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Speaker 1: I was listening to a weird Al song today where

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Speaker 1: he name drops Kanye, which, yeah, taky because taky, but

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Speaker 1: it's not, it's just a really it's just weird catalog.

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Speaker 2: Huh.

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Speaker 1: My kids were like on the way to school today,

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Speaker 1: were like, is weird Al still alive?

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Speaker 2: Weird Alse still alive? Ship? He's I have a oh

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Speaker 2: I should say I have a picture with him. He's

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Speaker 2: but a baby.

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Speaker 1: He's also Kermit vibes. Oh big Kermit vibes. Yeah, damn,

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Speaker 1: I guess sixty he's sixty five. Weird aw man beautiful,

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Speaker 1: still got it though, Taki Taki?

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Speaker 2: Where's he at now? Does he have like a not

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Speaker 2: like us one? Yet? He has been quiet and I

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Speaker 2: you know, where is he? Kendrick and Drake Beef? Did

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Speaker 2: he do a meet the grams? He actually dig you

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Speaker 2: for you? Yeah, I'm looking if you did. Pekaboo, that

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Speaker 2: would be fucking amazing. What are they talking about? They're

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Speaker 2: not talking about anything? What are they talking about? They're

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Speaker 2: talking about They're not talking about anything. Pika Boo. I

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Speaker 2: just I just freaked out a three month old Pika Boo.

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Speaker 1: Hello the Internet, and welcome to Season three, eighty six,

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Speaker 1: episode two of turd Eylands. Guys.

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Speaker 2: It's a production of iHeartRadio.

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Speaker 1: It's a podcast where we take a deep dive into

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Speaker 1: America's shared consciousness.

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Speaker 3: And it's Tuesday, April twenty ninth. Yeah, you know, we're

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Speaker 3: almost April. It's National Zipper Day.

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Speaker 2: Shout out zippers, shout out just fastening, just just zipping up,

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Speaker 2: you know what I mean, shut up? The national it's

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Speaker 2: the Peace Rose Day, National Peace Rose Day, which is

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Speaker 2: name of a like a famous rose that like a

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Speaker 2: French horticulturalist had sent out like before the invasion of

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Speaker 2: France by the Nazis to try and preserve his unique rose.

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Speaker 2: And we caught the peace rows here in America and

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Speaker 2: also national like in terms of like maintaining peace.

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Speaker 1: Yeah, was he he sent the roses to the Nazis

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Speaker 1: and was like, hey.

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Speaker 2: Well so he was in France and he's like the

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Speaker 2: I guess the sort of anecdote goes is, because the

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Speaker 2: invasion was imminent, he sent cuttings to people in Italy, Turkey,

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Speaker 2: Germany's somehow at that point is like Germany and the

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Speaker 2: US to be like, hold on to these, hold on

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Speaker 2: to these, and yeah, it ended up being I think

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Speaker 2: given out at like one of the first meetings of

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Speaker 2: the UN. And it's also but all that to say,

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Speaker 2: the most important thing is National Shrimp Scampy Day. So

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Speaker 2: let's get locked in.

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Speaker 1: Yeah, shout out to the letters YKK one your zippy.

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Speaker 2: By the way, Yeah, very famous. I mean we might

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Speaker 2: have to be. That's a Japanese group, Japanese Zipper or ykks.

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Speaker 2: Oh yeah, sorry tariffs. You know what I mean. Our

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Speaker 2: zipper game about to get fucked up. That's like the

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Speaker 2: goaded zipper too. Like we don't loll get only.

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Speaker 1: Wear pants that have USA on the zipper and those

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Speaker 1: always gets stale first. Yeah, most inopportune times. Yeah, oh man,

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Speaker 1: I can't believe we're almost through April. You know, I'm

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Speaker 1: gonna be the guy who keeps blaming the year be

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Speaker 1: like April twenty twenty five, bad one.

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Speaker 2: Can't wait. May things are looking up because you'll be

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Speaker 2: with us.

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Speaker 1: Yeah all right, my name is Jack O'Brien ak golf

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Speaker 1: till you dead, Golf golf till you dead.

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Speaker 3: Boswell, roll Boswell, rollo boswill roll.

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Speaker 2: On the green. That one courtesy of Sparkles.

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Speaker 1: On the Discord, who said when Joel Monique said, Trump

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Speaker 1: can golf till he's dead. That song just started playing

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Speaker 1: in my mind and I couldn't get it out. That is,

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Speaker 1: by the way, not a threat. That is just an

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Speaker 1: invitation for him to have a lot of fun golfing

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Speaker 1: for the rest of his long, healthy life. He cannot

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Speaker 1: off till he's dead because he's so powerful.

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Speaker 2: They need archives. The Neia Archives remix of that song

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Speaker 2: is really good. Yeah, a little drummond bass, Yeah yeah, yeah,

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Speaker 2: the why Why whyse not thekks Miles. I'm thrilled to

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Speaker 2: be joined by you, my co host, mister Miles. Thank

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Speaker 2: you so much for having me. Miles Gray, the Lord

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Speaker 2: of Lankersham aka the Showgun with No Gun, Experimental Black

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Speaker 2: and Knees visual artist, your boy Kusamah shout out to

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Speaker 2: Uh there was. It was Japanese America or Japanese Heritage

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Speaker 2: Night at Dodger Stadium last night, so you know, it

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Speaker 2: was very excited about that also because there was there's

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Speaker 2: unique merch that I'm trying to put my hands on. Yeah,

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Speaker 2: you know what i mean, You know what I mean,

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Speaker 2: You know what I mean. The game Uh no, no, no,

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Speaker 2: I did not make it to the game because her

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Speaker 2: magic You surprised me with tickets to see the Cowboy

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Speaker 2: Carter Tour. Wow, I was not able to go, not

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Speaker 2: that I even had tickets to the Dodger game. But yeah, yeah, yeah,

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Speaker 2: did that, did.

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Speaker 1: That And we will get your review tomorrow. We'll get

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Speaker 1: your review later. Yeah. Yeah, there's there's a press embargo

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Speaker 1: as they didn't want that coming through.

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Speaker 2: Miles.

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Speaker 1: We're thrilled to be joined once again in our third

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Speaker 1: seat by a research associate at the Leverholme Center for

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Speaker 1: the Future of Intelligence, where she researches AI from the

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Speaker 1: perspective of gender studies. She's the co host of the

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Speaker 1: great podcast A Good Robot. Please welcome back the brilliant

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Speaker 1: doctor Kerrie mcinn.

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Speaker 4: Doctor kay, Hi, thanks so much for having me back.

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Speaker 2: Thank you for accepting our offer to return, you know,

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Speaker 2: to class up the joint as I.

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Speaker 1: Say, international uh, you know, diplomacy going on. We had

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Speaker 1: to you know, work it out. But we're thrilled to

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Speaker 1: have you back. M h.

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Speaker 4: How have you been, I mean, how have we all been?

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Speaker 4: I don't really to answer that question anymore. So I'm

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Speaker 4: always like, I guess, in the big scheme of things,

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Speaker 4: I'm personally doing really well.

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Speaker 2: But yeah, yeah, thriving, Yeah, yeah, what's the energy? Like,

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Speaker 2: what's the energy like in Europe looking at the cess

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Speaker 2: pit that is a flame as known as the United States.

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Speaker 2: Is it like is it like a tooth like Iraq war?

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Speaker 2: Anger like I experienced going to Europe orre like what

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Speaker 2: the fuck are y'all doing over there? Or is it

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Speaker 2: like how we felt after Breggs there people like, oh,

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Speaker 2: is fucking themselves bad?

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Speaker 4: I feel like it's yeah, a lot of confusion and fear.

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Speaker 4: I don't know if it's necessarily even just anger as

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Speaker 4: much as it's just people being like what is happening?

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Speaker 4: Like what are you doing? But I think there is

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Speaker 4: a degree of like shell shock to it, where just

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Speaker 4: so much keeps happening that I think you start to

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Speaker 4: like become slightly numb, which is very bad but also

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Speaker 4: very understandable. I got a bit of a refresh from

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Speaker 4: that because I was back home in New Zealand in

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Speaker 4: March and like, there's nothing quite like being in a

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Speaker 4: country when you watch the news every day and like

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Speaker 4: nothing happens. It's like a tree fell near the motorway,

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Speaker 4: not even on the motorway, or you know, that kind

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Speaker 4: of level of needs right, right right, And then it

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Speaker 4: came back here and I was like, oh my goodness,

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Speaker 4: this is a very different level of news.

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Speaker 2: Yeah yeah, yeah, yeah, Well we're not getting them to it. Unfortunately.

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Speaker 2: Every day hurts every day. Damn you got anything for

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Speaker 2: that hurts it every day? Yeah? Yeah, I mean, but

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Speaker 2: that is the point. I think they want as many

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Speaker 2: people to turn off and sort of ignore what's happening.

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Speaker 2: But I think I think as things ramp up here,

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Speaker 2: people more and more realize how how much the norms,

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Speaker 2: as flawed as they were, were things that were worth

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Speaker 2: keeping and trying to improve rather than be like FuG everything,

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Speaker 2: get rid of it all and whatever this is.

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Speaker 1: Yeah, all right, well we are going to get to

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Speaker 1: know you a little bit better in a moment.

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Speaker 2: First, just a preview of some of the things we're

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Speaker 2: talking about.

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Speaker 1: First of all, we just want to get your expert

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Speaker 1: opinion on where where are we at generally with AI.

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Speaker 2: With with AI, like is a one, it's actually called

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Speaker 2: a one jack a one, like Linda McMahon would say, Yeah.

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Speaker 1: Are there cool things happening? Is it that we seem

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Speaker 1: to see a lot of bad ones happening in the

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Speaker 1: United States? We want to talk about this, this signal

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Speaker 1: chat that's been powering the right word shift in tech,

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Speaker 1: powered by Mark Andresen seems.

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Speaker 2: Like a cool guy.

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Speaker 1: It's the person whose name that I feel like, now

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Speaker 1: I have to learn after reading about this. We're gonna

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Speaker 1: see how Trump wants to use AI. We're gonna talk

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Speaker 1: about using AI large language models to entrap people online,

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Speaker 1: people who have signed over their likenesses and now not

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Speaker 1: so psyched about that decision. All of that plenty more.

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Speaker 1: But before we get to that, we do like to

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Speaker 1: ask our guests, what is something from your search history

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Speaker 1: that's revealing about who you?

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Speaker 4: Ooh, well, I mean, I guess do you know the

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Speaker 4: singer Lord. I'm a very big fan. She's just had

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Speaker 4: a new single out for the first time in like

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Speaker 4: four years, and she goes like very underground that just

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Speaker 4: like suddenly pops up again, and so I'm very excited

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Speaker 4: for it's hopefully going to be Lord's summer. And yeah,

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Speaker 4: I've been following the single following the kind of weird

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Speaker 4: sort of I don't know if you saw her botched

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Speaker 4: kind of mini and New York spontaneous concert that immediately

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Speaker 4: got canceled because you didn't have a permit was following that,

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Speaker 4: and yeah, I'm just excited for whatever happens next, hopefully

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Speaker 4: an album, hopefully not more botched concerts.

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Speaker 1: I was actually talking about Lord this morning to my

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Speaker 1: children because we were listening to the weird Al song

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Speaker 1: Foil on the drive to school, which is a royal, yeah,

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Speaker 1: which is royals and it's just about tinfoil, and they

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Speaker 1: were like, what was the song about? Originally I was like, well,

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Speaker 1: there's this really great artist named Lord that you should

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Speaker 1: have probably learned about before you heard the song. But anyways,

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Speaker 1: it didn't have a ton of time to dig into it.

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Speaker 2: But I'm glad.

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Speaker 1: Eric said, I'm on the I'm on the weird Al

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Speaker 1: watch to see what he's going to be dropping. He's

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Speaker 1: he's given us an even longer desert to walk through.

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Speaker 2: But what's you go? Oh yeah, sorry, I was curious,

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Speaker 2: Like I was. I saw somebody post on Blue Sky

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Speaker 2: some kind of meme about how Lord fans are experiencing

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Speaker 2: like this third album and comparing it to another artist

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Speaker 2: is like, what, how do you like? It felt like

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Speaker 2: where people really split on the second album and are

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Speaker 2: now looking for the third, Like what's as a Lord fan?

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Speaker 2: How do you look at her discography right now and

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Speaker 2: what do you hope? Like, Okay, can you just help

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Speaker 2: me understand what I like what people are talking about

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Speaker 2: with this new like Lord album.

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Speaker 4: Yeah, I mean pretty close to Her third album was

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Speaker 4: like very divisive, and so it kind of came out

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Speaker 4: and it was then like sort of twenty twenty one

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Speaker 4: was like just off the pandemic and it was super

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Speaker 4: kind of like breezy summary, like it was really different

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Speaker 4: everything should put out previously, and so I think people

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Speaker 4: felt pretty split on it. Whereas the second album, which

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Speaker 4: is also my personal favorite, Melodrama, was this kind of

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Speaker 4: like buzzy, poppy, kind of intense sort of dance album,

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Speaker 4: and it sounds like she's going back to like that

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Speaker 4: kind of sound again, and so I think people are

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Speaker 4: just like very excited. It's because, like post Charlie XCX,

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Speaker 4: I like want to hear her do this sound again.

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Speaker 2: Got it, got it? Got it? Oh, so they did

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Speaker 2: the thing, which like you're not doing the thing that

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Speaker 2: we liked you from the first album, and yeah, okay,

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Speaker 2: this makes it.

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Speaker 4: Yeah, like I kind of like that she does like

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Speaker 4: different stuff, but also, yeah, I think people are excited.

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Speaker 2: That always helps. That always helps me discover other genres.

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Speaker 2: When an artist I really liked started tinkering with something else,

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Speaker 2: and before I would be a little bit rigid about

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Speaker 2: it and be like I don't I didn't sign up

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Speaker 2: for this. But then you're like, well, I like you

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Speaker 2: as an artist, so thank you for opening my mind. Yeah, yeah,

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Speaker 2: kid A was hard for you, huh the kid kid

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Speaker 2: My God? Yeah, absolutely, I was just I was absolutely

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Speaker 2: spun by that one. He says, can't be taken back.

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Speaker 2: I did. I did. I want everything to be the Bens. Actually, no,

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Speaker 2: Pablo Honey for being honest. Yeah, the first one.

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Speaker 1: One, the well how did you experienced the Charlie XCX thing,

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Speaker 1: because that that was an interesting song. So they had

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Speaker 1: like a Charlie XCX had a song with Lord where

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Speaker 1: they talked about there's like a very honest song. They're like, yeah,

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Speaker 1: I kind of like hated you a little bit, like

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Speaker 1: but not really, but like I didn't know what you

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Speaker 1: were going through. No, No, it was like a song

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Speaker 1: where like they were it was like just talking about

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Speaker 1: them hanging out and like them being like kind of

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Speaker 1: just like a weird like celebrity friendship type thing.

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Speaker 2: I don't know where were you a fan, Carrie?

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Speaker 4: I loved it. Yeah, I never really loved like brat

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Speaker 4: in general, even though I'm like the least brat person

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Speaker 4: in the world because I like listen to what her

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Speaker 4: like party music. Couldn't go to bed at like nine

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Speaker 4: p thirty. I don't think I was the target.

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Speaker 1: What he had better get a sprain of bread in

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Speaker 1: at four o'clock so I can't have time to wind

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Speaker 1: down before.

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Speaker 4: Dinner prep with. But yeah, no, I love that. You know,

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Speaker 4: all those kinds of like sliding, awkward, ambivalent relationships. You know,

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Speaker 4: I thought they captured that really brilliantly.

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Speaker 2: Yeah awesome. What is something you think is underrated?

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Speaker 4: Super honest baff bags on planes, because like on these

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Speaker 4: European super cheap flights, they don't seem to do them anymore.

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Speaker 4: Because I found that out unfortunately last December by just

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Speaker 4: barfing on the plane because I could not find a

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Speaker 4: barf bag and I would not recommend that. So bring

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Speaker 4: your own bag as that first tip to people traveling

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Speaker 4: to Europe. A second, you know any airlines out there,

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Speaker 4: they must be so cheap, please bring them back. So, yeah,

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Speaker 4: I don't know when that happens. I feel like they

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Speaker 4: always used to have, you know that like sad little

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Speaker 4: baggie in the back seat for this exact scenario.

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Speaker 1: And I feel like I never used them back then,

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Speaker 1: And now I just had to like have my seven

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Speaker 1: year old throw up into my hands on a plane

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Speaker 1: because we couldn't find it in.

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Speaker 4: Time, Like, wait, did this happened to you as well?

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Speaker 2: Yeah, like within the passage two minutes and you weren't

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Speaker 2: flying easy Jet or Ryanair.

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Speaker 1: No, we were now and yeah, we were flying Asiana

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Speaker 1: and we had made it through a whole flight that

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Speaker 1: was like pretty bumpy and got back onto the tarmac

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Speaker 1: and something about the way it was like kind of

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Speaker 1: bouncing a little bit. He was just like, ohh And.

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Speaker 2: We did not have a lot of time. I'm not

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Speaker 2: looking forward to inevitably when I have to, because I

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Speaker 2: feel like that's the thing every parent has to do,

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Speaker 2: is to buy hand receive that offering from your child.

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Speaker 2: Like I remember doing that with my mom and I

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Speaker 2: was wearing overall. I have such a vivid memory. I

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Speaker 2: was wearing overalls and my mom just put it into

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Speaker 2: that front pocket of my own wow, And I was like,

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Speaker 2: there you go, just put that in your overall pocket

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Speaker 2: because Mommy did not drive this far to buy something

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Speaker 2: for us to leave now.

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Speaker 1: So I've never felt more helpless than walking up to

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Speaker 1: the flight attendant with a handful of.

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Speaker 2: Like you have something, I could put this in Jesus.

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Speaker 1: When did you get Because I don't really get a

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Speaker 1: lot of motion sickness, but I feel like flat flights

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Speaker 1: are generally pretty steady, and then there's like the quick turbulence.

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Speaker 1: But like motion sickness for me has always happened when

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Speaker 1: it's like a slow kind of I don't know, you know,

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Speaker 1: like you it's like sort of slowly attacking your equilibrium

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Speaker 1: went when did you have your onset?

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Speaker 4: Oh gosh, I mean I feel like I'm quite prone

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Speaker 4: to motion sickness anyway. But it was one of those

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Speaker 4: like you know planes, where it's like you get into

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Speaker 4: the air, the plane to me just like shaking like

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Speaker 4: one of those toys, and then like ten minutes later

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Speaker 4: they're like there seems to be some turbuluce, like we know,

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Speaker 4: don't worry, we got We're not getting out of our seats. See.

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Speaker 4: I think it was just a bit of a rocky ride.

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Speaker 4: But also I had like one of those giant like

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Speaker 4: American style. Even they give you like a leader of

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Speaker 4: coffee right before I went on. So it was also

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Speaker 4: self induced in some way. I was too I was

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Speaker 4: too primed for that to happen. But you know, git

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Speaker 4: two Holidays or whatever airline we were flying, you know,

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Speaker 4: bring back the.

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Speaker 1: Bag, right, had nothing to do with the air conditions

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Speaker 1: of the plane. They're like, yeah, sorry, the plane just

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Speaker 1: does that.

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Speaker 4: It's sh the bags out itself, you know.

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Speaker 2: Yeah, it shakes you.

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Speaker 1: And then they ask you you have to pay twenty

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Speaker 1: five dollars for barsh bags price.

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Speaker 2: It's an add on. It's not like I'm anecdotally. Some

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Speaker 2: people are saying it's because flights, because like the airplane

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Speaker 2: technology allows for smoother flights, people are just getting less sick,

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Speaker 2: Like yeah, in numbers, so then maybe they're dialing it back.

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Speaker 2: But I don't even if you.

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Speaker 4: Don't do it, I'm like, when the zero points there

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Speaker 4: are one, do it.

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Speaker 2: It's real bad everyone else. But I don't mind them

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Speaker 2: having a bag because I'd rather not have to see

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Speaker 2: somebody be like, excuse me, then you take this? Hey,

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Speaker 2: could you pass this up to the flight.

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Speaker 1: No, we're not supposed to be getting out of her seat,

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Speaker 1: But what is something that you think is overrated?

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Speaker 4: Actually another plane related one at No BARF. This time

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Speaker 4: I saw Emelia Perez the movie on the plane and

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Speaker 4: the whole time I did finish it, but the whole

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Speaker 4: time I was like, how did this get thirteen OSCAR nominations.

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Speaker 4: I'm sorry if you're big fans, but I was just like, like, genuinely,

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Speaker 4: I'm quite impressed it got that many because I was like,

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Speaker 4: this is just not like, regardless of what you think

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Speaker 4: about the politics and everything, I'm like, even just with

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Speaker 4: all of that stripped out and all the drama around it,

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Speaker 4: I was like, this is just a bad musical. It's

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Speaker 4: I really, I really find it quite interesting. So I

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Speaker 4: was like, well, kudos to them for.

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Speaker 2: It did so well, you know what, I respect, Yeah,

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Speaker 2: I respect the game there because you somehow got this

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Speaker 2: thing straight to the top. Yeah, I mean, idea, I'm

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Speaker 2: efficient with my movie watching. I only watch whatever we'll

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Speaker 2: win Best Picture of the Year before it happens, and

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Speaker 2: I did it this year again. You know, only saw Anora,

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Speaker 2: don't need to do anything else.

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Speaker 4: Yeah, yeah, yeah, the plane So that was great, but

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Speaker 4: I had to hold my headphone into the headphone jack

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Speaker 4: for the whole two plus hours of the film, and

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Speaker 4: I still I still really enjoyed that. So Nora was Yeah,

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Speaker 4: that was worth it.

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Speaker 2: Damn these budget airlines bark bad eggs. You got to

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Speaker 2: hold the fucking cable to the headphones in the jack

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Speaker 2: to be able to hear it. Oh.

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Speaker 1: I used to have to do that with my headphones

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Speaker 1: on my phone, like when back when like all headphones

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00:18:26,880 --> 00:18:29,680
Speaker 1: were wired, and like there was like something going on

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00:18:29,760 --> 00:18:32,479
Speaker 1: with my port and so I had to just always

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Speaker 1: like have it like jammed in. Oh.

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Speaker 2: Remember when airplanes used to have the little just plastic

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Speaker 2: tubing where the sound came through the plastic tubes into

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Speaker 2: your ears before like the digital like three and a

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Speaker 2: half millimeter cables. Oh, that was an era. I just

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Speaker 2: remember the fidelity sound. Yeah. I always remember stealing it

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Speaker 2: and like trying to like drink stuff with it because

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Speaker 2: it was just like a big tube with like foam

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Speaker 2: ear tips.

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Speaker 1: Yeah.

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00:18:56,600 --> 00:18:58,840
Speaker 2: It was like one step above just having a giant

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Speaker 2: horn that you stuck your ear, Yeah, like held on.

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Speaker 2: I remember being a kid and bringing the armrest up

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00:19:04,640 --> 00:19:06,800
Speaker 2: and dialing the volume up and just resting my ear

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Speaker 2: ear on the two little holes where the sound came out.

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Speaker 1: Wait, really, you didn't even know that's how it worked,

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00:19:12,760 --> 00:19:15,240
Speaker 1: Like a dumb part of me assumed that's how it worked.

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Speaker 2: But the tubes like, yeah, no, it was just a

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Speaker 2: plastic tube.

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Speaker 1: And here we are talking tech ye us like doctor

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Speaker 1: mc and Ernie here. Yeah, I will say, Miles, you

387
00:19:27,480 --> 00:19:30,920
Speaker 1: you should probably start advertising yourself as like you need

388
00:19:30,920 --> 00:19:33,720
Speaker 1: to sign the same pr agent as that octopus who

389
00:19:33,760 --> 00:19:38,040
Speaker 1: like predicted who would win the World Cup three times

390
00:19:38,040 --> 00:19:40,280
Speaker 1: in a row because you only watched the.

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Speaker 2: Whatever I stumble upon, the whatever you stumble upon? Yeah,

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00:19:42,920 --> 00:19:44,760
Speaker 2: they're like, what what did he say? I have to

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Speaker 2: read the vibes. And if too many older people are

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Speaker 2: like this is the movie of the year, I go,

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00:19:49,440 --> 00:19:52,920
Speaker 2: no it ain't. No, it ain't. And because of that

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Speaker 2: I will not watch it.

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Speaker 1: I think I think it would have won if they

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Speaker 1: hadn't had the backlash because it add to that same

399
00:20:01,040 --> 00:20:07,960
Speaker 1: green book crash thing, Oh right, like by writing it. Yeah,

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00:20:08,080 --> 00:20:12,840
Speaker 1: it's like the you know, a very complex issue and

401
00:20:12,840 --> 00:20:17,400
Speaker 1: then a movie written by somebody who has given ten

402
00:20:17,480 --> 00:20:20,560
Speaker 1: minutes of thought to it and written a script and

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Speaker 1: then they like put a bunch of you know, filmmaking

404
00:20:23,720 --> 00:20:28,240
Speaker 1: like good filmmaking work against that, although this one, I agree,

405
00:20:28,280 --> 00:20:31,359
Speaker 1: this is the music in the musical was not was

406
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Speaker 1: not outstanding, but shame shame that a shame that.

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Speaker 2: It was daring. They love a fucking daring movie. You know.

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Speaker 2: All right, well, it's wonderful to have you back.

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Speaker 1: We're going to take a quick break, and when we

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Speaker 1: come back, we are going to talk about technology beyond

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00:20:51,280 --> 00:20:54,200
Speaker 1: the little tube headphones that used to plug it directly

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Speaker 1: into your airplane seat armrest.

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Speaker 5: We will be right back, and we're back.

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Speaker 1: We're back, and we on this podcast we ask the

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Speaker 1: hard hitting questions such as where are we at with AI? Generally,

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00:21:20,720 --> 00:21:24,359
Speaker 1: just like generally, what's the vibes? Doctor McNerney.

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Speaker 2: I'm all so every because I remember when we first

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Speaker 2: had you on, we were like in the midst of

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Speaker 2: like e AI EI and andlay mommy EI. Yeah, yeah,

420
00:21:33,880 --> 00:21:38,440
Speaker 2: AI researchers giving us like the warning like this could

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Speaker 2: be the next it's gonna end the world. I'm I'm

422
00:21:41,520 --> 00:21:47,320
Speaker 2: taking cyanide now to preemptive excuse myself from this apocalypse.

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Speaker 2: And after a while, I think that's as we started

424
00:21:50,240 --> 00:21:52,160
Speaker 2: to speak to more and more experts, we all became

425
00:21:52,280 --> 00:21:55,280
Speaker 2: rightly unconvinced by its capabilities and like, oh, it's a

426
00:21:55,280 --> 00:21:58,680
Speaker 2: fancy computer program that's like a toy basically, but they're

427
00:21:58,680 --> 00:22:00,800
Speaker 2: saying you can do so much more. Then we see

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00:22:00,840 --> 00:22:03,480
Speaker 2: more and more articles about how people using AI are

429
00:22:03,520 --> 00:22:06,679
Speaker 2: like fucking up in like legal proceedings like the MyPillow

430
00:22:06,720 --> 00:22:10,560
Speaker 2: guy his lawyers use chat GPT recently and the judges

431
00:22:10,600 --> 00:22:14,200
Speaker 2: like they're like thirty critical errors in your like your

432
00:22:14,320 --> 00:22:16,359
Speaker 2: response here, Like I don't even know what this is.

433
00:22:16,800 --> 00:22:20,399
Speaker 2: But now I'm like, are we now sort of I

434
00:22:20,400 --> 00:22:22,159
Speaker 2: think because we're sort of past the thing of like

435
00:22:22,240 --> 00:22:25,800
Speaker 2: this is that world ender game changer, but still these

436
00:22:25,800 --> 00:22:28,280
Speaker 2: companies have to make the line go up. Are we

437
00:22:28,400 --> 00:22:30,560
Speaker 2: like now seeing more of a strategy to just kind

438
00:22:30,560 --> 00:22:34,240
Speaker 2: of normalize AI use to get people to slowly warm

439
00:22:34,320 --> 00:22:37,080
Speaker 2: up to it than sort of like the big explosive

440
00:22:37,160 --> 00:22:40,000
Speaker 2: sort of debut with like chat GPT and things that

441
00:22:40,040 --> 00:22:42,480
Speaker 2: we saw. Because I feel like now I hear more

442
00:22:42,520 --> 00:22:46,919
Speaker 2: people talk about AI when they're like miyazakifying themselves or

443
00:22:46,960 --> 00:22:49,960
Speaker 2: getting a recipe recommendation, rather than like this is going

444
00:22:49,960 --> 00:22:51,920
Speaker 2: to replace the entire medical field.

445
00:22:52,080 --> 00:22:55,280
Speaker 4: Yeah, I mean, I think normalization is the exact right word,

446
00:22:55,400 --> 00:22:57,680
Speaker 4: and I was just thinking this today as I went

447
00:22:57,760 --> 00:22:59,560
Speaker 4: on to Google Search, and of course they now have

448
00:22:59,680 --> 00:23:02,320
Speaker 4: that overview summary and even.

449
00:23:02,119 --> 00:23:11,320
Speaker 1: Some of my fe who you're talking about, my friend Gemini, Yeah.

450
00:23:09,359 --> 00:23:13,479
Speaker 4: They're yeah, some answers, you know, even things like that.

451
00:23:13,520 --> 00:23:15,800
Speaker 4: I think people like me who say probably wouldn't have

452
00:23:15,840 --> 00:23:19,040
Speaker 4: gone to chat GBT just to do like basic information searches,

453
00:23:19,280 --> 00:23:21,119
Speaker 4: I have to admit I do read the AI summary

454
00:23:21,200 --> 00:23:22,680
Speaker 4: is quite a lot, and you know, and I think

455
00:23:22,680 --> 00:23:24,680
Speaker 4: that it's a way of just kind of like making

456
00:23:24,680 --> 00:23:28,399
Speaker 4: it really frictionless and really really easy to immediately use

457
00:23:28,440 --> 00:23:30,199
Speaker 4: an AI tool. And I think we see that with

458
00:23:30,240 --> 00:23:32,919
Speaker 4: a lot of like AI roll out and look. And

459
00:23:33,000 --> 00:23:35,320
Speaker 4: on the one hand, like I'm not completely against the

460
00:23:35,400 --> 00:23:37,159
Speaker 4: use of these AI tools if you're really finding it

461
00:23:37,240 --> 00:23:39,679
Speaker 4: useful and if it's reliable, you're kind of getting the

462
00:23:39,720 --> 00:23:42,040
Speaker 4: full picture behind the tool and what it's for. But

463
00:23:42,080 --> 00:23:43,479
Speaker 4: on the other hand, like I think something I do

464
00:23:43,560 --> 00:23:45,919
Speaker 4: worry about is something that I think has to be

465
00:23:46,000 --> 00:23:48,800
Speaker 4: integrated into all our ethical tech use is like a

466
00:23:48,840 --> 00:23:51,240
Speaker 4: meaningful sense of the opt out or a meaningful sense

467
00:23:51,280 --> 00:23:53,359
Speaker 4: of the opt in. So like, do I have the

468
00:23:53,440 --> 00:23:55,200
Speaker 4: right in the ability to say, like NAP I don't

469
00:23:55,240 --> 00:23:57,720
Speaker 4: want to use this, and you know, am I being

470
00:23:57,920 --> 00:24:01,080
Speaker 4: like actively consulted and being able to consent to using

471
00:24:01,119 --> 00:24:04,320
Speaker 4: particular tools or programs and so on and so forth.

472
00:24:04,640 --> 00:24:06,960
Speaker 4: And yeah, I think the current rollout of AI tools

473
00:24:07,480 --> 00:24:09,639
Speaker 4: is not really complying with that particularly.

474
00:24:09,680 --> 00:24:12,760
Speaker 2: Well hmm right, Yeah, they're just excited.

475
00:24:12,840 --> 00:24:14,480
Speaker 1: They have a new toy that they want to work,

476
00:24:14,640 --> 00:24:16,439
Speaker 1: that they want to use that they're going to make

477
00:24:16,480 --> 00:24:20,560
Speaker 1: a you know, super Bowl commercial about so.

478
00:24:20,440 --> 00:24:23,280
Speaker 2: The Gouda cheese the most.

479
00:24:23,320 --> 00:24:27,560
Speaker 1: Did you see that, doctor McEnery that Google advertised their

480
00:24:27,640 --> 00:24:31,760
Speaker 1: AI product with a Super Bowl ad that had incorrect

481
00:24:31,760 --> 00:24:34,560
Speaker 1: information in it. The premise was, this is a cheese

482
00:24:34,640 --> 00:24:37,919
Speaker 1: farmer and he is a whiz when it comes to

483
00:24:37,960 --> 00:24:40,640
Speaker 1: making good cheese, but he's all thumbs when it comes

484
00:24:40,640 --> 00:24:44,600
Speaker 1: to writing marketing material. And so he it showed Google

485
00:24:44,640 --> 00:24:47,800
Speaker 1: AI like telling him facts that he could put on

486
00:24:47,880 --> 00:24:50,479
Speaker 1: his cheese. And one of the facts was that Gouda

487
00:24:50,680 --> 00:24:57,000
Speaker 1: cheese is the most popular type of cheese and six.

488
00:24:56,280 --> 00:24:58,240
Speaker 2: The world's cheese sales.

489
00:24:58,440 --> 00:25:02,159
Speaker 1: What which is just like on its surface, like obviously wrong,

490
00:25:02,359 --> 00:25:05,240
Speaker 1: and they still like put it in a in a

491
00:25:05,280 --> 00:25:08,480
Speaker 1: Super Bowl commercial, Like somebody caught it once. The ad

492
00:25:08,520 --> 00:25:11,040
Speaker 1: had like been up and so it didn't make it

493
00:25:11,119 --> 00:25:13,680
Speaker 1: to the super Bowl, but it made it like online

494
00:25:13,680 --> 00:25:16,480
Speaker 1: and was viewed millions of times. And it's just that

495
00:25:17,040 --> 00:25:20,320
Speaker 1: seems to be like it's if if it's not going

496
00:25:20,359 --> 00:25:23,080
Speaker 1: to be one hundred percent, if it's not going to

497
00:25:23,119 --> 00:25:25,320
Speaker 1: be right one hundred percent of the time, it's kind

498
00:25:25,320 --> 00:25:29,879
Speaker 1: of useless because it's just like that. I mean, yeah,

499
00:25:29,320 --> 00:25:33,439
Speaker 1: I feel like everybody should be like would be opting

500
00:25:33,440 --> 00:25:35,199
Speaker 1: out of it if they knew, like and just so

501
00:25:35,280 --> 00:25:37,640
Speaker 1: you know, like one out of like every I don't

502
00:25:37,680 --> 00:25:40,360
Speaker 1: know twenty of the things that you search is going

503
00:25:40,400 --> 00:25:43,080
Speaker 1: to be like blatantly wrong in a way that is

504
00:25:43,119 --> 00:25:46,840
Speaker 1: going to be humiliating to you. Yeah, Yang, we're not

505
00:25:46,840 --> 00:25:49,520
Speaker 1: going to tell you which one enjoy the product.

506
00:25:50,000 --> 00:25:51,720
Speaker 4: Yeah. I mean, first, I love the idea that it's

507
00:25:51,760 --> 00:25:53,840
Speaker 4: not even the ara but it's just big gudhas out

508
00:25:53,840 --> 00:25:56,960
Speaker 4: there trying to spread some misinformation about the popularity of

509
00:25:56,960 --> 00:25:59,600
Speaker 4: Guda cheese. But second, like, yeah, I feel like one

510
00:25:59,640 --> 00:26:01,840
Speaker 4: way of people describe it as this idea of like yeah,

511
00:26:01,880 --> 00:26:04,119
Speaker 4: something like chat GPT or the AI overview can be

512
00:26:04,200 --> 00:26:07,040
Speaker 4: useful for getting like a sense of a topic. But yeah,

513
00:26:07,040 --> 00:26:08,680
Speaker 4: I guess the issue for me, is like that often

514
00:26:08,720 --> 00:26:12,119
Speaker 4: requires quite a lot of expertise to be able to

515
00:26:12,160 --> 00:26:14,840
Speaker 4: know whether something is right or not. And so for example,

516
00:26:14,840 --> 00:26:17,720
Speaker 4: like my husband as from North Carolina, as a basketball fan,

517
00:26:17,840 --> 00:26:21,440
Speaker 4: like I am sort of forcibly inducted into the NBA

518
00:26:21,640 --> 00:26:23,800
Speaker 4: enough that yeah, I could probably tell that eighty percent

519
00:26:23,920 --> 00:26:27,000
Speaker 4: maybe actually that might be overestimating my knowledge, but yeah,

520
00:26:27,000 --> 00:26:30,280
Speaker 4: that twenty percent would be totally out of my knowledge.

521
00:26:30,320 --> 00:26:32,760
Speaker 4: And so I think that's my fears. If you're relying

522
00:26:32,840 --> 00:26:35,480
Speaker 4: on these tools. It's not that people can't tell that

523
00:26:35,600 --> 00:26:39,040
Speaker 4: something's wrong, but you know that when we're using it

524
00:26:39,080 --> 00:26:41,120
Speaker 4: for like a really wide range of applications, that does

525
00:26:41,160 --> 00:26:43,720
Speaker 4: actually require a lot of expertise in all those different things.

526
00:26:43,720 --> 00:26:46,560
Speaker 4: And I think, certainly, speaking for myself, like that's not

527
00:26:46,640 --> 00:26:48,880
Speaker 4: something I would be able to do or to discern.

528
00:26:49,440 --> 00:26:52,040
Speaker 2: Yeah, because I mean sure, like you think of it like, well,

529
00:26:52,080 --> 00:26:53,719
Speaker 2: if I get a B on my test, but if

530
00:26:53,720 --> 00:26:56,359
Speaker 2: you're asking something to like explain the Civil War and

531
00:26:56,400 --> 00:26:59,160
Speaker 2: you get all the generals and the battles and dates right,

532
00:26:59,480 --> 00:27:03,000
Speaker 2: but the that the reason for the American Civil War wrong,

533
00:27:03,320 --> 00:27:05,639
Speaker 2: where it's like and they all fought over you know,

534
00:27:06,000 --> 00:27:09,800
Speaker 2: economic rights, and then that's now it doesn't matter that

535
00:27:09,840 --> 00:27:12,520
Speaker 2: eighty percent is completely negated by this other piece of

536
00:27:12,680 --> 00:27:16,480
Speaker 2: information that has completely colored the you know, the description

537
00:27:16,600 --> 00:27:18,560
Speaker 2: of something. Now, That's why I'm like, even every time

538
00:27:18,600 --> 00:27:22,440
Speaker 2: I see those summaries, I'm like, no, Like I'll click

539
00:27:22,440 --> 00:27:24,680
Speaker 2: on the links that are saying like we're using these

540
00:27:24,720 --> 00:27:26,640
Speaker 2: to tell us, and I'll look at them like, this

541
00:27:26,680 --> 00:27:29,359
Speaker 2: is not really even exactly what's happening here, to the

542
00:27:29,359 --> 00:27:31,399
Speaker 2: point that I feel like it's causing more harm than

543
00:27:31,440 --> 00:27:34,520
Speaker 2: good because I've at least learned to try and just

544
00:27:34,600 --> 00:27:38,760
Speaker 2: research myself. You know. That's how I got my theories

545
00:27:38,800 --> 00:27:40,880
Speaker 2: on the Earth shape and things like that.

546
00:27:41,000 --> 00:27:43,560
Speaker 1: My research some interesting ideas on that. I don't know

547
00:27:43,600 --> 00:27:46,159
Speaker 1: if you have a couple hours, Doctor mcinnae.

548
00:27:46,240 --> 00:27:48,720
Speaker 2: Well, doctor, I've every time I've emailed doctor Carry, she's

549
00:27:48,840 --> 00:27:51,720
Speaker 2: respectfully declined to entertain the conversation, which I under said.

550
00:27:51,720 --> 00:27:53,040
Speaker 2: She has a luck going on. She has luck going on,

551
00:27:53,160 --> 00:27:53,520
Speaker 2: but I.

552
00:27:53,480 --> 00:27:55,040
Speaker 1: Mean it is she could be a useful source to

553
00:27:55,040 --> 00:27:57,160
Speaker 1: you though you're looking for somebody who's been in an airplane,

554
00:27:57,320 --> 00:27:57,520
Speaker 1: you know.

555
00:27:57,880 --> 00:27:59,240
Speaker 2: Yeah, I looked down.

556
00:28:00,480 --> 00:28:05,840
Speaker 1: Are there cool usees of AI that aren't getting attention?

557
00:28:05,960 --> 00:28:08,480
Speaker 1: Or I guess even like tech breakthroughs that you know,

558
00:28:08,560 --> 00:28:10,320
Speaker 1: we asked this the last time you were on But

559
00:28:10,480 --> 00:28:14,960
Speaker 1: like we're still in the early stages of AI. Are

560
00:28:15,000 --> 00:28:18,560
Speaker 1: there directions that have like popped out to you that

561
00:28:18,880 --> 00:28:21,399
Speaker 1: you know the future of technology and this technology in

562
00:28:21,400 --> 00:28:26,120
Speaker 1: particular could take that are promising for like the bettering

563
00:28:26,160 --> 00:28:28,760
Speaker 1: of the world for more than like twelve rich guys,

564
00:28:28,800 --> 00:28:31,200
Speaker 1: which I feel like is the current the current model.

565
00:28:31,280 --> 00:28:33,720
Speaker 1: It's like these twelve guys are like, Yeah, this would

566
00:28:33,720 --> 00:28:36,719
Speaker 1: be amazing if we could replace all the people.

567
00:28:37,440 --> 00:28:40,040
Speaker 2: See me with total row. I was with total Rope

568
00:28:40,160 --> 00:28:41,480
Speaker 2: from the Miyazaki movie.

569
00:28:42,280 --> 00:28:45,880
Speaker 1: But what where are you seeing hope?

570
00:28:46,040 --> 00:28:47,240
Speaker 2: Where are you seeing hope?

571
00:28:47,240 --> 00:28:49,960
Speaker 4: I guess I'm genuinely terrified and like say something you

572
00:28:50,000 --> 00:28:51,800
Speaker 4: would be like, that's the exact same amount so you

573
00:28:51,840 --> 00:28:58,080
Speaker 4: said two years ago and destroy Yeah, I mean I

574
00:28:58,160 --> 00:29:02,160
Speaker 4: think like I'm always quite excited more creative uses of

575
00:29:02,200 --> 00:29:04,400
Speaker 4: AI or people who are like really trying to think

576
00:29:04,440 --> 00:29:06,400
Speaker 4: about like instead of saying like how can we make

577
00:29:06,400 --> 00:29:08,320
Speaker 4: like one product that works for the whole world, like

578
00:29:08,400 --> 00:29:10,200
Speaker 4: which tends to be the approach of things like chatt

579
00:29:10,240 --> 00:29:13,080
Speaker 4: GBT and then like spor they obviously don't work for

580
00:29:13,120 --> 00:29:15,240
Speaker 4: the whole world and all these different cultural contexts and

581
00:29:15,240 --> 00:29:18,160
Speaker 4: all these different linguistic contexts, because I don't think a

582
00:29:18,200 --> 00:29:21,640
Speaker 4: single product can, but you know, I do think like

583
00:29:21,840 --> 00:29:24,719
Speaker 4: there are really interesting examples of groups like Tahuku Media

584
00:29:24,720 --> 00:29:26,880
Speaker 4: in New Zealand where I'm from, which have been like

585
00:29:27,000 --> 00:29:29,640
Speaker 4: using AI and machine learning teachiks to try and focus

586
00:29:29,680 --> 00:29:32,840
Speaker 4: on trail Mody or the indigenous language of New Zealand's

587
00:29:33,160 --> 00:29:36,520
Speaker 4: language preservation. And so because of like long histories of

588
00:29:36,600 --> 00:29:39,240
Speaker 4: kind of the state suppression of Terreo Modi, there was

589
00:29:39,280 --> 00:29:41,600
Speaker 4: like a period where like there weren't that many Native

590
00:29:41,640 --> 00:29:44,760
Speaker 4: Trail Moordi speakers. It was like really aggressively suppressed. And

591
00:29:44,840 --> 00:29:46,560
Speaker 4: now as a result, people are kind of trying to

592
00:29:46,600 --> 00:29:50,640
Speaker 4: really reinvest and support the kind of revitalization of Treo Mody.

593
00:29:51,080 --> 00:29:54,800
Speaker 4: And Peter Lucas Jones, who's this CEO, I believe Teku

594
00:29:54,880 --> 00:29:58,400
Speaker 4: Media and like their whole team have been really intentional

595
00:29:58,440 --> 00:30:00,680
Speaker 4: and sort of really world leading think and how they've

596
00:30:00,720 --> 00:30:03,360
Speaker 4: been trying to use AI machine learning for this. I

597
00:30:03,400 --> 00:30:05,239
Speaker 4: think a big part of their project though, is that

598
00:30:05,280 --> 00:30:09,120
Speaker 4: they're very insistent on like indigenous data sovereignty or making

599
00:30:09,120 --> 00:30:11,960
Speaker 4: sure that like their platforms aren't sold to big tech

600
00:30:12,080 --> 00:30:14,120
Speaker 4: or reliant on big tech, And I think I imagine

601
00:30:14,160 --> 00:30:17,400
Speaker 4: that's like a really challenging project because like this sort

602
00:30:17,400 --> 00:30:20,280
Speaker 4: of small handful of like big tech firms like are

603
00:30:20,360 --> 00:30:23,479
Speaker 4: incredibly dominant in this space, but a lot of that

604
00:30:23,480 --> 00:30:25,480
Speaker 4: has been around like no, you know, we really want

605
00:30:25,480 --> 00:30:27,560
Speaker 4: to make sure that this remains like technology by and

606
00:30:27,640 --> 00:30:30,640
Speaker 4: for MALDI people and for our organization. And so I

607
00:30:30,680 --> 00:30:34,160
Speaker 4: think projects like that I find like really really inspiring

608
00:30:34,200 --> 00:30:36,280
Speaker 4: and really important. But I think they're also just like

609
00:30:36,320 --> 00:30:39,640
Speaker 4: a great example of like AI development being done super well,

610
00:30:39,680 --> 00:30:41,400
Speaker 4: which is like you have a clear problem, and you

611
00:30:41,480 --> 00:30:44,000
Speaker 4: have clear ideas and ways that you think that AI

612
00:30:44,120 --> 00:30:46,560
Speaker 4: machine learning can help us address in part some of

613
00:30:46,560 --> 00:30:50,240
Speaker 4: this problem without like positioning it as the solution, because

614
00:30:50,240 --> 00:30:51,880
Speaker 4: I think if anyone comes out of the gate ands

615
00:30:51,920 --> 00:30:54,080
Speaker 4: like AI is going to solve this problem, that's when

616
00:30:54,080 --> 00:30:56,400
Speaker 4: I think you should always be a bit like, oh,

617
00:30:56,520 --> 00:30:58,480
Speaker 4: I don't think it is, especially if the problem is

618
00:30:58,480 --> 00:31:03,520
Speaker 4: something like really really massive, right discrimination is kind of like, well, look,

619
00:31:03,560 --> 00:31:05,640
Speaker 4: we just have to reject that one sort of straight

620
00:31:05,640 --> 00:31:07,360
Speaker 4: out the gate and kind of think a bit more

621
00:31:07,400 --> 00:31:08,640
Speaker 4: specifically about this.

622
00:31:08,840 --> 00:31:10,400
Speaker 2: And I'm sure those companies are like and when we

623
00:31:10,440 --> 00:31:12,520
Speaker 2: made that claim, that wasn't actually meant for you to

624
00:31:12,560 --> 00:31:14,640
Speaker 2: be the receiver of that message. It was for Wall

625
00:31:14,640 --> 00:31:18,200
Speaker 2: Street and investors when we said this thing will solve everything,

626
00:31:19,000 --> 00:31:21,440
Speaker 2: because like, yeah, I mean, like that application feels like

627
00:31:21,480 --> 00:31:23,720
Speaker 2: the kind of thing that like, you know, in the

628
00:31:23,840 --> 00:31:28,320
Speaker 2: US right now, Trump is very focused on eliminating any

629
00:31:28,360 --> 00:31:32,560
Speaker 2: semblance of equity or diversity inclusion. Obviously, as we've seen

630
00:31:32,640 --> 00:31:37,120
Speaker 2: like the woke DEI initiative crusade against all those things,

631
00:31:37,640 --> 00:31:40,479
Speaker 2: and that sounds like exactly the kind of thing that

632
00:31:40,480 --> 00:31:43,120
Speaker 2: Trump would be, like, that's not useful. It just has

633
00:31:43,160 --> 00:31:45,000
Speaker 2: to be this other thing that's a money making endeavor.

634
00:31:45,080 --> 00:31:48,720
Speaker 2: Because right now, his people, like the people within the administration,

635
00:31:49,000 --> 00:31:51,760
Speaker 2: like the head of Science and Technology, have said things

636
00:31:51,800 --> 00:31:55,400
Speaker 2: like Biden's AI policies were like divisive and it's all

637
00:31:55,520 --> 00:31:58,840
Speaker 2: been about all been about the redistribution, like redistribution in

638
00:31:58,880 --> 00:32:02,240
Speaker 2: the name of equity. And naturally Trump has fired many

639
00:32:02,320 --> 00:32:06,000
Speaker 2: of the AI experts Biden hired because it was clear

640
00:32:06,280 --> 00:32:08,840
Speaker 2: like obviously Biden hired them. He's like, there's a huge

641
00:32:08,880 --> 00:32:11,000
Speaker 2: bias problem with any of this stuff, and if it's

642
00:32:11,000 --> 00:32:13,000
Speaker 2: even going to be a product people use, like that's

643
00:32:13,640 --> 00:32:17,040
Speaker 2: probably a thing worth tackling. But now like it's become

644
00:32:17,080 --> 00:32:21,000
Speaker 2: sort of like normalized within this administration to say this

645
00:32:21,160 --> 00:32:24,080
Speaker 2: is all harmful now because it's trying to advance equity,

646
00:32:24,680 --> 00:32:27,160
Speaker 2: when like when we've spoken to people like you and

647
00:32:27,200 --> 00:32:29,360
Speaker 2: other experts, it's like, no, you have to get rid

648
00:32:29,400 --> 00:32:32,680
Speaker 2: of those inequities or else it doesn't even fucking work.

649
00:32:33,280 --> 00:32:35,720
Speaker 2: Like like when you're talking about things like being able

650
00:32:35,760 --> 00:32:38,920
Speaker 2: to someone who has like a darker complexion, how does

651
00:32:38,920 --> 00:32:42,480
Speaker 2: a like a self automated car even identify that pedestrian

652
00:32:42,520 --> 00:32:46,160
Speaker 2: as an object to avoid? Because again, these biases affect

653
00:32:46,200 --> 00:32:49,640
Speaker 2: all these different systems. But it feels like now like

654
00:32:49,760 --> 00:32:52,720
Speaker 2: at least from the American conservative side of things or

655
00:32:52,760 --> 00:32:57,080
Speaker 2: just generally the tech conservative movement is like, yeah, maybe

656
00:32:57,080 --> 00:33:00,479
Speaker 2: it's just like fine if it you know, misidentifies like

657
00:33:00,520 --> 00:33:03,640
Speaker 2: black people or doing these other things that just kind

658
00:33:03,680 --> 00:33:06,040
Speaker 2: of show that at the end of the day, we

659
00:33:06,040 --> 00:33:07,600
Speaker 2: would only it only needs to work in the way

660
00:33:07,600 --> 00:33:09,160
Speaker 2: that we want it to work, and all the other

661
00:33:09,200 --> 00:33:10,920
Speaker 2: applications whatever be damned.

662
00:33:10,720 --> 00:33:14,440
Speaker 1: Keeps identifying black people as traffic cones. Do we think

663
00:33:14,480 --> 00:33:16,480
Speaker 1: that we need Can we go to market with this

664
00:33:16,800 --> 00:33:18,840
Speaker 1: still or is that are we good here?

665
00:33:19,000 --> 00:33:22,480
Speaker 2: Well, that's yeah, And I'm just I'm amazed at how

666
00:33:23,840 --> 00:33:27,680
Speaker 2: you know, I think, how much like objectively, this is

667
00:33:27,680 --> 00:33:29,960
Speaker 2: a thing. If you want a product to work, it

668
00:33:30,040 --> 00:33:32,680
Speaker 2: has to be able to be used around the world.

669
00:33:33,000 --> 00:33:34,920
Speaker 2: So how useful is that in a place that doesn't

670
00:33:34,920 --> 00:33:38,480
Speaker 2: have like an ethnic majority that's all white people in

671
00:33:38,560 --> 00:33:41,320
Speaker 2: the same way like that? You know, again, these all

672
00:33:41,360 --> 00:33:43,600
Speaker 2: just feel very counterintuitive. But that seems to be the

673
00:33:43,680 --> 00:33:46,920
Speaker 2: name of this year, this year's theme generally.

674
00:33:47,000 --> 00:33:48,760
Speaker 4: Yeah, yeah, I mean the idea of this is the

675
00:33:48,840 --> 00:33:52,680
Speaker 4: year of counterintuitive thing. This really resonates. Yeah, and I

676
00:33:52,720 --> 00:33:54,880
Speaker 4: think it's like not only disappointing, because it's like I

677
00:33:54,880 --> 00:33:56,760
Speaker 4: do think that it shouldn't be super hard to buy

678
00:33:56,800 --> 00:33:59,080
Speaker 4: into the idea that like, yeah, AI that is like

679
00:33:59,160 --> 00:34:02,160
Speaker 4: more equitable, less biased, and more fear like genuinely is

680
00:34:02,200 --> 00:34:04,520
Speaker 4: actually in the long run good for everyone. Although I

681
00:34:04,560 --> 00:34:07,400
Speaker 4: know there's like a reactionary group that feels like any

682
00:34:07,480 --> 00:34:09,840
Speaker 4: kind of equity or equality is you know, kind of

683
00:34:09,840 --> 00:34:13,040
Speaker 4: impinging on their own kind of share of the pie.

684
00:34:13,400 --> 00:34:15,200
Speaker 4: But you know, I really think like AI ethics and

685
00:34:15,239 --> 00:34:17,839
Speaker 4: safety is for everyone. But yeah, I also think it's

686
00:34:17,920 --> 00:34:20,040
Speaker 4: very sad because like this has huge knock on effis

687
00:34:20,120 --> 00:34:22,200
Speaker 4: for the rest of the world, because like the US

688
00:34:22,320 --> 00:34:25,000
Speaker 4: is a world leader in AI and tech production, and

689
00:34:25,080 --> 00:34:27,239
Speaker 4: so yeah, if you have an environment that is kind

690
00:34:27,239 --> 00:34:30,200
Speaker 4: of saying let's throw ethics out the window, then that

691
00:34:30,239 --> 00:34:31,840
Speaker 4: does have knock on effis for the rest of the

692
00:34:31,840 --> 00:34:34,560
Speaker 4: world their buias and uses still a lot of this technology.

693
00:34:35,160 --> 00:34:37,960
Speaker 4: So yeah, I think like these rollbacks obviously massively affect

694
00:34:37,960 --> 00:34:41,480
Speaker 4: the US domestic context, but they certainly don't stop.

695
00:34:41,280 --> 00:34:44,239
Speaker 2: There, right, Is there any do you see any put

696
00:34:44,440 --> 00:34:47,239
Speaker 2: like this is as stupid on its phase as it

697
00:34:47,320 --> 00:34:49,839
Speaker 2: sounds right to be, like we have to we have

698
00:34:49,920 --> 00:34:53,160
Speaker 2: to stop with these inclusive efforts to address biases within

699
00:34:53,239 --> 00:34:57,839
Speaker 2: AI like models. There's no like it's as dumb as

700
00:34:57,880 --> 00:35:00,399
Speaker 2: that sounds, right, because in my mind and everything I've read,

701
00:35:00,440 --> 00:35:03,160
Speaker 2: people are like, no, no, no, like it it works

702
00:35:03,239 --> 00:35:07,239
Speaker 2: worse when it has all these like inbuilt biases, like

703
00:35:07,320 --> 00:35:10,680
Speaker 2: it will not work as good therefore is not viable.

704
00:35:10,800 --> 00:35:13,400
Speaker 2: So it is that is bad, right, There's no like

705
00:35:13,440 --> 00:35:16,360
Speaker 2: secret things like well, you know, some bias is good

706
00:35:16,480 --> 00:35:17,400
Speaker 2: for these things.

707
00:35:18,000 --> 00:35:20,759
Speaker 4: I mean, if someone if that is that is the

708
00:35:20,800 --> 00:35:23,239
Speaker 4: secret source, and please tell me. Definitely not what I

709
00:35:23,280 --> 00:35:25,360
Speaker 4: would think. I mean, yeah, I mean I think it

710
00:35:25,440 --> 00:35:27,000
Speaker 4: just comes down to again, like I think there's a

711
00:35:27,000 --> 00:35:29,680
Speaker 4: fundamental irony of saying, like, you know, the power of

712
00:35:29,920 --> 00:35:32,120
Speaker 4: these AI enabled tools and products is that you know,

713
00:35:32,200 --> 00:35:35,880
Speaker 4: we can perform all these like massive tasks at scale,

714
00:35:35,880 --> 00:35:37,520
Speaker 4: and this idea of again like a product that can

715
00:35:37,560 --> 00:35:39,239
Speaker 4: be sold across the world, a product that can be

716
00:35:39,320 --> 00:35:41,840
Speaker 4: used at scale with the kind of knowledge that this

717
00:35:41,960 --> 00:35:44,280
Speaker 4: only really works for like a very very narrow base.

718
00:35:44,680 --> 00:35:46,319
Speaker 4: I mean my assumption to be fair though, is like

719
00:35:46,360 --> 00:35:48,520
Speaker 4: I think that a lot of people who make products

720
00:35:48,520 --> 00:35:51,200
Speaker 4: that have, you know, these kinds of exclusions or biases

721
00:35:51,400 --> 00:35:54,040
Speaker 4: aren't necessarily going in being like, I know my product

722
00:35:54,080 --> 00:35:56,279
Speaker 4: is really biased, and I actually just like don't care,

723
00:35:56,480 --> 00:35:58,759
Speaker 4: Like I don't think that usually is it? I think

724
00:35:58,880 --> 00:36:00,960
Speaker 4: often to me, it's just this kind of mindset of

725
00:36:01,440 --> 00:36:03,640
Speaker 4: either we just like haven't really thought about it. I

726
00:36:03,640 --> 00:36:07,040
Speaker 4: think this particularly common with accessibility, which is that of

727
00:36:07,160 --> 00:36:09,680
Speaker 4: the accessibility has to be integrated and from the very

728
00:36:09,719 --> 00:36:12,000
Speaker 4: beginning of the design process it can't be slapped on

729
00:36:12,080 --> 00:36:14,279
Speaker 4: at the end, and too often I think that's how

730
00:36:14,280 --> 00:36:16,600
Speaker 4: it gets approached, and so people just haven't even begun

731
00:36:16,640 --> 00:36:19,400
Speaker 4: to think about you know, oh, like, will my language,

732
00:36:19,480 --> 00:36:22,120
Speaker 4: I mean, sorry, will my model work for disabled people?

733
00:36:22,200 --> 00:36:24,879
Speaker 4: My product work for people with these different kinds of

734
00:36:24,920 --> 00:36:27,200
Speaker 4: like physical disabilities. Like, you know, I think it's just

735
00:36:27,200 --> 00:36:30,759
Speaker 4: that the whole groups of populations just get ignored or

736
00:36:31,040 --> 00:36:33,319
Speaker 4: you know, maybe they've realized and they think, oh, it's

737
00:36:33,320 --> 00:36:35,640
Speaker 4: actually a really bad thing that this product doesn't work

738
00:36:35,680 --> 00:36:37,960
Speaker 4: for this particular group. But I think I'm just going

739
00:36:38,040 --> 00:36:39,359
Speaker 4: to make the trade off and decide, like, I think

740
00:36:39,400 --> 00:36:41,479
Speaker 4: that's a small enough consumer base that I can still

741
00:36:41,520 --> 00:36:43,600
Speaker 4: sell my product, and like, I don't think I'll get

742
00:36:43,600 --> 00:36:46,120
Speaker 4: too much into pushback. I think it'll be fine. And

743
00:36:46,160 --> 00:36:48,240
Speaker 4: I think this, you know, might often be the case

744
00:36:48,360 --> 00:36:51,800
Speaker 4: for populations that are perceived as being like very very small.

745
00:36:51,880 --> 00:36:53,960
Speaker 4: So I'm thinking of say, like trans or gender diverse

746
00:36:54,040 --> 00:36:58,279
Speaker 4: people who often get erased from certain data sets because

747
00:36:58,280 --> 00:37:00,000
Speaker 4: they're like, oh, well, this is like statistically a very

748
00:37:00,160 --> 00:37:02,719
Speaker 4: small percentage. It's like, yeah, but those people's exclusion like

749
00:37:02,800 --> 00:37:05,080
Speaker 4: really really matters. It has a huge impact on their

750
00:37:05,120 --> 00:37:07,480
Speaker 4: life if they go through a scanner and their body

751
00:37:07,560 --> 00:37:11,040
Speaker 4: is not recognized or seen as non normative, or if

752
00:37:11,040 --> 00:37:14,200
Speaker 4: they're excluded from different databases like these do have huge

753
00:37:14,320 --> 00:37:17,360
Speaker 4: knock on effects for people. So yeah, so I guess

754
00:37:17,360 --> 00:37:19,160
Speaker 4: I would say that, you know, the kinds of people

755
00:37:19,160 --> 00:37:21,680
Speaker 4: maybe who are not seeing AI ethics as a priority

756
00:37:22,000 --> 00:37:24,480
Speaker 4: aren't doing it because they're just like you know, oh,

757
00:37:24,520 --> 00:37:28,480
Speaker 4: you know, debiasing whatever. That's fine. I think it's Yeah,

758
00:37:28,520 --> 00:37:30,080
Speaker 4: it's probably more just to do with a lot of

759
00:37:30,080 --> 00:37:33,080
Speaker 4: different blinkers, like a certain kind of narrow mindset about

760
00:37:33,120 --> 00:37:36,239
Speaker 4: who your consumer is and who's like actually using these products.

761
00:37:36,680 --> 00:37:39,920
Speaker 2: Right, Yeah, it does feel probably, But for the Trump administration,

762
00:37:39,960 --> 00:37:42,040
Speaker 2: I'd say they're probably very much focused on the fact

763
00:37:42,040 --> 00:37:44,400
Speaker 2: that because there it doesn't matter. It's like, I don't know,

764
00:37:44,400 --> 00:37:46,440
Speaker 2: even if it makes everything unsafe, I just have to

765
00:37:46,480 --> 00:37:49,680
Speaker 2: say the words I don't like equity, and that's without

766
00:37:49,719 --> 00:37:51,239
Speaker 2: any consideration for what that means.

767
00:37:51,239 --> 00:37:54,040
Speaker 1: And if the whole world breaks, like the better for

768
00:37:54,160 --> 00:37:59,200
Speaker 1: him to consolidate power, you know, Like that yeah feels yeah.

769
00:37:59,239 --> 00:38:02,680
Speaker 1: I mean, did you see this Semaphore article about like

770
00:38:02,719 --> 00:38:07,520
Speaker 1: the group, the Mark Andresen group chats that like, so

771
00:38:07,600 --> 00:38:10,520
Speaker 1: he's been having like these signal chats since the days

772
00:38:10,520 --> 00:38:13,640
Speaker 1: of the pandemic, and it's like he has like gone

773
00:38:13,680 --> 00:38:16,680
Speaker 1: out of his way to bring in all of these

774
00:38:17,000 --> 00:38:22,520
Speaker 1: tech leaders and then like fucking super far right wing,

775
00:38:23,400 --> 00:38:27,719
Speaker 1: like people like Richard Hannania, like the guy who's like

776
00:38:27,719 --> 00:38:30,560
Speaker 1: an outright white supremacist, and like put these people in

777
00:38:30,640 --> 00:38:34,360
Speaker 1: group chats together. Like at one point he like brought

778
00:38:34,400 --> 00:38:39,120
Speaker 1: in some progressive people and then they wrote a New

779
00:38:39,200 --> 00:38:43,760
Speaker 1: York Times op ed criticizing laws that were banning critical

780
00:38:43,840 --> 00:38:47,080
Speaker 1: race theory, and he just like had a meltdown and

781
00:38:47,200 --> 00:38:50,640
Speaker 1: was like, you betrayed me by writing this thing, and

782
00:38:51,560 --> 00:38:53,200
Speaker 1: like kicked them out of the group chat, and like

783
00:38:53,280 --> 00:38:56,880
Speaker 1: since then, it's just all this like extreme right wing

784
00:38:57,120 --> 00:39:00,840
Speaker 1: propaganda that's being like kind of vomited back and forth

785
00:39:00,880 --> 00:39:05,440
Speaker 1: between these people who are like the biggest, most powerful

786
00:39:05,480 --> 00:39:08,839
Speaker 1: oligarchs and like the people who are in charge of

787
00:39:08,960 --> 00:39:12,920
Speaker 1: the direction that tech takes. And so I yeah, I

788
00:39:12,960 --> 00:39:15,840
Speaker 1: mean it feels like this whole thing is developing in

789
00:39:15,880 --> 00:39:20,239
Speaker 1: a way that feels particularly like non optimal and like

790
00:39:20,360 --> 00:39:24,480
Speaker 1: stupid and narrow minded and racist and white supremacist and

791
00:39:24,520 --> 00:39:28,719
Speaker 1: all those things, and like that this was very helpful

792
00:39:28,840 --> 00:39:32,920
Speaker 1: for a context. I just wonder like all of like

793
00:39:32,960 --> 00:39:37,400
Speaker 1: we're seeing a model that's being developed not in the

794
00:39:37,440 --> 00:39:41,400
Speaker 1: best way possible. It seems like like to say the least,

795
00:39:42,000 --> 00:39:46,480
Speaker 1: and we're probably going to like discount a lot of

796
00:39:46,520 --> 00:39:50,280
Speaker 1: these possibilities because they're so shitty at what they're doing.

797
00:39:50,320 --> 00:39:55,439
Speaker 1: But do you see that sort of white supremacist mindset

798
00:39:55,600 --> 00:39:58,960
Speaker 1: kind of pervading in the tech mainstream?

799
00:40:00,120 --> 00:40:00,840
Speaker 2: What a question?

800
00:40:02,840 --> 00:40:05,279
Speaker 4: Yeah? No, I mean I feel like I guess, like

801
00:40:05,640 --> 00:40:08,160
Speaker 4: what I do think this giesses towards. Is this like

802
00:40:08,400 --> 00:40:11,960
Speaker 4: very public repositioning of Silicon Valley, which I think always

803
00:40:11,960 --> 00:40:14,680
Speaker 4: had this like relatively liberal veneer, even if it's not

804
00:40:14,719 --> 00:40:17,640
Speaker 4: clear how deep those roots actually went kind of very

805
00:40:17,680 --> 00:40:20,520
Speaker 4: explicitly aligning themselves with the right and with Trump. And

806
00:40:20,560 --> 00:40:22,279
Speaker 4: it's a little bit hard to tell, like how much

807
00:40:22,280 --> 00:40:25,560
Speaker 4: of this is like political expediency people saying, well, clearly,

808
00:40:25,800 --> 00:40:28,840
Speaker 4: you know, I'm going to do anything to avoid heavier regulation,

809
00:40:29,000 --> 00:40:31,600
Speaker 4: we do anything to avoid this kind of like sort

810
00:40:31,600 --> 00:40:36,160
Speaker 4: of punitive regime that Trump's exacting on many different institutions,

811
00:40:36,200 --> 00:40:38,480
Speaker 4: So we're going to you know, put ourselves in his camp.

812
00:40:38,760 --> 00:40:40,759
Speaker 4: And how much of this is like an expression of

813
00:40:40,800 --> 00:40:44,160
Speaker 4: like deeper ideologies and deeper kind of political beliefs that

814
00:40:44,239 --> 00:40:46,759
Speaker 4: have maybe sat door mentor sort of have kind of

815
00:40:46,760 --> 00:40:50,480
Speaker 4: been cultivated within Silicon Valley and tech firms and now

816
00:40:50,680 --> 00:40:52,759
Speaker 4: kind of finally bursting onto the scene now that they

817
00:40:52,760 --> 00:40:54,799
Speaker 4: feel a bit more empowered, as there's kind of been

818
00:40:54,840 --> 00:40:57,960
Speaker 4: this like global shift towards the right. So yeah, I

819
00:40:58,000 --> 00:41:01,080
Speaker 4: don't know, honestly, like how much, which you know, we

820
00:41:01,160 --> 00:41:04,760
Speaker 4: can discern between sort of like the real deep feeling

821
00:41:04,840 --> 00:41:07,239
Speaker 4: and the kind of political expediency argument. But I think

822
00:41:07,280 --> 00:41:11,320
Speaker 4: it's just undeniable that you have people like particularly Mark Zuckerberg,

823
00:41:11,360 --> 00:41:14,120
Speaker 4: who would have been like normally more kind of center

824
00:41:14,560 --> 00:41:17,319
Speaker 4: possibly center left a lot of issues even though he

825
00:41:17,360 --> 00:41:21,440
Speaker 4: was obviously running like a massively exploitative, gigantic, quite dangerous

826
00:41:21,480 --> 00:41:25,719
Speaker 4: tech company now sort of really aggressively rebranding as a

827
00:41:25,800 --> 00:41:29,120
Speaker 4: kind of wooing Trump, but also sort of big into

828
00:41:29,160 --> 00:41:31,919
Speaker 4: a lot of these like hyper masculiness, tech bro sort

829
00:41:31,920 --> 00:41:35,399
Speaker 4: of languages and ideologies that I don't think certainly five

830
00:41:35,480 --> 00:41:38,360
Speaker 4: years ago we would have seen him sort of supporting

831
00:41:38,400 --> 00:41:40,200
Speaker 4: in the same way. So yeah, it feels like there's

832
00:41:40,200 --> 00:41:43,880
Speaker 4: been this sort of very visible shift in Silicon Valley.

833
00:41:43,880 --> 00:41:46,080
Speaker 4: But again, as someone who's not based in Silicon Valley,

834
00:41:46,120 --> 00:41:48,319
Speaker 4: like I probably couldn't tell you like how much that

835
00:41:48,360 --> 00:41:50,680
Speaker 4: feels like this this is actually the real face of

836
00:41:50,719 --> 00:41:53,800
Speaker 4: the Beast versus this is actually just like what people

837
00:41:53,840 --> 00:41:55,040
Speaker 4: are doing for the moment.

838
00:41:54,880 --> 00:41:57,719
Speaker 2: Yeah, because I mean you see how much like Andresen

839
00:41:57,880 --> 00:42:00,840
Speaker 2: got Silicon Valley money together to get Trump into office,

840
00:42:00,840 --> 00:42:03,160
Speaker 2: along with a bunch of other crypto people's like seventy

841
00:42:03,160 --> 00:42:06,080
Speaker 2: eight million dollars from like the and recent side of things,

842
00:42:06,600 --> 00:42:10,160
Speaker 2: and you know, like these group chats they do, it's

843
00:42:10,200 --> 00:42:14,279
Speaker 2: like our modern day smoke filled lounges where you get

844
00:42:14,320 --> 00:42:17,279
Speaker 2: to see these very powerful people sort of debate these

845
00:42:17,320 --> 00:42:20,000
Speaker 2: topics and get their takes out there that are like

846
00:42:20,880 --> 00:42:23,800
Speaker 2: wacky as hell. And that's why I think, like reading

847
00:42:23,800 --> 00:42:26,640
Speaker 2: this article, it was a little bit more like damn.

848
00:42:26,640 --> 00:42:29,080
Speaker 2: I mean, I don't know if everybody in this group

849
00:42:29,200 --> 00:42:31,680
Speaker 2: chet thinks that, but there are definitely some vocal people

850
00:42:31,719 --> 00:42:34,440
Speaker 2: in this group chat that definitely think they are the

851
00:42:34,480 --> 00:42:37,480
Speaker 2: ones who are going to solve these very complex issues,

852
00:42:37,920 --> 00:42:41,520
Speaker 2: but in the most like inelegant, one size fits all

853
00:42:41,640 --> 00:42:44,839
Speaker 2: kind of way. That's just all about power. And that's

854
00:42:44,880 --> 00:42:48,399
Speaker 2: when I'm like, oh, I think it's really these are

855
00:42:48,440 --> 00:42:51,120
Speaker 2: starting to blend together, especially as we see how much

856
00:42:51,640 --> 00:42:54,920
Speaker 2: how people's like media diets and the information they receive

857
00:42:55,080 --> 00:42:58,160
Speaker 2: are informed by what these people who are running these companies,

858
00:42:58,160 --> 00:43:01,200
Speaker 2: how they believe in like how information should move and

859
00:43:01,239 --> 00:43:05,080
Speaker 2: how people get siloed into information bubbles and things like that.

860
00:43:05,080 --> 00:43:06,959
Speaker 2: That's when I started to begin to be like, mmm,

861
00:43:07,680 --> 00:43:09,560
Speaker 2: this feels a little bit more like a smoking gun

862
00:43:09,600 --> 00:43:11,560
Speaker 2: when you hear like, you know, these ideas are being

863
00:43:11,760 --> 00:43:14,640
Speaker 2: like exchanged and knowing that like there's this guy Curtis

864
00:43:14,760 --> 00:43:16,760
Speaker 2: Yarvin that we talked about a few weeks ago who's

865
00:43:16,800 --> 00:43:20,319
Speaker 2: like basically like a tech monarchist who has a lot

866
00:43:20,360 --> 00:43:22,799
Speaker 2: of ideas that people like Elon Musk and Peter Teel

867
00:43:22,920 --> 00:43:26,279
Speaker 2: like are really subscribed to. And yeah, and like in

868
00:43:26,360 --> 00:43:28,759
Speaker 2: these group chats. It's is where these a lot of

869
00:43:28,760 --> 00:43:32,440
Speaker 2: these very influential people start getting introduced to these kinds

870
00:43:32,440 --> 00:43:35,280
Speaker 2: of ideas. So that's when I'm like, hmm, this feels

871
00:43:36,040 --> 00:43:41,320
Speaker 2: very sinister at best. I mean, like when you see

872
00:43:41,320 --> 00:43:43,680
Speaker 2: this and then thinking that these are the people that

873
00:43:43,760 --> 00:43:46,560
Speaker 2: control the levers that you know, just normal people who

874
00:43:46,600 --> 00:43:49,120
Speaker 2: are using the Internet and stuff get affected by their policies,

875
00:43:49,200 --> 00:43:51,799
Speaker 2: it feels like a little bit like they figured out

876
00:43:51,880 --> 00:43:55,239
Speaker 2: this is how they exercise like their immense control over

877
00:43:55,320 --> 00:43:58,279
Speaker 2: people in these sort of you know, less sort of

878
00:43:58,880 --> 00:44:00,480
Speaker 2: not like in the ways that we think in terms

879
00:44:00,520 --> 00:44:03,360
Speaker 2: of governance or whatever, but through the spread of information

880
00:44:03,440 --> 00:44:04,080
Speaker 2: and ideas.

881
00:44:04,760 --> 00:44:06,480
Speaker 4: Yeah, and I guess I feel like the signal chats

882
00:44:06,480 --> 00:44:08,520
Speaker 4: like raise two things about this kind of like pivot

883
00:44:08,520 --> 00:44:11,240
Speaker 4: to the right, which I think are both really important,

884
00:44:11,280 --> 00:44:13,560
Speaker 4: and like, I think the one is what you've pointed out,

885
00:44:13,640 --> 00:44:15,919
Speaker 4: is this like small scale influencing. Like I think often

886
00:44:15,960 --> 00:44:19,319
Speaker 4: when we talk about disinformation or misinformation, we're thinking about

887
00:44:19,320 --> 00:44:21,000
Speaker 4: that in terms of like, oh, someone makes like an

888
00:44:21,040 --> 00:44:23,880
Speaker 4: AI deep think and they like release it onto the internet,

889
00:44:23,880 --> 00:44:26,839
Speaker 4: and somehow that deep fake like convinces like many many

890
00:44:26,920 --> 00:44:29,319
Speaker 4: many people that this event occurred or didn't occur, and

891
00:44:29,320 --> 00:44:31,600
Speaker 4: it has like seismic effects, and like, you know, again,

892
00:44:31,640 --> 00:44:35,600
Speaker 4: I don't think that's necessarily how disinformation works. I think that, yeah,

893
00:44:35,600 --> 00:44:38,640
Speaker 4: these kinds of like small cultivated circles of trust and

894
00:44:38,800 --> 00:44:41,000
Speaker 4: maybe the things that we should be really really concerned about,

895
00:44:41,040 --> 00:44:43,120
Speaker 4: which is like what actually causes people to change their

896
00:44:43,160 --> 00:44:45,960
Speaker 4: mind or change behaviors, Like maybe these are the levers

897
00:44:45,960 --> 00:44:49,239
Speaker 4: that can like be significantly more effective. But yet the

898
00:44:49,280 --> 00:44:51,160
Speaker 4: second is kind of a point you were raising, and

899
00:44:51,200 --> 00:44:54,239
Speaker 4: I think does again like tie into what happens when

900
00:44:54,239 --> 00:44:55,799
Speaker 4: you build these bonds of trust, which is just like

901
00:44:55,840 --> 00:44:58,080
Speaker 4: following the money. And like you mentioned sort of the

902
00:44:58,239 --> 00:45:00,279
Speaker 4: massive amount of tech funding that got t I'm at

903
00:45:00,280 --> 00:45:02,680
Speaker 4: the office and I had a colleague who now wiks

904
00:45:02,680 --> 00:45:05,000
Speaker 4: to The Guardian, and he and the team kind of

905
00:45:05,080 --> 00:45:08,400
Speaker 4: just like tracked all the funding behind the Trump and

906
00:45:08,400 --> 00:45:11,080
Speaker 4: Harris campaigns, and what was just really astounding was just

907
00:45:11,120 --> 00:45:14,319
Speaker 4: like the extraordinary amount of money that specifically just came

908
00:45:14,360 --> 00:45:16,560
Speaker 4: from tech, and like that's been a really big change

909
00:45:16,560 --> 00:45:18,520
Speaker 4: in the last five years or so. Is just like

910
00:45:18,880 --> 00:45:21,520
Speaker 4: how much money sort of Silicon Valley has deally putting

911
00:45:21,520 --> 00:45:25,120
Speaker 4: it into political lobbying. And so I think, you know, yeah,

912
00:45:25,120 --> 00:45:28,960
Speaker 4: it's even regardless of like your political orientation, but like

913
00:45:29,040 --> 00:45:32,319
Speaker 4: particularly right now with the Trump administration, Like I think

914
00:45:32,360 --> 00:45:34,400
Speaker 4: that's something we should all be really concerned about.

915
00:45:34,920 --> 00:45:37,440
Speaker 2: Mm hmm, yeah. I mean, because it feels like this

916
00:45:37,520 --> 00:45:41,400
Speaker 2: is the best way. In some level, the tech industry

917
00:45:41,440 --> 00:45:43,520
Speaker 2: is sort of like, well, we're actually now the ones

918
00:45:43,560 --> 00:45:46,280
Speaker 2: that are really able to manufacture consent through social media

919
00:45:46,280 --> 00:45:50,279
Speaker 2: and misinformation, and you marry that with like a you know,

920
00:45:50,640 --> 00:45:54,239
Speaker 2: presidential administration that would really benefit from that kind of

921
00:45:54,280 --> 00:45:58,359
Speaker 2: like full court press propaganda. Kind of making and it

922
00:45:58,360 --> 00:46:01,399
Speaker 2: feels like, oh, everyone kind of wins in their own way,

923
00:46:01,480 --> 00:46:04,640
Speaker 2: although there are clearly some people intec have their regrets

924
00:46:04,640 --> 00:46:06,680
Speaker 2: after all the tariff chaos, and they're like no, no, no, no, no,

925
00:46:06,680 --> 00:46:11,719
Speaker 2: no no no not actually, also, don't just fuck that

926
00:46:11,760 --> 00:46:15,440
Speaker 2: one's actually really valuable. I just wanted less regulations and

927
00:46:15,480 --> 00:46:17,680
Speaker 2: for people to say the N word on Twitter more.

928
00:46:17,760 --> 00:46:20,120
Speaker 2: That was my whole thing, and now I have no

929
00:46:20,400 --> 00:46:24,400
Speaker 2: money or less money. But yeah, it's it feels like

930
00:46:24,480 --> 00:46:28,000
Speaker 2: it's just like the most clown show version of all this,

931
00:46:28,040 --> 00:46:30,080
Speaker 2: I think, which is also very frustrating to watch or

932
00:46:30,760 --> 00:46:32,960
Speaker 2: just have to sit idly buyas it's all happening to

933
00:46:33,040 --> 00:46:34,680
Speaker 2: us very intentional.

934
00:46:35,040 --> 00:46:40,080
Speaker 1: Very Also, it's just like really pathetic, just like seeing

935
00:46:40,080 --> 00:46:43,520
Speaker 1: how easy, easily influenced these people are. I mean, it

936
00:46:43,560 --> 00:46:47,520
Speaker 1: makes sense because it feels like they're from a church

937
00:46:47,600 --> 00:46:52,120
Speaker 1: that believes that whoever, wherever, the most money is equals

938
00:46:52,200 --> 00:46:54,680
Speaker 1: the right thing, and so you just like get them

939
00:46:54,680 --> 00:46:57,560
Speaker 1: on a group chat with a billionaire and they're like, well,

940
00:46:57,640 --> 00:47:00,400
Speaker 1: I mean that guy must be the smartest guy in

941
00:47:00,440 --> 00:47:03,080
Speaker 1: the world, and so right his ideas I have to

942
00:47:03,120 --> 00:47:03,520
Speaker 1: listen to.

943
00:47:03,960 --> 00:47:06,160
Speaker 4: Yeah, and I do think as well, like, you know,

944
00:47:06,239 --> 00:47:08,640
Speaker 4: to some extent, I'm like, yeah, to that point, much

945
00:47:08,719 --> 00:47:11,480
Speaker 4: less interested in what people like quote unquote really think

946
00:47:11,560 --> 00:47:13,640
Speaker 4: and much more interesting in just what they do. And

947
00:47:13,719 --> 00:47:16,160
Speaker 4: so to some extent, I'm like, yeah, you know, does

948
00:47:16,160 --> 00:47:19,360
Speaker 4: it really matter whether Mark Zuckerberg is personally invested in

949
00:47:19,400 --> 00:47:23,120
Speaker 4: these like you know, quite misogynist ideologies about like what

950
00:47:23,239 --> 00:47:25,239
Speaker 4: it means to be a man? Or doesn't matter that

951
00:47:25,280 --> 00:47:27,319
Speaker 4: he like has gone on Joe Rogan and now like

952
00:47:27,360 --> 00:47:29,319
Speaker 4: propagates a lot of these ideas and has you know,

953
00:47:29,440 --> 00:47:32,560
Speaker 4: publicly shift admitta to kind of align with or support

954
00:47:32,560 --> 00:47:35,000
Speaker 4: Trump's administration. Like that, to me is like what really

955
00:47:35,000 --> 00:47:37,279
Speaker 4: matters right now, which is that we have kind of

956
00:47:37,280 --> 00:47:39,799
Speaker 4: these active, kind of shifts of power in the tech

957
00:47:39,840 --> 00:47:43,200
Speaker 4: industry that go beyond kind of like politically signaling in

958
00:47:43,280 --> 00:47:45,799
Speaker 4: certain ways, Like they're really really tangible, And I think

959
00:47:45,880 --> 00:47:48,160
Speaker 4: Musk is like the flagship of that in terms of

960
00:47:48,160 --> 00:47:50,839
Speaker 4: someone who's just been like very very visible in the administration.

961
00:47:51,000 --> 00:47:52,839
Speaker 4: But you know, he's certainly not the only one.

962
00:47:53,800 --> 00:47:55,839
Speaker 1: Yeah, it feels like there's no shortage of these tech

963
00:47:55,880 --> 00:48:00,520
Speaker 1: billionaires who are dead set on a horrifying idea.

964
00:48:00,000 --> 00:48:03,160
Speaker 2: All right, let's take a quick break and we'll come

965
00:48:03,200 --> 00:48:06,799
Speaker 2: back and keep talking about AI. Will be right back,

966
00:48:17,280 --> 00:48:18,720
Speaker 2: and we're back. We're back.

967
00:48:19,520 --> 00:48:24,560
Speaker 1: And one of the trends that we've seen is the

968
00:48:24,680 --> 00:48:31,080
Speaker 1: police and other forces trying to use fake AI personas

969
00:48:31,719 --> 00:48:35,799
Speaker 1: who are there to get college students to admit that

970
00:48:35,840 --> 00:48:39,120
Speaker 1: they haven't like signed the National Pledge to support Israel

971
00:48:39,239 --> 00:48:43,520
Speaker 1: or whatever, and you know, or try and radicalize people

972
00:48:43,680 --> 00:48:46,200
Speaker 1: or you know, all the all the entrapment and shit

973
00:48:46,280 --> 00:48:49,120
Speaker 1: that we were seeing like as early as like the

974
00:48:49,160 --> 00:48:52,240
Speaker 1: Oughts with like nine to eleven and the quote unquote

975
00:48:52,239 --> 00:48:55,880
Speaker 1: war on Terror. But first of all, I'm just curious,

976
00:48:55,960 --> 00:48:58,399
Speaker 1: Like it seems like the sort of thing that would

977
00:48:58,400 --> 00:49:02,799
Speaker 1: be hard to get fooled by. But the Internet is

978
00:49:02,840 --> 00:49:05,920
Speaker 1: obviously massive and full of like stupid people. But like,

979
00:49:06,320 --> 00:49:12,480
Speaker 1: does this sort of application concern you? Are the models,

980
00:49:13,320 --> 00:49:18,279
Speaker 1: doctor McNerney, like advanced to a degree that like, you know,

981
00:49:18,520 --> 00:49:22,000
Speaker 1: I could get pulled in by a bot that like

982
00:49:22,080 --> 00:49:26,279
Speaker 1: claims to be a college professor who once has some

983
00:49:26,400 --> 00:49:27,680
Speaker 1: interesting literature for.

984
00:49:27,680 --> 00:49:28,120
Speaker 2: Me to read.

985
00:49:29,400 --> 00:49:32,279
Speaker 4: Yeah, I mean I guess for me, I feel like

986
00:49:32,320 --> 00:49:34,640
Speaker 4: this is how most of these scams work. Though, right

987
00:49:34,640 --> 00:49:36,440
Speaker 4: as you always think you're never going to get taken

988
00:49:36,480 --> 00:49:38,560
Speaker 4: in by one until you get taken in by one,

989
00:49:38,600 --> 00:49:40,920
Speaker 4: And I think they really, you know, they benefit from

990
00:49:41,040 --> 00:49:42,960
Speaker 4: that kind of sense of confidence, but also just like

991
00:49:43,040 --> 00:49:45,120
Speaker 4: more broadly from the fact that like we have to

992
00:49:45,160 --> 00:49:48,279
Speaker 4: have a degree of trust now interactions with people to

993
00:49:48,400 --> 00:49:51,600
Speaker 4: like function societally, and like that's what's so damaging about

994
00:49:51,600 --> 00:49:54,279
Speaker 4: these like fraudulent or fake uses of AI. It's like

995
00:49:54,320 --> 00:49:56,360
Speaker 4: they really affect that kind of shared trust.

996
00:49:56,800 --> 00:49:58,000
Speaker 2: Yeah, I get so many.

997
00:49:58,080 --> 00:50:00,360
Speaker 1: I think I'm like confident off of all the text

998
00:50:00,360 --> 00:50:02,759
Speaker 1: messages I get. They're like, Hi, do you want to

999
00:50:02,760 --> 00:50:05,680
Speaker 1: get lunch? And it's like a new number. I'm just like, well,

1000
00:50:06,080 --> 00:50:11,200
Speaker 1: I know this person's not real. So once again ninety

1001
00:50:11,320 --> 00:50:14,239
Speaker 1: nine and oh when it comes to telling when a

1002
00:50:14,239 --> 00:50:16,640
Speaker 1: bot is coming for me, but then I did get

1003
00:50:16,680 --> 00:50:19,920
Speaker 1: into a long term relationship with a bot, so but no,

1004
00:50:20,280 --> 00:50:24,880
Speaker 1: uh well, yeah, are there are there like strategies for

1005
00:50:24,960 --> 00:50:26,800
Speaker 1: telling whether you're chatting with it? Like is there a

1006
00:50:26,920 --> 00:50:32,480
Speaker 1: turing test? Like not obviously like specific questions like in

1007
00:50:32,520 --> 00:50:36,879
Speaker 1: a Blade Runner, but like overall things that they don't

1008
00:50:36,880 --> 00:50:37,720
Speaker 1: deal well with.

1009
00:50:37,960 --> 00:50:40,279
Speaker 2: Or that like a bit of misinformation here when you're

1010
00:50:40,280 --> 00:50:42,040
Speaker 2: a young kid and you're like you don't when a cop,

1011
00:50:42,040 --> 00:50:43,520
Speaker 2: you got to ask them if they're a cop, and

1012
00:50:43,560 --> 00:50:46,319
Speaker 2: they got to tell you right now right or else

1013
00:50:46,320 --> 00:50:48,560
Speaker 2: it's a trapman, Like you you're not an AI, right,

1014
00:50:48,600 --> 00:50:51,400
Speaker 2: I mean, is it that easy? Is there a silver bullet?

1015
00:50:52,800 --> 00:50:55,600
Speaker 4: That's a good question, because yeah, I mean I do

1016
00:50:55,680 --> 00:50:58,959
Speaker 4: think like part of the interesting thing I think about

1017
00:50:58,960 --> 00:51:00,760
Speaker 4: a lot of scams with this is that my colleague,

1018
00:51:00,760 --> 00:51:03,040
Speaker 4: the philosopher clear Bent, says, she's like, often they're not

1019
00:51:03,640 --> 00:51:07,000
Speaker 4: intending to try and make their contact the most believable

1020
00:51:07,120 --> 00:51:09,320
Speaker 4: with you, like when you get these like mass spam emails,

1021
00:51:09,360 --> 00:51:10,759
Speaker 4: Like I think this happens to most of us. As

1022
00:51:10,760 --> 00:51:14,400
Speaker 4: we look at something, we're like, that's obviously spam. And

1023
00:51:14,480 --> 00:51:16,600
Speaker 4: my colleague clear says, it's like, well, it's because like

1024
00:51:16,680 --> 00:51:19,759
Speaker 4: they don't want to be getting a huge pool of

1025
00:51:19,800 --> 00:51:22,400
Speaker 4: people who are like not actually that easily food getting

1026
00:51:22,400 --> 00:51:25,040
Speaker 4: bought in to the first step, because like these people

1027
00:51:25,040 --> 00:51:26,480
Speaker 4: are not going to go all the way like they

1028
00:51:26,480 --> 00:51:28,120
Speaker 4: want the people who they think will go all the

1029
00:51:28,120 --> 00:51:30,440
Speaker 4: way with them and hand over money or like who

1030
00:51:30,520 --> 00:51:33,200
Speaker 4: will like even not feel confident enough to challenge what's happening,

1031
00:51:33,280 --> 00:51:36,120
Speaker 4: or they'll be really like bored into what's happening. But

1032
00:51:36,160 --> 00:51:37,719
Speaker 4: you know, yeah, like the kind of core of like

1033
00:51:37,920 --> 00:51:40,680
Speaker 4: the scamming is not necessarily saying like oh, it's about

1034
00:51:40,960 --> 00:51:43,520
Speaker 4: crafting the perfect email to get people through the door.

1035
00:51:43,520 --> 00:51:45,960
Speaker 4: It's about like, no, who can we actually take to

1036
00:51:46,000 --> 00:51:47,880
Speaker 4: the end. And I think like, yeah, a lot of

1037
00:51:47,920 --> 00:51:49,799
Speaker 4: these AI chatbots, it's like we might look at this

1038
00:51:49,880 --> 00:51:52,359
Speaker 4: and say like, Okay, well this seems really sketchy, but

1039
00:51:52,400 --> 00:51:54,359
Speaker 4: like they just need to get enough people who are

1040
00:51:54,400 --> 00:51:55,960
Speaker 4: like oh okay. But actually, like a lot of the

1041
00:51:56,000 --> 00:51:59,120
Speaker 4: interfaces I use online are like not super great or like,

1042
00:51:59,239 --> 00:52:01,839
Speaker 4: you know, I might not find them very intuitive, and like, oh,

1043
00:52:01,920 --> 00:52:03,719
Speaker 4: this is kind of a scary thing that I've been

1044
00:52:03,719 --> 00:52:06,399
Speaker 4: suggested to do, or like they've made a threat about

1045
00:52:06,400 --> 00:52:08,319
Speaker 4: like my money or something like that. So I've got

1046
00:52:08,320 --> 00:52:12,400
Speaker 4: to now like stay engaged in this conversation. And you know,

1047
00:52:12,440 --> 00:52:14,319
Speaker 4: so I think like to some extent, like there is

1048
00:52:14,360 --> 00:52:18,200
Speaker 4: like a particular exploitation of vulnerability that happens in these

1049
00:52:18,280 --> 00:52:20,600
Speaker 4: in these scams. But yeah, in terms of like a

1050
00:52:20,719 --> 00:52:23,319
Speaker 4: touring test for these chat bots, I don't know, because

1051
00:52:23,320 --> 00:52:26,399
Speaker 4: I feel like, also, yeah, like I feel like people

1052
00:52:26,440 --> 00:52:28,400
Speaker 4: are often like oh, like I can totally tell when

1053
00:52:28,440 --> 00:52:31,120
Speaker 4: I'm changing to a chatbot, and I'm like, I don't

1054
00:52:31,160 --> 00:52:33,879
Speaker 4: know if I always can. I think you know it is.

1055
00:52:34,200 --> 00:52:36,480
Speaker 4: They can be like especially with something like chat GPT,

1056
00:52:37,120 --> 00:52:39,799
Speaker 4: Like you know, I know with people say anecdotally have

1057
00:52:39,880 --> 00:52:42,600
Speaker 4: talked about, say trying to discern whether like chat GPT

1058
00:52:42,719 --> 00:52:44,760
Speaker 4: has been used to write essays, like that's more common

1059
00:52:44,800 --> 00:52:47,279
Speaker 4: in the context that I'm in higher education, and like

1060
00:52:47,360 --> 00:52:51,280
Speaker 4: sometimes they can get like really clear, like really clear keywords,

1061
00:52:51,360 --> 00:52:53,560
Speaker 4: or they'll say okay, like the people keep using a

1062
00:52:53,600 --> 00:52:56,400
Speaker 4: phrase that is like not super normal to use in

1063
00:52:56,440 --> 00:52:58,920
Speaker 4: this discipline, but like probably is quite normal if you

1064
00:52:59,000 --> 00:53:01,480
Speaker 4: use AI. So they might have keywords like that, you

1065
00:53:01,560 --> 00:53:03,840
Speaker 4: might have really obvious tells that they've kept the prompted

1066
00:53:04,040 --> 00:53:06,400
Speaker 4: like okay, please write me and essay about it.

1067
00:53:07,520 --> 00:53:09,520
Speaker 3: So that's kind of okay, that's there is your essay

1068
00:53:09,960 --> 00:53:12,759
Speaker 3: question on the following prompt, right, But.

1069
00:53:12,800 --> 00:53:15,120
Speaker 4: Yeah, I mean I think to some extent, like you know,

1070
00:53:15,400 --> 00:53:17,520
Speaker 4: I do worry a little bit if people are like, no,

1071
00:53:17,600 --> 00:53:20,319
Speaker 4: I completely believe that I am like scam proof when

1072
00:53:20,360 --> 00:53:22,719
Speaker 4: it comes to AI, because I'm like I do think,

1073
00:53:22,880 --> 00:53:25,120
Speaker 4: you know, compared to like five years ago, like we

1074
00:53:25,200 --> 00:53:29,040
Speaker 4: do just have like much more sophisticated voice cloning technologies,

1075
00:53:29,200 --> 00:53:32,200
Speaker 4: language models and so on and so forth, that might make

1076
00:53:32,200 --> 00:53:34,040
Speaker 4: it a little bit harder to discern in that kind

1077
00:53:34,040 --> 00:53:36,239
Speaker 4: of like initial first pass. But this is just like

1078
00:53:36,320 --> 00:53:38,759
Speaker 4: my instinctive thoughts and like maybe this actually yeah, like

1079
00:53:38,840 --> 00:53:40,880
Speaker 4: the version of like a cop has to tell you

1080
00:53:40,920 --> 00:53:41,640
Speaker 4: a cop for AI.

1081
00:53:41,800 --> 00:53:44,080
Speaker 2: Yeah. I mean, if you're asking my opinion, Jack, I

1082
00:53:44,120 --> 00:53:46,200
Speaker 2: think it's great that the cops are using AI do

1083
00:53:46,239 --> 00:53:49,640
Speaker 2: and trap people online. I think I'm not concerned. I

1084
00:53:49,680 --> 00:53:53,280
Speaker 2: think I was just looking for guys. Yeah, we're you're

1085
00:53:53,960 --> 00:53:57,360
Speaker 2: as long as whatever makes the cops jobs easier, you know,

1086
00:53:57,520 --> 00:54:00,799
Speaker 2: I'm all for that. I back the blue, but that

1087
00:54:00,920 --> 00:54:03,840
Speaker 2: is too much overtime, Pey, No, I mean, but that

1088
00:54:04,000 --> 00:54:06,359
Speaker 2: is interesting, doctor carry to consider. Like I didn't think

1089
00:54:06,400 --> 00:54:08,959
Speaker 2: about how it's like you need it to be shit,

1090
00:54:09,719 --> 00:54:11,880
Speaker 2: so you put off the people that are going to

1091
00:54:11,920 --> 00:54:13,600
Speaker 2: be smart enough by the time it comes to the

1092
00:54:13,640 --> 00:54:16,160
Speaker 2: part where you ask money, because if they're already in

1093
00:54:16,320 --> 00:54:19,560
Speaker 2: at the most wacky version, like Carol, when are we

1094
00:54:19,600 --> 00:54:22,839
Speaker 2: going to play golf this weekend? And they're like, huh,

1095
00:54:23,000 --> 00:54:28,200
Speaker 2: I'm not Carol, and they're like exactly that funny.

1096
00:54:28,239 --> 00:54:32,279
Speaker 1: I got the most interesting investment opportunity right right right

1097
00:54:32,360 --> 00:54:33,879
Speaker 1: right from Carol's golf buddy.

1098
00:54:33,880 --> 00:54:36,520
Speaker 2: Who's Carol. I'm not sure. I don't know, I don't know.

1099
00:54:36,800 --> 00:54:40,319
Speaker 1: Yeah, yeah, I mean I feel like the images are

1100
00:54:40,440 --> 00:54:44,960
Speaker 1: the concerning place, Like it just the degree to which

1101
00:54:45,000 --> 00:54:49,759
Speaker 1: AI has made photoshop really really easy, Like there's just

1102
00:54:50,560 --> 00:54:53,360
Speaker 1: I don't know, it's degraded, the degree to it. Like

1103
00:54:53,400 --> 00:54:56,279
Speaker 1: there was just a story over the weekend where a

1104
00:54:56,320 --> 00:54:59,840
Speaker 1: lot of people were fooled by AI created mug shots

1105
00:55:00,160 --> 00:55:04,840
Speaker 1: of the judge that the Trouble Ministry arrested and like

1106
00:55:04,920 --> 00:55:08,240
Speaker 1: making it look like she's like weeping as she's being arrested.

1107
00:55:08,600 --> 00:55:12,279
Speaker 1: But like, I just feel like there's just no no

1108
00:55:12,520 --> 00:55:16,160
Speaker 1: way to believe any image that you see, Like you

1109
00:55:16,280 --> 00:55:20,520
Speaker 1: just need to keep googling ask ask whether it is

1110
00:55:20,600 --> 00:55:23,320
Speaker 1: AI or not. And then of course you're asking Google's

1111
00:55:23,320 --> 00:55:26,239
Speaker 1: AI whether it's AI or not, and you know they're

1112
00:55:26,239 --> 00:55:29,640
Speaker 1: probably in on it too. But what what would you say?

1113
00:55:29,680 --> 00:55:33,399
Speaker 1: What are the uses that you feel like are most

1114
00:55:33,520 --> 00:55:35,719
Speaker 1: concerning that you're you're kind of keeping an eye on

1115
00:55:35,719 --> 00:55:37,360
Speaker 1: that you see evolving the fastest.

1116
00:55:37,760 --> 00:55:40,239
Speaker 4: I mean, I think what you've actually pointed out to

1117
00:55:40,280 --> 00:55:41,959
Speaker 4: me is the thing that like I get the most

1118
00:55:41,960 --> 00:55:44,160
Speaker 4: worried about personally, which is less the production of like

1119
00:55:44,320 --> 00:55:48,319
Speaker 4: individuals pieces of media like video, deep fakes or photographs,

1120
00:55:48,320 --> 00:55:51,000
Speaker 4: even though those can be really really scary and quite

1121
00:55:51,040 --> 00:55:53,120
Speaker 4: concerning when you see them made. Like for me, it's

1122
00:55:53,120 --> 00:55:54,719
Speaker 4: like that broader point you said of like I don't

1123
00:55:54,719 --> 00:55:56,879
Speaker 4: really know what I can trust, and like I think

1124
00:55:57,040 --> 00:55:59,400
Speaker 4: the creation of deep fakes in and of themselves, like

1125
00:55:59,760 --> 00:56:02,400
Speaker 4: you know, they undermine sort of our broader trust in

1126
00:56:02,400 --> 00:56:04,759
Speaker 4: our information environment, and like this is happening at a

1127
00:56:04,800 --> 00:56:08,640
Speaker 4: time when out information environment is already very chalic and

1128
00:56:08,680 --> 00:56:11,480
Speaker 4: quite hostile, and so like, yeah, I really feel in particular,

1129
00:56:11,719 --> 00:56:13,200
Speaker 4: I mean I feel for all of us, but I

1130
00:56:13,239 --> 00:56:15,319
Speaker 4: really feel for young people kind of growing up in

1131
00:56:15,320 --> 00:56:20,040
Speaker 4: this like very intense, polarized, kind of extreme media environment.

1132
00:56:20,280 --> 00:56:22,640
Speaker 4: And I don't think that kind of the possibility of

1133
00:56:22,680 --> 00:56:25,279
Speaker 4: deep fakes and the sort of this idea that like

1134
00:56:25,320 --> 00:56:28,399
Speaker 4: anything creative could be fictitious is going to necessarily help

1135
00:56:28,440 --> 00:56:30,800
Speaker 4: that or help people believe that they can have access

1136
00:56:30,840 --> 00:56:34,120
Speaker 4: to kind of verifiable and true information. So like to

1137
00:56:34,120 --> 00:56:37,000
Speaker 4: give an example of this, like one example here is

1138
00:56:37,040 --> 00:56:39,319
Speaker 4: this idea of like the liar's dividend, which is like

1139
00:56:39,400 --> 00:56:43,280
Speaker 4: anyone could say something kind of mean or offensive or untrue,

1140
00:56:43,680 --> 00:56:46,120
Speaker 4: and then you know, when confronted about it, they could say, like, oh,

1141
00:56:46,120 --> 00:56:49,279
Speaker 4: that video clip's not true, like that was a deep fake,

1142
00:56:49,400 --> 00:56:52,080
Speaker 4: Like that's fake news, to use a very common term

1143
00:56:52,120 --> 00:56:55,880
Speaker 4: from US politics, And you know, it could be that actually,

1144
00:56:55,880 --> 00:56:58,319
Speaker 4: like ninety nine percent of people are like, no, we

1145
00:56:58,480 --> 00:57:01,160
Speaker 4: really think you said that. That sound like you like

1146
00:57:01,280 --> 00:57:03,120
Speaker 4: we've done all these checks and we think this is

1147
00:57:03,120 --> 00:57:05,600
Speaker 4: real footage. But like you can plant just snuffed out

1148
00:57:05,640 --> 00:57:08,040
Speaker 4: in people's minds. You can plant that one percent where

1149
00:57:08,080 --> 00:57:10,759
Speaker 4: people are like, but what if actually this is is

1150
00:57:10,800 --> 00:57:13,160
Speaker 4: a deep fake, Like what if this isn't true? And

1151
00:57:13,239 --> 00:57:14,799
Speaker 4: I think that's how that you kind of get this

1152
00:57:14,920 --> 00:57:19,560
Speaker 4: erosion of sort of the information landscape that makes everything

1153
00:57:19,560 --> 00:57:22,640
Speaker 4: that little bit shakier. Because yeah, again, like I think

1154
00:57:22,920 --> 00:57:25,440
Speaker 4: when it comes to say, like the impact of like

1155
00:57:25,560 --> 00:57:29,440
Speaker 4: AI generated misinformation sort of on things like elections, Like

1156
00:57:29,480 --> 00:57:31,800
Speaker 4: there was a lot of fear around this last year

1157
00:57:31,840 --> 00:57:34,120
Speaker 4: because last year was kind of had all these democratic

1158
00:57:34,160 --> 00:57:37,360
Speaker 4: elections called like the Year of Elections, and also sort

1159
00:57:37,400 --> 00:57:40,640
Speaker 4: of the emergence of like really really sophisticated generative AI

1160
00:57:40,760 --> 00:57:43,320
Speaker 4: tools coming out at the same time and it seems

1161
00:57:43,320 --> 00:57:45,560
Speaker 4: like actually the verdict on that is a little bit mixed.

1162
00:57:45,800 --> 00:57:48,400
Speaker 4: And so you know, one kind of study looked at

1163
00:57:48,400 --> 00:57:50,480
Speaker 4: this and they said, well, actually, of you know, just

1164
00:57:50,560 --> 00:57:52,760
Speaker 4: under eighty pieces and media we looked at of these

1165
00:57:52,760 --> 00:57:57,120
Speaker 4: AI generated sort of pieces attached to different political campaigns

1166
00:57:57,240 --> 00:57:59,600
Speaker 4: or elections, and like, you know, half of them like

1167
00:57:59,680 --> 00:58:04,120
Speaker 4: weren't necessarily misinformation, and then the other half of them like,

1168
00:58:04,160 --> 00:58:05,760
Speaker 4: you know, yes, a lot of like they were made

1169
00:58:05,800 --> 00:58:07,480
Speaker 4: by AI, but like a lot of them really were

1170
00:58:07,520 --> 00:58:09,760
Speaker 4: like more like cheap fakes or like they were not

1171
00:58:09,840 --> 00:58:13,760
Speaker 4: necessarily actually deeply reliant on AI tools. It could have

1172
00:58:13,840 --> 00:58:16,600
Speaker 4: quite easily been made something with like Photoshop for example.

1173
00:58:17,160 --> 00:58:19,400
Speaker 4: So yeah, AI has made the production of these particular

1174
00:58:19,400 --> 00:58:22,880
Speaker 4: pieces of media, but it's not necessarily actually like fundamentally

1175
00:58:23,000 --> 00:58:27,040
Speaker 4: changed what we're able to do necessarily in these particular cases.

1176
00:58:27,560 --> 00:58:29,400
Speaker 4: So yeah, so I think like when it comes to

1177
00:58:29,400 --> 00:58:31,640
Speaker 4: sort of like the cause effect of saying, like, you know,

1178
00:58:31,920 --> 00:58:35,080
Speaker 4: we're going to have these specific pieces of disinformation, they're

1179
00:58:35,080 --> 00:58:38,240
Speaker 4: gonna have these specific negative effects, I think, you know,

1180
00:58:38,320 --> 00:58:40,480
Speaker 4: the picture there is maybe not as extreme as some

1181
00:58:40,560 --> 00:58:43,600
Speaker 4: of the media fear around it last year was portraying,

1182
00:58:43,800 --> 00:58:45,720
Speaker 4: But yeah, I think this broader question of like how

1183
00:58:45,720 --> 00:58:47,800
Speaker 4: do we know what we can trust like that remains

1184
00:58:47,840 --> 00:58:48,640
Speaker 4: really central.

1185
00:58:49,240 --> 00:58:52,800
Speaker 2: Yeah, I feel like there's just also outside of obviously

1186
00:58:52,800 --> 00:58:56,000
Speaker 2: like the geopolitical things that are very front of mind,

1187
00:58:56,120 --> 00:58:58,160
Speaker 2: because that's you know, the things that has an effect

1188
00:58:58,160 --> 00:59:00,000
Speaker 2: on a lot of people. I just see it creeping

1189
00:59:00,040 --> 00:59:03,440
Speaker 2: into so many creative spaces that I'm like, it's cheapening

1190
00:59:03,600 --> 00:59:08,240
Speaker 2: so many things because like, for example, like Pinterest, if

1191
00:59:08,280 --> 00:59:12,080
Speaker 2: you look for anything around like architecture right now, I

1192
00:59:12,120 --> 00:59:14,959
Speaker 2: was like looking for something it's just like in because

1193
00:59:15,000 --> 00:59:18,120
Speaker 2: I'm in the process of hopefully like rebuilding my home

1194
00:59:18,520 --> 00:59:22,520
Speaker 2: of like trying to look at architecture pictures, eighty percent

1195
00:59:22,560 --> 00:59:26,240
Speaker 2: of them are bullshit AI slop images where you're like, oh,

1196
00:59:26,240 --> 00:59:28,400
Speaker 2: that's an interesting kitchen. You're like, why is the sink

1197
00:59:28,600 --> 00:59:31,520
Speaker 2: in the wall like this? Like wait, hold on what

1198
00:59:31,720 --> 00:59:33,760
Speaker 2: And I'm like, then none of these power chords like

1199
00:59:33,800 --> 00:59:36,320
Speaker 2: line up with where the lamps are, And it just

1200
00:59:36,360 --> 00:59:38,000
Speaker 2: sort of it like gets to the point where like

1201
00:59:38,200 --> 00:59:40,520
Speaker 2: this is this isn't even real stuff that I can

1202
00:59:40,720 --> 00:59:44,520
Speaker 2: draw inspiration from or even like on Spotify, like there's

1203
00:59:44,560 --> 00:59:49,440
Speaker 2: so much more AI generated music that is also kind

1204
00:59:49,480 --> 00:59:52,520
Speaker 2: of taking like shifting people's focus. Like I also see

1205
00:59:52,560 --> 00:59:54,640
Speaker 2: the way we listen to music is really changing, where

1206
00:59:54,680 --> 00:59:58,280
Speaker 2: it's much more passive and you're being fed music rather

1207
00:59:58,360 --> 01:00:01,320
Speaker 2: than like seeking music you used to you know, you

1208
01:00:01,400 --> 01:00:03,680
Speaker 2: used to go buy a CD, which I don't mind

1209
01:00:03,680 --> 01:00:06,800
Speaker 2: because I'm I love when I discover new music through

1210
01:00:06,880 --> 01:00:09,760
Speaker 2: these kinds of these sort of like algorithmic playlists and

1211
01:00:09,800 --> 01:00:12,480
Speaker 2: things like that. But it's also created like a whole

1212
01:00:12,520 --> 01:00:16,240
Speaker 2: other like genre of music that's like a fake artist

1213
01:00:16,400 --> 01:00:18,600
Speaker 2: that has like is just putting up sort of texture,

1214
01:00:19,320 --> 01:00:21,960
Speaker 2: like really uninteresting music to have in the background. It

1215
01:00:22,040 --> 01:00:25,160
Speaker 2: there's some really insidious that like word for it. It's

1216
01:00:25,200 --> 01:00:27,400
Speaker 2: like functional music or just something that it's like is

1217
01:00:27,440 --> 01:00:30,200
Speaker 2: not really meant to listen to, but just on passively.

1218
01:00:30,680 --> 01:00:32,280
Speaker 2: And when I see all that, I'm like, oh, this

1219
01:00:32,360 --> 01:00:35,680
Speaker 2: is this really also takes away from like the magic

1220
01:00:35,720 --> 01:00:39,560
Speaker 2: of human creativity and you know, just the the skills

1221
01:00:39,560 --> 01:00:43,560
Speaker 2: that people build through composition or design or whatever, you know,

1222
01:00:43,680 --> 01:00:45,840
Speaker 2: because everything is being flooded with things that are just

1223
01:00:45,920 --> 01:00:48,480
Speaker 2: kind of like based on it, but not you know,

1224
01:00:48,640 --> 01:00:51,360
Speaker 2: having that real sort of sincerity to it. And that's

1225
01:00:51,800 --> 01:00:53,760
Speaker 2: that's another one that I see happening more and more

1226
01:00:53,760 --> 01:00:55,640
Speaker 2: and more, and like, ooh, this feels like this is

1227
01:00:55,640 --> 01:00:57,800
Speaker 2: going to be a slippery slope too. Now I think

1228
01:00:57,840 --> 01:01:04,439
Speaker 2: it'll be fine anyways. Yeah, yeah, yeah, it's fine, it's fine. Yeah,

1229
01:01:04,480 --> 01:01:07,080
Speaker 2: It's all a one. It's all a one. As Linda.

1230
01:01:07,280 --> 01:01:08,040
Speaker 2: Did you see her.

1231
01:01:07,960 --> 01:01:10,840
Speaker 4: Say that, I unfortunately am aware of this.

1232
01:01:11,160 --> 01:01:16,760
Speaker 2: Yeah, Linda McMahon's secretary of Education repeatedly will describe AI

1233
01:01:17,000 --> 01:01:21,480
Speaker 2: as a one. Wow. Yeah, it's like as if we

1234
01:01:21,480 --> 01:01:26,400
Speaker 2: weren't just bad enough, bad bad all around, even if

1235
01:01:26,400 --> 01:01:28,640
Speaker 2: she's ready. Yeah, yeah, it was like, I mean, with

1236
01:01:29,040 --> 01:01:30,840
Speaker 2: the rise of a one and you're like, are you

1237
01:01:30,920 --> 01:01:33,480
Speaker 2: not even around people who are saying it out loud?

1238
01:01:34,120 --> 01:01:36,400
Speaker 2: Or do you love people with the worst fucking taste

1239
01:01:36,440 --> 01:01:37,320
Speaker 2: in steak sauce?

1240
01:01:37,840 --> 01:01:37,960
Speaker 1: Right?

1241
01:01:38,040 --> 01:01:40,000
Speaker 4: But that's that's why I worry though about, you know,

1242
01:01:40,160 --> 01:01:42,640
Speaker 4: the numbness I mentioned at the beginning, where like when

1243
01:01:42,640 --> 01:01:45,720
Speaker 4: I had that, I was just like, yeah, that sounds

1244
01:01:46,120 --> 01:01:48,200
Speaker 4: that sounds like what I would expect at this point

1245
01:01:48,240 --> 01:01:50,280
Speaker 4: someone out they're saying a one. I'm like, no, I

1246
01:01:50,320 --> 01:01:54,200
Speaker 4: should not be like right in any way have this normalized?

1247
01:01:54,240 --> 01:01:56,080
Speaker 4: But like I feel like we've hit that stage where

1248
01:01:56,120 --> 01:01:59,080
Speaker 4: it just seems to be this like total disregard for

1249
01:01:59,120 --> 01:02:02,720
Speaker 4: any kind of like strategic policy planning or expertise, and

1250
01:02:02,760 --> 01:02:06,200
Speaker 4: instead you get the kind of a ones. Yeah, well

1251
01:02:06,200 --> 01:02:06,800
Speaker 4: without there.

1252
01:02:07,000 --> 01:02:08,600
Speaker 2: Are my a ones, my day ones.

1253
01:02:10,120 --> 01:02:13,560
Speaker 1: Well, doctor mcnernie, has been a pleasure having you back

1254
01:02:13,600 --> 01:02:16,760
Speaker 1: on the daily Geist. Where can people find you? Follow you,

1255
01:02:16,800 --> 01:02:18,240
Speaker 1: hear you all that good stuff.

1256
01:02:18,800 --> 01:02:22,640
Speaker 4: Yeah, well, if you're interested in more conversations around feminism technology,

1257
01:02:22,720 --> 01:02:25,400
Speaker 4: then highly recommend you check out my podcast with doctor

1258
01:02:25,440 --> 01:02:28,920
Speaker 4: elenoald Ridge, The Good Robot Podcast. And yeah, if you're

1259
01:02:28,920 --> 01:02:30,880
Speaker 4: interested in kind of some of the topics of discussion

1260
01:02:31,280 --> 01:02:34,160
Speaker 4: we've been discussing today. A couple of other substacks and

1261
01:02:34,240 --> 01:02:37,240
Speaker 4: channels maybe to shout out is AI snake Oil is

1262
01:02:37,280 --> 01:02:39,520
Speaker 4: a really really great if you're interested in kind of

1263
01:02:39,840 --> 01:02:42,360
Speaker 4: maybe trying to debunk some of the hype around AI,

1264
01:02:42,520 --> 01:02:44,360
Speaker 4: trying to like get the grips of like, Okay, what

1265
01:02:44,400 --> 01:02:46,480
Speaker 4: can all these different AI tools do or not do?

1266
01:02:46,920 --> 01:02:49,000
Speaker 4: I think they do a really great job. And I

1267
01:02:49,080 --> 01:02:52,520
Speaker 4: think Mystery AI Hype Theater three thousand, I always get

1268
01:02:52,560 --> 01:02:55,200
Speaker 4: the name mixed up, but like that's also a good

1269
01:02:55,240 --> 01:02:58,120
Speaker 4: fun and they have a lot of fun. I think

1270
01:02:58,160 --> 01:03:01,080
Speaker 4: kind of trying to look through various sort of you know,

1271
01:03:01,240 --> 01:03:04,800
Speaker 4: slightly sketchy looking AI projects together and sort of you know,

1272
01:03:04,920 --> 01:03:08,560
Speaker 4: tearing them apart. So would definitely recommend those shows too nice.

1273
01:03:08,960 --> 01:03:11,920
Speaker 2: Is there a work of media or social media that

1274
01:03:12,000 --> 01:03:13,080
Speaker 2: you've been enjoying?

1275
01:03:13,680 --> 01:03:15,880
Speaker 4: Oh gosh, I mean, I'm not proud to say this,

1276
01:03:16,000 --> 01:03:18,200
Speaker 4: but I'm actually like writing a book chapter at the

1277
01:03:18,240 --> 01:03:21,600
Speaker 4: moment on like internet scam culture. And so as part

1278
01:03:21,640 --> 01:03:23,400
Speaker 4: of that, I've been going back to, you know, the

1279
01:03:23,440 --> 01:03:27,000
Speaker 4: great debacle of Fire Festival, And obviously I'm like, I

1280
01:03:27,000 --> 01:03:29,840
Speaker 4: think this is indicative of like much bigger societal problems.

1281
01:03:29,840 --> 01:03:32,360
Speaker 4: But the memes that didn't come out of Fire Festival

1282
01:03:32,400 --> 01:03:35,880
Speaker 4: were unfortunately spectacular, and so I have been going back

1283
01:03:36,080 --> 01:03:39,080
Speaker 4: through some of those, like in the now very mature

1284
01:03:39,080 --> 01:03:42,240
Speaker 4: with like five year old cheese sandwich memes that came

1285
01:03:42,280 --> 01:03:45,320
Speaker 4: out of Fire Festival. So that's been my like current

1286
01:03:45,400 --> 01:03:48,960
Speaker 4: social media, why are we doing that like old albums?

1287
01:03:49,000 --> 01:03:50,960
Speaker 2: Like you're like, man, I remember the memes when Trump

1288
01:03:51,000 --> 01:03:54,160
Speaker 2: got COVID, Remember the memes when the Queen died? Like

1289
01:03:54,680 --> 01:03:57,800
Speaker 2: just like you're like, man, the way I was get them?

1290
01:03:57,920 --> 01:04:00,680
Speaker 4: Yeah, like they are you just like you? So like

1291
01:04:00,720 --> 01:04:03,640
Speaker 4: here it was like when Liz trust uh was kind

1292
01:04:03,680 --> 01:04:06,040
Speaker 4: of you're really going through it like that was just

1293
01:04:06,080 --> 01:04:10,000
Speaker 4: like relentless political mediums. Uh RICI soon a can you

1294
01:04:10,000 --> 01:04:11,840
Speaker 4: ask your election? But yeah, you just kind of get

1295
01:04:11,840 --> 01:04:14,080
Speaker 4: swept away. So you know, maybe that's the new thing,

1296
01:04:14,120 --> 01:04:15,280
Speaker 4: the kind of meme album.

1297
01:04:16,080 --> 01:04:18,720
Speaker 2: I'm ready. I'm ready for a meme album. Miles, Where

1298
01:04:18,720 --> 01:04:21,000
Speaker 2: can people find you as their working media? You've been

1299
01:04:21,120 --> 01:04:25,000
Speaker 2: oh me, you find me everywhere they have at Symbols,

1300
01:04:25,040 --> 01:04:27,360
Speaker 2: at Miles of Gray. If you want to hear me

1301
01:04:28,080 --> 01:04:30,800
Speaker 2: sob this week about the state of the Lakers, that's

1302
01:04:30,840 --> 01:04:34,000
Speaker 2: on Miles and Jack got mad Mass He's ourn NBA podcast.

1303
01:04:36,160 --> 01:04:39,400
Speaker 2: I mean, whatever, whatever, whatever, whatever, you know, it's just fine.

1304
01:04:39,440 --> 01:04:40,600
Speaker 2: You know, we're just gonna have to come back from

1305
01:04:40,600 --> 01:04:42,080
Speaker 2: three to one, you know what I mean. He's done

1306
01:04:42,120 --> 01:04:44,320
Speaker 2: it before, we've done it before, and that's I guess

1307
01:04:44,320 --> 01:04:46,080
Speaker 2: that's what it calls for. That's what it calls for.

1308
01:04:46,440 --> 01:04:49,440
Speaker 2: Or I will just ignore everything and go full fetal

1309
01:04:49,520 --> 01:04:52,080
Speaker 2: and sob every night. But there's that, And then if

1310
01:04:52,120 --> 01:04:53,520
Speaker 2: you want to hear me talk to ninety day Fiance,

1311
01:04:53,640 --> 01:04:56,120
Speaker 2: I do that over at four twenty day Fiance. A

1312
01:04:56,160 --> 01:04:58,320
Speaker 2: post I like from Blue Sky is from Eric Columbus

1313
01:04:58,320 --> 01:05:01,600
Speaker 2: at Eric Columbus dot beaskuy dot soci. He quote tweeted

1314
01:05:02,000 --> 01:05:04,640
Speaker 2: a tweet from a reporter and it's first of all,

1315
01:05:04,640 --> 01:05:07,840
Speaker 2: the quote. The tweet that's been quoted says, because the

1316
01:05:07,880 --> 01:05:10,240
Speaker 2: Canadian elections had just happened, it said one couple at

1317
01:05:10,240 --> 01:05:12,640
Speaker 2: a voting station in Port Credit said they would rather

1318
01:05:12,720 --> 01:05:17,400
Speaker 2: not speak to American American media. They then apologized three times,

1319
01:05:17,440 --> 01:05:22,520
Speaker 2: and then they quote tweeted it with saying peak Canada. Yeah, yep,

1320
01:05:22,560 --> 01:05:24,439
Speaker 2: I get it, I get it, don't speak to don't,

1321
01:05:24,600 --> 01:05:27,320
Speaker 2: but I'm also sorry we won't. We can't speak to

1322
01:05:27,400 --> 01:05:28,080
Speaker 2: American med.

1323
01:05:28,560 --> 01:05:32,960
Speaker 1: Tweet I've been enjoying from kathle Hughey Lewis and the

1324
01:05:33,040 --> 01:05:37,560
Speaker 1: news at Treye Dessert tweeted instant deleted tweet Hall of

1325
01:05:37,640 --> 01:05:41,880
Speaker 1: Fame nominee, and then retweeted a New England Patriots tweet.

1326
01:05:42,200 --> 01:05:45,840
Speaker 1: So they drafted somebody named Kobe Minor with the very

1327
01:05:45,920 --> 01:05:50,400
Speaker 1: last pick in the NFL draft, and they tweeted an

1328
01:05:50,440 --> 01:05:56,880
Speaker 1: image of him with the words a minor because his

1329
01:05:56,960 --> 01:06:03,720
Speaker 1: last name's Minor. Amazing, Oh Kobe. Anyways, you can find

1330
01:06:03,760 --> 01:06:06,000
Speaker 1: me on Twitter at Jack underscorel Brian on Blue Sky

1331
01:06:06,040 --> 01:06:08,400
Speaker 1: at jack O b the Number One. You can find

1332
01:06:08,520 --> 01:06:11,000
Speaker 1: us on Twitter and Blue Sky at Daily Zeigeist. We're

1333
01:06:11,000 --> 01:06:13,560
Speaker 1: at the Daily Zeitgeist on Instagram. You can go to

1334
01:06:13,600 --> 01:06:16,440
Speaker 1: the description of the episode wherever you're listening to this

1335
01:06:16,640 --> 01:06:19,840
Speaker 1: and you can find the footnotes, which is where we

1336
01:06:19,880 --> 01:06:22,000
Speaker 1: link off to the information that we talked about in

1337
01:06:22,040 --> 01:06:22,880
Speaker 1: today's episode.

1338
01:06:22,920 --> 01:06:23,840
Speaker 2: We also link off to.

1339
01:06:23,800 --> 01:06:27,600
Speaker 1: A song that we think you might enjoy. Hey, Miles,

1340
01:06:27,680 --> 01:06:29,479
Speaker 1: is there a song that you think people might enjoy?

1341
01:06:29,640 --> 01:06:33,320
Speaker 2: Yeah, this is a group called Reo Costa Ko st

1342
01:06:33,520 --> 01:06:36,560
Speaker 2: A And this is a track called Ancients. And so

1343
01:06:36,640 --> 01:06:40,760
Speaker 2: if you like jungle, if you like uh, you know

1344
01:06:40,960 --> 01:06:43,240
Speaker 2: that that sort of like Came and Paula. This feels

1345
01:06:43,240 --> 01:06:45,800
Speaker 2: like kind of a mix between like sort of psychedelic

1346
01:06:45,880 --> 01:06:50,120
Speaker 2: e but funky with a lot of with falsetto vocals.

1347
01:06:50,400 --> 01:06:53,040
Speaker 2: I think it's great, very Look, you know this is great,

1348
01:06:53,120 --> 01:06:55,400
Speaker 2: great music. It's it's time to let our big toe

1349
01:06:55,440 --> 01:06:58,160
Speaker 2: shoot up in our boop. Okay. So this is Ancients

1350
01:06:58,200 --> 01:07:00,280
Speaker 2: by the group Real Costa check in Out.

1351
01:07:00,520 --> 01:07:02,600
Speaker 1: We link off to that in the footnote that All

1352
01:07:02,640 --> 01:07:04,600
Speaker 1: these Guys is a production by heart Radio from our

1353
01:07:04,640 --> 01:07:07,720
Speaker 1: podcast from iHeartRadio. Visit the iHeartRadio, app, Apple Podcast or

1354
01:07:07,720 --> 01:07:09,400
Speaker 1: wherever you listen to your favorite shows.

1355
01:07:09,480 --> 01:07:12,520
Speaker 2: That's gonna do it for us this morning.

1356
01:07:12,600 --> 01:07:14,800
Speaker 1: We are back this afternoon to tell you what is trending,

1357
01:07:15,120 --> 01:07:17,800
Speaker 1: and we will talk to you all then, Bye bye.

1358
01:07:18,360 --> 01:07:21,280
Speaker 2: The Daily Zeit Guys is executive produced by Catherine Law.

1359
01:07:21,480 --> 01:07:23,240
Speaker 4: Co produced by Bee Wang

1360
01:07:23,880 --> 01:07:28,840
Speaker 2: Co produced by Victor Wright, edited and engineered by Justin Connor,