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Speaker 1: Hello the Internet, and welcome to this episode of the

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Speaker 1: Weekly Zeitgeist. These are some of our favorite segments from

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Speaker 1: this week, all edited together into one NonStop infotainment la stravaganza. Yeah, So,

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Speaker 1: without further ado, here is the Weekly Zeitgeist. Miles. Yes,

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Speaker 1: we are thrilled, fortunate, blessed to be joined in our

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Speaker 1: third seat by an assistant professor of Technology, Operations and

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Speaker 1: Statistics at NYU, where his research focuses on the intersection

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Speaker 1: of machine learning and natural language processing, which, of course

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Speaker 1: means you know his interests include conversational agents, hierarchical models, yes,

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Speaker 1: steep learning, spectral clustering. You guys are all probably finishing

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Speaker 1: this sentence with spectral estimation of hidden Markov models, yes, obviously,

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Speaker 1: and of course time series analysis. Please welcome, doctor Joel, said,

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Speaker 1: Doc doctor Joel.

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Speaker 2: E Hi, thank you, Yeah, thank you Jack and Miles

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Speaker 2: for having me. It's a pleasure to be here.

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Speaker 1: The pleasure to have you.

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Speaker 3: We're glad you're here because we got we got a

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Speaker 3: lot of questions about a lot of stuff, some some

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Speaker 3: to mostly to do with what your area of expertise is.

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Speaker 3: Some things I just need a second opinion on, just around.

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Speaker 1: The career of Alan Iverson is it? Is it a

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Speaker 1: bad joke? If it's been used in like a cable

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Speaker 1: I believe that it's a Direct TV commercial is using

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Speaker 1: They're like and you, we have our very own AI

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Speaker 1: that helps you select shows. And then Alan Iversons sitting

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Speaker 1: on the couch with the person. Wait, does that make

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Speaker 1: it off limits? Yeah? Yeah, that's that's a real commercial.

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Speaker 1: And I'm going to be running with that joke all

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Speaker 1: day today because I'm not any better than the comedy

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Speaker 1: writers at Direct TV. I can never know how many

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Speaker 1: it is. Yeah, but what doctor said, Doc? Is that good?

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Speaker 1: Doctor said Doc? Is that a good way to address you?

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Speaker 4: Yeah?

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Speaker 2: Sure, I'm very easy about things. Okay, okay, how about doctor? Well,

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Speaker 2: I've never been called that. I had some students in

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Speaker 2: my class called me jay Wow.

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Speaker 1: Wow. Yes, yeah, is it joo so?

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Speaker 2: So the pronunciation is so, it's Brazilian Portuguese. That's what

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Speaker 2: I'm named after, but I go by I go by

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Speaker 2: Joao Jook jasying about it, doctor j even doctor j.

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Speaker 1: Amazing? All right? Well yeah, I mean the main thing

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Speaker 1: We've brought you here today to ask the big question

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Speaker 1: around AI, such as what is that? What is AI

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Speaker 1: and we'll follow from there. But you know, questions that

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Speaker 1: reveal that we're smart people. Obviously, Hey, Molly, was something

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Speaker 1: for your search history that's revealing about who you are?

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Speaker 5: Well? Right now, it's uh Crossroads by bone.

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Speaker 1: Thuck bone b Yeah, you did point out that the

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Speaker 1: actually the first lyrics of the song are bone bone bone, bone, bone, bone, bone, bone, bone, bone, bone.

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Speaker 5: Bone, bone, bone, bone bone. I'm also just reading about

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Speaker 5: remembering about the video that there's a part where there's

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Speaker 5: like a newborn baby that's died.

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Speaker 1: Yeah, and I don't know, people love when newborn babies

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Speaker 1: die and their music videos and.

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Speaker 5: The reader takes them all to heaven.

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Speaker 1: Yeah.

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Speaker 3: Wait, isn't there one part where like someone touches the

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Speaker 3: dude's eyes and no, they touched the forehead and the

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Speaker 3: eyes go black and oh that's right, and that's how

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Speaker 3: you know got it?

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Speaker 6: Yeah?

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Speaker 1: That to me, I was like, I don't know if

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Speaker 1: I want God to get me like that? That seems pretty.

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Speaker 5: Look I'll be I'll be real. I won't pretend to

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Speaker 5: have looked up something cool. The most recent thing I

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Speaker 5: searched for was the pop star Tate McCrae, who has

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Speaker 5: a viral TikTok song because I was.

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Speaker 1: Like, who is Tate McCray, please help?

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Speaker 5: She has a viral TikTok song. Her name is Tate McCray. Okay,

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Speaker 5: and she's a Canadian pop star. And so the video

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Speaker 5: is set in a hockey rink because it's about oh

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Speaker 5: hell yeah, she's getting getting back at her hockey player

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Speaker 5: boyfriend who just cheated on her.

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Speaker 3: Oh no, wow, I've never seen someone look more like

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Speaker 3: their name.

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Speaker 5: Yeah, look more like a Canadian pop star named Tate McCray.

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Speaker 7: Yeah.

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Speaker 3: Like I was like, if you just showed me, like,

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Speaker 3: who's this person, I'm like, that's Tate McCrae, Canadian pop star.

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Speaker 5: She's b Olivia Rodrigo.

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Speaker 1: Oh okay, but she just had a song.

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Speaker 5: That went really viral on TikTok, and so I was

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Speaker 5: like a person. The song is called Greedy, and it's

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Speaker 5: like she's.

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Speaker 1: The video looks like it is a Canadian spoof of

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Speaker 1: Hit Me Baby One More Time.

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Speaker 5: It does look like that. It's really funny.

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Speaker 1: It's like, what if hit Me Baby One More Time

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Speaker 1: but Canada?

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Speaker 5: Yeah, kind of, it's like if the Grimes video wasn't fascist.

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Speaker 5: It's like, I like when there's like a like a sports.

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Speaker 3: Oh, like that's that first video she had like at

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Speaker 3: the Monster Truck rally or whatever.

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Speaker 5: Yeah, where she was like I love Arenas.

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Speaker 1: Yo, Wait where did they shoot this video? Is she

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Speaker 1: based in La.

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Speaker 5: Tate McCrae, Yeah, why recognize?

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Speaker 3: Yeah, yeah, this is where. This is where we used

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Speaker 3: to play hockey and Bourbank I think is they shoot

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Speaker 3: everything there?

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Speaker 1: Like Pickwick?

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Speaker 3: Yeah yeah, the Picwica shirts, the Picckwick Ice skating place.

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Speaker 1: I'd have to see if I just went.

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Speaker 5: There for the first time. Dang. Yeah, there was a

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Speaker 5: big hockey boom in Los Angeles because of the Kings. Yeah, yeah,

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Speaker 5: you know this, but children of the Valley loved to

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Speaker 5: go play hockey in a climate where hockey has never

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Speaker 5: been played.

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Speaker 1: Yeah, it's true. It's it's so true.

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Speaker 3: And uh who else I remember the other Fate Like

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Speaker 3: I think I was like, honestly, I think it was

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Speaker 3: one of the first black andies players to ever play

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Speaker 3: hockey because this was like in the fucking eighties.

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Speaker 1: I was getting on the ice and then Kurt Russell

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Speaker 1: and Goldie you.

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Speaker 5: Were like, you just improved the game one thousand percent.

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Speaker 3: It was we were such a ragtag group. It was

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Speaker 3: like us, like some Armenian kids, some Canadian kids whose

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Speaker 3: like parents are like trying.

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Speaker 1: To the platform. Mighty Ducks, by the way, yeah exactly exactly,

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Speaker 1: the blazing group. This coach came back. He had just

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Speaker 1: got one of them was girl. We had two Mexican kids,

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Speaker 1: Jose and Joel. You know, we were the team. Photo

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Speaker 1: looked fucking lit. I don't know what this was a movie.

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Speaker 1: Yeah it was, and it was eighties, literally the Mighty Ducks.

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Speaker 5: But also it should be a movie about you and

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Speaker 5: your ragtag group of valley friends.

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Speaker 1: Going to New England and getting realizer hockey.

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Speaker 5: Playing against the Rinch kids who have all the hockey gear.

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Speaker 1: Yeah right, exactly right Minnesota. They're just like mean Canadians,

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Speaker 1: like just people people want to root against Canadian.

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Speaker 5: This video is funny because it's like, I like, I've

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Speaker 5: never seen anyone do the aesthetics of hockey in a

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Speaker 5: pop music video before, and it's like so Canadian obviously,

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Speaker 5: but there's a part where she's wearing like a goalie glove.

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Speaker 5: It's like she's dressed like sexy, but she's.

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Speaker 1: Wearing like pieces of hockey gear.

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Speaker 5: It's really funny. Yeah, it's like she's wearing like like

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Speaker 5: a crop top and basketball shorts.

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Speaker 1: Kind of yeah, there's like this lone old like like

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Speaker 1: Gordy Howe hockey glove. She's like holding that. I'm like, yo,

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Speaker 1: this is so old. It's like okay, okay, like Jason

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Speaker 1: Boorhees's hockey gear is a little Yeah, it's really funny.

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Speaker 5: It looks like like the Thanos glove or whatever too.

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Speaker 5: It was just like it's like not sexy. And then

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Speaker 5: she's driving a zamboni.

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Speaker 1: Jamie Jamie, Jamie Loftus's influence is felt across much of

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Speaker 1: the pop music landscape.

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Speaker 5: Yeah, James, it's very Jamie loft is codded of Tate mcray.

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Speaker 1: It's kind of funny, like the last shot being heard

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Speaker 1: driving a zamboni, like very like in a very weird like.

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Speaker 5: Yeah, it was like it's about being confident and sassy

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Speaker 5: taking control.

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Speaker 1: It's pretty funny. It's funny, and it's like a good.

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Speaker 5: It's a pretty good song. I heard it on TikTok

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Speaker 5: a million times and was like what is this And

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Speaker 5: now I know.

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Speaker 1: It's Tate mc graan. Now our listeners know, and that

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Speaker 1: is the sort of ship that they were not going

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Speaker 1: to hear from us. I can tell you that, bunch.

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Speaker 1: So this is a great search history you are bringing.

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Speaker 1: I'm bringing pop corner. Well, it's just like usually the things. Okay,

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Speaker 1: can I tell you the other thing I looked up?

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Speaker 1: This is more on the type of thing I normally

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Speaker 1: look up. But I found out that Well maybe whatever,

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Speaker 1: I'll never mind. I'll tell you guys later. Oh I

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Speaker 1: like that, I do. I want to make this.

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Speaker 5: Part go too long. I'll bring it up in the

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Speaker 5: jet fuel part.

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Speaker 1: Okay, the jet fuel doesn't melt. Nine eleven was last

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Speaker 1: week was going on what is What's something you think

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Speaker 1: is overrated?

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Speaker 6: You know what I think is overrated is celebrity worship

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Speaker 6: culture in America.

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Speaker 1: It is terrible. What is that? But we gave up

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Speaker 1: on God, so we need new gods.

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Speaker 6: Live your own life though, right, It's like really, yeah,

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Speaker 6: I don't know, I don't get it.

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Speaker 1: What aspect? I mean, what what spurred this? And you

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Speaker 1: what did you observe? And you're like, we're doing this again. Huh,

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Speaker 1: we're bowing at the prostrate at the feet of the Kardashian's.

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Speaker 6: The tiny bit personal too Well, we'll get into it later.

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Speaker 6: But yeah, I mean, I don't I don't want to

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Speaker 6: get into specifics because I don't want to name names.

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Speaker 1: But okay, it's for instance.

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Speaker 6: It's like, I'm an artist, right, and I'm very much

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Speaker 6: an independent artist, and I do sometimes work in mainstream things,

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Speaker 6: but a lot of my art that I create is

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Speaker 6: very much independently done. I have friends who are like,

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Speaker 6: oh my god, I can't come see your show. I

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Speaker 6: said that because this weekend I'm going to see Adele

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Speaker 6: and A pid a thousand dollars for my ticket. I

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Speaker 6: love I love Dell, but I'm like, bitch, does not

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Speaker 6: need your thousand dollars. I need to put twenty dollars

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Speaker 6: and bring five of your friends and come see my show.

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Speaker 6: So it's like that kind of thing, and like, right

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Speaker 6: now I'm dealing with this again. I have a future

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Speaker 6: film coming out, and we just got told by the

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Speaker 6: people were working on the marketing end of They're like, oh,

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Speaker 6: well there's like two really big films coming out that weekend, guys.

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Speaker 6: So it's like, I come see yours, and I'm like, great,

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Speaker 6: Like we need like more of this, like you know,

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Speaker 6: glitzy like whatever.

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Speaker 3: Yeah, yeah, wait, does the American Nation come out October sixth,

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Speaker 3: Is that what I read? Yeah, And we talked about

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Speaker 3: We talked about the congestion of the calendar, the film

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Speaker 3: calendar because Taylor Swift's movie comes out the thirteenth, and

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Speaker 3: that caused like ripple effect with every other studio be like,

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Speaker 3: all right, that scary movie we had, we can't do

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Speaker 3: it on Friday the thirteenth anymore because that Taylor Swift

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Speaker 3: is re entering.

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Speaker 1: That that's the second scariest date ten to six three.

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Speaker 6: So yeah, So that's another thing that I'm dealing with

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Speaker 6: in my film right now. I lost theaters and screens

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Speaker 6: because Taylor Swift is one of them. And then there's

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Speaker 6: another really huge Bollywood film that's coming on.

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Speaker 1: I'm like, God, damn it.

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Speaker 6: That's also an audience South Asian, right, So yeah.

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Speaker 1: Just god, is it unique to America? Would you say

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Speaker 1: the like celebrity worship? Is it more in America than

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Speaker 1: other places that you've been.

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Speaker 6: Do It's so weird in America too, Like why are

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Speaker 6: we obsessed with the queen and like the you know,

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Speaker 6: the royalty in the UK, it's so weird, like people

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Speaker 6: are having these parties to like watch marriages over I'm

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Speaker 6: like what is happening. Yeah, yeah, it's like weird in America,

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Speaker 6: but you know, I think the whole everybody does it everywhere, right,

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Speaker 6: the whole world.

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Speaker 1: Yeah. Or people just getting so caught up because they

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Speaker 1: share the same birthday as one of the royals. I

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Speaker 1: think that's really just weird behavior for sure. You know,

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Speaker 1: Miles is the same birthday as is the same birthday

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Speaker 1: as Miles. I thank you, thank you so much, thank

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Speaker 1: you so much.

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Speaker 3: But yeah, no, And I think, like, but it is

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Speaker 3: everywhere too, Like I look, you know, I'm Japanese too,

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Speaker 3: and I look in Japan, we have like it's called

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Speaker 3: idle culture, Like we literally call these celebrities fucking idols,

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Speaker 3: you know, and it's just this, you know, I think, why.

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Speaker 6: So much responsibility on these poor little people too?

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Speaker 1: I don't know.

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Speaker 6: Maybe some of them also just want to make art, guys,

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Speaker 6: they just want to They just let them, you know

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Speaker 6: what I mean.

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Speaker 1: Yeah, I feel like they don't mind it though they

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Speaker 1: like they don't. It's like I think there's a trap

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Speaker 1: where they like what they want it and pursue it

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Speaker 1: with the single mindedness of Captain AHB. But then like

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Speaker 1: once they get it, they're like oh my life is over.

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Speaker 1: I don't have like a life anymore, right, or like.

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Speaker 3: I can't go to the store or every person I

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Speaker 3: interact with I have to worry that they're trying to

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Speaker 3: get something from me because of the status. But hey,

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Speaker 3: it comes with a lot of money to rest your

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Speaker 3: sad little head on.

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Speaker 1: Usually when you ask them, they're like, yeah, but I

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Speaker 1: would not trade it for anything. Yeah, go back to

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Speaker 1: being like you no, no, no, no, no no no.

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Speaker 1: What is something probably that you take is underrated?

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Speaker 4: Okay, Like I said, I'm very bad at self care.

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Speaker 4: I just started using I have two dogs and they

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Speaker 4: shud a lot. I just started using a rumba. It's like,

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Speaker 4: let the robots take over, all right, you're doing it better. Okay,

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Speaker 4: this place is so much cleaner that I could ever

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Speaker 4: sweep it up.

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Speaker 5: Just take my job.

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Speaker 1: I don't care.

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Speaker 5: I don't care. You can do it robots.

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Speaker 1: You know new rumba Like how long go? Did you

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Speaker 1: get the room?

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Speaker 4: But so my brother gave it to me, so it's

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Speaker 4: an old room, but I just started using it. So

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Speaker 4: I'm like so excited.

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Speaker 7: I'm not going to say anything because it will disappoint you.

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Speaker 7: So I'll just let you learn that on your own.

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Speaker 5: Wait, what's disappointing about it?

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Speaker 7: Just there's a lot of that I can't do that.

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Speaker 7: It promises like that it can just like my father. No, really,

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Speaker 7: it'll get tangled up and like if you have long

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Speaker 7: hair at all, that shit, especially curly hair, will get

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Speaker 7: tangled up in it. It'll not You'll have to keep

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Speaker 7: cutting through.

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Speaker 1: Uh.

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Speaker 7: There's just a lot of upkeep. And then sometimes it's

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Speaker 7: like I don't understand that there's a carpet step. I

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Speaker 7: will keep knocking my little room behead against it.

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Speaker 4: Yeah, I got it for free and I had no expectations, and.

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Speaker 7: Oh, see that you're gonna it's awesome.

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Speaker 5: I believe.

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Speaker 7: I believe I want everyone to have that first week

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Speaker 7: that you have the roomba where you're like, it's yeah, possible.

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Speaker 1: Everything is possible.

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Speaker 5: That's how I feel.

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Speaker 1: Yeah. And sometimes it will like start like listening in

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00:14:49,680 --> 00:14:52,080
Speaker 1: on your conversations, like you'll be like, wait, why is

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Speaker 1: the room but in the corner of the room while

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00:14:54,200 --> 00:14:58,200
Speaker 1: we're like talking about or financial information, and why is

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Speaker 1: it we.

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Speaker 4: Why why does it have those Boston Dynamic dog legs

300
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Speaker 4: and climbing up the walls?

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Speaker 1: Where to get that gun. Yeah, my experience with the room,

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Speaker 1: so I like, I have the same question in my

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00:15:12,280 --> 00:15:14,120
Speaker 1: heart the second you brought up the room. But I

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Speaker 1: was like, oh, no, she doesn't know yet.

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Speaker 5: I don't know yet.

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00:15:17,280 --> 00:15:19,280
Speaker 1: Yeah I saw, I saw the light leave.

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Speaker 5: Both of our eyes dragged too.

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Speaker 4: I thought you'd be so excited and I was like, Oh,

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00:15:24,040 --> 00:15:24,920
Speaker 4: this is so fun.

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Speaker 1: And then but every time someone talks about the room,

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Speaker 1: but I'm like, what is it a new purchase? Because

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Speaker 1: I think they will solve it. Like I think eventually

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Speaker 1: it won't be bad like that, because like there are

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Speaker 1: things that does that are pretty cool, and we've.

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Speaker 7: We've made artificial hearts and ship like you're telling you

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Speaker 7: find the fucking room, but solution, Like I believe in us.

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Speaker 4: I love I love that we're talking about it like

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Speaker 4: we did it like sports teams. Yeah, of course we

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Speaker 4: made artificial hearts. Like, let's get on it, guys.

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Speaker 1: Go on. I mean, maybe the most advanced technology and

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Speaker 1: robotics is not going to be purchasable at a bed

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Speaker 1: bathroom beyond, Like maybe I was looking for it in

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Speaker 1: the wrong place, But how.

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Speaker 7: Dare I say that about the bath and beyond? What

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00:16:12,360 --> 00:16:14,400
Speaker 7: the fuck do you think beyond is about.

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Speaker 5: Exactly That's what the beyond was.

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Speaker 1: The singularity.

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Speaker 4: Also feel like I don't care how far technology advances

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Speaker 4: until we make a printer that works, you know what

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Speaker 4: I mean, Like just print, Just print when I want

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Speaker 4: you to print, and that I'll be excited about.

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Speaker 7: And that I also don't have to be like, oh,

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Speaker 7: I now need to get like new ink or some shit.

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Speaker 4: Own or prime or highlight or whatever the fuck the

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00:16:40,040 --> 00:16:42,080
Speaker 4: preacher needs that my fight.

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Speaker 7: Also needs, you know, Like I know I spend that

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00:16:46,080 --> 00:16:50,520
Speaker 7: at FENTI I don't yeah it things for you.

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Speaker 1: I don't yeah zeigang for anybody who's like ever worked

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00:16:53,920 --> 00:16:56,560
Speaker 1: at like a you know, in the government or something

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00:16:56,600 --> 00:17:01,400
Speaker 1: like that. Do the powers that be secretly have printers

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00:17:01,440 --> 00:17:03,920
Speaker 1: that work? Like do they just have Like I feel

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00:17:04,000 --> 00:17:07,080
Speaker 1: like that there is a lot of like you know,

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00:17:07,280 --> 00:17:12,720
Speaker 1: designed obsolescence and like designed like fuck ups with printers

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00:17:13,080 --> 00:17:16,520
Speaker 1: so that like the ink costs more than the printer itself,

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00:17:16,640 --> 00:17:20,679
Speaker 1: and like that's by design. Bank Man, Big Inc. I

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00:17:20,720 --> 00:17:23,639
Speaker 1: feel like they've been saying it. Somebody's probably figured out

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00:17:23,680 --> 00:17:26,879
Speaker 1: the printer, and they just like don't it's not profitable

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00:17:26,960 --> 00:17:29,840
Speaker 1: for them to sell a printer that is like, yeah,

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Speaker 1: this lasts ten years and just works.

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Speaker 4: You know, I will say, speaking of like the artificial

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Speaker 4: hearts and stuff. I have been in like academic settings

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Speaker 4: with professors who are like Nobel Prize winners, and they too,

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Speaker 4: who are creating these amazing technologies can only be foiled

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Speaker 4: by printers and PowerPoint presentations. That is the only thing

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00:17:52,240 --> 00:17:56,720
Speaker 4: that can defeat a professor successfully that tracks, that tracks, honestly.

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Speaker 1: Yeah, all right, let's take a quick break and we'll

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Speaker 1: come back and catch up on some news heading into

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Speaker 1: the weekend. We'll be right back and we're back. Shall

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Speaker 1: we talk about what do you want to talk about?

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Speaker 1: You want to talk about private equity? This was this

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00:18:20,960 --> 00:18:25,840
Speaker 1: is interesting. I saw a headline that was allow me

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Speaker 1: to pull up the exact headline because I was like,

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Speaker 1: what is this? It said, can private equity be ellipses nice?

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Speaker 1: And I was like, what the fuck is this?

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Speaker 3: And it's an interview by enslaved by Megan Greenwell where

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Speaker 3: she interviews one of the heads of KKR, which is

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Speaker 3: this like private equity firm that just bought the publisher

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Speaker 3: Simon and Schuster and KKR is doing something that is

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Speaker 3: kind of unheard of in the private equity world, they're

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Speaker 3: offering employees of the companies they purchased an ownership.

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Speaker 1: Stake, and you're like, what the what, what do you mean?

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Speaker 1: What the fuck is this?

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Speaker 3: They basically built in a deal where part of like

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Speaker 3: the total equity of the company gets put aside for employees,

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Speaker 3: and higher earning employees have the option to buy like

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Speaker 3: additional equity if they want, but everyone gets a piece

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Speaker 3: for free. And the idea here is that when the

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00:19:15,680 --> 00:19:19,959
Speaker 3: company sells or goes public, everybody gets a little taste

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Speaker 3: of something, not just the C suite, which is like normal.

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Speaker 1: I mean, I feel like tech companies have been doing

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00:19:26,080 --> 00:19:29,840
Speaker 1: that with like stock like get allowing employees to like

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00:19:29,920 --> 00:19:32,520
Speaker 1: have stock and stuff like that for a while, but

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00:19:32,560 --> 00:19:36,199
Speaker 1: then when it comes time to like actually distribute it,

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00:19:36,600 --> 00:19:39,720
Speaker 1: they always like find a way to Like these things

385
00:19:39,720 --> 00:19:43,560
Speaker 1: are tied to like forty page contracts, the employees aren't

386
00:19:43,560 --> 00:19:45,959
Speaker 1: gonna have time to like read or like you know,

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00:19:46,400 --> 00:19:50,679
Speaker 1: have their legal teams like pour over. Like some of

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00:19:50,800 --> 00:19:53,280
Speaker 1: some of the examples is like a trucking company, Like

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00:19:53,320 --> 00:19:57,119
Speaker 1: these truckers don't have fucking legal teams to like pour over. No,

390
00:19:57,320 --> 00:19:59,160
Speaker 1: make sure they're not getting fucked over.

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00:19:59,200 --> 00:20:01,520
Speaker 3: But like, so this one their golden goose. Of an

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00:20:01,520 --> 00:20:06,000
Speaker 3: example is this company called chi overhead Doors, where the

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00:20:06,040 --> 00:20:08,520
Speaker 3: private equity firm bought them. Everyone had their steak and

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00:20:08,560 --> 00:20:10,920
Speaker 3: when they sold, an average of one hundred and seventy

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00:20:10,920 --> 00:20:15,840
Speaker 3: five thousand was like distributed to the to the like

396
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Speaker 3: rank and file employees. Some longtime truck drivers they say

397
00:20:19,160 --> 00:20:21,840
Speaker 3: made as much as eight hundred thousand, and like even

398
00:20:21,880 --> 00:20:25,240
Speaker 3: in speaking to them, like these truck drivers are like, yeah,

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00:20:25,280 --> 00:20:27,680
Speaker 3: it's cool, Like I made the money. And also when

400
00:20:27,720 --> 00:20:29,960
Speaker 3: you have a stake in it, you actually begin to

401
00:20:29,960 --> 00:20:32,200
Speaker 3: see the inefficiencies in your own business. He's like, I

402
00:20:32,320 --> 00:20:34,600
Speaker 3: used to get forty cents a mile no matter where

403
00:20:34,640 --> 00:20:36,840
Speaker 3: I drove, and I didn't care because more miles meant

404
00:20:36,840 --> 00:20:39,400
Speaker 3: more money. But when I realized that that could affect

405
00:20:39,440 --> 00:20:42,800
Speaker 3: the sale price of the company, then we started actually

406
00:20:42,840 --> 00:20:46,280
Speaker 3: optimizing things. So it's like a very fucking like double

407
00:20:46,400 --> 00:20:49,119
Speaker 3: edged sword here, because overall makes no sense given what

408
00:20:49,160 --> 00:20:51,879
Speaker 3: private equity is about, and like they're bloodblust for just

409
00:20:51,960 --> 00:20:54,919
Speaker 3: chopping down the workforce to just you know, lower in

410
00:20:54,920 --> 00:20:58,800
Speaker 3: the name of lowering costs. But they say the private

411
00:20:58,800 --> 00:21:00,520
Speaker 3: equit say it's win win it says workers get the

412
00:21:00,560 --> 00:21:03,920
Speaker 3: chance for big payout in a voicing company decisions not really,

413
00:21:04,160 --> 00:21:08,479
Speaker 3: and investors get increased staff engagement and retention, which in

414
00:21:08,520 --> 00:21:12,119
Speaker 3: turn creates higher profits. So the optimistic read on this

415
00:21:12,280 --> 00:21:14,880
Speaker 3: is that it's an attempt to trying to make capitalism

416
00:21:14,880 --> 00:21:17,560
Speaker 3: a little more worker friendly. The cynical version is that

417
00:21:17,760 --> 00:21:20,520
Speaker 3: this is just corporate. This is like a corporate whitewashing scheme,

418
00:21:21,040 --> 00:21:23,240
Speaker 3: and it's meant for people to be like, oh, this

419
00:21:23,359 --> 00:21:25,800
Speaker 3: is like this could actually benefit people. In the interview

420
00:21:25,840 --> 00:21:28,119
Speaker 3: the guy from the firm, he's using a lot of

421
00:21:28,240 --> 00:21:31,680
Speaker 3: maybees and might bees when it comes to this becoming

422
00:21:31,760 --> 00:21:34,520
Speaker 3: like a huge payout for people, And it's all nice

423
00:21:34,520 --> 00:21:37,800
Speaker 3: and like a hypothetical context, but the outcomes begin to

424
00:21:37,880 --> 00:21:41,080
Speaker 3: differ based on what happens, even like when the equity

425
00:21:41,119 --> 00:21:43,359
Speaker 3: firm exits the company, like do they go private? Do

426
00:21:43,400 --> 00:21:45,920
Speaker 3: they sell it to another firm? And then what happens there?

427
00:21:45,920 --> 00:21:48,760
Speaker 3: Does that next firm even give a fuck? So I

428
00:21:48,800 --> 00:21:51,440
Speaker 3: think that, you know, it seems like they're one example

429
00:21:51,600 --> 00:21:54,359
Speaker 3: of like the truck drivers making eight hundred k, Like

430
00:21:54,400 --> 00:21:58,800
Speaker 3: the ownership stake incentivized workers to just start cutting down

431
00:21:58,880 --> 00:22:01,920
Speaker 3: on their own rather than the private equity firm coming

432
00:22:01,920 --> 00:22:03,720
Speaker 3: in and doing it. And I think the other thing

433
00:22:03,800 --> 00:22:06,159
Speaker 3: is that it's huge for retention, which seems to be

434
00:22:06,560 --> 00:22:09,280
Speaker 3: a huge cost of when like a new ownership, you

435
00:22:09,320 --> 00:22:11,720
Speaker 3: know team comes in, you're just fucking They're like, man,

436
00:22:11,760 --> 00:22:13,560
Speaker 3: fuck this place, I'm out of here. But not like

437
00:22:13,600 --> 00:22:15,919
Speaker 3: what if we give you a piece and now they

438
00:22:15,920 --> 00:22:18,920
Speaker 3: don't have to cycle through thousands of employees to retain

439
00:22:19,000 --> 00:22:21,199
Speaker 3: and train them. So to me, it seems like a

440
00:22:21,359 --> 00:22:25,959
Speaker 3: very elegant way of like cost savings while appearing to

441
00:22:26,000 --> 00:22:28,760
Speaker 3: be doing the right thing because not everyone's gonna get

442
00:22:28,760 --> 00:22:30,360
Speaker 3: paid out eight hundred thousand dollars.

443
00:22:30,520 --> 00:22:34,159
Speaker 1: Like one example, they sort of parade around. Yeah, I

444
00:22:34,160 --> 00:22:38,440
Speaker 1: mean there are probably versions of capitalism that work right

445
00:22:38,760 --> 00:22:42,040
Speaker 1: now there are none. No, there's not like there's probably

446
00:22:42,080 --> 00:22:46,000
Speaker 1: like a handful of times that but like there are

447
00:22:46,080 --> 00:22:49,680
Speaker 1: millions they stumble upon opportunities for it to work. It's

448
00:22:49,720 --> 00:22:51,960
Speaker 1: called accidental socialism.

449
00:22:51,320 --> 00:22:55,919
Speaker 5: If right, yeah, socialism works, it doesn't work because how

450
00:22:55,960 --> 00:22:59,040
Speaker 5: you have to explain somebody for it to exist, right,

451
00:22:59,359 --> 00:23:02,360
Speaker 5: So somebody yet somebody has to get fucked for.

452
00:23:02,359 --> 00:23:04,560
Speaker 1: Oh yeah function. The other thing that there's.

453
00:23:04,359 --> 00:23:06,960
Speaker 5: No it existing without somebody getting fucked.

454
00:23:07,240 --> 00:23:10,480
Speaker 3: The like in this interview too, when like, you know,

455
00:23:10,640 --> 00:23:13,359
Speaker 3: very pointedly, this journalist is asking like why don't you

456
00:23:13,400 --> 00:23:16,800
Speaker 3: just raise wages like when you come in, like how

457
00:23:16,800 --> 00:23:18,439
Speaker 3: about that to keep employees?

458
00:23:18,480 --> 00:23:21,159
Speaker 1: Like what what does that work? And then they use

459
00:23:21,200 --> 00:23:23,600
Speaker 1: this I gotta like, we wouldn't be writing this article

460
00:23:23,680 --> 00:23:26,480
Speaker 1: if we did that. Probably what they're.

461
00:23:27,560 --> 00:23:29,960
Speaker 5: I know, Megan Greenwell shout out Megan Greenwell.

462
00:23:29,680 --> 00:23:30,680
Speaker 1: Yeah is that who wrote this?

463
00:23:31,280 --> 00:23:34,720
Speaker 5: Yeah, and she understood, she's got she knows.

464
00:23:34,600 --> 00:23:37,240
Speaker 3: She's writing a book right now about how private equity

465
00:23:37,280 --> 00:23:39,800
Speaker 3: Fox workers. Like, so she didn't go into this interview

466
00:23:39,880 --> 00:23:41,760
Speaker 3: being like this is going to be great, Like it's

467
00:23:41,800 --> 00:23:43,400
Speaker 3: there's a lot of really pointed questions.

468
00:23:43,760 --> 00:23:47,679
Speaker 1: But yeah, Kim Stanley Robinson like talks about how the

469
00:23:48,680 --> 00:23:52,480
Speaker 1: like profit inherently, like profit is the only goal of

470
00:23:52,520 --> 00:23:56,400
Speaker 1: any of this shit, and profit inherently is exploitive cause

471
00:23:56,400 --> 00:24:00,560
Speaker 1: it's like getting more than your fair share. Yeah, that's

472
00:24:01,040 --> 00:24:03,600
Speaker 1: the point of profit. That's what profit means.

473
00:24:04,119 --> 00:24:06,280
Speaker 3: So in this they asked like why don't you just

474
00:24:06,359 --> 00:24:10,160
Speaker 3: like raise the salary, like like that's the easy way

475
00:24:10,200 --> 00:24:11,920
Speaker 3: to go, And he says like, look, you know, we

476
00:24:11,920 --> 00:24:15,120
Speaker 3: we manage all kinds of people's money. We including teachers,

477
00:24:15,760 --> 00:24:19,199
Speaker 3: teachers retirement funds. We also manage wealthy people's money. So

478
00:24:19,240 --> 00:24:21,000
Speaker 3: I don't want a cherry pick, but I'm just giving

479
00:24:21,000 --> 00:24:22,840
Speaker 3: you a flavor for why this is so tricky. If

480
00:24:22,840 --> 00:24:25,480
Speaker 3: you're managing teachers pension money and you want to just

481
00:24:25,640 --> 00:24:28,800
Speaker 3: raise everyone's salary that is on the backs of the teachers,

482
00:24:28,880 --> 00:24:31,880
Speaker 3: which is not ethical and it's not our money, and

483
00:24:31,920 --> 00:24:34,840
Speaker 3: that's like the fucking rationality used to just hide behind that.

484
00:24:34,880 --> 00:24:36,679
Speaker 3: You be like, then the tea, that's the tea, it's

485
00:24:37,000 --> 00:24:37,479
Speaker 3: the teachers.

486
00:24:37,480 --> 00:24:39,480
Speaker 1: We're actually doing it for the teachers of America.

487
00:24:39,600 --> 00:24:42,520
Speaker 3: Yeah, yeah, Yeah, Like like you come away from this

488
00:24:42,600 --> 00:24:45,399
Speaker 3: interview is still kind of being like okay, like this

489
00:24:45,480 --> 00:24:47,399
Speaker 3: seems not like a win win. It seems like a

490
00:24:47,440 --> 00:24:52,120
Speaker 3: win maybe win if the environmental factors are precisely correct.

491
00:24:52,400 --> 00:24:54,040
Speaker 5: It's like what they're giving you a cut of a

492
00:24:54,080 --> 00:24:56,480
Speaker 5: company that they're about to run into the ground.

493
00:24:56,880 --> 00:25:00,520
Speaker 1: Yeah, but they say if if you guys and figure

494
00:25:00,520 --> 00:25:03,280
Speaker 1: out how to save money, even better, because then we

495
00:25:03,280 --> 00:25:05,000
Speaker 1: don't have to be we don't have to just start

496
00:25:05,000 --> 00:25:06,200
Speaker 1: doing layoffs.

497
00:25:06,280 --> 00:25:11,720
Speaker 5: Like what's happening in Hollywood right now too. It's like

498
00:25:11,800 --> 00:25:15,360
Speaker 5: the thing that was happening in publishing where they run

499
00:25:15,400 --> 00:25:18,720
Speaker 5: all the big companies, like your old school publishers like

500
00:25:18,760 --> 00:25:20,600
Speaker 5: Simon and Schuster into the ground.

501
00:25:20,960 --> 00:25:25,320
Speaker 1: And then the company that just bought some Yeah okay,

502
00:25:25,320 --> 00:25:27,120
Speaker 1: you're talking to the people who just bought them from.

503
00:25:27,240 --> 00:25:28,960
Speaker 1: I think like it was like a carve out from

504
00:25:29,000 --> 00:25:32,560
Speaker 1: Paramount or something. Yeah. So yeah, they know how to plan.

505
00:25:32,680 --> 00:25:35,520
Speaker 5: Their plan is to is to extract profits and get

506
00:25:35,560 --> 00:25:38,199
Speaker 5: the fuck out of there. It's all and run all

507
00:25:38,200 --> 00:25:38,720
Speaker 5: the way down.

508
00:25:38,880 --> 00:25:41,000
Speaker 3: Oh yeah, and it's wild too because when this guy

509
00:25:41,040 --> 00:25:43,560
Speaker 3: says like we're not just here just purely for profits,

510
00:25:43,600 --> 00:25:45,960
Speaker 3: I'm like, this is then you wouldn't be running a

511
00:25:46,000 --> 00:25:49,640
Speaker 3: fucking private equity firm, right, Like so I love that,

512
00:25:49,720 --> 00:25:52,280
Speaker 3: like the words come out this way. But yeah, it

513
00:25:52,320 --> 00:25:55,200
Speaker 3: is a very it's an interesting, I think scheme from

514
00:25:55,240 --> 00:25:59,000
Speaker 3: private equity to try and you know, save money in

515
00:25:59,040 --> 00:26:02,440
Speaker 3: their own ways on employee retention while trying to incentivize

516
00:26:02,480 --> 00:26:05,280
Speaker 3: people to like stick around as long as possible.

517
00:26:04,840 --> 00:26:06,000
Speaker 1: Before the inevitable.

518
00:26:06,440 --> 00:26:08,120
Speaker 3: Because then they say stuff like we wouldn't just sell

519
00:26:08,160 --> 00:26:10,040
Speaker 3: it to like another company who like doesn't have the

520
00:26:10,080 --> 00:26:13,240
Speaker 3: same ethos. It's like, really, if the fucking money is right,

521
00:26:13,320 --> 00:26:17,120
Speaker 3: then then what right. I don't think that's the case.

522
00:26:17,320 --> 00:26:22,440
Speaker 1: This is funny that like the global corporate media will

523
00:26:22,560 --> 00:26:24,960
Speaker 1: like find these examples where like if you look at

524
00:26:25,000 --> 00:26:27,439
Speaker 1: it from a certain angle, it looks like, hey, this

525
00:26:27,480 --> 00:26:29,600
Speaker 1: thing's working and it's really like helping out the world.

526
00:26:29,640 --> 00:26:32,320
Speaker 5: Yeah, anytime they're like, actually, check out this cool.

527
00:26:32,160 --> 00:26:35,960
Speaker 1: Thing, it's like, really, no, they're doing something different.

528
00:26:36,000 --> 00:26:37,440
Speaker 5: So I'm making March Simpson noises.

529
00:26:39,640 --> 00:26:45,200
Speaker 1: My stomach starts making March Simpson noises. All right, let's

530
00:26:45,280 --> 00:26:49,040
Speaker 1: cheer yourselves up by talking about how it continues to

531
00:26:49,280 --> 00:26:52,840
Speaker 1: just keep raining shit on Rudy Giuliani. We've been hearing

532
00:26:52,880 --> 00:26:56,040
Speaker 1: for a while that he's broke, can't pay his legal bills,

533
00:26:56,400 --> 00:26:59,280
Speaker 1: and now his lawyers, the people who are supposed to

534
00:26:59,280 --> 00:27:02,120
Speaker 1: be representing him, are suing him for one point three

535
00:27:02,160 --> 00:27:06,760
Speaker 1: six million dollars in unpaid legal fees. He racked up

536
00:27:06,800 --> 00:27:09,719
Speaker 1: fees and expenses totally more than one point five million dollars,

537
00:27:09,760 --> 00:27:11,880
Speaker 1: but he only managed to pay two hundred and fourteen

538
00:27:12,119 --> 00:27:16,560
Speaker 1: thousand dollars. And his defense is just like it's like

539
00:27:16,640 --> 00:27:23,640
Speaker 1: really expensive, man, what the fuck cost so much? Are

540
00:27:23,640 --> 00:27:24,280
Speaker 1: you serious?

541
00:27:24,400 --> 00:27:24,640
Speaker 2: Dude?

542
00:27:24,640 --> 00:27:28,000
Speaker 1: No way, no way, it's this high. Yeah, no, it is,

543
00:27:28,160 --> 00:27:32,040
Speaker 1: by the way, so you know, he himself is a lawyer,

544
00:27:32,160 --> 00:27:35,879
Speaker 1: and while he was representing Trump, he reportedly charged twenty

545
00:27:35,920 --> 00:27:41,840
Speaker 1: thousand dollars a day. Okay, so you know he's I

546
00:27:41,920 --> 00:27:44,440
Speaker 1: like that, he really is the way, Rudy Juliani says,

547
00:27:44,520 --> 00:27:46,960
Speaker 1: it's a real shame when lawyers do things like this,

548
00:27:47,480 --> 00:27:49,480
Speaker 1: And all I will say is that their bill is

549
00:27:49,600 --> 00:27:54,800
Speaker 1: way in excess to anything approaching legitimate fees. Sure, sure, sure.

550
00:27:55,560 --> 00:27:58,200
Speaker 1: The lawyer that he's stiffed who is now suing him

551
00:27:58,359 --> 00:28:02,880
Speaker 1: is one of his like oldest best friends. His friendship

552
00:28:03,119 --> 00:28:06,640
Speaker 1: Giuliani dates back to the seventies when they were both

553
00:28:06,680 --> 00:28:10,840
Speaker 1: prosecutors in the US Attorney's Office in the Southern District

554
00:28:10,920 --> 00:28:13,679
Speaker 1: of New York. Wow, I wonder what the how that

555
00:28:13,720 --> 00:28:17,320
Speaker 1: friendship started, Like where like if this his friend like

556
00:28:17,400 --> 00:28:19,919
Speaker 1: Robert Costel and now is like look at you now, Rudy,

557
00:28:20,240 --> 00:28:23,840
Speaker 1: look at you, Look at you. He got nothing, You

558
00:28:23,960 --> 00:28:24,600
Speaker 1: got nothing.

559
00:28:25,160 --> 00:28:28,160
Speaker 3: I mean we've seen how recently it's been all hands

560
00:28:28,200 --> 00:28:30,240
Speaker 3: on deck to try and get him more money, because

561
00:28:30,320 --> 00:28:32,199
Speaker 3: it felt like he was going to Trump and be

562
00:28:32,280 --> 00:28:35,840
Speaker 3: like I got bills, dude, and like you need to

563
00:28:35,920 --> 00:28:38,000
Speaker 3: help me because like I know a lot of shit.

564
00:28:38,600 --> 00:28:38,680
Speaker 2: Uh.

565
00:28:38,800 --> 00:28:41,200
Speaker 3: And then recently there was a fucking one hundred thousand

566
00:28:41,360 --> 00:28:44,320
Speaker 3: dollars a plate fundraising dinner for Rudy Giuliani that.

567
00:28:44,400 --> 00:28:49,560
Speaker 1: Trump posted specifically. So yeah, love, I have a feeling

568
00:28:49,640 --> 00:28:52,280
Speaker 1: I wonder he probably was able to pay a lot

569
00:28:52,280 --> 00:28:54,960
Speaker 1: of that off. But anyway, who knows, who knows? He's

570
00:28:55,000 --> 00:28:55,720
Speaker 1: too busy.

571
00:28:55,440 --> 00:28:58,760
Speaker 5: Being Yeah, I like when the scammers get scammed, get yeah,

572
00:28:58,800 --> 00:28:59,720
Speaker 5: taken in for their.

573
00:28:59,640 --> 00:29:02,440
Speaker 1: Scam their own, by their own, the scammers.

574
00:29:02,040 --> 00:29:04,760
Speaker 5: Scam each other, yeah, or you're just like none of

575
00:29:04,760 --> 00:29:07,480
Speaker 5: these people have any loyalty to anyone but themselves. Of

576
00:29:07,480 --> 00:29:09,640
Speaker 5: course they're all gonna like fuck each other over at

577
00:29:09,640 --> 00:29:12,520
Speaker 5: the end and turn on each other. That to me

578
00:29:12,600 --> 00:29:14,719
Speaker 5: seemed like the lynchpin of the whole Trump thing was

579
00:29:14,760 --> 00:29:19,840
Speaker 5: like everyone he works everyone, he fucks everyone, but it's

580
00:29:19,840 --> 00:29:22,080
Speaker 5: like everyone he works with is like somebody who would

581
00:29:22,080 --> 00:29:23,400
Speaker 5: sell him out in a minute.

582
00:29:23,880 --> 00:29:25,520
Speaker 1: Yeah, and vice versa.

583
00:29:26,120 --> 00:29:28,560
Speaker 5: Yeah, like they're all that's the best part when they

584
00:29:28,600 --> 00:29:30,000
Speaker 5: all turn on each other at the end.

585
00:29:30,560 --> 00:29:34,000
Speaker 3: Yeah, I'm waiting to see how the dominoes fall, because

586
00:29:34,000 --> 00:29:36,400
Speaker 3: it's every time we hear about this stuff happening in

587
00:29:36,440 --> 00:29:39,920
Speaker 3: like Georgia or like Florida. There's more people who are like, actually,

588
00:29:39,960 --> 00:29:42,080
Speaker 3: they do not want to have the same defense team

589
00:29:42,080 --> 00:29:44,320
Speaker 3: as Trump now and realize that like they were being

590
00:29:44,400 --> 00:29:46,600
Speaker 3: like nudged to do what was the best for Trump

591
00:29:46,640 --> 00:29:48,040
Speaker 3: and not for them to be free.

592
00:29:48,680 --> 00:29:51,120
Speaker 1: You know, you know how these people like sell themselves

593
00:29:51,200 --> 00:29:54,480
Speaker 1: as like brilliant businessmen, and people are like, well, if

594
00:29:54,480 --> 00:29:57,760
Speaker 1: he could make a billion dollars, like, surely he could

595
00:29:57,840 --> 00:30:01,200
Speaker 1: run the country. Like, yeah, they're sc The thing that

596
00:30:01,240 --> 00:30:04,080
Speaker 1: they all have in common that they're good at is

597
00:30:04,120 --> 00:30:08,400
Speaker 1: the same skill displayed by if you've ever like been

598
00:30:08,440 --> 00:30:11,280
Speaker 1: out to dinner with a group in like high school

599
00:30:11,880 --> 00:30:14,320
Speaker 1: and everyone suddenly like, nah, I don't I don't have

600
00:30:14,440 --> 00:30:16,760
Speaker 1: enough money to pay this, Like what are you talking about?

601
00:30:16,800 --> 00:30:19,160
Speaker 1: Like you know, you know, like arguing over the bill

602
00:30:19,280 --> 00:30:22,479
Speaker 1: like that's before a homecoming dance. That is all that

603
00:30:22,680 --> 00:30:26,000
Speaker 1: is their skill. They were just the most stubborn of

604
00:30:26,040 --> 00:30:30,080
Speaker 1: the people who refused to pay for whatever they ordered

605
00:30:30,400 --> 00:30:33,440
Speaker 1: and are like, no, that's you see right here on

606
00:30:33,480 --> 00:30:38,160
Speaker 1: the menu it says twelve ninety nine, and so I

607
00:30:38,280 --> 00:30:43,120
Speaker 1: put in twelve dollars and it's like, well we all

608
00:30:43,160 --> 00:30:46,440
Speaker 1: also had drinks and no.

609
00:30:45,720 --> 00:30:49,040
Speaker 5: No, no, yeah, we all got those big Coca colas

610
00:30:49,080 --> 00:30:50,240
Speaker 5: because we're in high school.

611
00:30:50,400 --> 00:30:54,959
Speaker 1: Yeah, exactly through that like weird brown plastic cup that

612
00:30:55,120 --> 00:30:58,880
Speaker 1: was had the logo blast apps. You ordered the apps,

613
00:30:59,160 --> 00:31:01,360
Speaker 1: you demand, We ordered the You.

614
00:31:01,360 --> 00:31:05,120
Speaker 5: Ordered the the mojo potatoes at Shakey's where we're having

615
00:31:05,160 --> 00:31:05,720
Speaker 5: our dinner.

616
00:31:07,280 --> 00:31:11,640
Speaker 1: Drove someone here, so like that costs gas money? Oh man,

617
00:31:12,120 --> 00:31:17,200
Speaker 1: Just we'll argue until you're too exhausted to have I

618
00:31:17,240 --> 00:31:20,600
Speaker 1: remember we Molly, you'll appreciate this. Before a dance, we

619
00:31:20,640 --> 00:31:24,200
Speaker 1: went to miss Ellie's right there across from the Gilain

620
00:31:24,320 --> 00:31:28,600
Speaker 1: Maxwell in and out Michelli's. Yeah, yeah, yeah, yeah, but

621
00:31:28,960 --> 00:31:30,880
Speaker 1: that's how we say. That's how we always say to school,

622
00:31:30,880 --> 00:31:33,480
Speaker 1: We're going to miss Ellie's with the one seed.

623
00:31:33,560 --> 00:31:35,200
Speaker 5: That's why I would always be like, oh I say that,

624
00:31:35,840 --> 00:31:38,960
Speaker 5: I say the accurate Italian yeah than me.

625
00:31:39,880 --> 00:31:44,000
Speaker 1: But so we went and I remember one guy, just

626
00:31:44,120 --> 00:31:48,200
Speaker 1: one dude straight up just fucking just deny that they

627
00:31:48,320 --> 00:31:50,560
Speaker 1: ordered the food that you're like.

628
00:31:51,520 --> 00:31:53,160
Speaker 5: The that's so funny.

629
00:31:53,240 --> 00:31:55,320
Speaker 1: It was like, you know what, yeah.

630
00:31:54,880 --> 00:31:57,440
Speaker 5: I would have to respect that because you're like friends.

631
00:31:57,560 --> 00:31:59,280
Speaker 5: Well he's like, well where is it?

632
00:31:59,320 --> 00:32:03,560
Speaker 1: Then? Now shout out to my friends from you know

633
00:32:03,600 --> 00:32:06,200
Speaker 1: what they do now, and we all, every single one

634
00:32:06,240 --> 00:32:09,760
Speaker 1: of us was just like, nah, huh huh, I don't

635
00:32:09,760 --> 00:32:16,680
Speaker 1: give a fuck. I'm not just gas grade apple.

636
00:32:18,680 --> 00:32:21,040
Speaker 3: Well it wasn't me that it was your idea. We

637
00:32:21,120 --> 00:32:24,000
Speaker 3: came here because you said, yeah, it is not me.

638
00:32:24,160 --> 00:32:26,040
Speaker 3: Well she's not, so we can't get the discount.

639
00:32:26,400 --> 00:32:30,120
Speaker 5: Have you been Michelle's No, Oh my god, it's amazing.

640
00:32:30,160 --> 00:32:35,120
Speaker 5: There's one in Hollywood too. It's like the waiters also sing.

641
00:32:35,320 --> 00:32:37,040
Speaker 1: Yeah, oh hell yeah. Yeah.

642
00:32:37,040 --> 00:32:39,040
Speaker 5: It's like a piano bar, and your waiter will be

643
00:32:39,120 --> 00:32:42,520
Speaker 5: like your waiters, like here's your food, and then they

644
00:32:42,640 --> 00:32:46,120
Speaker 5: like turn around and take a microphone and sing memory

645
00:32:46,160 --> 00:32:47,080
Speaker 5: from Cats.

646
00:32:47,480 --> 00:32:48,080
Speaker 1: Yeah.

647
00:32:48,240 --> 00:32:54,160
Speaker 3: Yeah, and the food fantastically subpar so mad, you're so mad.

648
00:32:54,280 --> 00:32:57,240
Speaker 1: Yeah, the experience, you know, when.

649
00:32:57,040 --> 00:32:59,320
Speaker 3: You walk in, you're like, this food is not tasting great,

650
00:32:59,320 --> 00:33:02,360
Speaker 3: but it will taste like Italian food as we know

651
00:33:02,440 --> 00:33:04,000
Speaker 3: it by the American definition.

652
00:33:04,480 --> 00:33:08,840
Speaker 5: I love those like mid Italian restaurants from the fifties.

653
00:33:09,440 --> 00:33:11,680
Speaker 1: M hmm. Yeah.

654
00:33:11,760 --> 00:33:12,680
Speaker 5: But it's also like they.

655
00:33:12,520 --> 00:33:14,720
Speaker 1: Have after somebody right.

656
00:33:15,280 --> 00:33:18,000
Speaker 5: Yeah, it's like a fake balcony inside.

657
00:33:18,880 --> 00:33:24,240
Speaker 1: Yeah, it's like two inches deep.

658
00:33:24,720 --> 00:33:28,440
Speaker 5: Oh we should go, yeah, take you guys there. Yeah,

659
00:33:28,480 --> 00:33:30,640
Speaker 5: it's great because it's like a karaoke thing, but you

660
00:33:30,680 --> 00:33:32,120
Speaker 5: don't have to do karaoke.

661
00:33:32,480 --> 00:33:32,760
Speaker 1: Is like.

662
00:33:34,320 --> 00:33:38,840
Speaker 5: Waters are just singing. It's so weird. I love it, very.

663
00:33:39,320 --> 00:33:43,200
Speaker 1: Sea, Miles, where you get your taste for the old country. Yeah,

664
00:33:43,400 --> 00:33:46,760
Speaker 1: from Miss Ellis, as we would always incorrectly say in

665
00:33:46,760 --> 00:33:49,320
Speaker 1: my elementary school. And we will. We just can't. We

666
00:33:49,400 --> 00:33:52,360
Speaker 1: can't let that pronunciation go. That guy who was duck

667
00:33:52,400 --> 00:33:55,560
Speaker 1: in the bill, he went on to have a successful

668
00:33:56,200 --> 00:34:01,040
Speaker 1: like chain of daycare centers. Yeah, my god, profit king,

669
00:34:01,120 --> 00:34:06,840
Speaker 1: you know, he knows how profit man. Yeah, somebody's got it.

670
00:34:07,560 --> 00:34:10,399
Speaker 5: Somebody's got to get shorted. In that case, it was you.

671
00:34:10,880 --> 00:34:15,279
Speaker 1: Yeah, exactly. And all right, let's take a quick break

672
00:34:15,320 --> 00:34:18,440
Speaker 1: and come back and talk about something that we're all,

673
00:34:18,760 --> 00:34:21,440
Speaker 1: every single one of us. Every If you're hearing my

674
00:34:21,600 --> 00:34:24,200
Speaker 1: voice and you don't and you're not in the c

675
00:34:24,360 --> 00:34:27,919
Speaker 1: suite at Lockheed Martin, you got shorted on this ship.

676
00:34:28,480 --> 00:34:42,080
Speaker 1: We'll be right back and we're back. And Miles, I mean,

677
00:34:42,080 --> 00:34:44,319
Speaker 1: we we talk a lot about how we what we

678
00:34:44,400 --> 00:34:46,640
Speaker 1: think this future is going to look like, right.

679
00:34:46,560 --> 00:34:51,400
Speaker 3: Yeah, I mean I share the name with Miles Dyson him.

680
00:34:51,960 --> 00:34:54,600
Speaker 3: I started a little thing called the sky Net, you know,

681
00:34:54,840 --> 00:34:56,000
Speaker 3: that keeps me up all night.

682
00:34:56,239 --> 00:34:59,920
Speaker 1: Well, but seriousness, Hey he went out of here, miles,

683
00:35:00,160 --> 00:35:01,120
Speaker 1: you know, I did. I did.

684
00:35:02,040 --> 00:35:03,879
Speaker 3: Look, it's all about altruism, you know at the end.

685
00:35:04,719 --> 00:35:06,919
Speaker 3: But like I think for me personally, right, so much

686
00:35:06,960 --> 00:35:09,759
Speaker 3: of my so much of my understanding of science is

687
00:35:09,800 --> 00:35:14,759
Speaker 3: derived from film and television because I'm American and I

688
00:35:14,800 --> 00:35:18,759
Speaker 3: think with like with Ai. I think whenever I would

689
00:35:18,760 --> 00:35:20,520
Speaker 3: think of like, oh my god, it's like, you don't

690
00:35:20,520 --> 00:35:23,279
Speaker 3: know where this thing's gonna go. The place I think

691
00:35:23,320 --> 00:35:26,080
Speaker 3: it's gonna go is sky Net from Terminator.

692
00:35:26,200 --> 00:35:28,839
Speaker 1: I just see the shot from the sky of all

693
00:35:28,920 --> 00:35:33,200
Speaker 1: the missiles tit being launched, like coming down. I'm trying

694
00:35:33,200 --> 00:35:35,440
Speaker 1: to have a nice day at the park with my kids.

695
00:35:35,480 --> 00:35:38,239
Speaker 1: I'm watching through a chain link, and next ladies over

696
00:35:38,280 --> 00:35:41,840
Speaker 1: there shaking the chain link, skeletons.

697
00:35:41,239 --> 00:35:43,560
Speaker 3: Rattling the kids over there, get her out of here.

698
00:35:43,920 --> 00:35:46,560
Speaker 3: But I'm you know, first of all, it's sort of

699
00:35:46,560 --> 00:35:50,880
Speaker 3: twofold question, how how far off base is the idea

700
00:35:51,040 --> 00:35:53,279
Speaker 3: of like how many jumps am I making in my

701
00:35:53,400 --> 00:35:57,080
Speaker 3: brain to be like Chad GBT next stop sky Net?

702
00:35:57,160 --> 00:36:00,759
Speaker 3: And and then also because of that, what are all

703
00:36:00,800 --> 00:36:04,040
Speaker 3: of the ways that people like me are not considering

704
00:36:04,760 --> 00:36:08,480
Speaker 3: what those actual, real tangible effects will be of. And

705
00:36:08,719 --> 00:36:10,680
Speaker 3: I don't want to be like the most cynical version,

706
00:36:10,760 --> 00:36:14,239
Speaker 3: but you know, if we aren't careful and we have

707
00:36:14,520 --> 00:36:19,000
Speaker 3: very sort of individualistic or profit minded sort of motivations

708
00:36:19,040 --> 00:36:22,319
Speaker 3: to develop this kind of technology, like what does that

709
00:36:22,920 --> 00:36:25,080
Speaker 3: worst case sort of look or not worst case? But

710
00:36:25,080 --> 00:36:27,239
Speaker 3: what are the ways I'm actually not thinking of because

711
00:36:27,239 --> 00:36:29,840
Speaker 3: I'm too busy thinking about T one thousands, right.

712
00:36:29,760 --> 00:36:31,960
Speaker 1: Like I just to like add on to that when

713
00:36:32,239 --> 00:36:35,520
Speaker 1: when I think about the computer revolution, like for decades

714
00:36:35,719 --> 00:36:39,280
Speaker 1: it was these people in San Francisco talking about how

715
00:36:39,880 --> 00:36:42,000
Speaker 1: the future was going to be completely different and done

716
00:36:42,040 --> 00:36:47,040
Speaker 1: on computers or something called the Internet, and we were

717
00:36:47,360 --> 00:36:50,719
Speaker 1: just down here looking at a computer that was like

718
00:36:50,800 --> 00:36:55,560
Speaker 1: green dots, like not that good of a screen display.

719
00:36:56,080 --> 00:36:59,319
Speaker 1: And by the time it like filtered down to us

720
00:36:59,400 --> 00:37:02,399
Speaker 1: and the world does look totally different, it's like, well, shit,

721
00:37:02,920 --> 00:37:05,799
Speaker 1: like that's that turns out that was a big deal.

722
00:37:06,239 --> 00:37:12,239
Speaker 1: So yeah, I feel like I want to be constantly

723
00:37:12,560 --> 00:37:14,719
Speaker 1: like on our show, just asking me a question like

724
00:37:15,040 --> 00:37:19,320
Speaker 1: where is this actually taking us? Because some in the past,

725
00:37:19,400 --> 00:37:22,680
Speaker 1: I feel like it's been hard to predict and when

726
00:37:22,680 --> 00:37:25,160
Speaker 1: it did come it wasn't exactly what the you know,

727
00:37:25,280 --> 00:37:28,120
Speaker 1: tech oracles said it was going to be, and it

728
00:37:28,239 --> 00:37:31,920
Speaker 1: was like unexpected and weird and beautiful and banal and

729
00:37:32,000 --> 00:37:35,400
Speaker 1: weird ways. And so I'm curious to hear your thoughts

730
00:37:35,400 --> 00:37:40,640
Speaker 1: on like, once this technology reaches the level of consumers,

731
00:37:41,760 --> 00:37:45,200
Speaker 1: like of just your average ordinary person who's like not

732
00:37:45,960 --> 00:37:49,319
Speaker 1: teaching machine learning at m YU, Like, what what is

733
00:37:49,360 --> 00:37:52,879
Speaker 1: it going to look like? But first but obviously first kind,

734
00:37:53,000 --> 00:37:55,359
Speaker 1: but first guy, I.

735
00:37:55,320 --> 00:38:01,440
Speaker 2: Think it's kind of okay. So, so I think the

736
00:38:01,520 --> 00:38:09,839
Speaker 2: idea of worrying about some version of you know, this

737
00:38:09,960 --> 00:38:15,439
Speaker 2: technology going out of control, right is there's so many

738
00:38:15,600 --> 00:38:22,040
Speaker 2: checks and balances of ways in which we are thinking

739
00:38:22,040 --> 00:38:27,239
Speaker 2: about and the technology is sort of moving forward that

740
00:38:27,960 --> 00:38:31,920
Speaker 2: kind of you know, the idea of something emergent and

741
00:38:31,960 --> 00:38:34,640
Speaker 2: then not only as an emergent, but it's going to

742
00:38:34,719 --> 00:38:39,359
Speaker 2: take over and try to destroy civilization, right.

743
00:38:39,440 --> 00:38:43,400
Speaker 3: And also send and also send cybernetic organisms into the

744
00:38:43,440 --> 00:38:46,440
Speaker 3: past to ensure that those people do not grow up

745
00:38:46,480 --> 00:38:49,719
Speaker 3: to take arms against kind of like John Connor and.

746
00:38:49,760 --> 00:38:54,000
Speaker 2: His yeah, yeah, So I'm a firm believer that in

747
00:38:54,160 --> 00:38:56,960
Speaker 2: being able to send people to the future, But I

748
00:38:57,080 --> 00:38:59,200
Speaker 2: still don't think that we're not going to be able

749
00:38:59,239 --> 00:39:02,600
Speaker 2: to send people in the past. So that's that's maybe

750
00:39:02,960 --> 00:39:06,440
Speaker 2: one problem.

751
00:39:05,160 --> 00:39:12,719
Speaker 1: But that's just like your opinion, man, all right, is

752
00:39:12,760 --> 00:39:16,480
Speaker 1: going to happen. And by the way, i'm the Doc

753
00:39:16,560 --> 00:39:19,400
Speaker 1: Brown character, I'm going to be friend a young child.

754
00:39:19,760 --> 00:39:26,480
Speaker 1: Yeah right, exactly. Marty and Doc been friends like probably

755
00:39:26,520 --> 00:39:30,160
Speaker 1: like it seems like eight years, so like when Marty

756
00:39:30,280 --> 00:39:33,400
Speaker 1: was like nine years old, they became best friends. Anyways,

757
00:39:33,480 --> 00:39:38,319
Speaker 1: sorry about that forgetting side tracks so well.

758
00:39:38,360 --> 00:39:40,960
Speaker 2: So I think, you know, so what I'm what I'm

759
00:39:41,000 --> 00:39:44,359
Speaker 2: thinking about when we think about this kind of technology

760
00:39:44,400 --> 00:39:48,759
Speaker 2: and how it could be, how the technology itself could

761
00:39:48,760 --> 00:39:53,600
Speaker 2: go wrong. I don't think that that kind of integration

762
00:39:53,800 --> 00:39:58,799
Speaker 2: is likely, right. I think that the concept of you know,

763
00:39:58,840 --> 00:40:03,279
Speaker 2: the singularity, it's so far out, and I don't think

764
00:40:03,360 --> 00:40:08,440
Speaker 2: that we have a good ability or understanding of like consciousness.

765
00:40:08,719 --> 00:40:10,680
Speaker 2: And I'd also don't think we have a really good

766
00:40:10,760 --> 00:40:14,920
Speaker 2: understanding of like, Okay, you know, why would you know

767
00:40:15,120 --> 00:40:20,160
Speaker 2: these large language models start, you know, trying to harm us.

768
00:40:21,320 --> 00:40:25,640
Speaker 2: So there's all those steps and jumps and leaps and

769
00:40:25,719 --> 00:40:31,399
Speaker 2: all the intermediate pieces that just seems so incredibly unlikely. Now,

770
00:40:31,400 --> 00:40:34,960
Speaker 2: I know that a lot of the ai AGI people

771
00:40:35,000 --> 00:40:38,160
Speaker 2: who worry, they worry is, oh, well, we got to

772
00:40:38,160 --> 00:40:40,959
Speaker 2: worry about this tail race, right, and a human level

773
00:40:41,000 --> 00:40:44,480
Speaker 2: extinction event is worth worrying about. You and some people

774
00:40:44,480 --> 00:40:47,759
Speaker 2: worrying about that is probably not a bad thing. But

775
00:40:47,800 --> 00:40:52,759
Speaker 2: I just think it's very unlikely for the reasons of

776
00:40:52,840 --> 00:40:56,120
Speaker 2: those of all the chain that would need to happen.

777
00:40:57,200 --> 00:41:01,799
Speaker 2: And we're not very good at robots. Also, robots are

778
00:41:01,960 --> 00:41:04,960
Speaker 2: you know, actually much further away.

779
00:41:05,000 --> 00:41:08,360
Speaker 1: But that is one Okay, well we'll talk about it,

780
00:41:08,360 --> 00:41:10,279
Speaker 1: but put a pin in that because I want to

781
00:41:10,280 --> 00:41:13,280
Speaker 1: come back to that, to bad robots, to bad robots,

782
00:41:13,719 --> 00:41:15,640
Speaker 1: and how easy I just want to brag about how

783
00:41:15,680 --> 00:41:17,279
Speaker 1: easy it would be for me to beat one up.

784
00:41:17,400 --> 00:41:20,799
Speaker 1: But uh, all those dynamics, Yeah yeah, I got money

785
00:41:20,840 --> 00:41:21,480
Speaker 1: on you in that fight.

786
00:41:22,080 --> 00:41:25,040
Speaker 2: But the thing that I do want to point out

787
00:41:25,160 --> 00:41:28,480
Speaker 2: is that you know the concept of starting to build

788
00:41:28,520 --> 00:41:32,480
Speaker 2: some of these rules into the large language models, you know,

789
00:41:32,760 --> 00:41:36,560
Speaker 2: to try to say, okay, well, you know, try to

790
00:41:36,760 --> 00:41:42,719
Speaker 2: be benign, don't do harm. That's a really super good idea, right,

791
00:41:43,320 --> 00:41:46,760
Speaker 2: And I think that you know, even if you don't

792
00:41:46,760 --> 00:41:51,560
Speaker 2: believe in sky Net, actually believing in trying to incorporate

793
00:41:51,640 --> 00:41:55,279
Speaker 2: responsibility into the large language models. I think that is

794
00:41:55,360 --> 00:41:59,280
Speaker 2: something that's very important moving into some of the dangers though,

795
00:42:00,120 --> 00:42:04,000
Speaker 2: Like I think actually large language models and people like

796
00:42:04,080 --> 00:42:06,239
Speaker 2: Opening Eye was actually worried about this in one of

797
00:42:06,280 --> 00:42:11,880
Speaker 2: its first iterations of GPT was the ability for the

798
00:42:11,960 --> 00:42:18,840
Speaker 2: large language models to create misinformation and disinformation. And you know,

799
00:42:18,920 --> 00:42:22,719
Speaker 2: I think that that's a really bad use of the

800
00:42:22,760 --> 00:42:25,400
Speaker 2: technology and very very potentially harmful.

801
00:42:26,120 --> 00:42:29,479
Speaker 1: And it already seems to be what it's being used for.

802
00:42:29,800 --> 00:42:33,920
Speaker 1: Like the technology, like the ways that like companies are

803
00:42:33,960 --> 00:42:38,080
Speaker 1: trying to replace journalism with it, or you know, clickbait

804
00:42:38,160 --> 00:42:41,120
Speaker 1: article like just generate tons of clickbait articles that are

805
00:42:41,120 --> 00:42:44,080
Speaker 1: like targeted at people, is like it it feels like

806
00:42:44,120 --> 00:42:48,239
Speaker 1: it's already training in that in a lot of ways.

807
00:42:48,600 --> 00:42:53,759
Speaker 2: Yeah, Unfortunately, I think you're right like that that there

808
00:42:54,239 --> 00:42:58,719
Speaker 2: is this that there is this bad use of the

809
00:42:58,760 --> 00:43:01,840
Speaker 2: technology where it's like, oh, let's make this so that

810
00:43:01,880 --> 00:43:05,560
Speaker 2: it could be as persuasive as possible. You know, there's

811
00:43:05,640 --> 00:43:08,279
Speaker 2: obviously been a ton of research in marketing on like

812
00:43:08,320 --> 00:43:11,319
Speaker 2: figuring out, Okay, how do we you know, position this

813
00:43:11,480 --> 00:43:14,480
Speaker 2: product so that it's you know, you're more likely to

814
00:43:14,520 --> 00:43:16,680
Speaker 2: buy it, right, and the same thing can be now

815
00:43:16,680 --> 00:43:20,960
Speaker 2: applied to language of like, Okay, you know, for Jack O'Brien,

816
00:43:21,120 --> 00:43:24,719
Speaker 2: how am I going to make this tailored advertisement just

817
00:43:24,840 --> 00:43:27,600
Speaker 2: for him to be able to do to make him

818
00:43:27,600 --> 00:43:30,760
Speaker 2: buy this particular product that I'm selling, or to make

819
00:43:30,920 --> 00:43:32,080
Speaker 2: him not vote?

820
00:43:32,400 --> 00:43:32,560
Speaker 1: Right?

821
00:43:32,840 --> 00:43:35,880
Speaker 2: Yeah, right, you know those kind of I mean, I

822
00:43:36,160 --> 00:43:37,799
Speaker 2: think that this is scary, and I think that this

823
00:43:37,920 --> 00:43:41,640
Speaker 2: is in the now right, which you know, is something

824
00:43:41,719 --> 00:43:44,840
Speaker 2: that in some sense is going to be hard to

825
00:43:46,040 --> 00:43:51,319
Speaker 2: like really stop people from using this technology in that

826
00:43:51,440 --> 00:43:56,839
Speaker 2: direction without things like legislation. You know, there's just some

827
00:43:56,840 --> 00:44:03,719
Speaker 2: some components that need you know, more legislation, and I

828
00:44:03,760 --> 00:44:07,000
Speaker 2: think that's going to be hard to do but probably necessary.

829
00:44:07,400 --> 00:44:08,560
Speaker 1: Right, Yeah.

830
00:44:08,800 --> 00:44:11,920
Speaker 3: I could even see, like just even in politics, you

831
00:44:11,960 --> 00:44:14,640
Speaker 3: can be like, Okay, I need to actually figure out

832
00:44:14,680 --> 00:44:18,200
Speaker 3: the best campaign plan for this very specific demographic that

833
00:44:18,239 --> 00:44:20,840
Speaker 3: lives in this part of this state and for sure,

834
00:44:21,120 --> 00:44:23,719
Speaker 3: and then just imagining what happens. But I guess is

835
00:44:23,760 --> 00:44:25,600
Speaker 3: that also part of the slippery slope is that like

836
00:44:25,960 --> 00:44:28,160
Speaker 3: the reliance sort of gives way to like sort of

837
00:44:28,600 --> 00:44:30,600
Speaker 3: this like thing where it's like this actually is gonna

838
00:44:30,640 --> 00:44:33,840
Speaker 3: whatever this says is the solution to whatever problem. We

839
00:44:33,960 --> 00:44:36,520
Speaker 3: have and like just kind of throwing our hands up

840
00:44:36,560 --> 00:44:39,800
Speaker 3: and all just becoming totally reliant. I mean, i'd imagine

841
00:44:39,800 --> 00:44:42,880
Speaker 3: that's also seems like a place where we could easily

842
00:44:42,920 --> 00:44:46,240
Speaker 3: sort of slip into a problem where it's like, yeah,

843
00:44:45,800 --> 00:44:48,640
Speaker 3: the chatbot may give us this answer.

844
00:44:48,840 --> 00:44:51,560
Speaker 2: But well, or or that people are starting to make

845
00:44:51,719 --> 00:44:56,600
Speaker 2: like very homogeneous decisions and choices right where you would make,

846
00:44:56,920 --> 00:45:01,680
Speaker 2: you know, many different choices because you already have a

847
00:45:01,719 --> 00:45:05,320
Speaker 2: template that's been given to you by you know this AI.

848
00:45:05,960 --> 00:45:07,920
Speaker 2: You're like, oh, okay, well, you know, I'm just going

849
00:45:07,960 --> 00:45:11,200
Speaker 2: to follow you know, this choice or this decision that

850
00:45:11,280 --> 00:45:14,200
Speaker 2: it's going to make for me and not you know,

851
00:45:14,400 --> 00:45:17,640
Speaker 2: do one of the thousand different alternatives right, right, And

852
00:45:18,239 --> 00:45:20,399
Speaker 2: if you do that, I do that. Jack does that, right,

853
00:45:20,520 --> 00:45:23,040
Speaker 2: Like all of a sudden, we would have done vastly

854
00:45:23,040 --> 00:45:26,000
Speaker 2: different choices, But now we have this sort of weird,

855
00:45:26,320 --> 00:45:30,040
Speaker 2: sort of centering effect where all of us are actually

856
00:45:30,160 --> 00:45:35,160
Speaker 2: making much less, you know, very choices in our decision making, right,

857
00:45:35,920 --> 00:45:39,480
Speaker 2: which has I mean, it does possess real risk. I

858
00:45:39,520 --> 00:45:44,719
Speaker 2: mean imagine applying this to resume screen right, where you

859
00:45:44,719 --> 00:45:47,239
Speaker 2: can imagine the same type of problem, right, and those

860
00:45:47,280 --> 00:45:49,919
Speaker 2: are there's just so many scenarios that you can think

861
00:45:49,960 --> 00:45:55,560
Speaker 2: of where you know, we need to take care, right, Yeah,

862
00:45:55,160 --> 00:45:57,800
Speaker 2: and this is a good time to think about that.

863
00:45:58,040 --> 00:46:00,919
Speaker 1: Right, And we're, yeah, we're turning our free will over

864
00:46:01,120 --> 00:46:04,960
Speaker 1: to the care of algorithms and you know, the phones,

865
00:46:05,080 --> 00:46:07,640
Speaker 1: like the skinner boxes in our hands that we're carrying around,

866
00:46:07,719 --> 00:46:10,240
Speaker 1: and like that feels like a thing that's already happening.

867
00:46:10,280 --> 00:46:13,440
Speaker 1: AI might just make it a little bit more effective

868
00:46:13,719 --> 00:46:17,880
Speaker 1: and like quicker to respond and like, but yeah, it

869
00:46:18,400 --> 00:46:22,640
Speaker 1: feels like a lot of the concerns over AI that

870
00:46:22,680 --> 00:46:24,520
Speaker 1: makes the most sense to me are the ones that

871
00:46:24,560 --> 00:46:28,839
Speaker 1: are already happening. And yeah, just in like to kind

872
00:46:28,840 --> 00:46:30,839
Speaker 1: of tip my head a little bit, like the in

873
00:46:30,960 --> 00:46:34,520
Speaker 1: doing a lot of research on the kind of overall

874
00:46:34,600 --> 00:46:39,680
Speaker 1: general intelligence concerns that we've been talking about, the ones

875
00:46:39,719 --> 00:46:42,560
Speaker 1: where it like takes over and evades human control because

876
00:46:42,560 --> 00:46:46,839
Speaker 1: it wants to you know, wants to defeat humans, I

877
00:46:46,920 --> 00:46:51,400
Speaker 1: was surprised how full of shit those seem to be. Like,

878
00:46:51,880 --> 00:46:55,160
Speaker 1: for instance, the one story that got passed around a

879
00:46:55,200 --> 00:47:00,440
Speaker 1: lot last year where a drug like during a military

880
00:47:00,520 --> 00:47:06,200
Speaker 1: exercise and AI was being told not to like take

881
00:47:06,239 --> 00:47:09,960
Speaker 1: out human targets by its human operator, and so the

882
00:47:10,000 --> 00:47:14,880
Speaker 1: AI made the decision to kill the operator so that

883
00:47:14,960 --> 00:47:17,840
Speaker 1: it could then just like go rogue and start killing

884
00:47:17,880 --> 00:47:22,160
Speaker 1: whatever it wanted to. And that story, like I passed

885
00:47:22,200 --> 00:47:24,520
Speaker 1: around a lot. I think we've even like referenced it

886
00:47:24,680 --> 00:47:28,319
Speaker 1: on this show, and it's not true. First of all,

887
00:47:28,400 --> 00:47:30,440
Speaker 1: I think when it first got passed around, people were like,

888
00:47:30,440 --> 00:47:33,640
Speaker 1: it actually killed someone man, and it was it was

889
00:47:33,719 --> 00:47:36,200
Speaker 1: just a hypothetical exercise first of all, like just in

890
00:47:36,239 --> 00:47:38,319
Speaker 1: the story like as it was being passed around. And

891
00:47:38,400 --> 00:47:42,760
Speaker 1: second of all, it was then debunked, like the person

892
00:47:42,800 --> 00:47:45,239
Speaker 1: who said that it happened in the exercise later came

893
00:47:45,280 --> 00:47:49,560
Speaker 1: out and was like that actually, like that didn't happen.

894
00:47:49,920 --> 00:47:53,319
Speaker 1: But it seems like there is a real interest and

895
00:47:53,400 --> 00:47:56,680
Speaker 1: real incentive on behalf of the people who are like

896
00:47:56,800 --> 00:48:02,160
Speaker 1: set up to make money off of these ais to

897
00:48:02,239 --> 00:48:05,560
Speaker 1: like make them seem like they have this like godlike

898
00:48:05,680 --> 00:48:09,080
Speaker 1: reasoning power. There's this other story about where like they

899
00:48:09,120 --> 00:48:12,759
Speaker 1: were testing the I think it was GPT four to

900
00:48:13,840 --> 00:48:17,480
Speaker 1: see like like during the alignment testing. Alignment testing is

901
00:48:17,520 --> 00:48:21,000
Speaker 1: like trying to make sure that the AI's goals are

902
00:48:21,040 --> 00:48:25,040
Speaker 1: aligned with humanities and like making humans more happy and

903
00:48:25,239 --> 00:48:28,760
Speaker 1: they like ran a test to see where the GPT

904
00:48:28,960 --> 00:48:32,360
Speaker 1: four was basically like, I can't solve the capture, but

905
00:48:32,480 --> 00:48:34,840
Speaker 1: what I'm going to do is I'm going to reach

906
00:48:34,880 --> 00:48:38,040
Speaker 1: out to a task rabbit and hire a task rabbit

907
00:48:38,320 --> 00:48:42,000
Speaker 1: to solve the capture for me. And the GPT four

908
00:48:42,120 --> 00:48:44,080
Speaker 1: made up a li and said that he was visually

909
00:48:44,080 --> 00:48:47,600
Speaker 1: impaired and that's why he needed the task grabbit to

910
00:48:47,640 --> 00:48:49,920
Speaker 1: do that. And again it's like one of those stories

911
00:48:50,040 --> 00:48:53,680
Speaker 1: like it feels creepy and it gives you goosebumps, and

912
00:48:53,920 --> 00:48:57,280
Speaker 1: again it's not true. It's like they were the GPT

913
00:48:57,640 --> 00:49:00,520
Speaker 1: was being prompted by a human, Like it's very similar

914
00:49:00,560 --> 00:49:05,799
Speaker 1: to the self driving car myth, like that that self

915
00:49:05,880 --> 00:49:09,160
Speaker 1: driving car viral video that Elon Musk put out, where

916
00:49:09,239 --> 00:49:12,520
Speaker 1: it was being prompted by a human and like pre programmed,

917
00:49:12,640 --> 00:49:15,680
Speaker 1: and like that, there were all these ways that the

918
00:49:16,520 --> 00:49:23,239
Speaker 1: human agency involved with the kind of clever task that

919
00:49:23,960 --> 00:49:26,319
Speaker 1: we're worried about this thing having done. We're just like

920
00:49:26,400 --> 00:49:29,160
Speaker 1: taken out. We're just edited out so that they could

921
00:49:29,200 --> 00:49:34,200
Speaker 1: tell a story where it seemed like the GPT, for

922
00:49:34,719 --> 00:49:37,640
Speaker 1: which is like what powers chat GPT that like it

923
00:49:37,800 --> 00:49:41,279
Speaker 1: was doing something evil, right, and it's like so it's

924
00:49:42,320 --> 00:49:46,240
Speaker 1: I get how these stories get out there and become

925
00:49:47,440 --> 00:49:51,680
Speaker 1: like because they're they're the version of AI that we've

926
00:49:51,719 --> 00:49:56,439
Speaker 1: been preparing for because like and watching Terminator. But it's

927
00:49:56,600 --> 00:49:59,960
Speaker 1: surprising to me how much like Sam Altman, the head

928
00:50:00,120 --> 00:50:04,560
Speaker 1: of Open AI, like just leans into that shit and

929
00:50:04,760 --> 00:50:08,160
Speaker 1: is like he like he in an interview with like

930
00:50:08,200 --> 00:50:11,120
Speaker 1: the New Yorker, he was like, Yeah, I like keep

931
00:50:11,120 --> 00:50:15,080
Speaker 1: a Sinai capsule on me and like all these weapons

932
00:50:15,160 --> 00:50:18,880
Speaker 1: and like a gas mask from the Israeli military in

933
00:50:18,960 --> 00:50:23,560
Speaker 1: case like an AI takeover happens. And it's like, what,

934
00:50:24,480 --> 00:50:27,759
Speaker 1: like you, of all people should know that that is

935
00:50:27,840 --> 00:50:32,799
Speaker 1: complete bullshit, but it and it totally like takes the

936
00:50:32,920 --> 00:50:35,920
Speaker 1: eye off the ball, like how the actual dangers that

937
00:50:36,000 --> 00:50:38,920
Speaker 1: AI poses, which is that it's going to just like

938
00:50:39,080 --> 00:50:41,880
Speaker 1: flood the zone with shit, like it's going to keep

939
00:50:41,960 --> 00:50:46,680
Speaker 1: making the Internet and phones like more and more unusable

940
00:50:46,760 --> 00:50:49,799
Speaker 1: but also more and more like difficult to like tear

941
00:50:49,880 --> 00:50:53,600
Speaker 1: ourselves away from. Like I feel like we've already lost

942
00:50:53,680 --> 00:50:56,840
Speaker 1: the alignment battle in the sense that like we've already

943
00:50:56,920 --> 00:51:01,600
Speaker 1: lost any way in which our technology that is supposed

944
00:51:01,640 --> 00:51:04,120
Speaker 1: to like serve us and make our lives better and

945
00:51:04,200 --> 00:51:08,120
Speaker 1: enrich our happiness, like are doing that, Like they they

946
00:51:08,160 --> 00:51:10,960
Speaker 1: stop doing that a long time ago. Like that's why

947
00:51:11,000 --> 00:51:13,960
Speaker 1: I always like say that the more interesting question, like

948
00:51:14,000 --> 00:51:16,760
Speaker 1: with the questions that were being asked in philosophy classes

949
00:51:16,760 --> 00:51:19,799
Speaker 1: in the early two thousands about like singularity and like

950
00:51:19,880 --> 00:51:24,080
Speaker 1: suddenly this thing spins off and is we don't realize

951
00:51:24,120 --> 00:51:26,720
Speaker 1: we're no longer being served and like we're like twenty

952
00:51:26,719 --> 00:51:29,919
Speaker 1: steps behind, like that happened with capitalism a long time ago.

953
00:51:30,200 --> 00:51:33,960
Speaker 1: Like that we're like capitalism is so far beyond and

954
00:51:34,000 --> 00:51:37,839
Speaker 1: it's like, you know, we're no longer serving ourselves and

955
00:51:37,880 --> 00:51:40,640
Speaker 1: serving fellow humanity. We're like serving this idea of this

956
00:51:40,880 --> 00:51:45,080
Speaker 1: market that is just you know, repeatedly making decisions to

957
00:51:45,120 --> 00:51:49,120
Speaker 1: make itself more efficient, take the friction out of like

958
00:51:49,160 --> 00:51:54,520
Speaker 1: our consumption behavior. And yeah, I think AI is going

959
00:51:54,560 --> 00:51:57,040
Speaker 1: to definitely be a tool in that. But like that

960
00:51:57,239 --> 00:51:59,920
Speaker 1: is the ultimate battle that we're already losing.

961
00:52:00,280 --> 00:52:04,000
Speaker 2: Yeah, I mean I completely agree with you on the

962
00:52:04,600 --> 00:52:09,520
Speaker 2: on the front of like your phones are I mean,

963
00:52:09,880 --> 00:52:14,239
Speaker 2: the company is are trying to maximize their profits, which

964
00:52:14,320 --> 00:52:19,480
Speaker 2: mean you know, possibly you being on your phone at

965
00:52:19,520 --> 00:52:23,279
Speaker 2: the disservice of doing something else, like going outdoors and

966
00:52:23,320 --> 00:52:28,600
Speaker 2: doing exercise right where you know, doing something social. I

967
00:52:30,239 --> 00:52:33,400
Speaker 2: I do see on the other hand, that like you know,

968
00:52:33,719 --> 00:52:40,080
Speaker 2: maybe hey, I can have positive effects right where you know,

969
00:52:41,080 --> 00:52:45,200
Speaker 2: take tutoring for an example, or public health, where we

970
00:52:46,200 --> 00:52:49,080
Speaker 2: take the phone and take the technology and use it

971
00:52:49,200 --> 00:52:55,520
Speaker 2: for benefit, like providing information about public health by helping

972
00:52:56,239 --> 00:53:01,040
Speaker 2: students who don't have the ability to have a tutor

973
00:53:01,160 --> 00:53:04,480
Speaker 2: have a tutor, right, Like all of these kind of

974
00:53:04,560 --> 00:53:08,200
Speaker 2: things where let's take advantage of the fact that you know,

975
00:53:08,280 --> 00:53:11,319
Speaker 2: the majority of the world has phones, right and try

976
00:53:11,360 --> 00:53:16,040
Speaker 2: to use that technology for you know, a good positive

977
00:53:16,120 --> 00:53:21,279
Speaker 2: societal benefit. But like any tool in it, it has

978
00:53:22,160 --> 00:53:25,719
Speaker 2: usage that are both good and bad. And I do think, like,

979
00:53:25,880 --> 00:53:29,440
Speaker 2: you know, there's gonna be a lot of uses of

980
00:53:29,600 --> 00:53:32,720
Speaker 2: this technology that are not necessarily in the best interests

981
00:53:32,719 --> 00:53:34,600
Speaker 2: of humanity or individual people.

982
00:53:35,360 --> 00:53:35,560
Speaker 1: Right.

983
00:53:36,360 --> 00:53:38,839
Speaker 3: So it's like really like sort of the real X

984
00:53:38,880 --> 00:53:42,600
Speaker 3: factor is how the technology is being deployed and for

985
00:53:42,600 --> 00:53:45,480
Speaker 3: what purpose, not that the technology in and of itself

986
00:53:46,080 --> 00:53:49,000
Speaker 3: is like this runaway train that we're trying to rein in.

987
00:53:49,360 --> 00:53:52,399
Speaker 3: It's that, yeah, if you do phone scams, you might

988
00:53:52,440 --> 00:53:55,080
Speaker 3: come up with better you know, like both like voice

989
00:53:55,120 --> 00:53:58,520
Speaker 3: models to get this. Rather than doing one call per hour,

990
00:53:58,640 --> 00:54:01,040
Speaker 3: you can do a thousand calls in one hour running

991
00:54:01,080 --> 00:54:04,400
Speaker 3: the same script and same scam on people. Or to

992
00:54:04,440 --> 00:54:07,319
Speaker 3: your point about how do you persuade Jack O'Brien to

993
00:54:07,360 --> 00:54:10,279
Speaker 3: not vote or to buy X product? Then it's all

994
00:54:10,320 --> 00:54:13,399
Speaker 3: going in that direction versus the things like how can

995
00:54:13,440 --> 00:54:17,120
Speaker 3: we optimize like the ability of a person to learn

996
00:54:17,320 --> 00:54:19,919
Speaker 3: if they're in an environment that typically isn't one where

997
00:54:19,920 --> 00:54:23,040
Speaker 3: people have access to information or for the public health use.

998
00:54:23,080 --> 00:54:25,440
Speaker 3: And that's why, like I think in the end, because

999
00:54:25,800 --> 00:54:28,319
Speaker 3: we always see like like, yeah, this thing could be

1000
00:54:28,440 --> 00:54:31,279
Speaker 3: used for good, and it's almost every example is like yeah,

1001
00:54:31,320 --> 00:54:34,080
Speaker 3: and it just made two guys three billion dollars in

1002
00:54:34,160 --> 00:54:38,080
Speaker 3: two cents, and that's what happened with that, And now

1003
00:54:38,120 --> 00:54:40,920
Speaker 3: we all don't know if videos we see on Instagram

1004
00:54:40,920 --> 00:54:41,800
Speaker 3: are real anymore.

1005
00:54:41,880 --> 00:54:46,239
Speaker 1: Thanks. Yeah, Yeah, it's not. Yeah, it's not inherently a

1006
00:54:46,280 --> 00:54:50,560
Speaker 1: bad technology. It's that the system as is currently constituted,

1007
00:54:51,160 --> 00:54:55,280
Speaker 1: favors scams. Like that's why we have a scam artist

1008
00:54:55,520 --> 00:54:59,799
Speaker 1: as like our most recent president before this one and

1009
00:55:00,520 --> 00:55:05,520
Speaker 1: probably could be future president again, like because that is

1010
00:55:06,400 --> 00:55:10,799
Speaker 1: what our current system is designed for. And like it

1011
00:55:10,840 --> 00:55:16,440
Speaker 1: is just scamming people for money. That's why when blockchain

1012
00:55:16,560 --> 00:55:20,480
Speaker 1: technology like for has its like infant baby steps, it

1013
00:55:20,520 --> 00:55:23,920
Speaker 1: immediately becomes a thing that people use to scam one

1014
00:55:23,920 --> 00:55:29,560
Speaker 1: another because that is the software of our current society.

1015
00:55:29,719 --> 00:55:34,920
Speaker 1: So yeah, there are like tons of amazing possibilities with AI.

1016
00:55:35,600 --> 00:55:38,440
Speaker 1: I don't think we find them in the United States,

1017
00:55:38,520 --> 00:55:43,160
Speaker 1: like applying the AI tools to our current software. I

1018
00:55:43,200 --> 00:55:46,120
Speaker 1: would love for there to be a version of the

1019
00:55:46,160 --> 00:55:51,239
Speaker 1: future where AI is so smart and efficient that it

1020
00:55:51,520 --> 00:55:55,480
Speaker 1: changes that paradigm somehow and change it so that our

1021
00:55:55,520 --> 00:55:59,920
Speaker 1: software that our society runs on is no longer scams.

1022
00:56:00,560 --> 00:56:04,440
Speaker 2: Yeah, right, I mean there is a future where you know,

1023
00:56:05,160 --> 00:56:11,640
Speaker 2: AI starts to do a lot of tasks for us.

1024
00:56:11,760 --> 00:56:16,279
Speaker 2: You know, I think that there's starting to be, you know,

1025
00:56:16,400 --> 00:56:21,000
Speaker 2: depending it's mostly in Europe now, but also here it

1026
00:56:21,040 --> 00:56:25,080
Speaker 2: talks about like universal basic income. It's it's kind of

1027
00:56:26,080 --> 00:56:28,520
Speaker 2: I don't think that that's going to be the case.

1028
00:56:28,600 --> 00:56:31,600
Speaker 2: I don't think that in the US anybody has an

1029
00:56:31,640 --> 00:56:36,560
Speaker 2: appetite for that. Maybe in twenty thirty years, but you know,

1030
00:56:36,600 --> 00:56:40,160
Speaker 2: I do think that, you know, the there are certain

1031
00:56:40,160 --> 00:56:45,319
Speaker 2: things about my job right as a professor, about like

1032
00:56:45,480 --> 00:56:51,320
Speaker 2: writing and certain other components right where you know, here's

1033
00:56:51,360 --> 00:56:54,960
Speaker 2: some things where AI can help, right, And you know,

1034
00:56:55,120 --> 00:56:59,239
Speaker 2: today I use it as a tool to make me

1035
00:56:59,360 --> 00:57:03,000
Speaker 2: more efficient, and like, you know, okay, I want to

1036
00:57:03,680 --> 00:57:07,160
Speaker 2: have an editor look at this. Okay, Well you know

1037
00:57:08,320 --> 00:57:11,120
Speaker 2: it's not going to do as good as a real editor,

1038
00:57:11,200 --> 00:57:13,640
Speaker 2: but maybe as a cop as a you know, bad

1039
00:57:13,760 --> 00:57:18,640
Speaker 2: copy editor. Yeah it's good, you know, Like but you know,

1040
00:57:19,000 --> 00:57:22,400
Speaker 2: I mean I've been writing papers for a while. You know,

1041
00:57:22,640 --> 00:57:26,040
Speaker 2: when the undergraduates write the paper, it does an even

1042
00:57:26,080 --> 00:57:30,520
Speaker 2: better job, right because they have more room for growth, right,

1043
00:57:30,640 --> 00:57:33,760
Speaker 2: And and so I think that, you know, yeah, I

1044
00:57:34,040 --> 00:57:38,360
Speaker 2: think that on a micro level, these tools are now

1045
00:57:38,400 --> 00:57:41,640
Speaker 2: being used. I think that you know, Russian spambots have

1046
00:57:41,720 --> 00:57:46,800
Speaker 2: been most likely using large language model technology before chat

1047
00:57:46,840 --> 00:57:51,960
Speaker 2: GPT right, right, And so you know, I mean there

1048
00:57:51,960 --> 00:57:53,600
Speaker 2: are some ways that I think we're going in a

1049
00:57:53,640 --> 00:57:55,640
Speaker 2: positive and good direction, right.

1050
00:57:55,720 --> 00:57:55,880
Speaker 1: Yeah.

1051
00:57:56,240 --> 00:57:58,080
Speaker 3: Now, now it's like now that you say that, I'm thinking,

1052
00:57:58,120 --> 00:58:01,120
Speaker 3: I'm like we thought Cambridge Analytico is bad, and it's

1053
00:58:01,160 --> 00:58:03,760
Speaker 3: like what happens now when we like turn it up

1054
00:58:03,760 --> 00:58:06,680
Speaker 3: to one thousand with this kind of thing where it's like, yeah,

1055
00:58:06,720 --> 00:58:10,200
Speaker 3: here are all these voter files now like really figure

1056
00:58:10,200 --> 00:58:13,320
Speaker 3: out how to suppress a vote or get to sway people.

1057
00:58:13,320 --> 00:58:15,680
Speaker 3: And yeah, I again, it does feel like one of

1058
00:58:15,720 --> 00:58:18,480
Speaker 3: these things where in the right hands, right, we can

1059
00:58:18,600 --> 00:58:22,640
Speaker 3: create a world where people don't have to toil because

1060
00:58:22,680 --> 00:58:26,919
Speaker 3: we're able to automate those things. But then the next

1061
00:58:27,000 --> 00:58:30,440
Speaker 3: question becomes, how do we off ramp our culture of

1062
00:58:30,560 --> 00:58:34,680
Speaker 3: greed and wealth hoarding and concentrating all of our wealth

1063
00:58:35,080 --> 00:58:37,760
Speaker 3: into a way to say, like, well, we actually need

1064
00:58:37,800 --> 00:58:40,680
Speaker 3: to spread this out that way everyone can have their

1065
00:58:40,720 --> 00:58:44,760
Speaker 3: needs met materially, because our economy kind of runs on

1066
00:58:44,880 --> 00:58:47,680
Speaker 3: its own in certain sectors. Obviously other ones need real

1067
00:58:47,760 --> 00:58:50,160
Speaker 3: like human labor and things like that. But yeah, that's

1068
00:58:50,160 --> 00:58:52,720
Speaker 3: where you begin to see like, Okay, that's the fork

1069
00:58:52,760 --> 00:58:56,360
Speaker 3: in the road. Can we make that? Do we take

1070
00:58:56,440 --> 00:58:57,440
Speaker 3: the right path.

1071
00:58:57,280 --> 00:59:00,360
Speaker 1: Or does it turn into you know, just like some

1072
00:59:00,560 --> 00:59:02,960
Speaker 1: very half asked version where like only a fraction of

1073
00:59:03,000 --> 00:59:07,000
Speaker 1: the people that need like universal basic income are receiving it,

1074
00:59:07,440 --> 00:59:10,640
Speaker 1: while you know, we read more articles about why we

1075
00:59:10,640 --> 00:59:14,560
Speaker 1: don't see Van Vocht in Hollywood anymore. As Jack pointed

1076
00:59:14,640 --> 00:59:15,600
Speaker 1: out that yeah.

1077
00:59:15,520 --> 00:59:19,120
Speaker 3: Terribly worded thing about Vince Vaughan that AI could not

1078
00:59:19,200 --> 00:59:19,680
Speaker 3: get right.

1079
00:59:19,840 --> 00:59:22,280
Speaker 1: He couldn't get his name right, So like Van Vought,

1080
00:59:23,680 --> 00:59:26,520
Speaker 1: the one. So the sci fi dystopia that seems like

1081
00:59:26,920 --> 00:59:29,360
Speaker 1: not not necessarily dystopia, but the sci fi future that

1082
00:59:29,400 --> 00:59:33,120
Speaker 1: seems like it's most close at hand, and like given

1083
00:59:33,760 --> 00:59:37,080
Speaker 1: you know, doing a weekend's worth research on like where

1084
00:59:37,120 --> 00:59:39,920
Speaker 1: where this is and like where all the top thinkers

1085
00:59:39,960 --> 00:59:42,640
Speaker 1: think we're headed. The thing that makes that seems the

1086
00:59:42,680 --> 00:59:45,840
Speaker 1: closest reality to me is the movie Her, Like Her

1087
00:59:46,040 --> 00:59:50,240
Speaker 1: is kind of already here. There's already these applications that

1088
00:59:50,400 --> 00:59:56,440
Speaker 1: use chat GPT and you know GPT four two again,

1089
00:59:56,520 --> 00:59:58,880
Speaker 1: Like it really seems to me like and I don't

1090
00:59:58,920 --> 01:00:03,400
Speaker 1: necessarily mean this like in a dismissive way. This is

1091
01:00:04,080 --> 01:00:07,440
Speaker 1: probably you know, a good description of most jobs. Like that.

1092
01:00:07,680 --> 01:00:10,440
Speaker 1: The thing that David Fincher I was like listening to

1093
01:00:10,440 --> 01:00:12,600
Speaker 1: a podcast where they talked about how David Fincher says,

1094
01:00:12,640 --> 01:00:16,200
Speaker 1: like words are only ever spoken in his movies by

1095
01:00:16,280 --> 01:00:20,200
Speaker 1: characters in order to lie, because like that's how humans

1096
01:00:20,240 --> 01:00:23,080
Speaker 1: actually use language, is just to like find different ways

1097
01:00:23,080 --> 01:00:25,880
Speaker 1: to like lie about who they are, what they're you know,

1098
01:00:26,160 --> 01:00:29,360
Speaker 1: approaches to things. And like I think, you know, these

1099
01:00:29,560 --> 01:00:32,920
Speaker 1: language models the thing that they're really good at, like

1100
01:00:33,040 --> 01:00:35,360
Speaker 1: whether it be like up to this point where people

1101
01:00:35,360 --> 01:00:37,720
Speaker 1: are like, holy shit, this thing's alive. That's talking to me.

1102
01:00:38,080 --> 01:00:40,360
Speaker 1: Well it's not. And it's like just doing a really

1103
01:00:40,400 --> 01:00:43,160
Speaker 1: good approximation of that and like kind of fooling you

1104
01:00:43,200 --> 01:00:46,760
Speaker 1: a little bit, like take that to the ultimate extreme,

1105
01:00:46,760 --> 01:00:49,120
Speaker 1: and it's like it makes you think that you're in

1106
01:00:49,200 --> 01:00:53,560
Speaker 1: a like loving relationship with a partner, and like that's

1107
01:00:53,720 --> 01:00:57,320
Speaker 1: already how it's being used in some in some instances

1108
01:00:57,440 --> 01:00:59,960
Speaker 1: to great effect. So like that that seems to be

1109
01:01:00,120 --> 01:01:02,800
Speaker 1: one way that I could see that being becoming more

1110
01:01:02,840 --> 01:01:05,160
Speaker 1: and more a thing where people are like, yeah, I

1111
01:01:05,160 --> 01:01:07,960
Speaker 1: don't have like human relationships anymore. I get like my

1112
01:01:08,800 --> 01:01:14,640
Speaker 1: emotional needs met by like her technology essentially is that

1113
01:01:14,920 --> 01:01:18,240
Speaker 1: What would you say about that? And what is there

1114
01:01:18,280 --> 01:01:22,640
Speaker 1: a different fictional future that you see close at hand?

1115
01:01:23,640 --> 01:01:28,000
Speaker 2: So I think Her is great. I actually, I mean

1116
01:01:28,040 --> 01:01:32,040
Speaker 2: it's really funny because you know, I remember back in

1117
01:01:32,080 --> 01:01:35,480
Speaker 2: twenty seventeen, twenty eighteen where I was like, oh, her

1118
01:01:36,000 --> 01:01:39,560
Speaker 2: is like a great sort of this is where AI

1119
01:01:39,720 --> 01:01:42,800
Speaker 2: is going. And you know, for those of you who

1120
01:01:42,840 --> 01:01:46,400
Speaker 2: haven't seen the movie, it's a funny movie where you know,

1121
01:01:46,520 --> 01:01:50,320
Speaker 2: they have Scarlett Johansson is the voice of an AI

1122
01:01:50,440 --> 01:01:53,480
Speaker 2: agent which is on the phone. And I think that

1123
01:01:53,560 --> 01:01:56,280
Speaker 2: this is actually a pretty accurate description of what we're

1124
01:01:56,280 --> 01:01:59,160
Speaker 2: going to have in the future, not necessarily the relationship part,

1125
01:01:59,240 --> 01:02:03,280
Speaker 2: but the fact that all have this personal assistant, and

1126
01:02:03,320 --> 01:02:07,120
Speaker 2: the personal assistant will see so many aspects of our lives,

1127
01:02:07,200 --> 01:02:14,280
Speaker 2: right our calendars, our you know, meetings, our phone calls, everything,

1128
01:02:14,440 --> 01:02:17,200
Speaker 2: and so it'll be you know, this assistant that we

1129
01:02:17,280 --> 01:02:21,440
Speaker 2: all have that's helping us to like make things more productive.

1130
01:02:21,560 --> 01:02:27,840
Speaker 2: And as a function of the assistant to be effective,

1131
01:02:28,040 --> 01:02:31,680
Speaker 2: the assistant will probably want to have some connection with you,

1132
01:02:32,440 --> 01:02:37,520
Speaker 2: and that connection will be likely to allow you to

1133
01:02:37,600 --> 01:02:41,440
Speaker 2: trust it. And here's where the slippery slope comes from,

1134
01:02:41,680 --> 01:02:45,400
Speaker 2: you know, where you where you're like, oh, this understands me,

1135
01:02:45,640 --> 01:02:49,600
Speaker 2: and you start getting into a deeper relationship where you know,

1136
01:02:50,000 --> 01:02:52,160
Speaker 2: a lot of the fulfillment of a one on one

1137
01:02:52,200 --> 01:02:58,040
Speaker 2: connection can come with your smart assistant. And I do

1138
01:02:58,080 --> 01:03:00,720
Speaker 2: think that there's a little bit of a danger here. Like,

1139
01:03:01,000 --> 01:03:05,680
Speaker 2: you know, again, I'm not a psychologist. I'm someone who

1140
01:03:05,720 --> 01:03:11,080
Speaker 2: studies AI machine learning, but I do actually, you know,

1141
01:03:11,240 --> 01:03:17,360
Speaker 2: study how well machines can make sense of emotion and

1142
01:03:17,400 --> 01:03:25,280
Speaker 2: empathy and really you know, GPT four, which is the

1143
01:03:25,400 --> 01:03:28,560
Speaker 2: current state of art technology is actually already really good

1144
01:03:28,560 --> 01:03:32,520
Speaker 2: and understanding. I use that phrase with a quote, the

1145
01:03:32,880 --> 01:03:39,640
Speaker 2: phrase understanding, but really what you were feeling, right, how

1146
01:03:39,680 --> 01:03:43,920
Speaker 2: you're feeling under this scenario. And you can imagine that

1147
01:03:44,080 --> 01:03:48,720
Speaker 2: feeling heard is one of the most important parts of relationship, right,

1148
01:03:48,880 --> 01:03:51,840
Speaker 2: And if you're not feeling heard by somebody else and

1149
01:03:51,880 --> 01:03:55,120
Speaker 2: you're feeling heard by you know, your personal assistant, so

1150
01:03:55,200 --> 01:03:59,880
Speaker 2: that could shift relationships to the AI, which you know,

1151
01:04:00,240 --> 01:04:03,720
Speaker 2: could be fundamentally dangerous because it's an AI, not a

1152
01:04:03,760 --> 01:04:05,520
Speaker 2: real person, right.

1153
01:04:06,560 --> 01:04:09,640
Speaker 1: Yeah, the one I was talking about is replica that

1154
01:04:09,720 --> 01:04:14,320
Speaker 1: app R E P L I KA. Where they are

1155
01:04:14,760 --> 01:04:17,920
Speaker 1: you know, designing these things explicitly to like fill in

1156
01:04:17,960 --> 01:04:23,000
Speaker 1: as like a romantic partner, and at least with some people.

1157
01:04:23,040 --> 01:04:24,920
Speaker 1: And it's always hard to tell, did they find like

1158
01:04:25,280 --> 01:04:29,000
Speaker 1: the three people who are using this to fill an

1159
01:04:29,040 --> 01:04:32,680
Speaker 1: actual hole in their lives or is it actually taking

1160
01:04:32,680 --> 01:04:37,479
Speaker 1: off as a technology, But yeah, the personal assistant thing

1161
01:04:37,640 --> 01:04:42,320
Speaker 1: seems closer at hand than maybe people realized. Any other

1162
01:04:42,400 --> 01:04:45,480
Speaker 1: like kind of concrete changes that you think are coming

1163
01:04:46,160 --> 01:04:50,800
Speaker 1: to people's lives that they aren't ready for or haven't

1164
01:04:50,840 --> 01:04:55,160
Speaker 1: haven't really thought about or seen in another sci fi movie.

1165
01:04:56,960 --> 01:04:59,200
Speaker 2: Sci Fi movies are remarkably good in.

1166
01:04:59,240 --> 01:05:01,520
Speaker 1: Predictaous yeah, or maybe they have seen in a sci

1167
01:05:01,560 --> 01:05:02,680
Speaker 1: fi movie.

1168
01:05:03,080 --> 01:05:07,480
Speaker 2: No, But I think you know another another thing, that

1169
01:05:08,640 --> 01:05:12,640
Speaker 2: one potential that I think not enough people are talking

1170
01:05:12,680 --> 01:05:15,880
Speaker 2: about is how much better video games are going to become.

1171
01:05:17,120 --> 01:05:20,800
Speaker 2: So people are already integrating GPT four into video games,

1172
01:05:21,400 --> 01:05:23,400
Speaker 2: and I think our video games are just going to

1173
01:05:23,480 --> 01:05:28,040
Speaker 2: be so much better because you know, you have this

1174
01:05:28,160 --> 01:05:35,240
Speaker 2: ability to like interact now with a character AI that's

1175
01:05:35,320 --> 01:05:38,600
Speaker 2: not just like very boring and chatting with you, but

1176
01:05:38,680 --> 01:05:41,960
Speaker 2: they can actually be truly entertaining and fully interactive.

1177
01:05:42,920 --> 01:05:43,400
Speaker 1: Interesting.

1178
01:05:43,600 --> 01:05:45,920
Speaker 2: I think, you know, computer games are going to be

1179
01:05:47,000 --> 01:05:50,240
Speaker 2: much much better and possibly also more addictive as a

1180
01:05:50,280 --> 01:05:51,360
Speaker 2: function of being much.

1181
01:05:51,240 --> 01:05:54,800
Speaker 3: Better perfect, and then that will dull our appetite for

1182
01:05:54,880 --> 01:05:58,080
Speaker 3: revolution when our jobs are all taken by day. Yes,

1183
01:05:58,160 --> 01:06:00,440
Speaker 3: this is great, This is great, all right.

1184
01:06:00,560 --> 01:06:03,320
Speaker 1: I feel much better after having had this conversation.

1185
01:06:03,480 --> 01:06:06,680
Speaker 3: Dudes, grand that's auto is way better the kinds of

1186
01:06:06,720 --> 01:06:09,240
Speaker 3: conversations I have with people I would normally bludgeon on

1187
01:06:09,280 --> 01:06:12,280
Speaker 3: the street with my character something else.

1188
01:06:13,680 --> 01:06:18,320
Speaker 1: All right, that's gonna do it for this week's weekly Zeitgeist.

1189
01:06:18,440 --> 01:06:21,960
Speaker 1: Please like and review the show. If you like, the

1190
01:06:22,000 --> 01:06:27,280
Speaker 1: show means the world de Miles. He he needs your validation, folks.

1191
01:06:27,840 --> 01:06:30,960
Speaker 1: I hope you're having a great weekend and I will

1192
01:06:30,960 --> 01:07:05,800
Speaker 1: talk to him Monday. By