Speaker 1: Hello the Internet, and welcome to season three, twenty six,
Speaker 1: episode two of der Daily's Iday production of iHeartRadio.
Speaker 2: This is a podcast where.
Speaker 1: We take a deep dive into American share consciousness. And
Speaker 1: it is Tuesday, February twenty seventh, twenty twenty four.
Speaker 2: Oh yeah, it's I mean, what is that? That means?
Speaker 2: It's Tuesday that we said twenty seventh? Is that where
Speaker 2: we are?
Speaker 1: That's right?
Speaker 2: Seven two four. It's uh a Nausema Awareness Day. It's
Speaker 2: National Retro Day, so I guess honor your see through
Speaker 2: telephones and vinyl players. National Polar Bear Day, National Strawberry Day,
Speaker 2: National Koalua Day, for all the for for all my
Speaker 2: Kolua lovers out there, it's the day is yours. The
Speaker 2: day is the white Russian?
Speaker 1: Is Kolua white Russian? Or no?
Speaker 2: That's Bailey's right, I think so, I don't know. It's
Speaker 2: just much either way. It's just a way too much
Speaker 2: sugar and you will have a bad time if that's
Speaker 2: all you drink.
Speaker 1: So yeah, that's right. Well, my name is Jack O'Brien aga.
Speaker 1: Don't you wish your snack food were sweet like this?
Speaker 2: Don't you want more?
Speaker 1: Vague disk satisfiying bliss, don't you. That is courtesy of
Speaker 1: Cleo Universe, in reference to the fact that some of
Speaker 1: the greatest science of the late twentieth century was spent
Speaker 1: focusing on how to get the perfect balance of mouth
Speaker 1: feel and like enjoyment and dissatisfaction in nacho cheese doritos.
Speaker 2: And yeah, gotta keep on popping them.
Speaker 1: That's gotta gotta make them keep popping. That's uh, that's
Speaker 1: who we are as a civilization. Unfortunately yet time and
Speaker 1: I'm thrilled to be joined as always by my co
Speaker 1: host mister Miles.
Speaker 2: Graam, Miles Gray, Gag Boss, the Warren Bond rock Kinside Boss,
Speaker 2: the Warrant Bondai rock kin Side. And that is the
Speaker 2: reference to Senator Elizabeth Warren saying her dream blunt rotation
Speaker 2: was just to smoke with the rock and my body
Speaker 2: curled into itself and I became a stand and drifted
Speaker 2: off into the sea. Yes, thank you to Laceroni for
Speaker 2: that one, for letting us know that's what you're gonna
Speaker 2: pass the duchy on the rock hand side.
Speaker 1: On the rock hand side, I wonder what the rock
Speaker 1: would be like, Hi, I feel like, probably pretty fun.
Speaker 2: Because you know, he's like simmering conservative underneath the surface,
Speaker 2: saying like I'm not good. It would just be like
Speaker 2: getting high with Joe Rogue. It's like I feel like
Speaker 2: it's you know, he'd say something weird and then being
Speaker 2: like he'd probably like make an observation about your physique.
Speaker 2: He'ah good, Dude's like you're right handed. Huh yeah, I
Speaker 2: could tell man based on how your shoulders built. And
Speaker 2: you're like, okay, going on, yeah, yeah.
Speaker 1: Have some workout tips for you. I'm solicited. Don't need that, Miles.
Speaker 1: We are thrilled to be joined by the hosts of
Speaker 1: the Mystery AI Hype Theater three thousand podcast. It's doctor
Speaker 1: Alex Hannah and professor Emily and Bendas.
Speaker 2: Hello, Hello, welcome, Welcome to both of you.
Speaker 1: Yeah, in addition to being podcast hosts, the highest honor
Speaker 1: one can attain in our world, you both have some
Speaker 1: pretty impressive credits, so I just wanted to go through
Speaker 1: those as well. Of top Emily, you are a linguist
Speaker 1: and professor at the University of Washington, where you are
Speaker 1: director of the Computational Linguistics Laboratory. Alex you are director
Speaker 1: of research at the Distributed AI Research Institute. You're both
Speaker 1: widely published, you both received a number of academic awards,
Speaker 1: you both have PhDs. What are you doing on our podcast?
Speaker 1: Is how what you like? Our booker is really good
Speaker 1: shout out to super producer Victor, But this is I
Speaker 1: don't know what's going on here.
Speaker 3: Yeah, we're excited to be here. And part of what's
Speaker 3: going on is that, you know, we're talking to the
Speaker 3: world about how AI, so called AI isn't all that right,
Speaker 3: and so a chance to have this conversation is really
Speaker 3: helpful and important.
Speaker 1: We've been talking about that for a while, but you
Speaker 1: are listening to your podcast really drove some things home.
Speaker 1: So I'm very excited about this conversation.
Speaker 2: Ar Yeah. Every time we have like we've had doctor
Speaker 2: Kerrie mcinernie on before. We think it's been on your
Speaker 2: show as well, every time we pick and uh juau
Speaker 2: sayak out in New York. We were just always talking
Speaker 2: about like our own evolution with how we felt about AI,
Speaker 2: because before I was like, it's gonna take all the
Speaker 2: writing jobs, and then people are like it's not. I'm like,
Speaker 2: how do you know, Like here's some more information. I'm like,
Speaker 2: it's vaporware to make money. So it's always nice, like, yeah,
Speaker 2: it's always nice to kind of keep, you know, pushing
Speaker 2: my own understanding along because I also encounter, you know,
Speaker 2: a lot of friends and family who also kind of
Speaker 2: have the same thing. There's still at to like, this
Speaker 2: stuff looks like it's gonna change the world forever in
Speaker 2: a way we'll never know. And yes, so it's always
Speaker 2: great to have the input of actual learned experts on
Speaker 2: the time learned expertise.
Speaker 1: So we're going to get into all of that, but
Speaker 1: before we do, we like to get to know our
Speaker 1: guests a little bit better by asking you, what is
Speaker 1: something from your search history that is revealing about who
Speaker 1: you guys are.
Speaker 4: I have an exciting one I can start, Hey, this
Speaker 4: is Alex. Yeah, so I'm building a chicken coop because
Speaker 4: my second job, rather than more one of my third
Speaker 4: jobs is kind of being a little suburban farmer. And
Speaker 4: so I'm getting some chicks delivered from a hatchery. And
Speaker 4: they said you need to put this directly into the browder,
Speaker 4: and I'm like, what's a brooder? And I had looked
Speaker 4: up what a brooder is and then came up and
Speaker 4: found that it can be anything from a rubber maide
Speaker 4: tub where you put the chickens while they're very small,
Speaker 4: until like a repurposed rabbit hut. So then I started
Speaker 4: thinking all morning about how to build a chicken Breweder's.
Speaker 2: And wait, just so, it's just like a like it's
Speaker 2: like a pen, like a mini pen for the chien
Speaker 2: to just kind of.
Speaker 4: Yeah, it's like a little pen for the chicks, and
Speaker 4: you put in a heat lamp, you know, because they
Speaker 4: need to be warm. Small, and they don't. They have
Speaker 4: only got that chicken fuzz. They don't have the chicken feathers.
Speaker 2: Yeah, yeah, yeah, they.
Speaker 1: Need to me this sounds like a job for a
Speaker 1: shoe box. But that's probably too small, too small, small, Unfortunately,
Speaker 1: that's what they're getting. If I'm building a chicken, you
Speaker 1: can also what I got if you're in if you're
Speaker 1: in a if you're in a rush, you can use
Speaker 1: your bathtub if you have one of those, and put
Speaker 1: some puppy pads. Yeah.
Speaker 4: I learned a lot about this today as I was
Speaker 4: just waiting for another meeting to start and just read
Speaker 4: everything I.
Speaker 1: Could nice and of course that chicken coop is gonna
Speaker 1: come in handy free eggs during the robot apocalypse, right,
Speaker 1: which Sam Altman has told me is coming. So I
Speaker 1: have to assume that's also what you're thinking, right, right.
Speaker 2: I mean she's prepping, she's prepping for the eventual DEMID. Yeah.
Speaker 1: I think during.
Speaker 4: COVID, I think there was a run on chicken eggs,
Speaker 4: and then so I'd go to Costco and you couldn't
Speaker 4: get the gross of chicken eggs, And then I'd go, hey,
Speaker 4: and I went to my backyard.
Speaker 2: Market got them for eight bucks an egg if you
Speaker 2: want to.
Speaker 1: Yeah, yeah, they are still really expensive. Emily, how about you?
Speaker 1: What's something from your search history?
Speaker 3: So I took a look, and it's a bunch of
Speaker 3: boring stuff, like I can't remember it can't be bothered
Speaker 3: to remember the website of a certain journal that I
Speaker 3: was interested in. So I'm like searching the name of
Speaker 3: the journal. But then like down below that we've been
Speaker 3: watching for all mankind, which is this like alternate timeline
Speaker 3: thing that that that's a what happens if the Soviets
Speaker 3: landed on the moon first? Right, right, So it diverges
Speaker 3: in the nineteen sixties, but it keeps referencing actual history.
Speaker 3: So my search history is full of like, okay, so
Speaker 3: when did the Vietnam War actually end?
Speaker 1: Right?
Speaker 3: And like know which Apollo mission did what. So it's
Speaker 3: a bunch of queries like that, sort of comparing what's
Speaker 3: in the show to actual history.
Speaker 2: How does that line up based on your sort of
Speaker 2: cursory research as you watch the show?
Speaker 3: So interestingly, So there's there's a point where Ted Kennedy
Speaker 3: is like in the background being talked about not going
Speaker 3: to chap Equittic. Oh, and then an episode later he's president.
Speaker 1: Wow.
Speaker 3: So there's and I suspect there's way more of that
Speaker 3: kind of stuff that I'm not catching right, right.
Speaker 2: Right, just subtle things right right.
Speaker 1: The media is like and Ted Kennedy missed a barbecue
Speaker 1: this weekend in chap Equittic. That's that's super interesting. Yeah,
Speaker 1: this is you know, third or fourth person who's mentioned
Speaker 1: for all mankind to us, I think this is pushing
Speaker 1: it over the threshold to where I have to I
Speaker 1: have to watch this damn thing.
Speaker 3: It's enjoyable, but it's tense. I don't know anything worth
Speaker 3: Like people in outer space really creeps me out because
Speaker 3: like that that degree of like lowliness and sort of
Speaker 3: lack of failsafes, you know, like when all the fail
Speaker 3: says are there are the ones that you built and
Speaker 3: beyond that, you know, your sol that's that's creepy.
Speaker 2: It seems uncomfortable outer space.
Speaker 1: Yeah, what is something, Alex that you think is underrated?
Speaker 4: I was trying to think of something and the only
Speaker 4: thing I could come up with this morning was the
Speaker 4: Nintendo Virtual Boy.
Speaker 2: The red goggles on a tripod.
Speaker 4: The red goggles on a tripod, and I know, you know,
Speaker 4: and whatever. Twenty years later, thirty years later, I think
Speaker 4: at this point, you know, Apple has its Apple Vision
Speaker 4: Pro that looks pretty much the same except you can
Speaker 4: walk around with it right right right. All I remember
Speaker 4: is that I really really really wanted a Nintendo Virtual
Speaker 4: Boy when I was whatever ten or old. I was
Speaker 4: in nineteen ninety five, and you know, but then I
Speaker 4: was reading the Wikipedia page and it kind of said,
Speaker 4: you know, know it it was gonna be people headaches,
Speaker 4: and it was overpriced. And but I'm going to stand
Speaker 4: by my claim that it is underrated. It was ahead
Speaker 4: of its time and it was one of those products
Speaker 4: that completely you know, I don't know, I think, you know,
Speaker 4: if you could go back, maybe it was just maybe
Speaker 4: I don't know, I don't know what they could have
Speaker 4: done differently, but yeah, that's that's all I got.
Speaker 2: Was hell, have you seen? Okay? So I was the
Speaker 2: same way, like I used to subscribe to Nintendo Power
Speaker 2: all that shit. It was such a nerdy game kid,
Speaker 2: and when like those ads for it, it was like
Speaker 2: this is the future. It blew my mind. My parents
Speaker 2: obviously were like, that's a hell no for a month, right,
Speaker 2: how silly you would look?
Speaker 1: We don't yeah deal with or in public with that.
Speaker 4: Yeah, it's also like a couple of grand wasn't It
Speaker 4: was just ridiculously it.
Speaker 2: Was something I think it was like a few It
Speaker 2: was just something like more than like what a PlayStation cost,
Speaker 2: which was sort of like the height of it or
Speaker 2: something like. Anyway, I remember a kid in my school
Speaker 2: had one, brought it to school and we lined up
Speaker 2: to play it, and it was the most underwhelming experience
Speaker 2: that like it broke because like there's nothing VR about this.
Speaker 2: It's like lightly three D everything I feel like in
Speaker 2: this like monochromatic, like red scale kind of graphic thing.
Speaker 2: It just was really it was underwhelming, But I completely
Speaker 2: follow the same path you had. Alex and being like
Speaker 2: this is I need this?
Speaker 4: This was the future?
Speaker 2: Yeah yeah yeah, and yeah, like that's so funny. And
Speaker 2: never even thought about the Vision pro as being like
Speaker 2: the like the spiritual sequel.
Speaker 4: Exactly, it's the direct descendant. Yeah, you know the Nintendo
Speaker 4: Virtual Boy crawled, so the Apple Vision bro exactly right.
Speaker 1: How about you, Emily, what is something you think is underrated?
Speaker 3: So after listening to a couple of your episodes and
Speaker 3: like all the nineties nostalgia, I have to say gen x.
Speaker 2: Xx, Yeah, people I thought were the coolest.
Speaker 3: We've got some monsters among us, you know, yeah, yeah,
Speaker 3: but you know gen X were small. We're scrappy. I
Speaker 3: saw someone a millennial that I know, online posting something
Speaker 3: about how weird it is to talk to gen X
Speaker 3: folks about their internet experiences are a back. Don't cite
Speaker 3: the old magic to me.
Speaker 2: I was there when.
Speaker 4: It was created. Although I feel like I feel like
Speaker 4: it's it is. It's a bit meta, Emily, because I
Speaker 4: feel like gen xers are always going to say that
Speaker 4: they're underrated, like they're underappreciated, and it's it's kind of
Speaker 4: a class.
Speaker 3: It's our whole identity exactly.
Speaker 2: It is.
Speaker 4: It is a referential Yeah, it's a class feature of
Speaker 4: gen xers to say that they are unappreciated. And you know,
Speaker 4: you children have to struggle with AOL dial up. You know,
Speaker 4: how about this, you know, prior no internet or yeah, I.
Speaker 3: Mean unless you you don't even know how to read
Speaker 3: the math.
Speaker 2: Yeah, yeah, I grew up in l A. I know
Speaker 2: how to use a Thompson Guide or Thomas Guide.
Speaker 1: That's that was.
Speaker 2: That was our Google Maps before anything. But yeah, yeah, exactly.
Speaker 2: But yeah, like I think about too, like as like
Speaker 2: an older millennial, like gen X were all the people
Speaker 2: I thought were the coolest people growing up, Like this
Speaker 2: is like I want to be I want to be
Speaker 2: a hacker. I want to be like them of oh yeah,
Speaker 2: well hey, I don't know what are we going to
Speaker 2: say about ourselves as millennials. I wonder I think we're
Speaker 2: just like we're just dead inside on some level that
Speaker 2: we're like, yeah, whatever, cool we are.
Speaker 4: We're dead inside and we killed everything, right that the
Speaker 4: stock you know, millennials are killing, you know, the napkin industry.
Speaker 1: Right, but the toast is going to be so good, Yeah.
Speaker 4: It's going to be amazing. We you know, we killed
Speaker 4: housing somehow because we spent too much on avocado toast. Yes,
Speaker 4: and you know, you know tumoric lattes or whatever, those
Speaker 4: are good, Like they're good.
Speaker 3: Though maybe maybe even underrated.
Speaker 1: There will be two people will be proposing to each
Speaker 1: other with rings made of turmeric and avocado instead of diamonds,
Speaker 1: because you build the diamond industry.
Speaker 2: Our new currency. Yes, yeah, but we always talk about that.
Speaker 2: Framing is just basically millennials can't afford X thing, right,
Speaker 2: we can't. We're not killing the diamond industry. We don't
Speaker 2: have money for diamond industry. We don't have money for these.
Speaker 1: Other millennials have this weird trend of living five to
Speaker 1: an apartment because they love it. Yeah, what, Alex, what's
Speaker 1: something you think is overrated?
Speaker 4: Well, we're we're all that, We're all the AI stuff
Speaker 4: and so you know we're both Emily and I are
Speaker 4: gonna talk about parts of it. But image generators for sure.
Speaker 4: Text the image generator, I mean people think they're very flashy,
Speaker 4: but I mean it does a lot of copying. You know,
Speaker 4: I was going to talk at a San Francisco public
Speaker 4: library with Carlo Ortiz, who's a concept artist, and she
Speaker 4: had all these examples in which you know, give it
Speaker 4: a prompt and it would literally just copy kind of
Speaker 4: a game art and then put it on the new
Speaker 4: thing and have some kind of swivels around it. But
Speaker 4: the stuff is just I mean, and it's also I mean,
Speaker 4: the the kind of images the thing, it's this this
Speaker 4: distinctive style that esthetically, apart from all the problems with
Speaker 4: the stuff and the non consensual use and the data
Speaker 4: theft and all the awful kinds of you know, non
Speaker 4: consensual deep fake porn and the kind of like far
Speaker 4: right imagery, like, the stuff is just ugly, Like it's
Speaker 4: got this like every every person in it just looks sunken.
Speaker 4: They look like they've just seen some shit, you know,
Speaker 4: they've got some.
Speaker 1: Showers like it's a mistare.
Speaker 4: Yeah, just just glassy, and you're just you're just like
Speaker 4: what is the appeal?
Speaker 1: You know?
Speaker 4: And and so I just yeah, I just I don't
Speaker 4: like it.
Speaker 2: Yeah, I think every so many things when people try
Speaker 2: and do like real life, because I look at like
Speaker 2: the mid Journey subreddit on Reddit to see like what
Speaker 2: people are making, and that was sort of like my entermre,
Speaker 2: Like wow, these like like when people get the prompt. Right,
Speaker 2: it's interesting. They're like Simpsons but real life characters and
Speaker 2: also from but from a Korean drama. Like okay, so
Speaker 2: we're getting the Korean like the k drama irl Simpsons,
Speaker 2: but all the the aesthetic like all looks like like
Speaker 2: sort of like David la Chappelle's photography. It's weird. There's
Speaker 2: like this hyper stylized look to it that feels very specific,
Speaker 2: And I think the thing that interests me is sort
Speaker 2: of like, as someone who's terrible at visual art, it's
Speaker 2: like this way for someone with absolutely no talent in
Speaker 2: that area to be like I summon this thing I'm
Speaker 2: thinking of and you're like, fine, it's a bunch of copies.
Speaker 2: But like I think that's the thing that most people
Speaker 2: are like, oh cool, right, it made the thing.
Speaker 4: It made the thing it made mediocre like art.
Speaker 1: I had the thought AI is like a doesn't matter generator,
Speaker 1: like Star Trek has the matter generator, and it is
Speaker 1: like ship out a bunch of stuff that doesn't matter.
Speaker 1: That's like uninspired, but it like matters to you for
Speaker 1: a split second. You know, it's like, hey, wow, that
Speaker 1: is that's weird that like that just came from a
Speaker 1: text prompt, but it's it actually doesn't matter in any
Speaker 1: long term sense.
Speaker 4: Right, Yeah, that's I love that framing. I mean, what's
Speaker 4: that famous that the the like image of the like
Speaker 4: the diner, Yeah.
Speaker 1: The nighthawks. Is that what it's called?
Speaker 4: Yeah, yeah, yeah, yeah yeah yeah. And someone had this
Speaker 4: great thread. Well, no, it was a troll thread though,
Speaker 4: you know what I'm talking about. There was like the
Speaker 4: nighthawks that the image and and and then someone was
Speaker 4: like I thought it, like, look at this composition. It's
Speaker 4: so boring. What if they were happier? And it was
Speaker 4: kind of like a troll thread. I think it was, Like.
Speaker 3: I thought, I thought it was genuine. I thought that
Speaker 3: they were really saying I'm making it better with AI.
Speaker 4: I think it was. I think it was a troll thread.
Speaker 4: It's like, why are these people so sad.
Speaker 2: When if they were happy?
Speaker 4: What if it was daytime? Yeah, it was. It was
Speaker 4: sort of like that, like you know, and I thought
Speaker 4: it was a troll thread. Maybe I'm granting posters like
Speaker 4: a little too much like Grace here, but I think
Speaker 4: it was like the idea of like I made it interesting.
Speaker 4: Look at this?
Speaker 2: Why is it so dark?
Speaker 1: Yeah? Right? What if there was confetti.
Speaker 2: I know, what if like a swagged out pope was
Speaker 2: also sitting at.
Speaker 1: So much can image, but the swinded out pope did though.
Speaker 4: I thought it was real, and I was and then
Speaker 4: I was like, this is such a cool But again, it's.
Speaker 1: Not like chat GPT was like, you know it would
Speaker 1: be funny. It was somebody was like, you know what
Speaker 1: would be funny and gave that prompt to chat GBT
Speaker 1: and then it ended up being But like that's that's
Speaker 1: the thing that you guys talk about a lot, is
Speaker 1: just the erasure of like people are like, well and
Speaker 1: here's chat GBT doing this thing, and it's like that
Speaker 1: somebody told it to do and then programmed it to do,
Speaker 1: like it's doing a specific It is not a person
Speaker 1: who is coming up.
Speaker 2: It's like talking about like Picasso's paint brush, but just
Speaker 2: as the paint brush, like look what the paint brush did,
Speaker 2: Oh my god, dude, which is cool.
Speaker 1: And then there's like a little like Buddhism there that
Speaker 1: I appreciate, but I feel like that's not where it's
Speaker 1: headed unfortunately. Emily, do you have something that you think
Speaker 1: is overrated?
Speaker 3: Yeah? I mean the short answer is large language models
Speaker 3: are overrated, But I think we're going to get into
Speaker 3: that we are probably, so I'm going to shift to
Speaker 3: my secondary answer, which is actually to take a page
Speaker 3: from Alex's work to say that scale is overrepresented. That
Speaker 3: if the goal of taking something and scaling it to
Speaker 3: millions of people is like the thing, the only folks
Speaker 3: really benefiting from that are the capitalists behind it. Right,
Speaker 3: the product is worse. The impact on the societies or
Speaker 3: the communities that lost access to whatever their local solution
Speaker 3: was is worse. Right, So scale is the thing that
Speaker 3: so many people are chasing, especially in the Bay Area,
Speaker 3: but also up here in Seattle, and it's.
Speaker 2: Way over it all over in every place. Everyone will
Speaker 2: hear that all the time, and like, yeah, yeah.
Speaker 1: I worked on a website that had like really was
Speaker 1: having really fast growth, and you know, it is just
Speaker 1: based on like publishing these three articles a day and
Speaker 1: really like focusing on making them as good as possible.
Speaker 1: And unfortunately, once it got a lot of then the
Speaker 1: executives came in and their first question was how do
Speaker 1: we scale this? How do we scale this? Though, like,
Speaker 1: how do we get a hundred articles a day? And
Speaker 1: I was like, back in two thousand and ten. You know,
Speaker 1: but it's just been how capitalism thinks about this from
Speaker 1: the beginning. Yeah, and now they're doing it with movies.
Speaker 1: They're like, who needs movies? Now I could make myself
Speaker 1: the star of Oppenheimer. It's like, why would you want
Speaker 1: to do what.
Speaker 4: You seems like you missed the point of that movie.
Speaker 2: Okay, I want to be destroyer of world.
Speaker 1: Oh that may feel strong? Yeah, yeah, all right, Well
Speaker 1: let's take a quick break and we'll come back and
Speaker 1: get into it. We'll be right back, and we're back.
Speaker 1: We're back and yeah, So, as we mentioned, you know,
Speaker 1: we've done some episodes on how there's a lot of
Speaker 1: hype around it. Your podcast really helped me understand just
Speaker 1: how much of the current AI crazes really hype. There's
Speaker 1: this one anecdote I just wanted to mention where a
Speaker 1: physicist who works for a car company was brought into
Speaker 1: a meeting to talk about hydraulic breaks and one of
Speaker 1: the executives of the car company asked them if putting
Speaker 1: some AI in the brakes wouldn't help them operate more smoothly,
Speaker 1: which they were kind of confounded, And we're like, what
Speaker 1: does what do you like, I guess that gets to
Speaker 1: the big question I have a lot of the time
Speaker 1: with AI hype in the media, which is, what do
Speaker 1: these people think AI is?
Speaker 3: It's magic fairy dust, But you actually made it slightly
Speaker 3: more plausible by making it breaks. The story was pistons pistons, okay,
Speaker 3: and the engineer was like, it's metal. There's no circus here.
Speaker 1: Right, Yeah, But yeah, I mean, there are three kind
Speaker 1: of big ideas that I got a new perspective on
Speaker 1: from your show that I wanted to start with. The
Speaker 1: first one is kind of this distinction between limited and
Speaker 1: general intelligence or like general AI, which is something that
Speaker 1: I've heard mentioned a lot in the past couple of
Speaker 1: years in articles about chat GPT. They're basically trying to
Speaker 1: use this as a distinction to say, look, we've known
Speaker 1: that computers can beat a chess master for a while now,
Speaker 1: but the distinction that we are trying to sell to
Speaker 1: you is that this one is different because it's a
Speaker 1: general intelligence. It can kind of reason its way into
Speaker 1: doing lots of things. Yeah, and one of the details
Speaker 1: I heard you point out on your show is that
Speaker 1: it's still pretty limited, like it's still basically doing a
Speaker 1: single thing pretty well, like that the chat GPT is
Speaker 1: can you can you talk about that, like what is it?
Speaker 1: What is that distinction and how kind of imaginary it is?
Speaker 3: Yeah, So before Alice goes off on chess as a
Speaker 3: thing in this and we'll get there, Alex, you're right
Speaker 3: that chat GYPTA does one thing well. But the one
Speaker 3: thing that's doing well is coming up with plausible sequences
Speaker 3: of words.
Speaker 2: Right.
Speaker 3: The problem is that those plausible sequences of words can
Speaker 3: be on any topic. So it looks like it is
Speaker 3: doing well at playing trivia games and being a search
Speaker 3: engine and writing legal documents and giving medical diagnoses, and
Speaker 3: the less you know about the stuff you're asking it,
Speaker 3: the more plausible and authoritative it sounds. But in fact,
Speaker 3: underlyingly it's doing just one thing, predicting a plausible sequence
Speaker 3: of words, right.
Speaker 1: Yeah.
Speaker 4: And the thing about chess is that it wasn't too
Speaker 4: long ago that general intelligence meant chess playing, you know,
Speaker 4: and so yeah, you had you had these you know,
Speaker 4: during the Cold War, they would have these machines that
Speaker 4: could play chess, and then that was supposed to be
Speaker 4: a substitute for general AI. They thought that was general
Speaker 4: intelligence and it was.
Speaker 2: A big deal.
Speaker 4: When you know, IBM Watson beat Gary Kasparov, and you
Speaker 4: know IBM Watson was that deep people, Yes, yes, Watson
Speaker 4: Watson won Jeopardy, Jeopardy, yeah it it beat it, beat
Speaker 4: Ken Jennings and then so yeah, thank you. It was
Speaker 4: another one those IBM. And so then that was the
Speaker 4: kind of that was that was the bar. And then
Speaker 4: they're like, well, Tess is now too easy.
Speaker 2: We have to do.
Speaker 4: We have to do go go, you know. And then
Speaker 4: and then they got it into some real time strategy
Speaker 4: games like like StarCraft and and Doda too, and so
Speaker 4: you know, so there's always been this kind of thing
Speaker 4: that's been a stand in for intelligence, and this has
Speaker 4: been what the advertisement for, you know, for general intelligences.
Speaker 4: And so in addition to what Emily's saying, it's really
Speaker 4: good at doing this next word thing. I mean, it
Speaker 4: seems to achieve some acceptable baseline for all these different tasks.
Speaker 4: But then these tasks become like stand ins for general intelligence,
Speaker 4: and it's really it really gives away the game when
Speaker 4: you know, people like Mark Zuckerberg, you know, come out
Speaker 4: and they say, well, Matt is going to work on AGI.
Speaker 4: Now I don't really know what that is but you know,
Speaker 4: we're going to do.
Speaker 1: It, but you guys seem to like it as investors,
Speaker 1: and so therefore that is what we're going to call
Speaker 1: everything now.
Speaker 4: Yeah, yeah, it's it's it's literally turning back towards the
Speaker 4: machine and like dialing up the AGI nob and seeing
Speaker 4: if the crowd, you know, since seems to tear at it.
Speaker 1: Yeah, so I think Emily, you were saying that just
Speaker 1: you were specifically saying that they would take an existing sentence,
Speaker 1: like one of the training methods, take an existing sentence,
Speaker 1: come rubble word, computer guesses what that word is and
Speaker 1: then compares guests to what the actual word is, and
Speaker 1: like it got better and better at doing that until
Speaker 1: it seems like the computer is talking to you using
Speaker 1: the language a human would use in conversation. Like just
Speaker 1: hearing it put that way for some reason was like, oh,
Speaker 1: it's so it's like just better. It's doing the same
Speaker 1: thing that a chess computer does, but it's just more
Speaker 1: broadly impressive to people who don't know how to play chess.
Speaker 3: So it's it's not actually doing the same thing that
Speaker 3: a chess computer does. So what Deep Blue did was
Speaker 3: they just threw enough computation at to calculate it out,
Speaker 3: what are the outcomes of these moves, what opens up,
Speaker 3: what opens up? And you put enough calculation in for
Speaker 3: chess that's doable with computers of the era of Deep
Speaker 3: Blue Go. It turns out the search space is much
Speaker 3: bigger so the Alpha Go. What they did was they
Speaker 3: actually trained it more like a language model on the
Speaker 3: sequences of moves in games from really highly rated go players.
Speaker 3: Someone beat it recently by playing terribly and then it
Speaker 3: was like, I don't know what to do, yeah, because
Speaker 3: it was completely.
Speaker 2: Yeah, that's why I usually lose at chess.
Speaker 1: I'm so much better than everybody that the level confuses.
Speaker 3: So there was a big deal about how GPT four
Speaker 3: could play chess super well. And one of the things
Speaker 3: about most of the models that are out there right
Speaker 3: now is we know nothing about their training data or
Speaker 3: very little, like the companies are closed on the training data,
Speaker 3: even open AI right but people figured out that there's
Speaker 3: actually an enormous corpus of highly rated chess players chess
Speaker 3: games in the training data in the specific notation for
Speaker 3: GPT four, So when it's playing chess, it is doing
Speaker 3: predict a likely next token based on that part of
Speaker 3: its training data. But that's not what Deep Blue is
Speaker 3: up to. Deep Blue is a different strategy. And in
Speaker 3: all of these cases, there's this idea that, like, well,
Speaker 3: chess is something that smart people do, so if a
Speaker 3: computer can play chess, that means we've built something really smart,
Speaker 3: which is ridiculous. And I just want to be very
Speaker 3: clear that Alex and I aren't saying there's some better
Speaker 3: way to build AGI, right, We're saying there's better ways
Speaker 3: to seek out effective automation in our lives.
Speaker 1: I'm kind of.
Speaker 3: Speaking for you there, Alex, but I think we're I
Speaker 3: think we're in a great yeah.
Speaker 4: Or rather, I mean, even if we want automation and
Speaker 4: particular parts of our lives, you know, what is what
Speaker 4: is the function of making chess robots?
Speaker 1: Right?
Speaker 4: And you know, insofar is building chess robots in the
Speaker 4: Cold War? I mean a lot of it had to
Speaker 4: do with you know, Cold War kind of scientific documen,
Speaker 4: you know, the US versus the USSR and seeing if
Speaker 4: we could do better, you know, and you know, chess
Speaker 4: was one front getting to the moon. Speaking of the
Speaker 4: show which has already completely evaded me that we were
Speaker 4: talking about earlier for all mankind, for all mankind, thank you,
Speaker 4: please cut me out, make me sound smart.
Speaker 1: It's a tough title. It doesn't stick in the brain.
Speaker 1: And they should have used AI to name that show.
Speaker 1: Yeah exactly, Yeah.
Speaker 3: Yeah, historical sexism that makes it stick, maybe makes it
Speaker 3: a little.
Speaker 4: Bit slipper, right, yes, yeah, so it's the same thing,
Speaker 4: you know, get into the moon first. So I mean
Speaker 4: this becomes a stand in, you know, and it's you
Speaker 4: know a lot of it is frankly kind of showingism.
Speaker 4: You know, mine can do it better than yours, and
Speaker 4: you know, and and really getting to that and so
Speaker 4: you know, this association with chess, as m least said,
Speaker 4: of like this thing smart people can seem to do,
Speaker 4: not things like this automation useful in these people's lives, right,
Speaker 4: will it make these kind of runt tasks kind of
Speaker 4: easier for people? And said we sort of jumped over
Speaker 4: that completely to go, let's make this art kind of
Speaker 4: thing and oh, it's going to put out a bunch
Speaker 4: of you know, concept artists and writers. Even if it
Speaker 4: doesn't work, well, you know, we're going to scare the
Speaker 4: wits out of these people who do this for a living.
Speaker 2: What right, like is there I feel like you know,
Speaker 2: looking at Cees and just what happened at Cees. So
Speaker 2: many companies like it's got AI like this thing this
Speaker 2: refrigerators you AI. And I feel like for most people
Speaker 2: who are just consumers are just kind of passively getting
Speaker 2: this information. There's like a discrepancy between how it's being
Speaker 2: marketed and what it is actually doing. Right yeah, and
Speaker 2: can you And so part of me is like how
Speaker 2: the like why are all these C suite maniacs suddenly
Speaker 2: being like, dude, this is the future, Like you got
Speaker 2: to get in on it. And I know that a
Speaker 2: lot of it is hype, but can you sort of
Speaker 2: like help me understand like the hypeline or pipeline to
Speaker 2: how a company says ooh, this is what we do,
Speaker 2: and then how that creeps up to the capitalists where
Speaker 2: then they're like yeah, yeah, yeah, what what what and
Speaker 2: then begin to say like make these proclamations about what
Speaker 2: large language models are doing or how it's going to
Speaker 2: revolutionize things. What sort of like because from your perspective,
Speaker 2: can you just sort of be like just give us
Speaker 2: the the unfiltered like this is how this like this
Speaker 2: conversation is moving from these laboratories into places like Goldman Sachs.
Speaker 4: Yeah, I mean, I think a lot of a lot
Speaker 4: of hype seems to op rate via fomo. It's honestly, honestly,
Speaker 4: if you wants it kind of reductive kind of way,
Speaker 4: you need to get in on this at the ground level.
Speaker 4: If you don't, you're going to be missing out on
Speaker 4: huge opportunities, huge returns on investment, right. And one of
Speaker 4: the ways I would say that AI hype is kind
Speaker 4: of at least a bit qualitatively different then let's say
Speaker 4: I don't know crypto or n F T S or yeah,
Speaker 4: you know, you know DeFi is that you know, crypto
Speaker 4: has kind of shown itself to be such a such
Speaker 4: a you know, house of cards that you know it
Speaker 4: doesn't it takes only a few things like you're Sam
Speaker 4: Bankman free and going to jail, or you know, these
Speaker 4: these huge kinds of scandals happening with with finance and
Speaker 4: coin base, that you know, those things, and then it's
Speaker 4: the wild fluctuation of bitcoin and other types of crypto
Speaker 4: that at least you have some kind of proof of
Speaker 4: concepts on this on this chat GPT where it seems
Speaker 4: like this thing can do a lot, and so there's
Speaker 4: enough kind of stability in that where folks say, yeah,
Speaker 4: let's let's go ahead and get in on this. And
Speaker 4: if we don't get it on the ground floor, we're
Speaker 4: missing out on millions or billions of dollars in in ROI.
Speaker 4: And so you know, this is you know, but the hype,
Speaker 4: I mean, the hype is really at its base is
Speaker 4: kind of fomo element of it, and it's and I
Speaker 4: think it's just the the thing where, you know, it
Speaker 4: obscures so much of what's going going on under the hood,
Speaker 4: and folks just think it's magic. You know, you see
Speaker 4: gen jen ai. People say we're putting gen ai in
Speaker 4: this that that you could rub it on an engine.
Speaker 4: When yeah, when when when AI itself I mean itself,
Speaker 4: that kind of calling things AI, I mean it self
Speaker 4: has this marketing quality to it. It's really you know,
Speaker 4: but if you kind of look at the class of
Speaker 4: technologies have been called AI since the inception of AI
Speaker 4: and then mid nineteen fifties, it can be anything as
Speaker 4: basic as a logistic regression, which is a very basic
Speaker 4: statistical method that it's taught in stats. You know, one
Speaker 4: A one or two A one too. You know, these
Speaker 4: language models that we see open AI, howking, and so
Speaker 4: you could say AI isn't anything I've got AI, I've
Speaker 4: programmed by hand, I did the math by hand.
Speaker 1: Ooh right, you know right? Yeah.
Speaker 3: So I think what's part of what's going on here
Speaker 3: is that because of the way chat GPT was set
Speaker 3: up as a demo and then all these people did
Speaker 3: free pr for open a sort of sharing their screencaps,
Speaker 3: everybody has the sense that there's a machine that can
Speaker 3: actually reason right now, and then you call anything else AI,
Speaker 3: and that the lubiz factor from chat GPT is sort
Speaker 3: of like makes the whole thing sparkle. There's a funny
Speaker 3: story from this conference called NEUROPS, which is neural information
Speaker 3: processing systems, not real neurons. These are the fake ones
Speaker 3: that these things are built out of. It's like a
Speaker 3: mathematical function that's sort of an approximation of a nineteen
Speaker 3: forties notion of what a neuron is. And a few
Speaker 3: years back, I want to say, like.
Speaker 1: I sound impressed by the way.
Speaker 3: Around twenty sixteen twenty seventeen, I think some folks at
Speaker 3: NEUREPS did a parody. They put together this pitch deck
Speaker 3: of a company that was going to build AGI and
Speaker 3: it's ridiculous. Like the pitch deck is basically step three
Speaker 3: it dot step three profit, Like there's nothing in there.
Speaker 3: And this was just on the cusp of when AI
Speaker 3: went from being sort of a joke like you wouldn't
Speaker 3: call your work AI if you were serious, to where
Speaker 3: we are now. And what these people didn't realize when
Speaker 3: they were doing their pitch deck was that there were
Speaker 3: some folks in the audience who had already stepped over
Speaker 3: into that, like, you know, AI, true believer, We're going
Speaker 3: to make a lot of money on this step. People
Speaker 3: came up and offered them money for their fake company.
Speaker 1: That's great.
Speaker 2: Yeah, so it's say it's like the dot com boom. Yeah,
Speaker 2: there's just like what's the company called some dot com?
Speaker 1: They're ready for the next thing, right, they've like kind
Speaker 1: of stalled out post iPhone, and they're like ready for
Speaker 1: the next big thing, whether it's here or not. And
Speaker 1: so yeah, the g whiz afication or like the g
Speaker 1: whiz results from open AI and chat GPT I feel
Speaker 1: like they were like good enough, let's get this thing going,
Speaker 1: Let's get this machine cranking out. The idea of describing
Speaker 1: like the mistakes that chat GPT makes as hallucinations. Like
Speaker 1: the marketing that's being done across the board here is
Speaker 1: pretty impressive.
Speaker 3: The fact that CHATGPT even uses the word I right,
Speaker 3: that's a programming choice. Yeah, could have been otherwise and
Speaker 3: would have made things much much clearer.
Speaker 1: Right, Yeah, chat GPT is trained on the entire Internet.
Speaker 3: Not true.
Speaker 1: Yeah, that was something that I accepted when it first started.
Speaker 1: I'm like, well, this thing hallucinates and its brain is
Speaker 1: the entire Internet in what could go wrong?
Speaker 3: So the thing about the phrase entire internet is that
Speaker 3: the Internet is not a thing where you can go
Speaker 3: somewhere and download, right. It is distributed, and so if
Speaker 3: you are going to collect information from the Internet, you
Speaker 3: have to decide which URLs you're going to visit, and
Speaker 3: they aren't all listed in one place. So there's no
Speaker 3: way to say the entire Internet. There's some choices happening already.
Speaker 2: Yeah.
Speaker 4: And what they mean when they say the entire Internet,
Speaker 4: I mean most of the time, they mean most of
Speaker 4: the English available Internet, And you mean, and although they've
Speaker 4: got some, but then much of the work that has
Speaker 4: been translated has been actually machine translated from English into
Speaker 4: another language. So, for instance, you know, a bunch of
Speaker 4: the URLs that they have are from the Google Japanese
Speaker 4: patents and common crawl and the Dodge paper. Yeah, a
Speaker 4: lot of it's been machine translated from English into Japanese
Speaker 4: use and it's just available through like Google's Japanese patent site.
Speaker 4: And so a bunch of this stuff is you know,
Speaker 4: just just loaded in and.
Speaker 2: It's and so and so.
Speaker 4: I mentioned this data set common crawl, which isn't you know,
Speaker 4: in the GPT two paper I think they cite it,
Speaker 4: or it's the TPT three paper. It's I think it's
Speaker 4: a GPT three P pair And they said, yeah, it's
Speaker 4: the common crawl. Is this data set that they say
Speaker 4: they use, which is this kind of this weird like
Speaker 4: nonprofit I was collecting a bunch of data that could
Speaker 4: be useful for some limited scientific inquiry, but then it
Speaker 4: became now they've completely rebranded themselves. They're saying, we have
Speaker 4: the data that you know, fuels large language models and Mozilla,
Speaker 4: the Mozilla Foundation folks that also the corporation makes the
Speaker 4: web browser Firefox. They did this report on common crawl
Speaker 4: and kind of what's behind it too, and and find
Speaker 4: you know, the weird idiosyncrasies of it. But yeah, when
Speaker 4: they say the whole internet, I mean yeah, it's these
Speaker 4: currated data sets. You can't get this whole internet. That's
Speaker 4: that's just that's just a falsity.
Speaker 2: But for our listeners, you can get it from Jack
Speaker 2: and I. We do have that file.
Speaker 4: So if you're it's just it's just one large ZIP file.
Speaker 2: Yeah, yeah, exactly. Yeah, it's on pirate Bay. Check it out.
Speaker 1: Yeah yeah, and we read it every morning to prepare
Speaker 1: for this show.
Speaker 2: Just the whole thing to credit and downloaded.
Speaker 1: Yeah. Uh, let's take a quick break and we'll be
Speaker 1: right back. And we're back. So yeah, just in terms
Speaker 1: of juxtaposing, like what these companies are saying and the
Speaker 1: mainstream media is buying versus what is actually happening. So
Speaker 1: I was reminded of Sam Altman telling The New Yorker
Speaker 1: that he keeps like a bag with cyanide capsules ready
Speaker 1: to go in case of the AI apocalypse. So you know,
Speaker 1: he is an expert who gets billions of dollars richer
Speaker 1: if people think his technology is so powerful that he's
Speaker 1: like freaked out by it. So, but that that's something
Speaker 1: that I don't know I've seen reprinted in like long
Speaker 1: articles about the danger of AI. And then there's this
Speaker 1: other like more real world trend where like you you
Speaker 1: talk about a Google engineer who admitted they're not going
Speaker 1: to use any large language model as part of their
Speaker 1: families healthcare journey.
Speaker 4: Oh that was a Google That was a Google senior
Speaker 4: vice president, not a senior of Google VP. Greg Corrado
Speaker 4: is one of the heads of Google Health itself.
Speaker 1: There is yeah, and there's also this story from your
Speaker 1: show has a fresh Health segment at the end where
Speaker 1: you talk about just saying examples headlines that.
Speaker 2: Are just fresh.
Speaker 1: Yeah, it's just fresh out.
Speaker 4: It's new versions of.
Speaker 2: Yeah.
Speaker 1: The one about Duo Lingo from a recent episode where
Speaker 1: they're getting rid of human translators, firing them, cutting the workforce,
Speaker 1: replacing them with translation AI, even though the technology isn't
Speaker 1: there yet. But the point that you were making on
Speaker 1: the show is that they're willing to go forward with
Speaker 1: that because the user base won't notice the difference until
Speaker 1: they're in Spain and need to get to a hospital
Speaker 1: and asking for the biblioteca. You know, like it's they
Speaker 1: are specifically an audience who is not going to know
Speaker 1: how bad the product that they're getting is because they're
Speaker 1: just like not in a in a position to know
Speaker 1: that by the nature of the product. And so just
Speaker 1: this distinction between being hyped to the mainstream media and
Speaker 1: like these long read like New York or Atlantic articles
Speaker 1: as this is a future that we should be scared
Speaker 1: of because like it's going to become self aware and
Speaker 1: Sam Altman is freaked out, and then what it's actually doing,
Speaker 1: which is just making everything shittier around us, is I
Speaker 1: think a big kind of chunk that I took away
Speaker 1: from your show that was just like, oh, yeah, that
Speaker 1: makes way more sense. That feels much more likely to
Speaker 1: be how this thing progresses.
Speaker 3: Yeah, So the AI doumerism, which is when Altman says
Speaker 3: I've got my bug out bag and my sinanite capsules
Speaker 3: in case the robot apocalypse comes, or when Jeff Hinton,
Speaker 3: who's credited has a Turing Award for his work on
Speaker 3: the specific kind of computer program that's us see Gablo
Speaker 3: statistics that's behind these large language models. He's now concerned
Speaker 3: that it's on track to because I mean, smarter than us,
Speaker 3: and it's gonna like these piles of linear algebra are
Speaker 3: not going to combust into consciousness. And anytime someone is like,
Speaker 3: you know, pointing to that boogeyman, what they're doing is
Speaker 3: they're basically hiding the actions of the people in the
Speaker 3: picture who are using it to do things like make
Speaker 3: a shitty language learning product because it's cheaper to do
Speaker 3: it that way than to pay the people with the
Speaker 3: actual expertise to do the translations.
Speaker 4: Right, yeah, yeah, And it's just leading just to I mean,
Speaker 4: I like this kind of thought on this, I mean
Speaker 4: this kind of process. And Corey Doctor has got this
Speaker 4: kind of sister concept of AI with hype, which is
Speaker 4: in shitification, which I think that the Luistic Society America
Speaker 4: it was their their overall.
Speaker 3: Yeah, American dialect Society Dialects, Yeah, picked it as the
Speaker 3: overall word of the year for twenty twenty three. But
Speaker 3: in shtification is something very specific, right, It's not just
Speaker 3: like we've now we're now swimming in AI extruded junk, right,
Speaker 3: AI and quotes as always, It's something more the company
Speaker 3: create a platform that you sort of get lured in
Speaker 3: because initially it's really useful for So this is like
Speaker 3: you think about how Amazon was great for finding products
Speaker 3: sucked for your local brick and mortar businesses. But as
Speaker 3: a consumer it was super convenient because you could find things.
Speaker 3: But then the companies basically turn around and they extract
Speaker 3: all of the value that the customer is getting out
Speaker 3: of it, and then they turn around to the other parties.
Speaker 3: There are the people trying to sell things, and they
Speaker 3: extract value out of them. So you start off with
Speaker 3: this thing that's initially quite useful and usable, and then
Speaker 3: it getsified in the name of making profits for the platform.
Speaker 3: And that's like the specific thing about in certification.
Speaker 4: And I would say there's some kinds of processes you
Speaker 4: can think about indification, the kind of idea that you
Speaker 4: have to rush to a certain kind of market, that
Speaker 4: you have a monopoly on this kind of thing, And
Speaker 4: I guess yeah. I mean, the thing about large language
Speaker 4: models I think that we get we get on and
Speaker 4: talk a lot about, is that large language models are
Speaker 4: kind of born shitty with content. So it's not like
Speaker 4: the platform started and that platform monopoly led to this
Speaker 4: kind of process of incertification. It's more like you decided
Speaker 4: to make a tool that is a really good word
Speaker 4: prediction machine, and you use it for a substitute for
Speaker 4: places in which people are meant to be speaking kind
Speaker 4: of with their own tone, with their own voice, with
Speaker 4: their own forms and their own putting, so they're on
Speaker 4: expertise and then and so it's kind of yeah, it's
Speaker 4: it's bored and shitty, and so you know this, this
Speaker 4: kind of thing I think is really helpful. It makes
Speaker 4: me think of kind of a thing I think. I
Speaker 4: saw a few times on Twitter where people are like, well,
Speaker 4: if you're an expert in any of these fields and
Speaker 4: you read content by large language models, you actually know
Speaker 4: anything about this, you're going to know that it's absolute bullshit, right,
Speaker 4: you know, if you ask it to write you a short,
Speaker 4: you know, treatise on I don't know, sociological methodology, something
Speaker 4: specific that I know a little bit about, it's going
Speaker 4: to be absolute bullshit, right, But good enough to computer
Speaker 4: engineers and you know, higher ups at these companies.
Speaker 1: Yeah.
Speaker 3: So here's an example. The other day, I came across
Speaker 3: an article that supposedly quoted me out of this publication
Speaker 3: from India called Bihar Praba, and I'm like, I never
Speaker 3: said those words. I could see how someone might think
Speaker 3: I would. And then I searched my emails, like no,
Speaker 3: I never corresponded with any journalist at this outfit. So
Speaker 3: I wrote them, I said, fabricated quote. Please take it
Speaker 3: down in printer retraction, which they did, and they wrote
Speaker 3: back and said, oh yeah, that actually we prompted some
Speaker 3: large language models.
Speaker 1: To create that before us and posted yes, because it
Speaker 1: seems like you might have and the large language models
Speaker 1: they don't like that. That's isn't that? What hallucinations are
Speaker 1: a lot of the time is just the large language
Speaker 1: model making up stuff it seems like is what the
Speaker 1: person wants them to say exactly.
Speaker 3: But here's the whole thing. Every single thing output from
Speaker 3: a large language model has that property. It's just sometimes
Speaker 3: it seems.
Speaker 1: To make sense. The whole thing is trying to do
Speaker 1: a trick of like predict I figured out what you
Speaker 1: wanted me to say, ha ha, But it's like, well,
Speaker 1: but what I wanted you to say is not always
Speaker 1: that's not how I want my questions answered that. That's
Speaker 1: actually a wildly flawed way of coming up like answering people.
Speaker 1: It's definitely something that I do in my day to
Speaker 1: day life because I'm scared of conflict and a people pleaser.
Speaker 1: But that's not I'm not a good scientific instrument for
Speaker 1: that reason, you know, Like, but you.
Speaker 4: Just got you just an avoidant attachment style, which as
Speaker 4: a as another avoidance.
Speaker 1: They've just made me a scientific model that's terrible.
Speaker 4: Like you, I can't believe this.
Speaker 2: I was like, I don't know if you saw that
Speaker 2: headline where Tyler Perry was like, I was going to
Speaker 2: open an eight hundred million dollar studio, but I stopped
Speaker 2: the second I saw what Sora, this video generative AI
Speaker 2: could do, and I realized we're in trouble. Said quote,
Speaker 2: I had no idea until I saw recently the demonstrations
Speaker 2: of what it's able to do. It's shocking to me.
Speaker 2: And he's basically saying, He's like, you could make up
Speaker 2: a pilot and save millions of dollars. This is going
Speaker 2: to have all kinds of ramifications. That feels like quite,
Speaker 2: that feels like half like just ignorance because this person's like,
Speaker 2: oh my god, look like total wow factor but also
Speaker 2: maybe hype. But I'm also curious from your perspective, what
Speaker 2: what are the actual dangers that we're facing that you know,
Speaker 2: because right now I think everything is just all about
Speaker 2: these are the jobs it's going to take. I think
Speaker 2: in the l ll M episode where the LM predicted
Speaker 2: what that what the potential of llms were and the
Speaker 2: jobs that it could take in a very paper was Yeah,
Speaker 2: where it's like, huh, you know, like just sort of
Speaker 2: the unethical nature of how even these companies are doing
Speaker 2: research and creating data to support this.
Speaker 1: Can we just stop? Can you just stop and explain
Speaker 1: exactly what the methodology of that paper was? No?
Speaker 2: Please, I will. I will allow the experts to do it,
Speaker 2: because it's it's it's absolutely bonkers to hear because any
Speaker 2: person who's like been at any like tried to look
Speaker 2: at a study or something and you look at methodology like.
Speaker 3: So methodology is a very kind term.
Speaker 2: For what was. Yeah, truly truly, So, Alex.
Speaker 3: Do you want to summarize real quick what was?
Speaker 1: Then?
Speaker 3: There was two different things we were looking at. It
Speaker 3: was something that came out of Open AI and something
Speaker 3: from Goldman Sachs and the Golden Sacks one was silly,
Speaker 3: but not as silly as the Open Eye one.
Speaker 4: Yeah, I mean getting in the detail. And I went
Speaker 4: through this and I puzzle this paper. I like poked
Speaker 4: my friend as a labor sociologist, I'm like, what the
Speaker 4: hell is going on here? And you know, okay, so
Speaker 4: there's this kind of metric that you can use to
Speaker 4: judge how hard a task is that the government collects,
Speaker 4: and there's this kind of job classification. They rid him
Speaker 4: from you know, one to seven effectively. And so what
Speaker 4: Goldman Sachs said was, well, basically, anything from one to
Speaker 4: four probably a machine can do. And you're like, okay,
Speaker 4: that's kind of silly. That's huge assumptions there. I understand though,
Speaker 4: as a researcher you have to make some assumptions when
Speaker 4: you don't have great data. But what Opening Eye did
Speaker 4: is that they asked two entities what like how well
Speaker 4: like what could be automated? They asked one other open
Speaker 4: AI employees, hey, what jobs do you think could be automated?
Speaker 4: Already hilarious because you know they're they're you know, pretty
Speaker 4: doing those jobs. Yeah, they're not doing those doubts. They're
Speaker 4: pretty primed to think that their technology is great. And
Speaker 4: then they asked DPT for itself. They prompted and say, hey,
Speaker 4: can we automate these jobs? So you know, and so.
Speaker 1: You'll never guess what the answer was.
Speaker 4: You'll never guess. You'll never guess.
Speaker 3: They took the output of that as if it were data,
Speaker 3: and then like you know, these ridiculous graphs and blah
Speaker 3: blah blah. It's just like the whole thing is is
Speaker 3: is fantasy.
Speaker 2: Right, So is the danger there just sort of this
Speaker 2: reliance Like I guess, so if we're classifying the sort
Speaker 2: of threats to our sense of like how information is
Speaker 2: distributed or what is real or what is hype or whatever,
Speaker 2: or if they're they're actually taking jobs, what, what is
Speaker 2: something that I think people that people actually need to
Speaker 2: be aware of or to sort of prepare themselves for
Speaker 2: how this is going to disrupt things in a way
Speaker 2: that you know, isn't necessarily the end of the world,
Speaker 2: but definitely changing things for the for the worst.
Speaker 3: There's a whole bunch of things, and the one that
Speaker 3: I'm sort of most going on about these days is
Speaker 3: the threats to the information ecosystem, So I want to
Speaker 3: talk about that. But there's also things like automated decision
Speaker 3: systems being used by governments to decide who gets social
Speaker 3: benefits and who gets them yanked right, things like doctor
Speaker 3: Joybul and WHINNI worked with a group of people in
Speaker 3: I want to say in New York City who were
Speaker 3: pushing back against having a face recognition system installed as
Speaker 3: their like entry system, So they were going to be
Speaker 3: continuously surveilled because the company who owned the house that
Speaker 3: they lived in, or maybe it was I'm not sure
Speaker 3: if it was going.
Speaker 4: To be apartment apartment complex, yes, yeah.
Speaker 3: Yeah, wanted to basically use biometrics as a way to
Speaker 3: have them gain entry to their own homes where they lived.
Speaker 3: So there's dangerous of lots of lots of different kinds.
Speaker 3: The one that's maybe closest to what we've been talking
Speaker 3: about though, is these dangerous to the information ecosystem where
Speaker 3: you now have the synthetic media, like that news article
Speaker 3: I was talking about before being posted as if it
Speaker 3: were real, without any watermarks, without anything that either a
Speaker 3: person can look at and say, I know that's fake,
Speaker 3: Like I knew because it was my name, and I
Speaker 3: knew I didn't say that thing, right, but somebody else
Speaker 3: wouldn't have. And there's also not a machine readable watermarking
Speaker 3: in there, so you can just filter it out and
Speaker 3: this stuff goes and goes. So there was Oh a
Speaker 3: few months ago, someone posted a fake mushroom foraging guide
Speaker 3: that they had created using a large language model to
Speaker 3: Amazon as a self published book so that it's just
Speaker 3: up there as a book. And coming back around to Sora,
Speaker 3: those videos like they look impressive at first glance, but
Speaker 3: then just like Alex was saying about the art having
Speaker 3: this sort of facile style to it, there's similar things
Speaker 3: going on in the videos, but still like it should
Speaker 3: be watermarked, it should be obvious that you're looking at
Speaker 3: something fake. And what open ai has done is they've
Speaker 3: put this tiny, little faint thing down the lower right
Speaker 3: hand corner that looks like some sort of readout from
Speaker 3: a sensor going up and down, and then it swirls
Speaker 3: into the open ai logo and it's faint, and it's
Speaker 3: in the same spot that Twitter puts the button for
Speaker 3: turning the sound on and off. So it's hidden by that.
Speaker 3: If you're seeing these things on Twitter, and it's completely opaque, right,
Speaker 3: if you are not in the know, if you don't
Speaker 3: know what open ai is, if you don't know that
Speaker 3: fake videos might exist, that doesn't tell you anything, right.
Speaker 3: So these are the things I'm worried about.
Speaker 4: And the things I'm really worried about is, you know,
Speaker 4: these things doing a pretty terrible job at producing written
Speaker 4: content and videos and images. And so it's not that
Speaker 4: they could replace a human person, but it just takes
Speaker 4: a boss or a VP to think that it's good enough. Right,
Speaker 4: and then this replaces whole classes of occupation. So again
Speaker 4: talking to Carl Ortiz, who's a concept artist. She's done
Speaker 4: work for Marvel and and DC and huge studios and
Speaker 4: Magic Togathering and you know, and she's basically saying, after
Speaker 4: mid journey stability, AI produces this incredibly crappy content. Jobs
Speaker 4: for concept artists have really dried up. They've gone and
Speaker 4: it's really hard. And I mean, especially for folks who
Speaker 4: are just breaking into the industry. You might just be
Speaker 4: trying to get there their you know, their work out
Speaker 4: there for entry level jobs. They can't find anything right now.
Speaker 4: And so imagine what that's going to replace, what that's
Speaker 4: going to encroach on, right, I mean, that's kind of
Speaker 4: unique thing about kind of creative fields and coding fields
Speaker 4: and and and whatnot. And then I would say this, yeah,
Speaker 4: this this automated decision making kind of in government and hiring.
Speaker 4: I mean, those are those are you know, deathly you know, airfyed, right,
Speaker 4: And I mean this is already being deployed. I mean
Speaker 4: at the US Mexico border. There's kind of massive. The
Speaker 4: Markup actually just put out this interview with David Moss,
Speaker 4: who's at the Electronic Freedom Foundation, No either the Electronic
Speaker 4: Freedom Refession. I think he's at the ACLU. I have
Speaker 4: to look this up, but it's about basically a survey
Speaker 4: of like surveillance technology that's not the southern border, and
Speaker 4: Dave Moss is at EFF. The Markdown Putt put a
Speaker 4: published something about with him. It was like a virtual
Speaker 4: reality tour of servants technology or something wild like that.
Speaker 3: I was gonna say. There's also things like shot Spotter,
Speaker 3: which reports to be able to detect what a gun
Speaker 3: has been fired. And this has been deployed by police
Speaker 3: departments all over the country, and there's you know, there's
Speaker 3: no transparency into how it was evaluatedor how it even
Speaker 3: works or why you should believe it works. And so
Speaker 3: what we have is a system that tells the cops
Speaker 3: that they're running into an active shooter situation, which is
Speaker 3: definitely a recipe for reducing police violence.
Speaker 2: Right right, yeah, right, our tide Microsoft, Yeah, and that
Speaker 2: there is a reason an investigation that that showed that
Speaker 2: it's always almost always used in neighborhoods.
Speaker 4: Yeah, communisic, the communities of color. Yeah, the Wired the
Speaker 4: Wired piece on that basically.
Speaker 1: Yeah.
Speaker 4: I think they said about what seventy percent of the
Speaker 4: census tracts something ridiculous like that.
Speaker 3: Yeah, Yeah, it's absurd. And then there's things like EPIC,
Speaker 3: which is a healthcare electronic health records company, is partnering
Speaker 3: with Microsoft to push the synthetic text into so many
Speaker 3: different systems, so that you're going to get like reports
Speaker 3: in your patient records that were automatically generated and then
Speaker 3: supposedly checked by a doctor who doesn't have time to
Speaker 3: check it thoroughly, right, and they're going to be doing
Speaker 3: things like, you know, randomly putting information about BMI and
Speaker 3: when it's not even relevant, or misgendering people over the place,
Speaker 3: or you know, this kind of stuff is going to hurt.
Speaker 1: Yeah.
Speaker 3: And I want to add to the issue of like
Speaker 3: entry level jobs drying up for people who do for example, illustration,
Speaker 3: doctor joybol and Winni points out that we're getting what's
Speaker 3: called an apprentice gap, where those positions where the easy
Speaker 3: stuff gets automated. And I don't think this is just
Speaker 3: in creative fields. But the positions where you're doing the
Speaker 3: easy stuff and you're learning how to master it and
Speaker 3: you are working alongside someone who's doing the harder stuff.
Speaker 3: If that gets replaced by automation, then it becomes harder
Speaker 3: to train the people to do the stuff that requires expertise.
Speaker 1: Right, Yeah, And it's easier to do in creative fields
Speaker 1: because there's such a just inability for executives to you know,
Speaker 1: like they don't know it. They've never known anything about
Speaker 1: like what what is quality creative work? So I feel
Speaker 1: like it's much easier for them to just be like, yeah,
Speaker 1: get rid of that, and well, yeah, like the way
Speaker 1: that we'll find out about that that isn't working is
Speaker 1: the quality of the creative output will be far worse.
Speaker 4: Right, I mean what I mean, all those folks really
Speaker 4: tend to care about our you know, content and engagement metrics.
Speaker 4: You know, you can't actually have something that's kind of
Speaker 4: known for quality or creativity.
Speaker 2: Right.
Speaker 4: It does remind me a little bit about kind of
Speaker 4: you know, the first rebels against automation, the Luodites, and
Speaker 4: you know, the kind of the kind of way in
Speaker 4: which they did have this apprentice guilt system in which
Speaker 4: they trained for you know, a decade or so before
Speaker 4: they could you know, perhaps go ahead and open their
Speaker 4: own shop in a way that you know, those folks
Speaker 4: were replaced by these these water frames that were they
Speaker 4: called water frames because they were produced by hydraulic power.
Speaker 4: But then uh, you know, effectively powered by unskilled people
Speaker 4: and by unskilled usually children doing incredibly dangerous work. But yeah,
Speaker 4: folks that have been training for this, the apprentices were
Speaker 4: incredibly steaming mad.
Speaker 1: Yeah, and then we made their name a synonym for like.
Speaker 2: Hater.
Speaker 4: Yeah, there's been some nice efforts to reclaim them by
Speaker 4: Brian Merchant and yeah, some folks.
Speaker 2: Yeah.
Speaker 1: So just in the comparison to Crypto, it feels like
Speaker 1: the adoption here, the hype cycle here is more widespread
Speaker 1: than Crypto. Like with Crypto, there was that moment where
Speaker 1: we saw the person behind the curtain who was you know,
Speaker 1: the people that there was the fall of Crypto. With AI,
Speaker 1: I don't feel like there is any incentive for anyone
Speaker 1: to any of the stakeholders. I guess I got it
Speaker 1: involved to yeah, to just come out and be like, yeah,
Speaker 1: it was bullshit. You know, there's just so much buy
Speaker 1: in across the board where I guess we've already talked
Speaker 1: about where you see this going, But is there Do
Speaker 1: you think there's any hope for this getting kind of
Speaker 1: found out, the truth catching on, or do you think
Speaker 1: it's just going to have to be one hundred years
Speaker 1: from now when somebody changes their mind about AI haters
Speaker 1: and it's like, actually they were onto something the way
Speaker 1: we are about light eites.
Speaker 3: We're still trying. That's what we're up to with the podcast,
Speaker 3: right a lot of our other public scholarship. There was
Speaker 3: an interesting moment last week when chat gpt sort of
Speaker 3: glitched and was out putting sense. Yes, I mean it
Speaker 3: was it actually Spanglish or people just calling it because
Speaker 3: it had some Spanish in it.
Speaker 4: It had some, It wasn't all I mean it was
Speaker 4: doing some spangless stuff. With this stuff is just even
Speaker 4: more inscrutable than usual. It was just completely nonsense.
Speaker 3: Yeah, yeah, And that the sort of open eye statement
Speaker 3: about what went wrong basically described it for what it is.
Speaker 3: They had to like say something other than what they
Speaker 3: usually say. And then there was a whole thing with
Speaker 3: Google's image generator, which you know the so the baseline
Speaker 3: thing is super duper biased and like makes everybody look
Speaker 3: like white people. And there's this wonderful thread on Medium,
Speaker 3: and here I'm doing the thing wre I can't think
Speaker 3: of a person's name, but it's a wonderful post on
Speaker 3: Medium where someone goes through this thread of pictures that
Speaker 3: I think we're initially on Reddit, where someone asked one
Speaker 3: of these generators to create warriors from different cultures taking
Speaker 3: a selfie and they're all doing the American selfie smile.
Speaker 3: And so does this really uncanny Valley thing going on
Speaker 3: there where these people from all these different cultures are
Speaker 3: posing the way Americans pose, so huge bias in these things. Underlyingly,
Speaker 3: Google has some kind of a patch that basically whatever
Speaker 3: prompt you put in, they add some additional prompting to
Speaker 3: make the people look diverse. And then last week or
Speaker 3: so someone figured out that if you asked it to
Speaker 3: show you a painting of Nazi soldiers, they're not all
Speaker 3: white because of this patch, right right, So yeah, So
Speaker 3: Google's backpedaling of that I think was ridiculous. There was
Speaker 3: some statement in there about how they certainly don't intend
Speaker 3: for their system to put out historically inaccurate images. I'm like,
Speaker 3: what the hell, it's a fake image, like there's no
Speaker 3: accuracy there, no matter what. These mishaps maybe sort of
Speaker 3: pulled back the curtain for a broader group of people.
Speaker 3: There's some true believers out there who are not going
Speaker 3: to be reached. Sure, but I think it may have
Speaker 3: helped for some slice of society.
Speaker 2: Yeah, I know some people who are like, how do
Speaker 2: we know that Reinhardt Heydrich didn't have dreadlocks? Clear?
Speaker 1: Wow?
Speaker 2: But okay, sure, go off.
Speaker 4: Yeah, And I mean I think it's this is such
Speaker 4: an interesting question, right, is like where does when is
Speaker 4: the AI bubble going to pop?
Speaker 3: Right?
Speaker 4: And I mean, in some ways it feels like, you know,
Speaker 4: we're where, I mean, as much as we can do.
Speaker 4: We're kind of prodding it, right, you know, and seeing
Speaker 4: you know. And one thing that I off handedly said
Speaker 4: one one time and Emily loves is ridicule as praxis.
Speaker 4: So yeah, one thing you know, and I will in
Speaker 4: a turn of one of Emily's great quotes, is you know,
Speaker 4: oh gosh, I'm going to mess it up right now.
Speaker 4: It's I and I. It's it's the refuse to be impressed.
Speaker 4: Uh oh yeah, resist, the urge to be resist? Is
Speaker 4: this year to be impressed. It's much it's much better
Speaker 4: when when when she says it, and it's and it's
Speaker 4: and it's kind of the idea of like some of
Speaker 4: it is a bit of the sheene right, But it
Speaker 4: feels like at some point, you know, if in the
Speaker 4: kind of operations of these things, you know enough, there'll
Speaker 4: be enough buy in, especially with automation, that's hard to
Speaker 4: reverse a lot of the automation without just a huge fight.
Speaker 4: And so I mean, I think something that helps our
Speaker 4: you know, worker led efforts, you know, and one of
Speaker 4: the most awesome things that we've seen this is something
Speaker 4: a scholar, Blair a tear Frost, calls AI counter governance,
Speaker 4: she calls and one example she is is the w
Speaker 4: GA strike and how folks struck for one hundred and
Speaker 4: forty eight days and after strike they not only got
Speaker 4: a bunch of new kinds of guarantees for minimum pay
Speaker 4: when it came to streaming and the residuals they get
Speaker 4: for it, they also, you know, have to basically be
Speaker 4: informed if any AI is being used in the writer's room.
Speaker 4: They can't they can't be forced to edit or re
Speaker 4: edit or rewrite AI generated content. They have to be
Speaker 4: everything has to be basically above board. I mean, isn't
Speaker 4: as far as it could have gone and banned it
Speaker 4: out right right, But if there's any use of it
Speaker 4: has to be disclosed and you can't bring in these
Speaker 4: tools to hold you know, whole cloth to place the
Speaker 4: writer's room.
Speaker 1: Yeah. Well, Alex and Emily, it has been such a
Speaker 1: pleasure having you on the show. I feel like you
Speaker 1: could keep talking about this hours for days. But thank
Speaker 1: you so much for coming on. And yeah, I would
Speaker 1: love to have you back. Where can people find you?
Speaker 1: Follow you here? Your podcast all that good stuff.
Speaker 3: Yeah, so the podcast is called Mystery AI Hype Theater
Speaker 3: three thousand. Then you can find it anywhere find podcasts
Speaker 3: are streamed, but also on peer two if you prefer
Speaker 3: that kind of content as video. So we start off
Speaker 3: this Twitch and then show up that way. I'm on
Speaker 3: all the socials. I tend to be Emily M. Bender
Speaker 3: so Twitter or blue sky on Macedon. I'm Emily M.
Speaker 3: Bender at what are we dare institute that social and
Speaker 3: I'm very findable on the web, very googleable at the
Speaker 3: University of Washington. How about you, Alex?
Speaker 4: Yeah, and if you want to chech catch the twitch stream.
Speaker 4: It's Twitch, dot tv, slash dare, undiscore Institute, usually on Mondays,
Speaker 4: usually this time slot. So next week we'll be here. Wait,
Speaker 4: I forget, we'll be here eventually.
Speaker 3: Yes, today's to today's Tuesday though in.
Speaker 4: That's right, that's right, Yeah, it's actually not so Mondays.
Speaker 4: Check us out. You can catch me at Twitter and
Speaker 4: Blue Sky Alex Hannah no h at the end. And
Speaker 4: then yeah, our mast on server, dare, hyphencommunity, dot social,
Speaker 4: what else? I think those are all the socials we have?
Speaker 1: Yeah, amazing. Is there a work of media that you've
Speaker 1: been enjoying?
Speaker 3: So I am super excited about doctor Joeyblamuni's book Unmasking AI,
Speaker 3: came out last Halloween. It is a beautiful sort of
Speaker 3: combination memoir slash introduction into the world of how so
Speaker 3: called AI works, how policymaking is done around it, how
Speaker 3: activism is done around it. And I actually got to
Speaker 3: interview her here in Seattle at the University of Washington
Speaker 3: last week, which was amazing.
Speaker 1: Nice. How about you, Alex?
Speaker 4: Yeah, I'd recommend the podcast Worlds Beyond Number and it
Speaker 4: is a Dungeons and Dragons live play, actual play podcast
Speaker 4: that is put Together by Brendan Lee Mulligan, Abria Engar,
Speaker 4: Lou Wilson, and Erica is talking about writing that is
Speaker 4: not generated by AI, but generated by some very talented
Speaker 4: improv comedians and storytellers themselves.
Speaker 1: Acale, How do you scale?
Speaker 4: Oh my god, it is actually surprisingly I mean that
Speaker 4: podcast is actually I think the single most funded thing
Speaker 4: on Patreon. Birth a bunch of records for it. I
Speaker 4: had about forty thousand subscribers. People want good content, Yeah,
Speaker 4: I'm humans high art, you know. People want this vibrant storytelling,
Speaker 4: amazing creation, you know, and so the more we can
Speaker 4: we can plug that stuff the better.
Speaker 1: By the way, you can also find Mystery AI Hype
Speaker 1: Theater three thousand. Like where podcasts are? So in case
Speaker 1: you don't do Twitch. Yeah, like I was listening you know,
Speaker 1: Spotify or whatever.
Speaker 2: You weren't on Twitch.
Speaker 1: I wasn't on Twitch this weekend. I was taking it.
Speaker 1: I was taking a break. You know. It's just like
Speaker 1: too much, too much Twitch.
Speaker 2: Couldn't get a hyperion.
Speaker 1: That's right? Okay, all right, Miles, where can people find you?
Speaker 1: What's working media you've been enjoying.
Speaker 2: At the at base platforms at Miles of Gray also
Speaker 2: if you like the NBA, you can hear Jack and
Speaker 2: I just go on and on about that on our
Speaker 2: other podcast, Miles and Jack Got Mad Moosties. And if
Speaker 2: you like trash reality TV like me, I like I
Speaker 2: comfort myself with it, check me out on four to
Speaker 2: twenty Day Fiance where we talk about ninety day Fiance tweet.
Speaker 2: I like, this is just kind of it's a historical day.
Speaker 2: I would have to say for the Internet. We missed
Speaker 2: an anniversary on Sunday. Apparently the very famous clip of
Speaker 2: Peter Weber, the bowler who won and came out with
Speaker 2: the iconic phrase who do you think you are? I Am?
Speaker 2: That had its twelfth anniversary and I just will to
Speaker 2: play that audio day because it's one of our favorites.
Speaker 2: Here we go, are you?
Speaker 1: Who are all?
Speaker 2: Right? Who do you think you are? Who do you?
Speaker 1: Oh?
Speaker 2: Man?
Speaker 1: The whole thing is good. Also, the run up to
Speaker 1: that is great. That's right. I did it.
Speaker 2: I did it?
Speaker 1: God damn it?
Speaker 4: What I yeah? I play an amateur sport. And just
Speaker 4: like the exhilaration you get and you just say anything.
Speaker 2: Yeah, yeah, I know.
Speaker 4: That's I love that clip.
Speaker 2: I respect I think you are? I It's like yeah,
Speaker 2: you've you're you've glory fried your brain circuits and now
Speaker 2: that's what you're saying. And the other one from at
Speaker 2: the Dark Prowler. It's a screenshot of an Uber notification
Speaker 2: on the phone. It says, young Stroker, the body Snatcher
Speaker 2: will be joining you along your route. You're still on
Speaker 2: schedule to arrive by seven fifty four.
Speaker 1: Okay, by the way, chicken nuggets glory fried. Thanks, Yeah,
Speaker 1: that'd be great. Oh yeah you Oh tweet I've been enjoying.
Speaker 1: Caitlyn at Kitlyn at Kate Holes tweeted overnight oats sounds
Speaker 1: like the name of a race horse who's sucks. That
Speaker 1: was an old one that was apparently September twenty fifth,
Speaker 1: twenty twenty two.
Speaker 2: Good one, Go banger.
Speaker 1: You can find me on Twitter at Jack Underscore O'Brien.
Speaker 1: You can find us on Twitter at daily Zeitgeist. We're
Speaker 1: at the Daily Zeitgeist on Instagram. We have a Facebook
Speaker 1: fan page on a website Daily zeikegeist dot com where
Speaker 1: you post our episode and our footnote. We link off
Speaker 1: to the information that we talked about in today's episode,
Speaker 1: as well as a song that we think you might enjoy.
Speaker 1: Miles is there a song that you think people might enjoy.
Speaker 2: This is just a nice little instrumental clip, not clip,
Speaker 2: a full on track from a London based producer goes
Speaker 2: by Vegan veg y N and the track is called
Speaker 2: a Dream goes On Forever and it's a super dreamy track.
Speaker 2: It's just kind of an interesting production. So maybe you know,
Speaker 2: maybe you know, just check out some mid journey you
Speaker 2: know images, some sore AI videos and just blast this
Speaker 2: in your headphones. Just kind of go on for a dream,
Speaker 2: goes for.
Speaker 3: The music and leave the synthetic media.
Speaker 1: Just fully embraced into the music, close your eyes and
Speaker 1: actually the mid journey images that your brain will produce.
Speaker 1: I've found that like at night when I sleep, sometimes
Speaker 1: my brain produces Sora images.
Speaker 2: This is like you sound like you sound like a
Speaker 2: youth pastor talking to kids about God. Or it's like
Speaker 2: you know where it's really the AI is actually up
Speaker 2: in here kids?
Speaker 1: Yeah, want to get.
Speaker 4: Yeah yeah yeah. The real training data we had is
Speaker 4: the word of.
Speaker 1: God, exactly exactly what I need.
Speaker 2: The Daily Zeitgeist is a production of by Heart Radio.
Speaker 1: For more podcasts from my Heart Radio, visit the iHeart
Speaker 1: Radio ap Apple podcast or wherever you listen to your
Speaker 1: favorite shows, that it is going to do it for
Speaker 1: us this morning. We are back this afternoon to tell
Speaker 1: you what is trending, and we'll talk to you all then.
Speaker 2: Bye bye,