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Speaker 1: Hello the Internet, and welcome to season three oh six,

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Speaker 1: Episode two of Alley's I Guys Day production of iHeartRadio.

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Speaker 1: This is a podcast where we take a deep dive

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Speaker 1: into America's shared consciousness. And it is Tuesday, September twenty sixth,

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Speaker 1: twenty twenty three.

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Speaker 2: Ooh you ready.

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Speaker 1: You know what this is?

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Speaker 3: Right?

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Speaker 2: Oh, you know what this is right? For all my

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Speaker 2: pancake lovers, It's your day, is National Pancake Day. It's

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Speaker 2: also National Shamoo the Whale Day. Shout out Shamoo. It's

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Speaker 2: also for all my dumpling lovers out there, for me,

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Speaker 2: specifically my Joza lovers out there, National dumpling Day. Okay,

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Speaker 2: so you know, celebrate however you've seen fit depending on

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Speaker 2: your cultural disposition. It's also National Johnny Applesey Day. And

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Speaker 2: only bring that up because I remember in Christian school

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Speaker 2: we were made to sing that as like a song

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Speaker 2: before we went to lunch.

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Speaker 1: Man. The amount of pro Johnny Applesey propaganda that I

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Speaker 1: had to live with a lot of.

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Speaker 2: Approach, Yeah yeah, yeah, it's like like John Chapman or

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Speaker 2: something original.

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Speaker 1: I just really liked it, really fucked with apples like

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Speaker 1: the children must learn.

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Speaker 2: Yeah, I bet there's some dark ass ship probably behind

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Speaker 2: you know, like some a story like that. It's got

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Speaker 2: to be a milkshack.

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Speaker 1: Though, Yeah, for sure, very problematic. But the way, like

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Speaker 1: the amount of information that I consumed, Like I had

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Speaker 1: one of those my family had one of those book

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Speaker 1: sets where it was like important historical figures and like

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Speaker 1: right next to Lincoln was like Johnny Appleseed, and you

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Speaker 1: know they I was prepared for Johnny Appleseed to be

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Speaker 1: like one of the major figures in my.

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Speaker 2: Life, right and education. Did you know the song, No,

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Speaker 2: the Lord is good to me, And so I thank

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Speaker 2: the Lord for giving me the anxiety, the sun and

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Speaker 2: the rain and the apple seed. The Lord is good

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Speaker 2: to me, And then we could go eat lunch. But

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Speaker 2: we had to sing that ship before wow from the

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Speaker 2: from the Disney movie apparently.

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Speaker 1: So I wasn't even I didn't even get all of

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Speaker 1: the Johnny apples propaganda.

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Speaker 2: I got the Christian, I got the Christian capitalist. Johnny

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Speaker 2: apple Seed propaganda just mainlined into my little brain. He

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Speaker 2: wore a pot on his head, right, I mean I

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Speaker 2: think who sure like backwards kind of like Davy Rocket.

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Speaker 1: Yeah, anyways, this is all don't know the ship that

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Speaker 1: is probably specific to American audiences. Yeah, but my name

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Speaker 1: is Jack O'Brien aka start spreading the news eating today.

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Speaker 1: I want to eat a part of it pea can

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Speaker 1: pecan pie. These chattering tea are longing to chew right

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Speaker 1: through the nutty part of it, pig can pecan pie.

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Speaker 1: That has courtesy of Maxer twelve sixteen on the discord,

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Speaker 1: a little just real solid, right down the middle meatball

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Speaker 1: of a weird al parody and reference to our conversation

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Speaker 1: about peacock pie. For some reason, he was lobbing that

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Speaker 1: meatball up to you. Yeah, I saw in the discord.

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Speaker 1: I was tagging that, but you took that. Oh so

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Speaker 1: you just do other people's a case. Okay, introting? All right,

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Speaker 1: Well I'm throws to be joined as always by my

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Speaker 1: co host mister Miles Gras.

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Speaker 2: Okay, so you did my ak and I heard you

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Speaker 2: do this one on Friday, so allow me to do

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Speaker 2: it one more time, same Miles and Jackie.

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Speaker 3: Have you seen it yet? Ooh, it's just flying around

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Speaker 3: la la la, losing all my jets. Yeah, oh there

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Speaker 3: isn't were and his sauce based out. Oh Jetty, she's

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Speaker 3: a really neat. She's gotta stell the use a bomb

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Speaker 3: and shoot so fazy you can't even be seen.

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Speaker 1: Losing all my jets.

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Speaker 2: Shout out to the Department of Defense for losing all

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Speaker 2: of our multi million dollar killer fucking spacecrafts, and shout

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Speaker 2: out to Johnny Davis and Blinky Heck, one more time.

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Speaker 1: We're done. More time. Yeah, maybe we can like layer

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Speaker 1: those because you went high, I feel like I went

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Speaker 1: a little bit lower. Maybe maybe we could get a

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Speaker 1: little little harmony harmony. Yeah, anyways, Miles, Yeah, you're thrilled

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Speaker 1: to be joined in our third seat yes for today's

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Speaker 1: expert episode by a truly brilliant guest who I felt

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Speaker 1: especially stupid singing an aka in front of She's a

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Speaker 1: research associate at the lever Home Center for the Future

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

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Speaker 1: gender studies, critical race theory, and Asian diaspora studies. He

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Speaker 1: is also a research fellow at the AI Now Institute,

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Speaker 1: the co editor of the upcoming volume The Good Robot,

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Speaker 1: Feminist Voices on the Future of Technology, and the co

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Speaker 1: host of the Wonderful Good Robot podcast. Please welcome the

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Speaker 1: brilliant The talented doctor Kerry mcinnern doctor Belly.

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Speaker 4: Thanks so much for having me.

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Speaker 2: Thank you so much for joining us.

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Speaker 1: How different has this been? So far from what you

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Speaker 1: were expecting?

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Speaker 4: Oh my gosh, I don't have a song. I was like,

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Speaker 4: how do I say? I definitely don't have a song.

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Speaker 4: And I also don't know who Johnny Appleseed is. I

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Speaker 4: grew up at a New zeal End and so I

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Speaker 4: didn't have a book of like historical figures. I had

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Speaker 4: a book called watch Out these Creatures, Bite and Sting,

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Speaker 4: and it was like all the ways you could die

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Speaker 4: being killed by like a jellyfish or a stay or

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Speaker 4: like an octopus. I may leave you into Australia. And

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Speaker 4: so it kind of like traumatized me for the good

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Speaker 4: from childhood. But it does mean I didn't have the

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Speaker 4: great apple seed song that you.

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Speaker 1: Just say, yeah, yeah, did you guys have apples though,

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Speaker 1: because I was under the impression that the only reason

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Speaker 1: I had apples was a Johnny apple seed Yeah, and

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Speaker 1: the Lord and the Lord provided It's just all Kiwi's.

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Speaker 2: Right down there, pretty much.

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Speaker 4: Kiwis kiwi fruit, no apples, no songs, just no music.

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Speaker 2: It's just silent, is an excute. Part of my ignorance

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Speaker 2: is the kiwi is that like a native New Zealand fruit.

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Speaker 4: I'm hoping so the kiwi is a native New Zealand bird,

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Speaker 4: and then the kiwi fruit is what we call that

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Speaker 4: like fuzzy brown fruit. Yea, yeah, I feel like they

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Speaker 4: kind of mix up the two or they you know,

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Speaker 4: they're both brown and fuzzy and sort of.

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Speaker 2: Bulbous ignorance incarnate here, folks.

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Speaker 1: But if you do cut the bird in half, it

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Speaker 1: looks exactly like the kiwi fruit. Right, A lot of

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Speaker 1: people don't know that green with green with little horton

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Speaker 1: seed seed core.

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Speaker 2: Yeah.

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Speaker 1: All right, doctor McNerney, we have you here today to

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Speaker 1: talk to about AI. We've talked to last week's episode

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Speaker 1: about AI. We're thrilled to ask you our follow up questions,

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Speaker 1: but before we get to any of it, we do

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Speaker 1: like to get to know our guests a little bit

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Speaker 1: better and ask you what is something from your search history?

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Speaker 4: This is pretty embarrassing. I just got a Nintendo and

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Speaker 4: I'm like finally entering my game agol Era. I'm like,

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Speaker 4: this is going to be so fun. But my whole

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Speaker 4: such history, it's just me googling like very basic controls

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Speaker 4: and Zelda Breath of the Wilds. It's like how do

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Speaker 4: I jump? Like it's really awful. I went and look

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Speaker 4: it's like how do I befriend a dog? Like how

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Speaker 4: do I climb a mountain? And so it's not going well.

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Speaker 4: I need to outsource my zilta plane to like children

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Speaker 4: who will be much better than me.

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Speaker 2: Wait, but is it? Is it? You're not forcing you

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Speaker 2: to give up, right, You're just you're You're just acclimating

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Speaker 2: to the new environment, right.

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Speaker 4: I mean the kingdom is not going to be safe

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Speaker 4: whatever I'm meant to be doing. I'm just walking in

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Speaker 4: circles now for like an infinite amount of time. But

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Speaker 4: I'm having fun, which I guess is the main point.

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Speaker 2: Yeah, yeah, yeah, And wait, why did you or were you?

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Speaker 2: Were you like a game? Did you play games in

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Speaker 2: childhood and you're kind of coming back or this is

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Speaker 2: completely new, like this is all new territory for you.

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Speaker 4: I did, but I was also bad in childhood, Like

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Speaker 4: this isn't a story of like oh, I used to

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Speaker 4: be like super into games and I like fell out

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Speaker 4: of touch like how people were like amazing athletes his

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Speaker 4: children and then like pick it up again, like I'm

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Speaker 4: consistently awful a game such history is not a surprise.

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Speaker 2: Got it? Got it? I mean you were doing, you know,

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Speaker 2: worthwhile things like thinking about how to ethically deal with

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Speaker 2: all this technology while I was laughing my ass off

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Speaker 2: playing Donkey Kong Country, you being like, Aha, slap the

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Speaker 2: ground and bananas come out. That's a little hack people

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Speaker 2: didn't tell you about.

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Speaker 1: Our AI guests on last week's Expert episode was saying

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Speaker 1: the video games are one of the ways that he

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Speaker 1: thinks that the future, the future of AI is going

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Speaker 1: to impact our day to day lives. Is that Is

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Speaker 1: that something that you think about as you're out there

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Speaker 1: kind of just walking in circles in Zelda Breath of

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Speaker 1: the Wild.

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Speaker 4: I mean, I feel like I performed so poorly they

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Speaker 4: probably think I am like an NPC. I think it's

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Speaker 4: just like that is not a person with free will

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Speaker 4: like that following a fox around.

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Speaker 2: It just it just rotated seven hundred and twenty degrees

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Speaker 2: over and over for no reason. I guess.

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Speaker 1: So have they made games more fun for people who

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Speaker 1: suck at video games? That that's the innovation that I'm

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Speaker 1: looking for, because I was never very good at them.

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Speaker 2: Yeah.

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Speaker 1: I liked playing them, I like hearing about them. I

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Speaker 1: think I'm ready to enter my gamer girl era and

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Speaker 1: come back.

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Speaker 2: But yeah you should. I mean, yeah, look, Jack, they

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Speaker 2: already know you as the switch God because you've been

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Speaker 2: on Nindado so much. The controllers basically fuse to your body.

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Speaker 2: But there are like a lot of new or like

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Speaker 2: new like the new Star Wars game. You could just

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Speaker 2: set it to like, man, I'm not trying to do

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Speaker 2: all this like fancy shit. I just want to beat

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Speaker 2: people up or just mash the keypad over and over

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Speaker 2: and win like that, and you can you can't do

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Speaker 2: that because I think you know it is like it

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Speaker 2: is about being able to play at different skill levels

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Speaker 2: rather being like, oh, you don't know how to multi

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Speaker 2: use the force while you know you're doing your melee attacks.

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Speaker 2: Come on, some of us just want to be the kids.

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Speaker 2: Unraveled is good for that too, of like a fun

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Speaker 2: video game that is not like really requiring you to

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Speaker 2: have like these sort of highly developed video game skills

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Speaker 2: and it's an easy play.

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Speaker 1: Nice doctor mcinnerney, What is something you think is overrated?

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Speaker 4: I feel like I'm not gonna win myself any friends.

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Speaker 4: It'll be like how let lose friends and stop influencing people.

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Speaker 4: But all the Disney live action movies, I've hit a

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Speaker 4: stage where I'm like, no more live actions, Like The

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Speaker 4: Little Mermaid is beautiful. I love the original Cinderella, but

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Speaker 4: you know I don't I don't want to see more

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Speaker 4: and more of like the same movies again, Like it

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Speaker 4: makes you a bit sad and like what other stories

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Speaker 4: could be told if it weren't like always making these

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Speaker 4: live actions. But you know, maybe I'll be proven like

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Speaker 4: totally wrong and the next ones will be amazing, But

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Speaker 4: I'm ready for something else.

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Speaker 2: Just yeah, based on everyone's first like sort of reaction

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Speaker 2: like oh you saw how it wasn't like yeah it,

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Speaker 2: No one was like it was so good, I'm gonna

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Speaker 2: see it nine times. Everyone just kind of I think

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Speaker 2: they're they have to reconcile like their love for the

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Speaker 2: original source material with that and not fully be like

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Speaker 2: I didn't like it. They're like yeah, I mean it's

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Speaker 2: it's it's interesting.

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Speaker 1: You know, it's like their voice just goes up. Yeah,

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Speaker 1: you know it was like I spent my afternoon going

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Speaker 1: to that. I think that's right. I've never been more

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Speaker 1: confident in a prediction about the future of a like

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Speaker 1: subgenre of movies than in saying that they're not going

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Speaker 1: to suddenly figure something out about the Disney live action

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Speaker 1: reskins of the animations, like we we know what the

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Speaker 1: original cartoon UNEs look like and what happens in them.

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Speaker 1: We know what is possible here, right. I can't I

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Speaker 1: can't imagine a version like what they're gonna pull out

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Speaker 1: where we're like, oh, no, that is not did not

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Speaker 1: see that one coming?

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Speaker 2: Whoa that bear is talking and singing? Hold on, oh,

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Speaker 2: they already did that. That was the best one I

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Speaker 2: think was Jungle Book, and that was the first one

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Speaker 2: they did, and ever since, I feel like, yeah, been

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Speaker 2: diminishing returns man, although I didn't see The Little Mermaid,

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Speaker 2: so I cannot speak to that one.

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Speaker 4: Is you?

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Speaker 2: What's your favorite genre of film though, doctor carry Ooh?

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Speaker 4: I mean okay, so I really have a soft spot

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Speaker 4: for like old school superhero movies. But my like most recent,

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Speaker 4: most amazing film I film was the latest into the

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Speaker 4: Spider Verse film what was it? The Spider Verse? And

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Speaker 4: I just I love animation and that film It's so

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Speaker 4: witty and the visual is extraordinary, just great films. So

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Speaker 4: I oracle about how much I love these films at

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Speaker 4: work and everyone has to deal with me being like,

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Speaker 4: go watch it, So anyone listening, go watch it.

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Speaker 2: Amazing favorite favorite Spider character, Spider person in the universe.

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Speaker 4: Oh my goodness. I can't remember the name of it.

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Speaker 4: But the Light really like the really like dark noir

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Speaker 4: run for the first one. He's like from like all

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Speaker 4: those like nineteen thirties kind of like mystery films, you know,

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Speaker 4: in black and white. I kind of love that.

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Speaker 2: Yeah, yeah, it was it just spider Man Noirs.

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Speaker 1: Yeah, I think nor Spider Man. Maybe I don't know, or.

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Speaker 2: I'm sorry spider Man, nor Nor.

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Speaker 1: What is when you say old school superhero movies? Do

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Speaker 1: you mean like Christopher Reeves Superman? Or are you talking

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Speaker 1: about like the first Iron Man? The first Iron Man?

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Speaker 4: I think it's almost like less like specific films. I love,

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Speaker 4: like almost films that have that like really cheesy like

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Speaker 4: origin coming of age story, like you know me, it's

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Speaker 4: just really just about like they're discovering themselves and it's

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Speaker 4: such a simple narrative, and like I feel like I

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Speaker 4: should crave more complexity than that, and I should want

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Speaker 4: it to be a more nuanced stories. But sometimes it's

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Speaker 4: just really satisfying and you want this clean cut narrative

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Speaker 4: of this like good guy to beating all these bad guys.

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Speaker 4: When I want to relax, I really enjoy that. In

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Speaker 4: my day job, I think about lots of like complexity

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Speaker 4: and nuance and like what we do with the future

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Speaker 4: of society, and so you know, right, sometimes I find

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Speaker 4: it very relaxing just to be like sonless.

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Speaker 2: Yeah, you're like, yeah, I love a Joseph Campbell type

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Speaker 2: and flick. Just give me that hero's journey in everything.

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Speaker 4: It's gonna be okay, Yeah, it's gonna.

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Speaker 2: Go wrong, challenge your old self to become your new self.

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Speaker 1: Yeah, but DOCC is an example of what we're facing

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Speaker 1: with AI like in the immediate future. I think we

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Speaker 1: can all agree on that, right, Spiderman two, in.

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Speaker 2: A way, you're like, it's giving me the equivalent of

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Speaker 2: like eight arms. In a way you're like, oh boy.

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Speaker 1: What, what's something you I think is underrated?

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Speaker 4: Oh? Underrated? I mean I'm a big like cozy knight

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Speaker 4: in person, like I want to be one that's fun

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Speaker 4: party people who are like you know, is out raving.

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Speaker 4: But realistically at ten PM, I'm like the day is over,

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Speaker 4: I'm in bed, like I think a cozy knight in

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Speaker 4: especially Ortumn is coming. Like that's just my happy place,

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Speaker 4: me at home, my husband. Life is good.

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Speaker 2: Yeah, I like that, that's my That's my whole thing.

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Speaker 2: Especially we don't get seasons here, so the second there's

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Speaker 2: like just the feeling of like a chill, I'm like,

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Speaker 2: I need to start nine fires and just being near them.

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Speaker 1: And that is a problem that Miles has, and he's

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Speaker 1: working on his therapy.

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Speaker 2: I guess legally it's called arson or something, but what

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Speaker 2: I say is I just want everyone to be cozy.

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Speaker 1: Around the Greater Los Angeles metropolitan area.

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Speaker 2: I'm yeah, I cannot go within four miles of the

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Speaker 2: Angelus National Forest.

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Speaker 1: But whatever, they must see my light. Yeah.

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Speaker 2: I love coziness. I love a are you are you cozy?

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Speaker 2: Are you summertime Jack? If you had to pick between,

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Speaker 2: would you raither be summertime Jack or cozy Jack?

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Speaker 1: I do. I do love a summer especially I'm in

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Speaker 1: my swimming phase where I just I really like getting

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Speaker 1: in a body of water, get running into the ocean,

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Speaker 1: even when it's a little cold ocean. And yeah, so

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Speaker 1: I think I'm like in a summer phase. But I yeah,

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Speaker 1: I am feeling the need to get cozy.

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Speaker 2: Yeah, I'll always take cozy over summer. That's just me.

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Speaker 2: I know you you It's it's.

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Speaker 1: Like you fetishize winter. I do.

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Speaker 2: It's problematic, like that's why I would only date like

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Speaker 2: like women from the Northeast. Ye're like, I feel like

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Speaker 2: you're dating me like for this like exotic sort of life,

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Speaker 2: you think, I like, what's it like with the snow.

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Speaker 1: You're appropriating menace like Minnesota culture?

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Speaker 2: Right, Yeah, well, hey, look we're all trying.

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Speaker 1: Hey, I heard that. He said, Hey, all right, let's

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Speaker 1: take a quick break and we'll come right right back

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Speaker 1: and talk about AI. We'll be right back and we're back.

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Speaker 1: And doctor McNerney as mentioned, I am an idiot on

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Speaker 1: this AI stuff. I think I generally have like my

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Speaker 1: version of AI up until last week. I guess like

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Speaker 1: researching for our last expert episode was what I had

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Speaker 1: read in you know, mainstream articles that went viral and

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Speaker 1: films move like Hollywood films, and then like messing around

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Speaker 1: with open AI and or chat GPT. So I had

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Speaker 1: this kind of disconnect in my mind where it was

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Speaker 1: like from an outsider's perspective. We have this C plus

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Speaker 1: level like copywriter thing with like in chat GPT GPT

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Speaker 1: for and then like the Godfather of AI, who I'm

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Speaker 1: just trusting people is the Godfather of AI. But that's

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Speaker 1: what everyone uses that same phrase though, like the Godfather

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Speaker 1: of AI just quit Google and says we're all fucked

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Speaker 1: in the next couple of years. And I think it's

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Speaker 1: confusing to me because I don't know exactly, like I

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Speaker 1: can't even like picture the way how he thinks we're fucked.

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Speaker 1: And there was this letter that was like, we need

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Speaker 1: to pause development on AI for in the near future,

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Speaker 1: and I guess I'm just curious to hear your perspective

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Speaker 1: on that pause letter and what the kind of dangers

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Speaker 1: of AI are in the near future.

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Speaker 2: Yeah, because to Jack's point two, also, we were talking

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Speaker 2: with Juao Sadoc last week at NYU about it, and

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Speaker 2: like at the end of it, we're like, okay, so

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Speaker 2: it's not sky Net right from terminator, and they're like,

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Speaker 2: oh great, But then we realize there's a raft of

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Speaker 2: other things that come along with just not being the terminator.

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Speaker 2: So yeah, that's I'm also from a similar perspective where

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Speaker 2: I always assume sky Net.

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Speaker 4: Yeah, I mean, I think you're totally not alone in

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Speaker 4: the dominance of ideas like sky Nee the Terminator, because

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Speaker 4: so much of our cultural framework for understanding what AI

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Speaker 4: is comes from a very narrow set of movie It's

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Speaker 4: like the Terminator, like the Matrix, which always positions AI

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Speaker 4: is something that's going to dominate us, it's going to

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Speaker 4: take over the world, and it's going to control us.

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Speaker 4: And it's important to, I think highlight that that's definitely

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Speaker 4: not the only ideas that we have about AI. We've

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Speaker 4: got thousands of years of thinking about our relationship with

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Speaker 4: intelligent machines, and there's a lot of different cultural traditions

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Speaker 4: that have really different ways of thinking about our relationships

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Speaker 4: with AI and intelligent machines that could be much more positive,

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Speaker 4: much more harmonious. And so I do think our immediacy

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Speaker 4: of jumping to this idea of sky net is reflective

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Speaker 4: of very much where we are right now. Right I'm

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Speaker 4: in the UK, you're in the US. These are countries

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Speaker 4: that have a really long history of thinking about AI

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Speaker 4: and very binary terms. So yeah, I think it's important

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Speaker 4: that we think about these long term risks of AI.

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Speaker 4: And you mentioned the pause letter calling for a wholt

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Speaker 4: to generating more large language models like chat GPT until

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Speaker 4: it had a bit more of a moment to think

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Speaker 4: about to the long term consequences of these models. But

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Speaker 4: I think it's really important not just to think of

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Speaker 4: the long term risks, but to think about which long

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Speaker 4: term risks we prioritize, because I think the sky net

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Speaker 4: terminator fantasy eats up a lot of oxygen about how

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Speaker 4: we talk about AI's risks. But there's a lot of

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Speaker 4: different risks to AI poses. So another long term risk

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Speaker 4: that we don't talk about very much at all is

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Speaker 4: the climate cost of AI. Right because AI it's hugely

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Speaker 4: energy intensive. Data centers require a huge amount of water

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Speaker 4: to function, and we have this massive problem of e

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Speaker 4: waste produced by a lot of electronic systems. But that

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Speaker 4: long term problem of climate crisis is much less exciting.

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Speaker 4: It's really scary, it's really grounded, and a lot of

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Speaker 4: our experiences and so it just doesn't seem to get

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Speaker 4: as much airtime. So that's something that I think is

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Speaker 4: really important. It's changing the conversation a bit to say, Okay,

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Speaker 4: it's sometimes interesting, sometimes scary to think about the terminator option,

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Speaker 4: but what are some of the other long term options

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Speaker 4: that could really shape our lives.

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Speaker 1: Yeah, like the degree to which the deck is being

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Speaker 1: stacked towards the terminator option was surprising to me. Like

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Speaker 1: we dug in last week a little bit to the

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Speaker 1: two stories I had always heard that are like kind

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Speaker 1: of put into the terminator version of AI taking over

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Speaker 1: a category. There's the AI that killed a person in

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Speaker 1: a military exercise that decided to like eliminate its controller.

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Speaker 1: And then there's the AI that hired a task rabbit

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Speaker 1: to pass the CAPSHA test. And like in both cases

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Speaker 1: those are like the AI that killed a person in

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Speaker 1: the military exercise, like that was somebody claiming that, and

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Speaker 1: then when they went back they were like, oh, I

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Speaker 1: was just saying it could hypothetically do that.

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Speaker 2: It was a thought experiment.

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Speaker 1: Yeah, it was a thought experiment of what an AI

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Speaker 1: could do in the right circumstances. And the task rabbit

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Speaker 1: one was more similar to the self driving car elon

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Speaker 1: musk thing, where it was just there was a human

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Speaker 1: prompting it to do that thing. That seems creepy to

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Speaker 1: us when we start thinking about oh, it's like scheming

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Speaker 1: to get loose and get overcome the things that we're

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Speaker 1: using to keep it hemmed in. So it does feel

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Speaker 1: like there is an incentive structure set up for the

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Speaker 1: people in charge of some of these major AI companies

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Speaker 1: to get us to believe that shit, like to think

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Speaker 1: to only focus on the AI v. Humanity, Like AI

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Speaker 1: gets loose of its control of our controls for it

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Speaker 1: and takes over and starts like killing people version of it.

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Speaker 1: I'm just curious, like what are your thoughts on, Like

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Speaker 1: why why why are they incentivized to do that when

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Speaker 1: it would seem like you, well, you don't want to

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Speaker 1: make it. You don't want it to seem like this

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Speaker 1: self driving car will take over and.

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Speaker 2: Start kill your family.

435
00:23:38,720 --> 00:23:41,840
Speaker 1: Yeah, start killing your family. It's so powerful. But it

436
00:23:41,880 --> 00:23:46,480
Speaker 1: seems like with AI, they're more willing to buy into

437
00:23:46,560 --> 00:23:50,600
Speaker 1: that fantasy and like have that fantasy projected to people

438
00:23:50,640 --> 00:23:55,160
Speaker 1: who are are not as closely tied to the ins

439
00:23:55,160 --> 00:23:56,240
Speaker 1: and outs of the industry.

440
00:23:56,720 --> 00:23:59,160
Speaker 4: Yeah, I mean, I think that's such an important point

441
00:23:59,240 --> 00:24:02,440
Speaker 4: because it's a weird thing about the AI industry, right,

442
00:24:02,480 --> 00:24:04,600
Speaker 4: Like you would never have this kind of hypewave around

443
00:24:04,600 --> 00:24:07,239
Speaker 4: something like broccoli where you say, oh, the broccoli, if

444
00:24:07,240 --> 00:24:08,879
Speaker 4: you eat it could kill you or it could like

445
00:24:09,119 --> 00:24:11,520
Speaker 4: transform the world, and then you wouldn't expect that to

446
00:24:11,560 --> 00:24:13,520
Speaker 4: somehow get people to buy a lot more broccoli and

447
00:24:13,680 --> 00:24:16,240
Speaker 4: just be like, I don't want to eat broccoli now.

448
00:24:16,880 --> 00:24:22,959
Speaker 1: It's so fucking good and powerful you explode like maybe.

449
00:24:23,800 --> 00:24:26,920
Speaker 4: Makes it even worse broccoli that you can then also eat,

450
00:24:27,000 --> 00:24:30,680
Speaker 4: you know what broccoli would be doing. But I do

451
00:24:30,720 --> 00:24:34,280
Speaker 4: think that we see this real cultivation of hype around AI,

452
00:24:34,640 --> 00:24:36,760
Speaker 4: and that a lot of firms explicitly use that to

453
00:24:36,800 --> 00:24:38,919
Speaker 4: sell their products, and it gets people interested in it

454
00:24:38,960 --> 00:24:41,879
Speaker 4: because on the one hand, people are really scared about

455
00:24:41,920 --> 00:24:45,000
Speaker 4: the long term and short term impacts of AI. On

456
00:24:45,080 --> 00:24:47,040
Speaker 4: the other hand, it also scared then though, of getting

457
00:24:47,119 --> 00:24:49,400
Speaker 4: left behind. So you see more and more firms saying, well,

458
00:24:49,400 --> 00:24:52,400
Speaker 4: now I've got to buy the latest generative AI tool

459
00:24:52,480 --> 00:24:54,600
Speaker 4: so that I look really tech savvy and I look

460
00:24:54,720 --> 00:24:57,720
Speaker 4: pick forward and I look futuristic. And so it's part

461
00:24:57,760 --> 00:25:00,280
Speaker 4: of the bigger hype cycle, I think, to a lot

462
00:25:00,280 --> 00:25:02,920
Speaker 4: of tension towards their products, but also to make them

463
00:25:02,920 --> 00:25:06,120
Speaker 4: seem like this really edgy, desirable thing. But I think

464
00:25:06,160 --> 00:25:08,760
Speaker 4: what's also interesting about both the stories that you raise

465
00:25:08,880 --> 00:25:10,720
Speaker 4: is when you looked under the hood there was human

466
00:25:10,800 --> 00:25:13,760
Speaker 4: labor involved, right, There were people who were really central

467
00:25:14,000 --> 00:25:17,280
Speaker 4: to something that was attributed to an AI. And I

468
00:25:17,280 --> 00:25:19,439
Speaker 4: think that's a really common story we see with a

469
00:25:19,440 --> 00:25:22,200
Speaker 4: lot of the type around AI. Is often the way

470
00:25:22,200 --> 00:25:25,399
Speaker 4: we tell those stories erases the very real human labor

471
00:25:26,080 --> 00:25:28,920
Speaker 4: that drives those products, whether it's the artists who originally

472
00:25:28,920 --> 00:25:32,199
Speaker 4: made the images that train the generative AI through today,

473
00:25:32,240 --> 00:25:35,040
Speaker 4: the labelers all sorts of people who are really central

474
00:25:35,080 --> 00:25:36,840
Speaker 4: to those processes.

475
00:25:36,680 --> 00:25:40,119
Speaker 2: Right, And I know, like in your episode of The

476
00:25:40,160 --> 00:25:43,479
Speaker 2: Good Robot when you're discussing the pause letter, you know,

477
00:25:43,560 --> 00:25:47,960
Speaker 2: I think the I think the version that we see

478
00:25:48,000 --> 00:25:50,680
Speaker 2: as like sort of these short term threats, at least

479
00:25:50,800 --> 00:25:53,000
Speaker 2: in the most immediate way is like for me working

480
00:25:53,119 --> 00:25:56,160
Speaker 2: in and around entertainment and people who work in advertising

481
00:25:56,280 --> 00:25:59,160
Speaker 2: and seeing like an uptake in that section, I go, okay,

482
00:25:59,160 --> 00:26:02,520
Speaker 2: that's easy. Like I can see how a company immediately goes, yeah,

483
00:26:02,520 --> 00:26:04,600
Speaker 2: it's a tool, and then suddenly it's like and now

484
00:26:04,600 --> 00:26:06,640
Speaker 2: you're on your ass because we'll just use the tool

485
00:26:06,720 --> 00:26:08,359
Speaker 2: now and we don't even need a person to prompt it,

486
00:26:08,480 --> 00:26:10,800
Speaker 2: or we need many we just need fewer people to

487
00:26:10,840 --> 00:26:13,320
Speaker 2: operate it. So to me, I'm like Okay, that's an

488
00:26:13,480 --> 00:26:16,280
Speaker 2: obvious sort of thing I can see like on the horizon.

489
00:26:16,640 --> 00:26:18,520
Speaker 2: And you did talk about, well, there was a lot

490
00:26:18,520 --> 00:26:21,159
Speaker 2: of talk of these sort of long term existential or

491
00:26:21,240 --> 00:26:23,879
Speaker 2: quote unquote existential threats, that there were a lot of

492
00:26:24,119 --> 00:26:27,199
Speaker 2: things in the short term that we're actually ignoring. What

493
00:26:27,440 --> 00:26:29,760
Speaker 2: are those sort of things that we need to bring

494
00:26:29,800 --> 00:26:31,679
Speaker 2: a little bit more awareness to, Like I know you

495
00:26:31,720 --> 00:26:35,680
Speaker 2: mentioned the climate, and I look at it from my perspective,

496
00:26:35,720 --> 00:26:38,240
Speaker 2: I see like the just massive job loss that could happen.

497
00:26:38,840 --> 00:26:40,720
Speaker 2: But what are sort of like the more short term

498
00:26:40,720 --> 00:26:44,160
Speaker 2: things that kind of maybe are less sexy or interesting

499
00:26:44,200 --> 00:26:46,159
Speaker 2: to the people who just want to write about killer

500
00:26:46,240 --> 00:26:47,240
Speaker 2: terminators and things.

501
00:26:48,640 --> 00:26:51,399
Speaker 4: Yeah, I mean, I think less sexy is exactly the

502
00:26:51,480 --> 00:26:53,240
Speaker 4: right phrase for this, which is a lot of the

503
00:26:53,240 --> 00:26:56,600
Speaker 4: short term issues. I very much about entrenching existing forms

504
00:26:56,640 --> 00:26:59,639
Speaker 4: of inequality and making them worse. And that's often something

505
00:26:59,680 --> 00:27:02,000
Speaker 4: people don't really want to hear about because they don't

506
00:27:02,000 --> 00:27:05,240
Speaker 4: want to acknowledge these inequalities, or because it takes away

507
00:27:05,240 --> 00:27:07,560
Speaker 4: from the shiny units of AI. It makes it very

508
00:27:07,640 --> 00:27:10,960
Speaker 4: much like a lot of other technological revolutions that we've

509
00:27:11,000 --> 00:27:13,239
Speaker 4: already seen, and that's super boring. Like you don't want

510
00:27:13,280 --> 00:27:16,159
Speaker 4: to hear about how the wheel somehow brought about some

511
00:27:16,240 --> 00:27:21,400
Speaker 4: kind of inequality. We all know the big heart take

512
00:27:21,520 --> 00:27:23,919
Speaker 4: from today, But yeah, I mean something that I look at,

513
00:27:23,960 --> 00:27:27,600
Speaker 4: for example, are like very mundane but important technologies, like

514
00:27:27,760 --> 00:27:30,560
Speaker 4: technologies used in recruitment and hiring. So I look at

515
00:27:30,600 --> 00:27:33,160
Speaker 4: AI powered video interview tools and look at how that

516
00:27:33,200 --> 00:27:37,439
Speaker 4: affects people's particular you know, likelihood of being employed and

517
00:27:37,440 --> 00:27:39,680
Speaker 4: how they go through the workforce, and yep, it's less

518
00:27:39,680 --> 00:27:42,680
Speaker 4: exciting seeming than the terminator. But again, when you look

519
00:27:42,720 --> 00:27:45,000
Speaker 4: under the hood and dig into them, you're like, oh wow,

520
00:27:45,400 --> 00:27:49,640
Speaker 4: this could actually really really compound inequalities that we see

521
00:27:49,680 --> 00:27:52,080
Speaker 4: in the workforce under the guise of the idea that

522
00:27:52,080 --> 00:27:54,119
Speaker 4: these tools are going to make hiring more fair. And

523
00:27:54,160 --> 00:27:55,960
Speaker 4: that's a massive problem.

524
00:27:56,000 --> 00:27:58,280
Speaker 2: Right, so because like the idea with those hiring tools

525
00:27:58,359 --> 00:28:00,679
Speaker 2: like it will actually take away these sort of like

526
00:28:00,720 --> 00:28:04,280
Speaker 2: demographic cues that someone might use to like you know,

527
00:28:04,760 --> 00:28:07,359
Speaker 2: they apply their own biases to So in fact, it

528
00:28:07,440 --> 00:28:10,239
Speaker 2: is the most equitable way to hire. But is it

529
00:28:10,280 --> 00:28:13,640
Speaker 2: because of just the kinds of people that are creating

530
00:28:13,720 --> 00:28:16,560
Speaker 2: these sort of systems, Because they tends to be a

531
00:28:16,600 --> 00:28:19,800
Speaker 2: bit one note that that's inherently where like sort of

532
00:28:19,800 --> 00:28:21,480
Speaker 2: that like it begins to wobble a bit.

533
00:28:21,840 --> 00:28:23,960
Speaker 4: It's a mixture. So of course, yes, the lack of

534
00:28:23,960 --> 00:28:27,000
Speaker 4: diversity in the AI industry is like very stark. It's

535
00:28:27,040 --> 00:28:30,600
Speaker 4: also sadly in the uk an industry, where for example,

536
00:28:30,640 --> 00:28:33,920
Speaker 4: women's representation is actually getting worse, not better. So that's

537
00:28:33,960 --> 00:28:35,800
Speaker 4: a sad slap in the face for a lot of

538
00:28:35,800 --> 00:28:39,240
Speaker 4: the progress narrative that we want to see. But sometimes

539
00:28:39,240 --> 00:28:42,640
Speaker 4: it's not even necessarily that the people creating these tools

540
00:28:42,680 --> 00:28:45,680
Speaker 4: have bad intentions, maybe not even that they're using faulty

541
00:28:45,760 --> 00:28:48,160
Speaker 4: data sets or biased data sets. These are two of

542
00:28:48,160 --> 00:28:50,720
Speaker 4: the really big problems that are flaggs. But sometimes the

543
00:28:50,840 --> 00:28:54,760
Speaker 4: underlying idea behind their product is just fogus, Like it's

544
00:28:54,880 --> 00:28:57,760
Speaker 4: just a really bad concept and yet somehow it gets

545
00:28:57,800 --> 00:29:00,400
Speaker 4: brought to market again because of all this hyper AI.

546
00:29:00,960 --> 00:29:03,320
Speaker 4: So with the video interview tools that we look at,

547
00:29:03,360 --> 00:29:06,440
Speaker 4: for example, they basically claim that they can discern a

548
00:29:06,560 --> 00:29:10,320
Speaker 4: candidate's personality from their face and from their words, so

549
00:29:10,320 --> 00:29:12,880
Speaker 4: how they talk, how they move, They can decide how

550
00:29:12,920 --> 00:29:15,760
Speaker 4: extroverted you are, or how you know open you are,

551
00:29:15,840 --> 00:29:18,440
Speaker 4: how neurotic you are, how conscientious, you are all these

552
00:29:18,480 --> 00:29:23,320
Speaker 4: different markets of personality, to which I would say, firstly, no,

553
00:29:23,800 --> 00:29:26,320
Speaker 4: there's absolutely no when AI can do that. This is

554
00:29:26,520 --> 00:29:30,120
Speaker 4: just a very old kind of racial pseudoscience making its

555
00:29:30,120 --> 00:29:33,000
Speaker 4: way back into the mainstream, saying Okay, we can totally

556
00:29:33,040 --> 00:29:36,160
Speaker 4: guess your personality from your face, Like it's like your

557
00:29:36,200 --> 00:29:39,360
Speaker 4: friends looking at someone's profile picture on like Pinder or

558
00:29:39,400 --> 00:29:42,160
Speaker 4: whatever and being like they look like they'd be really

559
00:29:42,160 --> 00:29:44,360
Speaker 4: fun at a party. Like it's about that level of.

560
00:29:44,320 --> 00:29:45,760
Speaker 1: Accuracy, you know.

561
00:29:46,000 --> 00:29:48,760
Speaker 4: And then second, is that even a good way of

562
00:29:48,840 --> 00:29:50,960
Speaker 4: judging if someone's gonna be good for a job? Like

563
00:29:51,520 --> 00:29:54,080
Speaker 4: how extroverted do you want a person in the job

564
00:29:54,120 --> 00:29:56,720
Speaker 4: to be? Maybe in your job that's really really helpful

565
00:29:56,760 --> 00:29:59,640
Speaker 4: in my job, I don't know how helpful. So this

566
00:29:59,800 --> 00:30:01,840
Speaker 4: just kind of a lot of blow is at the

567
00:30:01,960 --> 00:30:04,720
Speaker 4: very you know, bottom of these products that we should

568
00:30:04,720 --> 00:30:05,960
Speaker 4: be worried about.

569
00:30:06,320 --> 00:30:11,200
Speaker 1: Just like a C minus level job hiring process, Like

570
00:30:11,240 --> 00:30:13,600
Speaker 1: that's what I feel like so many of the things,

571
00:30:13,640 --> 00:30:16,160
Speaker 1: like when you get down to them and see them

572
00:30:16,200 --> 00:30:20,200
Speaker 1: in action, they're like not that good. Like it does

573
00:30:20,280 --> 00:30:23,360
Speaker 1: feel like the whole thing is being hyped to a

574
00:30:23,400 --> 00:30:26,560
Speaker 1: large degree, and like that's something I heard from somebody

575
00:30:27,000 --> 00:30:30,600
Speaker 1: I know who like works in you know, like all

576
00:30:30,640 --> 00:30:33,320
Speaker 1: of my friends who work in finance or like any

577
00:30:33,360 --> 00:30:35,720
Speaker 1: of those things. Like my brain shuts off when he

578
00:30:35,760 --> 00:30:39,120
Speaker 1: starts talking about what he does. But he was saying,

579
00:30:39,640 --> 00:30:42,000
Speaker 1: like he pays attention to the market, and he was saying,

580
00:30:42,000 --> 00:30:45,720
Speaker 1: there's like a big thing propping up the stock market

581
00:30:45,800 --> 00:30:48,960
Speaker 1: right now is AI. And it really is like that,

582
00:30:48,960 --> 00:30:52,280
Speaker 1: that's where so much of people's wealth is tied up,

583
00:30:52,320 --> 00:30:54,840
Speaker 1: is in like the stock market, and it's just tied

584
00:30:54,960 --> 00:30:58,760
Speaker 1: to like what you can get people excited about in

585
00:30:58,840 --> 00:31:02,680
Speaker 1: a lot of cases. Really like that. From that perspective,

586
00:31:03,840 --> 00:31:07,160
Speaker 1: the incentive structure makes sense, Like you want people talking

587
00:31:07,200 --> 00:31:11,600
Speaker 1: about how your AI technology can do all these amazing

588
00:31:11,720 --> 00:31:16,240
Speaker 1: things because that literally makes you have more money than

589
00:31:16,280 --> 00:31:18,600
Speaker 1: you would have if they knew the truth about your

590
00:31:19,120 --> 00:31:20,320
Speaker 1: your product.

591
00:31:20,120 --> 00:31:23,440
Speaker 2: Without being like, yeah, wait, how many seven fingered Trump

592
00:31:23,800 --> 00:31:27,400
Speaker 2: pictures can we create? And right be like yeah, man,

593
00:31:27,640 --> 00:31:29,240
Speaker 2: fucking millions into this.

594
00:31:29,680 --> 00:31:33,080
Speaker 4: Yeah yeah, I mean that's kind of something that's really

595
00:31:33,080 --> 00:31:34,920
Speaker 4: come out of the last few years of how many

596
00:31:34,960 --> 00:31:37,360
Speaker 4: fans just use the label of AI to get funding.

597
00:31:37,440 --> 00:31:39,080
Speaker 4: Like I think there was a study a couple of

598
00:31:39,160 --> 00:31:42,960
Speaker 4: years ago that said like of European AI startups didn't

599
00:31:43,040 --> 00:31:45,239
Speaker 4: use AI, which I point, you're like, well, what are

600
00:31:45,280 --> 00:31:45,680
Speaker 4: you doing.

601
00:31:48,480 --> 00:31:51,480
Speaker 1: Well, we could though this podcast is actually an AI

602
00:31:51,560 --> 00:31:55,840
Speaker 1: podcast because eventually it could use AI. And before we

603
00:31:55,840 --> 00:31:59,479
Speaker 1: were recording, Miles was actually putting in. He asked an

604
00:31:59,520 --> 00:32:04,320
Speaker 1: AI to pitch him an episode of Friends in which

605
00:32:04,360 --> 00:32:07,720
Speaker 1: the cast and you know, the people on the the

606
00:32:07,800 --> 00:32:11,080
Speaker 1: friends on the show deal with the fallout from the

607
00:32:11,080 --> 00:32:14,080
Speaker 1: events of nine to eleven. And it wouldn't do it.

608
00:32:14,240 --> 00:32:16,920
Speaker 1: So we can't quite claim that we are an AI

609
00:32:17,160 --> 00:32:19,720
Speaker 1: podcast yet, but but we did it.

610
00:32:19,800 --> 00:32:22,040
Speaker 2: Did do it when I said, do it, pitch me

611
00:32:22,080 --> 00:32:26,000
Speaker 2: an episode where Joey and Monica drive Uber from Yeah,

612
00:32:26,040 --> 00:32:28,480
Speaker 2: and it did so clearly it because you can see

613
00:32:28,480 --> 00:32:30,520
Speaker 2: where these guardrails out. They're like, don't do nine to

614
00:32:30,520 --> 00:32:33,320
Speaker 2: eleven stuff. Though that's right now.

615
00:32:33,960 --> 00:32:36,800
Speaker 1: But I mean, so yeah, I think there's two things

616
00:32:36,840 --> 00:32:41,840
Speaker 1: we're talking about here, Like from from one perspective, like, yes,

617
00:32:41,880 --> 00:32:45,040
Speaker 1: you could put it in the category of like, well, yes,

618
00:32:45,080 --> 00:32:51,320
Speaker 1: the wheel makes racism or colonialism more. Frictionless is a

619
00:32:51,320 --> 00:32:54,080
Speaker 1: word that gets used a lot, but like literally in

620
00:32:54,080 --> 00:32:56,960
Speaker 1: the case of the wheel, frictionless. But AI and like

621
00:32:57,000 --> 00:33:01,000
Speaker 1: a lot of technology is designed to make groups of

622
00:33:01,080 --> 00:33:03,800
Speaker 1: people and like our interactions and the things that make

623
00:33:03,800 --> 00:33:06,640
Speaker 1: people money more frictionless. And that's something that you guys

624
00:33:06,680 --> 00:33:11,479
Speaker 1: have talked about on recent episodes of Good Robot. Like

625
00:33:12,080 --> 00:33:14,240
Speaker 1: there's this one example that really jumped out to me

626
00:33:14,400 --> 00:33:16,680
Speaker 1: that I think was from your most recent episode, or

627
00:33:16,760 --> 00:33:20,680
Speaker 1: at least the one that's up most recently right now

628
00:33:21,040 --> 00:33:24,680
Speaker 1: as we're recording this, where you guys were talking about

629
00:33:25,480 --> 00:33:29,400
Speaker 1: a company that asked a regulating body to make an

630
00:33:29,400 --> 00:33:33,040
Speaker 1: exception to a law around like a high risk use

631
00:33:33,040 --> 00:33:36,160
Speaker 1: of AI, and the law said that people had to

632
00:33:36,280 --> 00:33:41,160
Speaker 1: supervise the use of AI like just because it seemed dangerous,

633
00:33:41,200 --> 00:33:45,040
Speaker 1: and the company appealed to the regulating body by saying, well,

634
00:33:45,080 --> 00:33:47,760
Speaker 1: we just like that would cost too much and we

635
00:33:47,760 --> 00:33:50,080
Speaker 1: would never be able to like scale this and make

636
00:33:50,120 --> 00:33:53,840
Speaker 1: a profit. And it feels to me like our answer

637
00:33:53,920 --> 00:33:58,560
Speaker 1: as a civilization to that complaint needs to be that's

638
00:33:58,600 --> 00:34:02,280
Speaker 1: not our problem, like that then you shouldn't be doing it.

639
00:34:02,720 --> 00:34:06,440
Speaker 1: But instead it seems like the answer too often, not

640
00:34:06,600 --> 00:34:09,719
Speaker 1: just in AI, but just across the board, especially in

641
00:34:09,760 --> 00:34:12,680
Speaker 1: the US, is like, Okay, well we have to make

642
00:34:12,719 --> 00:34:15,360
Speaker 1: an exception so that they can make a profit around

643
00:34:15,360 --> 00:34:19,720
Speaker 1: this technology, or else the technology won't get developed because

644
00:34:20,320 --> 00:34:23,880
Speaker 1: the only thing that drives technological progress is like the

645
00:34:23,920 --> 00:34:27,560
Speaker 1: profit motive. But that's, you know, as I think you

646
00:34:27,600 --> 00:34:29,920
Speaker 1: guys talked about in that episode, that's never been the

647
00:34:29,960 --> 00:34:33,360
Speaker 1: best way to develop technology, Like it's it's been a

648
00:34:33,360 --> 00:34:38,320
Speaker 1: good way sometimes to democratize existing technology, but like that's

649
00:34:38,719 --> 00:34:42,080
Speaker 1: I don't know, I feel like that idea of you

650
00:34:42,440 --> 00:34:44,319
Speaker 1: have to make it profitable, you have to make it

651
00:34:44,360 --> 00:34:47,040
Speaker 1: easy on these companies to keep trying different things for

652
00:34:47,080 --> 00:34:50,960
Speaker 1: AI to become profitable is baked in at a like

653
00:34:51,560 --> 00:34:55,440
Speaker 1: cellular level at this point in how a lot of

654
00:34:55,960 --> 00:34:59,280
Speaker 1: you know, Western colonial civilizations operate.

655
00:35:00,080 --> 00:35:01,520
Speaker 4: I mean, I think too often a lot of the

656
00:35:01,560 --> 00:35:04,360
Speaker 4: technologies that shape our daily lives are made by a

657
00:35:04,640 --> 00:35:07,680
Speaker 4: very narrow set of people who ultimately aren't beholden to us.

658
00:35:07,880 --> 00:35:10,560
Speaker 4: They're beholden to their shareholders or to their boss and right,

659
00:35:10,640 --> 00:35:13,799
Speaker 4: so they don't really have our best interests at heart, right, Like,

660
00:35:13,920 --> 00:35:16,759
Speaker 4: for example, take this whole rebranding of like Twitter to

661
00:35:17,120 --> 00:35:19,880
Speaker 4: X by Musk. I remember waking up and finding my

662
00:35:20,040 --> 00:35:22,839
Speaker 4: little Twitter bird replaced with this huge X and just

663
00:35:22,880 --> 00:35:26,200
Speaker 4: being like, oh, converirstly because it was part of you know,

664
00:35:26,280 --> 00:35:30,360
Speaker 4: Twitter's low decline. But secondly it maybe you feel pretty

665
00:35:30,360 --> 00:35:33,919
Speaker 4: disappointed or really aware of the fact that one guy

666
00:35:33,960 --> 00:35:37,360
Speaker 4: could have such a huge impact on how literally millions

667
00:35:37,360 --> 00:35:41,280
Speaker 4: of people use a social networking platform that's actually super important,

668
00:35:41,640 --> 00:35:43,560
Speaker 4: you know, to their daily lives and has played a

669
00:35:43,600 --> 00:35:46,719
Speaker 4: huge role in activist movements and fostering different communities. And

670
00:35:46,760 --> 00:35:48,440
Speaker 4: I think that's a story we see time and time

671
00:35:48,480 --> 00:35:50,799
Speaker 4: again with some of these big tech companies, which is

672
00:35:51,200 --> 00:35:54,080
Speaker 4: not only do they have their own profit motive at heart,

673
00:35:54,360 --> 00:35:57,160
Speaker 4: they also they're not beholden in any way to the public,

674
00:35:57,160 --> 00:36:00,399
Speaker 4: and they're not being compelled by regulation to make good

675
00:36:00,400 --> 00:36:04,080
Speaker 4: decisions that necessarily benefit the public. So I think a

676
00:36:04,120 --> 00:36:07,560
Speaker 4: really important question going forward is how do we support

677
00:36:07,680 --> 00:36:10,840
Speaker 4: kinds of technology development that are very much based in

678
00:36:10,880 --> 00:36:13,680
Speaker 4: the communities that the technology is for. I think one

679
00:36:13,760 --> 00:36:16,640
Speaker 4: really big part of that is recognizing that so many

680
00:36:16,719 --> 00:36:20,000
Speaker 4: AI models, as you mentioned right, they're designed to be

681
00:36:20,080 --> 00:36:22,239
Speaker 4: scalable and that's how to make money, this idea that

682
00:36:22,280 --> 00:36:25,279
Speaker 4: you can apply them globally and universally, and I think

683
00:36:25,280 --> 00:36:28,760
Speaker 4: that's a big problem, partly because it often is really homogenizing.

684
00:36:28,920 --> 00:36:31,560
Speaker 4: It can involve like explitting a model from the US

685
00:36:31,680 --> 00:36:34,320
Speaker 4: usually out to the rest of the world, it's probably

686
00:36:34,360 --> 00:36:37,560
Speaker 4: not actually appropriate to using those contexts. But also a

687
00:36:37,560 --> 00:36:40,000
Speaker 4: lot of really exciting and good uses of technology I

688
00:36:40,040 --> 00:36:43,680
Speaker 4: think come from these really localized, specific, community based levels.

689
00:36:43,800 --> 00:36:46,280
Speaker 4: So sometimes I think it can be about thinking smaller

690
00:36:46,360 --> 00:36:47,120
Speaker 4: rather than bigger.

691
00:36:47,880 --> 00:36:50,600
Speaker 2: Yeah. Yeah, that was like another thing that struck me

692
00:36:50,640 --> 00:36:53,480
Speaker 2: about like just all the warnings and even in that

693
00:36:53,560 --> 00:36:57,239
Speaker 2: pause letter is sort of like the presumption that it's like, well,

694
00:36:57,280 --> 00:37:00,120
Speaker 2: all you motherfuckers are going to use this, so we

695
00:37:00,160 --> 00:37:01,920
Speaker 2: got to talk about it where it's like I don't know,

696
00:37:01,960 --> 00:37:03,759
Speaker 2: I don't even fucking know what it is, Like a

697
00:37:03,800 --> 00:37:06,440
Speaker 2: second ago, I thought it was Skynet, and now, like

698
00:37:06,960 --> 00:37:08,600
Speaker 2: you know, you have your company being like, yeah, we

699
00:37:08,680 --> 00:37:11,799
Speaker 2: now have enterprise AI tools like welcome. You're like, but

700
00:37:11,920 --> 00:37:14,640
Speaker 2: what am I huh like? And I think that's what's

701
00:37:14,640 --> 00:37:16,719
Speaker 2: an really interesting thing about this as like a sort

702
00:37:16,719 --> 00:37:20,799
Speaker 2: of technological advancement is before people really understand what it is,

703
00:37:21,320 --> 00:37:24,040
Speaker 2: there is like from the higher the powers that be

704
00:37:24,239 --> 00:37:26,520
Speaker 2: sort of going into it being like, well, this is it,

705
00:37:26,719 --> 00:37:30,240
Speaker 2: Like everyone's using it, but I'm still not sure how.

706
00:37:30,280 --> 00:37:32,880
Speaker 2: And I guess that probably feeds into this whole model

707
00:37:32,920 --> 00:37:36,520
Speaker 2: of generating as much you know, excitement market excitement about

708
00:37:36,520 --> 00:37:40,080
Speaker 2: AI is by taking the angle of like everything's different

709
00:37:40,320 --> 00:37:43,800
Speaker 2: because everyone is going to be using AI mostly y'all

710
00:37:43,840 --> 00:37:46,680
Speaker 2: don't know what that is, but get ready. And I

711
00:37:46,680 --> 00:37:48,719
Speaker 2: think that's what also makes it very confusing for me,

712
00:37:48,760 --> 00:37:51,880
Speaker 2: as just like a lay person outside of the text sphere,

713
00:37:52,400 --> 00:37:54,640
Speaker 2: to just be like, wait, so are we all using it?

714
00:37:54,680 --> 00:37:57,919
Speaker 2: And even now, I really I still can't see what

715
00:37:58,160 --> 00:38:01,200
Speaker 2: that is and how that benefits me. And I think

716
00:38:01,440 --> 00:38:03,839
Speaker 2: that's a big part of I'm sure your work too,

717
00:38:04,000 --> 00:38:07,040
Speaker 2: or even like any ethicist, is to understand like, well,

718
00:38:07,040 --> 00:38:09,959
Speaker 2: who does it? Ben Like, first, we're making this because

719
00:38:09,960 --> 00:38:13,920
Speaker 2: it benefits who and how? Yeah, And I think yeah,

720
00:38:14,040 --> 00:38:16,560
Speaker 2: I mean right now, it benefits the companies that are

721
00:38:16,560 --> 00:38:18,200
Speaker 2: making it. And it sort of feels like that's the

722
00:38:18,239 --> 00:38:20,560
Speaker 2: way it's being preserved or slightly being like, yeah, you

723
00:38:20,560 --> 00:38:22,520
Speaker 2: guys are gonna love this, but really we're going to

724
00:38:22,600 --> 00:38:24,480
Speaker 2: benefit from the adoption of this technology.

725
00:38:25,280 --> 00:38:27,360
Speaker 4: Yeah, I mean, I think that's this crucial question, is

726
00:38:27,400 --> 00:38:30,440
Speaker 4: this stepping back and saying, actually, is this inevitable? And

727
00:38:30,480 --> 00:38:32,360
Speaker 4: do we even want this in the first place? And

728
00:38:32,400 --> 00:38:35,280
Speaker 4: I think that's what really frustrated me about the pause

729
00:38:35,360 --> 00:38:37,200
Speaker 4: letter and about a number of kind of big text

730
00:38:37,200 --> 00:38:39,600
Speaker 4: figures signing on to it, is that they're very much

731
00:38:39,640 --> 00:38:43,560
Speaker 4: pushing this narrative of like, oh, this is like unstoppable

732
00:38:43,680 --> 00:38:45,960
Speaker 4: and it's inevitable and it's happening, We've got a fine

733
00:38:46,000 --> 00:38:46,799
Speaker 4: way to deal with it.

734
00:38:46,840 --> 00:38:47,359
Speaker 3: And it's like.

735
00:38:47,800 --> 00:38:51,040
Speaker 4: You're making it like you're the people literally making these

736
00:38:51,080 --> 00:38:53,800
Speaker 4: technologies and a lot of cases, so if you really

737
00:38:53,800 --> 00:38:58,840
Speaker 4: think it's an existential risk to humanity, stop, it's honestly

738
00:38:58,920 --> 00:39:02,319
Speaker 4: could even be that import But that's you know, what

739
00:39:02,480 --> 00:39:04,640
Speaker 4: makes me really then question their motives and sort of

740
00:39:04,680 --> 00:39:06,960
Speaker 4: coming forward with a lot of this kind of very

741
00:39:07,000 --> 00:39:10,000
Speaker 4: doom and gloom language. I think it's also interesting if

742
00:39:10,040 --> 00:39:13,279
Speaker 4: you look at, for example, countries as national AI strategies.

743
00:39:13,800 --> 00:39:15,520
Speaker 4: So if you look at say like China and the

744
00:39:15,640 --> 00:39:17,799
Speaker 4: UK and the US and these countries that are now

745
00:39:17,880 --> 00:39:20,600
Speaker 4: thinking about what their national long term AI strategy is

746
00:39:20,640 --> 00:39:23,640
Speaker 4: going to be, they also very much frame it around

747
00:39:23,640 --> 00:39:26,239
Speaker 4: the idea that AI is completely inevitable, that this is

748
00:39:26,280 --> 00:39:30,120
Speaker 4: going to be the transformative technology for imaging the future,

749
00:39:30,280 --> 00:39:34,359
Speaker 4: for geopolitical dominance, for economic supremacy. And again, I think

750
00:39:34,600 --> 00:39:36,680
Speaker 4: as an ethesis. What I really want people to do

751
00:39:36,760 --> 00:39:38,839
Speaker 4: is step back and say, I think we're actually at

752
00:39:38,840 --> 00:39:41,040
Speaker 4: a crossroad, so we can decide whether or not we

753
00:39:41,080 --> 00:39:44,440
Speaker 4: think these technologies are good for us, and whether they

754
00:39:44,480 --> 00:39:47,839
Speaker 4: are sustainable, whether they are a useful long term thing

755
00:39:47,880 --> 00:39:51,600
Speaker 4: for our societies, or actually whether the benefits of these

756
00:39:51,640 --> 00:39:55,200
Speaker 4: technologies are going to be experienced by very few people

757
00:39:55,239 --> 00:39:57,080
Speaker 4: and the costs are going to be borne by many.

758
00:39:58,040 --> 00:40:02,960
Speaker 1: Right. We talked last week about the scientific application that

759
00:40:03,600 --> 00:40:06,960
Speaker 1: you know, used deep learning to figure out the shapes

760
00:40:07,000 --> 00:40:11,439
Speaker 1: of proteins, the structures of proteins, and that could have

761
00:40:12,040 --> 00:40:16,520
Speaker 1: some beneficial uses, will probably have some beneficial uses for

762
00:40:16,880 --> 00:40:19,680
Speaker 1: you know, how we understand disease and medicine and how

763
00:40:19,680 --> 00:40:24,360
Speaker 1: we treat that. But there are ways to probably differentiate

764
00:40:24,400 --> 00:40:28,400
Speaker 1: and think about these things, like it's not you don't

765
00:40:28,440 --> 00:40:33,200
Speaker 1: just have to be either luddite or like AI pedal

766
00:40:33,280 --> 00:40:36,719
Speaker 1: to the floor. You know, let's just get out of

767
00:40:36,719 --> 00:40:40,000
Speaker 1: the way of the big companies. You know, it feels like,

768
00:40:40,560 --> 00:40:45,600
Speaker 1: but it is such a complicated technology that I think

769
00:40:45,640 --> 00:40:49,560
Speaker 1: there's going to be inherent cloudiness around how people understand

770
00:40:49,600 --> 00:40:54,680
Speaker 1: it and also manufactured cloudiness because it is in the

771
00:40:54,800 --> 00:41:00,560
Speaker 1: overall systems benefit the overall system, being like capitalism in

772
00:41:00,600 --> 00:41:05,160
Speaker 1: their benefit to generate like market excitement where there shouldn't

773
00:41:05,239 --> 00:41:07,280
Speaker 1: be any basically.

774
00:41:07,000 --> 00:41:09,200
Speaker 4: Yeah, I mean, I think it's easy to generate this

775
00:41:09,280 --> 00:41:11,800
Speaker 4: kind of nebulousness around AI because to some extent we

776
00:41:11,840 --> 00:41:14,080
Speaker 4: still don't really know what it is. It's still more

777
00:41:14,080 --> 00:41:16,799
Speaker 4: of a concept than anything else because the term AI

778
00:41:16,920 --> 00:41:20,160
Speaker 4: is like stretched and used to describe so many applications.

779
00:41:20,200 --> 00:41:23,440
Speaker 4: Like I spent two years interviewing engineers and data scientists

780
00:41:23,480 --> 00:41:26,040
Speaker 4: is a big tech firm, and they would sort of grumble, well,

781
00:41:26,120 --> 00:41:28,279
Speaker 4: fifteen or twenty years ago, you know, we didn't even

782
00:41:28,320 --> 00:41:30,520
Speaker 4: call this AI, and we were already doing it. It's

783
00:41:30,600 --> 00:41:32,480
Speaker 4: just a decision tree, you know. Again, it's kind of

784
00:41:32,520 --> 00:41:35,200
Speaker 4: part of that branding. But also we have these again

785
00:41:35,320 --> 00:41:38,239
Speaker 4: thousands of years of stories and thinking about what an

786
00:41:38,239 --> 00:41:41,120
Speaker 4: intelligent machine is, and that means we can get super

787
00:41:41,160 --> 00:41:45,320
Speaker 4: invested in super cloudy very very quickly. And yeah, I

788
00:41:45,320 --> 00:41:47,480
Speaker 4: don't want people to feel bad for being scared of

789
00:41:47,560 --> 00:41:51,879
Speaker 4: being cloudy about these technologies, like it is dense and confusing,

790
00:41:52,160 --> 00:41:55,120
Speaker 4: But at the same time, I do think that it

791
00:41:55,200 --> 00:41:57,239
Speaker 4: is really important to come back to that question of

792
00:41:57,280 --> 00:41:59,600
Speaker 4: what does this do for us. So, you know, the

793
00:41:59,680 --> 00:42:02,520
Speaker 4: question of like Luddism or being a lude I think

794
00:42:02,560 --> 00:42:05,400
Speaker 4: is really interesting because I you know, personally, I do

795
00:42:05,560 --> 00:42:08,440
Speaker 4: use AI applications. There's certain things about these technologies that

796
00:42:08,480 --> 00:42:11,400
Speaker 4: really excite me. But I'm really sympathetic to some of

797
00:42:11,400 --> 00:42:15,600
Speaker 4: the kind of old school ludyet who weren't necessarily anti technology,

798
00:42:15,880 --> 00:42:18,759
Speaker 4: but we're really against the kind of impacts that technology

799
00:42:18,760 --> 00:42:20,920
Speaker 4: we're having on their society. So the way that new

800
00:42:20,960 --> 00:42:23,480
Speaker 4: technology is like I think it would be things like

801
00:42:23,560 --> 00:42:27,319
Speaker 4: spinning and weaving, we're causing mass unemployment and the kind

802
00:42:27,400 --> 00:42:30,680
Speaker 4: of broader rammifications that was having for people in the

803
00:42:30,760 --> 00:42:33,560
Speaker 4: UK socially, and that kind of has quite a scary

804
00:42:33,680 --> 00:42:37,000
Speaker 4: parallel to today in terms of thinking about maybe what

805
00:42:37,080 --> 00:42:40,480
Speaker 4: AI will bring about for the rest of us who

806
00:42:40,520 --> 00:42:43,480
Speaker 4: maybe aren't researchers in a lab, but who maybe might

807
00:42:43,520 --> 00:42:45,560
Speaker 4: be replaced by some of these algorithms in terms of

808
00:42:45,560 --> 00:42:47,160
Speaker 4: our work and our output.

809
00:42:48,280 --> 00:42:51,960
Speaker 1: Yeah, can you talk at all about open source like

810
00:42:52,120 --> 00:42:56,400
Speaker 1: models of because you know, when we talk about this

811
00:42:56,560 --> 00:43:02,240
Speaker 1: idea that corporations have all power and are incentivized to

812
00:43:02,320 --> 00:43:05,360
Speaker 1: do whatever is going to make the most money, which

813
00:43:05,480 --> 00:43:07,160
Speaker 1: in a lot of cases is going to be the

814
00:43:07,200 --> 00:43:12,319
Speaker 1: thing that removes the friction from consumption decisions, and you

815
00:43:12,360 --> 00:43:16,480
Speaker 1: know just how people interact and do these things, which well,

816
00:43:16,719 --> 00:43:19,480
Speaker 1: as you guys talked about in your episode, like removing

817
00:43:19,480 --> 00:43:22,440
Speaker 1: the friction, Like friction can be really good. Sometimes sometimes

818
00:43:22,440 --> 00:43:25,640
Speaker 1: your system needs friction to stop and correct itself and

819
00:43:26,280 --> 00:43:30,400
Speaker 1: recognize when bad shit, when things are going wrong. But

820
00:43:31,000 --> 00:43:36,400
Speaker 1: you know, there's also a history in even in the US,

821
00:43:36,520 --> 00:43:41,640
Speaker 1: where corporations are racing to get to a development and

822
00:43:42,239 --> 00:43:48,520
Speaker 1: ultimately are beat by open source models of technological organizing

823
00:43:48,640 --> 00:43:51,879
Speaker 1: around like getting a specific solution. Do you have any

824
00:43:51,960 --> 00:43:55,520
Speaker 1: hope for like open source or in the future of AI.

825
00:43:56,320 --> 00:43:58,840
Speaker 4: Yeah, I mean, I think I'm really interested in community

826
00:43:59,040 --> 00:44:01,480
Speaker 4: forms of development, and I think open source is a

827
00:44:01,480 --> 00:44:05,160
Speaker 4: really interesting example. I think we've seen other interesting examples

828
00:44:05,160 --> 00:44:08,480
Speaker 4: around things like collective data labeling, and I think that

829
00:44:08,560 --> 00:44:11,319
Speaker 4: these kinds of collective movements, on the one hand, seem

830
00:44:11,360 --> 00:44:15,560
Speaker 4: like a really exciting community based alternative to the concentration

831
00:44:16,160 --> 00:44:19,600
Speaker 4: of power in a very very narrow segment of tech companies.

832
00:44:20,000 --> 00:44:22,680
Speaker 4: On the other hand, though, I think community work is

833
00:44:22,800 --> 00:44:26,719
Speaker 4: really hard work. We had doctor David Adlani on our podcast,

834
00:44:26,719 --> 00:44:29,800
Speaker 4: who's very important figure in Mascana, which is a grassroots

835
00:44:29,920 --> 00:44:33,120
Speaker 4: organization that aims to bring some of the over four

836
00:44:33,160 --> 00:44:37,920
Speaker 4: thousand African languages into natural language processing or NLP systems,

837
00:44:38,000 --> 00:44:40,080
Speaker 4: And he talks a lot about how the work he

838
00:44:40,120 --> 00:44:43,799
Speaker 4: does with Marskana is so valuable and so important, but

839
00:44:43,880 --> 00:44:47,600
Speaker 4: it's also really really hard because when you're working in

840
00:44:47,640 --> 00:44:51,360
Speaker 4: that kind of collective, decentralized environment, it can be much slower,

841
00:44:51,400 --> 00:44:52,680
Speaker 4: and as you said, there can be a lot more

842
00:44:52,719 --> 00:44:57,520
Speaker 4: friction in that process. But counter to this move fast,

843
00:44:57,520 --> 00:44:59,800
Speaker 4: break things kind of culture, sometimes that friction can be

844
00:44:59,800 --> 00:45:01,960
Speaker 4: really productive and it can help us slow down and

845
00:45:02,000 --> 00:45:05,200
Speaker 4: think about, you know, the decisions that we're making very intentionally,

846
00:45:05,320 --> 00:45:07,680
Speaker 4: rather than just kind of racing as fast as we

847
00:45:07,760 --> 00:45:10,720
Speaker 4: can to make the next newest, shiniest product.

848
00:45:12,120 --> 00:45:14,759
Speaker 2: I was almost like in your work too, you know,

849
00:45:14,800 --> 00:45:18,320
Speaker 2: you talk about how, you know, like looking at these technologies,

850
00:45:18,400 --> 00:45:21,719
Speaker 2: especially through a lens of like feminism and intersectionality and

851
00:45:22,000 --> 00:45:25,279
Speaker 2: you know, bipop communities and things like that, and I like

852
00:45:25,320 --> 00:45:27,440
Speaker 2: broadly in science there's like, you know, there's an issue

853
00:45:27,520 --> 00:45:31,520
Speaker 2: of like language hegemony in scientific research, where if things

854
00:45:31,560 --> 00:45:35,200
Speaker 2: aren't written in English a lot, sometimes studies just get

855
00:45:35,239 --> 00:45:38,680
Speaker 2: fucking ignored because like I don't speak Spanish or I

856
00:45:38,719 --> 00:45:42,040
Speaker 2: can't read Chinese. Therefore I don't know if this research

857
00:45:42,160 --> 00:45:45,400
Speaker 2: is being done, and therefore it just doesn't exist because

858
00:45:45,440 --> 00:45:49,279
Speaker 2: the larger community is like we all just think in English, like,

859
00:45:49,400 --> 00:45:51,680
Speaker 2: so how do you like, you know, specifically because you know,

860
00:45:52,040 --> 00:45:55,560
Speaker 2: in hearing the description of your work, help me understand,

861
00:45:55,560 --> 00:45:57,920
Speaker 2: like and the listeners too, like, of how we should

862
00:45:57,960 --> 00:46:00,080
Speaker 2: be looking at these things from that also from that

863
00:46:00,120 --> 00:46:02,520
Speaker 2: perspective too, because I think right now we're all caught

864
00:46:02,600 --> 00:46:05,239
Speaker 2: up and like it's talking sky at and it's not

865
00:46:05,880 --> 00:46:07,880
Speaker 2: you know, hold on, like there are other subtleties that

866
00:46:07,920 --> 00:46:11,680
Speaker 2: actually we should really think deeply about because to your point,

867
00:46:11,880 --> 00:46:13,840
Speaker 2: I feel like those are the dimensions of an emerging

868
00:46:13,840 --> 00:46:17,200
Speaker 2: technology or trend or something that gets ignored because to

869
00:46:17,239 --> 00:46:19,799
Speaker 2: your point, it's like the thing, well, of course it's

870
00:46:20,520 --> 00:46:24,359
Speaker 2: it's unequal, of course it's racist or whatever, But what

871
00:46:24,400 --> 00:46:26,600
Speaker 2: are those like what are those ways that people need

872
00:46:26,640 --> 00:46:28,520
Speaker 2: to really be thinking about this technology?

873
00:46:29,040 --> 00:46:31,239
Speaker 4: Yeah, I mean I think English language hegemony is a

874
00:46:31,280 --> 00:46:33,960
Speaker 4: really good example of this broader problem of the more

875
00:46:34,000 --> 00:46:37,120
Speaker 4: subtle kinds of exclusions that get built into these technologies.

876
00:46:37,200 --> 00:46:40,200
Speaker 4: Because I think we've all probably seen the cases of

877
00:46:40,320 --> 00:46:44,120
Speaker 4: AI systems that have been really horrifically and explicitly racist

878
00:46:44,320 --> 00:46:47,920
Speaker 4: or really horrifically sexist, from you know, Tay the chatbot

879
00:46:47,960 --> 00:46:51,920
Speaker 4: that started spousing horrific right wing racist propaganda and had

880
00:46:51,960 --> 00:46:55,799
Speaker 4: get taken down through to Amazon hiring tool that's systemically

881
00:46:55,800 --> 00:46:59,560
Speaker 4: discriminated against female candidates. These are really I think, avert

882
00:47:00,680 --> 00:47:02,560
Speaker 4: depictions of the kinds of harms a I can do.

883
00:47:02,960 --> 00:47:06,760
Speaker 4: But I think things like English language hegemony are also

884
00:47:06,840 --> 00:47:10,560
Speaker 4: incredibly important for showing how existing kinds of exclusions and

885
00:47:10,640 --> 00:47:13,560
Speaker 4: patterns of power get replicated in these tools. Because do

886
00:47:13,760 --> 00:47:17,120
Speaker 4: an English language speaker, very crucially, they might use chat

887
00:47:17,160 --> 00:47:19,520
Speaker 4: GPT and think this is great, this is what my

888
00:47:19,600 --> 00:47:22,320
Speaker 4: whole world looks like if they only speak English. Obvious

889
00:47:22,360 --> 00:47:24,680
Speaker 4: to anyone who is not a native English speaker who

890
00:47:24,719 --> 00:47:27,160
Speaker 4: doesn't only speak English, it's going to be an incredibly

891
00:47:27,160 --> 00:47:29,840
Speaker 4: different experience. And that's where I think we see the

892
00:47:29,880 --> 00:47:33,200
Speaker 4: benefits of these tools being really unequally distributed. I think

893
00:47:33,200 --> 00:47:36,880
Speaker 4: it's also important because there's such exclusions in which kinds

894
00:47:36,920 --> 00:47:40,360
Speaker 4: of languages and forms of communication can get translated into

895
00:47:40,360 --> 00:47:43,400
Speaker 4: these systems. So, for example, I work with a linguists

896
00:47:43,400 --> 00:47:45,360
Speaker 4: at the University of Newcastle, and she talks about the

897
00:47:45,360 --> 00:47:48,560
Speaker 4: fact that there's so many languages like signed languages and

898
00:47:48,680 --> 00:47:51,239
Speaker 4: languages that don't have a written language, but they're never

899
00:47:51,320 --> 00:47:53,640
Speaker 4: going to be translated into these tools and never are

900
00:47:53,680 --> 00:47:56,560
Speaker 4: going to benefit from them. You might think, Okay, well,

901
00:47:57,200 --> 00:48:01,399
Speaker 4: do these communities want those languages translate into an AI tool?

902
00:48:01,600 --> 00:48:04,680
Speaker 4: Maybe maybe not, I'd argua, of course that's up to them.

903
00:48:04,719 --> 00:48:07,320
Speaker 4: But those communities are still going to experience the negative

904
00:48:07,320 --> 00:48:10,160
Speaker 4: effects of AI, like the climate cost of these tools.

905
00:48:10,200 --> 00:48:12,719
Speaker 4: And so I think it's just really important, like you say,

906
00:48:12,800 --> 00:48:15,720
Speaker 4: to think about what kinds of hegemony are getting further

907
00:48:15,920 --> 00:48:18,280
Speaker 4: entrenched by AI powered technologies.

908
00:48:19,200 --> 00:48:21,920
Speaker 1: All right, great, let let's take a quick break and

909
00:48:22,160 --> 00:48:25,520
Speaker 1: we'll come back and finish up with a few questions.

910
00:48:25,600 --> 00:48:40,799
Speaker 1: We'll be right back and we're back and all right. So,

911
00:48:40,960 --> 00:48:44,920
Speaker 1: I mean, one thing that I want myself to just

912
00:48:44,960 --> 00:48:46,959
Speaker 1: like get out of this conversation is just a sense

913
00:48:47,000 --> 00:48:52,400
Speaker 1: of like ways that we like what we think AI

914
00:48:52,760 --> 00:48:57,200
Speaker 1: might look like, how it might impact what the world

915
00:48:57,239 --> 00:49:00,560
Speaker 1: around us looks like in the not too distant future.

916
00:49:00,760 --> 00:49:03,960
Speaker 1: And we've we've already talked about like some ways that

917
00:49:04,000 --> 00:49:09,040
Speaker 1: AI is being overrated as like a autonomous killbot, like

918
00:49:09,160 --> 00:49:13,800
Speaker 1: terminator style kilbot. You know, there's there's some other examples,

919
00:49:13,920 --> 00:49:18,520
Speaker 1: like on one of the episodes of your podcast, an

920
00:49:18,520 --> 00:49:22,920
Speaker 1: AI expert talks about getting an AI enhanced cancer scan

921
00:49:23,760 --> 00:49:27,640
Speaker 1: and assuming the scan was like taking three D video

922
00:49:27,840 --> 00:49:30,719
Speaker 1: and doing or doing like three D modeling, and it

923
00:49:30,760 --> 00:49:33,120
Speaker 1: was just putting a box on a two D image.

924
00:49:33,520 --> 00:49:36,319
Speaker 1: And I believe the guests like admits that they were

925
00:49:36,320 --> 00:49:42,440
Speaker 1: influenced by like Hollywood movies, and it seems like that's

926
00:49:42,560 --> 00:49:44,600
Speaker 1: what people who are trying to make money off of

927
00:49:44,600 --> 00:49:46,520
Speaker 1: this one. So like that we're going to have this

928
00:49:46,640 --> 00:49:51,840
Speaker 1: steady push to make us overrate misunderstand what the actual

929
00:49:51,960 --> 00:49:56,560
Speaker 1: promise of AI is going to look like, how what

930
00:49:56,640 --> 00:49:58,440
Speaker 1: tools are going to be given to us in the

931
00:49:58,480 --> 00:50:01,040
Speaker 1: near future, and like what are the tools actually able

932
00:50:01,080 --> 00:50:05,439
Speaker 1: to do? So I'd just be curious to hear from you,

933
00:50:05,520 --> 00:50:10,600
Speaker 1: like what do you think ways that AI might intervene

934
00:50:10,719 --> 00:50:13,920
Speaker 1: in our lives in the near future might already be

935
00:50:14,000 --> 00:50:17,239
Speaker 1: intervening in our lives? Well, what are those things that

936
00:50:17,320 --> 00:50:21,160
Speaker 1: we're underrating, Like what and what are the things that

937
00:50:21,320 --> 00:50:25,680
Speaker 1: you think are probably taking up too much of our

938
00:50:25,719 --> 00:50:28,200
Speaker 1: bandwidth in terms of how we're picturing AI.

939
00:50:30,080 --> 00:50:32,440
Speaker 4: Oh, that's a great question. I think to start with

940
00:50:32,480 --> 00:50:34,120
Speaker 4: the second half, to start with what I think is

941
00:50:34,160 --> 00:50:36,480
Speaker 4: maybe a little bit overrated, or maybe you'll take a

942
00:50:36,480 --> 00:50:38,880
Speaker 4: bit longer to pan out. I would say some of

943
00:50:38,880 --> 00:50:43,120
Speaker 4: these really high tech applications, things like AI and medicine,

944
00:50:43,160 --> 00:50:45,920
Speaker 4: for example, I think that we might not see them

945
00:50:45,960 --> 00:50:49,719
Speaker 4: expand beyond a very narrow set of controlled circumstances. I

946
00:50:49,760 --> 00:50:52,600
Speaker 4: think this idea that you know, soon every hospital will

947
00:50:52,600 --> 00:50:55,120
Speaker 4: have this tool, or say AI and education, that every

948
00:50:55,120 --> 00:50:57,319
Speaker 4: classroom is going to have access to this tool, I

949
00:50:57,320 --> 00:51:00,799
Speaker 4: think unfortually just really grossly over its demanding the kinds

950
00:51:00,800 --> 00:51:04,200
Speaker 4: of resources only in this country that schools and the

951
00:51:04,239 --> 00:51:06,400
Speaker 4: hospitals have. I was talking to a friend who's a

952
00:51:06,440 --> 00:51:09,000
Speaker 4: doctor here and she was saying, oh, well, what do

953
00:51:09,080 --> 00:51:11,160
Speaker 4: you think about AI in medicine, And then she kind

954
00:51:11,160 --> 00:51:13,800
Speaker 4: of stopped for a moment and just laughed and said, well,

955
00:51:14,080 --> 00:51:15,759
Speaker 4: I don't know why I'm asking you that, because my

956
00:51:15,800 --> 00:51:20,120
Speaker 4: hospital uses paper notes. And I said what, and she said, yeah,

957
00:51:20,200 --> 00:51:23,680
Speaker 4: our whole hospital is not computerized. And that was a

958
00:51:23,719 --> 00:51:27,160
Speaker 4: good reality check for me because it made me realize, like, wow,

959
00:51:27,200 --> 00:51:29,160
Speaker 4: actually some of the stuff that I'm thinking about is

960
00:51:29,280 --> 00:51:31,800
Speaker 4: so far away from the reality of what people like

961
00:51:32,160 --> 00:51:34,400
Speaker 4: my friend the doctor is having to deal with on

962
00:51:34,440 --> 00:51:37,040
Speaker 4: the ground. And same with schools. You know, here a

963
00:51:37,120 --> 00:51:39,000
Speaker 4: whole hundreds of schools I think have had to be

964
00:51:39,040 --> 00:51:41,040
Speaker 4: closed because there's some kind of issues with the concrete

965
00:51:41,040 --> 00:51:43,920
Speaker 4: that I might collapse, which is a horrific But b

966
00:51:44,120 --> 00:51:47,919
Speaker 4: is such a different level of infrastructural problem that thinking

967
00:51:47,920 --> 00:51:50,279
Speaker 4: about AI in the costume is really not on the

968
00:51:50,400 --> 00:51:59,400
Speaker 4: radar in that scenarios exactly. Yeah, autonomous, Yeah, it's like

969
00:51:59,400 --> 00:52:02,000
Speaker 4: a low Yeah, people like what if students cheat? And

970
00:52:02,000 --> 00:52:11,040
Speaker 4: you're like, look, yeah, you know. So those are things

971
00:52:11,040 --> 00:52:12,759
Speaker 4: that I think are maybe a little bit on the

972
00:52:12,800 --> 00:52:16,600
Speaker 4: overrated side. I think a way things are underrated, Like

973
00:52:16,800 --> 00:52:20,040
Speaker 4: I'm really interested in how AI can change how we

974
00:52:20,080 --> 00:52:22,680
Speaker 4: see the world and how we see ourselves, like I think,

975
00:52:22,800 --> 00:52:25,400
Speaker 4: you know, take for example, something like TikTok. You know,

976
00:52:25,440 --> 00:52:27,920
Speaker 4: I have to confess I'm a big TikTok user. I

977
00:52:27,960 --> 00:52:31,240
Speaker 4: love grolling a lot of mind list diarbage. It brings

978
00:52:31,280 --> 00:52:34,359
Speaker 4: me a lot of joy and peace. But even things

979
00:52:34,360 --> 00:52:37,040
Speaker 4: like AI powered beauty filters, right, like, I think that

980
00:52:37,080 --> 00:52:39,800
Speaker 4: has such a profound effect on how you just understand

981
00:52:39,840 --> 00:52:43,000
Speaker 4: and see yourself and your own faith. They're often really

982
00:52:43,080 --> 00:52:46,200
Speaker 4: imbued with gendered and racist kinds of assumptions as well.

983
00:52:46,200 --> 00:52:48,040
Speaker 4: I often look a lot whiter when I use a

984
00:52:48,040 --> 00:52:51,440
Speaker 4: beauty filter, even though I'm multiracial. Like all of those assumptions,

985
00:52:51,480 --> 00:52:53,920
Speaker 4: I think they get baked in that seep into our

986
00:52:53,960 --> 00:52:56,920
Speaker 4: lives and like really subtle ways, but I think collectively

987
00:52:57,040 --> 00:52:58,320
Speaker 4: can have quite a big impact.

988
00:52:59,120 --> 00:53:02,799
Speaker 1: Yeah, I was talking about the I don't know if

989
00:53:02,800 --> 00:53:05,120
Speaker 1: this is technically AI, but then again, I don't know

990
00:53:05,160 --> 00:53:08,920
Speaker 1: what AI is or if there's like a specific argument,

991
00:53:08,960 --> 00:53:12,560
Speaker 1: but like the the way that like iPhones take forty

992
00:53:12,600 --> 00:53:15,839
Speaker 1: pictures like consecutively and then like choose the best one

993
00:53:16,000 --> 00:53:18,960
Speaker 1: from the forty, and like the live photo setting like

994
00:53:19,239 --> 00:53:22,279
Speaker 1: feels like a thing and like it's really good at it,

995
00:53:22,560 --> 00:53:25,200
Speaker 1: and it's something that I had, like just it happened

996
00:53:25,320 --> 00:53:27,600
Speaker 1: on my phone with like an update and I was like, yeah,

997
00:53:27,600 --> 00:53:29,520
Speaker 1: this is just how you take pictures now, and like

998
00:53:29,600 --> 00:53:32,640
Speaker 1: pictures are way better than they used to be. I

999
00:53:32,640 --> 00:53:35,080
Speaker 1: think I saw somebody speculate that like some of the

1000
00:53:35,160 --> 00:53:39,919
Speaker 1: software like Photoshop and other things that have traditionally had

1001
00:53:40,160 --> 00:53:45,120
Speaker 1: sort of a difficult learning curve will suddenly get much

1002
00:53:45,200 --> 00:53:49,880
Speaker 1: easier for people to use. So yeah, I like those

1003
00:53:49,920 --> 00:53:53,880
Speaker 1: are like having actual tools that are like kind of

1004
00:53:53,920 --> 00:53:56,880
Speaker 1: easy to use and like very simple, straightforward. We know

1005
00:53:57,280 --> 00:54:01,879
Speaker 1: what the goal is here, and we're able to use

1006
00:54:02,320 --> 00:54:07,359
Speaker 1: these enhanced kind of programs to achieve that goal. Like

1007
00:54:07,400 --> 00:54:11,080
Speaker 1: that that feels more possible to me and like something

1008
00:54:11,120 --> 00:54:13,680
Speaker 1: that we might see in the not too distant future.

1009
00:54:14,000 --> 00:54:16,480
Speaker 2: Totally, that's like a pitch you can understand, like this

1010
00:54:16,520 --> 00:54:19,400
Speaker 2: will make it easier for you to photoshop a friend

1011
00:54:19,440 --> 00:54:22,320
Speaker 2: out and just put someone else in. You're like, okay,

1012
00:54:22,400 --> 00:54:25,480
Speaker 2: rather than right now, it's like this shit will end

1013
00:54:25,520 --> 00:54:28,040
Speaker 2: the world and you're like, well, what is it? I

1014
00:54:28,040 --> 00:54:31,040
Speaker 2: don't know, I don't know, I don't know, but it

1015
00:54:31,080 --> 00:54:33,799
Speaker 2: will fuck you up. And you're like huh, and I

1016
00:54:33,800 --> 00:54:37,200
Speaker 2: guess that's such a different proposition up front and earlier,

1017
00:54:37,280 --> 00:54:39,960
Speaker 2: doctor Carry was saying, like there are things that genuinely

1018
00:54:40,000 --> 00:54:42,960
Speaker 2: excite you about like this sort of like these emerging

1019
00:54:43,040 --> 00:54:45,799
Speaker 2: technologies from I, and I would love to hear from

1020
00:54:45,800 --> 00:54:48,600
Speaker 2: your vantage point because you are looking at this and

1021
00:54:48,680 --> 00:54:50,920
Speaker 2: a like like what is ethical and what is going

1022
00:54:51,000 --> 00:54:54,120
Speaker 2: to bring meaningful value to people? Like what are those things?

1023
00:54:54,160 --> 00:54:56,040
Speaker 2: That I can feel like, oh, yeah, yeah, I can

1024
00:54:56,080 --> 00:54:57,120
Speaker 2: get down with that future.

1025
00:54:57,960 --> 00:55:00,279
Speaker 4: Yeah. I mean, I recognize that I sound very doom

1026
00:55:00,320 --> 00:55:02,120
Speaker 4: and gloom a lot of the time that I speak,

1027
00:55:02,239 --> 00:55:04,080
Speaker 4: and for all my friends have to deal with my

1028
00:55:04,239 --> 00:55:08,840
Speaker 4: sort of constant existential anxiety about technologies. But at the

1029
00:55:08,880 --> 00:55:10,880
Speaker 4: same time, you know, I'm an active user of a

1030
00:55:10,920 --> 00:55:12,760
Speaker 4: lot of them, Like I use a lot of voice

1031
00:55:12,800 --> 00:55:16,480
Speaker 4: dictation software, voice editing software, you know, for when we

1032
00:55:16,520 --> 00:55:20,480
Speaker 4: record our podcasts, and these are things that genuinely make

1033
00:55:20,520 --> 00:55:22,799
Speaker 4: my life so much better and easier. I love being

1034
00:55:22,800 --> 00:55:24,880
Speaker 4: able to transcribe, you know, what I'm saying and have

1035
00:55:25,000 --> 00:55:27,160
Speaker 4: it appear on the screen and not have to type

1036
00:55:27,200 --> 00:55:29,200
Speaker 4: out my emails and have them a pair. I recognize

1037
00:55:29,200 --> 00:55:31,120
Speaker 4: you they're like, all very boring. I should have said

1038
00:55:31,120 --> 00:55:36,040
Speaker 4: something like I DJ not at all I write email

1039
00:55:36,080 --> 00:55:39,440
Speaker 4: of using voice. It's very you know, but I think

1040
00:55:39,520 --> 00:55:42,600
Speaker 4: kind of, you know, extrapolating out from that, you know,

1041
00:55:42,640 --> 00:55:46,600
Speaker 4: there's really amazing applications and accessibility, and when it comes

1042
00:55:46,640 --> 00:55:49,480
Speaker 4: to the kinds of access people can now have online

1043
00:55:49,719 --> 00:55:53,760
Speaker 4: because of advancements and aipowered tools, and so I think, though,

1044
00:55:54,120 --> 00:55:56,239
Speaker 4: you know, what's really central, there is kind of that

1045
00:55:56,320 --> 00:55:58,640
Speaker 4: broader vision of you know, what is the kind of

1046
00:55:58,640 --> 00:56:02,640
Speaker 4: benefit this is meaningfully bringing to society? What problem is

1047
00:56:02,680 --> 00:56:05,120
Speaker 4: being solved? And are the people who are like most

1048
00:56:05,120 --> 00:56:07,880
Speaker 4: affected by that problem the ones leading the conversation and

1049
00:56:07,920 --> 00:56:10,560
Speaker 4: saying what they need? Because too often I think we

1050
00:56:10,600 --> 00:56:13,839
Speaker 4: see tech developers creating stuff that actually no one really

1051
00:56:13,880 --> 00:56:15,960
Speaker 4: asked for. I'm sure you have like all seen that

1052
00:56:16,040 --> 00:56:18,960
Speaker 4: thing on Amazon when you're like, you're asked for that

1053
00:56:19,120 --> 00:56:24,279
Speaker 4: banana holder or avocado peeler or something. And too often

1054
00:56:24,280 --> 00:56:25,920
Speaker 4: I think tech add on to be a little bit

1055
00:56:26,000 --> 00:56:28,320
Speaker 4: like that. Whereas I think when we have like really

1056
00:56:28,360 --> 00:56:32,200
Speaker 4: interesting conscious development of say, for example, feminist tools that

1057
00:56:32,239 --> 00:56:36,279
Speaker 4: are designed to encourage good conversations things like that, that

1058
00:56:36,360 --> 00:56:38,719
Speaker 4: really makes me excited about the kinds of futures we

1059
00:56:38,760 --> 00:56:39,839
Speaker 4: could have with technology.

1060
00:56:40,480 --> 00:56:43,680
Speaker 2: Right, it almost feels like the danger here is when

1061
00:56:43,719 --> 00:56:45,920
Speaker 2: someone has like a technology and go and this is

1062
00:56:46,000 --> 00:56:48,279
Speaker 2: gonna be in every home, and you're like, this is

1063
00:56:48,280 --> 00:56:50,720
Speaker 2: a sales pitch. Actually yeah, because if you're not saying

1064
00:56:51,200 --> 00:56:53,759
Speaker 2: this is how we will work less and have more

1065
00:56:53,880 --> 00:56:58,560
Speaker 2: time to frolic, to enjoy our human existence, to connect

1066
00:56:58,640 --> 00:57:01,719
Speaker 2: with our families, then then like miss me with that,

1067
00:57:01,719 --> 00:57:03,960
Speaker 2: because it reminds me of Crypto. It reminds me of

1068
00:57:04,040 --> 00:57:06,799
Speaker 2: the metaverse, and it reminds you of Zuckerberg being like

1069
00:57:07,160 --> 00:57:12,879
Speaker 2: every worker will have this fucking headset on no no,

1070
00:57:13,000 --> 00:57:15,920
Speaker 2: but nice fucking tri asshole. And I feel like this

1071
00:57:15,960 --> 00:57:18,240
Speaker 2: is kind of like it has a similar tone of like,

1072
00:57:18,840 --> 00:57:22,400
Speaker 2: get ready, folks for this thing. And granted they have

1073
00:57:22,480 --> 00:57:24,760
Speaker 2: some a shiny toy in the form of these large

1074
00:57:24,840 --> 00:57:27,480
Speaker 2: language models that are fun to do, like do and

1075
00:57:27,520 --> 00:57:30,680
Speaker 2: really they're just skimming the internet and just giving it

1076
00:57:30,720 --> 00:57:33,320
Speaker 2: to you in a nice, tidy sentence. But yeah, like

1077
00:57:33,360 --> 00:57:35,880
Speaker 2: I feel like that is just kind of like I'm

1078
00:57:35,880 --> 00:57:38,400
Speaker 2: seeing that dimension when you see the hype around it,

1079
00:57:38,400 --> 00:57:40,800
Speaker 2: which feels a little more like y'all are talking about

1080
00:57:40,800 --> 00:57:43,560
Speaker 2: this to make money, whereas the other things that you're

1081
00:57:43,560 --> 00:57:47,640
Speaker 2: talking about, like accessibility and trying to democratize certain applications.

1082
00:57:47,680 --> 00:57:52,240
Speaker 2: With things like that, less there's less less scale involved

1083
00:57:52,280 --> 00:57:54,880
Speaker 2: with something like that, So me, yeah, it hasn't talked.

1084
00:57:54,640 --> 00:57:57,640
Speaker 4: About exactly, and I just think it's really important to

1085
00:57:57,640 --> 00:57:59,840
Speaker 4: recognize that. You know, I'm not saying that say things

1086
00:57:59,880 --> 00:58:03,160
Speaker 4: like smart home technology is like Amazon Alexa and things

1087
00:58:03,200 --> 00:58:06,040
Speaker 4: are like inherently bad products. I'm sure gaps of people

1088
00:58:06,160 --> 00:58:08,600
Speaker 4: like love having those technologies in their home. They find

1089
00:58:08,600 --> 00:58:10,880
Speaker 4: them useful. But I just think whenever you bring in

1090
00:58:10,880 --> 00:58:14,200
Speaker 4: a new technology, you also bring a new vulnerabilities, and

1091
00:58:14,240 --> 00:58:16,880
Speaker 4: you bring a new costs. And sometimes the hype wave

1092
00:58:17,000 --> 00:58:19,320
Speaker 4: means we focus on the benefits. You know, Oh, if

1093
00:58:19,320 --> 00:58:22,240
Speaker 4: you bring this to every home, everyone's life will be easier,

1094
00:58:22,400 --> 00:58:25,480
Speaker 4: if everyone will have a more seamless home experience, when

1095
00:58:25,520 --> 00:58:28,040
Speaker 4: actually that's not the case. There's real costs to bringing

1096
00:58:28,040 --> 00:58:30,919
Speaker 4: those tools into a lot of people's homes. Everything from,

1097
00:58:30,920 --> 00:58:33,280
Speaker 4: for example, the way that certain kinds of elder care

1098
00:58:33,360 --> 00:58:36,800
Speaker 4: is getting replaced by technological tools in the UK care system,

1099
00:58:37,280 --> 00:58:39,520
Speaker 4: through to the fact that those tools can really easily

1100
00:58:39,520 --> 00:58:42,400
Speaker 4: be used for the purposes of domestic abuse, into a

1101
00:58:42,520 --> 00:58:44,680
Speaker 4: partner violence, It can be used to control or trap

1102
00:58:44,720 --> 00:58:47,320
Speaker 4: someone in their home. And these are really ugly truths

1103
00:58:47,320 --> 00:58:49,720
Speaker 4: about those technologies, and they're often not the ones that

1104
00:58:49,760 --> 00:58:52,680
Speaker 4: are put at the heart of technology development. And so

1105
00:58:52,920 --> 00:58:55,200
Speaker 4: that's a lot of my job, I guess, is just

1106
00:58:55,240 --> 00:58:58,120
Speaker 4: to say, how do we put those vulnerabilities in those

1107
00:58:58,160 --> 00:59:01,440
Speaker 4: costs at the forefront, and then judge holistically whether or

1108
00:59:01,520 --> 00:59:04,040
Speaker 4: not we really want this product to exist.

1109
00:59:04,240 --> 00:59:06,400
Speaker 2: Yeah, but here's the thing. I got a lot of

1110
00:59:06,440 --> 00:59:10,960
Speaker 2: my stock portfolio tied up in these companies. I need

1111
00:59:10,960 --> 00:59:13,880
Speaker 2: these I need these fucking things to moon, if you

1112
00:59:13,920 --> 00:59:14,480
Speaker 2: know what I mean.

1113
00:59:14,960 --> 00:59:17,520
Speaker 1: So I want to end the episode just doing something

1114
00:59:17,520 --> 00:59:20,480
Speaker 1: that you guys do on The Good Robot, which is

1115
00:59:20,960 --> 00:59:25,320
Speaker 1: talk talk about some like books that are recommended or

1116
00:59:25,600 --> 00:59:27,600
Speaker 1: you know what works you recommend. You know, you talk

1117
00:59:27,680 --> 00:59:30,120
Speaker 1: on an episode earlier, I think it was in the

1118
00:59:30,160 --> 00:59:34,240
Speaker 1: summer about how a lot of the ideas and fears

1119
00:59:34,280 --> 00:59:38,400
Speaker 1: and hopes that we have currently are very similar to

1120
00:59:38,440 --> 00:59:44,000
Speaker 1: what we've seen in movies and like those like when

1121
00:59:44,000 --> 00:59:47,400
Speaker 1: you look at Elon Musk's ten favorite books, they're all

1122
00:59:47,440 --> 00:59:50,880
Speaker 1: like fed by this same like Isaac Asimov, like I

1123
00:59:51,120 --> 00:59:54,480
Speaker 1: robot thing I'd had coot. A lot of Kubrick's ideas

1124
00:59:54,600 --> 00:59:59,600
Speaker 1: are around AI are go back to this very specific

1125
00:59:59,840 --> 01:00:05,480
Speaker 1: verse version of technology where it inevitably turns into a

1126
01:00:05,560 --> 01:00:09,960
Speaker 1: mean psychopath who is set on dominating all of humanity.

1127
01:00:10,400 --> 01:00:13,520
Speaker 1: So as an alternative, because we do like to talk about,

1128
01:00:14,040 --> 01:00:18,200
Speaker 1: you know, the importance of expanding our imagination, Like you know,

1129
01:00:18,400 --> 01:00:20,280
Speaker 1: with regards to climate, a lot of it is like

1130
01:00:20,360 --> 01:00:25,080
Speaker 1: either it's either apocalypse or business as usual capitalism. There's

1131
01:00:25,120 --> 01:00:29,400
Speaker 1: not like people have a hard time imagining the alternative

1132
01:00:29,800 --> 01:00:33,439
Speaker 1: on AI. On the subject of AI, like, what are

1133
01:00:35,480 --> 01:00:38,000
Speaker 1: is there? First of all, is there any like mainstream

1134
01:00:38,080 --> 01:00:40,600
Speaker 1: kind of popular movie that you feel like actually like

1135
01:00:40,760 --> 01:00:44,720
Speaker 1: that example of AI like got it right? And then

1136
01:00:45,000 --> 01:00:48,480
Speaker 1: are there other kind of more obscure works that you

1137
01:00:48,480 --> 01:00:51,240
Speaker 1: would send people to in order to sort of feed

1138
01:00:51,280 --> 01:00:55,400
Speaker 1: their imagination of like what what a world with AI

1139
01:00:55,520 --> 01:00:56,720
Speaker 1: technology could look like?

1140
01:00:57,320 --> 01:00:59,959
Speaker 4: Yeah? I mean that question of the imagination and how

1141
01:01:00,160 --> 01:01:04,120
Speaker 4: our current imagination of AI is really narrow is super key,

1142
01:01:04,240 --> 01:01:06,760
Speaker 4: And you know, I think it's fascinating that, yes, you know,

1143
01:01:06,880 --> 01:01:09,040
Speaker 4: Musk and these folks have like a very narrow set

1144
01:01:09,120 --> 01:01:11,760
Speaker 4: of stories that they refer to. In those stories, it's

1145
01:01:11,800 --> 01:01:14,880
Speaker 4: always like, oh, the type of masculine take Bro makes

1146
01:01:14,880 --> 01:01:17,360
Speaker 4: an AI and then it looks like him and it

1147
01:01:17,400 --> 01:01:19,960
Speaker 4: takes over the world, and you're like, are you okay

1148
01:01:20,720 --> 01:01:22,840
Speaker 4: that you're like your bedtime story?

1149
01:01:23,960 --> 01:01:27,920
Speaker 1: Soopath bent on world domination thinks that all AI is

1150
01:01:27,960 --> 01:01:31,280
Speaker 1: going to be a sociopath bent on world domination. That's

1151
01:01:31,280 --> 01:01:33,560
Speaker 1: so weird, right, Yeah?

1152
01:01:33,640 --> 01:01:34,760
Speaker 4: Who would have guessed that?

1153
01:01:35,040 --> 01:01:35,320
Speaker 2: Yeah?

1154
01:01:35,360 --> 01:01:39,240
Speaker 4: But yeah, I mean, and something that I really like

1155
01:01:39,480 --> 01:01:42,600
Speaker 4: us stories, particularly science fiction stories that I love sci

1156
01:01:42,600 --> 01:01:45,160
Speaker 4: fi and fantasy. I'm like a huge believer in like

1157
01:01:45,560 --> 01:01:47,720
Speaker 4: the way that it can help us imagine different worlds.

1158
01:01:48,080 --> 01:01:51,120
Speaker 4: In terms of like mainstream films, I guess the one

1159
01:01:51,120 --> 01:01:52,640
Speaker 4: that I comes to the top of my head is

1160
01:01:52,680 --> 01:01:55,560
Speaker 4: Big Hero six. So the Disney Pixar film where you

1161
01:01:55,600 --> 01:01:58,760
Speaker 4: have an AI powered kind of we're about healthcare buddy

1162
01:01:59,000 --> 01:02:01,760
Speaker 4: called Bamax, And I think that's a really interesting example

1163
01:02:01,840 --> 01:02:04,000
Speaker 4: of an AI that you know, at one point in

1164
01:02:04,000 --> 01:02:06,360
Speaker 4: the film, he gets dressed up in armor and you know,

1165
01:02:06,920 --> 01:02:08,919
Speaker 4: the kid here to starts to try and use him

1166
01:02:09,000 --> 01:02:10,760
Speaker 4: as a kind of weapon. But like, this is an

1167
01:02:10,760 --> 01:02:12,600
Speaker 4: AI that so has it been designed to be a

1168
01:02:12,640 --> 01:02:16,760
Speaker 4: weapon that kind of resists being weaponized a lot of ways,

1169
01:02:16,880 --> 01:02:19,160
Speaker 4: And in that sense, it's really countercultural to a lot

1170
01:02:19,160 --> 01:02:21,600
Speaker 4: of the AI we see in Western film and cinema,

1171
01:02:21,640 --> 01:02:24,920
Speaker 4: which is often very weaponized. It's often either this like

1172
01:02:25,040 --> 01:02:28,480
Speaker 4: kind of sexy cyborg figure or it's this hyper masculine

1173
01:02:28,600 --> 01:02:31,840
Speaker 4: terminator figure. Yeah, so I find be Max is kind

1174
01:02:31,880 --> 01:02:33,600
Speaker 4: of gender lid and like just.

1175
01:02:33,600 --> 01:02:38,680
Speaker 1: A well, puffy balloon person. But that's also so accurate

1176
01:02:38,920 --> 01:02:42,800
Speaker 1: of what a young boy would do immediately with that

1177
01:02:43,000 --> 01:02:47,120
Speaker 1: is like a weapon. It's like the tweet I can

1178
01:02:47,120 --> 01:02:48,920
Speaker 1: tell you me and my friends would have killed et

1179
01:02:49,120 --> 01:02:54,720
Speaker 1: with hammers. It's like little little boys are monsters. But

1180
01:02:54,760 --> 01:02:55,960
Speaker 1: that that's a great example.

1181
01:02:56,240 --> 01:02:59,840
Speaker 4: Yeah, in terms of like some alternative like stories and ideas,

1182
01:03:00,160 --> 01:03:02,760
Speaker 4: started to do a really shameless self plug but we

1183
01:03:02,840 --> 01:03:05,600
Speaker 4: have a book coming out called The Robot Why Technology

1184
01:03:05,640 --> 01:03:09,480
Speaker 4: Needs Feminism, and I'm plugging it because it's really beautiful.

1185
01:03:09,480 --> 01:03:12,080
Speaker 4: We worked with a science fiction illustrator because Sinjon Lee,

1186
01:03:12,080 --> 01:03:14,960
Speaker 4: and there was myself and doctor Elinoaldrage, my co host.

1187
01:03:15,040 --> 01:03:17,200
Speaker 4: And the whole thing is these two thousand word essays,

1188
01:03:17,200 --> 01:03:19,800
Speaker 4: so they're really short and really punchy by lots of

1189
01:03:19,840 --> 01:03:22,160
Speaker 4: different guests with had on the podcast, and they all

1190
01:03:22,200 --> 01:03:25,120
Speaker 4: respond to this idea of good technology dot dot dot,

1191
01:03:25,160 --> 01:03:28,720
Speaker 4: So it might be good technology is free, or good technology,

1192
01:03:28,880 --> 01:03:31,400
Speaker 4: you know, challenges power, and the idea is you can

1193
01:03:31,520 --> 01:03:34,560
Speaker 4: just dip in read one. They're also illustrated, and then

1194
01:03:34,680 --> 01:03:36,480
Speaker 4: just dip out when you need a break or a moment.

1195
01:03:37,000 --> 01:03:39,360
Speaker 4: But yeah, I really would encourage you to pick that

1196
01:03:39,480 --> 01:03:43,000
Speaker 4: up because it just contains the most incredible feminist philosophers,

1197
01:03:43,040 --> 01:03:47,280
Speaker 4: technologists and vendors, activists who are really pushing forward different

1198
01:03:47,320 --> 01:03:49,880
Speaker 4: ideas of the kinds of technological futures we could have.

1199
01:03:50,320 --> 01:03:51,800
Speaker 4: I mean, apart from that, I read a lot of

1200
01:03:51,840 --> 01:03:56,200
Speaker 4: like Chinese, diasporic Asian American sci fi as well, particularly

1201
01:03:56,360 --> 01:03:58,440
Speaker 4: sci fi that's like thinking a lot about the climate

1202
01:03:58,480 --> 01:04:01,040
Speaker 4: crisis and AI and insects of that, and so I

1203
01:04:01,080 --> 01:04:03,880
Speaker 4: think a lot of those stories have really interesting and

1204
01:04:03,920 --> 01:04:07,600
Speaker 4: different perspectives on what AI is it can be from

1205
01:04:07,640 --> 01:04:10,560
Speaker 4: like Elliott de Badad's work, which explores, you know, the

1206
01:04:10,640 --> 01:04:13,880
Speaker 4: relationships between like humans and sentient mind shifts in this

1207
01:04:14,080 --> 01:04:17,560
Speaker 4: base Opera universe. Through the like lyrical lized Faltfish Girl,

1208
01:04:17,600 --> 01:04:20,680
Speaker 4: which is like a very dystopian imagining of like a

1209
01:04:20,680 --> 01:04:24,080
Speaker 4: future society based on labor from clones. Like all of

1210
01:04:24,120 --> 01:04:26,760
Speaker 4: these novels, I think just if they're like a lot

1211
01:04:26,760 --> 01:04:28,560
Speaker 4: more love and a lot more could ask, they're just

1212
01:04:29,000 --> 01:04:33,720
Speaker 4: absolutely interesting and imagine have been gorgeous ideas about the future.

1213
01:04:34,440 --> 01:04:37,880
Speaker 1: Amazing. Well, this has been such a fascinating conversation. Thank

1214
01:04:37,920 --> 01:04:40,000
Speaker 1: you so much for taking the time. At what I

1215
01:04:40,040 --> 01:04:42,840
Speaker 1: have to assume is midnight where you are right now,

1216
01:04:43,160 --> 01:04:47,200
Speaker 1: it's almost nine almost nine, Okay, yeah, that's too late. Well,

1217
01:04:47,240 --> 01:04:50,640
Speaker 1: thank you doctor McNerney for doing the show Where can

1218
01:04:50,680 --> 01:04:54,360
Speaker 1: people find you? Follow you, hear you all that good stuff.

1219
01:04:54,800 --> 01:04:56,600
Speaker 4: Oh yeah, thanks so much for having me on. It's

1220
01:04:56,600 --> 01:04:58,760
Speaker 4: been a bluff. Yeah, check out the good Robot. We're

1221
01:04:58,760 --> 01:05:01,720
Speaker 4: on YouTube, Apple's Spotify, and then keep an eye out

1222
01:05:01,760 --> 01:05:03,960
Speaker 4: for the book coming in February twenty twenty four.

1223
01:05:04,840 --> 01:05:05,320
Speaker 1: Amazing.

1224
01:05:05,520 --> 01:05:07,280
Speaker 2: Yeah, we'll have to have you back for that. Yeah.

1225
01:05:07,520 --> 01:05:10,040
Speaker 1: Yeah, absolutely. And is there a work of media that

1226
01:05:10,080 --> 01:05:10,920
Speaker 1: you've been enjoying?

1227
01:05:11,240 --> 01:05:15,120
Speaker 4: Ooh, media in terms of like a show, like a pot.

1228
01:05:15,920 --> 01:05:18,640
Speaker 1: Show, a podcast and tweet.

1229
01:05:18,800 --> 01:05:21,680
Speaker 4: Oh, I feel like I should choose something like really intellectual.

1230
01:05:21,680 --> 01:05:23,840
Speaker 4: But I actually just sent my husband this tweet that

1231
01:05:23,880 --> 01:05:27,720
Speaker 4: really got me because very me, which is it was

1232
01:05:27,880 --> 01:05:29,760
Speaker 4: it's actually a bit dark, but with this news headline

1233
01:05:29,760 --> 01:05:32,600
Speaker 4: about this hiker had been missing and the rescuers couldn't

1234
01:05:32,600 --> 01:05:34,640
Speaker 4: get in contact with them and they didn't get rescued

1235
01:05:34,680 --> 01:05:37,560
Speaker 4: for twenty four hours because they wouldn't answer their phone

1236
01:05:37,720 --> 01:05:41,800
Speaker 4: it was an unknown number. And this is just exactly me,

1237
01:05:42,040 --> 01:05:46,560
Speaker 4: like my husband like please, fuck you know what I'm like,

1238
01:05:47,240 --> 01:05:49,720
Speaker 4: I will not I will not answer it under a number.

1239
01:05:49,880 --> 01:05:52,320
Speaker 4: And so that brought me a little bit of joy today.

1240
01:05:53,080 --> 01:05:55,600
Speaker 1: Absolutely, And I feel like that's a great way that

1241
01:05:56,000 --> 01:05:58,960
Speaker 1: a great example of how technology has marched forward and

1242
01:05:59,120 --> 01:06:03,440
Speaker 1: completely real and like something that used to work phone communication,

1243
01:06:03,760 --> 01:06:09,160
Speaker 1: but we can't answer our phones anymore because the fucking spots. Miles,

1244
01:06:09,280 --> 01:06:11,240
Speaker 1: where can people find you? What's the work media you've

1245
01:06:11,240 --> 01:06:11,880
Speaker 1: been enjoying?

1246
01:06:12,000 --> 01:06:15,840
Speaker 2: Yeah at miles of Gray wherever they got at. I'm

1247
01:06:15,840 --> 01:06:22,080
Speaker 2: there soon to Blue Sky, shout out ill Will Christy Main. Yeah,

1248
01:06:22,360 --> 01:06:24,800
Speaker 2: you and you and I were hopping in okay codes

1249
01:06:24,800 --> 01:06:27,400
Speaker 2: we got we got the invite, just ch the blue Sky.

1250
01:06:27,840 --> 01:06:30,160
Speaker 2: So yeah there shortly. I'll also check Jack and Eye

1251
01:06:30,160 --> 01:06:32,760
Speaker 2: out on our basketball podcast, Miles and jackout man mas

1252
01:06:32,840 --> 01:06:36,440
Speaker 2: pease check me out on my ninety day Fiance podcast

1253
01:06:36,680 --> 01:06:40,720
Speaker 2: for twenty Day Fiance and also the true crime show

1254
01:06:40,800 --> 01:06:43,240
Speaker 2: The Good Thief, which has all episode eight episodes out,

1255
01:06:43,280 --> 01:06:46,640
Speaker 2: So please stream those binge those talking about the Greek

1256
01:06:46,720 --> 01:06:50,880
Speaker 2: robin Hood, the man who legitimately was kidnapping millionaires and

1257
01:06:51,200 --> 01:06:54,480
Speaker 2: doing some old fashioned wealth redistribution in the in a

1258
01:06:54,560 --> 01:06:58,280
Speaker 2: positive way, never hurting people either, never hurting people, like again,

1259
01:06:58,560 --> 01:07:01,200
Speaker 2: an ethical criminal if they're is such a thing. Some

1260
01:07:01,280 --> 01:07:03,920
Speaker 2: tweets I like, first one is from you know, shout

1261
01:07:03,920 --> 01:07:06,920
Speaker 2: out to the WGA and all the negotiators, because it

1262
01:07:06,960 --> 01:07:10,040
Speaker 2: looks like the WGA strike is they're close to getting

1263
01:07:10,080 --> 01:07:13,600
Speaker 2: something ratified. So we like that. This tweet is from

1264
01:07:13,680 --> 01:07:17,440
Speaker 2: Jeff Yang at Original Spin, tweeted just underscoring how WGA

1265
01:07:17,520 --> 01:07:21,000
Speaker 2: negotiators told the studios the union wouldn't go back to

1266
01:07:21,040 --> 01:07:24,120
Speaker 2: work until SAG also has a deal, because if the

1267
01:07:24,200 --> 01:07:27,720
Speaker 2: last five months proved anything, it's in together, win together.

1268
01:07:27,840 --> 01:07:30,680
Speaker 2: And there's this snippet from it might be a deadline,

1269
01:07:30,680 --> 01:07:33,160
Speaker 2: but it says quote the studios also inquired if once

1270
01:07:33,160 --> 01:07:35,560
Speaker 2: a tentative agreement is ratified by the scribes, if the

1271
01:07:35,600 --> 01:07:37,720
Speaker 2: writers would pick up their pens and hit their keyboards

1272
01:07:37,760 --> 01:07:40,800
Speaker 2: again very soon afterwards, the guild, from what we understand,

1273
01:07:41,000 --> 01:07:43,520
Speaker 2: made it clear that they would not be going back

1274
01:07:43,560 --> 01:07:46,960
Speaker 2: to work until SAG AFTRA also had a new agreement

1275
01:07:47,040 --> 01:07:51,600
Speaker 2: with the AMPTP, reflective of the wj WGA's feeling of

1276
01:07:51,680 --> 01:07:55,600
Speaker 2: solidarity between the two unions that has characterized their first

1277
01:07:55,680 --> 01:07:59,200
Speaker 2: mutual strikes since nineteen sixty love the solidarity, and I

1278
01:07:59,240 --> 01:08:01,680
Speaker 2: know AYATSI is also going to have a contract that

1279
01:08:01,800 --> 01:08:04,840
Speaker 2: is going to need to be renegotiated next year, and

1280
01:08:05,760 --> 01:08:08,320
Speaker 2: it looks like guess what, folks they did. It only

1281
01:08:08,360 --> 01:08:12,040
Speaker 2: listen to one thing, Yeah, putting your putting the tools down.

1282
01:08:12,120 --> 01:08:14,400
Speaker 2: Put the tools down and pick them other tools up,

1283
01:08:14,440 --> 01:08:17,040
Speaker 2: which is getting in your solidarity back. So we'd love

1284
01:08:17,120 --> 01:08:19,200
Speaker 2: to see that. And another tweet I like is from

1285
01:08:19,400 --> 01:08:24,080
Speaker 2: t Pain at t Pain just put bartender just doing

1286
01:08:24,120 --> 01:08:28,400
Speaker 2: her job. Me just this prudo of Kevin James.

1287
01:08:28,160 --> 01:08:32,000
Speaker 1: At the butt of Kevin James. It's going going, but it.

1288
01:08:32,080 --> 01:08:36,240
Speaker 2: Is trying to be like, hey, hey, can I get

1289
01:08:36,240 --> 01:08:39,639
Speaker 2: your eye content? Just just a ginger beer if you can.

1290
01:08:40,600 --> 01:08:41,320
Speaker 2: Those are my tools.

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01:08:41,840 --> 01:08:45,880
Speaker 1: Uh tweet I've been enjoying at b r n bn E.

1292
01:08:46,280 --> 01:08:48,960
Speaker 1: I don't know how to pronounce that, but their display

1293
01:08:49,040 --> 01:08:54,080
Speaker 1: name is bbbbbbb space bbbbbbb, which I have to assume

1294
01:08:54,200 --> 01:08:59,040
Speaker 1: stands for bone bone bone bone bone bom bone boom boom.

1295
01:08:59,120 --> 01:09:02,439
Speaker 1: But the tweet I usually don't use for my tweet

1296
01:09:02,560 --> 01:09:07,320
Speaker 1: the ones that things that I've retweeted, but this one

1297
01:09:07,439 --> 01:09:09,800
Speaker 1: got me so much I had to reshare it. The

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01:09:09,920 --> 01:09:12,400
Speaker 1: tweet is what if you went to ET's planet and

1299
01:09:12,479 --> 01:09:17,519
Speaker 1: all of the other ET's were wearing clothes that really,

1300
01:09:18,280 --> 01:09:20,800
Speaker 1: oh really fucked up? How I thought about Et?

1301
01:09:21,080 --> 01:09:22,960
Speaker 2: Yeah? Yo, what.

1302
01:09:26,680 --> 01:09:31,200
Speaker 1: You can find me on Twitter at Jack Underscore O'Brien

1303
01:09:31,600 --> 01:09:34,800
Speaker 1: and on threads at Jack Underscore, Oh Underscore Brian, and

1304
01:09:35,040 --> 01:09:39,760
Speaker 1: on Blue Skill. I'll have a username there eventually soon.

1305
01:09:40,280 --> 01:09:42,879
Speaker 1: And you can find us on Twitter at daily Zeikeeist

1306
01:09:43,120 --> 01:09:45,360
Speaker 1: d Daily Zekeeist on Instagram. We have a Facebook fan

1307
01:09:45,400 --> 01:09:47,720
Speaker 1: page on a website daily zeikeist dot com where we

1308
01:09:47,760 --> 01:09:51,120
Speaker 1: post our episodes and our footnotes. We link off to

1309
01:09:51,160 --> 01:09:53,160
Speaker 1: the information that we talked about in today's episode, as

1310
01:09:53,160 --> 01:09:55,559
Speaker 1: well as a song that we think you might enjoy. Miles,

1311
01:09:55,600 --> 01:09:57,200
Speaker 1: what's the song people might enjoy?

1312
01:09:57,760 --> 01:10:01,639
Speaker 2: H This is an artist, Detroit's very own John FM,

1313
01:10:02,240 --> 01:10:04,519
Speaker 2: and I just figure out what an appropriate title. It's

1314
01:10:04,560 --> 01:10:08,559
Speaker 2: called White Science, and the track is like very It

1315
01:10:08,600 --> 01:10:12,000
Speaker 2: feels like if, like like I don't know, like if

1316
01:10:12,040 --> 01:10:14,040
Speaker 2: Prince was making a shit in like the Late Nights.

1317
01:10:14,120 --> 01:10:16,840
Speaker 2: It has like a frinsy vibe like it's frincy and

1318
01:10:16,880 --> 01:10:19,920
Speaker 2: it's kind of got this like vocal modulator on it

1319
01:10:20,040 --> 01:10:23,080
Speaker 2: that feels a little bit princy. So and also it's

1320
01:10:23,120 --> 01:10:25,360
Speaker 2: just a really good track. So this is John FM

1321
01:10:25,920 --> 01:10:27,040
Speaker 2: with White Science.

1322
01:10:27,439 --> 01:10:31,120
Speaker 1: That's what Jack FM's mom calls him when he's in trouble.

1323
01:10:32,040 --> 01:10:33,559
Speaker 2: John FM, Get in here Now.

1324
01:10:33,800 --> 01:10:35,840
Speaker 1: The Daily ZEITGEI is a production of by Heart Radio.

1325
01:10:35,880 --> 01:10:38,280
Speaker 1: For more podcasts from my heart Radio is the iHeartRadio app,

1326
01:10:38,280 --> 01:10:40,640
Speaker 1: Apple podcast. Wherever you listen to your favorite shows, that

1327
01:10:40,800 --> 01:10:43,439
Speaker 1: is gonna do it for us this morning, back this

1328
01:10:43,479 --> 01:10:46,200
Speaker 1: afternoon to tell you what is trending and we'll talk

1329
01:10:46,240 --> 01:10:46,720
Speaker 1: to you all then.

1330
01:10:46,920 --> 01:10:49,840
Speaker 2: Bye bye