Speaker 1: They had that like continuous Seinfeld generator for a while,
Speaker 1: which I oh, really yeah, I forgot what happens. I
Speaker 1: think it started. I think it just went to and
Speaker 1: started doing racist things though.
Speaker 2: Started talking like Jerry Seinfeld.
Speaker 1: Yeah actually yeah exactly, it started talking like Sinfield and
Speaker 1: Michael Richards, right, yeah.
Speaker 3: The idea of he's doing great things.
Speaker 1: Oh lord, what's with it with these dudent protesters anyway?
Speaker 3: What's the deal? They all got the same tent? Who's
Speaker 3: giving him the tents? That is his material? Hello the Internet,
Speaker 3: and welcome to season three, forty four, Episode.
Speaker 2: Two of dar Day's gay.
Speaker 3: Production of iHeart Radio. And this is a podcast where
Speaker 3: we take a deep dive into America's shared consciousness, America's
Speaker 3: deep brain. If you will, A little typic cap because
Speaker 3: we're big AI fan, A little little spoiler. We're big
Speaker 3: AI fans, now, folks. I have seen the light come around.
Speaker 3: I think when it's all you see on social media,
Speaker 3: you're like.
Speaker 2: That this is all right?
Speaker 3: Oh yeah, maybe this is cool. Yeah. It's Tuesday, June
Speaker 3: twenty fifth, twenty twenty four.
Speaker 2: Oh yeah, big day. It's National Catfish Day, which is
Speaker 2: odd because it's also my partner's birthday at breakday? Are
Speaker 2: you have you been catfishing me this whole time? It's
Speaker 2: national straw? Guys, haven't met her person yet? Still right,
Speaker 2: but one of these days. No, Well, and every time
Speaker 2: I want a video chat, she says her phone's broken,
Speaker 2: so we just kind of stick to the phone call stuff.
Speaker 3: But it was also very normal. It's this stage in
Speaker 3: a marriage very normal.
Speaker 2: Also, this is so weird and this is just like
Speaker 2: some weird religious stuff. It's national leak? Did you know
Speaker 2: this is national day?
Speaker 3: Do you know what that even is?
Speaker 2: It's because you're six months You're six months away from Christmas.
Speaker 3: Noel words the most it's the most opposite time of
Speaker 3: the year. What does this even mean? Jesus gets a
Speaker 3: half bird instead of ding dong, they dong ding quote
Speaker 3: Doctor Seuss. All right, anyways, happy National Leon Day. We're
Speaker 3: gonna need to workshop that. Maybe we can run it
Speaker 3: through an AI comedy writer, because those are good and
Speaker 3: in no way just humans posing as AI comedy writers. Anyways,
Speaker 3: my name is Jack O'Brien aka Big Ass Plumpers to
Speaker 3: Thin Ass Fools Geist keep it coming like grim as Spooch.
Speaker 3: We kill it on the podcast break in all the
Speaker 3: news say, I'll be goddamned if there ain't more raccoons.
Speaker 3: That is courtesy of Halcyon Salad. Like that name, Halcion Salad.
Speaker 3: I see what you've done there, and I enjoy it.
Speaker 3: I'm thrilled to be joined as always by my co host,
Speaker 3: mister Miles Gray.
Speaker 2: It's Miles Baker.
Speaker 3: I got them bell big boots when the weather suit.
Speaker 2: Yeah, I got that sweat because I'm a dad. Shout
Speaker 2: out razagg on the discord because yeah, like I said,
Speaker 2: I have zip off cargo pants that go from pants
Speaker 2: to shorts because I'm a dad and you have that's
Speaker 2: like just mandatory swag.
Speaker 3: You unzip the bottom of the shorts, yep, which is
Speaker 3: what they're designed to do when it gets warm at
Speaker 3: the knee, and then you flip them. You invert the
Speaker 3: likes and put the cuff at your knee. So you've
Speaker 3: got little bell bottoms, like two part bell bottoms.
Speaker 2: Yeah, the three part bell bottoms. I guess I saw
Speaker 2: it on Pinterest. It could have been AI, but it
Speaker 2: looked like people where we get our fashion from. Yeah, yeah,
Speaker 2: it might have been completely off. But hey, Miles, most
Speaker 2: people say, hey, good luck on you. Yes, people say, hey, Miles,
Speaker 2: We're thrilled to be joined Yes by the hosts of
Speaker 2: the Mystery AI Hype Theater three thousand podcasts. Once again,
Speaker 2: it's doctor Alex Hannah and Professor Emily M.
Speaker 3: Bender. What's up, guys, welcome to the show.
Speaker 1: Hello, Hey, so I'm so glad to hear you there.
Speaker 1: What was the thing about the raccoon?
Speaker 3: A raccoon ripped a crow in half in my backyard.
Speaker 1: Gosh, like it's just wwe style just put it over,
Speaker 1: it's it's knee when it's strange.
Speaker 3: I didn't see it happen. I just found the crow,
Speaker 3: if grown friendly with a murder of crows in my backyard,
Speaker 3: and as one does, and found the crow's body right
Speaker 3: next to like a garbage bag garbage can that had
Speaker 3: been like flipped over like by what could have only
Speaker 3: been a raccoon. And we do have a raccoon family
Speaker 3: living nearby, so.
Speaker 4: If you have if you have an infra raccoons, have
Speaker 4: you ever seen the video where someone's feeding a raccoon
Speaker 4: cotton candy?
Speaker 2: No?
Speaker 3: Oh, and don't they like take it to the water.
Speaker 3: I've seen like.
Speaker 2: Wash it dissolved and like no, Yeah, they always watch
Speaker 2: their food.
Speaker 3: Yeah, very respect it. I just have a much healthier
Speaker 3: respect for raccoons. We should be treating raccoons like handguns.
Speaker 3: They're very impressive and dangerous and we should just give
Speaker 3: them the proper respect. They have a thumbs exactly, they
Speaker 3: and they hunt birds.
Speaker 2: So it's all I knew. I didn't realize the food
Speaker 2: washing thing. My mom famously has opened her home to
Speaker 2: possums and other neighborhood wildlife and where she lives, and
Speaker 2: I remember in the kitchen where the cat food is,
Speaker 2: there was like a bunch of kibble in the water
Speaker 2: bowl and I was like, what is going on? And
Speaker 2: Mom was like, I think that's what the raccoon does.
Speaker 2: And I was like oh, and it was said so
Speaker 2: casually that I was like in your home. He's like yeah,
Speaker 2: yeah yeah. But then it leaves and I was like,
Speaker 2: this is very okay.
Speaker 1: Well does it just does it use like the cat
Speaker 1: like door? Does it? Yeah?
Speaker 2: Yeah, it comes through, watches the kibble, has a few bites,
Speaker 2: and then takes off into the night. It's like a
Speaker 2: pit stop for.
Speaker 1: One of the credit Yeah, I'm leading, So I'm leading
Speaker 1: a raccoon based Dungeons and Dragons campaigns starting next week.
Speaker 1: That's going to be like, yes, it's going to be
Speaker 1: like a heist that takes place in the warehouse where
Speaker 1: where I play Roller Derby. Like, I'm very excited. I've
Speaker 1: got you got it really architected. I don't want to
Speaker 1: give any secrets in case any of my my player
Speaker 1: characters listen to this podcast.
Speaker 3: But right, okay, that sounds amazing And what a coincidence
Speaker 3: raccoons having a bit of a moment really on this podcast.
Speaker 1: Yeah.
Speaker 3: So in addition to being a host of the wonderful
Speaker 3: Mystery and Hype Theater three thousand podcast, which podcast hosts
Speaker 3: the highest honor one can attain in American life. But
Speaker 3: you both have some pretty impressive credits. Emily, you are
Speaker 3: a linguist and professor at the University of Washington, where
Speaker 3: you are director of the Computational Linguistics Laboratory.
Speaker 5: Yep, that's right, okay.
Speaker 3: Alex you are director of research at the Distributed AI
Speaker 3: Research Institute. Both widely published, both received a number of
Speaker 3: academic awards. Both have PhDs. We had you on the
Speaker 3: podcast a few months back, told everyone the truth about
Speaker 3: AI that a lot of the stuff that we're scared of,
Speaker 3: and a lot of the stuff we think it can
Speaker 3: do is not true. It's bullshit. And I sat back
Speaker 3: and was like, well, we'll see what AI does after
Speaker 3: this one. And it's just kept happening, you guys, what
Speaker 3: the what the heck can we do? If anything, it's
Speaker 3: gotten worse since we told everybody the truth what's happening?
Speaker 4: Truly, everybody seems to want to believes absurd so wild. Yeah,
Speaker 4: And part of what we do with the podcast actually
Speaker 4: is like try to be a focal point for a
Speaker 4: community of the people who are like, know, that's not right.
Speaker 4: Why does everybody around me seem to believe that it's like,
Speaker 4: you know, actually doing all these things. Yeah, so it's yeah,
Speaker 4: it's it's you know, that's what we say in our podcast,
Speaker 4: like every time we think we've reached peak, a hype,
Speaker 4: the summit, the bullshit mountain, we discover there's.
Speaker 2: Worse to come, Like yeah, right, Like oh yeah, this
Speaker 2: is just a base camp until you get to the
Speaker 2: real peak.
Speaker 1: Like right, well, it's just that you just keep on thinking,
Speaker 1: that keeps on becoming and there's more and more things
Speaker 1: that these CEOs just you know, are really just say
Speaker 1: incredible nonsense. I don't know if you saw this. I
Speaker 1: think it was the last week the chief technology officer
Speaker 1: of open Ai, mir Murti Muradi, yeah, who famously was
Speaker 1: I think it was an interview of sixty minutes and
Speaker 1: when they were talking about one of their tools, soa
Speaker 1: you know, they had asked if.
Speaker 3: They favorite filmmaker, Yeah exactly, my favorite Yeah, thank you,
Speaker 3: yeah exactly.
Speaker 1: Just so are really edging out, you know, David Lynch
Speaker 1: these days, and so, you know, and they asked her
Speaker 1: do you train the stuff on YouTube? And she she
Speaker 1: kind of grimaced, yeah, painfully. And I remember a great
Speaker 1: Twitter comment. I was like, well, if you're gonna just
Speaker 1: lie about stuff, you at least have to have a
Speaker 1: good poker face about it. Yes, And so the last
Speaker 1: week she was like, well, I think she's doing another
Speaker 1: interview and she was like, well, some some some creative
Speaker 1: jobs are going to go away, like some artists should
Speaker 1: be you know, some.
Speaker 2: Creative jobs maybe shouldn't have existed in the first place. Right,
Speaker 2: If like these jobs were in affront to God or something,
Speaker 2: some of them just shouldn't have even been there.
Speaker 3: But she does have a French accent, so it's really
Speaker 3: hard to be like, this is ridiculous.
Speaker 1: Let's he's Italian and.
Speaker 3: That's what's amazing about her having a friend. I don't.
Speaker 3: I'm not I'm not a cultured person. I don't know
Speaker 3: the difference between They're all French to me, I'm a man, right, Hey,
Speaker 3: she has a Canadian accent. I think. I'm not sure.
Speaker 1: I know it's only it's only plagiarism if it comes
Speaker 1: from the French region of.
Speaker 3: Italy's right, that's right. Yeah, we're going to get into
Speaker 3: that story and just yeah, all of the madness that
Speaker 3: has continued to happen, the bullshit has continued to rain
Speaker 3: even harder, it seems like, yeah, which, yes, does make
Speaker 3: the mountain go higher, unfortunately, the bullshit mountain. But before
Speaker 3: we get to that, Emily Alex, we do like to
Speaker 3: ask our guests, what is something from your search histories
Speaker 3: that's revealing about who you are? Alex? You want to
Speaker 3: kick us off?
Speaker 1: Oh gosh, okay, I don't. The thing is, I don't
Speaker 1: think so I use duck duckgo, and so it doesn't
Speaker 1: actually keep us search history and if I actually look
Speaker 1: at my Google history, it's actually going to be really shameful.
Speaker 1: It's going to be me like searching my own name
Speaker 1: to see it, like if people are like ship talking
Speaker 1: to me online.
Speaker 3: Now this isn't just how we tell if someone's honest,
Speaker 3: is if they actually give that answer, we're like, okay, so.
Speaker 1: Yeah, you actually search yourself. Yeah, actually, but I think
Speaker 1: the last thing I actually searched was like queer barbers
Speaker 1: in the Bay Area because I haven't had a haircut
Speaker 1: in like a year, and I think I need to
Speaker 1: turm up or get you know, air out the the
Speaker 1: sides of my head for pride months. So that's yeah,
Speaker 1: that's elastic.
Speaker 3: I searched, what are you going? You're going full shaved
Speaker 3: on the sides.
Speaker 1: Or maybe trim it a little bit and up the
Speaker 1: back and bring out the curls a little bit.
Speaker 3: So okay, love it? Yeah, on board.
Speaker 2: I wish I could've got a few more days in look.
Speaker 1: In July like discounts, Like it's like like after Valentine's Day?
Speaker 1: Do I get an undercut at fifty percent off?
Speaker 3: Now?
Speaker 2: Right? Exactly?
Speaker 3: Emily, how about you? What's something from your search history?
Speaker 4: So forgive the poor pronunciation of this and the rest
Speaker 4: of the story, because Spanish is not one of my languages.
Speaker 4: But Champurrado, Oh yeah, it's something I search instrs.
Speaker 3: Yeah.
Speaker 4: So I was in Mexico City for a conference last
Speaker 4: week and at one of the coffee breaks, they had
Speaker 4: coffee and decaf coffee, and then.
Speaker 1: They had.
Speaker 3: And the Spanish don't give us about that.
Speaker 2: What do you see when you see that?
Speaker 3: Way? Champarado Mexican hot chocolate.
Speaker 2: So yeah, you're literally Google results.
Speaker 4: So the the labels all had like translations into English,
Speaker 4: and so it was champurado with o hack and Chocolate'm like, yeah,
Speaker 4: I got that.
Speaker 5: What's chopado?
Speaker 4: And so I look it up because I want to
Speaker 4: know what I'm consuming before I consume it. And it's
Speaker 4: basically a corn flour based thick drink chocolate corn soup.
Speaker 5: It was amazing.
Speaker 3: Chocolate corn you had me until chocolate corn soup. But
Speaker 3: the corn is just a thickening yeah, chocolate drink.
Speaker 4: Yeah, slight corn flavor like think corn tortilla, not on
Speaker 4: the cob.
Speaker 3: Yeah yeah, oh yeah, yeah, yeah that sounds amazing.
Speaker 5: Yeah, it was really good.
Speaker 3: I love some corn flakes in a chocolate bar. Yeah,
Speaker 3: corn chocolate. There you go. Yeah, you got it.
Speaker 2: You gotta arrive in your own way.
Speaker 3: Corn chocolate.
Speaker 4: It was really good and just awesome that it was there,
Speaker 4: Like you know, the the coffee breaks had like the
Speaker 4: Mexican sweetbreads and stuff like that, but otherwise it was
Speaker 4: pretty standard like coffee break stuff, and all of a
Speaker 4: sudden there's this wonderful mystery drink.
Speaker 5: The big urns was. It was lovely.
Speaker 3: That sounds great. What is something you think is underrated? Emily?
Speaker 4: I think Seattle's weather is underrated. Oh yeah, everyone makes
Speaker 4: fun of our weather and like, you know, fine believe
Speaker 4: that we don't need lots of people coming here.
Speaker 5: And it's true it gets dark in the winter.
Speaker 4: But like almost any day, you can be outside and
Speaker 4: you are not in physical danger because you are outside.
Speaker 1: I guess that's that's I mean, if you're going for yeah,
Speaker 1: that's interesting, but I mean it's I mean, the winters
Speaker 1: are just so punishing though it's so gray.
Speaker 2: It's dark, but the weather it's dark, it looks it
Speaker 2: looks like shit, but experientially not bad for you. I mean,
Speaker 2: I yeah, I know it's when does like it doesn't
Speaker 2: get all gloomy. I imagine in the summer, right, you
Speaker 2: have wonderful blue skies, and you can.
Speaker 4: Enjoy the gorgeous fire season aside. But yeah, from sort
Speaker 4: of mid October to early January, it can be pretty
Speaker 4: like it's gray and so like when the sun is
Speaker 4: technically above the horizon, it's.
Speaker 5: A little hard to tell.
Speaker 4: Yeah, right right, So, but you know, compared to like Chicago,
Speaker 4: where you have maybe four liverable weeks a year between
Speaker 4: the too hot and the two cold.
Speaker 1: Wow, wow, I'll do that. Because my thing was going
Speaker 1: to be Chicago, because I was just there and I
Speaker 1: was going to say my answer was going to be,
Speaker 1: Chicago is the best American city. I stand on this
Speaker 1: like one.
Speaker 3: No, absolutely not true.
Speaker 1: No, I'll even deal. I'll even deal with I'll deal
Speaker 1: with the winter. I mean if I okay, I'll be honest.
Speaker 1: If I didn't, you know, if the weather in Chicago,
Speaker 1: if if I could bring Bay Area weather to Chicago,
Speaker 1: I would live in Chicago. I mean there's other reasons,
Speaker 1: but I mean it's it's look, the vibes, immaculate street festivals,
Speaker 1: the neighborhoods. It's the one place that's probably the food.
Speaker 1: It's still comparatively affordable compared.
Speaker 3: To the coasts.
Speaker 1: Radical history, you know, just you know, some of the
Speaker 1: best politics. Yeah, you know, I would say, fuga. They
Speaker 1: shot shot an issue there, the fugitive, Oh I did.
Speaker 1: That's a deep cut. Yeah, I mean they I think
Speaker 1: they've shot a lot of Batman movies there, because you know,
Speaker 1: the iconic kind of lower Whacker drive and they call
Speaker 1: it with them and it's yeah, yeah, that's pretty great city.
Speaker 4: Crappy weather, right, if you're going to dump on weather somewhere.
Speaker 4: Everyone makes fun of Seattle's weather.
Speaker 1: Honestly, Emily, this is a hot take. I'd rather take
Speaker 1: Chicago's weather than Seattle's weather.
Speaker 3: I can't. I can't do gray. I can do I
Speaker 3: feel like I'm a crossfire.
Speaker 1: I can bridge it.
Speaker 3: I cannot do gray.
Speaker 1: It's super pressing.
Speaker 4: Well, this is why I say, like, don't move to
Speaker 4: Seattle if you can't handle our weather. Like the people
Speaker 4: who move here and then complain about.
Speaker 2: The weather, what do you expect all of this? What
Speaker 2: they say is true about it being great, like, oh,
Speaker 2: I didn't expect you to be that gray?
Speaker 3: Right, I think people talk about it like that. All right, Alex,
Speaker 3: let's stay with you. What is you, guys, is overrated
Speaker 3: and please do it in a point counterpoint style also
Speaker 3: that contradicts one another.
Speaker 1: Well, I got to think about what's what's overrated these days.
Speaker 1: I just don't know what's in the I know this
Speaker 1: the name of the show is the Daily Zeitgeist, But
Speaker 1: I don't really know what's in the zeitgeist. I mean,
Speaker 1: I guess Taylor Swift, I mean, I don't really have
Speaker 1: Maybe that's controversial. I'm saying something that's hot take, but
Speaker 1: I guess that's maybe not controversial to people of of
Speaker 1: our you know, our generation.
Speaker 2: No so joining Dave Grol on the on the attack
Speaker 2: this weekend. Yeah, wait, what happened with David Girl, like
Speaker 2: implying that she's like He's like, well, we play our
Speaker 2: music live like raw live rock and roll, you know,
Speaker 2: unlike the Era's tour, you know, with the errors tour,
Speaker 2: and then everyone's like fuck you Dave or other people
Speaker 2: will being like exactly exactly. Yeah.
Speaker 1: It's just like yeah, yeah, I mean Dave Girl is
Speaker 1: also overrated, I guess. But like, I mean, I enjoyed Look,
Speaker 1: I enjoy ever long like the next like middle aged
Speaker 1: sort of like dad figure like, but I you know,
Speaker 1: I'm sure like I'm glad that she played every part
Speaker 1: in that song. It sounds good, but you know, yeah,
Speaker 1: it doesn't make you an authority on Taylor Swift. So yeah,
Speaker 1: so I think I'm undercutting my own point.
Speaker 3: But now let's go, Yeah you did in your own over.
Speaker 4: Yeah that's excellent because I don't even have an opinion about.
Speaker 3: Saw Tucker Carlson do that was that that was called crossfire?
Speaker 1: Or was that the crossfire was with what's his face?
Speaker 3: Carlson and Paul That was wasn't the one that the
Speaker 3: one that John Stewart came on and was like yeah,
Speaker 3: it was like this show is bad, and then like
Speaker 3: they cancel it a couple of weeks.
Speaker 1: Well then, but then there was at once show Hannity
Speaker 1: and Combs where Sean, you know, Sean Hannity was supposed
Speaker 1: to be a conservative voice, and then you know Combs
Speaker 1: where like I don't I don't even know the guy's
Speaker 1: first name. They just they kind of just had him
Speaker 1: as a token like liberal on and then they just
Speaker 1: it was on Fox News. So he just attacked him,
Speaker 1: you know, relentlessly.
Speaker 3: He wasn't allowed to read the news. He's like, you
Speaker 3: argue the liberal points, but you're actually not permitted to
Speaker 3: leave this room. We're gonna keep you in here, old
Speaker 3: boy style.
Speaker 2: For the Oh, that was the end of sixty minutes
Speaker 2: that Andy Rooney would do. There was part of sixty
Speaker 2: minutes was point counterpoint, and it would be Andy Rooney
Speaker 2: if that's her thinking, Jack.
Speaker 3: No, No, there was a show. Yeah, yeah, it was
Speaker 3: right when I got out of college and worked for
Speaker 3: ABC News and so everybody was always watching news and
Speaker 3: at that time there was a big show on CNN
Speaker 3: called Crossfire. It was Tucker, Yeah, Tucker Carlson was the conservative.
Speaker 3: Paul mcgala was the roll and they just like got
Speaker 3: on and yelled at each other.
Speaker 1: I'm looking at us now.
Speaker 3: Apparently good.
Speaker 1: Apparently there there was a they they had a revival
Speaker 1: and then in twenty thirteen and fourteen on the left
Speaker 1: was Stephanie Cutter and Van Jones and then Nuke king
Speaker 1: Ridge and se Cup on the right, and then whatever.
Speaker 1: And then whenever they needed breaking news they'd bring in
Speaker 1: Wolf Blitzer for some reason.
Speaker 5: Because Wolf the situation room.
Speaker 1: Yeah yeah, they released him from the cryogenic.
Speaker 3: Whose helicopter lifted from the situation room three rooms over
Speaker 3: to the Crossfire set. Just with dead pan. You know,
Speaker 3: we need you go into emotion on his face. Ever
Speaker 3: you guys ever seen the Wolf Blitzer episode of Celebrity
Speaker 3: Jeopardy No Yourself a favor is It hasn't been scrubbed yet.
Speaker 1: Is it as good as like the SNL parody? Is
Speaker 1: the Celebrity Jeopardy with like the Sean Connery it's just yeah, no.
Speaker 3: No, and also incorrect, just one after another, like negative
Speaker 3: went into the into the red. He's in there quickly separty. Well, Wolf,
Speaker 3: we're gonna spot you three thousand because we can't have
Speaker 3: somebody be in negative numbers going into going into Final Jeopardy.
Speaker 3: And I think Andy Richter was on with him and
Speaker 3: just destroyed. Was so good.
Speaker 1: That's so funny. Andy Richter like this the kind of
Speaker 1: crossover I didn't know I need.
Speaker 2: Yeah, it's still up there mostly from what I could tell,
Speaker 2: it's incredible.
Speaker 3: Yeah, I am an old person. All right, we still
Speaker 3: have Emily. You're overrated. What do you think is overrated?
Speaker 5: Big cars are overrated?
Speaker 6: Oh?
Speaker 4: Totally sort of half heartedly looking for our next car
Speaker 4: and can't find anything that is like small. And the
Speaker 4: other day I was in the parking lot for a
Speaker 4: grocery store in your hair mostly I can walk for groceries,
Speaker 4: but occasionally have to drive this other store. And half
Speaker 4: the spots were labeled compact, and like all of those
Speaker 4: spots were taken up two at a time by what
Speaker 4: we now have is regular cars, because somebody's decided that
Speaker 4: people in this country don't deserve normal sized cars.
Speaker 3: Yeah, they're so.
Speaker 2: I mean, it's still the point where like even the
Speaker 2: people who design parking lots are like, we have to
Speaker 2: tell the automobile manufacturers, like the standard we've set as
Speaker 2: people who like create parking lots, like they're pushing the
Speaker 2: boundaries of what we can actually do or how we
Speaker 2: measure things because the cars are so fucking big.
Speaker 4: And our streets around here in Seattle, we have a
Speaker 4: lot of neighborhood streets where there's like parking on both
Speaker 4: sides and then sort of just barely enough space for
Speaker 4: two normal cars to go through, right, or sometimes you
Speaker 4: have to like pull over to the car, and the
Speaker 4: bigger the car is like the harder that gets.
Speaker 3: I love that thing.
Speaker 2: I remember one of the times went to Seattle, seeing
Speaker 2: how everybody just parks on whatever side of the street
Speaker 2: in whatever direction they want.
Speaker 3: I was like, all right, I'm like, all right, I
Speaker 3: was not familiar.
Speaker 1: That's funny.
Speaker 5: Yeah, yeah, a little bit of chaos.
Speaker 4: It totally offends my spouse, who's like, that's not how
Speaker 4: parking works.
Speaker 1: That's it.
Speaker 3: Yeah, yeah, I love it. Automakers just seem to be
Speaker 3: getting bigger and heavier. They won't stop until they make
Speaker 3: a car that like is legally required to have a
Speaker 3: foghorn on it.
Speaker 4: Right, So the cyber truck, Yeah, cyber truck.
Speaker 3: I was going to ask, have you have you considered
Speaker 3: the cyber truck.
Speaker 5: I've seen one in person.
Speaker 4: They are hilarious, like you can't not laugh exactly.
Speaker 3: What it's such. It is an experiencing one in the wild.
Speaker 4: It's like, wow, I just want to say that what
Speaker 4: we really need is functional public transit, but like, short
Speaker 4: of that, we also need to not be doing bigger
Speaker 4: and bigger cars.
Speaker 1: Yeah yeah, yeah, no, I just I mean, I have
Speaker 1: a truck. I have a twenty twenty truck, and I wish,
Speaker 1: I really wish it was much smaller because it's hard
Speaker 1: to park. It's way too big. I mean, I think
Speaker 1: that peak of truck design it was like, you know,
Speaker 1: a nineteen eighty seven Toyota Tacoba long cab, you know,
Speaker 1: just you know where like, yeah, you had to bunch
Speaker 1: up your knees in the back if you want to
Speaker 1: fit four people on it. But you actually had a
Speaker 1: long you know, you actually had a truck bed that
Speaker 1: actually had you know, some carrying capacity, you know, and.
Speaker 2: It was a car you could absolutely run into the.
Speaker 3: Ground with nobody.
Speaker 1: Yeah, oh yeah, oh yes.
Speaker 2: Now people were like, my new Ford Lightning needs a
Speaker 2: software update.
Speaker 1: Oh god, well that's the thing, is like yeah, I
Speaker 1: mean like, I mean that's a big deal. I know,
Speaker 1: in like organ which is like the where they had
Speaker 1: right to repair a right to repair bill, and I
Speaker 1: mean in some ways the people that were kind of
Speaker 1: into it weirdly were like Google actually came out kind
Speaker 1: of into it. There was a Good for for Media
Speaker 1: podcast where they talked about this with an Apple because
Speaker 1: they have such a closed ecosystem was so against right
Speaker 1: to repair or you know, even if you have right
Speaker 1: to repair. They actually they'd add on all these things
Speaker 1: where you you'd still have to send them to an
Speaker 1: authorized dealers because of firmware issues or whatever. And then
Speaker 1: John Deere, like John Deere is this kind of thing
Speaker 1: where they have you know, so much of their tractors
Speaker 1: are computerized, and so there's like a lot of like
Speaker 1: these John Deeer hacking kinds of things that people who
Speaker 1: are outside of the US, you know, programming these kinds
Speaker 1: of hacks for for people running these tractors and can't
Speaker 1: run into their firm where.
Speaker 3: Yeah, the farmers have all the good the GPS something,
Speaker 3: but did.
Speaker 4: You hear about how the GPS was out for a
Speaker 4: while with there's actually is again for a for media podcast,
Speaker 4: but that the way those tractors work for like planting
Speaker 4: is so precise and the GPS. With the GPS off,
Speaker 4: they basically couldn't plant because then the seeds wouldn't be
Speaker 4: in the right spot for the next process, and so
Speaker 4: they had to wait. And there's a really narrow window
Speaker 4: of currently with our you know, currently genetically modified like
Speaker 4: very very specific corn that Santo owns, and so it
Speaker 4: was actually looking pretty bad for a while. I didn't
Speaker 4: hear any follow ups. So maybe the solar flail was
Speaker 4: short enough and the GPS came back online, but apparently.
Speaker 5: That was a big thing.
Speaker 3: It has to be a brief window because it goes
Speaker 3: from corn plant corn seed planted in the ground to
Speaker 3: like popcorn in the movie theater in two and a
Speaker 3: half weeks.
Speaker 4: Yeah, hyper, most of it doesn't even go to popcorn
Speaker 4: in the movie theater, right, most of it goes to
Speaker 4: animal feed or after.
Speaker 3: All, I think, yeah, right, right, yeah, all right, Well,
Speaker 3: let's take a quick break and we're going to come
Speaker 3: back and dive into why Miles and I are excited
Speaker 3: about the future of AI. We'll be right back. Crossfire
Speaker 3: and we're back. We're back. So just to for people
Speaker 3: who haven't listened to your previous appearance in a while,
Speaker 3: I feel like a broad over generalization, but it feels
Speaker 3: like the stuff that AI is actually being used for
Speaker 3: and capable of is not what we're being told about
Speaker 3: through the mainstream media. Like it is not an autonomous
Speaker 3: intelligence that is going to be the bad guy in
Speaker 3: a Mission Impossible movie. I mean, it is a bad
Speaker 3: guy in a Mission Impossible movie, but it's not going
Speaker 3: to be a bad guy in the in reality our
Speaker 3: way it is, yeah, the way that our actual president believes.
Speaker 3: That was an amazing reveal that Joe Biden basically watched
Speaker 3: Mission Impossible and was like, we gotta gotta worry about
Speaker 3: this AI stuff. Jack it's gonna know my next move.
Speaker 3: But it is more like the large language models are
Speaker 3: basically more sophisticated auto complete that is telling you what
Speaker 3: it's data set indicates you want to hear, or what
Speaker 3: its data set indicates will make you think. It is
Speaker 3: thinking talking like a person. In many cases that means
Speaker 3: what they call hallucinating. What is actually just making ship up?
Speaker 2: What are their jobs? Could you say that You're like, sorry,
Speaker 2: I was hallucinating and like okay.
Speaker 1: Oh but what you wouldn't last long as a pre cog.
Speaker 3: Yeah, I would be the worst precog on.
Speaker 2: The I R S. I was hallucinating on that last
Speaker 2: tax return.
Speaker 3: I think that's probably.
Speaker 4: About using this to do your tax return.
Speaker 1: Yeah right, there's actually, yeah, there's actually a I mean
Speaker 1: in California, there's a whatever the Department of Tax and Revenue.
Speaker 1: They've been There was some great reporting and how it
Speaker 1: matters by Karie Johnson, and he was talking about how
Speaker 1: they were using this thing, some language model to effectively
Speaker 1: advise the people who call in or advise the agents
Speaker 1: who respond to people to call into the California Franchise
Speaker 1: sax board and you know, and they're like, well, they're
Speaker 1: they're and they're like, well, you know, the the agents
Speaker 1: are still going to you know, have the last worry.
Speaker 1: But I'm just like, yeah, yeah, but they might they're overworked,
Speaker 1: Like are they going to just they're going to read
Speaker 1: this stuff or me and them.
Speaker 3: You know, right exactly. Oh, you're going to use this
Speaker 3: as an extra thing, just an extra expense to do
Speaker 3: the product, do your job even better. That doesn't sound
Speaker 3: like a company necessarily. Yeah. Yeah, So an interesting thing
Speaker 3: that we're seeing happen. You know, we pay attention when
Speaker 3: there's a an AI story that captures the zeitgeist. We
Speaker 3: covered the B minus version of a George Carlin routine
Speaker 3: that came out. They were like, a, I just brought
Speaker 3: George Carlin back from the dead. We covered Amazon Fresh
Speaker 3: having that store where the cameras know what you've taken
Speaker 3: and so even if you try and shoplift like the
Speaker 3: cameras know they're gonna catch it, and then you don't
Speaker 3: even have to check out, you just walk out and
Speaker 3: they like it, charges your account because of AI. And
Speaker 3: then what we're seeing is that when the truth emerges,
Speaker 3: it does not enter the zeitgeist because you guys cover
Speaker 3: it on your show which is why we're so thrilled
Speaker 3: to have you back. But you know, we have updates
Speaker 3: on those two stories, Carlin. That was just written by
Speaker 3: a person the Amazon Fresh. Those videos were being fed
Speaker 3: to people working in India to try to track where
Speaker 3: everything was going, which was why there was like a
Speaker 3: weird pause like as people were where they're like, oh,
Speaker 3: I think we got okay, yeah, we're we're just gonna
Speaker 3: do a best guess. But it's straight up like mechanical
Speaker 3: Turk like it's which again Amazon named one of their
Speaker 3: companies the Mechanical Turk, so they they know what's going on.
Speaker 3: They knew what they were planning to do here all along. Maybe,
Speaker 3: But is that kind of the model you're seeing is big,
Speaker 3: flashy announcement, this is what AI integration can do, and
Speaker 3: then when it falls short, people just kind of ignore
Speaker 3: it or how does it seem from where you're sitting.
Speaker 4: Yeah, we haven't seen really good implosions yet, and it's
Speaker 4: surprising because like the stuff that goes wrong goes like
Speaker 4: really really wrong, and people are like, yeah, well, it's
Speaker 4: just in its infancy, which is a really really annoying
Speaker 4: metaphor because it first of all suggests that this is
Speaker 4: something that is you know, like a human, like an
Speaker 4: animal at least that's that's a baby and can grow
Speaker 4: something that is learning over time. And also sort of
Speaker 4: like pulls on this idea that we should be kind
Speaker 4: to these systems because they're just little babies, right, and
Speaker 4: so if something goes wrong, it's like, well, no, that's
Speaker 4: that's just it's it's still learning. And we get all
Speaker 4: of these appeals to the future, like how good it
Speaker 4: is going to be in the future, And there is
Speaker 4: at this point, I think so much money sunk into
Speaker 4: this that people aren't ready to like let go and
Speaker 4: own up to the fact that yeah, so, and it
Speaker 4: is I guess too easy to hire exploited workers for
Speaker 4: poor pay, usually overseas to like backstop the stuff. There's
Speaker 4: also so you gave us the Amazon ghost stores actually
Speaker 4: being monitored by people in India. There was one of
Speaker 4: the self driving car companies admitted that their cars were
Speaker 4: being supervised by workers in Mexico.
Speaker 2: And remember the stats on the alex Yeah it was
Speaker 2: it was.
Speaker 1: It was, So was Eric Voight, the CEO of Cruise,
Speaker 1: and he had said And then there was this reporting
Speaker 1: on the New York Times where they said, you know,
Speaker 1: they used they used humans. And then he was like, wait,
Speaker 1: well wait, wait, wait, wait, you're you're really blowing out
Speaker 1: of proportion. We only use it something of three to
Speaker 1: five percent at a time, Like that's a huge it's
Speaker 1: a huge amount of out and and he posted this
Speaker 1: himself on on Hacker News, which is this, you know,
Speaker 1: kind of like, I don't know four chan for tech pros.
Speaker 1: I guess, well, I guess four four chan is four
Speaker 1: Chan for techpros. But I mean it's you know, but
Speaker 1: like with a little less overt racism. I guess, yeah,
Speaker 1: especially yeah, but it was still Yeah, but we're seeing
Speaker 1: this in a lot of different industries. At the end
Speaker 1: of the day, it's just this is outsourcing humans. Dan
Speaker 1: at Vertessi is a sociologist at Princeton. She has a
Speaker 1: piece in Tech Policy Press which the title is something
Speaker 1: like AI it's just forecasting. It was just outsourcing two
Speaker 1: point zero effectively. And yeah, we're seeing we're seeing a
Speaker 1: lot of the same patterns that we saw in the
Speaker 1: early nineties when these business process outsourcing or BPO organizations
Speaker 1: were really becoming all the rage in the US.
Speaker 2: Right. The other thing that I see a lot too,
Speaker 2: is like I felt early on, especially when we were
Speaker 2: talking about it. The thing that intrigued us, like when
Speaker 2: was when everyone's like, dude, this thing is gonna fucking
Speaker 2: end the world. It's how powerful AI is. Have I
Speaker 2: have a whole plan to take myself off this mortale
Speaker 2: if I have to the moment in which AI becomes
Speaker 2: sentient and takes over. And like I think it felt
Speaker 2: like maybe the markets were like, hey, man, you're scaring
Speaker 2: the kids. Man, do we have another way to talk
Speaker 2: about this? And I feel like recently I seem more
Speaker 2: of like together, when we harness human intelligence with AI,
Speaker 2: we can achieve a new level of existence and ideation
Speaker 2: that has not been seen ever in the course of
Speaker 2: human history. And I saw that in the Netflix j
Speaker 2: Loo movie, where like the entire crux of the film
Speaker 2: was this AI. AI skeptic had to embrace the AI
Speaker 2: in order to overcome the main problem conflict in the film,
Speaker 2: or just even now, like with the CTO of Open
Speaker 2: AI also doing a similar thing when talking about how AI,
Speaker 2: like some creative jobs are just gonna vanish, But that's
Speaker 2: because when the human mind harnesses the power of the AI,
Speaker 2: we're gonna come up with such new things that feels
Speaker 2: like the new thing, which is more like we got
Speaker 2: to embrace it so we can evolve into this next
Speaker 2: level of thinking, et cetera, computation or whatever you guys on.
Speaker 3: I was just gonna say, Mystery Hype Theater three thousand
Speaker 3: reads the research papers so that we don't have to,
Speaker 3: and Miles watches the j Lo movies so that.
Speaker 5: You don't have to.
Speaker 3: Got to know what they're saying.
Speaker 1: But I'm glad you're I'm glad you're watching the day
Speaker 1: because there's so many different cultural touchstones of this.
Speaker 3: Yeah.
Speaker 1: I had to look because I thought I thought the
Speaker 1: movie you were talking about was the the like sort
Speaker 1: of the autobiography This is Me Now a love story,
Speaker 1: and I'm like, there's a film and I was like,
Speaker 1: there's an AI subplot in that. I didn't know that
Speaker 1: j Loo's life was, you know, a complete cautionary tale
Speaker 1: about you know, AI and and the inevitability of it.
Speaker 1: But yeah, but sorry, Emily was about to say something.
Speaker 4: I just want to be starting so our colleagues to meet.
Speaker 4: Gabruin and mil Torres coined this acronym test reel, which
Speaker 4: stands for a bundle of ideologies that are all very
Speaker 4: closely related to each other. And what's interesting about the transition,
Speaker 4: you notice that they've basically moved from one part of
Speaker 4: the tescrol acronym to another. So the it's all the
Speaker 4: stuff that's based on these ideas of eugenics and of
Speaker 4: sort of really disinterest in any actual current humans in
Speaker 4: the service of these imagined people living as uploaded simulations
Speaker 4: in the far long future. It's utilitarianism made even more
Speaker 4: ridiculous by being taken to an an extreme endpoint. So
Speaker 4: this thing like it's going to kill us all comes
Speaker 4: partially from like the long term ism part of this
Speaker 4: which people are fixated, this idea of we have to
Speaker 4: it's ridiculous. They have a specific number, which is ten
Speaker 4: to the fifty eight, who are the future humans who
Speaker 4: are going to live as uploaded simulations in computer systems
Speaker 4: installed all over the galaxy. And these are people who
Speaker 4: clearly have never worked in it support because somehow the
Speaker 4: computers just keep running.
Speaker 2: Yeah yeah, yeah yeah.
Speaker 4: And the idea is that if we don't make sure
Speaker 4: that future comes about, then we collectively are missing out
Speaker 4: on the happiness of those ten to the fifty eight
Speaker 4: humans and that's such a big number that it doesn't
Speaker 4: matter what happens now, all right. And I always say
Speaker 4: when I relate this story that I wish I were
Speaker 4: making this up. Yeah, but there are actually people who
Speaker 4: believe this. And so that's where the sort of like,
Speaker 4: oh no, it's you know, it's going to end us.
Speaker 5: All stuff lives, and that's.
Speaker 4: The l the long termism part of tesculism. But this
Speaker 4: idea that we should join with the computers and become
Speaker 4: a better thing, that's the t that's the transhumanism. And
Speaker 4: it's all part of sort of the same bundle and
Speaker 4: way of thinking. And there's this great paper out in
Speaker 4: the publication called First Monday by mil Tors and to
Speaker 4: Meet Gibbrus, sort of documenting the way that all of
Speaker 4: these different ideologies are linked one to the next. There's
Speaker 4: overlaps in the people working on them, there's overlaps in
Speaker 4: the ideas, and it all goes back to eugenics and
Speaker 4: race science.
Speaker 3: Wow, okay, and it just had a onto.
Speaker 1: I mean, it's really so the dobrism and the boosterism
Speaker 1: are you know, two sides of the same cli even
Speaker 1: though they kind of pose each other to be different.
Speaker 1: So you if you imagine, there was an article and
Speaker 1: it was a very funny chart that was a company
Speaker 1: of this article that is an article in the Washington Post,
Speaker 1: Natasha Tiku, and she had this kind of grid and
Speaker 1: it was really funny because it was like on one
Speaker 1: end was this guy Eliza Yudowski who's like a big duomor.
Speaker 1: He had this thing in Time where he wrote an
Speaker 1: op ed in Time magazine he was like, you know, basically,
Speaker 1: if we need to, we have to be willing to
Speaker 1: do air strikes on data centers and then which he
Speaker 1: actually wrote and he actually said she speaking of Tucker Carlston.
Speaker 1: I think Tucker Carlson also was like, oh geez, maybe
Speaker 1: we should do that. And on the other end you
Speaker 1: have you know Sam Altman, who was like, in Mira Maradi.
Speaker 3: You got a cyber trust.
Speaker 2: Sorry, no, it's okay, I just want a cyber truck.
Speaker 1: You just want a cyber truck. I got to enter
Speaker 1: my social Security number one. Yeah, yeah, it's fine, I'll
Speaker 1: give you mine. I want to beat to you too,
Speaker 1: and so you know, but you know, they are actually
Speaker 1: two sides of the same coin because they basically want
Speaker 1: to and I mean in the middle was to meet
Speaker 1: who's on this test real paper. And also it's also
Speaker 1: my boss at the at Dare and and and I'm
Speaker 1: not I'm not saying that too. You know, kiss Acids
Speaker 1: we actually get along quite well. So but it's but
Speaker 1: it's you know, posed as you know, someone in this
Speaker 1: in this grid. But so Dubrius and Bistrooms them both
Speaker 1: basically see AIS this kind of inevitability. We're gonna get there,
Speaker 1: you know. And to me, I think the metaphor you know,
Speaker 1: you allude to Emily is kind of like thinking about
Speaker 1: this like a kid that needs to be formed. And
Speaker 1: in some in some ways I think it's I mean,
Speaker 1: it's colonialism. It's it's that it's manifest destiny. You know,
Speaker 1: it's always five years away, you know. And so but
Speaker 1: they but they both see development of AI to be
Speaker 1: really really critical to whatever is happening next when we
Speaker 1: can be like no one is asking for this ship
Speaker 1: and it's taking up you know, and I and we're
Speaker 1: seeing a lot of memes online are like, you know,
Speaker 1: I'm so glad that we are draining a lake every
Speaker 1: you know, to generate this image of and there's one
Speaker 1: of the images was like a toddler holding you know,
Speaker 1: a crucifix and with a helmet that says the police
Speaker 1: on it and their neck deep in water and they've
Speaker 1: got big kawhi you know, emoji eyes, right.
Speaker 2: And if you blur your eyes, it's Jesus's face. Yeah,
Speaker 2: because that's another huge thing.
Speaker 3: I see.
Speaker 2: There's so much AI nonsense art out there too.
Speaker 4: And it's it's super environmentally disastrous, right, this is this thing,
Speaker 4: and a lot of it is non consensual in the
Speaker 4: sense that, like, if you try to use Google these
Speaker 4: days to do a search, the first thing you get,
Speaker 4: depending on search with many searches, is the AI overview,
Speaker 4: which is the output of one of these text to
Speaker 4: text models, taking way more processing power than just returning
Speaker 4: the townlu links, which is that they used to do,
Speaker 4: and you can't turn it off.
Speaker 5: So if you use Google do.
Speaker 4: A search, you're stuck with that. You're stuck with its
Speaker 4: environmental impact.
Speaker 3: Yeah. The one positive I'd say that I've seen more
Speaker 3: and more since we last spoke is people being like, wait,
Speaker 3: who the fuck is this for? Exactly? Who is asking
Speaker 3: for this? Which I think is ultimately going to become
Speaker 3: a louder and louder question. Is this seems to be
Speaker 3: mainly for tech CEOs. Yeah, we're very wealthy, but especially
Speaker 3: like in countries that aren't, where corporations are not more
Speaker 3: powerful and valued more as humans than actual humans, like
Speaker 3: the United States. I feel like it will it will
Speaker 3: become more and more of an issue and then be
Speaker 3: a matter of figuring out if that message actually gets
Speaker 3: in in the US.
Speaker 4: But yeah, we can keep shouting it, and that's the
Speaker 4: great question, like.
Speaker 5: Who asked for this?
Speaker 3: Wait? Who asked for this? And who likes where it's going? Yeah?
Speaker 2: Yeah, because of the luster has worn off from the
Speaker 2: early you know days of those early chat GPT models
Speaker 2: that came out like this.
Speaker 3: Thing could be a doctor, this thing is could be
Speaker 3: a lawyer.
Speaker 2: And then most people kind of like the people that
Speaker 2: I know who are first impressed, like yeah, Like it
Speaker 2: helped me write an email. That's about the most I
Speaker 2: can do with it, because I hate writing formally and
Speaker 2: so it helped that. But beyond that, I don't know
Speaker 2: many like as personally, but and I know people that
Speaker 2: work in many different fields. It sort of ends up
Speaker 2: being like, yeah, I don't really have a use for
Speaker 2: it aside from like making funny songs that I share
Speaker 2: with my girlfriend that you know, we have a song
Speaker 2: about how she loves Chipotle, and it was in the
Speaker 2: style of you know, Crystal Waters. Yeah, and now that's it.
Speaker 1: Yeah, yeah, I mean I think that's I mean, I
Speaker 1: like this phrase that Emily coined, which is, you know,
Speaker 1: resist the urge to be impressed. Yeah, where it's like
Speaker 1: this this kind of thing where you know, you've got
Speaker 1: this proof of concept and it's got a little little
Speaker 1: g whiz to it, but beyond that, I mean, you know,
Speaker 1: are you going to use this in any kind of
Speaker 1: real I don't know, product cycle or ideation or whatever.
Speaker 1: And and there's just been I mean, we've had a
Speaker 1: tech lash for the last couple of years here and
Speaker 1: and I mean I think it's really the only people
Speaker 1: I ever see praising this. And I mean, maybe this
Speaker 1: is just a virtue of my timeline that it's creating,
Speaker 1: and Elon Musk hasn't absolutely destroyed it yet, But the
Speaker 1: only people I see praising it are typically people who
Speaker 1: are very very much in the industry. You know, they're
Speaker 1: the same people who are like I live in San
Speaker 1: Francisco and I support you know, London Breed saying she
Speaker 1: wants to clean up our streets from homeless people. And
Speaker 1: it's just the people. And I mean some of them
Speaker 1: are not out and out that fascist, but you know,
Speaker 1: they're they're they're they're brushing, They're brushing along with it
Speaker 1: everyone else I see, especially also a lot of people
Speaker 1: in technology. They're like, I work in technology, and I
Speaker 1: hate this bubble and I'm so tired about talking about
Speaker 1: this and i just wanted to go away.
Speaker 3: I mean, this is you know.
Speaker 1: And then I see teachers, you know, we see a
Speaker 1: lot of people from the professions, teachers, nurses, doctor. My
Speaker 1: sister is a nurse. He's like, I hate this stuff.
Speaker 1: My sister's a lawyer, and she's like, yeah, I've used
Speaker 1: this to sort of start a brief, but he gets
Speaker 1: so many things wrong. I need to double check everything.
Speaker 1: And at the end of the day, yeah, does it
Speaker 1: actually help. Yeah, probably not.
Speaker 2: But but it's just a little baby, now, Alex, it's
Speaker 2: just a little baby.
Speaker 3: It's just a little baby, A little baby.
Speaker 1: Would you want to take a little defenseless baby.
Speaker 3: It is so interesting too, because it's like and it
Speaker 3: could be a lawyer or a doctor, which is also
Speaker 3: a baby. I'm so impressed by my little baby. Like
Speaker 3: what they just said, they could be a lawyer or
Speaker 3: a doctor one day to.
Speaker 4: Watch for the phrase in its infancy in the coverage
Speaker 4: of this, it's all over the place.
Speaker 1: Yeah, shout up, shout shout up. And I just want
Speaker 1: to give a shout out to Ana Laurene Hoffman, who
Speaker 1: someone is a professor at You'd have a really good
Speaker 1: friend of mine and colleague of Emily and uh so
Speaker 1: she's done a lot of work and sort of talking
Speaker 1: about like these metaphors of this kind of baby noess
Speaker 1: and how it absolves AI of like truly being like
Speaker 1: racist and sexist and absolutely fucking up. It's like, it's
Speaker 1: just a little baby. Yeah, it's a baby with with
Speaker 1: with a billion dollar valuation or whatever. You know, however
Speaker 1: much it is.
Speaker 3: By having AI put in the like mentioned in the
Speaker 3: earnings call.
Speaker 2: But if we're going to keep that consistency with that metaphor,
Speaker 2: you'd be like, then, why are you, as the parent,
Speaker 2: wheeling this child out to do labor that it's wholly
Speaker 2: unprepared for.
Speaker 3: You look fucked up. Actually, so, Miles, I have kids
Speaker 3: that are a little older than yours, and so I
Speaker 3: could that is actually good parenting. To wheel them out
Speaker 3: and have them and it's brings in the cheddar.
Speaker 1: Yeah, Jack coming on the podcast Hot take child labor,
Speaker 1: actually child labor.
Speaker 3: And when it's your kid, come on. Those are called chores,
Speaker 3: and yes, if they're doing the chores for a multinational
Speaker 3: corporation against a little hazy, but.
Speaker 2: Yeah, take out an h eight hour chor shift that check.
Speaker 3: Hazy because of all that money coming in.
Speaker 2: You know.
Speaker 4: So on this point about like nobody wants this, I
Speaker 4: have a talk that I've been doing since summer of
Speaker 4: twenty three called chat GP Why And then the subtitle
Speaker 4: is when, if ever is synthetic text safe, appropriate and desirable?
Speaker 4: And it's meant for sort of non specialist audiences. I
Speaker 4: basically go through, Okay, what's a language model, what's the
Speaker 4: technology for in its original use cases?
Speaker 5: How did we get to the current thing?
Speaker 4: And then what would have to be true for you
Speaker 4: to actually want to use the output of one of
Speaker 4: these things?
Speaker 2: Right?
Speaker 4: So, the first thing is you'd want something that is
Speaker 4: ethically produced, so not based on stolen data, not based
Speaker 4: on exploited labor. Basically, don't have those second layers box. Okay,
Speaker 4: you also want something that somehow is not an environmental disaster.
Speaker 5: Also don't have that.
Speaker 4: Assuming we somehow got past those two hurdles, and it's
Speaker 4: things like, okay, well, you need a case where it's
Speaker 4: not going to be misleading anybody. It's got to be
Speaker 4: a case where you don't actually care about the stuff
Speaker 4: being accurate or truthful what comes out of it, including
Speaker 4: you know, being able to recognize and mitigate any biases.
Speaker 4: You also don't care about the output being original, so
Speaker 4: plagiarism's okay. And like by the time you've done all that,
Speaker 4: it's like, yeah, this use case of helping you draft
Speaker 4: an email because that's tedious, right, and is that worth
Speaker 4: the two that we started with there? Right, you know,
Speaker 4: the labor exploitation, data theft and environmental impacts.
Speaker 3: Right now we've just broken down all of human meaning
Speaker 3: for the past, like for civilizations, Like you know, none
Speaker 3: of the things that we've always seemed to think matter
Speaker 3: matter anymore. Because billion dollar companies needed like a new
Speaker 3: toy to hype up for the stock market. It sounds
Speaker 3: like that's very frustrating. Let's take a quick let's take
Speaker 3: a quick break. We'll come back, because I do want
Speaker 3: to talk about what it is being used for. I mean,
Speaker 3: AI is a very loose term the way it's being applied,
Speaker 3: But people want to use, you know, these new advances
Speaker 3: in computing to make themselves seem more profitable, and so
Speaker 3: I want to talk about what it is actually being
Speaker 3: used for. We'll be right back, and we're back. We're
Speaker 3: we're back, and on your show, you've done some good
Speaker 3: stuff on just the surveillance side of AI, which I
Speaker 3: mean that turns out a lot of the technology that
Speaker 3: we initially thought was promising was just eventually used for
Speaker 3: the purposes of marketing and surveillance in the end, and
Speaker 3: it seems like AI skipped all the promising stuff. And
Speaker 3: it's just like, what if we just went right to
Speaker 3: the right there harming harming people.
Speaker 1: Yeah, I will, I will say that kind of. I mean,
Speaker 1: you had mentioned that this term AI is kind of
Speaker 1: being used, Lucy Goosey, and you know, I mean we
Speaker 1: we AI is kind of synonymous with large language models
Speaker 1: and image generators. But you know, things that have been
Speaker 1: called AI also encompass things like biometric surveillance, like like
Speaker 1: different different systems which use this technology called quot unquote
Speaker 1: machine learning, and which is kind of this large scale
Speaker 1: patterned recognition. So a lot of it's being used, especially
Speaker 1: at the border, so doing things like trying to detect
Speaker 1: verify identities by voices or by faces. You probably see
Speaker 1: this if you've been in the airport. The TSA has
Speaker 1: been using this, and you can still voluntarily opt out
Speaker 1: for now, but they're really incentivizing it. I saw that
Speaker 1: TSA has this touchless thing now, which is a facial recognition,
Speaker 1: so you don't have to present your ID, you can
Speaker 1: just scan your face.
Speaker 4: And go and and like, don't do that, like take
Speaker 4: every option to opt out, and that the fact that
Speaker 4: those signs are there saying that this is optional. Penny,
Speaker 4: somebody actually Petty. Yeah, yeah, the only reason we had
Speaker 4: that science is because of her activism saying like this
Speaker 4: has to be clear to the travelers that it's actually
Speaker 4: optional and you can opt out, so it's posted there
Speaker 4: that you don't have.
Speaker 3: To do this. Yeah, all right, then I'm going to
Speaker 3: feel you up. Sorry, those are just the rules.
Speaker 1: Yeah, it's just it's absolutely but I mean it gets
Speaker 1: it gets you know, leveraged against people who fly to
Speaker 1: a lesser degree, but I mean folks who are refugees
Speaker 1: or asy le's you know, I mean people on the
Speaker 1: move really encountered to stuff incredibly violent ways. You know,
Speaker 1: they do things like try to they take their blood
Speaker 1: and say that, well we can we can associate your
Speaker 1: We're gonna you know, sequence your genome and safe you're
Speaker 1: actually from the country you say you're from, which is first,
Speaker 1: it's pseudoscience. I mean, basically all biologists have been like,
Speaker 1: you can't use this to determine if someone is X,
Speaker 1: y Z, like nationality, because nationalities are one political entities,
Speaker 1: they're not biological ones, and so like we can sort
Speaker 1: of pinpoint you to a region, but it says nothing
Speaker 1: to say of anything about the political borders of a country.
Speaker 1: There's a great book I started reading by Petro Molner
Speaker 1: which is called The Walls Have Eyes, which is about
Speaker 1: this kind of intense surveillance state or intense surveillance architecture.
Speaker 1: You know, it's being used in you know, typically in
Speaker 1: the border, the US Mexico border, but also the you know,
Speaker 1: the various points of entry in Europe where African migrants
Speaker 1: are fleeing to, you know, fleeing places like Sudan and
Speaker 1: Congo and the Tagrai region of Ethiopia. So just like
Speaker 1: and this is just some of the most violent kind
Speaker 1: of stuff he in it you can imagine, and it's
Speaker 1: way far away from you know, this kind of Oh,
Speaker 1: here's like a fake little child, you know, or a
Speaker 1: Jesus holding twelve thousand babies riding in America truck with
Speaker 1: the American flag on it, you know what I mean, right,
Speaker 1: that's yeah, So the reality is yeah, much more stark.
Speaker 4: And you see that, you see that one too many
Speaker 4: image matching, so you get all these false arrests. So
Speaker 4: people because the AI said that they matched the image
Speaker 4: from the grain surveillance video. And it's one of those
Speaker 4: things where it's bad if it works, because you have
Speaker 4: this like increased surveillance power of the state, and it's
Speaker 4: bad if it doesn't work, because you get all these
Speaker 4: false arrests.
Speaker 5: Like it's it's just a bad idea. It's just a
Speaker 5: don't and.
Speaker 4: It's not just image stuff. So we read a while
Speaker 4: back about a situation in Germany, I think, where asylum
Speaker 4: seekers were being vetted as to whether or not they
Speaker 4: spoke the right language using you know, so one of
Speaker 4: the things you can do with pattern matching is okay,
Speaker 4: language identification that this string what languages that come from
Speaker 4: but it was being done based on completely inadequate data
Speaker 4: sets by people who don't speak the languages, who are
Speaker 4: not in a position to actually vet the output of
Speaker 4: the machine. And so you have these folks who are
Speaker 4: in the worst imaginable situation, like you don't go seeking
Speaker 4: asylum on a.
Speaker 5: Lark, right, these other people?
Speaker 4: Yeah, yeah, and then they're getting denied because some algorithms said, oh,
Speaker 4: you don't speak the language from the place you claim
Speaker 4: to be coming from where the person your accent is
Speaker 4: wrong or your variety is wrong or whatever. And the
Speaker 4: person who's who's run this computer system has no way
Speaker 4: of actually checking its output, but they believe it, and
Speaker 4: then they get these asylum seekers turned away.
Speaker 3: Yeah, so how does that? You know?
Speaker 2: Everything you said? How should we feel that open AI
Speaker 2: recently welcome to their board the eighteenth director of the NSA,
Speaker 2: Paul nakasone, is that bad? Or what should we take
Speaker 2: from that one?
Speaker 4: How should we feel not at all surprised?
Speaker 2: Right?
Speaker 4: How should we feel when open AI it's like, okay,
Speaker 4: bad is whatever the rest.
Speaker 5: Of that is is bad?
Speaker 2: Yeah? It seems bad, man, Yeah, it seems like there's
Speaker 2: again we're talking like this technology to mass surveillance pipeline,
Speaker 2: and who better than someone who ran the fucking NSSA, Like.
Speaker 3: And I know the way it's being spun.
Speaker 2: It's like, you know, this is a part of cyber command,
Speaker 2: Like he inherently knows like how what the what the
Speaker 2: guardbrails need to be in terms of keeping us safe.
Speaker 2: But to me it just feels like, No, you brought
Speaker 2: in a surveillance pro, not someone who understands inherently like
Speaker 2: what this specific technology is, but more someone who's like
Speaker 2: learns how to harness technology for this other specific aim.
Speaker 4: Yeah, so surveillance is not synonymous with safe. Like the
Speaker 4: one the one kind of one use case for the
Speaker 4: word surveillance that I think actually was pro public safety
Speaker 4: is there's a study the long term seting in Seattle
Speaker 4: called the Seattle Flu Study, and they are doing what
Speaker 4: they call surveillance testing for flu viruses. So they get
Speaker 4: volunteers to come in and get swabbed, and they are
Speaker 4: keeping track of what viruses are circulating in our community. Right,
Speaker 4: I'm all for surveilling the viruses, especially if you can
Speaker 4: keep the people out of it.
Speaker 1: Yeah, I would, I would. I would add a wrinkle
Speaker 1: to that just because I think that, I mean, there's
Speaker 1: a lot of surveillance. I mean that's the kind of technology,
Speaker 1: that's the kind of terminology they use of health surveillance
Speaker 1: to detect kind of virus rates and whatnot. I would
Speaker 1: also add the wrinkle that like a lot of those
Speaker 1: you know, organizations are really trusted by distrusted by marginalized people,
Speaker 1: Like what are you going to do what to me?
Speaker 1: You know, like especially thinking like you know, like lots
Speaker 1: of lots of transfolks and like especially like under housed
Speaker 1: or unhoused transfers, and just like you're going to do
Speaker 1: what you want this day upon me for who you know.
Speaker 4: Right, So, yeah, understandably, especially because because surveillance in general
Speaker 4: like is not a safety thing, right, It's not. It
Speaker 4: is maybe a like safety for people within the walls
Speaker 4: of the walled garden thing, but that's not safety, right.
Speaker 4: That's the other thing about this is that what we
Speaker 4: call AI these days is predicated on enormous data collection, right,
Speaker 4: And so to one eccent, it's just sort of an
Speaker 4: excuse to go about claiming access to all that data.
Speaker 4: And once you have access to all that data, you
Speaker 4: can do things with it that have nothing to do
Speaker 4: with the large language models, and so there's you know,
Speaker 4: this is I think less typically less immediately like threatening
Speaker 4: to life and limb than the applications that Alex was
Speaker 4: starting with. But there's a lot of stuff where it's like, actually,
Speaker 4: we would be better off without all that information about
Speaker 4: us being out there. And there's an example that came
Speaker 4: up recently. So did you see this thing about the
Speaker 4: system called recall that came out with Windows eleven? So
Speaker 4: this thing, Oh god, this is such a mess. So
Speaker 4: initially it was going to be by default turned on.
Speaker 2: Oh yes, right, yeah, this is kind of like the
Speaker 2: Adobe story too.
Speaker 5: Yeah, yeah, every five.
Speaker 4: Seconds it takes a picture of your screen and then
Speaker 4: you can use that to like using AI search for
Speaker 4: stuff that you've sort of and their example is something stupid.
Speaker 4: It's like, yeah, I saw a recipe, but I don't
Speaker 4: remember where I saw it, So you want to be
Speaker 4: able to search back through your activity and like zero
Speaker 4: thought to what this means for people who are victims
Speaker 4: of intimate partner violence, right that they have this surveillance
Speaker 4: going on in their computer that eventually it ended up
Speaker 4: being shipped as off by default because the cybersecurity folks
Speaker 4: pushed back really hard. And by folks, I don't mean
Speaker 4: the people at Microsoft, I mean the people out in
Speaker 4: the world or saw this coming. Yeah, but that's another
Speaker 4: example of like surveillance in the name of AI that's
Speaker 4: supposed to be the sort of, you know, helpful little
Speaker 4: thing for you, but like no thought to what that
Speaker 4: means for people. And it's like, yeah, we're just going
Speaker 4: to turn this on by default because everybody wants this obviously, right.
Speaker 2: It's like, no, I know how to look through my history.
Speaker 2: Actually I've developed that skill. Yeah, I don't need you
Speaker 2: to take snapshots of my desktop every three seconds.
Speaker 3: Your show's covered so many kind of upsetting ways that
Speaker 3: it doesn't seem like it's people implementing AI. It's companies
Speaker 3: implementing AI in a lot of cases to do jobs
Speaker 3: that it's not capable of doing. There there's been incorrect
Speaker 3: obituaries Grock, the Elon Musk won the Twitter one made
Speaker 3: up fake headlines about Iran attacking Israel, and like public
Speaker 3: like put them out as like a major trending story.
Speaker 3: You have this great anecdote about a Facebook chatbot AI
Speaker 3: like responding to someone had this like very specific question
Speaker 3: they have, like a gifted disabled child. They were like, he,
Speaker 3: does anybody have experience with a gifted disabled like two
Speaker 3: E child with like this specific New York public school
Speaker 3: program And the chatbot responds, yes, I have experience with that,
Speaker 3: and just like made up because they knew that's what
Speaker 3: that's what they wanted to hear. And fortunately it was
Speaker 3: like clearly labeled as an AI chatbot. So the person
Speaker 3: was like, what the black mirror, but World Health Organization,
Speaker 3: you know, eating disorder institutions replacing therapists with AI, Like
Speaker 3: you just have all these examples of this going being
Speaker 3: used where it shouldn't be and things going badly, and
Speaker 3: like that. There's a detail that I think we talked
Speaker 3: about last time about dual Lingo, where the model where
Speaker 3: they let AI take over some of the stuff that
Speaker 3: like human teachers and translators were doing before. And you
Speaker 3: made the point that people who are learning the language,
Speaker 3: who are beginners are not in a position to notice
Speaker 3: that the quality has dropped. Yeah, And I feel like
Speaker 3: that's what we're seeing basically everywhere now is just the
Speaker 3: Internet is so big, they're just using it so many
Speaker 3: different places that it's hard to catch them all and
Speaker 3: then there is not an appetite to report on all
Speaker 3: the ways it's fucking up, and so it just everything
Speaker 3: is kind of getting slightly too drastically shittier at once. Yeah,
Speaker 3: and I don't know what to do with that.
Speaker 1: I would say, yeah, well go ahead, Emily.
Speaker 5: What you do with that is you make fun of it.
Speaker 4: That's one of our things is ridiculous process to like,
Speaker 4: you know, try to try to keep the mood up,
Speaker 4: but also just show it for.
Speaker 5: How ridiculous it is.
Speaker 4: And then the other thing is to really seek out
Speaker 4: the good journalism on this topic, because so much of
Speaker 4: it is either fake journalism output by a large language
Speaker 4: model these days, or journalists who are basically practicing access
Speaker 4: journalism who are doing the g's thing, who are reproducing
Speaker 4: press releases and so finding the people who are doing
Speaker 4: really good critical work and like supporting them I think
Speaker 4: is super important.
Speaker 1: You we're going to say, well, I was, well, though,
Speaker 1: you just teed me up really well, because I was
Speaker 1: actually going to say, you know, some of the people
Speaker 1: who are doing some of the best work on it
Speaker 1: are like four or four media, and you know, I
Speaker 1: want to give a shout out to them because they're
Speaker 1: you know, these folks are basically you know, they were
Speaker 1: at Motherboard and Motherboard you know, uh, the whole Vice
Speaker 1: Empire was basically you know, Sunset and and so they
Speaker 1: they laid off a bunch of people. So they started
Speaker 1: this kind of journalists owned and operated place, and you
Speaker 1: know that focus is specifically on tech and AI and
Speaker 1: these folks have been kind of in the game for
Speaker 1: so long they they know how to talk about this
Speaker 1: stuff without really having this kind of being bowled over.
Speaker 1: You know, there's people who play that access journalism, like
Speaker 1: like kar Swisher who like kind of poses herself as
Speaker 1: this person who is very antagonistic but like, you know.
Speaker 2: Right, just like fawning over like AI people, and.
Speaker 1: Like all the time, Well I trusted Elon Musse and
Speaker 1: tell I was like, well, why did you trust this
Speaker 1: man in the first place? Like that, you know, I
Speaker 1: was reading the uh the Peter Thiel biography, The Contrarian
Speaker 1: and you know, and like it's it's a very it's
Speaker 1: a very harowing read. I mean, it was fascinating, but
Speaker 1: it was very harrowing. It wasn't an it was pretty
Speaker 1: like critical, but like you know, they discuss the PayPal
Speaker 1: days you know twenty four years ago, when you know,
Speaker 1: Elon Musk was like, well, I want to name PayPal
Speaker 1: to X and then and then everybody was like, why
Speaker 1: the fuck would you do that? People are already using
Speaker 1: people are using PayPal as a verb. You know, that's
Speaker 1: effectively the same thing he did with Twitter, Like people
Speaker 1: are talking about tweet as a verb. Why would you say,
Speaker 1: you know, just it's been like an absolutely vapid human
Speaker 1: being with no business sense. Anyways, Journal, that was a
Speaker 1: very long way of saying Kara Switcher sucks, and then
Speaker 1: also saying that all saying also saying that there's lots
Speaker 1: of folks. There's a number of folks doing great stuff.
Speaker 1: So I mean folks at four or for Karen Howe
Speaker 1: who's independent but had been at The Atlantic and I
Speaker 1: T Tech Review and Wall Street Journal. Curry Johnson, who
Speaker 1: is at Wired is now at cal Matters. There's a
Speaker 1: lot of people that really report on AI from the
Speaker 1: perspective of like the people who it's harming, rather than
Speaker 1: starting from well this tool can do X, Y and Z, right,
Speaker 1: you know, we really should take these groups out their claims.
Speaker 1: But yeah, I mean the larger part of it is
Speaker 1: I mean, there's just so much stuff out there, you know,
Speaker 1: and it's it's so hard and it is like whack
Speaker 1: them all. And I mean we're we're not journalists by training.
Speaker 1: I mean we're sort of doing a journalistic thing right now.
Speaker 1: I think we're I would not say we are journalists.
Speaker 1: I always say we are doing a journalistic thing.
Speaker 2: We're doing journalism.
Speaker 1: We are not doing original reporting. S sure, but it
Speaker 1: is well and you know, I would you know, I'm not.
Speaker 1: I don't know, I'm not the I don't I don't
Speaker 1: know who decides this is the court of journalism. But
Speaker 1: you know, reporting in so far as looking at original
Speaker 1: papers and effectively being like, okay, this is marketing.
Speaker 3: This is why it's marketing. Know they're there, yeah, rather
Speaker 3: than you.
Speaker 1: Know, a whizbang c net article or something that comes
Speaker 1: out of a content mill and says Google just published
Speaker 1: this tool that says you can you know, find eighteen
Speaker 1: million materials. Who are that are you know complete? Almost
Speaker 1: like Okay, well let's look at those claims and upon
Speaker 1: what grounds to those claims stand? And and you know
Speaker 1: how that's that's a pretty.
Speaker 4: I think what we're doing is is first of all,
Speaker 4: sharing our expertise in our specific fields, but also like
Speaker 4: modeling for people how to be critical.
Speaker 5: Consumers of journalism.
Speaker 4: So journalism ja but yeah, definitely without training in journalism totally.
Speaker 3: Yeah. But I think we want to do we want
Speaker 3: to do the M and M articles. I mean, oh
Speaker 3: my gosh, there's this article that has like done our
Speaker 3: brains because it just has this series of sentences.
Speaker 2: That I don't know that because everything is degrading, like journalism.
Speaker 2: You know, there's that story about like the Daily Mail
Speaker 2: was like Natalie Portman was hooked on cocaine when she
Speaker 2: was at Harvard. You're like, no, that was from that
Speaker 2: rap she did on SNL and that was like a bit.
Speaker 2: But because this thing is street and then the Daily
Speaker 2: Mail had to be like at the end they corrected it.
Speaker 2: They're like, uh, just she was not. That was obviously
Speaker 2: satirical and that was due to human error, Like they
Speaker 2: really leaned into that.
Speaker 4: Like no, yeah, of course, did I say by the
Speaker 4: time that the fabricated quote of mine came out of
Speaker 4: one of these things and was printed as news.
Speaker 2: No?
Speaker 4: No, So I also like alex so search my own
Speaker 4: name because I talked to journalists or not that I
Speaker 4: like to see what's happening. And there was something in
Speaker 4: an outfit called Bihar Praba that attributed this quote to me,
Speaker 4: which is not something I'd ever said and not anybody
Speaker 4: remember talking to. So I emailed the editor and I said,
Speaker 4: please take down this fabricated quote in print of retraction
Speaker 4: because I never said that, and they did so the
Speaker 4: article got updated remove the thing attributed to me, and
Speaker 4: then there was a thing at the bottom saying we've
Speaker 4: attracted this. But what they didn't put publicly, but he
Speaker 4: told me over email is that the whole thing came
Speaker 4: out of Gemini.
Speaker 3: Oh wow.
Speaker 4: And they posted it as a news article of course.
Speaker 4: And you know the only reason I discovered it was
Speaker 4: it was my own name and like, I never said
Speaker 4: that thing.
Speaker 2: Well, I need your expertise here to decipher this food
Speaker 2: and wine article that was talking about how Eminem's was
Speaker 2: coming out with a pumpkin pie flavored eminem but very early.
Speaker 2: Normally pumpkin pie flavored things don't enter the market till
Speaker 2: around August, like around when fall comes. But Eminem is
Speaker 2: why we were covering it, because we are journalists in May,
Speaker 2: pumpkin spice already no, But again they were saying this
Speaker 2: is because apparently gen Z and millennial consumers are celebrating
Speaker 2: Halloween earlier. But this is this one section that completely
Speaker 2: wait wait, yeah, I don't know. That's what they're saying,
Speaker 2: according to their analysis, that we were that we apparent,
Speaker 2: So let me read this for you.
Speaker 3: Quote.
Speaker 2: The pre seasonal launch of the milk chocolate pumpkin pie
Speaker 2: Eminem's is a strategic move that taps into Mars' market research.
Speaker 2: This research indicates that gen Z and millennials plan to
Speaker 2: celebrate Halloween by dressing up and planning for the holiday
Speaker 2: about six point eight weeks beforehand. Well, six point eight
Speaker 2: weeks from Memorial Day is the fourth of July, so
Speaker 2: you still have plenty of time to latch onto a
Speaker 2: pop culture trend and turn it into a creative costume.
Speaker 3: I don't right, it doesn't It doesn't make any sense.
Speaker 1: I know, I'm fixating on six point eight.
Speaker 6: What does that even mean? Does that mean?
Speaker 3: And where did Memorial Day come from?
Speaker 1: And that?
Speaker 3: And what is six point eight weeks for Memorial Day?
Speaker 3: Because it's not any of the days that they said
Speaker 3: it was.
Speaker 5: They said July fourth.
Speaker 3: Wait, and also six point.
Speaker 2: Eight eight weeks isn't a real amount of time. That's
Speaker 2: forty seven point point six days? Yeah, what is what
Speaker 2: is even a six point eight a week?
Speaker 4: So if this were real, it's possible that they surveyed
Speaker 4: a bunch of people and they said when do you
Speaker 4: start planning your Halloween costume? And those people gave dates
Speaker 4: and then they average that and that's how you could
Speaker 4: get to it.
Speaker 3: I get that.
Speaker 5: I get that that's fair, but also it totally.
Speaker 4: Sounds like someone put into a large language model write
Speaker 4: an article about why millennials and gen z are planning
Speaker 4: their Halloween costumes earlier.
Speaker 5: So like it sounds like that.
Speaker 2: But also just so odd to say, well, six point
Speaker 2: eight weeks from Memorial Day is the fourth of July.
Speaker 2: This article didn't even come out like it came out
Speaker 2: after Memorial Day and yeah fourth It's just nothing made sense.
Speaker 2: And I was like, I don't fucking understand what they're
Speaker 2: doing to me right now. But again that's this is
Speaker 2: like the insidious part for me.
Speaker 5: But this appeared in Food and Wine.
Speaker 2: This is in Food and Wine magazine with a human
Speaker 2: like in the byline, and I actually d m this
Speaker 2: person on Instagram and I said, do you mind just
Speaker 2: clarifying this part, like I'm a little bit confused and
Speaker 2: I've I've gotten no response.
Speaker 1: I'm wondering if it's because I know that. I mean,
Speaker 1: there was some good coverage and futurism and they were
Speaker 1: talking about this company called adv on Commerce and the
Speaker 1: way that basically this this this company has been basically
Speaker 1: making AI generated articles for a lot of different publications,
Speaker 1: usually on products like product placement, right, And so it
Speaker 1: makes me think it's sort of like because food and wine,
Speaker 1: you know, may have been one of their forgot I
Speaker 1: forgot the article, but they had a bun they had
Speaker 1: like you know, better homes and gardening and you know,
Speaker 1: kind of these legacy articles like that. So I don't
Speaker 1: know if it's something of that or this journalist kind
Speaker 1: of said write me this thing and I'm just gonna
Speaker 1: drop it and then go with god, you know.
Speaker 2: Yeah. Yeah.
Speaker 3: My My other favorite example of is this headline I
Speaker 3: saw somewhere it's no big secret why Van Vaught isn't
Speaker 3: around anymore, and with a picture of Vince Vaughan, but
Speaker 3: they just like got his name completely wrong. It's no
Speaker 3: big secret why Van Vaught isn't around anymore.
Speaker 1: I'm like, I'm you know, if I was just scrolling
Speaker 1: and I just and I'd say like, yeah, you just
Speaker 1: like you know, I liked Van Vaught in the intern
Speaker 1: and then yeah, but then I but and then I
Speaker 1: would have looked at it, and then I would have
Speaker 1: double taped. I'm like, wait, wait, wait did he co
Speaker 1: star with oh when Mick Wilson or something?
Speaker 2: Yeah, yeah, yeah, Russell Wilson was in that.
Speaker 4: I think it was the Week reporting that you're thinking of,
Speaker 4: alex I Futures' did a bunch of it. But then
Speaker 4: AD Week had the whole thing about ad Von and
Speaker 4: I can't quite no, no.
Speaker 1: No, it was it was future. It was futurism, yeah,
Speaker 1: because because A Week had the thing on this program
Speaker 1: that Google was offering and they didn't have a name.
Speaker 4: Oh right, yeah, avas futurism. Yeah, but it totally sounds.
Speaker 3: Like what it is happening. Yeah.
Speaker 5: Yeah, I thought you were going to.
Speaker 4: Talk about the surveillance by Eminem thing. We said Eminem's
Speaker 4: So this was somewhere in Canada. There was an Eminem
Speaker 4: vending machine that was like taking pictures of the students
Speaker 4: while they were making their purchases. And I forget what
Speaker 4: the like O sensible purpose was, but the students found
Speaker 4: out and I got it removed.
Speaker 3: Wow, probably freaked out and made a big deal about it. Right, Well,
Speaker 3: I feel like we could talk to you guys once
Speaker 3: again for three hours. There's so much interesting stuff to
Speaker 3: talk about. Your show is so great. Thank you both
Speaker 3: for joining. Yeah, where can people find you? Follow you
Speaker 3: all that good stuff? Emily, we'll start with you.
Speaker 4: Well, first, there's a podcast Mystery AI hYP Theater three thousand,
Speaker 4: where you find any podcast you can find ours, And
Speaker 4: we've also started a newsletter. If you just search Mystery
Speaker 4: a Hip Theater three thousand newsletter, I think it'll turn up.
Speaker 4: And that's an irregular newsletter where we basically took the
Speaker 4: things that used to be sort of little tweet storms,
Speaker 4: and since the social media stuff has gotten fragmented, we're
Speaker 4: now creating newsletter posts with them. So it's you know,
Speaker 4: off the cuff discussions of things on Twitter, x in
Speaker 4: Macedon and Blue Sky. I'm Emily M Bender and I'm
Speaker 4: also reluctantly using LinkedIn as social media these days.
Speaker 3: So it's gonna be the one that survives them all
Speaker 3: because I know some people kind of need it.
Speaker 1: Really the cock really the cock verches of yeah, yeah, yeah,
Speaker 1: I met al you, Alex, Alex, Hannah h A N
Speaker 1: n A on Twitter Blue Sky. I barely use blue
Speaker 1: Sky or mass it on, but Twitter is the best
Speaker 1: place to find me. Also check out Dare Dare d
Speaker 1: a I R hyphen Institute dot org. And we're also
Speaker 1: Dare Underscore Institute on Twitter, Macedon and uh, we're not
Speaker 1: on Blue Sky yet, but we're on LinkedIn. But that's,
Speaker 1: you know, where you learned a lot about what our
Speaker 1: institute's doing. Lots of good stuff, amazing colleagues and whatnot.
Speaker 3: Yeah, amazing. And is there a work of media that
Speaker 3: you've been enjoying.
Speaker 5: Yes, I've got one for you.
Speaker 4: This I think started off as a tweet, but I
Speaker 4: saw it as a screencap on Macedon. So it's by
Speaker 4: Lama in a tux and the text is don't you
Speaker 4: understand that the human race is an endless number of
Speaker 4: monkeys and every day we produce an endless number of words.
Speaker 4: And one of us already wrote Hamlet.
Speaker 3: That's really good.
Speaker 1: That's such. That's such a hyper specific piece of media.
Speaker 1: I think I think last I think last time was
Speaker 1: on this. I was plugging Worlds Beyond Number, which is
Speaker 1: a podcast which I'm just absolutely in love with, which
Speaker 1: is a a Dungeons and Dragon's actual play podcast, but
Speaker 1: it's got amazing sound production. I would just like plug
Speaker 1: in everything on dropout dot tv. I mean this it's
Speaker 1: a screaming service. Honestly, it's uh, you know, Sam Reich,
Speaker 1: who is a Reich who is the son of Robert Reich,
Speaker 1: kind of liberal darling and former Department of Labor secretary
Speaker 1: in the Clinton administration, has turned college humor into an
Speaker 1: area of of like really great comedians. So they're putting
Speaker 1: out a lot of great stuff. So i'd say, you know,
Speaker 1: make some noises. Coming out with a new season today,
Speaker 1: which is it's it's a really great improv comedy thing
Speaker 1: and yeah, let's just let's just go with that.
Speaker 2: So just.
Speaker 3: Hilarious.
Speaker 1: Those very important interviews Vick Michalis I named one of
Speaker 1: my chickens vehicular manslaughter after an inside joke there, and
Speaker 1: another one Thomas Shrinkley. So yeah, just incredible, incredible stuff.
Speaker 3: Yeah, shout out to Sam, He's one of the best. Miles. Yes,
Speaker 3: where can people find you? Is there a workimedia you
Speaker 3: can enjoy?
Speaker 2: They have at symbols. Look for at Miles of Gray.
Speaker 2: I'm probably there. You can find Jack and I on
Speaker 2: our basketball podcast, Miles and Jack Got Mad, where we've
Speaker 2: wrapped up the NBA season and I have that streaming
Speaker 2: down my face with pain and anger as the Celtics
Speaker 2: win again. And also if you want to hear me
Speaker 2: talk about very serious stuff, I'm talking about ninety day
Speaker 2: Fiance on my other show for twenty Day Fiance, which
Speaker 2: you can check out wherever they have podcasts. A tweet
Speaker 2: I like first one is from a past guest, Josh
Speaker 2: Gondleman tweeted, I bet the best part of being in
Speaker 2: a threuttle as you have someone to do all three
Speaker 2: Beastie Boys PARSI karaoke. That's I guess one way to
Speaker 2: look at that. And then the one from other past gasts.
Speaker 2: Demia de Juibe at electro Lemon got his account hacked
Speaker 2: and he tweeted, Hi, Hello, it's DEMI. I got my
Speaker 2: account back. I feel the need to clarify that under
Speaker 2: no circumstances should you ever believe that I or anybody
Speaker 2: on this website is selling cheap MacBooks for charity or otherwise,
Speaker 2: And what benefit would my signature do.
Speaker 3: To a laptop. Oh yeah, thank you for.
Speaker 1: I actually remember because I followed Demi, and I remember
Speaker 1: when his account got hacked and I thought, man, that's
Speaker 1: really and I at first I thought it was a
Speaker 1: bit because Demi is hilenious. But then I'm just like,
Speaker 1: what the hell, it's funny.
Speaker 2: His follow up tweet was for anyone who thought I
Speaker 2: was doing a bit, what's the punchline?
Speaker 3: My jokes are never so ubdused.
Speaker 2: I love you if I want you to know it
Speaker 2: wasn't all that funny, and I want you to know quick.
Speaker 3: Yeah.
Speaker 1: No, I was also trying to find out what the
Speaker 1: punchline was, right right?
Speaker 3: Yeah?
Speaker 2: Wait for it so funny that part of you wants
Speaker 2: to be like, well, hold on, what are you doing here?
Speaker 3: Yeah? What's like, what's what's the deal here?
Speaker 2: You don't want to immediately just dismiss Demi because he's
Speaker 2: such a great comedic man.
Speaker 1: Yeah, but yeah, if you do want good Demi content,
Speaker 1: the who's Who's welcome at the cookout? You can find
Speaker 1: that's some dropout content that you can get for free
Speaker 1: on YouTube.
Speaker 3: There you go. Tweet I've been enjoying sleepy at Sleepy
Speaker 3: Underscore Nice tweeted, it's absurd that Diddy Kong wears a
Speaker 3: hat that says Nintendo. Patently ridiculous. There's no way he
Speaker 3: understands the significance. It would be like me unknowingly wearing
Speaker 3: a hat that coincidentally depicts the true form of the universe.
Speaker 3: Take it off, Kong.
Speaker 1: That's incredible.
Speaker 6: Oh my god, good, it's so fucking good, because yeah,
Speaker 6: the second he showed up, you're like, I don't know, Yeah, Brandon,
Speaker 6: he likes Nintendo.
Speaker 3: You can find me on Twitter at Jack Underscore. O'Brien,
Speaker 3: you can find us on Twitter at Daily Zeikegeist. We're
Speaker 3: at the Daily Zeike on Instagram. We have a Facebook
Speaker 3: fan page and a website Daily zeitgeist dot com, where
Speaker 3: we post our episodes and our foot Nope, no where
Speaker 3: we link off to the information that we talked about
Speaker 3: in today's episode, as well as a song that we
Speaker 3: think you might enjoy. Myles, what song do you think
Speaker 3: people might enjoy?
Speaker 2: I came across this track from like the fifties that
Speaker 2: is like not really populas, Like it was playing on
Speaker 2: the radio and I just when I hear it. When
Speaker 2: I heard it, I was like, wait, what is this
Speaker 2: song because I thought it was like maybe a like
Speaker 2: newer artist doing sort of a send up of like
Speaker 2: fifties music, like surf music. It's called out in the
Speaker 2: Sun parenthetical hey oh, and it is a bit like
Speaker 2: Belafonte's day Oh and kind of has this like sort
Speaker 2: of similar sort of cadence to the verse. But it's
Speaker 2: just like when I heard it, I'm like, this sounds
Speaker 2: like the kind of like song like Tarantino would pluck
Speaker 2: from obscurity and then put under like a really dark scene,
Speaker 2: and it just got like it's like a beat song,
Speaker 2: but there's this like darkness to it that I really love.
Speaker 2: But anyway, this is the beach Nuts without in the Sun,
Speaker 2: So yeah, check this song out.
Speaker 3: It's when did that actually come out? Is it recent?
Speaker 2: You know?
Speaker 3: It's from the fifties?
Speaker 2: Notes from the fifties, Like they're an actual band.
Speaker 3: Like the lyrics are like, hey there, girls, where are
Speaker 3: you going?
Speaker 2: And they're like, down to the beach is.
Speaker 3: Where we're going?
Speaker 2: Lyrics is so literal, but there's this like charm to
Speaker 2: it and the instrumentation is cool. So anyway, this is
Speaker 2: the beach Nuts without in the Sun parenthetical.
Speaker 3: Fail all right, well, we will link off to that
Speaker 3: in the footnotes todayly Zeitgeist is a production of iHeartRadio.
Speaker 3: For more podcasts from my heart Radio, visit the heart
Speaker 3: Radio app, Apple Podcasts, or wherever fine podcasts are given
Speaker 3: away for free. That's going to do it for us
Speaker 3: this yet morning. We're back this afternoon to tell you
Speaker 3: what is trending, and we will talk to y'all then,
Speaker 3: bye bye, bye bye