaker 2: Welcome to Stuff You Should Know, a production of iHeartRadio.

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Speaker 1: Hey, and welcome to the podcast. I'm Josh, and there's Chuck,

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Speaker 1: and Jerry's here, too. And this is Stuff You Should Know,

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Speaker 1: the day after Labor Day edition, which Chuck loves.

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Speaker 2: Yeah, you know, Emily showed me that article that informed

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Speaker 2: part of this episode the other day. Yeah. And we'll

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Speaker 2: get to it, but I started looking at this photographer's

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Speaker 2: doppelganger photo series.

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Speaker 1: Yeah.

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Speaker 2: And boy, some of those are crazy similar.

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

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Speaker 2: Yeah.

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Speaker 1: Those guys struck me as extremely close.

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Speaker 2: The two women with the bangs and the black tank

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Speaker 2: tops looked like identical twins to me. I could not

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Speaker 2: tell them apart.

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Speaker 1: Yeah, I feel like we shouldn't tease anybody anymore. There's

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Speaker 1: a guy, and like you said, we'll get to him

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

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

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Speaker 1: guess a Quebecois photographer, Francois Brunel. And for 20 years,

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Speaker 1: he's been assembling... pictures of doppelgangers, people who already knew

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Speaker 1: they had a doppelganger and wrote in doppelgangers he's looked

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Speaker 1: for and found on the street. And he brings them

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Speaker 1: together into his studio and photographs them together. But he

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Speaker 1: does some like really cool stuff with it. It's not

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Speaker 1: just like, here, you stand here and you stand next

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Speaker 1: to him. The way that he poses them and dresses

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Speaker 1: them and everything, like it creates like a sense of

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Speaker 1: intimacy between them. It makes you think you're looking at

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Speaker 1: identical twin photos.

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Speaker 2: Yeah. I mean, you know, some of them people favor

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Speaker 2: one another. And then when you have them styled to

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Speaker 2: look alike and posed alike, that informs what you're seeing.

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Speaker 2: But a handful of them, it was they look so

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Speaker 2: much like identical twins that it's just it's weird to

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Speaker 2: think that you could walk down the street one day

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Speaker 2: and meet someone who looks exactly like you.

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Speaker 1: Yeah. And I mean, like that is got to be

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Speaker 1: pretty eerie. Luckily, one of the great things about social

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Speaker 1: media is that people can post their random encounters. You

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Speaker 1: got a phone with a camera on it. You got

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Speaker 1: access to social media through that phone. You can just

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Speaker 1: snap a picture of you and your doppelganger, post it

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Speaker 1: to the internet, and let everyone go crazy. And that

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Speaker 1: does happen, it seems like, from time to time. But

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Speaker 1: this dude's whole jam, it's called I'm Not a Lookalike

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Speaker 1: with an exclamation point. It sounds like a Disney movie

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Speaker 1: from the 60s or something. But that's Francois Brunel's 20-year series.

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Speaker 2: Yeah, and not just... hey, Chuck, you look like every

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Speaker 2: tubby guy with a beard and glasses. It's like, that's lazy.

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Speaker 1: Sure.

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Speaker 2: I think when you're in the public eye, people tend

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Speaker 2: to do things like that to say, okay, you look

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Speaker 2: like all of these people. We're talking like real lookalikes.

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Speaker 1: Right.

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Speaker 2: Which I, you know, I think I've gotten a couple

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Speaker 2: here and there over the years that I was like, okay,

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Speaker 2: I could see that for sure. But doppelgangers are different.

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

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Speaker 1: a German word at its origin. And 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 medieval word. It was

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Speaker 1: actually coined at the end of the 18th century by

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Speaker 1: an author named Jean-Paul. which is an unlikely German name

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Speaker 1: if you ask me. But he wrote a book called Siebenkass. Yeah, Siebenkass.

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

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Speaker 1: who had a doppelganger, right?

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Speaker 2: Yeah. I think John Paul's hiding something with that name.

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Speaker 2: I do too.

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Speaker 1: He's actually a double.

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Speaker 2: I think there's a Dusseldorf at the end or something

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Speaker 2: that he left off.

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Speaker 1: That's right.

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Speaker 2: Yeah, so he wrote about it in that novel, but

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Speaker 2: as Livia is keen to point out, Livia helped us

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Speaker 2: with this one, he sort of mixes up the words.

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Speaker 2: When he was actually talking about a lookalike human, he

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Speaker 2: used the word with a T, doppeltganger, but did use

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Speaker 2: the word doppelganger in there as a meal with two

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Speaker 2: courses being served together. So I don't know, things just

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Speaker 2: got a little confused or people thought it was just

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Speaker 2: fine to drop the T. But doppelganger became the eventual

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Speaker 2: word that we use for, you know, a non-related lookalike.

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Speaker 2: And that's I don't know if we pointed that out.

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Speaker 2: The obvious thing here is that you're not not related

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Speaker 2: to these people at all.

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Speaker 1: Yeah. And the other thing about a doppelganger is, again,

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Speaker 1: like you said, it's not like you bear a passing

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Speaker 1: resemblance or you favor one another. Like you're as close

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Speaker 1: to the person's double body double as you can get.

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Speaker 1: And the closer you are, the more of a doppelganger

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Speaker 1: you are, basically. Right. But Jean-Paul, I can't say his

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Speaker 1: name any other way, he coined those terms. So it's

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Speaker 1: funny that he kind of got it wrong. Everybody else

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Speaker 1: is like, no, we're going to drop the T, like

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Speaker 1: you said. But the fact that he coined the term

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Speaker 1: doppelganger doesn't mean he invented the concept. It's actually a

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Speaker 1: much older concept. It just didn't have the same name

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Speaker 1: that we use now. Apparently, there was a longstanding superstition

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Speaker 1: in England, in Germany, dating back, I think, to the

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Speaker 1: medieval age, maybe before Chuck, where if you saw your

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Speaker 1: doppelganger three times, it meant you were about to die.

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Speaker 1: And that's still a, I think people still kind of

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Speaker 1: believe that one.

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Speaker 2: Yeah. And that's, I mean, there are all kinds of

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Speaker 2: old pieces of folklore in just about every country you

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Speaker 2: can explore that has some sort of, and it's usually not,

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Speaker 2: if you find your doppelganger, then, you know, good luck

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Speaker 2: will come your way. It always seems like it's some

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Speaker 2: kind of spooky negative thing will happen, whether you see

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Speaker 2: them three times, or whether, I mean, there was one,

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Speaker 2: I think, in the Norse mythology, where a spirit appears.

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Speaker 2: This one's just a little weird. It appears just like you,

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Speaker 2: but it beats you to wherever you're going. So if

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Speaker 2: you're traveling on a journey, your doppelganger would beat you there,

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Speaker 2: and people think that you would have arrived earlier. I

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Speaker 2: don't know if that's the solves tardiness for the North mythology,

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Speaker 2: but that one's not super evil, but it's usually, especially

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Speaker 2: when you get into literature, uh, some sort of, you know,

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Speaker 2: evil force or evil twin that's come along to wreck

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Speaker 2: things for you. Yeah.

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

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Speaker 1: put a button on that. Cause I did a little research. It, um,

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Speaker 1: it's called the Varduger. And the reason I know how

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Speaker 1: to pronounce it is because I looked up what the

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Speaker 1: O with the slash is pronounced like. And it's the

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Speaker 1: same thing as an O with the umlaut.

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Speaker 2: Yeah.

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Speaker 1: I didn't know that before, did you?

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Speaker 2: Yeah.

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Speaker 1: Well, you might be a little more of a metal

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Speaker 1: fan than me.

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Speaker 2: It's cool looking, just like the umlaut.

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Speaker 1: Super cool. I think it's cooler than the umlaut. It's

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Speaker 1: got like a slash through it, you know, like, take

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Speaker 1: that O. Whereas the umlaut's like, I'm adorning this O.

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Speaker 1: It's a little different.

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Speaker 2: All right. Edgar Allan Poe wrote a story called William Wilson. Uh,

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Speaker 2: sort of like I mentioned before about, you know, evil

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Speaker 2: things happening and narrator meets a boy at school. Uh,

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Speaker 2: and this one had the same name, I'm presuming William Wilson.

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Speaker 2: Otherwise it'd be a weird title. And this boy though

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Speaker 2: only speaks in a whisper. So, uh, it seems like

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Speaker 2: all through literature, it's always some sort of a creepy

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Speaker 2: shadowy figure.

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Speaker 1: Yeah, there's also Dostoevsky put kind of a spin on

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Speaker 1: it where he introduced a doppelganger to the main character

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Speaker 1: in a novel called, or a novella, I think, called

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Speaker 1: The Double. And I love this. In it, the doppelganger

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Speaker 1: is like a more likable version of the main character

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Speaker 1: and ends up breaking the main character mentally. I like that.

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

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

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Speaker 1: along and people are like, I like you a lot more.

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Speaker 2: Yeah.

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Speaker 1: It reminds me, have you seen that Paul Rudd show,

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Speaker 1: Living With Yourself? No. I don't know how it escaped,

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Speaker 1: but they did a season of it in 2019, didn't

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Speaker 1: follow up on it, but he plays himself and his

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Speaker 1: doppelganger to great comedic effect. But it's also... Like really

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

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Speaker 2: Well, that's the other thing. And we'll talk about some

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Speaker 2: more in history. But, of course, with TV, oftentimes it's

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Speaker 2: flipped to a more fun thing. And it's played for laughs.

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Speaker 2: I don't think Three's Company had a doppelganger episode, but

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Speaker 2: it wouldn't surprise me. Because it can be played for

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Speaker 2: sort of mixed identity hijinks as well. Totally.

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Speaker 1: Totally. Like one where Mr. Furley was into Jack, but

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Speaker 1: it was really his doppelganger.

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Speaker 2: Yeah, and I mean, it'd probably be like his twin

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Speaker 2: brother who came to visit in that case, but.

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Speaker 1: This still counts. An unknown twin still in the canon

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Speaker 1: counts as a doppelganger.

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Speaker 2: Do you think?

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Speaker 1: Well, yeah, I think so.

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Speaker 2: All right. Well, you just negated what I said, that

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Speaker 2: they're not related, but. Well, okay.

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Speaker 1: That's true. Once you find out, here's the thing. This

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Speaker 1: actually proves your point, Chuck. Once the characters find out, oh,

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Speaker 1: it's a twin, mystery solved, no longer a doppelganger, it

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Speaker 1: becomes a twin. But up to that point when they're

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Speaker 1: crossing paths with one another and never in the same

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Speaker 1: room at the same time.

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Speaker 2: That's a central mystery.

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Speaker 1: It seems doppelganger. Yes, exactly.

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Speaker 2: But Furley doesn't have a brother. Or does he?

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Speaker 1: Why is he acting so weird?

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Speaker 2: There have been some true historical figures over the years

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Speaker 2: that bought into this stuff. Abraham Lincoln. He and his wife,

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Speaker 2: they had all kinds of kind of interesting beliefs.

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Speaker 1: It was the age of spiritualism.

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Speaker 2: Yeah, for sure. So you have to take that into consideration,

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Speaker 2: I think. But Lincoln supposedly saw his image three times,

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Speaker 2: I think, in the mirror in 1860, which.

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Speaker 2: What was his wife's name? Mary Todd?

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Speaker 1: Mary Todd.

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Speaker 2: Mary Todd said, you know, you're going to serve two

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Speaker 2: terms as president. That's what that means. But you're going

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Speaker 2: to die before the end of the second term.

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Speaker 1: And he said... Well, good night, dear.

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Speaker 2: But see, I take issue with some of these listed

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Speaker 2: historically because they're more ghostly specters than real human doppelgangers.

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Speaker 1: So John Donne's story is strictly by location. I would

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Speaker 1: not call it a doppelganger one where he saw his

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Speaker 1: wife a few times while he was in Paris and

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Speaker 1: she was back in England and she was actually ill

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Speaker 1: and their child had died. By location doesn't count. But

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Speaker 1: Percy Bysshe Shelley, he saw his doppelganger and died a

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Speaker 1: few weeks later in a sailing accident. I would say

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Speaker 1: that one counts in the lore. And then Catherine the

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Speaker 1: Great's was pretty good too, right?

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Speaker 2: Yeah, and I think Percy Bysshe Shelley's doppelganger had a

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Speaker 2: t-shirt on that said, I'd rather be sailing.

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Speaker 1: That's right.

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Speaker 2: Which made it even weirder.

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Speaker 1: No, his t-shirt said no plates because that was his

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Speaker 1: third choice.

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Speaker 2: Who was the other one?

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Speaker 1: Catherine the Great?

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Speaker 2: Yeah, this was a real one too, actually. Supposedly there

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Speaker 2: was a servant, right, who discovered a double like that

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Speaker 2: somehow got in there, faked their way in there, and

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Speaker 2: was sitting on the throne. So this was a case

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Speaker 2: where someone was, I don't think, trying to like, you know,

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Speaker 2: realistically take over as queen, but at least got in

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Speaker 2: there and was like, I'm going to sit on the

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Speaker 2: throne and I got past the guards because I look

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Speaker 2: like you.

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Speaker 1: Yeah, I also saw it interpreted as like phantasmagoric too.

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Speaker 2: Oh, like it didn't happen?

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Speaker 1: No, that it was like a phantom or like a

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Speaker 1: supernatural doppelganger rather than like a double or something like that.

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Speaker 2: 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 Goethe, one of the reasons I like this

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Speaker 1: anecdote is because I love saying Goethe's name. Mm-hmm. He

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Speaker 1: was riding one time from Alsace to, I want to

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

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Speaker 2: I mean, sure. It's the name of that author.

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Speaker 1: And he passed himself on the road. And his doppelganger

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

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

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Speaker 1: to Alsace going the other way. And he realized he

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Speaker 1: was wearing that same outfit that his doppelganger had been

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Speaker 1: wearing eight years before. And he was like, this is

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Speaker 1: pretty awesome.

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Speaker 2: And that was a Time Life Books thing?

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Speaker 1: Yeah. I remember it very well. Yeah.

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Speaker 2: That sounds about right.

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Speaker 1: I didn't know how to say Goethe's name at the time, though.

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Speaker 1: So I was like, this really weird thing happened to Goethe.

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Speaker 1: All right.

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Speaker 2: Maybe we should take a little break now that we've

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Speaker 2: sort of given some past examples of what may or

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Speaker 2: may not be doppelgangers. And we'll talk about real doppelgangers,

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Speaker 2: real live humans walking around that look like you right

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Speaker 2: after this. All right, so we mentioned the photographer, Francois Brunel,

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Speaker 2: who's been doing this project. It's called I'm Not a Lookalike,

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Speaker 2: where he takes pictures of these people.

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Speaker 1: I'm sorry, Chuck. There's an exclamation point at the end

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Speaker 1: of that. Yeah.

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Speaker 2: I'm not a lookalike. I encourage people to go check

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Speaker 2: this out when they're in a safe space and you're

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Speaker 2: not driving or whatever to look at some of these

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Speaker 2: photos because it's amazing. I mean, some of them, like

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Speaker 2: I said, look to my eye like identical twins. The

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Speaker 2: other two sort of gentlemen probably in their 60s with

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Speaker 2: the little mustache. Did you see those guys?

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Speaker 1: The shirtless ones? No. No.

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Speaker 2: The picture I saw they had on like little, uh,

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Speaker 2: sort of like a newsboy caps.

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

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Speaker 2: They look, I mean, they look like identical twins and, um,

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Speaker 2: so cool. Those are the ones that this is the

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Speaker 2: article that Emily sent me where it talked about that project.

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Speaker 2: And then it got into a really interesting study because

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Speaker 2: if you think, well, you know, what is a doppelganger?

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Speaker 2: I've always, since I was a kid, I've always sort of, um,

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Speaker 2: realize that there are only so many facial combinations you

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Speaker 2: can have and with the amount of we talked about

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Speaker 2: this over the years in the show too like over

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Speaker 2: the years and through history uh especially seems to happen

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Speaker 2: if you're looking through like yearbooks from like the 40s

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Speaker 2: and 50s and 60s you'll like see people that look

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Speaker 2: like people you know or like yourself there are just

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Speaker 2: only so many ways you can rearrange a face until

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Speaker 2: you're bound at some point to get someone who maybe

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Speaker 2: favors somebody a little bit or even more or to

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Speaker 2: the point where they look just alike and And so

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Speaker 2: you might think, well, what does that have to say

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Speaker 2: about what's going on inside? Is there anything to DNA?

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Speaker 2: And they looked into that, right?

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Speaker 1: Yeah, I guess Brunel's work caught the attention of a

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Speaker 1: researcher named Manel Esteller from Spain, from the Carreras Leukemia

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Speaker 1: Research Institute, who said, I want to try to figure

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Speaker 1: this out. And these people are like a perfect population sample.

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Speaker 1: So he took, I think, 32 pairs of doppelgangers. ran

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Speaker 1: them through three different facial recognition programs. And half of them,

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Speaker 1: I guess 16 of the pairs, as far as the

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Speaker 1: AI was concerned in the programs, they were identical twins.

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Speaker 1: That's what the AI took them for in 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 very similar genes

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Speaker 1: in some key places, especially like facial construction.

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Speaker 2: Yeah. And they were also, and again, this doesn't like

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Speaker 2: lessen the study any to me, but they were generally

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Speaker 2: from the same country or the same ethnic background. Although

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Speaker 2: I did see it gender varied because there was one

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Speaker 2: where it was a man and a woman who looked

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Speaker 2: a lot alike in those photos that they put together,

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Speaker 2: which is pretty interesting. But they looked into how they

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Speaker 2: matched up, I think, with their microbiome epigenetically, which is

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Speaker 2: basically how your behaviors in your environment is going to

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Speaker 2: affect your genes. And then just your straight up genome.

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Speaker 2: And what they found out was they did share a

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Speaker 2: lot of what are called SNPs, single nucleotide polymorphisms, which

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Speaker 2: are the variations in our DNA that create most of

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Speaker 2: the genetic variation that people have with one another. So

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Speaker 2: they shared a lot of them. And I wish we

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Speaker 2: would have gotten like the raw numbers because all it

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Speaker 2: says is like, you know, they shared many SNPs, whereas

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Speaker 2: I'd like to see sort of the actual data, which

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Speaker 2: I couldn't get to. But what they did find was

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Speaker 2: that epigenetically and with their microbiomes, they were basically random.

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Speaker 2: So it was only genetically that they shared something.

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Speaker 1: Well, yeah. And they said, well, actually, I think this

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Speaker 1: is probably what accounts for a lot of the differences

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Speaker 1: in identical twins. Like people look at identical twins and

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Speaker 1: they're like, oh, you're identical. But if you really start

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Speaker 1: to look at them closely, you're like, actually, there's a

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Speaker 1: lot more differences than you'd think between them. And even

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Speaker 1: though they share the same genes, I think 99% of

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Speaker 1: the same DNA, what makes identical twins slightly different is

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Speaker 1: in their epigenome. And this study basically found the same

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Speaker 1: thing with doppelgangers. They can share a lot of similar

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Speaker 1: genes that give them the same facial traits or builds

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Speaker 1: or I think even habits they found. A lot of

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

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

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Speaker 1: But they found that, like you said, the differences were

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Speaker 1: found in their epigenome. And I don't know if, I

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

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Speaker 1: microbiome might have provided some differences too. It's basically nature

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

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

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Speaker 1: when we're basically bound to look the same genetically.

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Speaker 2: Yeah, I think the microbiome was the same as the

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Speaker 2: epigenetic finding, which was, I think the quote was, it

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Speaker 2: generally wasn't any closer than two randos.

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Speaker 1: Gotcha.

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Speaker 2: That's what they said in the paper, randos.

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Speaker 1: They did. They did. I'm trying to find real quick the...

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Speaker 1: The data? The number of SMPs. I can't find it

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Speaker 1: that quick. There's a lot of numbers and weird, like,

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Speaker 1: capital C, lowercase p, capital G. I'm like, I don't

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Speaker 1: know what that is. But I will tell you this.

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Speaker 1: I looked up those older guys in the news caps.

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Speaker 2: Right.

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Speaker 1: They are spitting image. Yeah. And not only are they

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Speaker 1: spitting image of one another, they're spitting image of the

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Speaker 1: Lennox air conditioning man. They look exactly like him. that guy.

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Speaker 1: Down to the hat.

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Speaker 2: Well, I guess if that guy ever, you know, leaves

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Speaker 2: the earth and they've got two backups.

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Speaker 1: I mean, what's it called when you get three doppelgangers together?

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Speaker 2: I don't know. A droppelganger?

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Speaker 1: I guess so.

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Speaker 2: What I'd be curious to see is if, and maybe

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Speaker 2: I'll look more just for my own funsies into... the

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Speaker 2: photographer's project, because what I'm curious about is if these,

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Speaker 2: like how these people get along and if they like

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Speaker 2: formed friendships, if they liked each other, if they had

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Speaker 2: similar interests, you know, that's, that's pretty fascinating to me.

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Speaker 1: I think so too. And again, like that's, that's the

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Speaker 1: kind of stuff that can take a, a real turn,

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Speaker 1: I guess, depending on how you were raised or even like,

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Speaker 1: you know, were you raised in your power lines? Great.

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Speaker 1: That means you're into baseball cards, that kind of thing

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Speaker 1: that we just haven't put our finger on yet. But

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Speaker 1: it does seem like, you know, it's becoming clearer and clearer.

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Speaker 1: It's not just genetics. It's not just epigenetics. It's all

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Speaker 1: of that stuff put together. And it seems like so

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Speaker 1: wildly unattainable, that information that because it's so complicated and

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Speaker 1: there's so many variables and so many factors. But if

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Speaker 1: you step back and think about it, there's a finite

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Speaker 1: number of variables and factors. As many as it seems

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Speaker 1: to us, it's a finite number. It's not infinite, which

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Speaker 1: means someday we could conceivably get a grasp on exactly

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Speaker 1: what makes a person them, which I find really exciting.

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Speaker 2: You know what would be funny is if they really

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Speaker 2: got along and they were like, oh my God, we

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Speaker 2: have so many similarities. We look just alike. this is

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Speaker 2: such a weird thing. And they were having lunch and

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Speaker 2: one of them was like, boy, what a shame that

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Speaker 2: Hillary lost the right. And the other one might unveils

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Speaker 2: a Make America Great Again t-shirt or something.

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Speaker 1: Drops their fork.

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Speaker 2: They just get up and part ways silently. Yeah.

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Speaker 1: That would be the best case scenario, I think.

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Speaker 2: Yeah. So this study, this next one kind of irritates

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Speaker 2: me a little bit. It was from 2015 and I

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Speaker 2: think was well-intentioned, but there was an Australian biologist who

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Speaker 2: named Tegan Lucas, who got off to a great start,

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Speaker 2: looked at 4,000 faces from the U.S. Anthropometric Survey database,

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Speaker 2: which is something the U.S. Army puts together.

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Speaker 1: Tegan Lucas is listening to this now. She's like, oh,

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Speaker 1: you got off to a great start on that U.S.

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Speaker 1: Anthropometric Survey, Chuck.

430
00:22:13,109 --> 00:22:16,790
Speaker 2: Well, I think Tegan Lucas overachieved because what they did

431
00:22:16,830 --> 00:22:22,959
Speaker 2: was looked across eight facial features and calculated like the

432
00:22:23,000 --> 00:22:24,899
Speaker 2: chances that you'll look just like someone else is one

433
00:22:24,920 --> 00:22:27,820
Speaker 2: in a trillion. I see these pictures of these doppelgangers.

434
00:22:27,880 --> 00:22:30,280
Speaker 2: I'm like, there's just no way because I'm looking at

435
00:22:30,320 --> 00:22:34,460
Speaker 2: the evidence. And it turns out that Tegan really got

436
00:22:34,609 --> 00:22:39,010
Speaker 2: into like measuring the millimeter of a nostril and stuff

437
00:22:39,050 --> 00:22:42,450
Speaker 2: like that. And the comparison, it was just too identical.

438
00:22:42,510 --> 00:22:45,290
Speaker 2: Like I think with identical twins, you wouldn't get that close.

439
00:22:46,310 --> 00:22:48,050
Speaker 1: No, but I think that's what she was going for

440
00:22:48,109 --> 00:22:51,760
Speaker 1: is like, you know, would it be possible for two

441
00:22:52,260 --> 00:22:54,959
Speaker 1: identical people to be walking around? And one of the

442
00:22:55,080 --> 00:22:59,609
Speaker 1: applications of our research is if we're creating facial recognition systems,

443
00:22:59,640 --> 00:23:02,150
Speaker 1: which we've done an episode on before, it was pretty

444
00:23:02,170 --> 00:23:06,030
Speaker 1: eerie if I remember correctly. But if we're creating these things,

445
00:23:06,090 --> 00:23:10,390
Speaker 1: if we're building computers to recognize people's faces, are there

446
00:23:10,410 --> 00:23:12,750
Speaker 1: going to be any kind of like false positives out

447
00:23:12,770 --> 00:23:16,640
Speaker 1: there because there's two people out there who are that similar?

448
00:23:16,760 --> 00:23:21,570
Speaker 1: And the chances are, really low unless we just keep

449
00:23:21,650 --> 00:23:24,540
Speaker 1: going population-wise. And even with the number of people out today,

450
00:23:24,840 --> 00:23:27,560
Speaker 1: I frankly don't think it's that low of a chance

451
00:23:27,600 --> 00:23:30,720
Speaker 1: that there are two people who are identical across eight

452
00:23:30,800 --> 00:23:34,500
Speaker 1: measures of your face. And she wouldn't reveal what the

453
00:23:34,540 --> 00:23:38,550
Speaker 1: eight measures were because apparently her work was that groundbreaking

454
00:23:38,590 --> 00:23:41,570
Speaker 1: that she's like, I can't let anybody know what to

455
00:23:42,390 --> 00:23:43,690
Speaker 1: hide or disguise.

456
00:23:43,710 --> 00:23:44,210
Speaker 2: Okay.

457
00:23:44,859 --> 00:23:48,859
Speaker 1: But the point is it's eight different dimensions. And like

458
00:23:48,880 --> 00:23:51,580
Speaker 1: you said, I think like nostril width, ear length, that

459
00:23:51,619 --> 00:23:54,900
Speaker 1: kind of stuff, down to the millimeter. And she found

460
00:23:54,920 --> 00:23:57,400
Speaker 1: that it's like a one in a trillion chance that

461
00:23:57,420 --> 00:24:00,340
Speaker 1: there are two people walking around like that. The thing

462
00:24:00,400 --> 00:24:02,350
Speaker 1: is there's eight billion people on the planet.

463
00:24:03,290 --> 00:24:06,070
Speaker 2: So that means there's no doppelgangers.

464
00:24:06,270 --> 00:24:09,390
Speaker 1: Well, it's not close to a trillion, but it's not

465
00:24:09,470 --> 00:24:11,750
Speaker 1: as far off from a trillion as like if there

466
00:24:11,790 --> 00:24:13,919
Speaker 1: were two people on the planet. So if you have

467
00:24:13,960 --> 00:24:15,740
Speaker 1: two people on the planet and a one in a

468
00:24:15,780 --> 00:24:18,060
Speaker 1: trillion chance that they're going to look alike, the chance

469
00:24:18,100 --> 00:24:20,379
Speaker 1: that they're going to look like is basically nothing. But

470
00:24:20,400 --> 00:24:23,000
Speaker 1: as you get closer to a trillion, there is a

471
00:24:23,140 --> 00:24:25,240
Speaker 1: really good chance that there are going to be two

472
00:24:25,300 --> 00:24:28,359
Speaker 1: people walking around that are identical. And we're at eight million.

473
00:24:28,400 --> 00:24:31,600
Speaker 1: So we're at, I think, a one in 125 chance

474
00:24:31,920 --> 00:24:34,720
Speaker 1: that today on Earth there are two people who look

475
00:24:34,790 --> 00:24:39,090
Speaker 1: exactly alike across those eight measures. The point is for

476
00:24:39,170 --> 00:24:41,070
Speaker 1: you and me to just be looking at stuff.

477
00:24:41,090 --> 00:24:41,270
Speaker 2: Uh-huh.

478
00:24:42,119 --> 00:24:44,540
Speaker 1: We don't need those eight measures to be exactly right.

479
00:24:44,619 --> 00:24:47,659
Speaker 1: If they're close enough, that's when people look like doppelgangers

480
00:24:47,700 --> 00:24:50,600
Speaker 1: to you and me. But we're not artificial intelligence. We're

481
00:24:50,660 --> 00:24:52,020
Speaker 1: sentient beings. Right.

482
00:24:52,080 --> 00:24:58,070
Speaker 2: Well, yeah, we're artificial. And I think we're intelligent. But

483
00:24:58,109 --> 00:24:59,419
Speaker 2: we're not those two things together.

484
00:25:00,119 --> 00:25:02,230
Speaker 1: Are we artificial? Sure.

485
00:25:02,250 --> 00:25:03,670
Speaker 2: I just want to try and freak people out.

486
00:25:03,609 --> 00:25:04,790
Speaker 1: A little bit. Oh, okay.

487
00:25:04,970 --> 00:25:06,550
Speaker 2: The Josh bot and the Chuck bot are hard at

488
00:25:06,570 --> 00:25:06,889
Speaker 2: work here.

489
00:25:07,630 --> 00:25:08,750
Speaker 1: Does not compute.

490
00:25:09,270 --> 00:25:12,030
Speaker 2: Well, I think what we're arriving at is a point

491
00:25:12,070 --> 00:25:14,550
Speaker 2: that we failed to make earlier, which is there is

492
00:25:14,609 --> 00:25:19,310
Speaker 2: no agreed upon definition of what constitutes a doppelganger. Like

493
00:25:19,390 --> 00:25:21,929
Speaker 2: how alike do you have to look to someone to

494
00:25:21,970 --> 00:25:23,030
Speaker 2: say you're a doppelganger?

495
00:25:23,910 --> 00:25:24,130
Speaker 1: Right.

496
00:25:24,530 --> 00:25:26,860
Speaker 2: You know, like I look like the guy, I look

497
00:25:26,880 --> 00:25:30,920
Speaker 2: like the bass player for Jason Isbell in the 400 unit.

498
00:25:31,820 --> 00:25:33,619
Speaker 2: But is he my doppelganger? Probably not.

499
00:25:35,520 --> 00:25:38,090
Speaker 1: Okay, so one of the reasons why I think people

500
00:25:38,130 --> 00:25:40,210
Speaker 1: would kind of look at you and then the bass

501
00:25:40,270 --> 00:25:40,990
Speaker 1: player for.

502
00:25:40,840 --> 00:25:44,180
Speaker 2: … Jason Isbell in the 400 unit.

503
00:25:44,780 --> 00:25:48,000
Speaker 1: Sure. And put you guys together and said, oh, they

504
00:25:48,040 --> 00:25:51,560
Speaker 1: really look alike is because … we use like shortcuts

505
00:25:51,600 --> 00:25:54,920
Speaker 1: when we're looking at, at people's faces or even just

506
00:25:54,980 --> 00:25:57,300
Speaker 1: people in general. So let's say you're both wearing the

507
00:25:57,320 --> 00:26:00,200
Speaker 1: same hat and the same, you both have a similar beard,

508
00:26:00,240 --> 00:26:01,620
Speaker 1: I'm guessing. Yeah.

509
00:26:02,070 --> 00:26:03,290
Speaker 2: I want to name him cause I don't want to

510
00:26:03,330 --> 00:26:05,490
Speaker 2: just keep calling that cause I love that group. Uh,

511
00:26:06,010 --> 00:26:08,530
Speaker 2: his name is Jimbo Hart. And when you look at

512
00:26:08,570 --> 00:26:09,629
Speaker 2: a picture of Jimbo Hart.

513
00:26:09,690 --> 00:26:11,790
Speaker 1: He, you know, it's a great name too.

514
00:26:12,090 --> 00:26:14,270
Speaker 2: Like look him up. He's got the beard. He's, you know,

515
00:26:14,290 --> 00:26:16,130
Speaker 2: when I'm reading, I got on my black room glasses.

516
00:26:16,850 --> 00:26:18,840
Speaker 2: I like to wear my hats these days. My, uh,

517
00:26:19,720 --> 00:26:22,080
Speaker 2: you know, various sort of cowboy hats and fedoras.

518
00:26:22,880 --> 00:26:23,080
Speaker 1: Sure.

519
00:26:23,740 --> 00:26:25,080
Speaker 2: Yeah. I mean, the guy looks like me.

520
00:26:25,800 --> 00:26:28,610
Speaker 1: Okay. So if you put you guys together, you're both

521
00:26:28,630 --> 00:26:30,330
Speaker 1: wearing the same glasses, you got a beard, you got

522
00:26:30,609 --> 00:26:32,310
Speaker 1: hats on and all that stuff. People would be like

523
00:26:32,410 --> 00:26:35,929
Speaker 1: spitting image, but take the glasses off, shave the beard,

524
00:26:35,950 --> 00:26:38,750
Speaker 1: take the hat off and then put you guys together.

525
00:26:39,109 --> 00:26:42,040
Speaker 1: How close would you really be? And probably not that close.

526
00:26:42,280 --> 00:26:44,180
Speaker 2: I can tell you, I can see him without a

527
00:26:44,220 --> 00:26:45,580
Speaker 2: beard and he's not nearly as close.

528
00:26:46,580 --> 00:26:49,440
Speaker 1: OK, right. So there you go. Like we make these

529
00:26:49,500 --> 00:26:52,880
Speaker 1: shortcuts with our face or with our minds to just

530
00:26:52,920 --> 00:26:55,080
Speaker 1: kind of as like kind of a shorthand for like, oh,

531
00:26:55,100 --> 00:26:57,880
Speaker 1: that's Chuck. I don't have to analyze them anymore because

532
00:26:57,920 --> 00:27:00,020
Speaker 1: that uses up a lot of brain power and energy.

533
00:27:00,420 --> 00:27:03,080
Speaker 1: And I'm kind of low on carbs today. So I

534
00:27:03,140 --> 00:27:05,399
Speaker 1: just know that that's Chuck where it really turned out

535
00:27:05,440 --> 00:27:07,700
Speaker 1: to be Jimbo Hart. The basis for.

536
00:27:07,480 --> 00:27:09,350
Speaker 2: The 400 unit.

537
00:27:09,880 --> 00:27:10,199
Speaker 1: That's right.

538
00:27:11,990 --> 00:27:14,610
Speaker 2: Uh, right. And you know, you've seen this and here's

539
00:27:14,650 --> 00:27:18,230
Speaker 2: a good example of how this can play tricks on people. Uh,

540
00:27:18,270 --> 00:27:19,910
Speaker 2: and this is just one example. A lot of people

541
00:27:19,970 --> 00:27:22,330
Speaker 2: have done that kind of fun thing where you switch

542
00:27:22,490 --> 00:27:26,070
Speaker 2: out a, a face on someone and put it on

543
00:27:26,130 --> 00:27:29,630
Speaker 2: someone else. And it's like, it should be obvious, but

544
00:27:29,670 --> 00:27:32,210
Speaker 2: it's like, it just looks like a weird uncanny version

545
00:27:32,260 --> 00:27:32,859
Speaker 2: of that person.

546
00:27:32,880 --> 00:27:33,540
Speaker 1: Right.

547
00:27:33,780 --> 00:27:36,460
Speaker 2: Uh, the one that Livia picked out was from MIT

548
00:27:37,080 --> 00:27:41,020
Speaker 2: when they put George Bush's face on Dick Cheney's head.

549
00:27:42,030 --> 00:27:46,609
Speaker 2: And Bill Clinton's face on Al Gore's head, that dates

550
00:27:46,670 --> 00:27:50,090
Speaker 2: the creation of that particular meme or whatever it was.

551
00:27:51,520 --> 00:27:54,260
Speaker 2: The Dick Cheney one looks like you got to look

552
00:27:54,280 --> 00:27:57,220
Speaker 2: a bunch of times before you realize that's George Bush's face.

553
00:27:58,520 --> 00:28:01,920
Speaker 2: The Al Gore one, I mean, it's so clearly Clinton.

554
00:28:02,300 --> 00:28:05,730
Speaker 2: It's just kind of funny looking. But the Bush-Cheney swap

555
00:28:05,810 --> 00:28:08,469
Speaker 2: actually kind of worked in that, I guess, goes to

556
00:28:08,490 --> 00:28:11,230
Speaker 2: sort of prove that you look at things like hair.

557
00:28:12,300 --> 00:28:15,480
Speaker 2: And stuff like that as, you know, that big Clinton

558
00:28:15,530 --> 00:28:17,910
Speaker 2: mop or that big Al Gore mop that he had

559
00:28:17,970 --> 00:28:20,350
Speaker 2: or Cheney's bald head. And that's a big clue.

560
00:28:21,070 --> 00:28:23,629
Speaker 1: Yeah. And also like the I think one of the

561
00:28:23,970 --> 00:28:26,830
Speaker 1: foundations of this swap is that it can't be like

562
00:28:26,990 --> 00:28:30,680
Speaker 1: an egregious change. Right. Like if you remember back to,

563
00:28:30,700 --> 00:28:32,830
Speaker 1: I think it was Conan that used to put Steve

564
00:28:32,850 --> 00:28:36,030
Speaker 1: Buscemi eyes on different celebrities and see what they look like.

565
00:28:36,590 --> 00:28:39,410
Speaker 1: That's an egregious change. It's not going to work. That

566
00:28:39,450 --> 00:28:43,080
Speaker 1: was great. Dick Cheney and George Bush's eyes look similar

567
00:28:43,120 --> 00:28:45,760
Speaker 1: enough that if you put George Bush's eyes on Dick

568
00:28:45,800 --> 00:28:48,900
Speaker 1: Cheney's face, you're not really going to notice because it's

569
00:28:48,920 --> 00:28:51,979
Speaker 1: not that egregious of a change. And there was a study,

570
00:28:52,020 --> 00:28:56,950
Speaker 1: I think, from 1977. swinging, groovy study. And it basically

571
00:28:57,230 --> 00:29:00,760
Speaker 1: tried to figure out how people register faces in what order.

572
00:29:00,800 --> 00:29:02,640
Speaker 1: And they found that first we look at the eyes,

573
00:29:03,020 --> 00:29:05,020
Speaker 1: then we look at the mouth, and then we look

574
00:29:05,040 --> 00:29:07,420
Speaker 1: at the nose. And those are the things we kind

575
00:29:07,460 --> 00:29:10,950
Speaker 1: of take in. But there's other research, and I think

576
00:29:10,970 --> 00:29:13,830
Speaker 1: this is also kind of like supported by the same study.

577
00:29:14,730 --> 00:29:16,530
Speaker 1: Like when you look at someone's face, you don't go,

578
00:29:16,590 --> 00:29:18,930
Speaker 1: all right, let me see the eyes. You got that.

579
00:29:19,270 --> 00:29:21,670
Speaker 1: Let me see the mouth. Right. And then the nose.

580
00:29:21,930 --> 00:29:23,410
Speaker 2: That makes you a serial killer.

581
00:29:23,870 --> 00:29:27,520
Speaker 1: Right. Right. Oh, I want to kill that person. Right. No,

582
00:29:27,640 --> 00:29:29,580
Speaker 1: like we just look at the whole thing, look at

583
00:29:29,600 --> 00:29:32,000
Speaker 1: the whole package, which is why it's so easy for

584
00:29:32,040 --> 00:29:36,190
Speaker 1: us to overlook differences between even identical twins or doppelgangers

585
00:29:36,490 --> 00:29:39,490
Speaker 1: and make people look closer together than they are because

586
00:29:39,550 --> 00:29:40,940
Speaker 1: our brains are lazy and fat.

587
00:29:41,820 --> 00:29:44,140
Speaker 2: That's right. And it also depends on as far as

588
00:29:44,180 --> 00:29:46,860
Speaker 2: whether you might look like someone about, you know, it

589
00:29:47,080 --> 00:29:49,240
Speaker 2: just sort of makes sense if you look, and it's

590
00:29:49,260 --> 00:29:51,680
Speaker 2: hard to say that someone's average looking, but what we

591
00:29:51,720 --> 00:29:54,160
Speaker 2: mean by that is, and Livia points out is, you know,

592
00:29:54,240 --> 00:29:57,590
Speaker 2: you're not six foot nine and have flaming red hair

593
00:29:57,620 --> 00:30:00,160
Speaker 2: and freckles. Like, Those things will make you very much

594
00:30:00,200 --> 00:30:02,500
Speaker 2: stand out. Whereas if you live in a population of,

595
00:30:03,780 --> 00:30:06,640
Speaker 2: you know, middle Georgia and you, you know, you're a

596
00:30:06,640 --> 00:30:08,680
Speaker 2: little tubby, you've got a beard, you got dark hair,

597
00:30:08,700 --> 00:30:13,010
Speaker 2: and you dress a certain way, then you might look

598
00:30:13,030 --> 00:30:13,730
Speaker 2: like a lot of people.

599
00:30:14,870 --> 00:30:20,790
Speaker 1: Sure. And that actually raises a recent internet sensation. A

600
00:30:21,310 --> 00:30:24,490
Speaker 1: guy named Neil Douglas was, I think, flying to Ireland

601
00:30:24,550 --> 00:30:27,810
Speaker 1: on a Ryanair flight. Yeah. And went to get in

602
00:30:27,850 --> 00:30:30,030
Speaker 1: his seat and saw that there was somebody sitting in

603
00:30:30,130 --> 00:30:32,470
Speaker 1: his seat. And the person looked up and it was

604
00:30:32,530 --> 00:30:33,510
Speaker 1: his doppelganger.

605
00:30:33,570 --> 00:30:34,510
Speaker 2: It was so great.

606
00:30:34,950 --> 00:30:36,830
Speaker 1: I mean, isn't that even more bizarre? Like it wasn't

607
00:30:36,870 --> 00:30:38,350
Speaker 1: in like the back of the plane. Like this guy

608
00:30:38,390 --> 00:30:40,900
Speaker 1: was in his seat, too, which makes it even more eerie.

609
00:30:41,570 --> 00:30:44,010
Speaker 1: And so they snapped a selfie and, like, posted it.

610
00:30:44,070 --> 00:30:47,260
Speaker 1: And everyone just went bonkers because these guys had no

611
00:30:47,360 --> 00:30:50,080
Speaker 1: idea who the other one was. They weren't related. But

612
00:30:50,100 --> 00:30:52,300
Speaker 1: they looked a lot like each other. But there was

613
00:30:52,320 --> 00:30:55,260
Speaker 1: this BBC article that covered it where they basically said, OK,

614
00:30:55,280 --> 00:30:57,440
Speaker 1: but let's really stop and think about this. That's amazing.

615
00:30:57,860 --> 00:30:59,640
Speaker 1: And the fact that he was in the other guy's

616
00:30:59,660 --> 00:31:03,200
Speaker 1: seat is even more amazing. But how unlikely is it

617
00:31:03,220 --> 00:31:06,320
Speaker 1: that these two, you know— were on the same plane.

618
00:31:06,820 --> 00:31:09,040
Speaker 1: And they figured out that based on a number of

619
00:31:09,100 --> 00:31:12,710
Speaker 1: different factors, like the hair, the face, the beard, the nose,

620
00:31:13,350 --> 00:31:17,350
Speaker 1: I think the brown eyes, that there's something like maybe

621
00:31:17,350 --> 00:31:22,190
Speaker 1: 80,000 people walking around out there that look just like them, essentially.

622
00:31:22,290 --> 00:31:24,150
Speaker 2: Yeah, 74,000. That's a lot of people.

623
00:31:24,820 --> 00:31:27,600
Speaker 1: It is a lot of people that look essentially like

624
00:31:27,700 --> 00:31:30,380
Speaker 1: those two enough that the internet would go crazy if

625
00:31:30,400 --> 00:31:32,140
Speaker 1: you put two of them together and said, these two

626
00:31:32,180 --> 00:31:35,380
Speaker 1: are strangers and they just met. And posted a picture

627
00:31:35,460 --> 00:31:35,620
Speaker 1: of it.

628
00:31:36,100 --> 00:31:39,400
Speaker 2: It was a pretty fun picture because they're so delighted, clearly,

629
00:31:40,120 --> 00:31:43,320
Speaker 2: with seeing each other. And then another guy, there's a

630
00:31:43,780 --> 00:31:46,740
Speaker 2: triple picture somewhere, too, of another guy that was like, hey,

631
00:31:46,820 --> 00:31:50,120
Speaker 2: I'm one of those 74,000. And they put all three

632
00:31:50,160 --> 00:31:52,770
Speaker 2: of them close to each other and they look pretty similar.

633
00:31:53,880 --> 00:31:58,310
Speaker 2: The one sort of difference here is in height is

634
00:31:58,470 --> 00:32:01,400
Speaker 2: one of the dudes was 5'7, the other was 6'3,

635
00:32:01,850 --> 00:32:05,450
Speaker 2: because there's other pictures of them. out drinking together in Ireland.

636
00:32:05,470 --> 00:32:07,210
Speaker 2: I guess they kind of hung out and stuff, which

637
00:32:07,280 --> 00:32:07,650
Speaker 2: is great.

638
00:32:08,040 --> 00:32:10,060
Speaker 1: Well, I read they kept running into each other and

639
00:32:10,100 --> 00:32:12,680
Speaker 1: finally were like, okay, let's go get a pint together.

640
00:32:13,160 --> 00:32:15,400
Speaker 1: Like they were at the same hotel and like they

641
00:32:15,420 --> 00:32:17,240
Speaker 1: kept running into one another. So they went with it.

642
00:32:17,520 --> 00:32:21,040
Speaker 2: See, if you believe in like weird fate or intervention,

643
00:32:21,080 --> 00:32:24,110
Speaker 2: you might wonder if like, maybe I should give some

644
00:32:24,130 --> 00:32:25,890
Speaker 2: thought to what this means. Like, am I supposed to

645
00:32:26,570 --> 00:32:29,010
Speaker 2: do something special with this person? Right.

646
00:32:29,350 --> 00:32:33,190
Speaker 1: If the movie Serendipity with John Cusack really spoke to you.

647
00:32:34,310 --> 00:32:36,470
Speaker 2: I never even saw that. You mentioned that movie a lot.

648
00:32:36,930 --> 00:32:38,570
Speaker 2: What happened in that movie?

649
00:32:38,630 --> 00:32:39,950
Speaker 1: I don't want to spoil it for anybody.

650
00:32:39,970 --> 00:32:43,340
Speaker 2: Was there a doppelganger? No. Oh, okay.

651
00:32:43,600 --> 00:32:46,100
Speaker 1: But they kept running into one another, and it just

652
00:32:46,260 --> 00:32:46,920
Speaker 1: kind of was like.

653
00:32:47,040 --> 00:32:49,680
Speaker 2: Oh, it's just the chance of true love over and over.

654
00:32:49,700 --> 00:32:51,420
Speaker 1: It was a good movie, okay?

655
00:32:51,440 --> 00:32:54,340
Speaker 2: Didn't they go to some ice cream shop in New York?

656
00:32:54,380 --> 00:32:55,180
Speaker 2: Famous ice cream shop?

657
00:32:55,720 --> 00:32:59,840
Speaker 1: I don't remember that. I think you're talking about Midnight Cowboy. Okay.

658
00:32:59,940 --> 00:33:02,950
Speaker 2: Yeah, that's right. Let's go get some ice cream.

659
00:33:04,530 --> 00:33:08,710
Speaker 1: That was a great Jon Voight, Chuck. There was another

660
00:33:08,820 --> 00:33:11,540
Speaker 1: one that came along recently, too, that I think is

661
00:33:11,580 --> 00:33:14,240
Speaker 1: even better than the other one. There's a dude named

662
00:33:14,300 --> 00:33:17,600
Speaker 1: Sean McArdle who is hanging around a pool in Vegas.

663
00:33:18,320 --> 00:33:22,830
Speaker 1: And his double, Chuck, just look it up, Sean McArdle doppelganger.

664
00:33:23,460 --> 00:33:25,740
Speaker 1: His double happened to be floating along in the same

665
00:33:25,780 --> 00:33:28,610
Speaker 1: pool wearing the exact same glasses. Apparently, they were the

666
00:33:28,740 --> 00:33:31,410
Speaker 1: only two people at the pool wearing glasses. Not sunglasses,

667
00:33:31,450 --> 00:33:35,230
Speaker 1: but glasses glasses. Okay. The same glasses, same hat. They

668
00:33:35,290 --> 00:33:37,910
Speaker 1: actually do have like the same build and same height

669
00:33:37,950 --> 00:33:41,190
Speaker 1: and everything. They are the spitting image of one another.

670
00:33:41,370 --> 00:33:44,080
Speaker 1: And they were just randomly in the same pool in

671
00:33:44,100 --> 00:33:45,200
Speaker 1: Las Vegas at the same time.

672
00:33:45,240 --> 00:33:46,160
Speaker 2: That's so funny.

673
00:33:46,680 --> 00:33:46,920
Speaker 1: Yeah.

674
00:33:47,320 --> 00:33:49,860
Speaker 2: I think my first thought would be like, what kind

675
00:33:49,880 --> 00:33:51,580
Speaker 2: of crimes can we commit? Yeah.

676
00:33:52,220 --> 00:33:54,180
Speaker 1: Well, yeah. We'll get to that in a different episode.

677
00:33:54,200 --> 00:33:54,940
Speaker 2: Let's rob a bank.

678
00:33:55,960 --> 00:33:57,990
Speaker 1: Mm-hmm. That's my first thought.

679
00:33:58,090 --> 00:34:01,430
Speaker 2: That's terrible. We're just kidding. Are we due for another break?

680
00:34:01,470 --> 00:34:01,710
Speaker 1: Yeah?

681
00:34:01,750 --> 00:34:02,270
Speaker 2: Or should we wait?

682
00:34:02,290 --> 00:34:03,969
Speaker 1: I think so. Yeah, let's take a break. All right.

683
00:34:04,030 --> 00:34:06,030
Speaker 2: We'll take a break and come back and sort this

684
00:34:06,090 --> 00:34:06,350
Speaker 2: all out.

685
00:34:25,739 --> 00:34:28,700
Speaker 1: So one of the reasons I think, Chuck, that we

686
00:34:29,300 --> 00:34:33,290
Speaker 1: have like a propensity to notice doppelgangers and also just

687
00:34:33,310 --> 00:34:37,390
Speaker 1: be just crazed by them is that we are really,

688
00:34:37,450 --> 00:34:41,570
Speaker 1: really good at picking out faces. So good, in fact,

689
00:34:41,610 --> 00:34:44,049
Speaker 1: that we have a region of our brain that it's

690
00:34:44,070 --> 00:34:51,330
Speaker 1: starting to become clear is intended exclusively for seeing faces, right?

691
00:34:51,840 --> 00:34:54,890
Speaker 1: And that actually bucks the current trend of understanding with

692
00:34:54,930 --> 00:34:57,730
Speaker 1: neuroscience where different parts of the brain get recruited to

693
00:34:57,790 --> 00:35:01,049
Speaker 1: kind of specialize in different things from sensory input over time.

694
00:35:02,150 --> 00:35:04,630
Speaker 1: You put a lot of sound in one part of

695
00:35:04,670 --> 00:35:05,830
Speaker 1: the brain, it's going to be like, all right, fine,

696
00:35:05,870 --> 00:35:09,190
Speaker 1: I'll handle sound from now on. This, it seems you're

697
00:35:09,370 --> 00:35:13,760
Speaker 1: born with. this ability to recognize faces. The region's called

698
00:35:13,780 --> 00:35:16,990
Speaker 1: the fusiform gyrus. And they've done some really interesting studies

699
00:35:17,050 --> 00:35:18,690
Speaker 1: about it. And I think we should actually do an

700
00:35:18,770 --> 00:35:23,310
Speaker 1: entire episode on the fusiform gyrus and face blindness.

701
00:35:23,830 --> 00:35:24,470
Speaker 2: Hubba hubba.

702
00:35:25,190 --> 00:35:28,090
Speaker 1: I know. Can you tell I'm hot?

703
00:35:28,890 --> 00:35:31,790
Speaker 2: I'm all about it. Because this one experiment is something

704
00:35:31,810 --> 00:35:34,810
Speaker 2: I would actually like to do. It's pretty freaky. They

705
00:35:34,830 --> 00:35:38,830
Speaker 2: got people in a room. They gave them a mild

706
00:35:38,930 --> 00:35:43,319
Speaker 2: electric current. straight to the fusiform gyrus. Isn't that right?

707
00:35:44,000 --> 00:35:44,160
Speaker 1: Yep.

708
00:35:44,739 --> 00:35:47,859
Speaker 2: And they said, you know, we're going to do this

709
00:35:47,920 --> 00:35:49,460
Speaker 2: and just look at me and tell me what you see.

710
00:35:50,160 --> 00:35:55,009
Speaker 2: And basically they shapeshifted in front of their very face

711
00:35:55,590 --> 00:35:59,410
Speaker 2: when they gave them this electrical stimulation. So it changed

712
00:35:59,469 --> 00:36:03,490
Speaker 2: their perception of what they look like. It didn't change

713
00:36:03,530 --> 00:36:06,450
Speaker 2: their height or weight or their clothing or their skin

714
00:36:06,489 --> 00:36:10,560
Speaker 2: color or anything like that. But it sounds like it literally,

715
00:36:11,239 --> 00:36:14,120
Speaker 2: in their own perception at least, transformed what their face

716
00:36:14,160 --> 00:36:16,490
Speaker 2: looked like, which has got to be really weird.

717
00:36:17,190 --> 00:36:21,370
Speaker 1: Yeah. Oh, man, I can't imagine seeing that, especially while

718
00:36:21,410 --> 00:36:24,509
Speaker 1: you're undergoing open brain surgery and you're conscious. And then

719
00:36:24,530 --> 00:36:26,600
Speaker 1: they start messing with parts of it, right?

720
00:36:26,700 --> 00:36:26,919
Speaker 2: Right.

721
00:36:27,600 --> 00:36:30,160
Speaker 1: There was a follow-up study, too, that I find just

722
00:36:30,219 --> 00:36:34,070
Speaker 1: even more fascinating. They recruited some participants who were blind

723
00:36:34,110 --> 00:36:38,040
Speaker 1: from birth, so they've never seen a human face ever

724
00:36:38,140 --> 00:36:41,650
Speaker 1: with their eyes. And they gave them— they put them in

725
00:36:41,670 --> 00:36:45,270
Speaker 1: an MRI, and they gave them models of, like, 3D

726
00:36:45,350 --> 00:36:48,700
Speaker 1: models of faces and had them rub the faces, and

727
00:36:48,739 --> 00:36:51,040
Speaker 1: the fusiform gyrus lit up. I love that. So, like,

728
00:36:51,239 --> 00:36:54,779
Speaker 1: it is strictly for faces. They also— they were like, okay, well,

729
00:36:54,820 --> 00:36:57,480
Speaker 1: maybe it's just, you know, it's for other things, too.

730
00:36:57,739 --> 00:37:01,140
Speaker 1: They gave them 3D models of cubes and other stuff

731
00:37:01,520 --> 00:37:04,859
Speaker 1: that weren't faces. Nothing happened in the fusiform gyrus. So

732
00:37:05,180 --> 00:37:07,290
Speaker 1: we have a place in the brain that's strictly for

733
00:37:07,350 --> 00:37:11,549
Speaker 1: recognizing faces. And I think that's probably why we can

734
00:37:11,630 --> 00:37:15,230
Speaker 1: pick out doppelgangers so easily, because our brains are kind

735
00:37:15,270 --> 00:37:18,219
Speaker 1: of attuned to look for faces everywhere. Yeah.

736
00:37:18,340 --> 00:37:20,279
Speaker 2: And, you know, if you're thinking this might be, like

737
00:37:20,340 --> 00:37:22,920
Speaker 2: I mentioned, committing a crime sort of in passing, but

738
00:37:23,480 --> 00:37:25,500
Speaker 2: if you think this could be a big problem as

739
00:37:25,540 --> 00:37:31,410
Speaker 2: far as the advent and more widespread use of facial recognition, AI,

740
00:37:31,489 --> 00:37:34,980
Speaker 2: especially with CCTV and trying to catch bad people doing

741
00:37:35,020 --> 00:37:37,940
Speaker 2: bad things. And we've talked about this in the past on,

742
00:37:37,960 --> 00:37:41,960
Speaker 2: I think, a couple of different episodes. But that stuff

743
00:37:42,020 --> 00:37:45,740
Speaker 2: works okay, but it doesn't work great with dark skin.

744
00:37:45,760 --> 00:37:49,820
Speaker 2: It doesn't work great for people of color, black people,

745
00:37:49,980 --> 00:37:53,680
Speaker 2: Asian people. It gets confused a lot. And this is already,

746
00:37:55,090 --> 00:37:59,110
Speaker 2: you know, people have been wrongly imprisoned because facial recognition

747
00:37:59,250 --> 00:38:00,610
Speaker 2: AI has made a mistake.

748
00:38:01,310 --> 00:38:04,680
Speaker 1: Yeah. There's one guy as recently as 2019 from New

749
00:38:04,700 --> 00:38:09,460
Speaker 1: Jersey named Najir Parks, and he had to end up

750
00:38:09,560 --> 00:38:13,310
Speaker 1: spending five grand to defend himself, spent, I think, 20

751
00:38:13,310 --> 00:38:18,050
Speaker 1: days in jail possibly because he was accused or I

752
00:38:18,070 --> 00:38:21,920
Speaker 1: guess an AI facial recognition system accused him of shoplifting

753
00:38:22,040 --> 00:38:24,200
Speaker 1: and then trying to hit a police officer with his car.

754
00:38:25,050 --> 00:38:28,390
Speaker 1: And it turns out he'd been 30 miles away, but

755
00:38:28,530 --> 00:38:31,090
Speaker 1: he had been misidentified by this. And I think in

756
00:38:31,150 --> 00:38:34,010
Speaker 1: our episode, we talked about this at length, that facial

757
00:38:34,070 --> 00:38:38,660
Speaker 1: recognition systems are really good at recognizing white men's faces.

758
00:38:38,940 --> 00:38:39,160
Speaker 2: Right.

759
00:38:39,700 --> 00:38:41,840
Speaker 1: But other people, it just starts to go down and

760
00:38:41,880 --> 00:38:43,879
Speaker 1: down until you get to the point where a black

761
00:38:43,920 --> 00:38:48,420
Speaker 1: woman's face is 40 times likelier to come up with

762
00:38:48,460 --> 00:38:52,830
Speaker 1: a false positive than a light-skinned man. Right. And apparently

763
00:38:52,850 --> 00:38:55,410
Speaker 1: it's just the data that they're trained on. They just said,

764
00:38:55,750 --> 00:38:57,910
Speaker 1: let's just give them photos of white men and I'm

765
00:38:57,969 --> 00:39:00,140
Speaker 1: sure it'll be fine. And it just hasn't been fine.

766
00:39:00,540 --> 00:39:02,060
Speaker 2: Well, I mean, that's one of the problems with it

767
00:39:02,100 --> 00:39:04,420
Speaker 2: all is it's just working from a database. So it's

768
00:39:04,460 --> 00:39:07,920
Speaker 2: not like it can take a face and find someone

769
00:39:07,960 --> 00:39:10,520
Speaker 2: just somewhere on planet Earth. Like you got to be

770
00:39:10,540 --> 00:39:11,500
Speaker 2: on a database somehow.

771
00:39:12,600 --> 00:39:16,450
Speaker 1: Right. But I'm saying the AI is trained on photos

772
00:39:16,890 --> 00:39:19,169
Speaker 1: and it comes to understand a face is a face

773
00:39:19,210 --> 00:39:22,140
Speaker 1: based on the photos that it's trained on. And I

774
00:39:22,180 --> 00:39:26,610
Speaker 1: guess they didn't select enough photos of people of color,

775
00:39:26,830 --> 00:39:29,270
Speaker 1: photos of women. I don't know why, but it seems

776
00:39:29,310 --> 00:39:32,000
Speaker 1: like they got really good at white men because they

777
00:39:32,040 --> 00:39:34,380
Speaker 1: were given more photos of white men. That's how I

778
00:39:34,420 --> 00:39:35,120
Speaker 1: take it. Yeah.

779
00:39:35,219 --> 00:39:36,100
Speaker 2: No, that's how I took it too.

780
00:39:36,980 --> 00:39:39,719
Speaker 1: So Chuck, if let's say that you are a doppelganger

781
00:39:39,760 --> 00:39:45,469
Speaker 1: of somebody, somebody famous like Madonna or Johnny Depp or- Rizzo.

782
00:39:45,690 --> 00:39:50,500
Speaker 1: Let's say Rizzo, too, just to get the kids involved. No, Lizzo.

783
00:39:52,700 --> 00:39:55,060
Speaker 2: I was like, oh, God.

784
00:39:55,560 --> 00:40:00,290
Speaker 1: I was thinking back to Ratso Rizzo, I think. So

785
00:40:00,469 --> 00:40:02,730
Speaker 1: if you're a doppelganger of Lizzo's, what would you do

786
00:40:02,770 --> 00:40:03,550
Speaker 1: with that talent?

787
00:40:04,030 --> 00:40:07,810
Speaker 2: Well, you could go hang out on Hollywood Boulevard near

788
00:40:07,830 --> 00:40:11,300
Speaker 2: Man's Chinese Theater and make some money walking around charging

789
00:40:11,300 --> 00:40:14,690
Speaker 2: $ 5 a picture. You could get employed full time with

790
00:40:14,730 --> 00:40:18,970
Speaker 2: an agency to, you know, get sent out. If someone

791
00:40:18,989 --> 00:40:23,370
Speaker 2: wants Bill Gates at their party and you are a lookalike,

792
00:40:23,450 --> 00:40:25,570
Speaker 2: then you can go to their party and make a

793
00:40:26,530 --> 00:40:27,590
Speaker 2: couple hundred bucks probably.

794
00:40:28,400 --> 00:40:33,020
Speaker 1: Probably. You could also work on movies, apparently. There's lots

795
00:40:33,080 --> 00:40:35,100
Speaker 1: of jobs for you. Yeah.

796
00:40:35,160 --> 00:40:40,660
Speaker 2: I mean, this one is, it's less face, like, When

797
00:40:40,680 --> 00:40:44,060
Speaker 2: you're body doubling someone or photo doubling or standing in,

798
00:40:44,680 --> 00:40:47,020
Speaker 2: kind of in that order, or did I get that wrong?

799
00:40:47,440 --> 00:40:50,620
Speaker 2: Stand in is least important that you actually look like someone,

800
00:40:51,120 --> 00:40:54,710
Speaker 2: but you should be the same size as somebody because

801
00:40:55,150 --> 00:40:58,290
Speaker 2: they're lighting you for camera and stuff like that. Like

802
00:40:58,310 --> 00:41:01,930
Speaker 2: you can't, you know, The Rock can't have a body

803
00:41:01,969 --> 00:41:05,770
Speaker 2: double or a stand in that looks like me. You know?

804
00:41:05,790 --> 00:41:08,540
Speaker 1: I don't know if you wore one of those like

805
00:41:08,560 --> 00:41:09,480
Speaker 1: ripped t-shirts.

806
00:41:09,620 --> 00:41:13,660
Speaker 2: Yeah, right. I can get away with it. But, you know,

807
00:41:14,980 --> 00:41:17,359
Speaker 2: if you do look like a celebrity and you want

808
00:41:17,380 --> 00:41:20,200
Speaker 2: to get work as a photo double, then that certainly

809
00:41:20,239 --> 00:41:22,219
Speaker 2: doesn't hurt if you look like them in the face.

810
00:41:22,340 --> 00:41:25,210
Speaker 2: But body size and skin tone and stuff like that

811
00:41:25,250 --> 00:41:26,950
Speaker 2: is more important as for doubling.

812
00:41:27,670 --> 00:41:30,730
Speaker 1: But the places where it really kind of comes in handy,

813
00:41:30,870 --> 00:41:33,490
Speaker 1: if you really look like the person in the face

814
00:41:33,690 --> 00:41:35,870
Speaker 1: and the build and the hair and the mustache and

815
00:41:35,910 --> 00:41:39,259
Speaker 1: all that, is to serve as like a decoy, a

816
00:41:39,300 --> 00:41:44,339
Speaker 1: political decoy. Because throughout history, the more hated you are,

817
00:41:44,500 --> 00:41:48,600
Speaker 1: either within your country or internationally or both, the more

818
00:41:48,660 --> 00:41:52,120
Speaker 1: you need more than probably one political decoys, which is

819
00:41:52,580 --> 00:41:54,460
Speaker 1: essentially somebody who's saying like, yeah, I look like you,

820
00:41:54,500 --> 00:41:57,220
Speaker 1: I'll go do the boring stuff, but also risk my

821
00:41:57,280 --> 00:41:59,810
Speaker 1: life because somebody might assassinate you at some point.

822
00:42:00,250 --> 00:42:01,850
Speaker 2: Yeah, and I get the sense in some of these

823
00:42:01,890 --> 00:42:03,890
Speaker 2: that it's a little less like, I look like you,

824
00:42:03,930 --> 00:42:07,830
Speaker 2: I'd like to do this, Then Kim Jong-un says, hey,

825
00:42:07,870 --> 00:42:08,730
Speaker 2: you guys, come with me.

826
00:42:09,570 --> 00:42:09,790
Speaker 1: Right.

827
00:42:10,110 --> 00:42:11,950
Speaker 2: You're going to double me now. This is your new job.

828
00:42:12,790 --> 00:42:15,330
Speaker 2: Because he, of course, has been listed as having body

829
00:42:15,370 --> 00:42:19,960
Speaker 2: doubles to the point where there's been speculation fueled about

830
00:42:19,980 --> 00:42:22,160
Speaker 2: whether or not he was sick in the hospital or

831
00:42:22,200 --> 00:42:27,420
Speaker 2: actually dead. Saddam Hussein had a bunch of decoys. They

832
00:42:27,460 --> 00:42:31,410
Speaker 2: made a movie called The Devil's Double about a man

833
00:42:31,469 --> 00:42:37,850
Speaker 2: that was his son's double. Uday. And they made a, uh,

834
00:42:38,770 --> 00:42:40,280
Speaker 2: I meant to see it actually. It looked kind of good,

835
00:42:40,910 --> 00:42:42,160
Speaker 2: but I haven't seen it. That was out in 2011.

836
00:42:42,160 --> 00:42:46,339
Speaker 2: It was called the devil's double and Latif Yahia, um,

837
00:42:46,700 --> 00:42:49,319
Speaker 2: basically spoke out a lot afterward that like I was

838
00:42:49,480 --> 00:42:52,220
Speaker 2: Uday Hussein's double and he was a very bad guy.

839
00:42:52,239 --> 00:42:54,580
Speaker 2: So it was not probably the best job.

840
00:42:55,860 --> 00:43:02,170
Speaker 1: Um, apparently Stalin had at least four, um, doubles. And then,

841
00:43:02,900 --> 00:43:05,280
Speaker 1: This one is amazing to me. There is a British

842
00:43:05,320 --> 00:43:10,060
Speaker 1: general named Bernard Montgomery, and he hired a double by

843
00:43:10,100 --> 00:43:13,520
Speaker 1: the name of M.E. Clifton James. Great name. He was

844
00:43:13,620 --> 00:43:18,230
Speaker 1: Australian and he posed as Bernard Montgomery and went around,

845
00:43:18,250 --> 00:43:22,870
Speaker 1: I would guess, Europe talking about allied plans in the

846
00:43:22,910 --> 00:43:25,370
Speaker 1: hopes that like his his plans would be or that

847
00:43:25,570 --> 00:43:28,890
Speaker 1: the talks would be overheard by German spies. And the plans,

848
00:43:28,950 --> 00:43:31,680
Speaker 1: of course, were fake. And they were meant to serve

849
00:43:31,880 --> 00:43:35,200
Speaker 1: as a decoy or distraction for the Nazis. And they

850
00:43:35,260 --> 00:43:38,870
Speaker 1: think that possibly had an influence on the storming of Normandy.

851
00:43:39,390 --> 00:43:41,969
Speaker 2: That's pretty cool. There's a movie in there somewhere, or

852
00:43:41,989 --> 00:43:42,569
Speaker 2: at least a scene.

853
00:43:43,950 --> 00:43:46,710
Speaker 1: Surely. Easily. They could have thrown it in as bonus

854
00:43:46,750 --> 00:43:49,530
Speaker 1: material on the Operation Mincemeat movie, you know? Yeah.

855
00:43:49,760 --> 00:43:50,319
Speaker 2: Did you see that?

856
00:43:51,080 --> 00:43:55,799
Speaker 1: Yeah. Was it good? I liked our episode better, but yes,

857
00:43:56,600 --> 00:43:59,799
Speaker 1: it was good. It was a fine movie, sure.

858
00:43:59,820 --> 00:44:00,980
Speaker 2: Yeah, I need to check that out.

859
00:44:01,780 --> 00:44:03,680
Speaker 1: I saw one recently. This one has nothing to do

860
00:44:03,719 --> 00:44:07,400
Speaker 1: with anything, but it was a five-part series in Swedish

861
00:44:07,960 --> 00:44:13,120
Speaker 1: about the assassination of their prime minister in 1986, Olaf Palme.

862
00:44:13,920 --> 00:44:18,700
Speaker 1: And it is really fascinating because they think that one

863
00:44:18,719 --> 00:44:22,910
Speaker 1: of the main witnesses was actually the assassin. And they

864
00:44:22,969 --> 00:44:24,989
Speaker 1: kind of recreate how it would have happened and they

865
00:44:25,010 --> 00:44:27,770
Speaker 1: dive into his life. It's really, really good. It's worth watching.

866
00:44:27,810 --> 00:44:29,890
Speaker 1: I think that's on Netflix too. By the way, I

867
00:44:29,930 --> 00:44:34,549
Speaker 1: don't work for Netflix. Right. You got anything else? I

868
00:44:34,570 --> 00:44:39,460
Speaker 1: got nothing else. That's Doppelgangers then, everybody. I hope you

869
00:44:39,480 --> 00:44:42,060
Speaker 1: enjoyed this one. I sure did. I know Chuck did.

870
00:44:42,080 --> 00:44:43,880
Speaker 1: And if you want to know more about Doppelgangers, you

871
00:44:43,900 --> 00:44:45,940
Speaker 1: can start searching on the internet. And there's a lot

872
00:44:45,980 --> 00:44:49,339
Speaker 1: of cool posts from social media of people meeting their

873
00:44:49,360 --> 00:44:52,359
Speaker 1: Doppelgangers that will just knock your socks off. And since

874
00:44:52,420 --> 00:44:54,440
Speaker 1: I said that, of course, it's time for listener mail.

875
00:44:57,040 --> 00:45:01,440
Speaker 2: This is in response to the Kissin' Cousins episode from

876
00:45:02,380 --> 00:45:04,860
Speaker 2: a friend from the Ashkenazi Jewish community.

877
00:45:05,460 --> 00:45:10,200
Speaker 1: I've got 0.2% Ashkenazi in me. Oh, yeah? I also

878
00:45:10,239 --> 00:45:12,160
Speaker 1: have some Neanderthal, too.

879
00:45:12,739 --> 00:45:15,760
Speaker 2: All right. Hey, guys, just listen to the episode Kissin' Cousins.

880
00:45:16,400 --> 00:45:20,120
Speaker 2: I am from an insular Ashkenazi Jewish community. I'm sure

881
00:45:20,160 --> 00:45:22,480
Speaker 2: you understand us being insular makes sense. the gene pool

882
00:45:22,500 --> 00:45:25,520
Speaker 2: that much smaller. We also tend to marry younger than

883
00:45:25,540 --> 00:45:27,259
Speaker 2: the rest of the Western world. Thus, in pretty much

884
00:45:27,300 --> 00:45:30,220
Speaker 2: every high school in our community, we do genetic testing

885
00:45:30,860 --> 00:45:34,560
Speaker 2: for recessive genes for defects or serious illnesses common in

886
00:45:34,680 --> 00:45:38,280
Speaker 2: Ashkenazi Jews. We don't get the results to the test,

887
00:45:38,360 --> 00:45:40,620
Speaker 2: only an ID number. A big part of our culture

888
00:45:40,640 --> 00:45:43,770
Speaker 2: is being set up by matchmakers. Before meeting for the

889
00:45:43,790 --> 00:45:46,630
Speaker 2: first time, both families will call the screening center with

890
00:45:46,650 --> 00:45:48,910
Speaker 2: their ID number and be told if it's safe for

891
00:45:48,930 --> 00:45:50,089
Speaker 2: them to go ahead.

892
00:45:49,770 --> 00:45:52,910
Speaker 1: With the matchmaking. It's amazing. It is.

893
00:45:53,150 --> 00:45:54,950
Speaker 2: I met my current partner on my own, and we

894
00:45:54,989 --> 00:45:56,489
Speaker 2: decided we were ready to settle down. It was a

895
00:45:56,530 --> 00:45:59,770
Speaker 2: little scarier to call and see if we were compatible

896
00:45:59,810 --> 00:46:02,489
Speaker 2: because we'd already decided we wanted to be together. Can

897
00:46:02,930 --> 00:46:03,569
Speaker 2: you imagine that?

898
00:46:04,350 --> 00:46:06,590
Speaker 1: Yeah, no. I'll bet that was pretty nerve-wracking.

899
00:46:07,090 --> 00:46:08,250
Speaker 2: The call was more to see if we need to

900
00:46:08,290 --> 00:46:10,989
Speaker 2: speak to a geneticist before having children than to see

901
00:46:11,030 --> 00:46:12,690
Speaker 2: if we should get together for the first time at all.

902
00:46:13,170 --> 00:46:16,430
Speaker 2: But thankfully, we were good to go. Separately, I had

903
00:46:16,469 --> 00:46:18,669
Speaker 2: a close friend whose husband passed away a few years ago.

904
00:46:19,190 --> 00:46:22,150
Speaker 2: She had the option of marrying her brother-in-law. Remember, we

905
00:46:22,170 --> 00:46:25,220
Speaker 2: talked about that in the episode, which she had no

906
00:46:25,280 --> 00:46:29,000
Speaker 2: interest in doing. She did take a pair of his shoes, though,

907
00:46:29,120 --> 00:46:34,120
Speaker 2: and spit on them rather than him. So that part

908
00:46:34,140 --> 00:46:36,160
Speaker 2: of the Old Testament is kind of still kept in

909
00:46:36,200 --> 00:46:39,000
Speaker 2: some places. And of course, I'm not speaking for all

910
00:46:39,100 --> 00:46:41,690
Speaker 2: Ashkenazi Jews, only what I see in my community. Just

911
00:46:41,710 --> 00:46:43,550
Speaker 2: thought you might be interested to know how a group

912
00:46:44,110 --> 00:46:46,910
Speaker 2: is trying to keep tradition while staying safe and up

913
00:46:46,930 --> 00:46:50,719
Speaker 2: to date with science in the 21st century. and this

914
00:46:50,780 --> 00:46:53,380
Speaker 2: is a lovely sign-off here, wishing you the strength to

915
00:46:53,420 --> 00:46:55,660
Speaker 2: continue for a long time coming.

916
00:46:56,480 --> 00:46:56,760
Speaker 1: Nice.

917
00:46:56,940 --> 00:46:57,960
Speaker 2: And that's from Elkie.

918
00:46:58,680 --> 00:47:00,700
Speaker 1: Thanks a lot, Elkie. That was a great email. I

919
00:47:00,719 --> 00:47:03,200
Speaker 1: appreciate that big time. And if you want to be

920
00:47:03,239 --> 00:47:06,350
Speaker 1: like Elkie and send us a super fascinating email, you

921
00:47:06,370 --> 00:47:08,730
Speaker 1: can do that too. All you have to do is

922
00:47:08,790 --> 00:47:16,770
Speaker 1: write to stuffpodcast at iheartradio.com, press send, and see what happens. Right, Chuck?

923
00:47:17,110 --> 00:47:23,439
Speaker 2: That's right. Stuff You Should Know is a production of iHeartRadio.

924
00:47:23,940 --> 00:47:28,180
Speaker 2: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

925
00:47:28,340 --> 00:47:30,120
Speaker 2: or wherever you listen to your favorite shows.