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Speaker 1: Hello the Internet, and welcome to this episode of the

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Speaker 1: Weekly Zeitgeist. These are some of our favorite segments from

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Speaker 1: this week, all edited together into one NonStop infotainment laugh stravaganza. Yeah, So,

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Speaker 1: without further ado, here is the Weekly Zeitgeist. Miles. It

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Speaker 1: is a full it is we needed. We needed this

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Speaker 1: for our system. We've been having all these damn experts

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Speaker 1: on lately. We needed intelligent guests, thoughtful guests, intelligent thoughtful,

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Speaker 1: intelligent guest. We needed a pure chaos episode. We are

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Speaker 1: thrilled to be joined in our thirty by comedian, a writer,

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Speaker 1: an actor stand up albums with Blake album Stuffed Boy

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Speaker 1: Live from the Pandemic All debut number one on iTunes.

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Speaker 2: Samson.

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Speaker 1: His album Twelve Years of Voicemails from tug Last to

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Speaker 1: Blake Wexler charted on Billboard. Please welcome, the Hilarious, the Chaotic.

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Speaker 1: He's riding a recumbent bike in short shorts and his

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Speaker 1: plumpers are on full display.

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Speaker 2: It's Blake. This is Blake Wexler.

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Speaker 3: A k A.

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Speaker 4: I have a special, a stand up comedy special, but

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Speaker 4: geist my peak, I have two plumpers.

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Speaker 5: What the hell am I doing here? I am a wexpert.

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Speaker 5: I'm a wexpert, baby. This is i Heeart Media at

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Speaker 5: its finest, Blake wexerm joined by Jack and Miles.

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Speaker 6: Thank you so much for having me.

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Speaker 2: Show now too.

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Speaker 5: Yes, I have data. We're gonna dive into the data.

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Speaker 5: We're gonna talk to people on the ground and we

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Speaker 5: are gonna say that they're long.

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Speaker 7: On that.

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Speaker 2: We have data that we're diving into. Today.

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Speaker 1: We're gonna bring up the electoral map here and I'm

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Speaker 1: going to show you which you know groups are reporting

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Speaker 1: as blake.

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Speaker 2: How are you doing?

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Speaker 1: Man?

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Speaker 5: This is the Electoral Community College. That's right, it's not

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Speaker 5: it's a two year associates. I'm doing great, guys, Thank

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Speaker 5: you so much for.

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Speaker 2: Electoral Online College.

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Speaker 5: Phoenix Phoenix exactly. This phoenix will not rise from the ashes.

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Speaker 5: It has burnt itself to death. But yeah, no, I'm

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Speaker 5: so psyched to be here as usual.

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Speaker 2: Great. You gotta start a little late.

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Speaker 1: We're busy integrating the Damian Lillard trade into our into

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Speaker 1: our beings, into our personhood.

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Speaker 2: Didn't see that coming.

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Speaker 6: It's hard even gonna play for them, didn't he He

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Speaker 6: only wanted to go to Miami, right, And now he's

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Speaker 6: going to the Miami of the North Milwaukee milwa. Yeah.

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Speaker 5: I want a body of water up there, right, one

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Speaker 5: of those one of those great lakes.

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Speaker 6: It's like, I'll be yeah that.

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Speaker 2: Yeah, He's like, you're not going to Miami. But the

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Speaker 2: team does have M and I in the first two

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Speaker 2: letters of this yeah, so you.

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Speaker 6: Got to read it. You got to read the whole word.

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Speaker 1: They paid attention to the opening sound and the ending

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Speaker 1: sound of the word city that he was asking to

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Speaker 1: be traded to, nothing in between. May me, I think

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Speaker 1: this trade is really fun. I know this isn't our

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Speaker 1: NBA podcast, but I think it's fun. It brings Damian Lillard,

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Speaker 1: puts him on a team that gives him a real

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Speaker 1: chance of winning a title. It makes Giannis relevant again.

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Speaker 1: Jannis is probably our most fun and lovable celebrity basketball

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Speaker 1: player at the moment. So I'm I'm.

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Speaker 6: Here for James Harden. I think James Harden is the

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Speaker 6: most fun you know, la one.

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Speaker 1: I think we can take our self hating Sixers fandom

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Speaker 1: off the off the mic, but my Miles suggested a

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Speaker 1: good new new line for for us when people ask

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Speaker 1: if we're Sixer fans, that we we only recognize one

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Speaker 1: team of sixers, Miles, you wanna tell him what it is?

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Speaker 2: The January six Ers.

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Speaker 1: Yeah, that's the only that's the only sixers I recognize,

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Speaker 1: and I'm not a fan of them, and I don't

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Speaker 1: even know about another another group, Carmen. We do like

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Speaker 1: to ask our guests, Yeah, what is something from your

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Speaker 1: search history?

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Speaker 2: Well, I was.

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Speaker 8: Gonna say your first topic is a good one, but

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Speaker 8: we'll go back to the no fault divorce. But I'm

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Speaker 8: gonna tell you know what, And this is not even

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Speaker 8: a plug or anything, but Carrie Washington's new memoir. She's

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Speaker 8: just doing interviews and it just came out. And here's

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Speaker 8: the thing. I had to google it because I'm reading

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Speaker 8: everything about it. I haven't gotten the book yet. But

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Speaker 8: she also, like me, found out that her biological father

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Speaker 8: was not who she grew up with.

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Speaker 2: Oh oh well, and that.

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Speaker 8: Was kept from her whole life until she got booked

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Speaker 8: on you know, Henry Lewis Kate's Junior skipped Junior's show

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Speaker 8: on PBS.

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Speaker 2: Oh for real.

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Speaker 8: Yeah, and then I'm not telling you anything that's not

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Speaker 8: out there. But then they contacted her or she was

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Speaker 8: all excited and her parents were excited until they said, well,

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Speaker 8: you need to do a DNA test, and then her

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Speaker 8: parents were like, oh, excuse me, carry.

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Speaker 2: He's talk to you. Take a second, you have something

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Speaker 2: to tell you. That is the premise of the show.

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Speaker 2: So yeah, yeah, so.

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Speaker 8: And it's funny because she, I mean, this also happened

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Speaker 8: to me as well, which was what I write about.

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Speaker 8: But what she describes really really well, which I so

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Speaker 8: appreciate because it's not necessarily something people would think about,

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Speaker 8: is she, like me, grew up her whole lives knowing

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Speaker 8: something was wrong, something was off, almost like feeling like

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Speaker 8: something was wrong with our bodies, like we didn't things

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Speaker 8: didn't match up and that and it was really uncomfortable.

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Speaker 8: Is very like a subconscious thing, and it's lots of

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Speaker 8: anxiety and perfectionism, all this sort of stuff just happens

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Speaker 8: and you don't even know why, right, and then this

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Speaker 8: all this stuff comes out and you're like, oh, my

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Speaker 8: parents have and keeping the truth from me my whole life,

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Speaker 8: and you imagine living with them and then then they

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Speaker 8: know and every time they look at you. So yeah,

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Speaker 8: all to say, people, please talk to your children, tell

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Speaker 8: them the true, Please tell them the truth. But that

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Speaker 8: was my last that was my last.

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Speaker 2: And Google search.

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Speaker 1: Are you like, how radical is the radical honesty with

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Speaker 1: the kids? Are you like a no Santa Claus thing?

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Speaker 1: Or are you like, how how far are we going?

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Speaker 1: Are we saying like straight up? Look Santa Clauss because

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Speaker 1: their parents are liars.

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Speaker 8: First of all, my child figured it out before I

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Speaker 8: even had to say. She's like, she's that kind of kid.

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Speaker 8: She's like, she's my kid. She was like, so I

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Speaker 8: know who you are. I know who you are at hole,

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Speaker 8: so you could stop it, but please keep writing it

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Speaker 8: all my presence because it's cute. But I think that

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Speaker 8: you know, like she she mentions as well, Carrie as like,

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Speaker 8: when the secret is another human being? Right, you gotta talk, right,

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Speaker 8: you're creating Basically, I say to people it's like look,

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Speaker 8: and I get asked this a lot when I give talks.

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Speaker 8: It's like, well, well, what is it?

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Speaker 7: Is it?

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Speaker 8: Entitlement? Like, what is it that makes you feeling it? Look,

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Speaker 8: a secret is yours as long as it's yours. But

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Speaker 8: when you create another human being that is no longer yours,

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Speaker 8: that is actually a person, so it's no longer a secret.

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Speaker 8: It's a human And we can say as parents like well,

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Speaker 8: you're my kid. Listen, kids do not please everyone.

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Speaker 9: Get this through your head.

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Speaker 8: Kids not belong to you. They are not belongings. They

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Speaker 8: are people, so you must treat them as such. And

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Speaker 8: they're separate humans and you need to treat them that way.

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Speaker 8: And part of that is being very honest about how

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Speaker 8: they came about and what's going on in your life.

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Speaker 8: But I will tell you a funny thing is I

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Speaker 8: don't my daughter and I we talk a lot, but

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Speaker 8: one of my nieces said something about my first husband.

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Speaker 8: I can't remember how old my daughter was, but she

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Speaker 8: was like maybe ten, and she was like, you've been

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Speaker 8: married before.

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Speaker 2: Like she was like, you kept this from me, And.

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Speaker 8: I was like, I was like, girl, I just didn't

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Speaker 8: think about it. Tell you, like, it was a long

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Speaker 8: time ago, and it's so that's not a secret. It

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Speaker 8: was just I just didn't think about it.

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Speaker 2: But it was. It was hilarious, right right. I think

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Speaker 2: if like to your point, like, if a secret alters

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Speaker 2: someone's total understanding of themselves and the world they live in,

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Speaker 2: it's like, no, then that's not a secret. That's a

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Speaker 2: potential like psychic bomb that you have to diffuse as

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Speaker 2: quickly as possible.

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Speaker 8: Yeah, tremendous bomb and it affects your whole likely it

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Speaker 8: literally affected me from the time I was a kid,

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Speaker 8: like I remember it. It's definitely there. And you can't

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Speaker 8: subconsciously carrying a secret like that and then living with

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Speaker 8: your secret, right, Like what does that do to you

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Speaker 8: and your relationship with that person? And my thing is like, look,

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Speaker 8: you keep secrets in lines from people that you love.

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Speaker 8: That creates such a big distance that person can't be

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Speaker 8: your real friend or your real like love or you're

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Speaker 8: real whatever, like where it's just a huge distance that

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Speaker 8: separates you from people you love.

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Speaker 1: So right, gosh, come clean. I have to have the

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Speaker 1: courage to come clean. Doctor mcinnerney, what is something you

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Speaker 1: think is overrated?

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Speaker 7: I feel like I'm not gonna win myself any friends

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Speaker 7: will be like how to let lose friends and stop

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Speaker 7: influencing people. But all the Disney live action movies, I've

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Speaker 7: hit a stage where I'm like, no more live actions,

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Speaker 7: Like The Little Mermaid is beautiful. I love the original Cinderella,

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Speaker 7: but you know I don't. I don't want to see

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Speaker 7: more and more of like the same movies again, Like

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Speaker 7: it makes me a bit sad and like what other

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Speaker 7: stories could be told if it weren't like always making

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Speaker 7: these live actions. But you know, maybe I'll be proven

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Speaker 7: like totally wrong and the next one will be amazing.

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Speaker 9: But I'm ready for something else.

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Speaker 2: Just yeah. Based on everyone's first like sort of reaction,

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Speaker 2: like oh, you saw how it was and you like,

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Speaker 2: yeah it. No one was like it was so good,

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Speaker 2: I'm gonna see it nine times. Everyone just kind of

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Speaker 2: I think they're they have to reconcile like their love

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Speaker 2: for the original source material with that and not fully

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Speaker 2: be like I didn't like it. They're like yeah, I

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Speaker 2: mean it's it's it's interesting. You know, It's like their

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Speaker 2: voice just goes up.

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Speaker 1: Yeah.

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Speaker 2: I was like glad, I spent my afternoon going to that.

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Speaker 2: I think that's right.

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Speaker 1: I've never been more confident in a prediction about the

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Speaker 1: future of a like sub genre of movies than in

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Speaker 1: saying that they're not going to suddenly figure something out

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Speaker 1: about the Disney live action reskins of the animations, Like

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Speaker 1: we we know what the original cartoons look like and

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Speaker 1: what happens in them.

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Speaker 2: We know what is possible here, right.

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Speaker 1: I can't I can't imagine a version like what what

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Speaker 1: they're gonna pull out where we're like, oh no, that

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Speaker 1: is not did not see that one coming? Whoa that

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Speaker 1: bear is talking and singing? Hold on, oh they already

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Speaker 1: did that. That was the best one I think was

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Speaker 1: Jungle Book and that was the first one they did,

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Speaker 1: and ever since, I feel like, yeah, been diminishing returns man,

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Speaker 1: although I didn't see The Little Mermaid, so I cannot

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Speaker 1: speak to that one.

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Speaker 2: Is you? What's your favorite genre of film though, doctor

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Speaker 2: carry Ooh?

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Speaker 7: I mean okay, so I really have a soft spot

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Speaker 7: for like old school superhero movies. But my most recent

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Speaker 7: most amazing film I film was the latest into the

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Speaker 7: Spider Verse film, what was it? The Spider Verse? Yeah,

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Speaker 7: and I just I love animation and that film. It's

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Speaker 7: so witty and the visuals are.

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Speaker 9: Extraordinary, just great films.

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Speaker 7: So I oracle about how much I love these films

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Speaker 7: at work and everyone has to deal with me being like, go.

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Speaker 9: Watch it, So anyone listening, go watch it.

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Speaker 2: Amazing favorite favorite Spider character Spider person in the universe.

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Speaker 7: Oh my goodness, I can't remember the name of it.

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Speaker 7: But the Light really like really like dark noir runs

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Speaker 7: for the first one. He's like from like all those

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Speaker 7: like nineteen thirties kind of like right, mystery films, they've

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Speaker 7: all in black and white.

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Speaker 9: I kind of love that.

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Speaker 2: Yeah, yeah, it was it just Spider Man, Noirs, Yeah,

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Speaker 2: I think nor Spider Man.

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Speaker 10: Maybe I don't know, or I'm sorry Spider Man, nor

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Speaker 10: Nor What is when you say old school superhero movies,

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Speaker 10: do you mean like Christopher Reeve's Superman or are you

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Speaker 10: talking about.

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Speaker 2: Like the first Iron Man? The First Iron Man.

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Speaker 9: I think it's almost like less like specific films.

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Speaker 7: I love, like almost films that have that like really

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Speaker 7: cheesy like origin coming of age story, Like you know,

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Speaker 7: for me, it's just really just about like they're discovering

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Speaker 7: themselves and it's such a simple narrative, and like I

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Speaker 7: feel like I should crave more complexity than that, and

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Speaker 7: I should want it to be more nuanced.

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Speaker 9: Stories, but sometimes it's just really satisfying and he wants.

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Speaker 7: This clean cut narrative of this like good guy to

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Speaker 7: beating all these bad guys when I want to relax,

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Speaker 7: I really enjoy that. In my dage, I think about

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Speaker 7: lots of like complexity and new wance and like what

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Speaker 7: we do with the future of society, and so you know, right,

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Speaker 7: sometimes I find it very relaxing just to be like souless.

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Speaker 2: Yeah, you're like, yeah, I love a Joseph Campbell type

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Speaker 2: and flick. Just give me that hero's journey in everything.

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Speaker 9: Okay, Yeah, yeah, it's gonna.

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Speaker 2: Go wrong, challenge your old self to become your new self.

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Speaker 1: Yes, but Doc is an example of what we're facing

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Speaker 1: with AI like in the immediate future.

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Speaker 2: I think we can all agree on that, right, Spiderman two.

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Speaker 2: In a way, you're like, it's giving me the equivalent

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Speaker 2: of like eight arms. In a way, you're like, oh boy,

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Speaker 2: what is something doctor Higgins do you think is underrated? Underrated?

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Speaker 2: Asking people for help?

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Speaker 3: And I know that, like I know that some I

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Speaker 3: listened to the show daily, and I know people have

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Speaker 3: such good responses. But that was the only thing that

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Speaker 3: came to mind for me in terms of underrated. And

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Speaker 3: so that's why I'm on the show. Come to my

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Speaker 3: live show on October eleventh. Yeah, well the Saints come

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Speaker 3: listen to us babbel about absolutely nothing but no seriously

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Speaker 3: asking for help. And I think I'm the older I'm getting,

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Speaker 3: you know, I think that there's this you know, and

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Speaker 3: again this could be the social justice in me. But

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Speaker 3: I think, as you know, a marginalized person, we're always

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Speaker 3: expected to have the answers to everything. We're always supposed

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Speaker 3: to be the person like the world teaches you to

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Speaker 3: be the smartest person in the room all the time,

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Speaker 3: and I've just gotten to the point now where it's like, yes,

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Speaker 3: I have a doctorate, but I'm still not the smartest

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Speaker 3: person in the room. Like wouldn't anything say like support me, bitch,

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Speaker 3: Like that is literally how I feel all the time now,

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Speaker 3: Like I'm like, if I need help, I'm anna ask.

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Speaker 3: I'm not that girl that's like I've got it together,

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Speaker 3: like I'm good, Like my life is great. No, my

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Speaker 3: life is literally three second it's a string from falling apart.

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Speaker 3: So like, please help me, Like if you can help

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Speaker 3: me in any way, whether it be like you know,

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Speaker 3: giving me you know, a network or an email or

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Speaker 3: a connect to something like ever below saying help me, right,

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Speaker 3: because this life is hard, like just it's it's really

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Speaker 3: really hard.

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Speaker 2: So oh yeah, and I think there's also, yeah, there's

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Speaker 2: people don't want to want to struggle in public. That's

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Speaker 2: a huge reason why people don't ask for help or

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Speaker 2: act like they don't like they got their shit all

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Speaker 2: figured out. But it is like the most liberating and

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Speaker 2: sort of like life affirming thing when you ask for

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Speaker 2: like you'll be surprised you ask for help, the help

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Speaker 2: that you will receive from people that are like in

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Speaker 2: your circle. And I think that's also one of the

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Speaker 2: underrated aspects of asking for it is like you will

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Speaker 2: also realize like, damn, I go people on my corner. Yeah,

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Speaker 2: I'd say this is probably the most underrated thing of

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Speaker 2: my adult life. This is this is a great underrated

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Speaker 2: It's something I've had to do for like addiction issues

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Speaker 2: I've had to do in like it's made my marriage better,

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Speaker 2: and just in terms of it being good advice. We

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Speaker 2: did this thing like there's this hack, yeah, one hack

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Speaker 2: that Ben Franklin wants you to know. But there's this

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Speaker 2: thing that Ben Franklin talks about that he realizes it's

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Speaker 2: this like counterintuitive thing that if you ask someone for

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Speaker 2: a favor.

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Speaker 1: They and they do the favor for you, they will

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Speaker 1: like you more. And it's a weird because you're showing

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Speaker 1: vulnerability and there's like something like subconsciously of them like

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Speaker 1: doing something for you, like their brain is like, well,

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Speaker 1: they must be cool then, because I just did this

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Speaker 1: thing for them. So it's like it's all around, Like

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Speaker 1: just being willing to be vulnerable enough to ask for

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Speaker 1: help opens this world of possibilities.

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Speaker 2: And increase, you know, just everything.

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Speaker 1: It makes your life better and richer in so many ways,

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Speaker 1: you know, decreases loneliness, which is such a huge problem,

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Speaker 1: like right now, Yeah, and all that being said, I

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Speaker 1: can speak about it for a long time and like

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Speaker 1: I still don't do it enough.

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Speaker 2: Because it's hard.

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Speaker 3: It is hard, and I think I was gonna say

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Speaker 3: real quick, I think a really important thing is, you know,

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Speaker 3: masculinity is as a as a like a thing, right,

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Speaker 3: whether you talk about toxic masculine or masculine as a whole,

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Speaker 3: like we're all three men. And I think so being

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Speaker 3: even even for me being non binary, there's still this

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Speaker 3: presentation of I'm male presenting to people in some way,

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Speaker 3: and so I'm supposed to never be emotional enough to

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Speaker 3: say I'm struggling. And I actually had to like acknowledge

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Speaker 3: that a couple of like days ago, like two weeks ago.

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Speaker 3: I was literally sitting in my office going like here

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Speaker 3: comes the ideations, here comes to like no one loves you,

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Speaker 3: no one cares like you're you're feeling very alone. So

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Speaker 3: I had to hit my therapist back up and I

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Speaker 3: had to say like, hey, I'm going I'm I'm literally

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Speaker 3: slipping into this mindset again of being lonely and feeling

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Speaker 3: like the world doesn't need me here, like I need help.

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Speaker 3: And she was like, you know, let me get you back,

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Speaker 3: and you know, let me get you back on the roster.

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Speaker 3: And so I think that that's something that like I

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Speaker 3: try to emulate and everything that I do. That like

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Speaker 3: just because you see you know the shows, you see

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Speaker 3: me on the network, and you see me on TV,

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Speaker 3: and you see me with you know, celebrities. I mean

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Speaker 3: I went to dinner with the celeb friend of mine

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Speaker 3: just this weekend. People are like, oh, that's so cool.

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Speaker 3: I'm like yeah, but we were sitting at the table

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Speaker 3: literally almost crying with these other because we're all struggling,

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Speaker 3: like we're all going through it. So yeah, it's just

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Speaker 3: you know, asks for help literally, like people are not

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Speaker 3: going to judge you for.

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Speaker 2: Asking for help. So yeah, and that it's okay to

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Speaker 2: be struggling. I think that's the other hard part is

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Speaker 2: like whenever I start getting like, you know, I start

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Speaker 2: having I start ruminating on shit or whatever. It's like, man,

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Speaker 2: you feel mine, you get your shit together whatever. It's like, no, yeah,

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Speaker 2: that's not that's not how you get out of it.

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Speaker 2: You get out of it by being like Okay, okay,

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Speaker 2: that's where we're at right now. Let's try and find

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Speaker 2: a way to sort of pivot to something that feels

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Speaker 2: a little bit better and then incrementally get out of there.

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Speaker 2: But the whole like brute force of like I don't

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Speaker 2: need to feel like this, damn it. Like it's yeah,

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Speaker 2: guess what, it only makes shit worse. Yeah, what if

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Speaker 2: you just said no to sadness? Though, if you just

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Speaker 2: said no, like to drugs, Yeah, Like I don't know that.

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Speaker 2: That seems just like yeah, it's just going to make

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Speaker 2: me do more more sadness. That's right, more sadness. All right,

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Speaker 2: let's take a quick break and we'll come back and

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Speaker 2: speaking of sadness, we'll talk about the Republican Party and

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Speaker 2: how things are going for them. We'll be right back

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Speaker 2: and we're back.

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Speaker 1: And doctor McNerney, as mentioned, I am an idiot on

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Speaker 1: this AI stuff.

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Speaker 2: I think I generally have like my version of AI up.

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Speaker 1: Until last week. I guess like researching for our last

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Speaker 1: Expert episode was what I had read in you know,

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Speaker 1: mainstream articles that went viral and films move Hollywood films

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Speaker 1: and then like messing around with open AI and or

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Speaker 1: chat GPT. So I had this kind of disconnect in

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Speaker 1: my mind where it was like, from an outsider's perspective,

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Speaker 1: we have this C plus level like copywriter thing with

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Speaker 1: like in chat GPT, GPT for and then like the

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Speaker 1: Godfather of AI, who I'm just trusting people is the

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Speaker 1: Godfather of AI, but that's what everyone uses, that same phrase,

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Speaker 1: like the Godfather of AI just quit Google and says

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Speaker 1: we're all fucked in the next.

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Speaker 2: Couple of years. And I think it's confusing to.

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Speaker 1: Me because I don't know exactly, like I can't even

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Speaker 1: like picture the way how he thinks we're fucked. And

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Speaker 1: there was this letter that was like we need to

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Speaker 1: pause development on AI for in the near future, and

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Speaker 1: I guess I'm just curious to hear your perspective on

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Speaker 1: that pause letter and what the kind of dangers of

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Speaker 1: AI are in the near future.

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Speaker 2: Yeah, because to Jack's point two, also, we were talking

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Speaker 2: with Juao Sadoc last week at NYU about it, and

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Speaker 2: like at the end of it, we're like, Okay, so

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Speaker 2: it's not sky Net right from Terminator and they're like,

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Speaker 2: oh great, But then we realize there's a raft of

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Speaker 2: other things that come along with just not being the Terminator.

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Speaker 2: So yeah, that's I'm also from a similar perspective where

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Speaker 2: I always assume sky Net.

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Speaker 7: Yeah, I mean, I think you're totally not alone in

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Speaker 7: the dominance of ideas like sky Nee the Terminator, because

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Speaker 7: so much of our cultural framework for understanding what AI

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Speaker 7: is comes from a very narrow set of movie It's

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Speaker 7: like the Terminator, like the Matrix, which always be this

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Speaker 7: just AI is something that's going to dominate us. It's

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Speaker 7: going to take over the world, and it's going to

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Speaker 7: control us, and it's important to I think highlight that

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Speaker 7: that's definitely not the only ideas that we have about AI.

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Speaker 7: We've got thousands of years of thinking about our relationship

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Speaker 7: with intelligent machines, and there's a lot of different cultural

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Speaker 7: traditions that have really different ways of thinking about our

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Speaker 7: relationships with AI and intelligent machines that could be much

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Speaker 7: more positive, much more harmonious. And so I do think

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Speaker 7: our immediacy of jumping to this idea of sky net

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Speaker 7: is reflective of very much where we are right now. Right,

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Speaker 7: I'm in the UK, you're in the US. These are

436
00:22:19,200 --> 00:22:21,520
Speaker 7: countries that have a really long history of thinking about

437
00:22:21,520 --> 00:22:25,000
Speaker 7: AI and very binary terms. So yes, I think it's

438
00:22:25,000 --> 00:22:27,600
Speaker 7: important that we think about these long term risks of AI.

439
00:22:27,720 --> 00:22:30,359
Speaker 7: And you mentioned the pause letter calling for a wholt

440
00:22:30,400 --> 00:22:34,200
Speaker 7: to generating more large language models like chat GPT until

441
00:22:34,200 --> 00:22:36,120
Speaker 7: it had a bit more of a moment to think

442
00:22:36,160 --> 00:22:38,919
Speaker 7: about to the long term consequences of these models. But

443
00:22:39,040 --> 00:22:42,000
Speaker 7: I think it's really important not just to think of

444
00:22:42,200 --> 00:22:44,920
Speaker 7: the long term risks, but to think about which long

445
00:22:45,000 --> 00:22:48,120
Speaker 7: term risks we prioritize, because I think the sky net

446
00:22:48,200 --> 00:22:51,560
Speaker 7: terminated fantasy eats up a lot of oxygen about how

447
00:22:51,560 --> 00:22:54,000
Speaker 7: we talk about AI's risks. But there's a lot of

448
00:22:54,080 --> 00:22:57,360
Speaker 7: different risks to AI poses. So another long term risk

449
00:22:57,440 --> 00:22:59,679
Speaker 7: that we don't talk about very much at all is

450
00:22:59,720 --> 00:23:02,600
Speaker 7: the I'm at cost of AI right because AI it's

451
00:23:02,760 --> 00:23:06,439
Speaker 7: hugely energy intensive. Data centers require a huge amount of

452
00:23:06,480 --> 00:23:09,639
Speaker 7: water to function, and we have this massive problem of

453
00:23:09,640 --> 00:23:13,119
Speaker 7: e waste produced by a lot of electronic systems. But

454
00:23:13,359 --> 00:23:17,320
Speaker 7: that long term problem of climate crisis is much less exciting.

455
00:23:17,400 --> 00:23:20,080
Speaker 7: It's really scary, it's really grounded and a lot of

456
00:23:20,119 --> 00:23:22,680
Speaker 7: our experiences, and so it just doesn't seem to get

457
00:23:22,680 --> 00:23:24,960
Speaker 7: as much airtime. So that's something that I think is

458
00:23:25,000 --> 00:23:28,400
Speaker 7: really important, is changing the conversation a bit to say, Okay,

459
00:23:28,840 --> 00:23:32,359
Speaker 7: it's sometimes interesting, sometimes scary to think about the terminator option,

460
00:23:32,560 --> 00:23:35,080
Speaker 7: but what are some of the other long term options

461
00:23:35,080 --> 00:23:36,760
Speaker 7: that could really shape our lives.

462
00:23:37,480 --> 00:23:41,320
Speaker 2: Yeah, Like the degree to which the deck is being

463
00:23:41,400 --> 00:23:45,479
Speaker 2: stacked towards the terminator option was surprising to me, Like

464
00:23:45,520 --> 00:23:48,480
Speaker 2: we dug in last week a little bit to the

465
00:23:48,520 --> 00:23:52,040
Speaker 2: two stories I had always heard that are like kind

466
00:23:52,040 --> 00:23:55,840
Speaker 2: of put into the terminator version of AI taking over

467
00:23:55,880 --> 00:24:00,359
Speaker 2: a category. There's the AI that like killed a person

468
00:24:00,400 --> 00:24:05,840
Speaker 2: in a military exercise that decided to like eliminate its controller.

469
00:24:06,320 --> 00:24:08,119
Speaker 2: And then there was the AI.

470
00:24:08,000 --> 00:24:11,439
Speaker 1: That hired a task rabbit to pass the CAPSHA test.

471
00:24:11,640 --> 00:24:15,080
Speaker 1: And like in both cases those are like the AI

472
00:24:15,160 --> 00:24:17,560
Speaker 1: that killed a person in the military exercise, Like that

473
00:24:17,760 --> 00:24:21,560
Speaker 1: was somebody claiming that, and then when they went back

474
00:24:21,560 --> 00:24:23,120
Speaker 1: they were like, oh, I was just saying it could

475
00:24:23,240 --> 00:24:24,480
Speaker 1: hypothetically do that.

476
00:24:24,800 --> 00:24:26,520
Speaker 2: It was a thought experiment.

477
00:24:26,640 --> 00:24:29,440
Speaker 1: Yeah, it was a thought experiment of what an AI

478
00:24:29,640 --> 00:24:34,200
Speaker 1: could do in the right circumstances, and the task Rabbit

479
00:24:34,280 --> 00:24:38,200
Speaker 1: one was more similar to the self driving car Elon

480
00:24:38,320 --> 00:24:40,119
Speaker 1: Musk thing where it was just there was a human

481
00:24:40,200 --> 00:24:43,919
Speaker 1: prompting it to do that thing that seems creepy to

482
00:24:44,040 --> 00:24:47,320
Speaker 1: us when we start thinking about, oh, it's like scheming

483
00:24:47,440 --> 00:24:51,880
Speaker 1: to get loose and get overcome the things that we're

484
00:24:52,119 --> 00:24:56,800
Speaker 1: using to keep it hemmed in. So it does feel

485
00:24:56,880 --> 00:25:01,800
Speaker 1: like there is an incentive structure set up for the

486
00:25:02,359 --> 00:25:05,160
Speaker 1: people in charge of some of these major A companies

487
00:25:05,240 --> 00:25:09,639
Speaker 1: to get us to believe that shit like to think

488
00:25:09,680 --> 00:25:14,359
Speaker 1: to only focus on the AI v. Humanity, Like AI

489
00:25:15,160 --> 00:25:17,880
Speaker 1: gets loose of its control of our controls for it

490
00:25:18,000 --> 00:25:22,200
Speaker 1: and takes over and starts like killing people version of it.

491
00:25:22,280 --> 00:25:24,800
Speaker 1: I'm just curious, like what are your thoughts on, Like

492
00:25:24,880 --> 00:25:29,719
Speaker 1: why why why are they incentivized to do that when

493
00:25:29,760 --> 00:25:31,960
Speaker 1: it would seem like, well, you don't want to make it,

494
00:25:32,320 --> 00:25:35,199
Speaker 1: you don't want it to seem like this self driving

495
00:25:35,200 --> 00:25:37,240
Speaker 1: car will take over and.

496
00:25:37,600 --> 00:25:39,639
Speaker 2: Start kill your family.

497
00:25:39,920 --> 00:25:43,000
Speaker 1: Yeah, start killing your family. It's so powerful, But it

498
00:25:43,040 --> 00:25:47,679
Speaker 1: seems like with AI, they're more willing to buy into

499
00:25:47,720 --> 00:25:51,800
Speaker 1: that fantasy and like have that fantasy projected to people

500
00:25:51,840 --> 00:25:56,359
Speaker 1: who are are not as closely tied to the ins

501
00:25:56,359 --> 00:25:57,400
Speaker 1: and outs of the industry.

502
00:25:57,880 --> 00:25:59,959
Speaker 9: Yeah, I mean, I think that's such a point. I'm

503
00:26:00,080 --> 00:26:03,080
Speaker 9: point because it's a weird thing about the AI.

504
00:26:02,920 --> 00:26:04,920
Speaker 7: Industry, right, Like you would never have this kind of

505
00:26:05,000 --> 00:26:07,720
Speaker 7: hype wave around something like broccoli where you say, oh,

506
00:26:07,800 --> 00:26:09,600
Speaker 7: the broccoli, if you eat it could kill you or

507
00:26:09,640 --> 00:26:12,119
Speaker 7: it could like transform the world, And then you wouldn't

508
00:26:12,160 --> 00:26:14,040
Speaker 7: expect that to somehow get people to buy a lot

509
00:26:14,040 --> 00:26:16,560
Speaker 7: more broccoli and just be like, I don't want to

510
00:26:16,560 --> 00:26:17,600
Speaker 7: eat broccoli.

511
00:26:17,200 --> 00:26:22,480
Speaker 1: Now that's so fucking good and powerful make you explode,

512
00:26:22,760 --> 00:26:24,160
Speaker 1: like maybe like.

513
00:26:25,160 --> 00:26:28,120
Speaker 7: It even worse broccoli that you can then also eat,

514
00:26:28,160 --> 00:26:31,679
Speaker 7: you know what that broccoli would be doing. But I

515
00:26:31,680 --> 00:26:34,480
Speaker 7: do think that we see this real cultivation of hype

516
00:26:34,520 --> 00:26:37,679
Speaker 7: around AI, and that a lot of firms explicitly use

517
00:26:37,720 --> 00:26:39,959
Speaker 7: that to sell their products. It gets people interested in

518
00:26:39,960 --> 00:26:42,640
Speaker 7: it because on the one hand, people are really scared

519
00:26:42,720 --> 00:26:45,679
Speaker 7: about the long term and short term impacts of AI.

520
00:26:46,119 --> 00:26:47,960
Speaker 7: On the other hand, it also scared then though, of

521
00:26:47,960 --> 00:26:50,560
Speaker 7: getting left behind. So you see more and more firms saying, well,

522
00:26:50,560 --> 00:26:53,600
Speaker 7: now I've got to buy the latest generative AI tool

523
00:26:53,680 --> 00:26:55,800
Speaker 7: so that I look really tech savvy and I look

524
00:26:55,880 --> 00:26:58,920
Speaker 7: pick forward and I look futuristic, and so it's part

525
00:26:58,960 --> 00:27:01,240
Speaker 7: of the bigger hype psyche, I think, to draw a

526
00:27:01,280 --> 00:27:03,919
Speaker 7: lot of tension towards set products, but also to make

527
00:27:03,960 --> 00:27:07,119
Speaker 7: them seem like this really edgy, desirable thing. But I

528
00:27:07,160 --> 00:27:09,639
Speaker 7: think what's also interesting about both the stories that you

529
00:27:09,720 --> 00:27:11,600
Speaker 7: raise is when you looked under the hood, there was

530
00:27:11,680 --> 00:27:15,360
Speaker 7: human labor involved, right, There were people who were really central.

531
00:27:15,200 --> 00:27:17,520
Speaker 9: To something that was attributed to an AI.

532
00:27:18,040 --> 00:27:20,400
Speaker 7: And I think that's a really common story we see

533
00:27:20,440 --> 00:27:21,800
Speaker 7: with a lot of the type around AI.

534
00:27:22,160 --> 00:27:24,800
Speaker 9: Is often the way we tell those stories.

535
00:27:24,400 --> 00:27:28,360
Speaker 7: Eraises a very real human labor that drives those products,

536
00:27:28,400 --> 00:27:30,879
Speaker 7: whether it's the artists who originally made the images that

537
00:27:31,000 --> 00:27:34,920
Speaker 7: train the generative AI through today the labelers all sorts

538
00:27:34,960 --> 00:27:37,879
Speaker 7: of people who are really central to those processes.

539
00:27:37,880 --> 00:27:41,320
Speaker 2: Right, And I know, like in your episode of The

540
00:27:41,359 --> 00:27:44,679
Speaker 2: Good Robot when you're discussing the pause letter, you know,

541
00:27:44,720 --> 00:27:48,840
Speaker 2: I think that the I think the version that we

542
00:27:48,920 --> 00:27:51,600
Speaker 2: see as like sort of these short term threats at

543
00:27:51,680 --> 00:27:53,600
Speaker 2: least and the most immediate way is like for me

544
00:27:53,800 --> 00:27:56,480
Speaker 2: working in and around entertainment and people who work in

545
00:27:56,600 --> 00:28:00,399
Speaker 2: advertising and seeing like an uptake in that section, I go, oh, okay,

546
00:28:00,400 --> 00:28:03,680
Speaker 2: that's easy. Like I can see how a company immediately goes, yeah,

547
00:28:03,720 --> 00:28:05,760
Speaker 2: it's a tool, and then suddenly it's like and now

548
00:28:05,800 --> 00:28:08,000
Speaker 2: you're on your ass because we'll just use the tool now,

549
00:28:08,040 --> 00:28:09,560
Speaker 2: and we don't even need a person to prompt it

550
00:28:09,640 --> 00:28:11,960
Speaker 2: or we need many We just need fewer people to

551
00:28:12,000 --> 00:28:14,560
Speaker 2: operate it. So to me, I'm like, Okay, that's an

552
00:28:14,680 --> 00:28:17,480
Speaker 2: obvious sort of thing I can see, like on the horizon.

553
00:28:17,800 --> 00:28:19,679
Speaker 2: And you did talk about, well, there was a lot

554
00:28:19,720 --> 00:28:22,359
Speaker 2: of talk of these sort of long term existential or

555
00:28:22,440 --> 00:28:25,080
Speaker 2: quote unquote existential threats that there were a lot of

556
00:28:25,280 --> 00:28:28,399
Speaker 2: things in the short term that we're actually ignoring. What

557
00:28:28,640 --> 00:28:30,960
Speaker 2: are those sort of things that we need to bring

558
00:28:31,000 --> 00:28:32,879
Speaker 2: a little bit more awareness to, Like I know you

559
00:28:32,920 --> 00:28:36,840
Speaker 2: mentioned the climate, and I look at it from my perspective,

560
00:28:36,880 --> 00:28:39,440
Speaker 2: I see like the just massive job loss that could happen.

561
00:28:40,000 --> 00:28:41,880
Speaker 2: But what are sort of like the more short term

562
00:28:41,920 --> 00:28:45,320
Speaker 2: things that kind of maybe are less sexy or interesting

563
00:28:45,400 --> 00:28:47,320
Speaker 2: to the people who just want to write about killer

564
00:28:47,440 --> 00:28:48,440
Speaker 2: terminators and things.

565
00:28:49,840 --> 00:28:52,560
Speaker 7: Yeah, I mean, I think less sexy is exactly the

566
00:28:52,680 --> 00:28:54,400
Speaker 7: right phrase for this, which is a lot of the

567
00:28:54,440 --> 00:28:57,800
Speaker 7: short term issues. I very much about entrenching existing forms

568
00:28:57,800 --> 00:29:01,240
Speaker 7: of inequality and making them worse. That's often something people

569
00:29:01,360 --> 00:29:03,360
Speaker 7: don't really want to hear about because they don't want

570
00:29:03,360 --> 00:29:06,560
Speaker 7: to acknowledge its inequalities, or because it takes away from

571
00:29:06,600 --> 00:29:09,040
Speaker 7: the shiny units of AI. It makes it very much

572
00:29:09,120 --> 00:29:12,880
Speaker 7: like a lot of other technological revolutions that we're already seen,

573
00:29:12,920 --> 00:29:14,680
Speaker 7: and that's super boring, Like you don't want to hear

574
00:29:14,720 --> 00:29:21,720
Speaker 7: about how the wheel somehow brought about some kind of inequality.

575
00:29:19,920 --> 00:29:22,560
Speaker 9: Are the big heart.

576
00:29:22,400 --> 00:29:24,800
Speaker 7: Take for today, But yeah, I mean something that I

577
00:29:24,800 --> 00:29:28,600
Speaker 7: look at, for example, are like very mundane but important technologies,

578
00:29:28,680 --> 00:29:31,600
Speaker 7: like technologies used in recruitment and hiring. So I look

579
00:29:31,640 --> 00:29:34,160
Speaker 7: at AI powered video interview tools and look at how

580
00:29:34,200 --> 00:29:38,480
Speaker 7: that affects people's particular you know, likelihood of being employed

581
00:29:38,520 --> 00:29:40,560
Speaker 7: and how they go through the workforce, and yep, it's

582
00:29:40,640 --> 00:29:43,680
Speaker 7: less exciting seeming than the terminator. But again, when you

583
00:29:43,720 --> 00:29:45,480
Speaker 7: look under the hood and dig into them, you're like,

584
00:29:45,560 --> 00:29:50,520
Speaker 7: oh wow, this could actually really really compound inequalities that

585
00:29:50,560 --> 00:29:52,840
Speaker 7: we see in the workforce under the guise of the

586
00:29:52,880 --> 00:29:55,080
Speaker 7: idea that these tools are going to make hiring more fair,

587
00:29:55,200 --> 00:29:57,040
Speaker 7: and that's a massive problem.

588
00:29:57,200 --> 00:29:59,480
Speaker 2: Right, So, because like the idea of those hiring tools

589
00:29:59,520 --> 00:30:02,640
Speaker 2: like will actually take away these sort of like demographic

590
00:30:02,760 --> 00:30:06,080
Speaker 2: cues that someone might use to like, you know, they

591
00:30:06,080 --> 00:30:08,680
Speaker 2: apply their own biases to so in fact it is

592
00:30:08,760 --> 00:30:11,800
Speaker 2: the most equitable way to hire. But is it because

593
00:30:11,800 --> 00:30:15,080
Speaker 2: of just the kinds of people that are creating these

594
00:30:15,120 --> 00:30:18,000
Speaker 2: sort of systems, because they tends to be a bit

595
00:30:18,080 --> 00:30:21,160
Speaker 2: one note that that's inherently where like sort of that

596
00:30:21,360 --> 00:30:22,680
Speaker 2: like it begins to wobble a bit.

597
00:30:23,000 --> 00:30:23,760
Speaker 9: It's a mixture.

598
00:30:23,800 --> 00:30:25,840
Speaker 7: So of course, yes, the lack of diversity in the

599
00:30:25,880 --> 00:30:29,080
Speaker 7: AI industry is like very stark. It's also sadly in

600
00:30:29,120 --> 00:30:32,880
Speaker 7: the uk an industry where for example, women's representation is

601
00:30:32,920 --> 00:30:34,520
Speaker 7: actually getting worse, not better.

602
00:30:34,720 --> 00:30:35,800
Speaker 9: So that's a sad.

603
00:30:35,640 --> 00:30:37,560
Speaker 7: Slap in the face for a lot of the progress

604
00:30:37,640 --> 00:30:41,040
Speaker 7: narrative that we want to see. But sometimes it's not

605
00:30:41,120 --> 00:30:44,960
Speaker 7: even necessarily that the people creating these tools have bad intentions,

606
00:30:45,280 --> 00:30:47,800
Speaker 7: maybe not even that they're using faulty data sets or

607
00:30:47,800 --> 00:30:49,920
Speaker 7: biased data sets. These are two of the really big

608
00:30:49,960 --> 00:30:53,760
Speaker 7: problems that are flagged, But sometimes the underlying idea behind

609
00:30:53,760 --> 00:30:56,720
Speaker 7: their product is just fogus, Like it's just a really

610
00:30:56,760 --> 00:30:59,720
Speaker 7: bad concept and yet somehow it gets brought to market

611
00:31:00,160 --> 00:31:02,720
Speaker 7: because of all this hype around AI. So with the

612
00:31:02,920 --> 00:31:05,640
Speaker 7: video interview tools that we look at, for example, they

613
00:31:05,640 --> 00:31:09,920
Speaker 7: basically claim that they can discern a candidates personality from

614
00:31:10,000 --> 00:31:12,120
Speaker 7: their face and from their words, So how they talk,

615
00:31:12,200 --> 00:31:15,040
Speaker 7: how they move, They can decide how extroverted you are,

616
00:31:15,160 --> 00:31:18,040
Speaker 7: or how you know open you are, how neurotic you are,

617
00:31:18,040 --> 00:31:21,280
Speaker 7: how conscientious you are, all these different markets of personality

618
00:31:21,720 --> 00:31:25,800
Speaker 7: to which I would say, firstly, no, there's absolutely no

619
00:31:25,840 --> 00:31:28,720
Speaker 7: when AI can do that. This is just a very

620
00:31:28,760 --> 00:31:32,000
Speaker 7: old kind of racial pseudoscience making its way back into

621
00:31:32,040 --> 00:31:35,480
Speaker 7: the mainstream, saying Okay, we can totally guess your personality

622
00:31:35,520 --> 00:31:38,360
Speaker 7: from your face, Like it's like your friends looking at

623
00:31:38,400 --> 00:31:41,360
Speaker 7: someone's profile picture on like Pinder or whatever and being

624
00:31:41,440 --> 00:31:44,240
Speaker 7: like they look like they'd be really fun at a party.

625
00:31:44,360 --> 00:31:45,600
Speaker 7: Like it's about that level of.

626
00:31:45,520 --> 00:31:46,959
Speaker 2: Accuracy, you know.

627
00:31:47,200 --> 00:31:49,960
Speaker 7: And then second, is that even a good way of

628
00:31:50,040 --> 00:31:52,160
Speaker 7: judging if someone's gonna be good for a job, Like

629
00:31:52,720 --> 00:31:55,240
Speaker 7: how extroverted do you want a person in the job

630
00:31:55,320 --> 00:31:57,920
Speaker 7: to be? Maybe in your job that's really really helpful.

631
00:31:57,960 --> 00:32:00,640
Speaker 7: In my job, I don't know how help well. So

632
00:32:00,800 --> 00:32:03,040
Speaker 7: there's just kind of a lot of laws at the

633
00:32:03,160 --> 00:32:05,920
Speaker 7: very you know, bottom of these products that we should

634
00:32:05,920 --> 00:32:07,160
Speaker 7: be worried about.

635
00:32:07,520 --> 00:32:12,400
Speaker 1: Just like a C minus level job hiring process. Like

636
00:32:12,440 --> 00:32:14,800
Speaker 1: that's what I feel like so many of the things,

637
00:32:14,840 --> 00:32:17,360
Speaker 1: like when you get down to them and see them

638
00:32:17,360 --> 00:32:21,400
Speaker 1: in action, they're like not that good. Like it does

639
00:32:21,480 --> 00:32:24,520
Speaker 1: feel like the whole thing is being hyped to a

640
00:32:24,600 --> 00:32:27,760
Speaker 1: large degree. And like that's something I heard from somebody

641
00:32:28,160 --> 00:32:31,760
Speaker 1: I know who like works in you know, like all

642
00:32:31,800 --> 00:32:34,480
Speaker 1: of my friends who work in finance or like any

643
00:32:34,520 --> 00:32:36,920
Speaker 1: of those things. Like my brain shuts off when he

644
00:32:36,920 --> 00:32:40,320
Speaker 1: starts talking about what he does. But he was saying,

645
00:32:40,800 --> 00:32:43,160
Speaker 1: like he pays attention to the market, and he was saying,

646
00:32:43,200 --> 00:32:46,920
Speaker 1: there's like a big thing propping up the stock market

647
00:32:46,960 --> 00:32:50,120
Speaker 1: right now is AI. And it really is like that,

648
00:32:50,120 --> 00:32:53,479
Speaker 1: that's where so much of people's wealth is tied up,

649
00:32:53,520 --> 00:32:56,040
Speaker 1: is in like the stock market, and it's just tied

650
00:32:56,120 --> 00:33:00,120
Speaker 1: to like what you can get people excited about a

651
00:33:00,160 --> 00:33:03,880
Speaker 1: lot of cases. So it really like that from that perspective,

652
00:33:05,040 --> 00:33:06,760
Speaker 1: the incentive structure makes sense.

653
00:33:07,040 --> 00:33:08,640
Speaker 2: Like you want people talking.

654
00:33:08,360 --> 00:33:12,800
Speaker 1: About how your AI technology can do all these amazing

655
00:33:12,880 --> 00:33:17,440
Speaker 1: things because that literally makes you have more money than

656
00:33:17,480 --> 00:33:19,600
Speaker 1: you would have if they knew the truth.

657
00:33:19,360 --> 00:33:22,920
Speaker 2: About your your product without being like yeah, wait, how

658
00:33:22,920 --> 00:33:27,080
Speaker 2: many seven fingered Trump pictures can we create and right

659
00:33:27,200 --> 00:33:30,440
Speaker 2: be like, yeah, man, fucking up millions into this.

660
00:33:30,880 --> 00:33:34,200
Speaker 7: Yeah yeah, I mean that's kind of something that's really

661
00:33:34,280 --> 00:33:36,120
Speaker 7: come out of the last few years is how many

662
00:33:36,120 --> 00:33:38,560
Speaker 7: fans just use the label of AI to get funding.

663
00:33:38,640 --> 00:33:40,240
Speaker 7: Like I think there was a study a couple of

664
00:33:40,320 --> 00:33:44,120
Speaker 7: years ago that said, like of European AI startups didn't

665
00:33:44,240 --> 00:33:46,400
Speaker 7: use AI, which I point, You're like, well, what are

666
00:33:46,480 --> 00:33:46,880
Speaker 7: you doing?

667
00:33:49,680 --> 00:33:52,640
Speaker 1: Well, we could though, this podcast is actually an AI

668
00:33:52,760 --> 00:33:57,040
Speaker 1: podcast because eventually it could use AI. And before we

669
00:33:57,040 --> 00:34:00,680
Speaker 1: were recording, Miles was actually putting in. He asked an

670
00:34:00,720 --> 00:34:05,520
Speaker 1: AI to pitch him an episode of Friends in which

671
00:34:05,560 --> 00:34:09,520
Speaker 1: the cast and you know, the people on the Friends

672
00:34:09,880 --> 00:34:12,560
Speaker 1: on the show deal with the fallout from the events

673
00:34:12,560 --> 00:34:15,560
Speaker 1: of nine to eleven. And it wouldn't do it. So

674
00:34:15,600 --> 00:34:20,280
Speaker 1: we can't quite claim that we are an AI podcast yet, but.

675
00:34:19,800 --> 00:34:21,960
Speaker 2: But we did it. Did do it when I said,

676
00:34:22,000 --> 00:34:25,040
Speaker 2: do it, pitch me an episode where Joey and Monica

677
00:34:25,120 --> 00:34:28,520
Speaker 2: drive Uber right from Yeah, and it did so clearly

678
00:34:28,600 --> 00:34:31,040
Speaker 2: it because you can see where these guardrails out. They're like,

679
00:34:31,080 --> 00:34:34,640
Speaker 2: don't do nine to eleven stuff though, that's right now.

680
00:34:35,160 --> 00:34:38,040
Speaker 1: But I mean, so yeah, I think there's two things

681
00:34:38,040 --> 00:34:43,000
Speaker 1: we're talking about here, like from from one perspective, like, yes,

682
00:34:43,080 --> 00:34:46,160
Speaker 1: you could put it in the category of like, well, yes,

683
00:34:46,280 --> 00:34:52,520
Speaker 1: the wheel makes racism or colonialism more Frictionless is a

684
00:34:52,520 --> 00:34:55,279
Speaker 1: word that gets used a lot, but like literally in

685
00:34:55,280 --> 00:34:58,160
Speaker 1: the case of the wheel, frictionless, but AI and like

686
00:34:58,160 --> 00:35:02,040
Speaker 1: a lot of technology is designed to you make groups

687
00:35:02,040 --> 00:35:04,799
Speaker 1: of people and like our interactions and the things that

688
00:35:04,840 --> 00:35:07,640
Speaker 1: make people money more frictionless. And that's something that you

689
00:35:07,640 --> 00:35:12,160
Speaker 1: guys have talked about on recent episodes of Good Robot,

690
00:35:12,560 --> 00:35:15,319
Speaker 1: Like there's this one example that really jumped out to

691
00:35:15,360 --> 00:35:17,760
Speaker 1: me that I think was from your most recent episode,

692
00:35:17,840 --> 00:35:21,520
Speaker 1: or at least the one that's up most recently right

693
00:35:21,560 --> 00:35:25,160
Speaker 1: now as we're recording this, where you guys were talking

694
00:35:25,360 --> 00:35:30,399
Speaker 1: about a company that asked a regulating body to make

695
00:35:30,440 --> 00:35:33,959
Speaker 1: an exception to a law around like a high risk

696
00:35:34,080 --> 00:35:37,239
Speaker 1: use of AI, and the law said that people had

697
00:35:37,280 --> 00:35:41,400
Speaker 1: to supervise the use of AI like just because it

698
00:35:41,440 --> 00:35:44,800
Speaker 1: seemed dangerous, and the company appealed to the regulating body

699
00:35:44,840 --> 00:35:48,120
Speaker 1: by saying, well, we just like that would cost too

700
00:35:48,200 --> 00:35:50,760
Speaker 1: much and we would never be able to like scale

701
00:35:50,800 --> 00:35:54,239
Speaker 1: this and make a profit. And it feels to me,

702
00:35:54,360 --> 00:35:57,760
Speaker 1: like our answer as a civilization to that complaint needs

703
00:35:57,840 --> 00:36:02,319
Speaker 1: to be that's not our problem, Like that's then you

704
00:36:02,360 --> 00:36:05,680
Speaker 1: shouldn't be doing it. But instead, it seems like the

705
00:36:05,719 --> 00:36:09,680
Speaker 1: answer too often, not just in AI, but just across

706
00:36:09,760 --> 00:36:13,319
Speaker 1: the board, especially in the US, is like, Okay, well

707
00:36:13,360 --> 00:36:15,600
Speaker 1: we have to make an exception so that they can

708
00:36:15,640 --> 00:36:18,600
Speaker 1: make a profit around this technology, or else the technology

709
00:36:19,080 --> 00:36:24,000
Speaker 1: won't get developed because the only thing that drives technological

710
00:36:24,040 --> 00:36:27,840
Speaker 1: progress is like the profit motive. But that's you know,

711
00:36:28,120 --> 00:36:30,400
Speaker 1: as I think you guys talked about in that episode,

712
00:36:30,400 --> 00:36:33,040
Speaker 1: that's never been the best way to develop technology, Like

713
00:36:33,400 --> 00:36:38,360
Speaker 1: it's it's been a good way sometimes to democratize existing technology,

714
00:36:38,360 --> 00:36:40,279
Speaker 1: but like that's I don't know.

715
00:36:40,320 --> 00:36:41,000
Speaker 2: I feel like that.

716
00:36:41,200 --> 00:36:45,040
Speaker 1: Idea of you have to make it profitable, you have

717
00:36:45,120 --> 00:36:47,560
Speaker 1: to make it easy on these companies to keep trying

718
00:36:47,600 --> 00:36:51,080
Speaker 1: different things for AI to become profitable is baked in

719
00:36:51,520 --> 00:36:55,920
Speaker 1: at a like cellular level at this point in how

720
00:36:56,160 --> 00:36:59,839
Speaker 1: a lot of you know, Western colonial civilization.

721
00:37:01,000 --> 00:37:02,640
Speaker 7: Yeah, I mean, I think too often a lot of

722
00:37:02,640 --> 00:37:05,399
Speaker 7: the technologies that shape our daily lives are made by

723
00:37:05,480 --> 00:37:08,400
Speaker 7: a very narrow set of people who ultimately aren't beholden

724
00:37:08,440 --> 00:37:11,040
Speaker 7: to us. They're beholden to their shareholders or to their

725
00:37:11,080 --> 00:37:13,080
Speaker 7: boss and right, so they don't really have our best

726
00:37:13,680 --> 00:37:16,439
Speaker 7: interests at heart, right, Like, for example, take this whole

727
00:37:16,440 --> 00:37:19,759
Speaker 7: rebranding of like Twitter to X by Musk. I remember

728
00:37:19,840 --> 00:37:22,880
Speaker 7: waking up and finding my little Twitter bird replaced with

729
00:37:22,960 --> 00:37:26,000
Speaker 7: this huge X and just being like ugh. Con firstly

730
00:37:26,040 --> 00:37:29,200
Speaker 7: because it was part of you know, Twitter's low decline,

731
00:37:29,480 --> 00:37:33,120
Speaker 7: but secondly it maybe you feel pretty disappointed or really

732
00:37:33,160 --> 00:37:35,919
Speaker 7: aware of the fact that one guy could have such

733
00:37:35,960 --> 00:37:38,719
Speaker 7: a huge impact on how literally millions of.

734
00:37:38,680 --> 00:37:40,880
Speaker 9: People use a social networking.

735
00:37:40,440 --> 00:37:43,800
Speaker 7: Platform that's actually super important, you know, to their daily

736
00:37:43,880 --> 00:37:46,120
Speaker 7: lives and has played a huge role in activist movements

737
00:37:46,120 --> 00:37:48,800
Speaker 7: and fostering different communities. And I think that's a story

738
00:37:48,840 --> 00:37:50,560
Speaker 7: we see time and time again with some of these

739
00:37:50,560 --> 00:37:53,200
Speaker 7: big tech companies, which is not only do they have

740
00:37:53,239 --> 00:37:56,719
Speaker 7: their own profit motive at heart, they also they're not

741
00:37:56,760 --> 00:37:58,839
Speaker 7: beholden in any way to the public, and they're not

742
00:37:58,880 --> 00:38:02,520
Speaker 7: being compelled by relation to make good decisions that.

743
00:38:02,520 --> 00:38:03,880
Speaker 9: Necessarily benefit the public.

744
00:38:04,320 --> 00:38:07,120
Speaker 7: So I think a really important question going forward is

745
00:38:07,320 --> 00:38:11,080
Speaker 7: how do we support kinds of technology development that are

746
00:38:11,280 --> 00:38:13,759
Speaker 7: very much based in the communities that the technology is for.

747
00:38:14,320 --> 00:38:17,160
Speaker 7: I think one really big part of that is recognizing

748
00:38:17,160 --> 00:38:20,640
Speaker 7: that so many AI models, as you mentioned right, they're

749
00:38:20,680 --> 00:38:22,800
Speaker 7: designed to be scalable and that's how they make money.

750
00:38:22,840 --> 00:38:25,560
Speaker 7: This idea that you can apply them globally and universally,

751
00:38:26,000 --> 00:38:28,160
Speaker 7: and I think that's a big problem, partly because it

752
00:38:28,200 --> 00:38:31,719
Speaker 7: often is really homogenizing. It can involve like explitting a

753
00:38:31,760 --> 00:38:34,279
Speaker 7: model from the US usually out to the rest of

754
00:38:34,320 --> 00:38:34,720
Speaker 7: the world.

755
00:38:34,800 --> 00:38:37,040
Speaker 9: It's probably not actually appropriate.

756
00:38:36,560 --> 00:38:39,279
Speaker 7: To using those contexts. But also a lot of really

757
00:38:39,320 --> 00:38:41,759
Speaker 7: exciting and good uses of technology I think come from

758
00:38:41,800 --> 00:38:45,600
Speaker 7: these really localized, specific, community based levels. So sometimes I

759
00:38:45,640 --> 00:38:48,320
Speaker 7: think it can be about thinking smaller rather than bigger.

760
00:38:49,080 --> 00:38:51,799
Speaker 2: Yeah. Yeah, that was like another thing that struck me

761
00:38:51,840 --> 00:38:54,640
Speaker 2: about like just all the warnings and even in that

762
00:38:54,719 --> 00:38:58,439
Speaker 2: pause letter is sort of like the presumption that it's like, well,

763
00:38:58,440 --> 00:39:01,200
Speaker 2: all you motherfuckers are going to use this, so we

764
00:39:01,280 --> 00:39:03,120
Speaker 2: got to talk about it where it's like I don't know,

765
00:39:03,160 --> 00:39:04,920
Speaker 2: I don't even fucking know what it is. Like a

766
00:39:04,960 --> 00:39:07,640
Speaker 2: second ago, I thought it was skyingad and now like

767
00:39:08,120 --> 00:39:09,799
Speaker 2: you know, you have your company being like, yeah, we

768
00:39:09,840 --> 00:39:13,000
Speaker 2: now have enterprise AI tools like welcome. You're like, but

769
00:39:13,120 --> 00:39:15,799
Speaker 2: what am I huh like? And I think That's what's

770
00:39:15,840 --> 00:39:17,919
Speaker 2: an really interesting thing about this as like a sort

771
00:39:17,920 --> 00:39:21,960
Speaker 2: of technological advancement, is before people really understand what it is,

772
00:39:22,520 --> 00:39:25,239
Speaker 2: there is like from the higher the powers that be

773
00:39:25,440 --> 00:39:27,680
Speaker 2: sort of going into it being like, well, this is it?

774
00:39:27,920 --> 00:39:31,400
Speaker 2: Like everyone's using it, but I'm still not sure how.

775
00:39:31,440 --> 00:39:34,080
Speaker 2: And I guess that probably feeds into this whole model

776
00:39:34,120 --> 00:39:37,680
Speaker 2: of generating as much, you know, excitement market excitement about

777
00:39:37,719 --> 00:39:41,280
Speaker 2: AI is by taking the angle of like everything's different

778
00:39:41,520 --> 00:39:44,600
Speaker 2: because everyone is going to be using AI. Most of

779
00:39:44,680 --> 00:39:47,759
Speaker 2: y'all don't know what that is, but get ready. And

780
00:39:47,800 --> 00:39:49,880
Speaker 2: I think that's what also makes it very confusing for me,

781
00:39:49,960 --> 00:39:53,040
Speaker 2: as just like a lay person outside of the text sphere,

782
00:39:53,600 --> 00:39:55,840
Speaker 2: to just be like, wait, so are we all using it?

783
00:39:55,880 --> 00:39:59,000
Speaker 2: And even now I really I still can't see what

784
00:39:59,360 --> 00:40:02,399
Speaker 2: that is and how that benefits me. And I think

785
00:40:02,600 --> 00:40:05,000
Speaker 2: that's a big part of I'm sure your work too,

786
00:40:05,200 --> 00:40:08,200
Speaker 2: or even like any ethicist, is to understand like, well,

787
00:40:08,239 --> 00:40:11,160
Speaker 2: who does it? Ben Like, first, we're making this because

788
00:40:11,160 --> 00:40:13,319
Speaker 2: it benefits who and how?

789
00:40:13,600 --> 00:40:13,839
Speaker 7: Yeah?

790
00:40:13,960 --> 00:40:16,960
Speaker 2: I think, yeah, I mean right now it benefits the

791
00:40:17,000 --> 00:40:18,880
Speaker 2: companies that are making it. And it sort of feels

792
00:40:18,880 --> 00:40:21,680
Speaker 2: like that's the way it's being presentved or slightly being like, yeah,

793
00:40:21,680 --> 00:40:23,480
Speaker 2: you guys are gonna love this, but really it's we're

794
00:40:23,480 --> 00:40:25,680
Speaker 2: going to benefit from the adoption of this technology.

795
00:40:26,440 --> 00:40:28,600
Speaker 7: Yeah, I mean, I think that's this crucial question. Is

796
00:40:28,600 --> 00:40:31,640
Speaker 7: this stepping back and saying, actually, is this inevitable? And

797
00:40:31,680 --> 00:40:33,560
Speaker 7: do we even want this in the first place? And

798
00:40:33,600 --> 00:40:36,480
Speaker 7: I think that's what really frustrated me about the paut

799
00:40:36,520 --> 00:40:38,319
Speaker 7: letter and about a number of kind of big tech

800
00:40:38,400 --> 00:40:40,799
Speaker 7: figures signing on to it, is that they're very much

801
00:40:40,840 --> 00:40:44,759
Speaker 7: pushing this narrative of like, oh, this is like unstoppable

802
00:40:44,880 --> 00:40:47,160
Speaker 7: and it's inevitable and it's happening. We've got a fine

803
00:40:47,200 --> 00:40:49,840
Speaker 7: way to deal with it. And it's like you're making

804
00:40:49,880 --> 00:40:53,080
Speaker 7: it like you're the people literally making these technologies and

805
00:40:53,120 --> 00:40:55,160
Speaker 7: a lot of cases, so I do you really think

806
00:40:55,200 --> 00:40:57,239
Speaker 7: it's an existential risk to humanity?

807
00:40:58,960 --> 00:41:01,600
Speaker 9: It's the fun of could even be that simple?

808
00:41:02,040 --> 00:41:04,759
Speaker 7: But that's you know what makes me really then question

809
00:41:04,840 --> 00:41:06,839
Speaker 7: their motives and sort of coming forward with a lot

810
00:41:06,840 --> 00:41:10,080
Speaker 7: of this kind of very doom and gloom language. I

811
00:41:10,080 --> 00:41:12,240
Speaker 7: think it's also interesting if you look at, for example,

812
00:41:12,440 --> 00:41:15,560
Speaker 7: countries as national AI strategies. So if you look at

813
00:41:15,600 --> 00:41:18,040
Speaker 7: say like China and the UK and the US and

814
00:41:18,080 --> 00:41:20,520
Speaker 7: these countries that are now thinking about what their national

815
00:41:20,520 --> 00:41:23,000
Speaker 7: long term AI strategy is going to be, they also

816
00:41:23,160 --> 00:41:25,759
Speaker 7: very much frame it around the idea that AI is

817
00:41:25,760 --> 00:41:29,520
Speaker 7: completely inevitable, that this is going to be the transformative

818
00:41:29,560 --> 00:41:34,239
Speaker 7: technology for imaging the future, for geopolitical dominance, for economic supremacy.

819
00:41:34,600 --> 00:41:36,799
Speaker 7: And again, I think as an ethesis to what I

820
00:41:36,840 --> 00:41:38,919
Speaker 7: really want people to do is step back and say,

821
00:41:39,200 --> 00:41:41,000
Speaker 7: I think we're actually at a crossroad so we can

822
00:41:41,040 --> 00:41:44,359
Speaker 7: decide whether or not we think these technologies are good

823
00:41:44,360 --> 00:41:47,239
Speaker 7: for us, and whether they are sustainable, whether they are

824
00:41:47,600 --> 00:41:51,240
Speaker 7: a useful long term thing for our societies, or actually

825
00:41:51,400 --> 00:41:53,960
Speaker 7: whether the benefits of these technologies are going to be

826
00:41:54,040 --> 00:41:57,239
Speaker 7: experienced by very few people and the costs are going

827
00:41:57,280 --> 00:41:59,160
Speaker 7: to be borne by many.

828
00:41:59,239 --> 00:42:04,160
Speaker 1: Right, we talked last week about the scientific application that

829
00:42:04,760 --> 00:42:08,160
Speaker 1: you know, used deep learning to figure out the shapes

830
00:42:08,200 --> 00:42:12,600
Speaker 1: of proteins, the structures of proteins, and that could have

831
00:42:13,239 --> 00:42:17,719
Speaker 1: some beneficial uses, will probably have some beneficial uses for

832
00:42:18,080 --> 00:42:20,879
Speaker 1: you know, how we understand disease and medicine and how

833
00:42:20,880 --> 00:42:25,560
Speaker 1: we treat that. But there are ways to probably differentiate

834
00:42:25,600 --> 00:42:29,560
Speaker 1: and think about these things like it's not you don't

835
00:42:29,600 --> 00:42:34,400
Speaker 1: just have to be either luddite or like AI pedal

836
00:42:34,440 --> 00:42:37,920
Speaker 1: to the floor. You know, let's just get out of

837
00:42:37,920 --> 00:42:41,040
Speaker 1: the way of the big companies. You know, it feels

838
00:42:41,080 --> 00:42:46,560
Speaker 1: like but it is such a complicated technology that I

839
00:42:46,600 --> 00:42:50,000
Speaker 1: think there's going to be inherent cloudiness around how people

840
00:42:50,200 --> 00:42:55,400
Speaker 1: understand it and also manufactured cloudiness because it is in

841
00:42:55,800 --> 00:42:59,879
Speaker 1: the overall system's benefit. The overall system being like CAP,

842
00:43:01,000 --> 00:43:05,160
Speaker 1: it's in their benefit to generate like market excitement where

843
00:43:05,560 --> 00:43:06,960
Speaker 1: there shouldn't be any.

844
00:43:07,520 --> 00:43:10,320
Speaker 7: Basically, yeah, I mean, I think it's easy to generate

845
00:43:10,320 --> 00:43:12,880
Speaker 7: this kind of nebulousness around AI because to some extent,

846
00:43:12,920 --> 00:43:14,000
Speaker 7: we still don't really know.

847
00:43:14,080 --> 00:43:14,640
Speaker 9: What it is.

848
00:43:14,680 --> 00:43:17,240
Speaker 7: It's still more of a concept than anything else, because

849
00:43:17,360 --> 00:43:19,720
Speaker 7: the term AI is like stretched and used to describe

850
00:43:19,760 --> 00:43:23,800
Speaker 7: so many applications. Like I spent two years interviewing engineers

851
00:43:23,800 --> 00:43:25,839
Speaker 7: and data scientists is a big tech firm, and they

852
00:43:25,840 --> 00:43:28,640
Speaker 7: would sort of grumble, well, fifteen or twenty years ago,

853
00:43:28,719 --> 00:43:30,480
Speaker 7: you know, we didn't even call this AI, and we

854
00:43:30,480 --> 00:43:33,200
Speaker 7: were already doing it. It's just a decision tree, you know. Again,

855
00:43:33,239 --> 00:43:35,719
Speaker 7: it's kind of part of that branding. But also we

856
00:43:35,760 --> 00:43:38,680
Speaker 7: have these again thousands of years of stories and thinking

857
00:43:38,719 --> 00:43:41,400
Speaker 7: about what an intelligent machine is and that means we

858
00:43:41,440 --> 00:43:45,680
Speaker 7: can get super invested in super cloudy very very quickly.

859
00:43:46,080 --> 00:43:47,880
Speaker 7: And yeah, I don't want people to feel bad for

860
00:43:48,000 --> 00:43:51,080
Speaker 7: being scared of being cloudy about these technologies, like it

861
00:43:51,120 --> 00:43:54,680
Speaker 7: is dense and confusing, but at the same time, I

862
00:43:54,760 --> 00:43:57,680
Speaker 7: do think that it is really important to come back

863
00:43:57,680 --> 00:44:00,160
Speaker 7: to that question of what does this do for us? So,

864
00:44:00,560 --> 00:44:03,000
Speaker 7: you know, the question of like Luddism or being a

865
00:44:03,080 --> 00:44:05,799
Speaker 7: lude I think is really interesting because I you know,

866
00:44:05,880 --> 00:44:08,839
Speaker 7: personally I do use AI applications. There's certain things about

867
00:44:08,840 --> 00:44:12,080
Speaker 7: these technologies that really excite me. But I'm really sympathetic

868
00:44:12,160 --> 00:44:14,439
Speaker 7: to some of the kind of old school ludyet who

869
00:44:14,760 --> 00:44:18,680
Speaker 7: weren't necessarily anti technology, but we're really against the kind

870
00:44:18,719 --> 00:44:21,239
Speaker 7: of impacts that technology we're having on their society.

871
00:44:21,400 --> 00:44:23,960
Speaker 9: So the way that new technology is like I think

872
00:44:23,960 --> 00:44:24,399
Speaker 9: it would be.

873
00:44:24,360 --> 00:44:28,120
Speaker 7: Things like spinning and weaving, we're causing mass unemployment and

874
00:44:28,200 --> 00:44:31,720
Speaker 7: the kind of broader grammifications that was having for people

875
00:44:31,760 --> 00:44:33,520
Speaker 7: in the UK socially, and.

876
00:44:33,440 --> 00:44:36,120
Speaker 9: That kind of has quite a scary parallel to.

877
00:44:35,800 --> 00:44:38,799
Speaker 7: Today in terms of thinking about maybe what AI will

878
00:44:38,840 --> 00:44:42,240
Speaker 7: bring about for the rest of us who maybe aren't

879
00:44:42,280 --> 00:44:45,240
Speaker 7: researchers in a lab but who maybe might be replaced

880
00:44:45,239 --> 00:44:47,280
Speaker 7: by some of these algorithms in terms of our work

881
00:44:47,400 --> 00:44:48,400
Speaker 7: and our output.

882
00:44:49,440 --> 00:44:53,160
Speaker 1: Yeah, can you talk at all about open source like

883
00:44:53,320 --> 00:44:57,600
Speaker 1: models of because you know, when we talk about this

884
00:44:57,760 --> 00:45:02,960
Speaker 1: idea that orations have all this power and are incentivized

885
00:45:03,040 --> 00:45:06,240
Speaker 1: to do whatever is going to make the most money,

886
00:45:06,280 --> 00:45:08,239
Speaker 1: which in a lot of cases is going to be

887
00:45:08,280 --> 00:45:13,040
Speaker 1: the thing that removes the friction from consumption decisions. And

888
00:45:13,400 --> 00:45:16,799
Speaker 1: you know, just how people interact and do these things

889
00:45:16,840 --> 00:45:19,640
Speaker 1: which well, as you guys talked about in your episode,

890
00:45:19,680 --> 00:45:22,439
Speaker 1: like removing the friction, Like, friction can be really good

891
00:45:22,520 --> 00:45:26,120
Speaker 1: sometimes sometimes your system needs friction to stop and correct

892
00:45:26,120 --> 00:45:31,080
Speaker 1: itself and recognize when bad shit, when things are going wrong.

893
00:45:31,480 --> 00:45:37,000
Speaker 1: But you know, there's also a history in even in

894
00:45:37,040 --> 00:45:41,680
Speaker 1: the US, where corporations are racing to get to a

895
00:45:41,800 --> 00:45:47,400
Speaker 1: development and ultimately are beat by open source models of

896
00:45:47,520 --> 00:45:52,759
Speaker 1: technological organizing around like getting a specific solution. Do you

897
00:45:52,800 --> 00:45:56,280
Speaker 1: have any hope for like open source in the future

898
00:45:56,320 --> 00:45:56,720
Speaker 1: of AI.

899
00:45:57,520 --> 00:46:00,000
Speaker 7: Yeah, I mean, I think I'm really interested in community

900
00:46:00,239 --> 00:46:02,640
Speaker 7: forms of development, and I think open source is a

901
00:46:02,640 --> 00:46:06,360
Speaker 7: really interesting example. I think we've seen other interesting examples

902
00:46:06,360 --> 00:46:09,680
Speaker 7: around things like collective data labeling, and I think that

903
00:46:09,719 --> 00:46:12,479
Speaker 7: these kinds of collective movements on the one hand, seem

904
00:46:12,560 --> 00:46:16,760
Speaker 7: like a really exciting, community based alternative to the concentration

905
00:46:17,320 --> 00:46:20,800
Speaker 7: of power in a very very narrow segment of tech companies.

906
00:46:21,160 --> 00:46:23,840
Speaker 9: On the other hand, though, I think community work is

907
00:46:24,000 --> 00:46:25,120
Speaker 9: really hard work.

908
00:46:25,360 --> 00:46:28,680
Speaker 7: We had doctor David Adlani on our podcast, who's very

909
00:46:28,680 --> 00:46:31,920
Speaker 7: important figure in Mascana, which is a grassroots organization that

910
00:46:32,080 --> 00:46:35,160
Speaker 7: aims to bring some of the over four thousand African

911
00:46:35,200 --> 00:46:39,839
Speaker 7: languages into natural language processing or NLP systems, and he

912
00:46:39,880 --> 00:46:41,600
Speaker 7: talks a lot about how the work he does with

913
00:46:41,600 --> 00:46:45,600
Speaker 7: Masakana is so valuable and so important, but it's also

914
00:46:45,960 --> 00:46:49,359
Speaker 7: really really hard because when you're working in that kind

915
00:46:49,440 --> 00:46:52,680
Speaker 7: of collective, decentralized environment, it can be much slower, and

916
00:46:52,680 --> 00:46:54,200
Speaker 7: as you said, there can be a lot more friction

917
00:46:54,360 --> 00:46:58,919
Speaker 7: in that process. But counter to this move fast break

918
00:46:58,960 --> 00:47:01,239
Speaker 7: things kind of culture, sometimes that friction can be really

919
00:47:01,239 --> 00:47:03,720
Speaker 7: productive and it can help us slow down and think about,

920
00:47:03,920 --> 00:47:06,719
Speaker 7: you know, the decisions that we're making very intentionally, rather

921
00:47:06,760 --> 00:47:09,120
Speaker 7: than just kind of racing as fast as we can

922
00:47:09,239 --> 00:47:11,920
Speaker 7: to make the next newest, shiniest product.

923
00:47:13,320 --> 00:47:15,640
Speaker 2: I was almost like, you're like in your work too,

924
00:47:15,760 --> 00:47:18,560
Speaker 2: you know, you talk about how, you know, like looking

925
00:47:18,600 --> 00:47:21,520
Speaker 2: at these technologies, especially through a lens of like feminism

926
00:47:21,560 --> 00:47:25,560
Speaker 2: and intersectionality and you know, bipop communities and things like that,

927
00:47:25,960 --> 00:47:27,960
Speaker 2: and I know, like broadly in science there's like, you know,

928
00:47:28,000 --> 00:47:31,840
Speaker 2: there's an issue of like language hegemony in scientific research

929
00:47:31,960 --> 00:47:35,160
Speaker 2: where if things aren't written in English a lot, sometimes

930
00:47:35,560 --> 00:47:38,520
Speaker 2: studies just get fucking ignored because like I don't speak

931
00:47:38,520 --> 00:47:42,239
Speaker 2: Spanish or I can't read Chinese. Therefore I don't know

932
00:47:42,280 --> 00:47:45,480
Speaker 2: if this research is being done, and therefore it just

933
00:47:45,520 --> 00:47:48,640
Speaker 2: doesn't exist because the larger community is like we all

934
00:47:48,719 --> 00:47:51,440
Speaker 2: just think in English, Like, so how do you like

935
00:47:51,480 --> 00:47:54,800
Speaker 2: you know specifically because you know, when hearing the description

936
00:47:54,920 --> 00:47:58,120
Speaker 2: of your work help me understand, like and the listeners too,

937
00:47:58,200 --> 00:48:00,120
Speaker 2: like of how we should be looking at these things

938
00:48:00,120 --> 00:48:02,359
Speaker 2: from that also from that perspective too, because I think

939
00:48:02,600 --> 00:48:04,640
Speaker 2: right now we're all caught up and like it's talking

940
00:48:04,719 --> 00:48:08,040
Speaker 2: sky at and it's not you know, hold on, like

941
00:48:08,040 --> 00:48:10,640
Speaker 2: there are other subtleties that actually we should really think

942
00:48:10,680 --> 00:48:13,600
Speaker 2: deeply about because to your point, I feel like those

943
00:48:13,600 --> 00:48:16,640
Speaker 2: are the dimensions of an emerging technology or trend or

944
00:48:16,680 --> 00:48:19,560
Speaker 2: something that gets ignored because to your point, it's like

945
00:48:19,719 --> 00:48:23,120
Speaker 2: the thing. Well, of course it's unequal, of course it's

946
00:48:23,480 --> 00:48:26,480
Speaker 2: racist or whatever, But what are those like, what are

947
00:48:26,480 --> 00:48:28,799
Speaker 2: those ways that people need to really be thinking about

948
00:48:28,840 --> 00:48:29,719
Speaker 2: this technology?

949
00:48:30,239 --> 00:48:32,440
Speaker 7: Yeah, I mean, I think English language hegemony is a

950
00:48:32,480 --> 00:48:35,120
Speaker 7: really good example of this broader problem of the more

951
00:48:35,200 --> 00:48:38,319
Speaker 7: subtle kinds of exclusions that get built into these technologies,

952
00:48:38,400 --> 00:48:41,360
Speaker 7: because I think we've all probably seen the cases of

953
00:48:41,480 --> 00:48:45,360
Speaker 7: AI systems that have been really horrifically and explicitly racist

954
00:48:45,480 --> 00:48:49,120
Speaker 7: or really horrifically sexist, from you know, Tay the chatbot

955
00:48:49,160 --> 00:48:53,120
Speaker 7: that started spouting horrific right wing racist propaganda and had

956
00:48:53,160 --> 00:48:57,000
Speaker 7: get taken down through to Amazon hiring tool that's systemically

957
00:48:57,000 --> 00:49:00,760
Speaker 7: discriminated against female candidates. These are really I think avert

958
00:49:01,880 --> 00:49:03,759
Speaker 7: depictions of the kinds of harms a I can do.

959
00:49:04,160 --> 00:49:07,960
Speaker 7: But I think things like English language hegemony are also

960
00:49:08,040 --> 00:49:11,759
Speaker 7: incredibly important for showing how existing kinds of exclusions and

961
00:49:11,840 --> 00:49:14,759
Speaker 7: patterns of power get replicated in these tools. Because to

962
00:49:14,920 --> 00:49:18,319
Speaker 7: an English language speaker, very crucially, they might use chat

963
00:49:18,360 --> 00:49:20,719
Speaker 7: GPT and think this is great, this is what my

964
00:49:20,800 --> 00:49:23,480
Speaker 7: whole world looks like if they only speak English. Obvious

965
00:49:23,560 --> 00:49:25,759
Speaker 7: to anyone who is not a native English speaker or

966
00:49:25,760 --> 00:49:27,799
Speaker 7: who doesn't only speak English. It's going to be an

967
00:49:27,840 --> 00:49:30,840
Speaker 7: incredibly different experience. And that's where I think we see

968
00:49:30,920 --> 00:49:34,239
Speaker 7: the benefits of these tools being really unequally distributed. I

969
00:49:34,239 --> 00:49:37,680
Speaker 7: think it's also important because there's such exclusions in which

970
00:49:37,800 --> 00:49:41,239
Speaker 7: kinds of languages and forms of communication can get translated

971
00:49:41,280 --> 00:49:44,120
Speaker 7: into these systems. So, for example, I work with a

972
00:49:44,160 --> 00:49:46,479
Speaker 7: linguists at the University of Newcastle and she talks about

973
00:49:46,480 --> 00:49:49,360
Speaker 7: the fact that there's so many languages like signed languages

974
00:49:49,680 --> 00:49:52,160
Speaker 7: and languages that don't have a written language, but they're

975
00:49:52,200 --> 00:49:54,680
Speaker 7: never going to be translated into these tools and never

976
00:49:54,719 --> 00:49:57,760
Speaker 7: are going to benefit from them. You might think, Okay, well,

977
00:49:58,360 --> 00:50:02,600
Speaker 7: do these communities want those languages translated into an AI tool?

978
00:50:02,800 --> 00:50:05,680
Speaker 9: Maybe maybe not. I'd argue, of course that's up to them.

979
00:50:05,880 --> 00:50:08,520
Speaker 7: But those communities are still going to experience the negative

980
00:50:08,520 --> 00:50:11,360
Speaker 7: effects of AI, like the climate cost of these tools,

981
00:50:11,400 --> 00:50:13,919
Speaker 7: and so I think it's just really important, like you said,

982
00:50:13,960 --> 00:50:16,920
Speaker 7: to think about what kinds of hegemony are getting further

983
00:50:17,120 --> 00:50:19,440
Speaker 7: entrenched by AI powered technologies.

984
00:50:20,400 --> 00:50:23,560
Speaker 1: All right, great, let's take a quick break and we'll

985
00:50:23,560 --> 00:50:24,399
Speaker 1: come back and.

986
00:50:24,440 --> 00:50:26,719
Speaker 2: Finish up with a few questions.

987
00:50:26,760 --> 00:50:39,440
Speaker 1: We'll be right back, and we're back and there is

988
00:50:39,520 --> 00:50:44,960
Speaker 1: somehow still a thing having been born in nineteen eighty

989
00:50:45,120 --> 00:50:48,680
Speaker 1: and like lived in like all my early memories are

990
00:50:48,719 --> 00:50:53,320
Speaker 1: in a decade where it was just your brain on drugs,

991
00:50:53,360 --> 00:50:56,960
Speaker 1: like just this weird, straightforward, just say noted drugs.

992
00:50:57,040 --> 00:50:58,040
Speaker 2: I thought drugs were a.

993
00:50:57,960 --> 00:51:00,840
Speaker 1: Thing people were going to make me do, like force

994
00:51:01,560 --> 00:51:03,640
Speaker 1: into my blood stream when I.

995
00:51:03,600 --> 00:51:06,000
Speaker 2: Got to high school. Well that's only Seattle, Jack, If

996
00:51:06,040 --> 00:51:08,000
Speaker 2: you drive through Seattle, that will happen to you. That

997
00:51:08,040 --> 00:51:10,600
Speaker 2: will happen. They will force it actually through your car.

998
00:51:10,640 --> 00:51:12,040
Speaker 2: They have through your car window.

999
00:51:12,200 --> 00:51:14,560
Speaker 1: Yeah, well you can't. You have to have your windows up.

1000
00:51:14,600 --> 00:51:18,600
Speaker 1: Come on, they're they're gonna blow heroin into you and

1001
00:51:18,680 --> 00:51:20,799
Speaker 1: you you have to have the air recycling in your

1002
00:51:20,800 --> 00:51:21,840
Speaker 1: air in your car.

1003
00:51:23,400 --> 00:51:28,440
Speaker 2: Heroin gets in. But so, I I think we assumed

1004
00:51:28,520 --> 00:51:31,000
Speaker 2: that the DARE program went away, most of us, right

1005
00:51:32,080 --> 00:51:33,960
Speaker 2: until I saw I saw some people outside of a

1006
00:51:34,000 --> 00:51:37,799
Speaker 2: best Buy, like best by. Yeah did you see that?

1007
00:51:38,120 --> 00:51:39,920
Speaker 2: What you saw their people outside of the best Buy?

1008
00:51:40,239 --> 00:51:43,160
Speaker 3: Yeah it was the best Buy. Yeah, there people stand

1009
00:51:43,160 --> 00:51:45,000
Speaker 3: outside of the best spot. I don't know if they

1010
00:51:45,000 --> 00:51:49,719
Speaker 3: have like like there's a contract, right, Yeah, but that's

1011
00:51:49,719 --> 00:51:50,920
Speaker 3: where I saw their people too.

1012
00:51:51,040 --> 00:51:53,960
Speaker 2: I've seen their people outside two best buys before with

1013
00:51:54,080 --> 00:51:57,160
Speaker 2: like trying to get you to sign something, and all

1014
00:51:57,200 --> 00:51:59,319
Speaker 2: I said was like, y'all are what the fuck? I

1015
00:51:59,360 --> 00:52:03,680
Speaker 2: was like, Noah, and I'm here. I'm here for a

1016
00:52:03,719 --> 00:52:06,120
Speaker 2: mouse pad. Now leave me the fuck alone.

1017
00:52:06,480 --> 00:52:11,480
Speaker 1: So yeah, yeah, I mean there there's obviously an epidemic

1018
00:52:11,600 --> 00:52:15,680
Speaker 1: of overdose deaths. Like the thing that Pewee Herman was

1019
00:52:15,719 --> 00:52:18,160
Speaker 1: saying in that ps A that we listened to up

1020
00:52:18,200 --> 00:52:21,160
Speaker 1: top is that was not true of crack, Like crack

1021
00:52:21,200 --> 00:52:24,600
Speaker 1: didn't have like an epidemic of od's, I don't think,

1022
00:52:24,800 --> 00:52:28,040
Speaker 1: but it did. Like that that is happening with fentanyl,

1023
00:52:28,320 --> 00:52:31,839
Speaker 1: like ventanyl is causing a massive spike and overdoses like

1024
00:52:32,760 --> 00:52:35,560
Speaker 1: and you don't know if there's too much and people

1025
00:52:35,600 --> 00:52:37,640
Speaker 1: like that does seem to be like the thing that

1026
00:52:37,680 --> 00:52:41,680
Speaker 1: they told us about drugs in the eighties has absolutely

1027
00:52:42,280 --> 00:52:42,880
Speaker 1: come true.

1028
00:52:43,120 --> 00:52:50,760
Speaker 2: It's called yeah, it might be, but one will happen

1029
00:52:50,880 --> 00:52:53,640
Speaker 2: and we will go fucking hog wild over this ship.

1030
00:52:54,080 --> 00:52:57,640
Speaker 1: Yes, so they've decided, Okay, this is real, no more

1031
00:52:57,640 --> 00:53:00,880
Speaker 1: fucking around. Let's go back to that idea that was

1032
00:53:01,080 --> 00:53:04,360
Speaker 1: proven to not work and was just a way to

1033
00:53:05,080 --> 00:53:10,840
Speaker 1: get money to funnel money to cops, right essentially. Yeah,

1034
00:53:10,880 --> 00:53:13,560
Speaker 1: So the DARE program is being brought back to several

1035
00:53:13,560 --> 00:53:17,200
Speaker 1: school districts following a prolonged absence, this time with a

1036
00:53:17,239 --> 00:53:21,240
Speaker 1: specific focus on fentanyl. Doesn't seem like they've learned anything

1037
00:53:21,640 --> 00:53:24,960
Speaker 1: from the first time, Like that. The thing that people

1038
00:53:26,040 --> 00:53:28,280
Speaker 1: say was wrong with the DARE program in the first place.

1039
00:53:28,320 --> 00:53:31,879
Speaker 1: First of all, that it was cops, cops going into classrooms, Yep,

1040
00:53:32,080 --> 00:53:36,400
Speaker 1: yeah that part. Second of all, they were doing it

1041
00:53:36,440 --> 00:53:41,920
Speaker 1: too early. They were targeting kids who weren't really they're

1042
00:53:41,920 --> 00:53:45,560
Speaker 1: trying to like get it in kids' brains before, like

1043
00:53:45,680 --> 00:53:47,719
Speaker 1: drugs were a real thing to them.

1044
00:53:48,080 --> 00:53:50,799
Speaker 2: It's just like it. That's why it didn't work for me.

1045
00:53:51,360 --> 00:53:54,720
Speaker 3: My drug was power Rangers at it, right, I don't

1046
00:53:54,760 --> 00:53:57,000
Speaker 3: like I have no idea what a crack pipe is,

1047
00:53:57,200 --> 00:53:57,480
Speaker 3: you know.

1048
00:53:57,560 --> 00:53:59,960
Speaker 2: Yeah, totally or like I knew it from a movie.

1049
00:54:00,120 --> 00:54:01,799
Speaker 2: But like other than that, I was like, yeah, I

1050
00:54:01,800 --> 00:54:05,200
Speaker 2: mean I've seen seen New Jack City, but that's not

1051
00:54:05,480 --> 00:54:08,759
Speaker 2: that's not how I live. So yeah, because I think

1052
00:54:08,800 --> 00:54:10,919
Speaker 2: what I think, I was in fifth grade.

1053
00:54:10,760 --> 00:54:13,479
Speaker 3: Yeah, same, I was probably in like fourth or fifth

1054
00:54:13,520 --> 00:54:15,840
Speaker 3: grade when they started pushing there down my throat.

1055
00:54:15,640 --> 00:54:17,520
Speaker 2: And I just remember being like this is if anything,

1056
00:54:17,640 --> 00:54:21,600
Speaker 2: you're educating me on how like how drugs are. Yeah,

1057
00:54:21,880 --> 00:54:24,080
Speaker 2: that's all that was. That was like my first real

1058
00:54:24,160 --> 00:54:27,920
Speaker 2: conversation about drugs, where like the cop brought drugs into

1059
00:54:27,960 --> 00:54:30,720
Speaker 2: the classroom and we passed it around and smell that pipe.

1060
00:54:31,000 --> 00:54:33,320
Speaker 2: That's the worst smell you're ever gonna smell. I remember

1061
00:54:33,480 --> 00:54:38,520
Speaker 2: saying that. I was like, that smells good. Actually cigarette

1062
00:54:38,800 --> 00:54:42,719
Speaker 2: for failure, But yeah, yeah, it was.

1063
00:54:42,800 --> 00:54:46,200
Speaker 1: I didn't realize it was created by U, the l

1064
00:54:46,239 --> 00:54:49,680
Speaker 1: a p D. Chief of Police Area Gate Baby, who

1065
00:54:49,960 --> 00:54:55,520
Speaker 1: his thoughts on recreational drug users is that they should

1066
00:54:55,800 --> 00:54:58,160
Speaker 1: be taken out and shot.

1067
00:54:58,560 --> 00:55:06,000
Speaker 2: Mmm. So well, okay, a very like humane approach to Yeah,

1068
00:55:06,520 --> 00:55:09,360
Speaker 2: drug it waste before do Tarte. Yeah.

1069
00:55:09,800 --> 00:55:14,919
Speaker 1: Tarte got his ship from Darrel, the Johnny apple Seed

1070
00:55:14,960 --> 00:55:18,480
Speaker 1: of the DARE program. But at its height, DARRE was

1071
00:55:18,520 --> 00:55:22,120
Speaker 1: being taught at a whopping seventy five percent of schools.

1072
00:55:22,680 --> 00:55:26,200
Speaker 1: By nineteen ninety eight, it was reportedly costing taxpayers six

1073
00:55:26,320 --> 00:55:32,719
Speaker 1: hundred million dollars a year. Yeah, and the presentations were

1074
00:55:32,800 --> 00:55:37,320
Speaker 1: just full of racist dog whistles, or just not dog whistles,

1075
00:55:37,320 --> 00:55:41,560
Speaker 1: just racism, like pointing to the broken black family as

1076
00:55:41,600 --> 00:55:43,040
Speaker 1: the source of the drug crisis.

1077
00:55:44,000 --> 00:55:46,279
Speaker 2: Yeah, it's going to it for Tommy White School. Have

1078
00:55:46,400 --> 00:55:49,359
Speaker 2: ob be like Miles, you know about that? Yeah?

1079
00:55:49,120 --> 00:55:52,200
Speaker 3: Yeah, does your parents your family? Yeah, you're like, I

1080
00:55:52,239 --> 00:55:52,960
Speaker 3: have no idea what this is.

1081
00:55:52,960 --> 00:55:54,279
Speaker 2: I would see your mom picking you up.

1082
00:55:54,640 --> 00:55:58,560
Speaker 3: Why yeah, whish dad, You're like, man, get out of

1083
00:55:58,560 --> 00:56:01,120
Speaker 3: my line. But the thing aways bugged me about Dare

1084
00:56:01,320 --> 00:56:03,000
Speaker 3: was like, it's like the gum theory. Do you I

1085
00:56:03,040 --> 00:56:04,759
Speaker 3: don't know if you all are familiar what I call it,

1086
00:56:04,760 --> 00:56:06,520
Speaker 3: the gum theory. It may not be real, but I

1087
00:56:06,520 --> 00:56:08,680
Speaker 3: think about it. It's like when you tell some Look,

1088
00:56:08,719 --> 00:56:11,000
Speaker 3: when you tell kids to not chew gum in school,

1089
00:56:11,360 --> 00:56:13,720
Speaker 3: they're gonna chew gum because you're.

1090
00:56:13,680 --> 00:56:15,960
Speaker 2: Telling them underground gum racket.

1091
00:56:16,080 --> 00:56:18,040
Speaker 3: Yeah, there was a whole like I was the girl

1092
00:56:18,080 --> 00:56:20,680
Speaker 3: that was bringing the gum to school to give to everybody.

1093
00:56:20,800 --> 00:56:22,040
Speaker 2: Like what are you so like?

1094
00:56:22,200 --> 00:56:24,200
Speaker 3: Dare was always like it was so dumb to me

1095
00:56:24,239 --> 00:56:27,280
Speaker 3: because I'm going, you're telling kids not to quote unquote

1096
00:56:27,320 --> 00:56:29,440
Speaker 3: do something, and all you're doing is you're just piquing

1097
00:56:29,520 --> 00:56:32,160
Speaker 3: their interest. But yes, it was very much riddled in

1098
00:56:32,239 --> 00:56:34,920
Speaker 3: anti blackness, and that is something that I've always had

1099
00:56:34,960 --> 00:56:37,440
Speaker 3: an issue with when it comes to this program's.

1100
00:56:37,000 --> 00:56:41,960
Speaker 2: Yeah, you're paying the least cool, least trustworthy people in

1101
00:56:42,000 --> 00:56:46,000
Speaker 2: your community to come into school and talk to the

1102
00:56:45,680 --> 00:56:51,160
Speaker 2: k the part of your community that most despises them, right, and.

1103
00:56:51,120 --> 00:56:54,480
Speaker 3: They put drugs on them. They put drugs on people.

1104
00:56:54,840 --> 00:56:56,759
Speaker 3: So how are you going to tell me? Yeah, I

1105
00:56:56,800 --> 00:56:59,800
Speaker 3: don't know. I've always had issues with DARE. I've always

1106
00:56:59,800 --> 00:57:02,480
Speaker 3: had she's with the police. But I will say very

1107
00:57:02,560 --> 00:57:04,799
Speaker 3: openly that I think DARE is just a way to

1108
00:57:04,840 --> 00:57:08,120
Speaker 3: make black people look worse than what the world already does.

1109
00:57:08,200 --> 00:57:11,719
Speaker 2: So yeah, just reinforce like the police perspective or what

1110
00:57:12,080 --> 00:57:16,400
Speaker 2: drug use even, Yeah, it's it's black people mostly. And

1111
00:57:16,400 --> 00:57:19,000
Speaker 2: then also like getting kids like snitch on their parents

1112
00:57:19,080 --> 00:57:25,080
Speaker 2: and shit like that. It's like they had I remember

1113
00:57:25,160 --> 00:57:28,240
Speaker 2: them having a box that you put a non airs

1114
00:57:28,280 --> 00:57:31,680
Speaker 2: notes into the DARE box and then at the end

1115
00:57:31,680 --> 00:57:35,600
Speaker 2: of the class, the DARE officer would read the questions.

1116
00:57:36,240 --> 00:57:39,520
Speaker 2: And this was just a thing that smartasses used.

1117
00:57:39,240 --> 00:57:44,800
Speaker 1: To like funny, make them read stupid shit. But yeah,

1118
00:57:44,840 --> 00:57:50,200
Speaker 1: apparently it ended like that that DARE box resulted in

1119
00:57:50,560 --> 00:57:54,920
Speaker 1: children like knarking on their parents and being removed from

1120
00:57:55,040 --> 00:57:58,760
Speaker 1: their own homes by social services because they revealed that

1121
00:57:58,800 --> 00:58:02,760
Speaker 1: their parents were taking drugs aka smoking a little bit

1122
00:58:02,760 --> 00:58:10,440
Speaker 1: of pot. So really cool problem solving there, creating fucking

1123
00:58:10,800 --> 00:58:12,840
Speaker 1: just situations.

1124
00:58:12,840 --> 00:58:15,960
Speaker 2: But like to doctor John's point, like it it's like

1125
00:58:16,240 --> 00:58:20,880
Speaker 2: verifiable that DARE did fuck all to prevent kids from

1126
00:58:20,920 --> 00:58:22,400
Speaker 2: using drugs, right.

1127
00:58:22,400 --> 00:58:26,920
Speaker 1: Yeah, yeah, yeah, the stats are officially in and it

1128
00:58:28,240 --> 00:58:32,600
Speaker 1: either had no effect or made children slightly more likely

1129
00:58:32,680 --> 00:58:35,640
Speaker 1: to use drugs and alcohol at an early age.

1130
00:58:35,760 --> 00:58:39,240
Speaker 2: I'm I'm telling you it did because I legitimately wanted

1131
00:58:39,240 --> 00:58:45,360
Speaker 2: to smoke. PCP told us, Yeah, he said, I'm not joking.

1132
00:58:45,840 --> 00:58:49,400
Speaker 2: I'm hearing my best friend. I know, but this is

1133
00:58:49,440 --> 00:58:52,400
Speaker 2: my fucking tannel, like my loven year old brain. Right.

1134
00:58:53,640 --> 00:58:55,760
Speaker 2: First of all, the first thing we always ask him

1135
00:58:55,800 --> 00:58:58,000
Speaker 2: at the top of every day class was can we

1136
00:58:58,080 --> 00:59:01,040
Speaker 2: hold your gun? Yeah? He never and he was like no,

1137
00:59:01,160 --> 00:59:02,880
Speaker 2: there's no way, but we asked it every time. He's like,

1138
00:59:02,920 --> 00:59:04,640
Speaker 2: you guys have to stop asking well, can you pull

1139
00:59:04,680 --> 00:59:08,240
Speaker 2: it out and show us the bullets? No, He's like

1140
00:59:08,280 --> 00:59:09,920
Speaker 2: we have to get going. He's like what is that?

1141
00:59:10,200 --> 00:59:11,880
Speaker 2: And then like we have kids who like new guns

1142
00:59:11,920 --> 00:59:13,600
Speaker 2: and he would always get annoyed. But so he's talked

1143
00:59:13,600 --> 00:59:17,280
Speaker 2: about this, like so we're gonna talk about PCP Angelush

1144
00:59:17,480 --> 00:59:20,320
Speaker 2: like that. I got this exact same speech. It's crazy.

1145
00:59:20,400 --> 00:59:22,439
Speaker 2: He said. He went to a call at a jack

1146
00:59:22,480 --> 00:59:24,520
Speaker 2: in the box where a guy had just beamed up,

1147
00:59:24,840 --> 00:59:28,240
Speaker 2: smoked some PCP and was fighting the police there and

1148
00:59:28,240 --> 00:59:30,000
Speaker 2: they had they needed back up. He said. When he

1149
00:59:30,040 --> 00:59:33,720
Speaker 2: got there, this guy who smoked PCP lifted a fucking

1150
00:59:33,800 --> 00:59:37,560
Speaker 2: dumpster about the head. I remember that story and threw

1151
00:59:37,640 --> 00:59:39,200
Speaker 2: it out a cop car.

1152
00:59:39,840 --> 00:59:43,320
Speaker 11: And me and my friends were all like, oh. We

1153
00:59:43,320 --> 00:59:47,280
Speaker 11: were like, yes, are you for powers? Yeah, We're like

1154
00:59:47,320 --> 00:59:49,560
Speaker 11: how does it give you? How does it give you strength?

1155
00:59:49,640 --> 00:59:52,040
Speaker 11: He's like, I'm not sure. It's like the drugs, the

1156
00:59:52,080 --> 00:59:53,880
Speaker 11: drugs duty It's like. But then like we're like, what

1157
00:59:54,000 --> 00:59:54,200
Speaker 11: is it?

1158
00:59:54,280 --> 00:59:56,560
Speaker 2: Like you your muscles, like could you work Like I

1159
00:59:56,600 --> 01:00:00,560
Speaker 2: remember asking like if you worked out on PCP, would

1160
01:00:00,600 --> 01:00:03,919
Speaker 2: you keep that strength? And he was so again irritated

1161
01:00:03,960 --> 01:00:08,720
Speaker 2: by my curiosity around PCP induced superhuman strength. But yeah,

1162
01:00:08,880 --> 01:00:10,400
Speaker 2: he just got mad. He's like, God, this is not

1163
01:00:10,480 --> 01:00:14,800
Speaker 2: a cool thing. And and it's funny that everybody, I

1164
01:00:14,800 --> 01:00:17,760
Speaker 2: guess someone everybody heard this version of PCP gives you

1165
01:00:17,800 --> 01:00:20,840
Speaker 2: superhuman strength, to the point where, like YouTube came out.

1166
01:00:21,360 --> 01:00:25,040
Speaker 2: I was I was scouring YouTube for footage of somebody

1167
01:00:25,080 --> 01:00:27,240
Speaker 2: on PCP with that kind of strength. Never found it,

1168
01:00:27,280 --> 01:00:29,800
Speaker 2: and I was like, this was this cop lying to me?

1169
01:00:30,440 --> 01:00:34,439
Speaker 2: And he was, oh, oh mm hmm, you believe that shit? Yes,

1170
01:00:34,520 --> 01:00:35,000
Speaker 2: cop life.

1171
01:00:35,200 --> 01:00:39,080
Speaker 1: And so as the stats are coming out, the DARE

1172
01:00:39,200 --> 01:00:43,400
Speaker 1: program is doing their own research, you know, and they're like,

1173
01:00:43,520 --> 01:00:46,760
Speaker 1: all right, well, we're gonna get the DOJ to fund

1174
01:00:46,840 --> 01:00:50,520
Speaker 1: this study to see what the like, what actually happened,

1175
01:00:50,520 --> 01:00:51,720
Speaker 1: what's actually going on here?

1176
01:00:51,920 --> 01:00:52,040
Speaker 3: Right?

1177
01:00:52,160 --> 01:00:54,920
Speaker 2: And a month later, for the first time in memory,

1178
01:00:55,560 --> 01:00:59,160
Speaker 2: the DOJ refused to publish a study it had funded

1179
01:00:59,400 --> 01:01:03,560
Speaker 2: for the first time I ever, wow, because it found

1180
01:01:03,920 --> 01:01:07,800
Speaker 2: that the DARE program was useless and was taking money

1181
01:01:07,800 --> 01:01:11,800
Speaker 2: away from programs that actually worked behind the scenes. DARE

1182
01:01:12,400 --> 01:01:16,120
Speaker 2: went ballistic, and they just kept saying this one like

1183
01:01:16,240 --> 01:01:19,640
Speaker 2: every time they get they get attacked. The DARE program

1184
01:01:20,200 --> 01:01:23,560
Speaker 2: is like criticizing dares like kicking your mother or saying

1185
01:01:23,560 --> 01:01:26,960
Speaker 2: that apple pie doesn't taste good. And then like a

1186
01:01:27,000 --> 01:01:29,880
Speaker 2: decade later they were criticized or like proven to be

1187
01:01:30,080 --> 01:01:35,240
Speaker 2: fraudulent again, and their response was, it's like kicking Santa Claus.

1188
01:01:35,280 --> 01:01:39,360
Speaker 2: To me, we're as pure as the driven snow so like,

1189
01:01:40,080 --> 01:01:42,760
Speaker 2: we're as pure as the Colombian cocaine we put in

1190
01:01:42,760 --> 01:01:45,440
Speaker 2: front of children. You're weekend Lumbian flake.

1191
01:01:45,880 --> 01:01:47,920
Speaker 3: I just want to know where the money went, Like,

1192
01:01:48,160 --> 01:01:50,480
Speaker 3: there was a lot of money that was being moved

1193
01:01:50,480 --> 01:01:52,640
Speaker 3: in the nineties around there, and I'm just, well, ye

1194
01:01:52,840 --> 01:01:53,360
Speaker 3: know where.

1195
01:01:53,200 --> 01:01:54,960
Speaker 6: It went right right right place?

1196
01:01:55,240 --> 01:01:58,640
Speaker 2: Two corrupt cops. I'm wondering about where where, Like did

1197
01:01:58,680 --> 01:02:01,760
Speaker 2: it go into some like but none of benevolent police fund?

1198
01:02:01,880 --> 01:02:03,960
Speaker 2: I wonder or it was just like if you got

1199
01:02:03,960 --> 01:02:05,880
Speaker 2: that Dare gig, They're like, oh shit, man, you're off

1200
01:02:05,880 --> 01:02:08,720
Speaker 2: street street the street beat. Oh you're just doing fucking

1201
01:02:08,760 --> 01:02:12,000
Speaker 2: Dare classes. Oh that's right right there they were.

1202
01:02:12,240 --> 01:02:18,000
Speaker 1: They were confiscating luxury cars like Porsches and BMW's and

1203
01:02:18,040 --> 01:02:21,920
Speaker 1: turning them into Dare vehicles, like with a Dare decal

1204
01:02:22,080 --> 01:02:24,800
Speaker 1: on it that they got to drive around. They were like,

1205
01:02:24,880 --> 01:02:28,440
Speaker 1: They're like, yeah, well it becomes pretty apparent who's winning here, though,

1206
01:02:28,480 --> 01:02:31,000
Speaker 1: you know what I'm saying, right, And then the kid's like, wow,

1207
01:02:31,120 --> 01:02:33,760
Speaker 1: the police department buys you guys cars like no, we

1208
01:02:33,800 --> 01:02:34,560
Speaker 1: can't afford that.

1209
01:02:34,680 --> 01:02:37,360
Speaker 2: Drug dealers get rich enough to buy cars like this,

1210
01:02:37,480 --> 01:02:41,880
Speaker 2: And then you're like, oh, Okay, that's the wave. Yeah.

1211
01:02:41,920 --> 01:02:44,760
Speaker 3: In reality, they were doing the same exact thing that

1212
01:02:44,880 --> 01:02:50,800
Speaker 3: drug dealers were doing. They were selling you something. It's yeah,

1213
01:02:50,880 --> 01:02:51,919
Speaker 3: it's ridiculous.

1214
01:02:52,920 --> 01:02:57,200
Speaker 2: But amazingly Dare still uses the Dare box, like they

1215
01:02:57,240 --> 01:03:01,760
Speaker 2: still really have the write down questions for folks. It's anonymous,

1216
01:03:01,760 --> 01:03:03,760
Speaker 2: purely anonymous.

1217
01:03:03,640 --> 01:03:06,520
Speaker 1: Psych We're actually going to investigate your parents if you

1218
01:03:06,960 --> 01:03:09,520
Speaker 1: say that you know what any of these things are?

1219
01:03:10,120 --> 01:03:13,480
Speaker 2: Right that, uh, that's smell familiar to you? Oh yeah,

1220
01:03:13,960 --> 01:03:19,520
Speaker 2: and where to Like. I could totally see being like, oh, yeah,

1221
01:03:19,560 --> 01:03:21,280
Speaker 2: I know, I know what that smells. That's weed, and

1222
01:03:21,280 --> 01:03:23,840
Speaker 2: then being like, well, yeah, how do you know that? Uh?

1223
01:03:23,880 --> 01:03:31,000
Speaker 2: I my parents? Yeah, just looking panicking and throwing your

1224
01:03:31,000 --> 01:03:34,520
Speaker 2: parents under the bus. Yeah, I don't know. I don't know.

1225
01:03:34,560 --> 01:03:37,480
Speaker 2: I was at a reggae festival recently. That's why I know.

1226
01:03:37,840 --> 01:03:40,520
Speaker 2: Next question, no questions. I need a lawyer. Offer for

1227
01:03:40,600 --> 01:03:44,520
Speaker 2: the questions. You're honored. Yeah, all right, that's gonna do it.

1228
01:03:44,680 --> 01:03:49,160
Speaker 1: For this week's weekly Zeitgeist, Please like and review the show.

1229
01:03:49,760 --> 01:03:53,600
Speaker 1: If you like the show, uh means the world to Miles.

1230
01:03:53,680 --> 01:03:55,720
Speaker 1: He he needs your validation.

1231
01:03:55,880 --> 01:03:58,920
Speaker 2: Folks. I hope you're having a great weekend.

1232
01:03:59,160 --> 01:04:48,520
Speaker 1: And I will all team Monday Bye.