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Speaker 1: They had that like continuous Seinfeld generator for a while,

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Speaker 1: which I oh, really yeah, I forgot what happens. I

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Speaker 1: think it started. I think it just went to and

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Speaker 1: started doing racist things though.

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Speaker 2: Started talking like Jerry Seinfeld.

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Speaker 1: Yeah actually yeah exactly, it started talking like Sinfield and

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Speaker 1: Michael Richards, right, yeah.

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Speaker 3: The idea of he's doing great things.

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Speaker 1: Oh lord, what's with it with these dudent protesters anyway?

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Speaker 3: What's the deal? They all got the same tent? Who's

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Speaker 3: giving him the tents? That is his material? Hello the Internet,

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Speaker 3: and welcome to season three, forty four, Episode.

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Speaker 2: Two of dar Day's gay.

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Speaker 3: Production of iHeart Radio. And this is a podcast where

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Speaker 3: we take a deep dive into America's shared consciousness, America's

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Speaker 3: deep brain. If you will, A little typic cap because

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Speaker 3: we're big AI fan, A little little spoiler. We're big

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Speaker 3: AI fans, now, folks. I have seen the light come around.

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Speaker 3: I think when it's all you see on social media,

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Speaker 3: you're like.

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Speaker 2: That this is all right?

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Speaker 3: Oh yeah, maybe this is cool. Yeah. It's Tuesday, June

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Speaker 3: twenty fifth, twenty twenty four.

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Speaker 2: Oh yeah, big day. It's National Catfish Day, which is

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Speaker 2: odd because it's also my partner's birthday at breakday? Are

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Speaker 2: you have you been catfishing me this whole time? It's

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Speaker 2: national straw? Guys, haven't met her person yet? Still right,

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Speaker 2: but one of these days. No, Well, and every time

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Speaker 2: I want a video chat, she says her phone's broken,

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Speaker 2: so we just kind of stick to the phone call stuff.

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Speaker 3: But it was also very normal. It's this stage in

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Speaker 3: a marriage very normal.

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Speaker 2: Also, this is so weird and this is just like

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Speaker 2: some weird religious stuff. It's national leak? Did you know

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Speaker 2: this is national day?

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Speaker 3: Do you know what that even is?

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Speaker 2: It's because you're six months You're six months away from Christmas.

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Speaker 3: Noel words the most it's the most opposite time of

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Speaker 3: the year. What does this even mean? Jesus gets a

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Speaker 3: half bird instead of ding dong, they dong ding quote

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Speaker 3: Doctor Seuss. All right, anyways, happy National Leon Day. We're

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Speaker 3: gonna need to workshop that. Maybe we can run it

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Speaker 3: through an AI comedy writer, because those are good and

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Speaker 3: in no way just humans posing as AI comedy writers. Anyways,

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Speaker 3: my name is Jack O'Brien aka Big Ass Plumpers to

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Speaker 3: Thin Ass Fools Geist keep it coming like grim as Spooch.

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Speaker 3: We kill it on the podcast break in all the

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Speaker 3: news say, I'll be goddamned if there ain't more raccoons.

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Speaker 3: That is courtesy of Halcyon Salad. Like that name, Halcion Salad.

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Speaker 3: I see what you've done there, and I enjoy it.

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Speaker 3: I'm thrilled to be joined as always by my co host,

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Speaker 3: mister Miles Gray.

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Speaker 2: It's Miles Baker.

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Speaker 3: I got them bell big boots when the weather suit.

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Speaker 2: Yeah, I got that sweat because I'm a dad. Shout

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Speaker 2: out razagg on the discord because yeah, like I said,

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Speaker 2: I have zip off cargo pants that go from pants

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Speaker 2: to shorts because I'm a dad and you have that's

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Speaker 2: like just mandatory swag.

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Speaker 3: You unzip the bottom of the shorts, yep, which is

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Speaker 3: what they're designed to do when it gets warm at

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Speaker 3: the knee, and then you flip them. You invert the

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Speaker 3: likes and put the cuff at your knee. So you've

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Speaker 3: got little bell bottoms, like two part bell bottoms.

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Speaker 2: Yeah, the three part bell bottoms. I guess I saw

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Speaker 2: it on Pinterest. It could have been AI, but it

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Speaker 2: looked like people where we get our fashion from. Yeah, yeah,

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Speaker 2: it might have been completely off. But hey, Miles, most

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Speaker 2: people say, hey, good luck on you. Yes, people say, hey, Miles,

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Speaker 2: We're thrilled to be joined Yes by the hosts of

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Speaker 2: the Mystery AI Hype Theater three thousand podcasts. Once again,

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Speaker 2: it's doctor Alex Hannah and Professor Emily M.

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Speaker 3: Bender. What's up, guys, welcome to the show.

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Speaker 1: Hello, Hey, so I'm so glad to hear you there.

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Speaker 1: What was the thing about the raccoon?

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Speaker 3: A raccoon ripped a crow in half in my backyard.

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Speaker 1: Gosh, like it's just wwe style just put it over,

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Speaker 1: it's it's knee when it's strange.

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Speaker 3: I didn't see it happen. I just found the crow,

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Speaker 3: if grown friendly with a murder of crows in my backyard,

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Speaker 3: and as one does, and found the crow's body right

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Speaker 3: next to like a garbage bag garbage can that had

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Speaker 3: been like flipped over like by what could have only

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Speaker 3: been a raccoon. And we do have a raccoon family

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Speaker 3: living nearby, so.

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Speaker 4: If you have if you have an infra raccoons, have

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Speaker 4: you ever seen the video where someone's feeding a raccoon

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Speaker 4: cotton candy?

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Speaker 2: No?

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Speaker 3: Oh, and don't they like take it to the water.

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Speaker 3: I've seen like.

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Speaker 2: Wash it dissolved and like no, Yeah, they always watch

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Speaker 2: their food.

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Speaker 3: Yeah, very respect it. I just have a much healthier

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Speaker 3: respect for raccoons. We should be treating raccoons like handguns.

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Speaker 3: They're very impressive and dangerous and we should just give

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Speaker 3: them the proper respect. They have a thumbs exactly, they

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Speaker 3: and they hunt birds.

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Speaker 2: So it's all I knew. I didn't realize the food

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Speaker 2: washing thing. My mom famously has opened her home to

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Speaker 2: possums and other neighborhood wildlife and where she lives, and

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Speaker 2: I remember in the kitchen where the cat food is,

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Speaker 2: there was like a bunch of kibble in the water

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Speaker 2: bowl and I was like, what is going on? And

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Speaker 2: Mom was like, I think that's what the raccoon does.

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Speaker 2: And I was like oh, and it was said so

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Speaker 2: casually that I was like in your home. He's like yeah,

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Speaker 2: yeah yeah. But then it leaves and I was like,

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Speaker 2: this is very okay.

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Speaker 1: Well does it just does it use like the cat

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Speaker 1: like door? Does it? Yeah?

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Speaker 2: Yeah, it comes through, watches the kibble, has a few bites,

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Speaker 2: and then takes off into the night. It's like a

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Speaker 2: pit stop for.

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Speaker 1: One of the credit Yeah, I'm leading, So I'm leading

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Speaker 1: a raccoon based Dungeons and Dragons campaigns starting next week.

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Speaker 1: That's going to be like, yes, it's going to be

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Speaker 1: like a heist that takes place in the warehouse where

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Speaker 1: where I play Roller Derby. Like, I'm very excited. I've

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Speaker 1: got you got it really architected. I don't want to

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Speaker 1: give any secrets in case any of my my player

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Speaker 1: characters listen to this podcast.

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Speaker 3: But right, okay, that sounds amazing And what a coincidence

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Speaker 3: raccoons having a bit of a moment really on this podcast.

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Speaker 1: Yeah.

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Speaker 3: So in addition to being a host of the wonderful

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Speaker 3: Mystery and Hype Theater three thousand podcast, which podcast hosts

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Speaker 3: the highest honor one can attain in American life. But

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Speaker 3: you both have some pretty impressive credits. Emily, you are

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Speaker 3: a linguist and professor at the University of Washington, where

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Speaker 3: you are director of the Computational Linguistics Laboratory.

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Speaker 5: Yep, that's right, okay.

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Speaker 3: Alex you are director of research at the Distributed AI

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Speaker 3: Research Institute. Both widely published, both received a number of

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Speaker 3: academic awards. Both have PhDs. We had you on the

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Speaker 3: podcast a few months back, told everyone the truth about

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Speaker 3: AI that a lot of the stuff that we're scared of,

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Speaker 3: and a lot of the stuff we think it can

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Speaker 3: do is not true. It's bullshit. And I sat back

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Speaker 3: and was like, well, we'll see what AI does after

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Speaker 3: this one. And it's just kept happening, you guys, what

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Speaker 3: the what the heck can we do? If anything, it's

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Speaker 3: gotten worse since we told everybody the truth what's happening?

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Speaker 4: Truly, everybody seems to want to believes absurd so wild. Yeah,

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Speaker 4: And part of what we do with the podcast actually

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Speaker 4: is like try to be a focal point for a

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Speaker 4: community of the people who are like, know, that's not right.

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Speaker 4: Why does everybody around me seem to believe that it's like,

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Speaker 4: you know, actually doing all these things. Yeah, so it's yeah,

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Speaker 4: it's it's you know, that's what we say in our podcast,

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Speaker 4: like every time we think we've reached peak, a hype,

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Speaker 4: the summit, the bullshit mountain, we discover there's.

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Speaker 2: Worse to come, Like yeah, right, Like oh yeah, this

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Speaker 2: is just a base camp until you get to the

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Speaker 2: real peak.

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Speaker 1: Like right, well, it's just that you just keep on thinking,

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Speaker 1: that keeps on becoming and there's more and more things

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Speaker 1: that these CEOs just you know, are really just say

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Speaker 1: incredible nonsense. I don't know if you saw this. I

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Speaker 1: think it was the last week the chief technology officer

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Speaker 1: of open Ai, mir Murti Muradi, yeah, who famously was

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Speaker 1: I think it was an interview of sixty minutes and

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Speaker 1: when they were talking about one of their tools, soa

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Speaker 1: you know, they had asked if.

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Speaker 3: They favorite filmmaker, Yeah exactly, my favorite Yeah, thank you,

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Speaker 3: yeah exactly.

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Speaker 1: Just so are really edging out, you know, David Lynch

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Speaker 1: these days, and so, you know, and they asked her

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Speaker 1: do you train the stuff on YouTube? And she she

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Speaker 1: kind of grimaced, yeah, painfully. And I remember a great

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Speaker 1: Twitter comment. I was like, well, if you're gonna just

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Speaker 1: lie about stuff, you at least have to have a

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Speaker 1: good poker face about it. Yes, And so the last

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Speaker 1: week she was like, well, I think she's doing another

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Speaker 1: interview and she was like, well, some some some creative

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Speaker 1: jobs are going to go away, like some artists should

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Speaker 1: be you know, some.

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Speaker 2: Creative jobs maybe shouldn't have existed in the first place. Right,

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Speaker 2: If like these jobs were in affront to God or something,

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Speaker 2: some of them just shouldn't have even been there.

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Speaker 3: But she does have a French accent, so it's really

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Speaker 3: hard to be like, this is ridiculous.

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Speaker 1: Let's he's Italian and.

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Speaker 3: That's what's amazing about her having a friend. I don't.

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Speaker 3: I'm not I'm not a cultured person. I don't know

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Speaker 3: the difference between They're all French to me, I'm a man, right, Hey,

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Speaker 3: she has a Canadian accent. I think. I'm not sure.

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Speaker 1: I know it's only it's only plagiarism if it comes

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Speaker 1: from the French region of.

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Speaker 3: Italy's right, that's right. Yeah, we're going to get into

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Speaker 3: that story and just yeah, all of the madness that

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Speaker 3: has continued to happen, the bullshit has continued to rain

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Speaker 3: even harder, it seems like, yeah, which, yes, does make

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Speaker 3: the mountain go higher, unfortunately, the bullshit mountain. But before

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Speaker 3: we get to that, Emily Alex, we do like to

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Speaker 3: ask our guests, what is something from your search histories

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Speaker 3: that's revealing about who you are? Alex? You want to

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Speaker 3: kick us off?

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Speaker 1: Oh gosh, okay, I don't. The thing is, I don't

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Speaker 1: think so I use duck duckgo, and so it doesn't

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Speaker 1: actually keep us search history and if I actually look

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Speaker 1: at my Google history, it's actually going to be really shameful.

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Speaker 1: It's going to be me like searching my own name

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Speaker 1: to see it, like if people are like ship talking

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Speaker 1: to me online.

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Speaker 3: Now this isn't just how we tell if someone's honest,

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Speaker 3: is if they actually give that answer, we're like, okay, so.

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Speaker 1: Yeah, you actually search yourself. Yeah, actually, but I think

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Speaker 1: the last thing I actually searched was like queer barbers

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Speaker 1: in the Bay Area because I haven't had a haircut

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Speaker 1: in like a year, and I think I need to

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Speaker 1: turm up or get you know, air out the the

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Speaker 1: sides of my head for pride months. So that's yeah,

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Speaker 1: that's elastic.

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Speaker 3: I searched, what are you going? You're going full shaved

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Speaker 3: on the sides.

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Speaker 1: Or maybe trim it a little bit and up the

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Speaker 1: back and bring out the curls a little bit.

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Speaker 3: So okay, love it? Yeah, on board.

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Speaker 2: I wish I could've got a few more days in look.

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Speaker 1: In July like discounts, Like it's like like after Valentine's Day?

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Speaker 1: Do I get an undercut at fifty percent off?

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Speaker 3: Now?

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Speaker 2: Right? Exactly?

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Speaker 3: Emily, how about you? What's something from your search history?

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Speaker 4: So forgive the poor pronunciation of this and the rest

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Speaker 4: of the story, because Spanish is not one of my languages.

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Speaker 4: But Champurrado, Oh yeah, it's something I search instrs.

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Speaker 3: Yeah.

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Speaker 4: So I was in Mexico City for a conference last

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Speaker 4: week and at one of the coffee breaks, they had

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Speaker 4: coffee and decaf coffee, and then.

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Speaker 1: They had.

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Speaker 3: And the Spanish don't give us about that.

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Speaker 2: What do you see when you see that?

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Speaker 3: Way? Champarado Mexican hot chocolate.

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Speaker 2: So yeah, you're literally Google results.

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Speaker 4: So the the labels all had like translations into English,

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Speaker 4: and so it was champurado with o hack and Chocolate'm like, yeah,

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Speaker 4: I got that.

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Speaker 5: What's chopado?

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Speaker 4: And so I look it up because I want to

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Speaker 4: know what I'm consuming before I consume it. And it's

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Speaker 4: basically a corn flour based thick drink chocolate corn soup.

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Speaker 5: It was amazing.

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Speaker 3: Chocolate corn you had me until chocolate corn soup. But

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Speaker 3: the corn is just a thickening yeah, chocolate drink.

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Speaker 4: Yeah, slight corn flavor like think corn tortilla, not on

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Speaker 4: the cob.

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Speaker 3: Yeah yeah, oh yeah, yeah, yeah that sounds amazing.

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Speaker 5: Yeah, it was really good.

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Speaker 3: I love some corn flakes in a chocolate bar. Yeah,

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Speaker 3: corn chocolate. There you go. Yeah, you got it.

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Speaker 2: You gotta arrive in your own way.

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Speaker 3: Corn chocolate.

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Speaker 4: It was really good and just awesome that it was there,

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Speaker 4: Like you know, the the coffee breaks had like the

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Speaker 4: Mexican sweetbreads and stuff like that, but otherwise it was

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Speaker 4: pretty standard like coffee break stuff, and all of a

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Speaker 4: sudden there's this wonderful mystery drink.

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Speaker 5: The big urns was. It was lovely.

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Speaker 3: That sounds great. What is something you think is underrated? Emily?

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Speaker 4: I think Seattle's weather is underrated. Oh yeah, everyone makes

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Speaker 4: fun of our weather and like, you know, fine believe

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Speaker 4: that we don't need lots of people coming here.

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Speaker 5: And it's true it gets dark in the winter.

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Speaker 4: But like almost any day, you can be outside and

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Speaker 4: you are not in physical danger because you are outside.

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Speaker 1: I guess that's that's I mean, if you're going for yeah,

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Speaker 1: that's interesting, but I mean it's I mean, the winters

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Speaker 1: are just so punishing though it's so gray.

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Speaker 2: It's dark, but the weather it's dark, it looks it

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Speaker 2: looks like shit, but experientially not bad for you. I mean,

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Speaker 2: I yeah, I know it's when does like it doesn't

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Speaker 2: get all gloomy. I imagine in the summer, right, you

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Speaker 2: have wonderful blue skies, and you can.

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Speaker 4: Enjoy the gorgeous fire season aside. But yeah, from sort

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Speaker 4: of mid October to early January, it can be pretty

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Speaker 4: like it's gray and so like when the sun is

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Speaker 4: technically above the horizon, it's.

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Speaker 5: A little hard to tell.

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Speaker 4: Yeah, right right, So, but you know, compared to like Chicago,

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Speaker 4: where you have maybe four liverable weeks a year between

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Speaker 4: the too hot and the two cold.

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Speaker 1: Wow, wow, I'll do that. Because my thing was going

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Speaker 1: to be Chicago, because I was just there and I

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Speaker 1: was going to say my answer was going to be,

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Speaker 1: Chicago is the best American city. I stand on this

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Speaker 1: like one.

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Speaker 3: No, absolutely not true.

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Speaker 1: No, I'll even deal. I'll even deal with I'll deal

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Speaker 1: with the winter. I mean if I okay, I'll be honest.

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Speaker 1: If I didn't, you know, if the weather in Chicago,

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Speaker 1: if if I could bring Bay Area weather to Chicago,

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Speaker 1: I would live in Chicago. I mean there's other reasons,

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Speaker 1: but I mean it's it's look, the vibes, immaculate street festivals,

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Speaker 1: the neighborhoods. It's the one place that's probably the food.

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Speaker 1: It's still comparatively affordable compared.

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Speaker 3: To the coasts.

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Speaker 1: Radical history, you know, just you know, some of the

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Speaker 1: best politics. Yeah, you know, I would say, fuga. They

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Speaker 1: shot shot an issue there, the fugitive, Oh I did.

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Speaker 1: That's a deep cut. Yeah, I mean they I think

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Speaker 1: they've shot a lot of Batman movies there, because you know,

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Speaker 1: the iconic kind of lower Whacker drive and they call

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Speaker 1: it with them and it's yeah, yeah, that's pretty great city.

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Speaker 4: Crappy weather, right, if you're going to dump on weather somewhere.

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Speaker 4: Everyone makes fun of Seattle's weather.

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Speaker 1: Honestly, Emily, this is a hot take. I'd rather take

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Speaker 1: Chicago's weather than Seattle's weather.

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Speaker 3: I can't. I can't do gray. I can do I

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Speaker 3: feel like I'm a crossfire.

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Speaker 1: I can bridge it.

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Speaker 3: I cannot do gray.

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Speaker 1: It's super pressing.

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Speaker 4: Well, this is why I say, like, don't move to

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Speaker 4: Seattle if you can't handle our weather. Like the people

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Speaker 4: who move here and then complain about.

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Speaker 2: The weather, what do you expect all of this? What

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Speaker 2: they say is true about it being great, like, oh,

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Speaker 2: I didn't expect you to be that gray?

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Speaker 3: Right, I think people talk about it like that. All right, Alex,

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Speaker 3: let's stay with you. What is you, guys, is overrated

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Speaker 3: and please do it in a point counterpoint style also

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Speaker 3: that contradicts one another.

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Speaker 1: Well, I got to think about what's what's overrated these days.

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Speaker 1: I just don't know what's in the I know this

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Speaker 1: the name of the show is the Daily Zeitgeist, But

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Speaker 1: I don't really know what's in the zeitgeist. I mean,

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Speaker 1: I guess Taylor Swift, I mean, I don't really have

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Speaker 1: Maybe that's controversial. I'm saying something that's hot take, but

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Speaker 1: I guess that's maybe not controversial to people of of

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Speaker 1: our you know, our generation.

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Speaker 2: No so joining Dave Grol on the on the attack

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Speaker 2: this weekend. Yeah, wait, what happened with David Girl, like

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Speaker 2: implying that she's like He's like, well, we play our

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Speaker 2: music live like raw live rock and roll, you know,

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Speaker 2: unlike the Era's tour, you know, with the errors tour,

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Speaker 2: and then everyone's like fuck you Dave or other people

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Speaker 2: will being like exactly exactly. Yeah.

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Speaker 1: It's just like yeah, yeah, I mean Dave Girl is

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Speaker 1: also overrated, I guess. But like, I mean, I enjoyed Look,

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Speaker 1: I enjoy ever long like the next like middle aged

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Speaker 1: sort of like dad figure like, but I you know,

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Speaker 1: I'm sure like I'm glad that she played every part

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Speaker 1: in that song. It sounds good, but you know, yeah,

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Speaker 1: it doesn't make you an authority on Taylor Swift. So yeah,

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Speaker 1: so I think I'm undercutting my own point.

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Speaker 3: But now let's go, Yeah you did in your own over.

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Speaker 4: Yeah that's excellent because I don't even have an opinion about.

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Speaker 3: Saw Tucker Carlson do that was that that was called crossfire?

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Speaker 1: Or was that the crossfire was with what's his face?

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Speaker 3: Carlson and Paul That was wasn't the one that the

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Speaker 3: one that John Stewart came on and was like yeah,

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Speaker 3: it was like this show is bad, and then like

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Speaker 3: they cancel it a couple of weeks.

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Speaker 1: Well then, but then there was at once show Hannity

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Speaker 1: and Combs where Sean, you know, Sean Hannity was supposed

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Speaker 1: to be a conservative voice, and then you know Combs

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Speaker 1: where like I don't I don't even know the guy's

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Speaker 1: first name. They just they kind of just had him

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Speaker 1: as a token like liberal on and then they just

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Speaker 1: it was on Fox News. So he just attacked him,

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Speaker 1: you know, relentlessly.

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Speaker 3: He wasn't allowed to read the news. He's like, you

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Speaker 3: argue the liberal points, but you're actually not permitted to

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Speaker 3: leave this room. We're gonna keep you in here, old

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Speaker 3: boy style.

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Speaker 2: For the Oh, that was the end of sixty minutes

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Speaker 2: that Andy Rooney would do. There was part of sixty

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Speaker 2: minutes was point counterpoint, and it would be Andy Rooney

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Speaker 2: if that's her thinking, Jack.

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Speaker 3: No, No, there was a show. Yeah, yeah, it was

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Speaker 3: right when I got out of college and worked for

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Speaker 3: ABC News and so everybody was always watching news and

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Speaker 3: at that time there was a big show on CNN

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Speaker 3: called Crossfire. It was Tucker, Yeah, Tucker Carlson was the conservative.

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Speaker 3: Paul mcgala was the roll and they just like got

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Speaker 3: on and yelled at each other.

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Speaker 1: I'm looking at us now.

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Speaker 3: Apparently good.

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Speaker 1: Apparently there there was a they they had a revival

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Speaker 1: and then in twenty thirteen and fourteen on the left

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Speaker 1: was Stephanie Cutter and Van Jones and then Nuke king

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Speaker 1: Ridge and se Cup on the right, and then whatever.

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Speaker 1: And then whenever they needed breaking news they'd bring in

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Speaker 1: Wolf Blitzer for some reason.

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Speaker 5: Because Wolf the situation room.

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Speaker 1: Yeah yeah, they released him from the cryogenic.

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Speaker 3: Whose helicopter lifted from the situation room three rooms over

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Speaker 3: to the Crossfire set. Just with dead pan. You know,

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Speaker 3: we need you go into emotion on his face. Ever

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Speaker 3: you guys ever seen the Wolf Blitzer episode of Celebrity

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Speaker 3: Jeopardy No Yourself a favor is It hasn't been scrubbed yet.

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Speaker 1: Is it as good as like the SNL parody? Is

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Speaker 1: the Celebrity Jeopardy with like the Sean Connery it's just yeah, no.

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Speaker 3: No, and also incorrect, just one after another, like negative

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Speaker 3: went into the into the red. He's in there quickly separty. Well, Wolf,

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Speaker 3: we're gonna spot you three thousand because we can't have

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Speaker 3: somebody be in negative numbers going into going into Final Jeopardy.

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Speaker 3: And I think Andy Richter was on with him and

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Speaker 3: just destroyed. Was so good.

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Speaker 1: That's so funny. Andy Richter like this the kind of

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Speaker 1: crossover I didn't know I need.

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Speaker 2: Yeah, it's still up there mostly from what I could tell,

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Speaker 2: it's incredible.

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Speaker 3: Yeah, I am an old person. All right, we still

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Speaker 3: have Emily. You're overrated. What do you think is overrated?

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Speaker 5: Big cars are overrated?

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Speaker 6: Oh?

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Speaker 4: Totally sort of half heartedly looking for our next car

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Speaker 4: and can't find anything that is like small. And the

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Speaker 4: other day I was in the parking lot for a

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Speaker 4: grocery store in your hair mostly I can walk for groceries,

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Speaker 4: but occasionally have to drive this other store. And half

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Speaker 4: the spots were labeled compact, and like all of those

417
00:22:12,240 --> 00:22:15,440
Speaker 4: spots were taken up two at a time by what

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Speaker 4: we now have is regular cars, because somebody's decided that

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Speaker 4: people in this country don't deserve normal sized cars.

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Speaker 3: Yeah, they're so.

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Speaker 2: I mean, it's still the point where like even the

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Speaker 2: people who design parking lots are like, we have to

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Speaker 2: tell the automobile manufacturers, like the standard we've set as

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Speaker 2: people who like create parking lots, like they're pushing the

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Speaker 2: boundaries of what we can actually do or how we

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Speaker 2: measure things because the cars are so fucking big.

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Speaker 4: And our streets around here in Seattle, we have a

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Speaker 4: lot of neighborhood streets where there's like parking on both

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Speaker 4: sides and then sort of just barely enough space for

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Speaker 4: two normal cars to go through, right, or sometimes you

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Speaker 4: have to like pull over to the car, and the

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Speaker 4: bigger the car is like the harder that gets.

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Speaker 3: I love that thing.

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Speaker 2: I remember one of the times went to Seattle, seeing

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Speaker 2: how everybody just parks on whatever side of the street

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Speaker 2: in whatever direction they want.

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Speaker 3: I was like, all right, I'm like, all right, I

438
00:23:04,720 --> 00:23:05,440
Speaker 3: was not familiar.

439
00:23:05,680 --> 00:23:06,160
Speaker 1: That's funny.

440
00:23:06,200 --> 00:23:07,800
Speaker 5: Yeah, yeah, a little bit of chaos.

441
00:23:08,040 --> 00:23:11,040
Speaker 4: It totally offends my spouse, who's like, that's not how

442
00:23:11,119 --> 00:23:12,160
Speaker 4: parking works.

443
00:23:12,280 --> 00:23:12,560
Speaker 1: That's it.

444
00:23:13,560 --> 00:23:17,080
Speaker 3: Yeah, yeah, I love it. Automakers just seem to be

445
00:23:17,400 --> 00:23:20,600
Speaker 3: getting bigger and heavier. They won't stop until they make

446
00:23:20,640 --> 00:23:23,159
Speaker 3: a car that like is legally required to have a

447
00:23:23,240 --> 00:23:24,440
Speaker 3: foghorn on it.

448
00:23:24,680 --> 00:23:27,760
Speaker 4: Right, So the cyber truck, Yeah, cyber truck.

449
00:23:27,920 --> 00:23:30,120
Speaker 3: I was going to ask, have you have you considered

450
00:23:30,200 --> 00:23:31,080
Speaker 3: the cyber truck.

451
00:23:31,359 --> 00:23:32,480
Speaker 5: I've seen one in person.

452
00:23:32,600 --> 00:23:36,000
Speaker 4: They are hilarious, like you can't not laugh exactly.

453
00:23:35,760 --> 00:23:39,320
Speaker 3: What it's such. It is an experiencing one in the wild.

454
00:23:39,600 --> 00:23:41,480
Speaker 4: It's like, wow, I just want to say that what

455
00:23:41,600 --> 00:23:44,560
Speaker 4: we really need is functional public transit, but like, short

456
00:23:44,600 --> 00:23:46,600
Speaker 4: of that, we also need to not be doing bigger

457
00:23:46,600 --> 00:23:47,200
Speaker 4: and bigger cars.

458
00:23:47,520 --> 00:23:50,359
Speaker 1: Yeah yeah, yeah, no, I just I mean, I have

459
00:23:50,480 --> 00:23:53,480
Speaker 1: a truck. I have a twenty twenty truck, and I wish,

460
00:23:54,119 --> 00:23:56,920
Speaker 1: I really wish it was much smaller because it's hard

461
00:23:56,960 --> 00:23:59,760
Speaker 1: to park. It's way too big. I mean, I think

462
00:23:59,800 --> 00:24:03,680
Speaker 1: that peak of truck design it was like, you know,

463
00:24:03,760 --> 00:24:08,440
Speaker 1: a nineteen eighty seven Toyota Tacoba long cab, you know,

464
00:24:08,840 --> 00:24:11,359
Speaker 1: just you know where like, yeah, you had to bunch

465
00:24:11,480 --> 00:24:13,399
Speaker 1: up your knees in the back if you want to

466
00:24:13,520 --> 00:24:16,159
Speaker 1: fit four people on it. But you actually had a

467
00:24:16,240 --> 00:24:19,440
Speaker 1: long you know, you actually had a truck bed that

468
00:24:20,000 --> 00:24:24,119
Speaker 1: actually had you know, some carrying capacity, you know, and.

469
00:24:24,480 --> 00:24:26,919
Speaker 2: It was a car you could absolutely run into the.

470
00:24:27,000 --> 00:24:27,879
Speaker 3: Ground with nobody.

471
00:24:27,960 --> 00:24:29,800
Speaker 1: Yeah, oh yeah, oh yes.

472
00:24:30,040 --> 00:24:32,320
Speaker 2: Now people were like, my new Ford Lightning needs a

473
00:24:32,440 --> 00:24:33,520
Speaker 2: software update.

474
00:24:33,720 --> 00:24:36,320
Speaker 1: Oh god, well that's the thing, is like yeah, I

475
00:24:36,359 --> 00:24:38,359
Speaker 1: mean like, I mean that's a big deal. I know,

476
00:24:38,480 --> 00:24:40,479
Speaker 1: in like organ which is like the where they had

477
00:24:40,560 --> 00:24:43,480
Speaker 1: right to repair a right to repair bill, and I

478
00:24:43,560 --> 00:24:45,879
Speaker 1: mean in some ways the people that were kind of

479
00:24:45,960 --> 00:24:50,040
Speaker 1: into it weirdly were like Google actually came out kind

480
00:24:50,040 --> 00:24:52,800
Speaker 1: of into it. There was a Good for for Media

481
00:24:52,880 --> 00:24:56,359
Speaker 1: podcast where they talked about this with an Apple because

482
00:24:56,400 --> 00:24:59,280
Speaker 1: they have such a closed ecosystem was so against right

483
00:24:59,359 --> 00:25:02,000
Speaker 1: to repair or you know, even if you have right

484
00:25:02,040 --> 00:25:04,040
Speaker 1: to repair. They actually they'd add on all these things

485
00:25:04,040 --> 00:25:05,639
Speaker 1: where you you'd still have to send them to an

486
00:25:05,640 --> 00:25:09,879
Speaker 1: authorized dealers because of firmware issues or whatever. And then

487
00:25:10,000 --> 00:25:12,560
Speaker 1: John Deere, like John Deere is this kind of thing

488
00:25:12,600 --> 00:25:15,840
Speaker 1: where they have you know, so much of their tractors

489
00:25:15,880 --> 00:25:18,399
Speaker 1: are computerized, and so there's like a lot of like

490
00:25:18,600 --> 00:25:23,080
Speaker 1: these John Deeer hacking kinds of things that people who

491
00:25:23,119 --> 00:25:26,240
Speaker 1: are outside of the US, you know, programming these kinds

492
00:25:26,280 --> 00:25:29,440
Speaker 1: of hacks for for people running these tractors and can't

493
00:25:29,480 --> 00:25:30,640
Speaker 1: run into their firm where.

494
00:25:32,080 --> 00:25:36,560
Speaker 3: Yeah, the farmers have all the good the GPS something,

495
00:25:37,080 --> 00:25:37,480
Speaker 3: but did.

496
00:25:37,400 --> 00:25:39,320
Speaker 4: You hear about how the GPS was out for a

497
00:25:39,400 --> 00:25:44,040
Speaker 4: while with there's actually is again for a for media podcast,

498
00:25:44,160 --> 00:25:47,120
Speaker 4: but that the way those tractors work for like planting

499
00:25:47,359 --> 00:25:52,200
Speaker 4: is so precise and the GPS. With the GPS off,

500
00:25:52,240 --> 00:25:55,600
Speaker 4: they basically couldn't plant because then the seeds wouldn't be

501
00:25:55,600 --> 00:25:57,600
Speaker 4: in the right spot for the next process, and so

502
00:25:57,720 --> 00:25:59,840
Speaker 4: they had to wait. And there's a really narrow window

503
00:25:59,880 --> 00:26:02,680
Speaker 4: of currently with our you know, currently genetically modified like

504
00:26:03,080 --> 00:26:06,639
Speaker 4: very very specific corn that Santo owns, and so it

505
00:26:06,720 --> 00:26:08,480
Speaker 4: was actually looking pretty bad for a while. I didn't

506
00:26:08,480 --> 00:26:10,480
Speaker 4: hear any follow ups. So maybe the solar flail was

507
00:26:10,520 --> 00:26:13,040
Speaker 4: short enough and the GPS came back online, but apparently.

508
00:26:12,760 --> 00:26:13,320
Speaker 5: That was a big thing.

509
00:26:15,280 --> 00:26:17,560
Speaker 3: It has to be a brief window because it goes

510
00:26:17,600 --> 00:26:20,600
Speaker 3: from corn plant corn seed planted in the ground to

511
00:26:20,800 --> 00:26:23,480
Speaker 3: like popcorn in the movie theater in two and a

512
00:26:23,480 --> 00:26:24,080
Speaker 3: half weeks.

513
00:26:24,440 --> 00:26:30,080
Speaker 4: Yeah, hyper, most of it doesn't even go to popcorn

514
00:26:30,080 --> 00:26:31,639
Speaker 4: in the movie theater, right, most of it goes to

515
00:26:31,840 --> 00:26:33,400
Speaker 4: animal feed or after.

516
00:26:33,280 --> 00:26:36,840
Speaker 3: All, I think, yeah, right, right, yeah, all right, Well,

517
00:26:37,119 --> 00:26:39,359
Speaker 3: let's take a quick break and we're going to come

518
00:26:39,440 --> 00:26:43,879
Speaker 3: back and dive into why Miles and I are excited

519
00:26:44,240 --> 00:26:48,600
Speaker 3: about the future of AI. We'll be right back. Crossfire

520
00:27:00,520 --> 00:27:04,760
Speaker 3: and we're back. We're back. So just to for people

521
00:27:04,840 --> 00:27:09,040
Speaker 3: who haven't listened to your previous appearance in a while,

522
00:27:09,600 --> 00:27:13,280
Speaker 3: I feel like a broad over generalization, but it feels

523
00:27:13,359 --> 00:27:16,240
Speaker 3: like the stuff that AI is actually being used for

524
00:27:16,560 --> 00:27:20,800
Speaker 3: and capable of is not what we're being told about

525
00:27:21,040 --> 00:27:25,000
Speaker 3: through the mainstream media. Like it is not an autonomous

526
00:27:25,119 --> 00:27:27,760
Speaker 3: intelligence that is going to be the bad guy in

527
00:27:27,880 --> 00:27:30,879
Speaker 3: a Mission Impossible movie. I mean, it is a bad

528
00:27:30,920 --> 00:27:32,720
Speaker 3: guy in a Mission Impossible movie, but it's not going

529
00:27:32,760 --> 00:27:35,479
Speaker 3: to be a bad guy in the in reality our

530
00:27:35,520 --> 00:27:39,879
Speaker 3: way it is, yeah, the way that our actual president believes.

531
00:27:41,440 --> 00:27:45,840
Speaker 3: That was an amazing reveal that Joe Biden basically watched

532
00:27:45,920 --> 00:27:48,800
Speaker 3: Mission Impossible and was like, we gotta gotta worry about

533
00:27:48,840 --> 00:27:52,240
Speaker 3: this AI stuff. Jack it's gonna know my next move.

534
00:27:52,960 --> 00:27:56,640
Speaker 3: But it is more like the large language models are

535
00:27:57,480 --> 00:28:01,359
Speaker 3: basically more sophisticated auto complete that is telling you what

536
00:28:01,680 --> 00:28:05,200
Speaker 3: it's data set indicates you want to hear, or what

537
00:28:05,320 --> 00:28:08,159
Speaker 3: its data set indicates will make you think. It is

538
00:28:08,240 --> 00:28:11,920
Speaker 3: thinking talking like a person. In many cases that means

539
00:28:12,359 --> 00:28:15,960
Speaker 3: what they call hallucinating. What is actually just making ship up?

540
00:28:16,720 --> 00:28:19,160
Speaker 2: What are their jobs? Could you say that You're like, sorry,

541
00:28:19,240 --> 00:28:22,800
Speaker 2: I was hallucinating and like okay.

542
00:28:25,440 --> 00:28:27,880
Speaker 1: Oh but what you wouldn't last long as a pre cog.

543
00:28:28,119 --> 00:28:31,960
Speaker 3: Yeah, I would be the worst precog on.

544
00:28:32,000 --> 00:28:34,120
Speaker 2: The I R S. I was hallucinating on that last

545
00:28:34,160 --> 00:28:34,760
Speaker 2: tax return.

546
00:28:34,800 --> 00:28:36,040
Speaker 3: I think that's probably.

547
00:28:37,240 --> 00:28:38,880
Speaker 4: About using this to do your tax return.

548
00:28:39,280 --> 00:28:42,600
Speaker 1: Yeah right, there's actually, yeah, there's actually a I mean

549
00:28:42,640 --> 00:28:46,800
Speaker 1: in California, there's a whatever the Department of Tax and Revenue.

550
00:28:46,920 --> 00:28:49,840
Speaker 1: They've been There was some great reporting and how it

551
00:28:49,880 --> 00:28:53,520
Speaker 1: matters by Karie Johnson, and he was talking about how

552
00:28:53,600 --> 00:28:57,880
Speaker 1: they were using this thing, some language model to effectively

553
00:28:58,960 --> 00:29:02,479
Speaker 1: advise the people who call in or advise the agents

554
00:29:02,520 --> 00:29:06,200
Speaker 1: who respond to people to call into the California Franchise

555
00:29:06,240 --> 00:29:09,520
Speaker 1: sax board and you know, and they're like, well, they're

556
00:29:09,600 --> 00:29:11,880
Speaker 1: they're and they're like, well, you know, the the agents

557
00:29:11,880 --> 00:29:14,920
Speaker 1: are still going to you know, have the last worry.

558
00:29:14,960 --> 00:29:17,400
Speaker 1: But I'm just like, yeah, yeah, but they might they're overworked,

559
00:29:17,480 --> 00:29:19,239
Speaker 1: Like are they going to just they're going to read

560
00:29:19,280 --> 00:29:20,240
Speaker 1: this stuff or me and them.

561
00:29:20,240 --> 00:29:22,680
Speaker 3: You know, right exactly. Oh, you're going to use this

562
00:29:22,760 --> 00:29:25,920
Speaker 3: as an extra thing, just an extra expense to do

563
00:29:26,080 --> 00:29:29,240
Speaker 3: the product, do your job even better. That doesn't sound

564
00:29:29,560 --> 00:29:34,800
Speaker 3: like a company necessarily. Yeah. Yeah, So an interesting thing

565
00:29:34,840 --> 00:29:37,959
Speaker 3: that we're seeing happen. You know, we pay attention when

566
00:29:38,000 --> 00:29:41,880
Speaker 3: there's a an AI story that captures the zeitgeist. We

567
00:29:42,360 --> 00:29:45,400
Speaker 3: covered the B minus version of a George Carlin routine

568
00:29:46,000 --> 00:29:48,640
Speaker 3: that came out. They were like, a, I just brought

569
00:29:48,680 --> 00:29:52,160
Speaker 3: George Carlin back from the dead. We covered Amazon Fresh

570
00:29:52,640 --> 00:29:56,560
Speaker 3: having that store where the cameras know what you've taken

571
00:29:57,040 --> 00:29:59,400
Speaker 3: and so even if you try and shoplift like the

572
00:29:59,520 --> 00:30:02,400
Speaker 3: cameras know they're gonna catch it, and then you don't

573
00:30:02,400 --> 00:30:04,360
Speaker 3: even have to check out, you just walk out and

574
00:30:04,520 --> 00:30:08,040
Speaker 3: they like it, charges your account because of AI. And

575
00:30:08,200 --> 00:30:11,840
Speaker 3: then what we're seeing is that when the truth emerges,

576
00:30:12,640 --> 00:30:16,400
Speaker 3: it does not enter the zeitgeist because you guys cover

577
00:30:16,520 --> 00:30:18,360
Speaker 3: it on your show which is why we're so thrilled

578
00:30:18,440 --> 00:30:20,800
Speaker 3: to have you back. But you know, we have updates

579
00:30:20,800 --> 00:30:23,840
Speaker 3: on those two stories, Carlin. That was just written by

580
00:30:23,880 --> 00:30:29,520
Speaker 3: a person the Amazon Fresh. Those videos were being fed

581
00:30:29,720 --> 00:30:35,080
Speaker 3: to people working in India to try to track where

582
00:30:35,120 --> 00:30:37,280
Speaker 3: everything was going, which was why there was like a

583
00:30:37,360 --> 00:30:41,200
Speaker 3: weird pause like as people were where they're like, oh,

584
00:30:41,600 --> 00:30:43,960
Speaker 3: I think we got okay, yeah, we're we're just gonna

585
00:30:44,160 --> 00:30:47,560
Speaker 3: do a best guess. But it's straight up like mechanical

586
00:30:47,640 --> 00:30:51,400
Speaker 3: Turk like it's which again Amazon named one of their

587
00:30:51,480 --> 00:30:55,680
Speaker 3: companies the Mechanical Turk, so they they know what's going on.

588
00:30:55,880 --> 00:30:59,120
Speaker 3: They knew what they were planning to do here all along. Maybe,

589
00:30:59,680 --> 00:31:02,800
Speaker 3: But is that kind of the model you're seeing is big,

590
00:31:02,880 --> 00:31:07,080
Speaker 3: flashy announcement, this is what AI integration can do, and

591
00:31:07,240 --> 00:31:10,560
Speaker 3: then when it falls short, people just kind of ignore

592
00:31:10,600 --> 00:31:14,880
Speaker 3: it or how does it seem from where you're sitting.

593
00:31:15,360 --> 00:31:19,520
Speaker 4: Yeah, we haven't seen really good implosions yet, and it's

594
00:31:19,560 --> 00:31:22,040
Speaker 4: surprising because like the stuff that goes wrong goes like

595
00:31:22,240 --> 00:31:25,400
Speaker 4: really really wrong, and people are like, yeah, well, it's

596
00:31:25,480 --> 00:31:28,560
Speaker 4: just in its infancy, which is a really really annoying

597
00:31:28,640 --> 00:31:31,320
Speaker 4: metaphor because it first of all suggests that this is

598
00:31:31,360 --> 00:31:34,200
Speaker 4: something that is you know, like a human, like an

599
00:31:34,240 --> 00:31:36,280
Speaker 4: animal at least that's that's a baby and can grow

600
00:31:36,800 --> 00:31:40,200
Speaker 4: something that is learning over time. And also sort of

601
00:31:40,400 --> 00:31:42,600
Speaker 4: like pulls on this idea that we should be kind

602
00:31:42,840 --> 00:31:45,920
Speaker 4: to these systems because they're just little babies, right, and

603
00:31:45,960 --> 00:31:47,880
Speaker 4: so if something goes wrong, it's like, well, no, that's

604
00:31:47,960 --> 00:31:50,840
Speaker 4: that's just it's it's still learning. And we get all

605
00:31:50,920 --> 00:31:52,760
Speaker 4: of these appeals to the future, like how good it

606
00:31:52,800 --> 00:31:54,200
Speaker 4: is going to be in the future, And there is

607
00:31:54,600 --> 00:31:57,240
Speaker 4: at this point, I think so much money sunk into

608
00:31:57,360 --> 00:32:01,280
Speaker 4: this that people aren't ready to like let go and

609
00:32:01,720 --> 00:32:04,000
Speaker 4: own up to the fact that yeah, so, and it

610
00:32:04,160 --> 00:32:07,760
Speaker 4: is I guess too easy to hire exploited workers for

611
00:32:07,920 --> 00:32:11,520
Speaker 4: poor pay, usually overseas to like backstop the stuff. There's

612
00:32:11,520 --> 00:32:14,240
Speaker 4: also so you gave us the Amazon ghost stores actually

613
00:32:14,280 --> 00:32:16,720
Speaker 4: being monitored by people in India. There was one of

614
00:32:16,840 --> 00:32:21,240
Speaker 4: the self driving car companies admitted that their cars were

615
00:32:21,280 --> 00:32:22,840
Speaker 4: being supervised by workers in Mexico.

616
00:32:23,360 --> 00:32:25,480
Speaker 2: And remember the stats on the alex Yeah it was

617
00:32:25,600 --> 00:32:25,760
Speaker 2: it was.

618
00:32:26,080 --> 00:32:29,720
Speaker 1: It was, So was Eric Voight, the CEO of Cruise,

619
00:32:30,400 --> 00:32:32,560
Speaker 1: and he had said And then there was this reporting

620
00:32:32,680 --> 00:32:35,160
Speaker 1: on the New York Times where they said, you know,

621
00:32:35,480 --> 00:32:38,960
Speaker 1: they used they used humans. And then he was like, wait,

622
00:32:39,040 --> 00:32:41,520
Speaker 1: well wait, wait, wait, wait, you're you're really blowing out

623
00:32:41,520 --> 00:32:45,360
Speaker 1: of proportion. We only use it something of three to

624
00:32:45,520 --> 00:32:48,800
Speaker 1: five percent at a time, Like that's a huge it's

625
00:32:48,840 --> 00:32:52,400
Speaker 1: a huge amount of out and and he posted this

626
00:32:52,720 --> 00:32:55,920
Speaker 1: himself on on Hacker News, which is this, you know,

627
00:32:56,120 --> 00:32:59,000
Speaker 1: kind of like, I don't know four chan for tech pros.

628
00:32:59,000 --> 00:33:01,080
Speaker 1: I guess, well, I guess four four chan is four

629
00:33:01,160 --> 00:33:03,520
Speaker 1: Chan for techpros. But I mean it's you know, but

630
00:33:03,800 --> 00:33:08,200
Speaker 1: like with a little less overt racism. I guess, yeah,

631
00:33:09,240 --> 00:33:12,440
Speaker 1: especially yeah, but it was still Yeah, but we're seeing

632
00:33:12,480 --> 00:33:14,200
Speaker 1: this in a lot of different industries. At the end

633
00:33:14,240 --> 00:33:17,479
Speaker 1: of the day, it's just this is outsourcing humans. Dan

634
00:33:17,520 --> 00:33:22,400
Speaker 1: at Vertessi is a sociologist at Princeton. She has a

635
00:33:22,560 --> 00:33:25,880
Speaker 1: piece in Tech Policy Press which the title is something

636
00:33:26,000 --> 00:33:29,760
Speaker 1: like AI it's just forecasting. It was just outsourcing two

637
00:33:29,760 --> 00:33:32,720
Speaker 1: point zero effectively. And yeah, we're seeing we're seeing a

638
00:33:32,760 --> 00:33:35,720
Speaker 1: lot of the same patterns that we saw in the

639
00:33:35,840 --> 00:33:40,640
Speaker 1: early nineties when these business process outsourcing or BPO organizations

640
00:33:40,720 --> 00:33:42,720
Speaker 1: were really becoming all the rage in the US.

641
00:33:43,680 --> 00:33:45,640
Speaker 2: Right. The other thing that I see a lot too,

642
00:33:45,800 --> 00:33:48,080
Speaker 2: is like I felt early on, especially when we were

643
00:33:48,120 --> 00:33:50,440
Speaker 2: talking about it. The thing that intrigued us, like when

644
00:33:50,600 --> 00:33:52,880
Speaker 2: was when everyone's like, dude, this thing is gonna fucking

645
00:33:53,080 --> 00:33:56,920
Speaker 2: end the world. It's how powerful AI is. Have I

646
00:33:57,000 --> 00:34:00,360
Speaker 2: have a whole plan to take myself off this mortale

647
00:34:00,440 --> 00:34:02,800
Speaker 2: if I have to the moment in which AI becomes

648
00:34:02,840 --> 00:34:05,920
Speaker 2: sentient and takes over. And like I think it felt

649
00:34:05,960 --> 00:34:07,920
Speaker 2: like maybe the markets were like, hey, man, you're scaring

650
00:34:07,960 --> 00:34:09,680
Speaker 2: the kids. Man, do we have another way to talk

651
00:34:09,719 --> 00:34:12,279
Speaker 2: about this? And I feel like recently I seem more

652
00:34:12,360 --> 00:34:17,400
Speaker 2: of like together, when we harness human intelligence with AI,

653
00:34:17,920 --> 00:34:21,320
Speaker 2: we can achieve a new level of existence and ideation

654
00:34:21,600 --> 00:34:23,719
Speaker 2: that has not been seen ever in the course of

655
00:34:23,840 --> 00:34:26,719
Speaker 2: human history. And I saw that in the Netflix j

656
00:34:26,880 --> 00:34:30,000
Speaker 2: Loo movie, where like the entire crux of the film

657
00:34:30,239 --> 00:34:35,080
Speaker 2: was this AI. AI skeptic had to embrace the AI

658
00:34:35,360 --> 00:34:38,719
Speaker 2: in order to overcome the main problem conflict in the film,

659
00:34:39,120 --> 00:34:42,320
Speaker 2: or just even now, like with the CTO of Open

660
00:34:42,400 --> 00:34:45,480
Speaker 2: AI also doing a similar thing when talking about how AI,

661
00:34:45,960 --> 00:34:48,239
Speaker 2: like some creative jobs are just gonna vanish, But that's

662
00:34:48,320 --> 00:34:52,200
Speaker 2: because when the human mind harnesses the power of the AI,

663
00:34:52,280 --> 00:34:54,360
Speaker 2: we're gonna come up with such new things that feels

664
00:34:54,400 --> 00:34:56,520
Speaker 2: like the new thing, which is more like we got

665
00:34:56,640 --> 00:34:59,640
Speaker 2: to embrace it so we can evolve into this next

666
00:34:59,800 --> 00:35:04,120
Speaker 2: level of thinking, et cetera, computation or whatever you guys on.

667
00:35:04,840 --> 00:35:07,200
Speaker 3: I was just gonna say, Mystery Hype Theater three thousand

668
00:35:07,280 --> 00:35:10,360
Speaker 3: reads the research papers so that we don't have to,

669
00:35:10,560 --> 00:35:13,160
Speaker 3: and Miles watches the j Lo movies so that.

670
00:35:13,440 --> 00:35:14,040
Speaker 5: You don't have to.

671
00:35:14,400 --> 00:35:15,319
Speaker 3: Got to know what they're saying.

672
00:35:15,600 --> 00:35:17,960
Speaker 1: But I'm glad you're I'm glad you're watching the day

673
00:35:18,040 --> 00:35:21,040
Speaker 1: because there's so many different cultural touchstones of this.

674
00:35:21,440 --> 00:35:21,640
Speaker 3: Yeah.

675
00:35:21,680 --> 00:35:23,320
Speaker 1: I had to look because I thought I thought the

676
00:35:23,400 --> 00:35:26,359
Speaker 1: movie you were talking about was the the like sort

677
00:35:26,400 --> 00:35:29,760
Speaker 1: of the autobiography This is Me Now a love story,

678
00:35:29,960 --> 00:35:32,480
Speaker 1: and I'm like, there's a film and I was like,

679
00:35:32,600 --> 00:35:37,160
Speaker 1: there's an AI subplot in that. I didn't know that

680
00:35:37,640 --> 00:35:42,240
Speaker 1: j Loo's life was, you know, a complete cautionary tale

681
00:35:42,320 --> 00:35:46,400
Speaker 1: about you know, AI and and the inevitability of it.

682
00:35:46,600 --> 00:35:49,920
Speaker 1: But yeah, but sorry, Emily was about to say something.

683
00:35:50,200 --> 00:35:54,839
Speaker 4: I just want to be starting so our colleagues to meet.

684
00:35:54,880 --> 00:35:58,879
Speaker 4: Gabruin and mil Torres coined this acronym test reel, which

685
00:35:58,920 --> 00:36:01,160
Speaker 4: stands for a bundle of ideologies that are all very

686
00:36:01,200 --> 00:36:03,680
Speaker 4: closely related to each other. And what's interesting about the transition,

687
00:36:03,760 --> 00:36:07,320
Speaker 4: you notice that they've basically moved from one part of

688
00:36:07,520 --> 00:36:10,960
Speaker 4: the tescrol acronym to another. So the it's all the

689
00:36:11,000 --> 00:36:14,640
Speaker 4: stuff that's based on these ideas of eugenics and of

690
00:36:14,880 --> 00:36:19,360
Speaker 4: sort of really disinterest in any actual current humans in

691
00:36:19,480 --> 00:36:23,920
Speaker 4: the service of these imagined people living as uploaded simulations

692
00:36:23,960 --> 00:36:28,160
Speaker 4: in the far long future. It's utilitarianism made even more

693
00:36:28,239 --> 00:36:31,120
Speaker 4: ridiculous by being taken to an an extreme endpoint. So

694
00:36:31,680 --> 00:36:33,520
Speaker 4: this thing like it's going to kill us all comes

695
00:36:34,040 --> 00:36:36,279
Speaker 4: partially from like the long term ism part of this

696
00:36:36,480 --> 00:36:38,440
Speaker 4: which people are fixated, this idea of we have to

697
00:36:39,040 --> 00:36:42,120
Speaker 4: it's ridiculous. They have a specific number, which is ten

698
00:36:42,160 --> 00:36:44,480
Speaker 4: to the fifty eight, who are the future humans who

699
00:36:44,520 --> 00:36:48,640
Speaker 4: are going to live as uploaded simulations in computer systems

700
00:36:48,719 --> 00:36:51,759
Speaker 4: installed all over the galaxy. And these are people who

701
00:36:51,800 --> 00:36:55,239
Speaker 4: clearly have never worked in it support because somehow the

702
00:36:55,280 --> 00:36:56,279
Speaker 4: computers just keep running.

703
00:36:56,320 --> 00:36:57,719
Speaker 2: Yeah yeah, yeah yeah.

704
00:36:58,080 --> 00:37:01,520
Speaker 4: And the idea is that if we don't make sure

705
00:37:01,719 --> 00:37:05,759
Speaker 4: that future comes about, then we collectively are missing out

706
00:37:05,840 --> 00:37:07,680
Speaker 4: on the happiness of those ten to the fifty eight

707
00:37:07,760 --> 00:37:09,439
Speaker 4: humans and that's such a big number that it doesn't

708
00:37:09,480 --> 00:37:11,719
Speaker 4: matter what happens now, all right. And I always say

709
00:37:11,760 --> 00:37:13,680
Speaker 4: when I relate this story that I wish I were

710
00:37:13,760 --> 00:37:16,160
Speaker 4: making this up. Yeah, but there are actually people who

711
00:37:16,239 --> 00:37:18,200
Speaker 4: believe this. And so that's where the sort of like,

712
00:37:18,280 --> 00:37:20,000
Speaker 4: oh no, it's you know, it's going to end us.

713
00:37:20,040 --> 00:37:22,440
Speaker 5: All stuff lives, and that's.

714
00:37:22,280 --> 00:37:24,400
Speaker 4: The l the long termism part of tesculism. But this

715
00:37:24,520 --> 00:37:28,240
Speaker 4: idea that we should join with the computers and become

716
00:37:28,280 --> 00:37:31,600
Speaker 4: a better thing, that's the t that's the transhumanism. And

717
00:37:31,680 --> 00:37:34,279
Speaker 4: it's all part of sort of the same bundle and

718
00:37:34,360 --> 00:37:36,360
Speaker 4: way of thinking. And there's this great paper out in

719
00:37:36,440 --> 00:37:39,160
Speaker 4: the publication called First Monday by mil Tors and to

720
00:37:39,200 --> 00:37:42,360
Speaker 4: Meet Gibbrus, sort of documenting the way that all of

721
00:37:42,440 --> 00:37:45,680
Speaker 4: these different ideologies are linked one to the next. There's

722
00:37:45,719 --> 00:37:47,800
Speaker 4: overlaps in the people working on them, there's overlaps in

723
00:37:47,840 --> 00:37:50,120
Speaker 4: the ideas, and it all goes back to eugenics and

724
00:37:50,160 --> 00:37:50,680
Speaker 4: race science.

725
00:37:51,360 --> 00:37:53,680
Speaker 3: Wow, okay, and it just had a onto.

726
00:37:53,760 --> 00:37:57,480
Speaker 1: I mean, it's really so the dobrism and the boosterism

727
00:37:57,520 --> 00:38:00,919
Speaker 1: are you know, two sides of the same cli even

728
00:38:00,960 --> 00:38:02,960
Speaker 1: though they kind of pose each other to be different.

729
00:38:03,040 --> 00:38:05,719
Speaker 1: So you if you imagine, there was an article and

730
00:38:06,000 --> 00:38:08,760
Speaker 1: it was a very funny chart that was a company

731
00:38:08,760 --> 00:38:11,160
Speaker 1: of this article that is an article in the Washington Post,

732
00:38:12,440 --> 00:38:15,279
Speaker 1: Natasha Tiku, and she had this kind of grid and

733
00:38:15,360 --> 00:38:18,160
Speaker 1: it was really funny because it was like on one

734
00:38:18,360 --> 00:38:22,120
Speaker 1: end was this guy Eliza Yudowski who's like a big duomor.

735
00:38:22,280 --> 00:38:24,319
Speaker 1: He had this thing in Time where he wrote an

736
00:38:24,320 --> 00:38:28,560
Speaker 1: op ed in Time magazine he was like, you know, basically,

737
00:38:28,880 --> 00:38:31,400
Speaker 1: if we need to, we have to be willing to

738
00:38:31,440 --> 00:38:34,600
Speaker 1: do air strikes on data centers and then which he

739
00:38:34,680 --> 00:38:37,880
Speaker 1: actually wrote and he actually said she speaking of Tucker Carlston.

740
00:38:37,920 --> 00:38:40,319
Speaker 1: I think Tucker Carlson also was like, oh geez, maybe

741
00:38:40,320 --> 00:38:42,239
Speaker 1: we should do that. And on the other end you

742
00:38:42,320 --> 00:38:47,720
Speaker 1: have you know Sam Altman, who was like, in Mira Maradi.

743
00:38:47,600 --> 00:38:48,800
Speaker 3: You got a cyber trust.

744
00:38:48,800 --> 00:38:52,160
Speaker 2: Sorry, no, it's okay, I just want a cyber truck.

745
00:38:52,400 --> 00:38:55,319
Speaker 1: You just want a cyber truck. I got to enter

746
00:38:55,360 --> 00:38:59,440
Speaker 1: my social Security number one. Yeah, yeah, it's fine, I'll

747
00:38:59,440 --> 00:39:01,000
Speaker 1: give you mine. I want to beat to you too,

748
00:39:01,640 --> 00:39:04,720
Speaker 1: and so you know, but you know, they are actually

749
00:39:04,760 --> 00:39:07,560
Speaker 1: two sides of the same coin because they basically want

750
00:39:07,680 --> 00:39:09,439
Speaker 1: to and I mean in the middle was to meet

751
00:39:09,520 --> 00:39:12,359
Speaker 1: who's on this test real paper. And also it's also

752
00:39:12,440 --> 00:39:15,160
Speaker 1: my boss at the at Dare and and and I'm

753
00:39:15,160 --> 00:39:18,120
Speaker 1: not I'm not saying that too. You know, kiss Acids

754
00:39:18,400 --> 00:39:20,960
Speaker 1: we actually get along quite well. So but it's but

755
00:39:21,080 --> 00:39:24,320
Speaker 1: it's you know, posed as you know, someone in this

756
00:39:24,600 --> 00:39:27,279
Speaker 1: in this grid. But so Dubrius and Bistrooms them both

757
00:39:27,360 --> 00:39:32,680
Speaker 1: basically see AIS this kind of inevitability. We're gonna get there,

758
00:39:33,440 --> 00:39:36,520
Speaker 1: you know. And to me, I think the metaphor you know,

759
00:39:36,600 --> 00:39:38,960
Speaker 1: you allude to Emily is kind of like thinking about

760
00:39:38,960 --> 00:39:41,279
Speaker 1: this like a kid that needs to be formed. And

761
00:39:41,680 --> 00:39:43,880
Speaker 1: in some in some ways I think it's I mean,

762
00:39:43,920 --> 00:39:47,360
Speaker 1: it's colonialism. It's it's that it's manifest destiny. You know,

763
00:39:47,560 --> 00:39:51,320
Speaker 1: it's always five years away, you know. And so but

764
00:39:51,440 --> 00:39:54,520
Speaker 1: they but they both see development of AI to be

765
00:39:54,680 --> 00:39:58,520
Speaker 1: really really critical to whatever is happening next when we

766
00:39:58,600 --> 00:40:00,680
Speaker 1: can be like no one is asking for this ship

767
00:40:01,280 --> 00:40:05,120
Speaker 1: and it's taking up you know, and I and we're

768
00:40:05,120 --> 00:40:09,080
Speaker 1: seeing a lot of memes online are like, you know,

769
00:40:10,120 --> 00:40:12,839
Speaker 1: I'm so glad that we are draining a lake every

770
00:40:13,160 --> 00:40:16,319
Speaker 1: you know, to generate this image of and there's one

771
00:40:16,320 --> 00:40:20,080
Speaker 1: of the images was like a toddler holding you know,

772
00:40:20,400 --> 00:40:24,640
Speaker 1: a crucifix and with a helmet that says the police

773
00:40:24,760 --> 00:40:27,439
Speaker 1: on it and their neck deep in water and they've

774
00:40:27,480 --> 00:40:30,959
Speaker 1: got big kawhi you know, emoji eyes, right.

775
00:40:31,520 --> 00:40:34,799
Speaker 2: And if you blur your eyes, it's Jesus's face. Yeah,

776
00:40:34,880 --> 00:40:36,440
Speaker 2: because that's another huge thing.

777
00:40:36,520 --> 00:40:36,799
Speaker 3: I see.

778
00:40:36,840 --> 00:40:40,040
Speaker 2: There's so much AI nonsense art out there too.

779
00:40:41,160 --> 00:40:44,879
Speaker 4: And it's it's super environmentally disastrous, right, this is this thing,

780
00:40:45,200 --> 00:40:47,759
Speaker 4: and a lot of it is non consensual in the

781
00:40:47,800 --> 00:40:49,239
Speaker 4: sense that, like, if you try to use Google these

782
00:40:49,320 --> 00:40:51,480
Speaker 4: days to do a search, the first thing you get,

783
00:40:51,760 --> 00:40:54,360
Speaker 4: depending on search with many searches, is the AI overview,

784
00:40:54,400 --> 00:40:56,200
Speaker 4: which is the output of one of these text to

785
00:40:56,280 --> 00:40:59,720
Speaker 4: text models, taking way more processing power than just returning

786
00:40:59,719 --> 00:41:01,279
Speaker 4: the townlu links, which is that they used to do,

787
00:41:01,840 --> 00:41:03,200
Speaker 4: and you can't turn it off.

788
00:41:03,760 --> 00:41:04,840
Speaker 5: So if you use Google do.

789
00:41:04,840 --> 00:41:06,520
Speaker 4: A search, you're stuck with that. You're stuck with its

790
00:41:06,560 --> 00:41:07,400
Speaker 4: environmental impact.

791
00:41:08,040 --> 00:41:11,160
Speaker 3: Yeah. The one positive I'd say that I've seen more

792
00:41:11,200 --> 00:41:15,200
Speaker 3: and more since we last spoke is people being like, wait,

793
00:41:15,280 --> 00:41:19,880
Speaker 3: who the fuck is this for? Exactly? Who is asking

794
00:41:20,080 --> 00:41:22,879
Speaker 3: for this? Which I think is ultimately going to become

795
00:41:22,920 --> 00:41:26,080
Speaker 3: a louder and louder question. Is this seems to be

796
00:41:26,880 --> 00:41:32,800
Speaker 3: mainly for tech CEOs. Yeah, we're very wealthy, but especially

797
00:41:32,880 --> 00:41:36,719
Speaker 3: like in countries that aren't, where corporations are not more

798
00:41:36,800 --> 00:41:39,840
Speaker 3: powerful and valued more as humans than actual humans, like

799
00:41:39,880 --> 00:41:42,000
Speaker 3: the United States. I feel like it will it will

800
00:41:42,040 --> 00:41:45,040
Speaker 3: become more and more of an issue and then be

801
00:41:45,120 --> 00:41:49,000
Speaker 3: a matter of figuring out if that message actually gets

802
00:41:49,080 --> 00:41:49,959
Speaker 3: in in the US.

803
00:41:50,160 --> 00:41:52,680
Speaker 4: But yeah, we can keep shouting it, and that's the

804
00:41:52,719 --> 00:41:53,319
Speaker 4: great question, like.

805
00:41:53,360 --> 00:41:54,320
Speaker 5: Who asked for this?

806
00:41:54,800 --> 00:41:59,080
Speaker 3: Wait? Who asked for this? And who likes where it's going? Yeah?

807
00:41:59,320 --> 00:42:02,239
Speaker 2: Yeah, because of the luster has worn off from the

808
00:42:02,360 --> 00:42:05,600
Speaker 2: early you know days of those early chat GPT models

809
00:42:05,640 --> 00:42:06,360
Speaker 2: that came out like this.

810
00:42:06,480 --> 00:42:08,560
Speaker 3: Thing could be a doctor, this thing is could be

811
00:42:08,640 --> 00:42:09,160
Speaker 3: a lawyer.

812
00:42:09,560 --> 00:42:11,120
Speaker 2: And then most people kind of like the people that

813
00:42:11,200 --> 00:42:13,880
Speaker 2: I know who are first impressed, like yeah, Like it

814
00:42:14,040 --> 00:42:16,239
Speaker 2: helped me write an email. That's about the most I

815
00:42:16,280 --> 00:42:18,800
Speaker 2: can do with it, because I hate writing formally and

816
00:42:19,000 --> 00:42:21,719
Speaker 2: so it helped that. But beyond that, I don't know

817
00:42:21,880 --> 00:42:24,200
Speaker 2: many like as personally, but and I know people that

818
00:42:24,280 --> 00:42:27,719
Speaker 2: work in many different fields. It sort of ends up

819
00:42:27,760 --> 00:42:29,440
Speaker 2: being like, yeah, I don't really have a use for

820
00:42:29,560 --> 00:42:32,040
Speaker 2: it aside from like making funny songs that I share

821
00:42:32,080 --> 00:42:35,120
Speaker 2: with my girlfriend that you know, we have a song

822
00:42:35,120 --> 00:42:37,719
Speaker 2: about how she loves Chipotle, and it was in the

823
00:42:37,800 --> 00:42:41,680
Speaker 2: style of you know, Crystal Waters. Yeah, and now that's it.

824
00:42:42,360 --> 00:42:44,640
Speaker 1: Yeah, yeah, I mean I think that's I mean, I

825
00:42:44,880 --> 00:42:48,360
Speaker 1: like this phrase that Emily coined, which is, you know,

826
00:42:48,480 --> 00:42:51,360
Speaker 1: resist the urge to be impressed. Yeah, where it's like

827
00:42:51,520 --> 00:42:54,359
Speaker 1: this this kind of thing where you know, you've got

828
00:42:54,480 --> 00:42:57,120
Speaker 1: this proof of concept and it's got a little little

829
00:42:57,239 --> 00:43:01,160
Speaker 1: g whiz to it, but beyond that, I mean, you know,

830
00:43:01,320 --> 00:43:02,920
Speaker 1: are you going to use this in any kind of

831
00:43:03,040 --> 00:43:08,280
Speaker 1: real I don't know, product cycle or ideation or whatever.

832
00:43:08,480 --> 00:43:11,400
Speaker 1: And and there's just been I mean, we've had a

833
00:43:11,560 --> 00:43:14,279
Speaker 1: tech lash for the last couple of years here and

834
00:43:14,640 --> 00:43:18,120
Speaker 1: and I mean I think it's really the only people

835
00:43:18,239 --> 00:43:20,600
Speaker 1: I ever see praising this. And I mean, maybe this

836
00:43:20,760 --> 00:43:24,000
Speaker 1: is just a virtue of my timeline that it's creating,

837
00:43:24,080 --> 00:43:27,600
Speaker 1: and Elon Musk hasn't absolutely destroyed it yet, But the

838
00:43:27,680 --> 00:43:30,200
Speaker 1: only people I see praising it are typically people who

839
00:43:30,280 --> 00:43:34,359
Speaker 1: are very very much in the industry. You know, they're

840
00:43:34,360 --> 00:43:37,160
Speaker 1: the same people who are like I live in San

841
00:43:37,239 --> 00:43:41,239
Speaker 1: Francisco and I support you know, London Breed saying she

842
00:43:41,400 --> 00:43:43,719
Speaker 1: wants to clean up our streets from homeless people. And

843
00:43:43,840 --> 00:43:46,359
Speaker 1: it's just the people. And I mean some of them

844
00:43:46,400 --> 00:43:48,920
Speaker 1: are not out and out that fascist, but you know,

845
00:43:49,000 --> 00:43:51,880
Speaker 1: they're they're they're they're brushing, They're brushing along with it

846
00:43:52,600 --> 00:43:56,040
Speaker 1: everyone else I see, especially also a lot of people

847
00:43:56,200 --> 00:43:59,120
Speaker 1: in technology. They're like, I work in technology, and I

848
00:43:59,360 --> 00:44:03,280
Speaker 1: hate this bubble and I'm so tired about talking about

849
00:44:03,320 --> 00:44:05,560
Speaker 1: this and i just wanted to go away.

850
00:44:05,640 --> 00:44:07,200
Speaker 3: I mean, this is you know.

851
00:44:07,719 --> 00:44:10,080
Speaker 1: And then I see teachers, you know, we see a

852
00:44:10,120 --> 00:44:14,279
Speaker 1: lot of people from the professions, teachers, nurses, doctor. My

853
00:44:14,400 --> 00:44:16,360
Speaker 1: sister is a nurse. He's like, I hate this stuff.

854
00:44:16,719 --> 00:44:19,239
Speaker 1: My sister's a lawyer, and she's like, yeah, I've used

855
00:44:19,280 --> 00:44:21,719
Speaker 1: this to sort of start a brief, but he gets

856
00:44:21,800 --> 00:44:25,000
Speaker 1: so many things wrong. I need to double check everything.

857
00:44:25,160 --> 00:44:27,239
Speaker 1: And at the end of the day, yeah, does it

858
00:44:27,360 --> 00:44:29,360
Speaker 1: actually help. Yeah, probably not.

859
00:44:29,719 --> 00:44:32,759
Speaker 2: But but it's just a little baby, now, Alex, it's

860
00:44:32,840 --> 00:44:33,720
Speaker 2: just a little baby.

861
00:44:34,040 --> 00:44:36,640
Speaker 3: It's just a little baby, A little baby.

862
00:44:36,760 --> 00:44:40,400
Speaker 1: Would you want to take a little defenseless baby.

863
00:44:40,640 --> 00:44:42,320
Speaker 3: It is so interesting too, because it's like and it

864
00:44:42,400 --> 00:44:44,840
Speaker 3: could be a lawyer or a doctor, which is also

865
00:44:45,000 --> 00:44:50,560
Speaker 3: a baby. I'm so impressed by my little baby. Like

866
00:44:50,640 --> 00:44:52,920
Speaker 3: what they just said, they could be a lawyer or

867
00:44:53,000 --> 00:44:55,000
Speaker 3: a doctor one day to.

868
00:44:55,160 --> 00:44:58,120
Speaker 4: Watch for the phrase in its infancy in the coverage

869
00:44:58,160 --> 00:44:59,360
Speaker 4: of this, it's all over the place.

870
00:45:00,480 --> 00:45:02,640
Speaker 1: Yeah, shout up, shout shout up. And I just want

871
00:45:02,680 --> 00:45:04,600
Speaker 1: to give a shout out to Ana Laurene Hoffman, who

872
00:45:04,840 --> 00:45:08,600
Speaker 1: someone is a professor at You'd have a really good

873
00:45:08,600 --> 00:45:11,080
Speaker 1: friend of mine and colleague of Emily and uh so

874
00:45:11,560 --> 00:45:13,319
Speaker 1: she's done a lot of work and sort of talking

875
00:45:13,320 --> 00:45:16,680
Speaker 1: about like these metaphors of this kind of baby noess

876
00:45:16,719 --> 00:45:19,840
Speaker 1: and how it absolves AI of like truly being like

877
00:45:20,080 --> 00:45:23,480
Speaker 1: racist and sexist and absolutely fucking up. It's like, it's

878
00:45:23,480 --> 00:45:25,440
Speaker 1: just a little baby. Yeah, it's a baby with with

879
00:45:25,800 --> 00:45:29,359
Speaker 1: with a billion dollar valuation or whatever. You know, however

880
00:45:29,560 --> 00:45:30,000
Speaker 1: much it is.

881
00:45:30,560 --> 00:45:33,840
Speaker 3: By having AI put in the like mentioned in the

882
00:45:33,920 --> 00:45:34,520
Speaker 3: earnings call.

883
00:45:34,840 --> 00:45:37,600
Speaker 2: But if we're going to keep that consistency with that metaphor,

884
00:45:37,719 --> 00:45:39,720
Speaker 2: you'd be like, then, why are you, as the parent,

885
00:45:39,800 --> 00:45:42,520
Speaker 2: wheeling this child out to do labor that it's wholly

886
00:45:42,640 --> 00:45:43,279
Speaker 2: unprepared for.

887
00:45:43,600 --> 00:45:47,399
Speaker 3: You look fucked up. Actually, so, Miles, I have kids

888
00:45:47,480 --> 00:45:49,680
Speaker 3: that are a little older than yours, and so I

889
00:45:49,719 --> 00:45:52,359
Speaker 3: could that is actually good parenting. To wheel them out

890
00:45:52,400 --> 00:45:55,960
Speaker 3: and have them and it's brings in the cheddar.

891
00:45:56,640 --> 00:46:01,480
Speaker 1: Yeah, Jack coming on the podcast Hot take child labor,

892
00:46:01,640 --> 00:46:03,120
Speaker 1: actually child labor.

893
00:46:03,200 --> 00:46:08,240
Speaker 3: And when it's your kid, come on. Those are called chores,

894
00:46:08,360 --> 00:46:11,280
Speaker 3: and yes, if they're doing the chores for a multinational

895
00:46:11,400 --> 00:46:13,799
Speaker 3: corporation against a little hazy, but.

896
00:46:14,000 --> 00:46:17,040
Speaker 2: Yeah, take out an h eight hour chor shift that check.

897
00:46:16,960 --> 00:46:18,880
Speaker 3: Hazy because of all that money coming in.

898
00:46:19,280 --> 00:46:19,480
Speaker 2: You know.

899
00:46:20,760 --> 00:46:23,200
Speaker 4: So on this point about like nobody wants this, I

900
00:46:23,280 --> 00:46:25,200
Speaker 4: have a talk that I've been doing since summer of

901
00:46:25,280 --> 00:46:28,560
Speaker 4: twenty three called chat GP Why And then the subtitle

902
00:46:28,640 --> 00:46:32,040
Speaker 4: is when, if ever is synthetic text safe, appropriate and desirable?

903
00:46:32,400 --> 00:46:34,640
Speaker 4: And it's meant for sort of non specialist audiences. I

904
00:46:34,680 --> 00:46:36,520
Speaker 4: basically go through, Okay, what's a language model, what's the

905
00:46:36,560 --> 00:46:38,880
Speaker 4: technology for in its original use cases?

906
00:46:38,920 --> 00:46:40,080
Speaker 5: How did we get to the current thing?

907
00:46:40,440 --> 00:46:42,160
Speaker 4: And then what would have to be true for you

908
00:46:42,200 --> 00:46:44,360
Speaker 4: to actually want to use the output of one of

909
00:46:44,400 --> 00:46:44,799
Speaker 4: these things?

910
00:46:44,880 --> 00:46:45,000
Speaker 2: Right?

911
00:46:45,040 --> 00:46:46,680
Speaker 4: So, the first thing is you'd want something that is

912
00:46:46,960 --> 00:46:49,759
Speaker 4: ethically produced, so not based on stolen data, not based

913
00:46:49,800 --> 00:46:54,240
Speaker 4: on exploited labor. Basically, don't have those second layers box. Okay,

914
00:46:55,080 --> 00:46:57,800
Speaker 4: you also want something that somehow is not an environmental disaster.

915
00:46:58,400 --> 00:46:59,200
Speaker 5: Also don't have that.

916
00:47:01,040 --> 00:47:03,440
Speaker 4: Assuming we somehow got past those two hurdles, and it's

917
00:47:03,440 --> 00:47:05,640
Speaker 4: things like, okay, well, you need a case where it's

918
00:47:05,719 --> 00:47:10,080
Speaker 4: not going to be misleading anybody. It's got to be

919
00:47:10,120 --> 00:47:12,360
Speaker 4: a case where you don't actually care about the stuff

920
00:47:12,480 --> 00:47:15,720
Speaker 4: being accurate or truthful what comes out of it, including

921
00:47:16,400 --> 00:47:19,080
Speaker 4: you know, being able to recognize and mitigate any biases.

922
00:47:19,760 --> 00:47:22,640
Speaker 4: You also don't care about the output being original, so

923
00:47:22,760 --> 00:47:25,279
Speaker 4: plagiarism's okay. And like by the time you've done all that,

924
00:47:25,719 --> 00:47:28,680
Speaker 4: it's like, yeah, this use case of helping you draft

925
00:47:28,719 --> 00:47:32,359
Speaker 4: an email because that's tedious, right, and is that worth

926
00:47:32,480 --> 00:47:34,520
Speaker 4: the two that we started with there? Right, you know,

927
00:47:34,800 --> 00:47:37,960
Speaker 4: the labor exploitation, data theft and environmental impacts.

928
00:47:38,239 --> 00:47:41,760
Speaker 3: Right now we've just broken down all of human meaning

929
00:47:42,200 --> 00:47:46,759
Speaker 3: for the past, like for civilizations, Like you know, none

930
00:47:46,800 --> 00:47:50,080
Speaker 3: of the things that we've always seemed to think matter

931
00:47:50,719 --> 00:47:54,680
Speaker 3: matter anymore. Because billion dollar companies needed like a new

932
00:47:54,760 --> 00:47:57,760
Speaker 3: toy to hype up for the stock market. It sounds

933
00:47:57,840 --> 00:48:00,440
Speaker 3: like that's very frustrating. Let's take a quick let's take

934
00:48:00,480 --> 00:48:02,400
Speaker 3: a quick break. We'll come back, because I do want

935
00:48:02,440 --> 00:48:05,279
Speaker 3: to talk about what it is being used for. I mean,

936
00:48:05,360 --> 00:48:08,240
Speaker 3: AI is a very loose term the way it's being applied,

937
00:48:08,320 --> 00:48:12,080
Speaker 3: But people want to use, you know, these new advances

938
00:48:12,160 --> 00:48:16,080
Speaker 3: in computing to make themselves seem more profitable, and so

939
00:48:16,400 --> 00:48:18,440
Speaker 3: I want to talk about what it is actually being

940
00:48:18,560 --> 00:48:31,960
Speaker 3: used for. We'll be right back, and we're back. We're

941
00:48:32,760 --> 00:48:36,879
Speaker 3: we're back, and on your show, you've done some good

942
00:48:36,920 --> 00:48:41,680
Speaker 3: stuff on just the surveillance side of AI, which I

943
00:48:41,760 --> 00:48:45,800
Speaker 3: mean that turns out a lot of the technology that

944
00:48:45,880 --> 00:48:51,080
Speaker 3: we initially thought was promising was just eventually used for

945
00:48:51,360 --> 00:48:54,719
Speaker 3: the purposes of marketing and surveillance in the end, and

946
00:48:54,880 --> 00:48:57,960
Speaker 3: it seems like AI skipped all the promising stuff. And

947
00:48:58,040 --> 00:49:00,160
Speaker 3: it's just like, what if we just went right to

948
00:49:00,280 --> 00:49:05,000
Speaker 3: the right there harming harming people.

949
00:49:05,480 --> 00:49:08,440
Speaker 1: Yeah, I will, I will say that kind of. I mean,

950
00:49:08,880 --> 00:49:12,279
Speaker 1: you had mentioned that this term AI is kind of

951
00:49:12,360 --> 00:49:15,960
Speaker 1: being used, Lucy Goosey, and you know, I mean we

952
00:49:16,280 --> 00:49:20,560
Speaker 1: we AI is kind of synonymous with large language models

953
00:49:20,600 --> 00:49:23,640
Speaker 1: and image generators. But you know, things that have been

954
00:49:23,760 --> 00:49:30,840
Speaker 1: called AI also encompass things like biometric surveillance, like like

955
00:49:31,040 --> 00:49:35,560
Speaker 1: different different systems which use this technology called quot unquote

956
00:49:35,600 --> 00:49:39,320
Speaker 1: machine learning, and which is kind of this large scale

957
00:49:39,440 --> 00:49:44,000
Speaker 1: patterned recognition. So a lot of it's being used, especially

958
00:49:44,440 --> 00:49:48,720
Speaker 1: at the border, so doing things like trying to detect

959
00:49:49,200 --> 00:49:53,920
Speaker 1: verify identities by voices or by faces. You probably see

960
00:49:53,960 --> 00:49:56,680
Speaker 1: this if you've been in the airport. The TSA has

961
00:49:56,719 --> 00:49:59,920
Speaker 1: been using this, and you can still voluntarily opt out

962
00:50:00,320 --> 00:50:03,160
Speaker 1: for now, but they're really incentivizing it. I saw that

963
00:50:03,280 --> 00:50:06,960
Speaker 1: TSA has this touchless thing now, which is a facial recognition,

964
00:50:07,560 --> 00:50:09,880
Speaker 1: so you don't have to present your ID, you can

965
00:50:10,000 --> 00:50:11,320
Speaker 1: just scan your face.

966
00:50:11,160 --> 00:50:14,640
Speaker 4: And go and and like, don't do that, like take

967
00:50:14,800 --> 00:50:17,040
Speaker 4: every option to opt out, and that the fact that

968
00:50:17,120 --> 00:50:20,879
Speaker 4: those signs are there saying that this is optional. Penny,

969
00:50:20,960 --> 00:50:24,200
Speaker 4: somebody actually Petty. Yeah, yeah, the only reason we had

970
00:50:24,239 --> 00:50:26,800
Speaker 4: that science is because of her activism saying like this

971
00:50:26,960 --> 00:50:28,840
Speaker 4: has to be clear to the travelers that it's actually

972
00:50:28,880 --> 00:50:31,279
Speaker 4: optional and you can opt out, so it's posted there

973
00:50:31,320 --> 00:50:31,920
Speaker 4: that you don't have.

974
00:50:32,000 --> 00:50:34,480
Speaker 3: To do this. Yeah, all right, then I'm going to

975
00:50:34,520 --> 00:50:37,040
Speaker 3: feel you up. Sorry, those are just the rules.

976
00:50:37,520 --> 00:50:40,439
Speaker 1: Yeah, it's just it's absolutely but I mean it gets

977
00:50:40,560 --> 00:50:45,040
Speaker 1: it gets you know, leveraged against people who fly to

978
00:50:45,120 --> 00:50:48,600
Speaker 1: a lesser degree, but I mean folks who are refugees

979
00:50:48,719 --> 00:50:51,239
Speaker 1: or asy le's you know, I mean people on the

980
00:50:51,400 --> 00:50:55,719
Speaker 1: move really encountered to stuff incredibly violent ways. You know,

981
00:50:55,880 --> 00:50:59,200
Speaker 1: they do things like try to they take their blood

982
00:50:59,440 --> 00:51:03,040
Speaker 1: and say that, well we can we can associate your

983
00:51:03,600 --> 00:51:06,920
Speaker 1: We're gonna you know, sequence your genome and safe you're

984
00:51:07,000 --> 00:51:09,560
Speaker 1: actually from the country you say you're from, which is first,

985
00:51:09,600 --> 00:51:13,840
Speaker 1: it's pseudoscience. I mean, basically all biologists have been like,

986
00:51:13,960 --> 00:51:16,759
Speaker 1: you can't use this to determine if someone is X,

987
00:51:16,880 --> 00:51:22,480
Speaker 1: y Z, like nationality, because nationalities are one political entities,

988
00:51:22,520 --> 00:51:27,000
Speaker 1: they're not biological ones, and so like we can sort

989
00:51:27,040 --> 00:51:29,360
Speaker 1: of pinpoint you to a region, but it says nothing

990
00:51:29,520 --> 00:51:33,640
Speaker 1: to say of anything about the political borders of a country.

991
00:51:34,520 --> 00:51:37,760
Speaker 1: There's a great book I started reading by Petro Molner

992
00:51:38,640 --> 00:51:42,000
Speaker 1: which is called The Walls Have Eyes, which is about

993
00:51:42,040 --> 00:51:47,719
Speaker 1: this kind of intense surveillance state or intense surveillance architecture.

994
00:51:48,239 --> 00:51:51,040
Speaker 1: You know, it's being used in you know, typically in

995
00:51:51,640 --> 00:51:56,239
Speaker 1: the border, the US Mexico border, but also the you know,

996
00:51:56,360 --> 00:52:00,880
Speaker 1: the various points of entry in Europe where African migrants

997
00:52:01,040 --> 00:52:05,600
Speaker 1: are fleeing to, you know, fleeing places like Sudan and

998
00:52:05,719 --> 00:52:09,920
Speaker 1: Congo and the Tagrai region of Ethiopia. So just like

999
00:52:10,680 --> 00:52:12,799
Speaker 1: and this is just some of the most violent kind

1000
00:52:12,800 --> 00:52:15,560
Speaker 1: of stuff he in it you can imagine, and it's

1001
00:52:15,680 --> 00:52:18,719
Speaker 1: way far away from you know, this kind of Oh,

1002
00:52:18,880 --> 00:52:21,880
Speaker 1: here's like a fake little child, you know, or a

1003
00:52:22,000 --> 00:52:26,520
Speaker 1: Jesus holding twelve thousand babies riding in America truck with

1004
00:52:26,600 --> 00:52:28,759
Speaker 1: the American flag on it, you know what I mean, right,

1005
00:52:29,200 --> 00:52:33,360
Speaker 1: that's yeah, So the reality is yeah, much more stark.

1006
00:52:34,320 --> 00:52:37,000
Speaker 4: And you see that, you see that one too many

1007
00:52:37,760 --> 00:52:40,800
Speaker 4: image matching, so you get all these false arrests. So

1008
00:52:40,880 --> 00:52:43,520
Speaker 4: people because the AI said that they matched the image

1009
00:52:43,520 --> 00:52:46,359
Speaker 4: from the grain surveillance video. And it's one of those

1010
00:52:46,360 --> 00:52:50,560
Speaker 4: things where it's bad if it works, because you have

1011
00:52:50,680 --> 00:52:53,239
Speaker 4: this like increased surveillance power of the state, and it's

1012
00:52:53,280 --> 00:52:54,880
Speaker 4: bad if it doesn't work, because you get all these

1013
00:52:54,920 --> 00:52:55,479
Speaker 4: false arrests.

1014
00:52:55,520 --> 00:52:57,320
Speaker 5: Like it's it's just a bad idea. It's just a

1015
00:52:57,440 --> 00:52:58,279
Speaker 5: don't and.

1016
00:52:58,320 --> 00:53:01,080
Speaker 4: It's not just image stuff. So we read a while

1017
00:53:01,200 --> 00:53:04,640
Speaker 4: back about a situation in Germany, I think, where asylum

1018
00:53:04,680 --> 00:53:08,520
Speaker 4: seekers were being vetted as to whether or not they

1019
00:53:08,680 --> 00:53:12,040
Speaker 4: spoke the right language using you know, so one of

1020
00:53:12,040 --> 00:53:14,200
Speaker 4: the things you can do with pattern matching is okay,

1021
00:53:14,400 --> 00:53:18,560
Speaker 4: language identification that this string what languages that come from

1022
00:53:18,920 --> 00:53:21,960
Speaker 4: but it was being done based on completely inadequate data

1023
00:53:22,000 --> 00:53:24,319
Speaker 4: sets by people who don't speak the languages, who are

1024
00:53:24,400 --> 00:53:27,120
Speaker 4: not in a position to actually vet the output of

1025
00:53:27,160 --> 00:53:28,839
Speaker 4: the machine. And so you have these folks who are

1026
00:53:28,880 --> 00:53:31,840
Speaker 4: in the worst imaginable situation, like you don't go seeking

1027
00:53:31,880 --> 00:53:33,719
Speaker 4: asylum on a.

1028
00:53:33,760 --> 00:53:35,480
Speaker 5: Lark, right, these other people?

1029
00:53:37,280 --> 00:53:43,399
Speaker 4: Yeah, yeah, and then they're getting denied because some algorithms said, oh,

1030
00:53:43,440 --> 00:53:45,359
Speaker 4: you don't speak the language from the place you claim

1031
00:53:45,400 --> 00:53:48,560
Speaker 4: to be coming from where the person your accent is

1032
00:53:48,560 --> 00:53:50,680
Speaker 4: wrong or your variety is wrong or whatever. And the

1033
00:53:50,719 --> 00:53:53,960
Speaker 4: person who's who's run this computer system has no way

1034
00:53:54,040 --> 00:53:56,080
Speaker 4: of actually checking its output, but they believe it, and

1035
00:53:56,120 --> 00:53:57,759
Speaker 4: then they get these asylum seekers turned away.

1036
00:53:58,080 --> 00:53:59,759
Speaker 3: Yeah, so how does that? You know?

1037
00:54:00,160 --> 00:54:03,879
Speaker 2: Everything you said? How should we feel that open AI

1038
00:54:04,160 --> 00:54:09,080
Speaker 2: recently welcome to their board the eighteenth director of the NSA,

1039
00:54:09,440 --> 00:54:14,879
Speaker 2: Paul nakasone, is that bad? Or what should we take

1040
00:54:14,920 --> 00:54:15,399
Speaker 2: from that one?

1041
00:54:15,640 --> 00:54:17,880
Speaker 4: How should we feel not at all surprised?

1042
00:54:18,040 --> 00:54:18,120
Speaker 2: Right?

1043
00:54:18,239 --> 00:54:19,960
Speaker 4: How should we feel when open AI it's like, okay,

1044
00:54:20,080 --> 00:54:21,280
Speaker 4: bad is whatever the rest.

1045
00:54:21,160 --> 00:54:21,919
Speaker 5: Of that is is bad?

1046
00:54:22,200 --> 00:54:26,839
Speaker 2: Yeah? It seems bad, man, Yeah, it seems like there's

1047
00:54:26,840 --> 00:54:30,000
Speaker 2: again we're talking like this technology to mass surveillance pipeline,

1048
00:54:30,040 --> 00:54:33,480
Speaker 2: and who better than someone who ran the fucking NSSA, Like.

1049
00:54:33,880 --> 00:54:35,280
Speaker 3: And I know the way it's being spun.

1050
00:54:35,320 --> 00:54:37,800
Speaker 2: It's like, you know, this is a part of cyber command,

1051
00:54:37,880 --> 00:54:41,440
Speaker 2: Like he inherently knows like how what the what the

1052
00:54:41,520 --> 00:54:43,759
Speaker 2: guardbrails need to be in terms of keeping us safe.

1053
00:54:43,800 --> 00:54:45,640
Speaker 2: But to me it just feels like, No, you brought

1054
00:54:45,680 --> 00:54:49,279
Speaker 2: in a surveillance pro, not someone who understands inherently like

1055
00:54:49,719 --> 00:54:52,080
Speaker 2: what this specific technology is, but more someone who's like

1056
00:54:52,520 --> 00:54:56,080
Speaker 2: learns how to harness technology for this other specific aim.

1057
00:54:57,080 --> 00:55:00,600
Speaker 4: Yeah, so surveillance is not synonymous with safe. Like the

1058
00:55:00,680 --> 00:55:03,000
Speaker 4: one the one kind of one use case for the

1059
00:55:03,040 --> 00:55:05,840
Speaker 4: word surveillance that I think actually was pro public safety

1060
00:55:06,280 --> 00:55:08,680
Speaker 4: is there's a study the long term seting in Seattle

1061
00:55:08,719 --> 00:55:12,080
Speaker 4: called the Seattle Flu Study, and they are doing what

1062
00:55:12,120 --> 00:55:15,000
Speaker 4: they call surveillance testing for flu viruses. So they get

1063
00:55:15,080 --> 00:55:17,080
Speaker 4: volunteers to come in and get swabbed, and they are

1064
00:55:17,200 --> 00:55:20,040
Speaker 4: keeping track of what viruses are circulating in our community. Right,

1065
00:55:20,560 --> 00:55:23,200
Speaker 4: I'm all for surveilling the viruses, especially if you can

1066
00:55:23,239 --> 00:55:24,000
Speaker 4: keep the people out of it.

1067
00:55:24,200 --> 00:55:26,360
Speaker 1: Yeah, I would, I would. I would add a wrinkle

1068
00:55:26,440 --> 00:55:28,439
Speaker 1: to that just because I think that, I mean, there's

1069
00:55:28,480 --> 00:55:30,480
Speaker 1: a lot of surveillance. I mean that's the kind of technology,

1070
00:55:30,520 --> 00:55:32,840
Speaker 1: that's the kind of terminology they use of health surveillance

1071
00:55:33,280 --> 00:55:36,759
Speaker 1: to detect kind of virus rates and whatnot. I would

1072
00:55:36,760 --> 00:55:38,919
Speaker 1: also add the wrinkle that like a lot of those

1073
00:55:39,920 --> 00:55:43,759
Speaker 1: you know, organizations are really trusted by distrusted by marginalized people,

1074
00:55:43,840 --> 00:55:44,920
Speaker 1: Like what are you going to do what to me?

1075
00:55:45,120 --> 00:55:48,799
Speaker 1: You know, like especially thinking like you know, like lots

1076
00:55:48,840 --> 00:55:52,760
Speaker 1: of lots of transfolks and like especially like under housed

1077
00:55:52,840 --> 00:55:54,920
Speaker 1: or unhoused transfers, and just like you're going to do

1078
00:55:55,000 --> 00:55:56,920
Speaker 1: what you want this day upon me for who you know.

1079
00:55:57,239 --> 00:56:02,080
Speaker 4: Right, So, yeah, understandably, especially because because surveillance in general

1080
00:56:02,200 --> 00:56:05,040
Speaker 4: like is not a safety thing, right, It's not. It

1081
00:56:05,200 --> 00:56:09,200
Speaker 4: is maybe a like safety for people within the walls

1082
00:56:09,239 --> 00:56:11,600
Speaker 4: of the walled garden thing, but that's not safety, right.

1083
00:56:11,640 --> 00:56:16,040
Speaker 4: That's the other thing about this is that what we

1084
00:56:16,160 --> 00:56:19,799
Speaker 4: call AI these days is predicated on enormous data collection, right,

1085
00:56:19,920 --> 00:56:21,799
Speaker 4: And so to one eccent, it's just sort of an

1086
00:56:21,840 --> 00:56:24,879
Speaker 4: excuse to go about claiming access to all that data.

1087
00:56:25,440 --> 00:56:27,160
Speaker 4: And once you have access to all that data, you

1088
00:56:27,200 --> 00:56:28,640
Speaker 4: can do things with it that have nothing to do

1089
00:56:28,840 --> 00:56:31,920
Speaker 4: with the large language models, and so there's you know,

1090
00:56:32,000 --> 00:56:35,480
Speaker 4: this is I think less typically less immediately like threatening

1091
00:56:35,560 --> 00:56:38,360
Speaker 4: to life and limb than the applications that Alex was

1092
00:56:38,400 --> 00:56:41,400
Speaker 4: starting with. But there's a lot of stuff where it's like, actually,

1093
00:56:41,440 --> 00:56:44,360
Speaker 4: we would be better off without all that information about

1094
00:56:44,440 --> 00:56:47,560
Speaker 4: us being out there. And there's an example that came

1095
00:56:47,640 --> 00:56:49,200
Speaker 4: up recently. So did you see this thing about the

1096
00:56:49,280 --> 00:56:53,840
Speaker 4: system called recall that came out with Windows eleven? So

1097
00:56:54,360 --> 00:56:56,320
Speaker 4: this thing, Oh god, this is such a mess. So

1098
00:56:56,960 --> 00:56:58,719
Speaker 4: initially it was going to be by default turned on.

1099
00:56:59,040 --> 00:57:01,160
Speaker 2: Oh yes, right, yeah, this is kind of like the

1100
00:57:01,200 --> 00:57:02,120
Speaker 2: Adobe story too.

1101
00:57:02,239 --> 00:57:03,560
Speaker 5: Yeah, yeah, every five.

1102
00:57:03,480 --> 00:57:05,920
Speaker 4: Seconds it takes a picture of your screen and then

1103
00:57:06,040 --> 00:57:08,840
Speaker 4: you can use that to like using AI search for

1104
00:57:08,880 --> 00:57:11,240
Speaker 4: stuff that you've sort of and their example is something stupid.

1105
00:57:11,239 --> 00:57:12,560
Speaker 4: It's like, yeah, I saw a recipe, but I don't

1106
00:57:12,560 --> 00:57:13,759
Speaker 4: remember where I saw it, So you want to be

1107
00:57:13,760 --> 00:57:16,240
Speaker 4: able to search back through your activity and like zero

1108
00:57:16,400 --> 00:57:19,520
Speaker 4: thought to what this means for people who are victims

1109
00:57:19,720 --> 00:57:24,800
Speaker 4: of intimate partner violence, right that they have this surveillance

1110
00:57:24,880 --> 00:57:27,200
Speaker 4: going on in their computer that eventually it ended up

1111
00:57:27,440 --> 00:57:30,720
Speaker 4: being shipped as off by default because the cybersecurity folks

1112
00:57:30,880 --> 00:57:33,000
Speaker 4: pushed back really hard. And by folks, I don't mean

1113
00:57:33,040 --> 00:57:34,560
Speaker 4: the people at Microsoft, I mean the people out in

1114
00:57:34,600 --> 00:57:37,040
Speaker 4: the world or saw this coming. Yeah, but that's another

1115
00:57:37,120 --> 00:57:40,960
Speaker 4: example of like surveillance in the name of AI that's

1116
00:57:40,960 --> 00:57:43,560
Speaker 4: supposed to be the sort of, you know, helpful little

1117
00:57:43,640 --> 00:57:45,440
Speaker 4: thing for you, but like no thought to what that

1118
00:57:45,560 --> 00:57:46,840
Speaker 4: means for people. And it's like, yeah, we're just going

1119
00:57:46,880 --> 00:57:49,760
Speaker 4: to turn this on by default because everybody wants this obviously, right.

1120
00:57:50,080 --> 00:57:52,920
Speaker 2: It's like, no, I know how to look through my history.

1121
00:57:53,160 --> 00:57:56,680
Speaker 2: Actually I've developed that skill. Yeah, I don't need you

1122
00:57:56,800 --> 00:57:59,640
Speaker 2: to take snapshots of my desktop every three seconds.

1123
00:58:00,080 --> 00:58:03,320
Speaker 3: Your show's covered so many kind of upsetting ways that

1124
00:58:04,120 --> 00:58:07,360
Speaker 3: it doesn't seem like it's people implementing AI. It's companies

1125
00:58:07,440 --> 00:58:11,280
Speaker 3: implementing AI in a lot of cases to do jobs

1126
00:58:11,360 --> 00:58:14,840
Speaker 3: that it's not capable of doing. There there's been incorrect

1127
00:58:14,880 --> 00:58:20,000
Speaker 3: obituaries Grock, the Elon Musk won the Twitter one made

1128
00:58:20,040 --> 00:58:23,880
Speaker 3: up fake headlines about Iran attacking Israel, and like public

1129
00:58:24,080 --> 00:58:27,080
Speaker 3: like put them out as like a major trending story.

1130
00:58:27,720 --> 00:58:31,000
Speaker 3: You have this great anecdote about a Facebook chatbot AI

1131
00:58:31,560 --> 00:58:35,120
Speaker 3: like responding to someone had this like very specific question

1132
00:58:35,280 --> 00:58:38,160
Speaker 3: they have, like a gifted disabled child. They were like, he,

1133
00:58:38,320 --> 00:58:42,400
Speaker 3: does anybody have experience with a gifted disabled like two

1134
00:58:42,480 --> 00:58:46,720
Speaker 3: E child with like this specific New York public school

1135
00:58:47,000 --> 00:58:51,200
Speaker 3: program And the chatbot responds, yes, I have experience with that,

1136
00:58:51,280 --> 00:58:53,840
Speaker 3: and just like made up because they knew that's what

1137
00:58:54,040 --> 00:58:57,360
Speaker 3: that's what they wanted to hear. And fortunately it was

1138
00:58:57,400 --> 00:58:59,919
Speaker 3: like clearly labeled as an AI chatbot. So the person

1139
00:59:00,160 --> 00:59:05,920
Speaker 3: was like, what the black mirror, but World Health Organization,

1140
00:59:06,080 --> 00:59:10,960
Speaker 3: you know, eating disorder institutions replacing therapists with AI, Like

1141
00:59:11,320 --> 00:59:17,240
Speaker 3: you just have all these examples of this going being

1142
00:59:17,960 --> 00:59:22,280
Speaker 3: used where it shouldn't be and things going badly, and

1143
00:59:23,280 --> 00:59:25,880
Speaker 3: like that. There's a detail that I think we talked

1144
00:59:25,880 --> 00:59:32,280
Speaker 3: about last time about dual Lingo, where the model where

1145
00:59:32,440 --> 00:59:35,360
Speaker 3: they let AI take over some of the stuff that

1146
00:59:35,480 --> 00:59:39,840
Speaker 3: like human teachers and translators were doing before. And you

1147
00:59:39,960 --> 00:59:42,720
Speaker 3: made the point that people who are learning the language,

1148
00:59:42,720 --> 00:59:45,080
Speaker 3: who are beginners are not in a position to notice

1149
00:59:45,200 --> 00:59:48,560
Speaker 3: that the quality has dropped. Yeah, And I feel like

1150
00:59:48,680 --> 00:59:52,360
Speaker 3: that's what we're seeing basically everywhere now is just the

1151
00:59:52,480 --> 00:59:55,960
Speaker 3: Internet is so big, they're just using it so many

1152
00:59:56,040 --> 01:00:00,120
Speaker 3: different places that it's hard to catch them all and

1153
01:00:00,240 --> 01:00:04,960
Speaker 3: then there is not an appetite to report on all

1154
01:00:05,040 --> 01:00:08,920
Speaker 3: the ways it's fucking up, and so it just everything

1155
01:00:09,080 --> 01:00:14,840
Speaker 3: is kind of getting slightly too drastically shittier at once. Yeah,

1156
01:00:15,440 --> 01:00:17,440
Speaker 3: and I don't know what to do with that.

1157
01:00:18,680 --> 01:00:21,400
Speaker 1: I would say, yeah, well go ahead, Emily.

1158
01:00:21,840 --> 01:00:23,280
Speaker 5: What you do with that is you make fun of it.

1159
01:00:23,400 --> 01:00:25,760
Speaker 4: That's one of our things is ridiculous process to like,

1160
01:00:26,200 --> 01:00:28,360
Speaker 4: you know, try to try to keep the mood up,

1161
01:00:28,440 --> 01:00:29,480
Speaker 4: but also just show it for.

1162
01:00:29,520 --> 01:00:30,800
Speaker 5: How ridiculous it is.

1163
01:00:31,600 --> 01:00:33,600
Speaker 4: And then the other thing is to really seek out

1164
01:00:34,000 --> 01:00:36,160
Speaker 4: the good journalism on this topic, because so much of

1165
01:00:36,200 --> 01:00:39,200
Speaker 4: it is either fake journalism output by a large language

1166
01:00:39,200 --> 01:00:43,480
Speaker 4: model these days, or journalists who are basically practicing access

1167
01:00:43,560 --> 01:00:46,160
Speaker 4: journalism who are doing the g's thing, who are reproducing

1168
01:00:46,200 --> 01:00:48,520
Speaker 4: press releases and so finding the people who are doing

1169
01:00:48,600 --> 01:00:50,720
Speaker 4: really good critical work and like supporting them I think

1170
01:00:50,800 --> 01:00:51,520
Speaker 4: is super important.

1171
01:00:52,360 --> 01:00:53,760
Speaker 1: You we're going to say, well, I was, well, though,

1172
01:00:53,920 --> 01:00:55,600
Speaker 1: you just teed me up really well, because I was

1173
01:00:55,640 --> 01:00:57,840
Speaker 1: actually going to say, you know, some of the people

1174
01:00:57,840 --> 01:00:59,680
Speaker 1: who are doing some of the best work on it

1175
01:00:59,720 --> 01:01:02,920
Speaker 1: are like four or four media, and you know, I

1176
01:01:03,040 --> 01:01:04,560
Speaker 1: want to give a shout out to them because they're

1177
01:01:04,720 --> 01:01:08,880
Speaker 1: you know, these folks are basically you know, they were

1178
01:01:08,920 --> 01:01:14,200
Speaker 1: at Motherboard and Motherboard you know, uh, the whole Vice

1179
01:01:15,240 --> 01:01:20,000
Speaker 1: Empire was basically you know, Sunset and and so they

1180
01:01:20,120 --> 01:01:22,840
Speaker 1: they laid off a bunch of people. So they started

1181
01:01:22,880 --> 01:01:25,880
Speaker 1: this kind of journalists owned and operated place, and you

1182
01:01:26,000 --> 01:01:29,960
Speaker 1: know that focus is specifically on tech and AI and

1183
01:01:30,080 --> 01:01:33,120
Speaker 1: these folks have been kind of in the game for

1184
01:01:33,280 --> 01:01:36,560
Speaker 1: so long they they know how to talk about this

1185
01:01:36,640 --> 01:01:40,480
Speaker 1: stuff without really having this kind of being bowled over.

1186
01:01:41,200 --> 01:01:44,800
Speaker 1: You know, there's people who play that access journalism, like

1187
01:01:45,440 --> 01:01:48,560
Speaker 1: like kar Swisher who like kind of poses herself as

1188
01:01:48,680 --> 01:01:52,440
Speaker 1: this person who is very antagonistic but like, you know.

1189
01:01:52,520 --> 01:01:55,120
Speaker 2: Right, just like fawning over like AI people, and.

1190
01:01:55,920 --> 01:02:00,160
Speaker 1: Like all the time, Well I trusted Elon Musse and

1191
01:02:00,320 --> 01:02:01,840
Speaker 1: tell I was like, well, why did you trust this

1192
01:02:02,040 --> 01:02:04,720
Speaker 1: man in the first place? Like that, you know, I

1193
01:02:04,840 --> 01:02:09,160
Speaker 1: was reading the uh the Peter Thiel biography, The Contrarian

1194
01:02:09,800 --> 01:02:12,960
Speaker 1: and you know, and like it's it's a very it's

1195
01:02:13,000 --> 01:02:16,240
Speaker 1: a very harowing read. I mean, it was fascinating, but

1196
01:02:16,400 --> 01:02:19,200
Speaker 1: it was very harrowing. It wasn't an it was pretty

1197
01:02:19,320 --> 01:02:23,920
Speaker 1: like critical, but like you know, they discuss the PayPal

1198
01:02:24,080 --> 01:02:27,480
Speaker 1: days you know twenty four years ago, when you know,

1199
01:02:27,680 --> 01:02:31,000
Speaker 1: Elon Musk was like, well, I want to name PayPal

1200
01:02:31,120 --> 01:02:34,840
Speaker 1: to X and then and then everybody was like, why

1201
01:02:34,920 --> 01:02:37,320
Speaker 1: the fuck would you do that? People are already using

1202
01:02:37,760 --> 01:02:40,960
Speaker 1: people are using PayPal as a verb. You know, that's

1203
01:02:41,200 --> 01:02:44,120
Speaker 1: effectively the same thing he did with Twitter, Like people

1204
01:02:44,160 --> 01:02:46,560
Speaker 1: are talking about tweet as a verb. Why would you say,

1205
01:02:46,600 --> 01:02:51,000
Speaker 1: you know, just it's been like an absolutely vapid human

1206
01:02:51,080 --> 01:02:55,240
Speaker 1: being with no business sense. Anyways, Journal, that was a

1207
01:02:55,360 --> 01:02:58,800
Speaker 1: very long way of saying Kara Switcher sucks, and then

1208
01:02:59,080 --> 01:03:02,720
Speaker 1: also saying that all saying also saying that there's lots

1209
01:03:02,720 --> 01:03:05,320
Speaker 1: of folks. There's a number of folks doing great stuff.

1210
01:03:05,320 --> 01:03:08,160
Speaker 1: So I mean folks at four or for Karen Howe

1211
01:03:08,240 --> 01:03:12,280
Speaker 1: who's independent but had been at The Atlantic and I

1212
01:03:12,360 --> 01:03:15,280
Speaker 1: T Tech Review and Wall Street Journal. Curry Johnson, who

1213
01:03:15,360 --> 01:03:19,080
Speaker 1: is at Wired is now at cal Matters. There's a

1214
01:03:19,120 --> 01:03:22,520
Speaker 1: lot of people that really report on AI from the

1215
01:03:22,560 --> 01:03:25,680
Speaker 1: perspective of like the people who it's harming, rather than

1216
01:03:25,760 --> 01:03:29,240
Speaker 1: starting from well this tool can do X, Y and Z, right,

1217
01:03:29,520 --> 01:03:32,480
Speaker 1: you know, we really should take these groups out their claims.

1218
01:03:32,840 --> 01:03:34,520
Speaker 1: But yeah, I mean the larger part of it is

1219
01:03:34,600 --> 01:03:37,240
Speaker 1: I mean, there's just so much stuff out there, you know,

1220
01:03:37,360 --> 01:03:39,919
Speaker 1: and it's it's so hard and it is like whack

1221
01:03:40,000 --> 01:03:44,400
Speaker 1: them all. And I mean we're we're not journalists by training.

1222
01:03:44,560 --> 01:03:47,920
Speaker 1: I mean we're sort of doing a journalistic thing right now.

1223
01:03:50,280 --> 01:03:53,880
Speaker 1: I think we're I would not say we are journalists.

1224
01:03:53,920 --> 01:03:56,040
Speaker 1: I always say we are doing a journalistic thing.

1225
01:03:58,120 --> 01:03:59,200
Speaker 2: We're doing journalism.

1226
01:03:59,600 --> 01:04:03,160
Speaker 1: We are not doing original reporting. S sure, but it

1227
01:04:03,360 --> 01:04:06,880
Speaker 1: is well and you know, I would you know, I'm not.

1228
01:04:06,960 --> 01:04:09,040
Speaker 1: I don't know, I'm not the I don't I don't

1229
01:04:09,040 --> 01:04:11,720
Speaker 1: know who decides this is the court of journalism. But

1230
01:04:11,880 --> 01:04:14,440
Speaker 1: you know, reporting in so far as looking at original

1231
01:04:14,560 --> 01:04:18,280
Speaker 1: papers and effectively being like, okay, this is marketing.

1232
01:04:18,400 --> 01:04:22,000
Speaker 3: This is why it's marketing. Know they're there, yeah, rather

1233
01:04:22,120 --> 01:04:22,680
Speaker 3: than you.

1234
01:04:22,720 --> 01:04:28,760
Speaker 1: Know, a whizbang c net article or something that comes

1235
01:04:28,800 --> 01:04:32,840
Speaker 1: out of a content mill and says Google just published

1236
01:04:33,040 --> 01:04:36,400
Speaker 1: this tool that says you can you know, find eighteen

1237
01:04:36,480 --> 01:04:39,560
Speaker 1: million materials. Who are that are you know complete? Almost

1238
01:04:39,600 --> 01:04:42,400
Speaker 1: like Okay, well let's look at those claims and upon

1239
01:04:42,600 --> 01:04:45,880
Speaker 1: what grounds to those claims stand? And and you know

1240
01:04:46,040 --> 01:04:47,560
Speaker 1: how that's that's a pretty.

1241
01:04:48,360 --> 01:04:50,800
Speaker 4: I think what we're doing is is first of all,

1242
01:04:50,880 --> 01:04:53,240
Speaker 4: sharing our expertise in our specific fields, but also like

1243
01:04:53,480 --> 01:04:55,280
Speaker 4: modeling for people how to be critical.

1244
01:04:55,000 --> 01:04:56,600
Speaker 5: Consumers of journalism.

1245
01:04:57,960 --> 01:05:02,680
Speaker 4: So journalism ja but yeah, definitely without training in journalism totally.

1246
01:05:02,760 --> 01:05:04,680
Speaker 3: Yeah. But I think we want to do we want

1247
01:05:04,720 --> 01:05:07,439
Speaker 3: to do the M and M articles. I mean, oh

1248
01:05:07,680 --> 01:05:11,760
Speaker 3: my gosh, there's this article that has like done our

1249
01:05:11,880 --> 01:05:15,520
Speaker 3: brains because it just has this series of sentences.

1250
01:05:15,560 --> 01:05:18,840
Speaker 2: That I don't know that because everything is degrading, like journalism.

1251
01:05:18,960 --> 01:05:20,840
Speaker 2: You know, there's that story about like the Daily Mail

1252
01:05:20,960 --> 01:05:23,360
Speaker 2: was like Natalie Portman was hooked on cocaine when she

1253
01:05:23,480 --> 01:05:25,160
Speaker 2: was at Harvard. You're like, no, that was from that

1254
01:05:25,360 --> 01:05:28,440
Speaker 2: rap she did on SNL and that was like a bit.

1255
01:05:28,640 --> 01:05:31,720
Speaker 2: But because this thing is street and then the Daily

1256
01:05:31,800 --> 01:05:33,840
Speaker 2: Mail had to be like at the end they corrected it.

1257
01:05:33,920 --> 01:05:36,480
Speaker 2: They're like, uh, just she was not. That was obviously

1258
01:05:36,640 --> 01:05:39,360
Speaker 2: satirical and that was due to human error, Like they

1259
01:05:39,480 --> 01:05:40,520
Speaker 2: really leaned into that.

1260
01:05:40,760 --> 01:05:42,280
Speaker 4: Like no, yeah, of course, did I say by the

1261
01:05:42,320 --> 01:05:44,880
Speaker 4: time that the fabricated quote of mine came out of

1262
01:05:44,920 --> 01:05:46,479
Speaker 4: one of these things and was printed as news.

1263
01:05:46,680 --> 01:05:46,720
Speaker 2: No?

1264
01:05:46,960 --> 01:05:50,320
Speaker 4: No, So I also like alex so search my own

1265
01:05:50,400 --> 01:05:51,920
Speaker 4: name because I talked to journalists or not that I

1266
01:05:52,000 --> 01:05:54,040
Speaker 4: like to see what's happening. And there was something in

1267
01:05:54,120 --> 01:05:57,400
Speaker 4: an outfit called Bihar Praba that attributed this quote to me,

1268
01:05:57,440 --> 01:05:59,960
Speaker 4: which is not something I'd ever said and not anybody

1269
01:06:00,200 --> 01:06:03,240
Speaker 4: remember talking to. So I emailed the editor and I said,

1270
01:06:03,520 --> 01:06:06,360
Speaker 4: please take down this fabricated quote in print of retraction

1271
01:06:06,440 --> 01:06:08,880
Speaker 4: because I never said that, and they did so the

1272
01:06:08,960 --> 01:06:11,760
Speaker 4: article got updated remove the thing attributed to me, and

1273
01:06:11,840 --> 01:06:13,680
Speaker 4: then there was a thing at the bottom saying we've

1274
01:06:13,680 --> 01:06:16,120
Speaker 4: attracted this. But what they didn't put publicly, but he

1275
01:06:16,200 --> 01:06:18,360
Speaker 4: told me over email is that the whole thing came

1276
01:06:18,400 --> 01:06:19,040
Speaker 4: out of Gemini.

1277
01:06:19,920 --> 01:06:20,360
Speaker 3: Oh wow.

1278
01:06:20,400 --> 01:06:22,840
Speaker 4: And they posted it as a news article of course.

1279
01:06:22,840 --> 01:06:25,400
Speaker 4: And you know the only reason I discovered it was

1280
01:06:25,440 --> 01:06:27,640
Speaker 4: it was my own name and like, I never said

1281
01:06:27,680 --> 01:06:27,960
Speaker 4: that thing.

1282
01:06:28,320 --> 01:06:31,560
Speaker 2: Well, I need your expertise here to decipher this food

1283
01:06:31,600 --> 01:06:35,000
Speaker 2: and wine article that was talking about how Eminem's was

1284
01:06:35,080 --> 01:06:39,760
Speaker 2: coming out with a pumpkin pie flavored eminem but very early.

1285
01:06:40,000 --> 01:06:42,720
Speaker 2: Normally pumpkin pie flavored things don't enter the market till

1286
01:06:42,720 --> 01:06:45,360
Speaker 2: around August, like around when fall comes. But Eminem is

1287
01:06:45,400 --> 01:06:51,160
Speaker 2: why we were covering it, because we are journalists in May,

1288
01:06:51,880 --> 01:06:55,520
Speaker 2: pumpkin spice already no, But again they were saying this

1289
01:06:55,640 --> 01:06:59,440
Speaker 2: is because apparently gen Z and millennial consumers are celebrating

1290
01:06:59,560 --> 01:07:03,280
Speaker 2: Halloween earlier. But this is this one section that completely

1291
01:07:03,520 --> 01:07:08,320
Speaker 2: wait wait, yeah, I don't know. That's what they're saying,

1292
01:07:08,440 --> 01:07:12,640
Speaker 2: according to their analysis, that we were that we apparent,

1293
01:07:12,800 --> 01:07:13,800
Speaker 2: So let me read this for you.

1294
01:07:14,120 --> 01:07:14,400
Speaker 3: Quote.

1295
01:07:14,840 --> 01:07:17,520
Speaker 2: The pre seasonal launch of the milk chocolate pumpkin pie

1296
01:07:17,520 --> 01:07:20,880
Speaker 2: Eminem's is a strategic move that taps into Mars' market research.

1297
01:07:21,160 --> 01:07:24,320
Speaker 2: This research indicates that gen Z and millennials plan to

1298
01:07:24,440 --> 01:07:27,960
Speaker 2: celebrate Halloween by dressing up and planning for the holiday

1299
01:07:28,280 --> 01:07:32,200
Speaker 2: about six point eight weeks beforehand. Well, six point eight

1300
01:07:32,240 --> 01:07:35,000
Speaker 2: weeks from Memorial Day is the fourth of July, so

1301
01:07:35,120 --> 01:07:37,320
Speaker 2: you still have plenty of time to latch onto a

1302
01:07:37,400 --> 01:07:40,200
Speaker 2: pop culture trend and turn it into a creative costume.

1303
01:07:41,680 --> 01:07:46,760
Speaker 3: I don't right, it doesn't It doesn't make any sense.

1304
01:07:46,800 --> 01:07:53,600
Speaker 1: I know, I'm fixating on six point eight.

1305
01:07:55,640 --> 01:07:57,400
Speaker 6: What does that even mean? Does that mean?

1306
01:07:57,440 --> 01:07:59,160
Speaker 3: And where did Memorial Day come from?

1307
01:07:59,280 --> 01:07:59,480
Speaker 1: And that?

1308
01:07:59,720 --> 01:08:02,320
Speaker 3: And what is six point eight weeks for Memorial Day?

1309
01:08:02,320 --> 01:08:05,080
Speaker 3: Because it's not any of the days that they said

1310
01:08:05,120 --> 01:08:05,360
Speaker 3: it was.

1311
01:08:05,800 --> 01:08:07,080
Speaker 5: They said July fourth.

1312
01:08:07,600 --> 01:08:09,240
Speaker 3: Wait, and also six point.

1313
01:08:09,040 --> 01:08:11,520
Speaker 2: Eight eight weeks isn't a real amount of time. That's

1314
01:08:11,640 --> 01:08:15,120
Speaker 2: forty seven point point six days? Yeah, what is what

1315
01:08:15,280 --> 01:08:16,679
Speaker 2: is even a six point eight a week?

1316
01:08:17,240 --> 01:08:21,960
Speaker 4: So if this were real, it's possible that they surveyed

1317
01:08:22,000 --> 01:08:23,720
Speaker 4: a bunch of people and they said when do you

1318
01:08:23,760 --> 01:08:26,439
Speaker 4: start planning your Halloween costume? And those people gave dates

1319
01:08:26,520 --> 01:08:28,320
Speaker 4: and then they average that and that's how you could

1320
01:08:28,320 --> 01:08:28,600
Speaker 4: get to it.

1321
01:08:28,640 --> 01:08:28,960
Speaker 3: I get that.

1322
01:08:29,439 --> 01:08:32,680
Speaker 5: I get that that's fair, but also it totally.

1323
01:08:32,400 --> 01:08:35,840
Speaker 4: Sounds like someone put into a large language model write

1324
01:08:35,880 --> 01:08:40,160
Speaker 4: an article about why millennials and gen z are planning

1325
01:08:40,200 --> 01:08:41,320
Speaker 4: their Halloween costumes earlier.

1326
01:08:41,360 --> 01:08:43,120
Speaker 5: So like it sounds like that.

1327
01:08:43,479 --> 01:08:45,599
Speaker 2: But also just so odd to say, well, six point

1328
01:08:45,640 --> 01:08:48,240
Speaker 2: eight weeks from Memorial Day is the fourth of July.

1329
01:08:48,520 --> 01:08:50,840
Speaker 2: This article didn't even come out like it came out

1330
01:08:50,920 --> 01:08:55,280
Speaker 2: after Memorial Day and yeah fourth It's just nothing made sense.

1331
01:08:55,360 --> 01:08:57,720
Speaker 2: And I was like, I don't fucking understand what they're

1332
01:08:57,760 --> 01:09:00,519
Speaker 2: doing to me right now. But again that's this is

1333
01:09:00,600 --> 01:09:01,960
Speaker 2: like the insidious part for me.

1334
01:09:02,080 --> 01:09:04,479
Speaker 5: But this appeared in Food and Wine.

1335
01:09:04,720 --> 01:09:06,719
Speaker 2: This is in Food and Wine magazine with a human

1336
01:09:07,600 --> 01:09:10,160
Speaker 2: like in the byline, and I actually d m this

1337
01:09:10,280 --> 01:09:12,160
Speaker 2: person on Instagram and I said, do you mind just

1338
01:09:12,240 --> 01:09:15,200
Speaker 2: clarifying this part, like I'm a little bit confused and

1339
01:09:15,800 --> 01:09:17,519
Speaker 2: I've I've gotten no response.

1340
01:09:18,160 --> 01:09:20,559
Speaker 1: I'm wondering if it's because I know that. I mean,

1341
01:09:20,600 --> 01:09:23,920
Speaker 1: there was some good coverage and futurism and they were

1342
01:09:24,000 --> 01:09:28,360
Speaker 1: talking about this company called adv on Commerce and the

1343
01:09:28,439 --> 01:09:32,120
Speaker 1: way that basically this this this company has been basically

1344
01:09:32,760 --> 01:09:37,680
Speaker 1: making AI generated articles for a lot of different publications,

1345
01:09:38,360 --> 01:09:42,880
Speaker 1: usually on products like product placement, right, And so it

1346
01:09:43,000 --> 01:09:46,000
Speaker 1: makes me think it's sort of like because food and wine,

1347
01:09:46,640 --> 01:09:48,439
Speaker 1: you know, may have been one of their forgot I

1348
01:09:48,600 --> 01:09:51,240
Speaker 1: forgot the article, but they had a bun they had

1349
01:09:51,320 --> 01:09:54,560
Speaker 1: like you know, better homes and gardening and you know,

1350
01:09:54,640 --> 01:09:56,800
Speaker 1: kind of these legacy articles like that. So I don't

1351
01:09:56,840 --> 01:09:59,880
Speaker 1: know if it's something of that or this journalist kind

1352
01:09:59,880 --> 01:10:02,080
Speaker 1: of said write me this thing and I'm just gonna

1353
01:10:02,120 --> 01:10:04,320
Speaker 1: drop it and then go with god, you know.

1354
01:10:05,479 --> 01:10:07,320
Speaker 2: Yeah. Yeah.

1355
01:10:07,520 --> 01:10:10,840
Speaker 3: My My other favorite example of is this headline I

1356
01:10:10,920 --> 01:10:13,760
Speaker 3: saw somewhere it's no big secret why Van Vaught isn't

1357
01:10:13,800 --> 01:10:16,800
Speaker 3: around anymore, and with a picture of Vince Vaughan, but

1358
01:10:16,880 --> 01:10:23,400
Speaker 3: they just like got his name completely wrong. It's no

1359
01:10:23,520 --> 01:10:27,479
Speaker 3: big secret why Van Vaught isn't around anymore.

1360
01:10:29,920 --> 01:10:33,400
Speaker 1: I'm like, I'm you know, if I was just scrolling

1361
01:10:33,600 --> 01:10:35,800
Speaker 1: and I just and I'd say like, yeah, you just

1362
01:10:35,840 --> 01:10:37,920
Speaker 1: like you know, I liked Van Vaught in the intern

1363
01:10:38,400 --> 01:10:41,720
Speaker 1: and then yeah, but then I but and then I

1364
01:10:41,760 --> 01:10:43,000
Speaker 1: would have looked at it, and then I would have

1365
01:10:43,040 --> 01:10:45,760
Speaker 1: double taped. I'm like, wait, wait, wait did he co

1366
01:10:46,000 --> 01:10:49,040
Speaker 1: star with oh when Mick Wilson or something?

1367
01:10:49,280 --> 01:10:52,120
Speaker 2: Yeah, yeah, yeah, Russell Wilson was in that.

1368
01:10:53,000 --> 01:10:54,960
Speaker 4: I think it was the Week reporting that you're thinking of,

1369
01:10:55,040 --> 01:10:56,479
Speaker 4: alex I Futures' did a bunch of it. But then

1370
01:10:56,520 --> 01:10:58,240
Speaker 4: AD Week had the whole thing about ad Von and

1371
01:10:58,360 --> 01:10:59,720
Speaker 4: I can't quite no, no.

1372
01:11:00,360 --> 01:11:02,840
Speaker 1: No, it was it was future. It was futurism, yeah,

1373
01:11:02,880 --> 01:11:05,879
Speaker 1: because because A Week had the thing on this program

1374
01:11:06,760 --> 01:11:09,000
Speaker 1: that Google was offering and they didn't have a name.

1375
01:11:09,120 --> 01:11:13,600
Speaker 4: Oh right, yeah, avas futurism. Yeah, but it totally sounds.

1376
01:11:13,360 --> 01:11:14,559
Speaker 3: Like what it is happening. Yeah.

1377
01:11:14,720 --> 01:11:16,320
Speaker 5: Yeah, I thought you were going to.

1378
01:11:16,320 --> 01:11:19,320
Speaker 4: Talk about the surveillance by Eminem thing. We said Eminem's

1379
01:11:19,479 --> 01:11:22,679
Speaker 4: So this was somewhere in Canada. There was an Eminem

1380
01:11:22,760 --> 01:11:25,599
Speaker 4: vending machine that was like taking pictures of the students

1381
01:11:26,000 --> 01:11:27,880
Speaker 4: while they were making their purchases. And I forget what

1382
01:11:27,960 --> 01:11:30,240
Speaker 4: the like O sensible purpose was, but the students found

1383
01:11:30,280 --> 01:11:32,160
Speaker 4: out and I got it removed.

1384
01:11:32,680 --> 01:11:40,040
Speaker 3: Wow, probably freaked out and made a big deal about it. Right, Well,

1385
01:11:40,439 --> 01:11:42,639
Speaker 3: I feel like we could talk to you guys once

1386
01:11:42,640 --> 01:11:46,400
Speaker 3: again for three hours. There's so much interesting stuff to

1387
01:11:46,479 --> 01:11:48,800
Speaker 3: talk about. Your show is so great. Thank you both

1388
01:11:48,880 --> 01:11:53,000
Speaker 3: for joining. Yeah, where can people find you? Follow you

1389
01:11:53,280 --> 01:11:55,560
Speaker 3: all that good stuff? Emily, we'll start with you.

1390
01:11:56,240 --> 01:11:59,400
Speaker 4: Well, first, there's a podcast Mystery AI hYP Theater three thousand,

1391
01:11:59,520 --> 01:12:02,600
Speaker 4: where you find any podcast you can find ours, And

1392
01:12:02,720 --> 01:12:05,840
Speaker 4: we've also started a newsletter. If you just search Mystery

1393
01:12:05,880 --> 01:12:08,320
Speaker 4: a Hip Theater three thousand newsletter, I think it'll turn up.

1394
01:12:08,880 --> 01:12:11,600
Speaker 4: And that's an irregular newsletter where we basically took the

1395
01:12:11,680 --> 01:12:14,360
Speaker 4: things that used to be sort of little tweet storms,

1396
01:12:14,880 --> 01:12:17,559
Speaker 4: and since the social media stuff has gotten fragmented, we're

1397
01:12:17,600 --> 01:12:20,240
Speaker 4: now creating newsletter posts with them. So it's you know,

1398
01:12:20,640 --> 01:12:26,000
Speaker 4: off the cuff discussions of things on Twitter, x in

1399
01:12:26,400 --> 01:12:30,479
Speaker 4: Macedon and Blue Sky. I'm Emily M Bender and I'm

1400
01:12:30,479 --> 01:12:34,160
Speaker 4: also reluctantly using LinkedIn as social media these days.

1401
01:12:34,520 --> 01:12:38,320
Speaker 3: So it's gonna be the one that survives them all

1402
01:12:38,400 --> 01:12:40,760
Speaker 3: because I know some people kind of need it.

1403
01:12:41,080 --> 01:12:47,080
Speaker 1: Really the cock really the cock verches of yeah, yeah, yeah,

1404
01:12:47,120 --> 01:12:50,720
Speaker 1: I met al you, Alex, Alex, Hannah h A N

1405
01:12:50,840 --> 01:12:56,559
Speaker 1: n A on Twitter Blue Sky. I barely use blue

1406
01:12:56,600 --> 01:12:58,880
Speaker 1: Sky or mass it on, but Twitter is the best

1407
01:12:58,920 --> 01:13:02,800
Speaker 1: place to find me. Also check out Dare Dare d

1408
01:13:03,040 --> 01:13:07,080
Speaker 1: a I R hyphen Institute dot org. And we're also

1409
01:13:07,240 --> 01:13:13,040
Speaker 1: Dare Underscore Institute on Twitter, Macedon and uh, we're not

1410
01:13:13,120 --> 01:13:16,000
Speaker 1: on Blue Sky yet, but we're on LinkedIn. But that's,

1411
01:13:16,680 --> 01:13:18,280
Speaker 1: you know, where you learned a lot about what our

1412
01:13:18,360 --> 01:13:23,599
Speaker 1: institute's doing. Lots of good stuff, amazing colleagues and whatnot.

1413
01:13:24,160 --> 01:13:28,320
Speaker 3: Yeah, amazing. And is there a work of media that

1414
01:13:28,800 --> 01:13:29,679
Speaker 3: you've been enjoying.

1415
01:13:30,320 --> 01:13:31,479
Speaker 5: Yes, I've got one for you.

1416
01:13:31,600 --> 01:13:33,080
Speaker 4: This I think started off as a tweet, but I

1417
01:13:33,080 --> 01:13:35,160
Speaker 4: saw it as a screencap on Macedon. So it's by

1418
01:13:35,439 --> 01:13:38,439
Speaker 4: Lama in a tux and the text is don't you

1419
01:13:38,560 --> 01:13:41,000
Speaker 4: understand that the human race is an endless number of

1420
01:13:41,080 --> 01:13:43,599
Speaker 4: monkeys and every day we produce an endless number of words.

1421
01:13:43,800 --> 01:13:45,400
Speaker 4: And one of us already wrote Hamlet.

1422
01:13:47,800 --> 01:13:48,559
Speaker 3: That's really good.

1423
01:13:49,080 --> 01:13:52,840
Speaker 1: That's such. That's such a hyper specific piece of media.

1424
01:13:53,520 --> 01:13:56,320
Speaker 1: I think I think last I think last time was

1425
01:13:56,360 --> 01:13:59,080
Speaker 1: on this. I was plugging Worlds Beyond Number, which is

1426
01:13:59,120 --> 01:14:02,360
Speaker 1: a podcast which I'm just absolutely in love with, which

1427
01:14:02,439 --> 01:14:06,080
Speaker 1: is a a Dungeons and Dragon's actual play podcast, but

1428
01:14:06,200 --> 01:14:09,559
Speaker 1: it's got amazing sound production. I would just like plug

1429
01:14:09,640 --> 01:14:12,320
Speaker 1: in everything on dropout dot tv. I mean this it's

1430
01:14:12,320 --> 01:14:17,320
Speaker 1: a screaming service. Honestly, it's uh, you know, Sam Reich,

1431
01:14:17,400 --> 01:14:21,320
Speaker 1: who is a Reich who is the son of Robert Reich,

1432
01:14:21,560 --> 01:14:25,280
Speaker 1: kind of liberal darling and former Department of Labor secretary

1433
01:14:25,360 --> 01:14:29,960
Speaker 1: in the Clinton administration, has turned college humor into an

1434
01:14:30,000 --> 01:14:34,920
Speaker 1: area of of like really great comedians. So they're putting

1435
01:14:34,960 --> 01:14:38,360
Speaker 1: out a lot of great stuff. So i'd say, you know,

1436
01:14:38,680 --> 01:14:41,120
Speaker 1: make some noises. Coming out with a new season today,

1437
01:14:41,240 --> 01:14:44,519
Speaker 1: which is it's it's a really great improv comedy thing

1438
01:14:44,760 --> 01:14:47,439
Speaker 1: and yeah, let's just let's just go with that.

1439
01:14:47,680 --> 01:14:47,960
Speaker 2: So just.

1440
01:14:49,800 --> 01:14:50,320
Speaker 3: Hilarious.

1441
01:14:50,439 --> 01:14:54,400
Speaker 1: Those very important interviews Vick Michalis I named one of

1442
01:14:54,439 --> 01:14:59,560
Speaker 1: my chickens vehicular manslaughter after an inside joke there, and

1443
01:14:59,720 --> 01:15:04,439
Speaker 1: another one Thomas Shrinkley. So yeah, just incredible, incredible stuff.

1444
01:15:04,840 --> 01:15:09,280
Speaker 3: Yeah, shout out to Sam, He's one of the best. Miles. Yes,

1445
01:15:09,880 --> 01:15:12,280
Speaker 3: where can people find you? Is there a workimedia you

1446
01:15:12,439 --> 01:15:12,960
Speaker 3: can enjoy?

1447
01:15:13,160 --> 01:15:15,960
Speaker 2: They have at symbols. Look for at Miles of Gray.

1448
01:15:16,200 --> 01:15:19,640
Speaker 2: I'm probably there. You can find Jack and I on

1449
01:15:19,760 --> 01:15:23,639
Speaker 2: our basketball podcast, Miles and Jack Got Mad, where we've

1450
01:15:23,880 --> 01:15:28,200
Speaker 2: wrapped up the NBA season and I have that streaming

1451
01:15:28,320 --> 01:15:30,960
Speaker 2: down my face with pain and anger as the Celtics

1452
01:15:31,000 --> 01:15:33,519
Speaker 2: win again. And also if you want to hear me

1453
01:15:33,560 --> 01:15:36,360
Speaker 2: talk about very serious stuff, I'm talking about ninety day

1454
01:15:36,400 --> 01:15:39,800
Speaker 2: Fiance on my other show for twenty Day Fiance, which

1455
01:15:39,840 --> 01:15:42,880
Speaker 2: you can check out wherever they have podcasts. A tweet

1456
01:15:42,960 --> 01:15:47,760
Speaker 2: I like first one is from a past guest, Josh

1457
01:15:47,840 --> 01:15:51,479
Speaker 2: Gondleman tweeted, I bet the best part of being in

1458
01:15:51,560 --> 01:15:53,800
Speaker 2: a threuttle as you have someone to do all three

1459
01:15:53,920 --> 01:15:58,519
Speaker 2: Beastie Boys PARSI karaoke. That's I guess one way to

1460
01:15:58,560 --> 01:16:01,760
Speaker 2: look at that. And then the one from other past gasts.

1461
01:16:01,800 --> 01:16:05,360
Speaker 2: Demia de Juibe at electro Lemon got his account hacked

1462
01:16:05,400 --> 01:16:07,280
Speaker 2: and he tweeted, Hi, Hello, it's DEMI. I got my

1463
01:16:07,320 --> 01:16:09,720
Speaker 2: account back. I feel the need to clarify that under

1464
01:16:09,800 --> 01:16:13,320
Speaker 2: no circumstances should you ever believe that I or anybody

1465
01:16:13,400 --> 01:16:17,800
Speaker 2: on this website is selling cheap MacBooks for charity or otherwise,

1466
01:16:18,120 --> 01:16:20,280
Speaker 2: And what benefit would my signature do.

1467
01:16:20,400 --> 01:16:22,640
Speaker 3: To a laptop. Oh yeah, thank you for.

1468
01:16:23,240 --> 01:16:26,040
Speaker 1: I actually remember because I followed Demi, and I remember

1469
01:16:26,120 --> 01:16:29,120
Speaker 1: when his account got hacked and I thought, man, that's

1470
01:16:29,240 --> 01:16:31,439
Speaker 1: really and I at first I thought it was a

1471
01:16:31,520 --> 01:16:35,080
Speaker 1: bit because Demi is hilenious. But then I'm just like,

1472
01:16:35,240 --> 01:16:36,519
Speaker 1: what the hell, it's funny.

1473
01:16:36,800 --> 01:16:39,439
Speaker 2: His follow up tweet was for anyone who thought I

1474
01:16:39,600 --> 01:16:42,240
Speaker 2: was doing a bit, what's the punchline?

1475
01:16:43,840 --> 01:16:45,680
Speaker 3: My jokes are never so ubdused.

1476
01:16:45,920 --> 01:16:48,200
Speaker 2: I love you if I want you to know it

1477
01:16:48,320 --> 01:16:50,360
Speaker 2: wasn't all that funny, and I want you to know quick.

1478
01:16:50,840 --> 01:16:51,040
Speaker 3: Yeah.

1479
01:16:51,120 --> 01:16:53,320
Speaker 1: No, I was also trying to find out what the

1480
01:16:53,360 --> 01:16:54,840
Speaker 1: punchline was, right right?

1481
01:16:55,080 --> 01:16:55,280
Speaker 3: Yeah?

1482
01:16:55,600 --> 01:16:59,800
Speaker 2: Wait for it so funny that part of you wants

1483
01:16:59,840 --> 01:17:01,599
Speaker 2: to be like, well, hold on, what are you doing here?

1484
01:17:01,840 --> 01:17:03,599
Speaker 3: Yeah? What's like, what's what's the deal here?

1485
01:17:03,640 --> 01:17:05,960
Speaker 2: You don't want to immediately just dismiss Demi because he's

1486
01:17:05,960 --> 01:17:07,000
Speaker 2: such a great comedic man.

1487
01:17:07,160 --> 01:17:09,839
Speaker 1: Yeah, but yeah, if you do want good Demi content,

1488
01:17:10,040 --> 01:17:13,559
Speaker 1: the who's Who's welcome at the cookout? You can find

1489
01:17:13,640 --> 01:17:15,880
Speaker 1: that's some dropout content that you can get for free

1490
01:17:16,040 --> 01:17:16,599
Speaker 1: on YouTube.

1491
01:17:17,080 --> 01:17:21,120
Speaker 3: There you go. Tweet I've been enjoying sleepy at Sleepy

1492
01:17:21,160 --> 01:17:24,439
Speaker 3: Underscore Nice tweeted, it's absurd that Diddy Kong wears a

1493
01:17:24,560 --> 01:17:28,600
Speaker 3: hat that says Nintendo. Patently ridiculous. There's no way he

1494
01:17:28,800 --> 01:17:32,599
Speaker 3: understands the significance. It would be like me unknowingly wearing

1495
01:17:32,640 --> 01:17:36,040
Speaker 3: a hat that coincidentally depicts the true form of the universe.

1496
01:17:36,479 --> 01:17:37,760
Speaker 3: Take it off, Kong.

1497
01:17:39,479 --> 01:17:40,680
Speaker 1: That's incredible.

1498
01:17:41,479 --> 01:17:47,120
Speaker 6: Oh my god, good, it's so fucking good, because yeah,

1499
01:17:47,200 --> 01:17:50,879
Speaker 6: the second he showed up, you're like, I don't know, Yeah, Brandon,

1500
01:17:51,280 --> 01:17:52,120
Speaker 6: he likes Nintendo.

1501
01:17:53,000 --> 01:17:56,360
Speaker 3: You can find me on Twitter at Jack Underscore. O'Brien,

1502
01:17:56,400 --> 01:17:59,000
Speaker 3: you can find us on Twitter at Daily Zeikegeist. We're

1503
01:17:59,000 --> 01:18:01,799
Speaker 3: at the Daily Zeike on Instagram. We have a Facebook

1504
01:18:01,880 --> 01:18:05,240
Speaker 3: fan page and a website Daily zeitgeist dot com, where

1505
01:18:05,280 --> 01:18:08,360
Speaker 3: we post our episodes and our foot Nope, no where

1506
01:18:08,360 --> 01:18:10,559
Speaker 3: we link off to the information that we talked about

1507
01:18:10,960 --> 01:18:12,800
Speaker 3: in today's episode, as well as a song that we

1508
01:18:12,840 --> 01:18:15,639
Speaker 3: think you might enjoy. Myles, what song do you think

1509
01:18:15,720 --> 01:18:16,800
Speaker 3: people might enjoy?

1510
01:18:17,479 --> 01:18:21,880
Speaker 2: I came across this track from like the fifties that

1511
01:18:22,240 --> 01:18:24,760
Speaker 2: is like not really populas, Like it was playing on

1512
01:18:24,800 --> 01:18:26,559
Speaker 2: the radio and I just when I hear it. When

1513
01:18:26,600 --> 01:18:27,960
Speaker 2: I heard it, I was like, wait, what is this

1514
01:18:28,120 --> 01:18:30,840
Speaker 2: song because I thought it was like maybe a like

1515
01:18:31,000 --> 01:18:33,360
Speaker 2: newer artist doing sort of a send up of like

1516
01:18:33,439 --> 01:18:36,280
Speaker 2: fifties music, like surf music. It's called out in the

1517
01:18:36,439 --> 01:18:40,200
Speaker 2: Sun parenthetical hey oh, and it is a bit like

1518
01:18:40,320 --> 01:18:44,240
Speaker 2: Belafonte's day Oh and kind of has this like sort

1519
01:18:44,240 --> 01:18:47,360
Speaker 2: of similar sort of cadence to the verse. But it's

1520
01:18:47,520 --> 01:18:49,639
Speaker 2: just like when I heard it, I'm like, this sounds

1521
01:18:49,720 --> 01:18:52,639
Speaker 2: like the kind of like song like Tarantino would pluck

1522
01:18:52,680 --> 01:18:55,719
Speaker 2: from obscurity and then put under like a really dark scene,

1523
01:18:56,080 --> 01:18:57,920
Speaker 2: and it just got like it's like a beat song,

1524
01:18:58,000 --> 01:19:00,479
Speaker 2: but there's this like darkness to it that I really love.

1525
01:19:00,840 --> 01:19:04,400
Speaker 2: But anyway, this is the beach Nuts without in the Sun,

1526
01:19:05,240 --> 01:19:06,400
Speaker 2: So yeah, check this song out.

1527
01:19:06,479 --> 01:19:08,880
Speaker 3: It's when did that actually come out? Is it recent?

1528
01:19:09,080 --> 01:19:09,320
Speaker 2: You know?

1529
01:19:09,360 --> 01:19:10,080
Speaker 3: It's from the fifties?

1530
01:19:10,120 --> 01:19:11,880
Speaker 2: Notes from the fifties, Like they're an actual band.

1531
01:19:12,000 --> 01:19:15,479
Speaker 3: Like the lyrics are like, hey there, girls, where are

1532
01:19:15,600 --> 01:19:16,200
Speaker 3: you going?

1533
01:19:16,520 --> 01:19:18,880
Speaker 2: And they're like, down to the beach is.

1534
01:19:18,960 --> 01:19:19,960
Speaker 3: Where we're going?

1535
01:19:21,040 --> 01:19:24,880
Speaker 2: Lyrics is so literal, but there's this like charm to

1536
01:19:25,000 --> 01:19:27,479
Speaker 2: it and the instrumentation is cool. So anyway, this is

1537
01:19:27,560 --> 01:19:30,679
Speaker 2: the beach Nuts without in the Sun parenthetical.

1538
01:19:30,200 --> 01:19:33,560
Speaker 3: Fail all right, well, we will link off to that

1539
01:19:33,760 --> 01:19:36,760
Speaker 3: in the footnotes todayly Zeitgeist is a production of iHeartRadio.

1540
01:19:36,800 --> 01:19:38,880
Speaker 3: For more podcasts from my heart Radio, visit the heart

1541
01:19:38,960 --> 01:19:42,120
Speaker 3: Radio app, Apple Podcasts, or wherever fine podcasts are given

1542
01:19:42,120 --> 01:19:44,040
Speaker 3: away for free. That's going to do it for us

1543
01:19:44,280 --> 01:19:46,720
Speaker 3: this yet morning. We're back this afternoon to tell you

1544
01:19:46,800 --> 01:19:48,880
Speaker 3: what is trending, and we will talk to y'all then,

1545
01:19:49,040 --> 01:19:51,360
Speaker 3: bye bye, bye bye