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Speaker 1: Hey guys.

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Speaker 2: So I've always wanted to do some sort of like

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Speaker 2: top Episodes of the Year rundown thing ey.

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Speaker 1: This year, I had.

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Speaker 2: A little extra time before taking off for the holiday,

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Speaker 2: and so kind of threw something together just based on

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Speaker 2: what the episodes were that you listened to the most.

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Speaker 2: In future years, I'd like to open it up for voting,

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Speaker 2: get your input. But for this year, we're just gonna

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Speaker 2: be rerunning each of the top five episodes while we're

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Speaker 2: on holiday break. And here we are. We're up to

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Speaker 2: number one. This is the most listened to episode of

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Speaker 2: the year twenty twenty three. For a year where bullshit

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Speaker 2: about AI dominated the headlines, bullshit AI headlines dominated my

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Speaker 2: brain like the Van Vaught thing. I think it's very

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Speaker 2: appropriate that the most listened to episode was about bullshit AI.

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Speaker 2: It's another one of our expert guest interview episodes. I

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Speaker 2: hope you enjoy listening back to it as much as

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Speaker 2: I did, and I hope you're having a great holiday.

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Speaker 2: We'll see you in the new year. Thanks everyone, Bye,

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Speaker 2: Hello the Internet, and welcome to season three oh six,

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Speaker 2: Episode two of Alley's I Guys Say production of iHeartRadio.

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Speaker 2: This is a podcast where we take a deep debt

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Speaker 2: into America's shared consciousness. And it is Tuesday, September twenty sixth,

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Speaker 2: twenty twenty three.

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Speaker 1: Ooh you ready, you know what this is right? Ooh,

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Speaker 1: you know what this is right?

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Speaker 3: For all my pancake lovers, it's your day, is National

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Speaker 3: Pancake Day.

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Speaker 1: It's also National.

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Speaker 3: Shamoo the Whale Day, Shout out Shamoo. It's also for

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Speaker 3: all my dumpling lovers out there, for me, specifically my

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Speaker 3: Gioza lovers out there, National Dumpling Day. Okay, so you know,

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Speaker 3: celebrate however you've seen fit depending on your cultural disposition.

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Speaker 3: It's also National Johnny Appleseed Day. And only bring that

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Speaker 3: up because I remember in Christian school we were made

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Speaker 3: to sing that as like a song before we went

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Speaker 3: to lunch man.

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Speaker 2: The amount of pro Johnny Applesey propaganda that I had

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Speaker 2: to live with a lot of pro.

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Speaker 1: Yeah, yeah, yeah, It's like it was like John Chapman

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Speaker 1: or something original.

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Speaker 2: I just really liked it, really fucked with apples like

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Speaker 2: the children must learn.

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Speaker 1: Yeah.

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Speaker 3: I bet there's some dark ass shit probably behind, you know,

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Speaker 3: like some a story like that.

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Speaker 1: It's got to be a milkshick.

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Speaker 2: Though, Yeah, for sure, very problematic. But the way, like

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Speaker 2: the amount of information that I consumed, Like I had

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Speaker 2: one of those my family had one of those book

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Speaker 2: sets where it was like important historical figures and like

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Speaker 2: right next to Lincoln was like Johnny Appleseed, and you

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Speaker 2: know they I was prepared for Johnny Appleseed to be

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Speaker 2: like one of the major figures in my.

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Speaker 1: Life, right and education. Did you know the song? No,

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Speaker 1: the Lord is good to me, And so I thank.

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Speaker 2: The Lord for giving me the things I need, the

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Speaker 2: sun and the rain and the apple seed.

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Speaker 1: The Lord is good to me. And then we could

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Speaker 1: go eat lunch.

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Speaker 3: But we had to sing that ship before wow from

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Speaker 3: the from the Disney movie apparently.

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Speaker 2: So I wasn't even I didn't even get all of

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Speaker 2: the Johnny Applesy propaganda.

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Speaker 3: I got the Christian, I got the Christian capitalist Johnny

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Speaker 3: apple Seed propaganda just mainlined into.

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Speaker 1: My little brain. You were a pot on his head, right,

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Speaker 1: I mean I think who sure like.

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Speaker 2: Backwards kind of like Davy rocket. Yeah, anyways, this is

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Speaker 2: all I don't know. The ship that is probably specific

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Speaker 2: to American audiences.

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Speaker 1: Yeah, but my.

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Speaker 2: Name's Jack O'Brien aka start spreading the news eating today.

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Speaker 2: I want to eat a part of it pecan pecan pie.

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Speaker 2: These chattering teeth are longing to chew through the nutty

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Speaker 2: part of it, pig can pecan pie. That is courtesy

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Speaker 2: of Maxer twelve sixteen on the discord, A little just

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Speaker 2: real solid, right down the middle meatball of a weird

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Speaker 2: al parody and reference to our conversation about peacan pie.

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Speaker 2: For some reason, he was lobbing that meatball up to you.

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Speaker 3: Yeah, I saw in the discord. I was tagging that,

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Speaker 3: but you took it. Oh so you just do other

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Speaker 3: people's a case. Okay, interesting, All right.

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Speaker 2: Well I'm throwst to be joined as always by my

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Speaker 2: co host mister Miles Gras.

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Speaker 3: Okay, so you did my AKA and I heard you

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Speaker 3: do this one on Friday, so allow me to do

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Speaker 3: it one more time.

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Speaker 1: Same, Miles and Jackie. Have you seen it yet?

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Speaker 4: Ooh it's just flying around La La La losing all

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Speaker 4: my jets.

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Speaker 5: Yeah.

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Speaker 4: Oh there's a wee and his saw spaced out. Oh, Jetty,

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Speaker 4: she's a really neat. She's gotta stell the use a

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Speaker 4: bomb and shoot so fanzy, you can't even be seen.

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Speaker 3: La La losing all my Jets, shout out to the

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Speaker 3: Department of Defense for losing all of our multi million

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Speaker 3: dollar killer fucking spacecrafts, and shout out to Johnny Davis

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Speaker 3: and Blinky Heck.

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Speaker 1: One more time. We're done more time.

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Speaker 2: Yeah, maybe we can like layer those because you went high,

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Speaker 2: I feel like I went a little bit lower.

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Speaker 1: Maybe maybe we could get a little little harmony harmony. Yeah.

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Speaker 2: Anyways, Miles, Yeah, you're thrilled to be joined in our

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Speaker 2: third seat Yes for today's expert episode by truly brilliant

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Speaker 2: guests who I felt especially stupid singing an AKA in

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Speaker 2: front of She's a research associate at the Lever Holme

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Speaker 2: Center for the Future of Intelligence, where she researches AI

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Speaker 2: from the perspective of gender studies, critical race theory, and

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Speaker 2: Asian diaspora studies. He is also a research fellow at

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Speaker 2: the AI Now Institute, the co editor of the upcoming

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Speaker 2: volume The Good Robot, Feminist Voices on the Future of Technology,

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Speaker 2: and the co host of the Wonderful Good Robot podcast.

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Speaker 2: Please welcome the brilliant, the talented doctor Kerrie mcinnarn.

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Speaker 1: Doctor.

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Speaker 5: Hey, thanks so much for having me.

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Speaker 1: Thank you so much for joining us.

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Speaker 2: How different has this been so far from what you

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Speaker 2: were expecting.

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Speaker 5: Oh my gosh, I don't have a song.

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Speaker 2: I was like, how do I say?

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Speaker 5: I definitely don't have a song. And I also don't

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Speaker 5: know who Johnny applesey it is. I grew up at

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Speaker 5: a New zeal edge and so I didn't have a

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Speaker 5: book of like historical figures. I had a book called

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Speaker 5: watch Out these Creatures, bite and Sting, and it was

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Speaker 5: like all the ways you could die being killed by

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Speaker 5: like a jelly fish or like an octopus. I may

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Speaker 5: leave you into Australia. And so it kind of like

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Speaker 5: traumatized me for the good from childhood. But it does

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Speaker 5: mean I didn't have the great apple seed song that you.

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Speaker 1: Just say, did you guys have apples?

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Speaker 2: Though, because I was under the impression that the only

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Speaker 2: reason I had apples was a Johnny apple seed. Yeah,

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Speaker 2: and the Lord and the Lord provided.

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Speaker 1: It's just all kiwis right down there.

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Speaker 5: Pretty much. Kiwis kiwi fruit, no apples, no songs, just

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Speaker 5: no music. It's just silent.

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Speaker 3: Is an extcute part of my ignorance is the kiwi

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Speaker 3: is that like a native New Zealand fruit.

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Speaker 5: I'm hoping so the kiwi is a native New Zealand bird.

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Speaker 5: And then the Kiwi fruit is what we call that,

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Speaker 5: like fuzzy brown fruit. Yeah. I feel like they kind

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Speaker 5: of mix up the two or they you know, they're

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Speaker 5: both brown and fuzzy and sort of bulbous.

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Speaker 1: Ignorance incarnate here, folks.

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Speaker 2: But if you do cut the bird in half, it

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Speaker 2: looks exactly like the kiwi fruit. Right, A lot of

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Speaker 2: people don't know that green with green with little.

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Speaker 1: Horton seed seed core. Yeah.

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Speaker 2: All right, doctor McNerney, we have you here today to

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Speaker 2: talk to us about AI. We've talked to last week's

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Speaker 2: episode about AI. We're thrilled to ask you our follow

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Speaker 2: up questions, but before we get to any of it,

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Speaker 2: we do like to get to know our guests a

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Speaker 2: little bit better and ask you what is something from

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Speaker 2: your search history.

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Speaker 5: This is pretty embarrassing. I just got a Nintendo and

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Speaker 5: I'm like finally entering my game Ago Era. I'm like,

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Speaker 5: this is gonna be so fun. But my whole such

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Speaker 5: history it's just me googling like very basic controls and

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Speaker 5: Zelda Breath of the Wilds. It's like, how do I jump?

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Speaker 5: Like it's really awful. I wait and look, it's like

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Speaker 5: how do I befriend a dog? Like how do I

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Speaker 5: climb a mountain? And so it's not going well, I

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Speaker 5: need to outsource my zilta plane to like children who

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Speaker 5: will be much better than me.

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Speaker 1: Wait, but is it? Is it?

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Speaker 3: You're not forcing you to give up, right, You're just

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Speaker 3: you're You're just acclimating to the new environment, right.

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Speaker 5: I mean the Kingdom is not going to be safe

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Speaker 5: whatever I'm meant to be doing. I'm just walking in

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Speaker 5: circles now for like an infinite amount of time. But

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Speaker 5: I'm having fun, which I guess is the main point.

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Speaker 1: Yeah, yeah, yeah, And wait, why did you or were you?

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Speaker 1: Were you like a game?

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Speaker 3: Did you play games in childhood and you're kind of

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Speaker 3: coming back or this is completely new, like this is

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Speaker 3: all new territory for you.

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Speaker 5: I did, but I was also bad in childhood, Like

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Speaker 5: this is a story of like oh, I used to

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Speaker 5: be like super into the game, and I like fell

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Speaker 5: out of touch, like how people were like amazing athletes

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Speaker 5: as children and then like pick it up again, like

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Speaker 5: I'm consistently awful again. So the such just three is

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Speaker 5: not a surprise.

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Speaker 1: Got it?

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Speaker 5: Got it?

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Speaker 3: I mean you were doing you know, worthwhile things like

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Speaker 3: thinking about how to ethically deal with all this technology.

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Speaker 3: While I was laughing my ass off playing Donkey Kong Country,

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Speaker 3: you being like, Aha, slapped the ground and bananas come out.

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Speaker 1: That's a little hack people didn't tell you about.

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Speaker 2: Our AI guests on last week's Expert episode was saying

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Speaker 2: the video games are one of the ways that he

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Speaker 2: thinks that the future, the future of AI is going

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Speaker 2: to impact our day to day lives. Is that is

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Speaker 2: that something that you think about as you're out there

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Speaker 2: kind of just walking in circles in Zelda Breath of

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Speaker 2: the Wild.

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Speaker 5: I mean, I feel like I performed so poorly they

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Speaker 5: probably think I am like an NPC. It's just like

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Speaker 5: that is not a person with free will like that

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Speaker 5: following a fox.

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Speaker 1: Around It just it just rotated seven hundred and twenty

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Speaker 1: degrees over and over for no reason. I guess.

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Speaker 2: So have they made games more fun for people who

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Speaker 2: suck at video games? That that's the innovation that I'm

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Speaker 2: looking for, because I was never very good at them. Yeah,

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Speaker 2: I liked playing them, I like hearing about them. I

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Speaker 2: think I'm ready to enter my gamer girl era and

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Speaker 2: come back.

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Speaker 1: But yeah you should. I mean, yeah, look, Jack.

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Speaker 3: They already know you as the switch God because you've

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Speaker 3: been on Nindado so much. The controllers basically fuse to

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Speaker 3: your body, but there are like a lot of new

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Speaker 3: or like new like the new Star Wars game. You

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Speaker 3: could just set it to like, Man, I'm not trying

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Speaker 3: to do all this like fancy shit. I just want

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Speaker 3: to beat people up or just mash the keypad over

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Speaker 3: and over and win like that, and you can. You

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Speaker 3: can't do that cause I think you know it is

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Speaker 3: like it is about being able to play at different

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Speaker 3: skill levels rather than like, oh, you don't know how

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Speaker 3: to multi use the force while you know you're doing

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Speaker 3: your melee attacks.

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Speaker 1: Come on, some of us just want to be the kids.

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Speaker 3: Unraveled is good for that too, of like a fun

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Speaker 3: video game that is not like really requiring you to

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Speaker 3: have like these sort of highly developed video game skills

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Speaker 3: and it's an easy play.

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Speaker 2: Nice doctor mcinnerney, What is something you think is overrated?

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Speaker 5: I feel like I'm not gonna win myself any friends.

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Speaker 5: It'll be like how let loose friends and stop influencing people.

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Speaker 5: But all the Disney live action movies, I've hit a

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Speaker 5: stage where I'm like, no more live act since The

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Speaker 5: Little Mermage is beautiful. I love the original Cinderella, but

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Speaker 5: you know I don't I don't want to see more

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Speaker 5: and more of like the same movies again. Like it

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Speaker 5: makes me a bit sad and like what other stories

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Speaker 5: could be told if it weren't like always making these

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Speaker 5: live actions. But you know, maybe I'll be proven like

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Speaker 5: totally wrong and the next ones will be amazing, but

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Speaker 5: I'm ready for something else.

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Speaker 3: Just yeah, based on everyone's first like sort of reaction

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Speaker 3: like oh you saw how it wasn't like yeah it

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Speaker 3: No one was like it was so good, I'm gonna

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Speaker 3: see it nine times. Everyone just kind of I think

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Speaker 3: they're they have to reconcile like their love for the

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Speaker 3: original source material with that and.

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Speaker 1: Not fully be like I didn't like it. They're like yeah,

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Speaker 1: I mean it's it's yeah, it's interesting.

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Speaker 3: You know.

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Speaker 2: It's like their voice just goes up.

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Speaker 1: Yeah, you know, it was like.

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Speaker 2: I spent my afternoon going to that.

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Speaker 1: I think that's right.

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Speaker 2: I've never been more confident in a prediction about the

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Speaker 2: future of a like subgenre of movies than in saying

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Speaker 2: that they're not going to suddenly figure something out about

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Speaker 2: the Disney Live action reskins of the animations, like we

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Speaker 2: know what the original cartoons look like and what happens

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Speaker 2: in them.

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

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Speaker 2: I can't imagine a version like what they're gonna pull

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Speaker 2: out where we're.

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Speaker 1: Like, oh no, that.

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Speaker 2: Is not did not see that one coming? Whoa that

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Speaker 2: bear is talking and singing? Hold on, oh they already

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

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

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Speaker 2: and ever since I feel like, yeah, been diminishing returns Man,

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Speaker 2: although I didn't see The Little Mermaid, so I cannot

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Speaker 2: speak to that one.

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Speaker 1: Is you what's your favorite genre of film though, doctor Kerry?

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Speaker 5: Ooh? I mean okay, so I really have a soft

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Speaker 5: spot for like old school superhero movies. At my most

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Speaker 5: recent most amazing film I film was the latest into

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Speaker 5: the Spider Verse film what was it? Spider the Spider Verse?

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Speaker 5: And I just I love animation and that film. It's

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Speaker 5: so witty and the visuals are extraordinary, just great films.

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Speaker 5: So I oracle about how much I love these films

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Speaker 5: at work and everyone has to deal with me being like,

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Speaker 5: go watch it, So anyone listening, go watch it.

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Speaker 3: Amazing favorite favorite Spider character, Spider person in the universe.

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Speaker 5: Oh my goodness, I can't remember the name of it,

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Speaker 5: but the Light really like the really like dark noir

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Speaker 5: run for the first one. He's like from like all

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Speaker 5: those like nineteen thirties kind of like mystery films, you know,

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Speaker 5: all in black and white. I kind of love that.

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Speaker 1: Yeah, yeah, it was it just Spider Man Noirs. Yeah,

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

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Speaker 1: I'm sorry spider Man nor Nor.

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Speaker 2: What is when you say old school superhero movies? Do

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Speaker 2: you mean like Christopher Reeve's Superman or are you talking about.

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Speaker 1: Like the first Iron Man? The first Iron Man.

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Speaker 5: I think it's almost like less like specific films. I love,

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Speaker 5: like almost films that have that like really cheesy like

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Speaker 5: origin coming of age story, Like you know, it's just

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Speaker 5: really just about like they're discovering themselves and it's such

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Speaker 5: a simple narrative, and like I feel like I should

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Speaker 5: crave more complexity than that, and I should want it

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Speaker 5: to be more nuanced stories, but sometimes it's just really satisfying,

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Speaker 5: and you want this clean cut narrative of this like

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Speaker 5: good guy to beating all these bad guys. When I

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Speaker 5: want to relax, I really enjoy that. In my day job,

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Speaker 5: I think about lots of like complexity and nuances and

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Speaker 5: like what we do with the future of society, and

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Speaker 5: so you know, sometimes I find it very relaxing just

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Speaker 5: to be like soundless.

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Speaker 3: Yeah, you're like, yeah, I love a Joseph Campbell type flick.

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Speaker 1: Just give me that hero's journey in everything it's.

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Speaker 5: Gonna be okay, Yeah, it's gonna go wrong.

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Speaker 1: Challenge your old self to become your new self.

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Speaker 2: Yeah, but DOCC is an example of what we're facing

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Speaker 2: with AI Like in the immediate future, I think we

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Speaker 2: can all have on that Spiderman two.

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Speaker 3: In a way, You're like, it's giving me the equivalent

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Speaker 3: of like eight arms.

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Speaker 2: In a way you're like, oh boy, what's something you

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Speaker 2: think is underrated?

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Speaker 5: Oh? Underrated? I mean I'm a big like cozy knight

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Speaker 5: in person, Like I want to be one of those

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Speaker 5: fun party people who are like, you know, is out

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Speaker 5: raving are Realistically at ten pm, I'm like, the day

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Speaker 5: is over. I'm in bed, like I think a cozy

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Speaker 5: Knight in especially autumn is coming, Like that's just my

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Speaker 5: happy place, me at home, my husband. Life is good.

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Speaker 3: Yeah, I like that. That's my That's my whole thing.

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Speaker 3: Especially we don't get seasons here, so the second there's

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Speaker 3: like just the feeling of like a chill.

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Speaker 1: I'm like, I need to start nine fires and just

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Speaker 1: being near them.

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Speaker 2: And that is the problem that Miles has, and he's

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Speaker 2: working on his therapy.

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Speaker 3: I guess legally it's called arson or something, but what

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Speaker 3: I say is I just want everyone to be cozy

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Speaker 3: around the Greater Los Angeles metropolitan area. I'm yeah, I

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Speaker 3: cannot go within four miles of the Angela's National Forest.

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

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Speaker 3: I love coziness. I love a are you are you cozy?

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Speaker 3: Are you summertime Jack? If you had to pick between,

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Speaker 3: would you rather be summertime Jack or cozy Jack?

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Speaker 1: I do.

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Speaker 2: I do love a summer especially I'm in my swimming

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Speaker 2: phase where I just I really like getting in a

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Speaker 2: body of water, get running into the ocean, even when

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Speaker 2: it's a little cold ocean. And yeah, so I think

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Speaker 2: I'm like in a summer phase, but I yeah, I

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Speaker 2: am feeling the need to get cozy.

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Speaker 3: Yeah, I'll always take cozy over summer.

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Speaker 1: That's just me.

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Speaker 2: I know, you you it's it's like you fetishize winter.

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Speaker 1: I do.

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Speaker 3: It's problematic, like that's why I would only date like

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Speaker 3: like women from the Northeast. I feel like you're dating

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Speaker 3: me like for this like exotic sort of life, you think,

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Speaker 3: I like, what's it like with the snow.

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Speaker 2: You're appropriating Minnesota, like Minnesota culture, right, Yeah, well, hey,

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Speaker 2: look we're all trying.

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Speaker 1: Hey, I heard that.

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Speaker 2: He said, Hey, all right, let's take a quick break

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Speaker 2: and we'll come right right back and talk about AI.

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Speaker 2: We'll be right back, and we're back. We're and doctor

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Speaker 2: McNerney as mentioned, I am an idiot on this AI stuff.

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Speaker 2: I think I generally have like my version of AI

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Speaker 2: up until last week. I guess like researching for our

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Speaker 2: last expert episode was what I had read in you know,

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Speaker 2: mainstream articles that went viral and films mood like Hollywood films,

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Speaker 2: and then like messing around with open AI and or

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Speaker 2: chat GPT, So I had this kind of disconnect in

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Speaker 2: my mind where it was like, from an outsider's perspective,

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Speaker 2: we have this C plus level like copywriter thing with

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Speaker 2: like in chat GPT, GPT for and then like the

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Speaker 2: Godfather of AI, who I'm just trusting people is the

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Speaker 2: godfather of AI. But that's what everyone uses, that same phrase,

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Speaker 2: like the godfather of AI just quit Google and says

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Speaker 2: we're all fucked in the next couple of years. And

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Speaker 2: I think it's confusing to me because I don't know exactly,

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Speaker 2: like I can't even like picture the way how he

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Speaker 2: thinks we're fucked. And there was this letter that was like,

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Speaker 2: we need to pause development on AI for in the

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Speaker 2: near few sure, And I guess I'm just curious to

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Speaker 2: hear your perspective on that pause letter and what the

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Speaker 2: kind of dangers of AI are in the near future.

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Speaker 3: Yeah, because to Jack's point two, Also, we were talking

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Speaker 3: with Juao Sadok last week at NYU about it, and

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Speaker 3: like at the end of it, we're like, okay, so

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Speaker 3: it's not sky Net right from terminator and they're like,

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Speaker 3: oh great, But then we realize there's a raft of

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Speaker 3: other things that come along with just not being the terminator.

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Speaker 3: So yeah, that's I'm also from a similar perspective where

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Speaker 3: I always assume sky Net.

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Speaker 5: Yeah, I mean, I think you're totally not alone in

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Speaker 5: the dominant of ideas like sky Nee the terminator because

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Speaker 5: so much of our cultural framework for understanding what AI

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Speaker 5: is comes from a very narrow set of movie It's

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Speaker 5: like the terminator, like the matrix, which always positions AI

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Speaker 5: is something that's going to dominate us. It's going to

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Speaker 5: take over the world, and it's going to control us.

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Speaker 5: And it's important too, I think highlight that that's definitely

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Speaker 5: not the only ideas that we have about AI. We've

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Speaker 5: got thousands of years of thinking about our relationship with

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Speaker 5: intelligent machines, and there's a lot of different cultural traditions

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Speaker 5: that have really different ways of thinking about our relationships

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Speaker 5: with AI and intelligent machines that could be much more positive,

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Speaker 5: much more harmonious. And so I do think our immediacy

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Speaker 5: of jumping to this idea of sky net is reflective

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Speaker 5: of very much where we are right now. Right I'm

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Speaker 5: in the UK, you're in the US. These are countries

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Speaker 5: that have a really long history of thinking about AI

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Speaker 5: and very binary terms. So yeah, I think it's important

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Speaker 5: that we think about these long term risks of AI.

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Speaker 5: And you mentioned the pause letter calling for a wholt

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Speaker 5: to generating more large language models like chat GPT until

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Speaker 5: it had a bit more of a moment to think

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Speaker 5: about to the long term consequences of these models. But

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Speaker 5: I think it's really important not just to think of

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Speaker 5: the long term risks, but to think about which long

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Speaker 5: term risks we prioritize, because I think the sky neet

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Speaker 5: terminating fantasy eats up a lot of oxygen about how

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Speaker 5: we talk about AI's risk. But there's a lot of

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Speaker 5: different risks that AI poses. So another long term risk

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Speaker 5: that we don't talk about very much at all is

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Speaker 5: the climate cost of AI. Right, because AI it's hugely

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Speaker 5: energy intensive. Data centers require a huge amount of water

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Speaker 5: to function, and we have this massive problem of e

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Speaker 5: waste produced by a lot of electronic systems. But that

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Speaker 5: long term problem of climate crisis is much less exciting.

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Speaker 5: It's really scary, it's really grounded, and a lot of

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Speaker 5: our experiences and so it just doesn't seem to get

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Speaker 5: as much airtime. So that's something that I think is

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Speaker 5: really important, is changing the conversation a bit to say, Okay,

435
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Speaker 5: it's sometimes interesting, sometimes scary to think about the terminator option,

436
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Speaker 5: but what are some of the other long term options

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Speaker 5: that could really shape our lives.

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Speaker 2: Yeah, like the degree to which the deck is being

439
00:22:50,240 --> 00:22:54,320
Speaker 2: stacked towards the terminator option was surprising to me, Like

440
00:22:54,400 --> 00:22:57,320
Speaker 2: we dug in last week a little bit to the

441
00:22:57,359 --> 00:23:00,920
Speaker 2: two stories I had always heard that are like kind

442
00:23:00,920 --> 00:23:04,720
Speaker 2: of put into the terminator version of AI taking over

443
00:23:04,760 --> 00:23:05,440
Speaker 2: a category.

444
00:23:05,840 --> 00:23:09,040
Speaker 1: There's the AI that killed.

445
00:23:08,760 --> 00:23:12,439
Speaker 2: A person in a military exercise that decided to like

446
00:23:12,800 --> 00:23:16,959
Speaker 2: eliminate its controller, and then there was the AI that

447
00:23:17,080 --> 00:23:20,600
Speaker 2: hired a task rabbit to pass the CAPSHA test, And

448
00:23:20,800 --> 00:23:24,119
Speaker 2: like in both cases those are like the AI that

449
00:23:24,240 --> 00:23:26,719
Speaker 2: killed a person in the military exercise, like that was

450
00:23:27,680 --> 00:23:30,560
Speaker 2: somebody claiming that, and then when they went back they

451
00:23:30,560 --> 00:23:32,879
Speaker 2: were like, oh, I was just saying it could hypothetically

452
00:23:32,960 --> 00:23:33,360
Speaker 2: do that.

453
00:23:33,640 --> 00:23:35,360
Speaker 1: It was a thought experiment.

454
00:23:35,480 --> 00:23:38,320
Speaker 2: Yeah, it was a thought experiment of what an AI

455
00:23:38,480 --> 00:23:43,040
Speaker 2: could do in the right circumstances. And the task rabbit

456
00:23:43,119 --> 00:23:47,040
Speaker 2: one was more similar to the self driving car Elon

457
00:23:47,160 --> 00:23:48,960
Speaker 2: Musk thing, where it was just there was a human

458
00:23:49,080 --> 00:23:52,760
Speaker 2: prompting it to do that thing that seems creepy to

459
00:23:52,880 --> 00:23:55,639
Speaker 2: us when we like start thinking about, oh, it's like

460
00:23:55,720 --> 00:24:00,440
Speaker 2: scheming to get loose and get overcome the things that

461
00:24:00,480 --> 00:24:05,080
Speaker 2: we're using to keep it hemmed in. So it does

462
00:24:05,280 --> 00:24:09,679
Speaker 2: feel like there is an incentive structure set up for

463
00:24:10,640 --> 00:24:13,400
Speaker 2: the people in charge of some of these major AI

464
00:24:13,520 --> 00:24:18,119
Speaker 2: companies to get us to believe that shit, like to

465
00:24:18,240 --> 00:24:22,800
Speaker 2: think to only focus on the AI v. Humanity, Like

466
00:24:22,920 --> 00:24:26,520
Speaker 2: AI gets loose of its control of our controls for

467
00:24:26,560 --> 00:24:30,840
Speaker 2: it and takes over and starts like killing people version

468
00:24:30,880 --> 00:24:33,480
Speaker 2: of it. I'm just curious, like what are your thoughts on,

469
00:24:33,560 --> 00:24:38,320
Speaker 2: Like why why why are they incentivized to do that

470
00:24:38,440 --> 00:24:40,480
Speaker 2: when it would seem like, well, you don't want to

471
00:24:40,480 --> 00:24:43,280
Speaker 2: make it. You don't want it to seem like this

472
00:24:43,359 --> 00:24:46,080
Speaker 2: self driving car will take over and.

473
00:24:46,440 --> 00:24:48,520
Speaker 1: Start kill your family.

474
00:24:48,760 --> 00:24:51,879
Speaker 2: Yeah, start killing your family. It's so powerful. But it

475
00:24:51,920 --> 00:24:56,520
Speaker 2: seems like with AI, they're more willing to buy into

476
00:24:56,560 --> 00:25:00,639
Speaker 2: that fantasy and like have that fantasy projected to people

477
00:25:00,680 --> 00:25:05,320
Speaker 2: who are not as closely tied to the ins and

478
00:25:05,359 --> 00:25:06,280
Speaker 2: outs of the industry.

479
00:25:06,720 --> 00:25:09,200
Speaker 5: Yeah, I mean, I think that's such an important point

480
00:25:09,280 --> 00:25:12,480
Speaker 5: because it's a weird thing about the AI industry, right,

481
00:25:12,520 --> 00:25:14,639
Speaker 5: Like you would never have this kind of hypewave around

482
00:25:14,640 --> 00:25:17,240
Speaker 5: something like broccoli where you say, oh, the broccoli, if

483
00:25:17,280 --> 00:25:18,919
Speaker 5: you eat it could kill you or it could like

484
00:25:19,160 --> 00:25:21,560
Speaker 5: transform the world, and then you wouldn't expect that to

485
00:25:21,600 --> 00:25:23,560
Speaker 5: somehow get people to buy a lot more broccoli and

486
00:25:23,680 --> 00:25:26,000
Speaker 5: just feel like I don't want to eat broccoli.

487
00:25:26,040 --> 00:25:31,360
Speaker 2: Now that's so fucking good and powerful. If you explode

488
00:25:31,600 --> 00:25:33,000
Speaker 2: like maybe.

489
00:25:33,840 --> 00:25:36,639
Speaker 5: Makes it even worse brocci that you can then also

490
00:25:36,760 --> 00:25:40,520
Speaker 5: eat you know what broccoli would be doing. But I

491
00:25:40,560 --> 00:25:43,200
Speaker 5: do think that we see this real cultivation of hype

492
00:25:43,359 --> 00:25:46,520
Speaker 5: around AI and that a lot of firms explicitly use

493
00:25:46,560 --> 00:25:48,680
Speaker 5: that to sell their products, and it gets people interested

494
00:25:48,720 --> 00:25:51,040
Speaker 5: in it because on the one hand, people are really

495
00:25:51,080 --> 00:25:54,520
Speaker 5: scared about the long term and short term impacts of AI.

496
00:25:54,960 --> 00:25:56,800
Speaker 5: On the other hand, it also scared then though, of

497
00:25:56,840 --> 00:25:59,359
Speaker 5: getting left behind. So you see more and more firms saying, well,

498
00:25:59,440 --> 00:26:02,440
Speaker 5: now I've got to buy the latest generative AI tool

499
00:26:02,520 --> 00:26:04,640
Speaker 5: so that I look really tech savvy and I look

500
00:26:04,720 --> 00:26:07,760
Speaker 5: peck forward and I look futuristic, and so it's part

501
00:26:07,800 --> 00:26:10,119
Speaker 5: of this bigger hype cycle, I think, to draw a

502
00:26:10,160 --> 00:26:12,760
Speaker 5: lot of tension towards their products, but also to make

503
00:26:12,800 --> 00:26:16,000
Speaker 5: them seem like this really edgy, desirable thing. But I

504
00:26:16,000 --> 00:26:18,480
Speaker 5: think what's also interesting about both the stories that you

505
00:26:18,600 --> 00:26:20,439
Speaker 5: raise is when you looked under the hood, there was

506
00:26:20,520 --> 00:26:23,280
Speaker 5: human labor involved, right There were people who were really

507
00:26:23,320 --> 00:26:27,040
Speaker 5: central to something that was attributed to an AI, And

508
00:26:27,240 --> 00:26:29,359
Speaker 5: I think that's a really common story we see with

509
00:26:29,400 --> 00:26:32,040
Speaker 5: a lot of the type around AI is often the

510
00:26:32,080 --> 00:26:35,040
Speaker 5: way we tell those stories. Eraises a very real human

511
00:26:35,160 --> 00:26:38,440
Speaker 5: labor that drives those products, whether it's the artists who

512
00:26:38,480 --> 00:26:42,240
Speaker 5: originally made the images that trained the generative AI through today,

513
00:26:42,280 --> 00:26:45,080
Speaker 5: the labelers all sorts of people who are really central

514
00:26:45,119 --> 00:26:46,800
Speaker 5: to those processes.

515
00:26:46,720 --> 00:26:50,159
Speaker 3: Right, And I know, like in your episode of The

516
00:26:50,200 --> 00:26:53,520
Speaker 3: Good Robot when you're discussing the pause letter, you know,

517
00:26:53,600 --> 00:26:57,680
Speaker 3: I think that the I think the version that we

518
00:26:57,760 --> 00:27:00,480
Speaker 3: see as like sort of these short term threats, at

519
00:27:00,560 --> 00:27:02,439
Speaker 3: least in the most immediate way is like for me

520
00:27:02,640 --> 00:27:05,359
Speaker 3: working in and around entertainment and people who work in

521
00:27:05,440 --> 00:27:09,200
Speaker 3: advertising and seeing like an uptake in that section, I go, Okay,

522
00:27:09,200 --> 00:27:12,560
Speaker 3: that's easy. Like I can see how a company immediately goes, yeah,

523
00:27:12,560 --> 00:27:14,640
Speaker 3: it's a tool, and then suddenly it's like and now

524
00:27:14,640 --> 00:27:16,680
Speaker 3: you're on your ass because we'll just use the tool

525
00:27:16,760 --> 00:27:18,399
Speaker 3: now and we don't even need a person to prompt it,

526
00:27:18,520 --> 00:27:20,840
Speaker 3: or we need many we just need fewer people to

527
00:27:20,880 --> 00:27:23,359
Speaker 3: operate it. So to me, I'm like, Okay, that's an

528
00:27:23,520 --> 00:27:26,320
Speaker 3: obvious sort of thing I can see, like on the horizon.

529
00:27:26,680 --> 00:27:28,560
Speaker 3: And you did talk about, well, there was a lot

530
00:27:28,560 --> 00:27:31,200
Speaker 3: of talk of these sort of long term existential or

531
00:27:31,280 --> 00:27:33,919
Speaker 3: quote unquote existential threats that there were a lot of

532
00:27:34,160 --> 00:27:37,240
Speaker 3: things in the short term that we're actually ignoring. What

533
00:27:37,480 --> 00:27:39,800
Speaker 3: are those sort of things that we need to bring

534
00:27:39,840 --> 00:27:41,720
Speaker 3: a little bit more awareness to, Like I know you

535
00:27:41,760 --> 00:27:45,720
Speaker 3: mentioned the climate, and I look at it from my perspective,

536
00:27:45,720 --> 00:27:48,280
Speaker 3: I see like the just massive job loss that could happen.

537
00:27:48,880 --> 00:27:50,760
Speaker 3: But what are sort of like the more short term

538
00:27:50,760 --> 00:27:54,200
Speaker 3: things that kind of maybe are less sexy or interesting

539
00:27:54,240 --> 00:27:56,160
Speaker 3: to the people who just want to write about killer

540
00:27:56,280 --> 00:27:57,280
Speaker 3: terminators and things.

541
00:27:58,680 --> 00:28:01,439
Speaker 5: Yeah, I mean, I think less sexy is exactly the

542
00:28:01,520 --> 00:28:03,280
Speaker 5: right phrase for this, which is a lot of the

543
00:28:03,280 --> 00:28:06,680
Speaker 5: short term issues. I very much about entrenching existing forms

544
00:28:06,680 --> 00:28:09,680
Speaker 5: of inequality and making them worse. And that's often something

545
00:28:09,760 --> 00:28:12,040
Speaker 5: people don't really want to hear about because they don't

546
00:28:12,040 --> 00:28:15,280
Speaker 5: want to acknowledge its inequalities, or because it takes away

547
00:28:15,280 --> 00:28:17,600
Speaker 5: from the shiny units of AI. It makes it very

548
00:28:17,680 --> 00:28:21,000
Speaker 5: much like a lot of other technological revolutions that we've

549
00:28:21,040 --> 00:28:23,280
Speaker 5: already seen, and that's super boring. Like you don't want

550
00:28:23,320 --> 00:28:26,199
Speaker 5: to hear about how the wheel somehow brought about some

551
00:28:26,280 --> 00:28:32,880
Speaker 5: kind of inequality the big heart take for today. But yeah,

552
00:28:32,880 --> 00:28:34,760
Speaker 5: I mean something that I look at, for example, are

553
00:28:34,760 --> 00:28:38,760
Speaker 5: like very mundane but important technologies, like technologies used in

554
00:28:38,800 --> 00:28:41,640
Speaker 5: recruitment and hiring. So I look at AI powered video

555
00:28:41,720 --> 00:28:45,200
Speaker 5: interview tools and look at how that affects people's particular

556
00:28:45,680 --> 00:28:47,880
Speaker 5: you know, likelihood of being employed and how they go

557
00:28:47,920 --> 00:28:50,719
Speaker 5: through the workforce, and yep, it's less exciting seeming than

558
00:28:50,760 --> 00:28:53,280
Speaker 5: the terminator. But again, when you look under the hood

559
00:28:53,280 --> 00:28:55,840
Speaker 5: and dig into them, you're like, oh wow, this could

560
00:28:55,840 --> 00:28:59,760
Speaker 5: actually really really compound inequalities that we see in the

561
00:28:59,800 --> 00:29:02,280
Speaker 5: work force under the guise of the idea that these

562
00:29:02,280 --> 00:29:04,480
Speaker 5: tools are going to make hiring more fair. And that's

563
00:29:04,520 --> 00:29:06,040
Speaker 5: a massive problem.

564
00:29:06,040 --> 00:29:08,280
Speaker 3: Right, so because like the idea with those hiring tools

565
00:29:08,400 --> 00:29:10,719
Speaker 3: like it will actually take away these sort of like

566
00:29:10,760 --> 00:29:14,320
Speaker 3: demographic cues that someone might use to like you know,

567
00:29:14,800 --> 00:29:17,400
Speaker 3: they apply their own biases to So in fact, it

568
00:29:17,440 --> 00:29:20,280
Speaker 3: is the most equitable way to hire. But is it

569
00:29:20,320 --> 00:29:23,680
Speaker 3: because of just the kinds of people that are creating

570
00:29:23,760 --> 00:29:26,600
Speaker 3: these sort of systems, Because they tends to be a

571
00:29:26,640 --> 00:29:29,840
Speaker 3: bit one note that that's inherently where like sort of

572
00:29:29,840 --> 00:29:31,520
Speaker 3: that like it begins to wobble a bit.

573
00:29:31,880 --> 00:29:34,000
Speaker 5: It's a mixture. So of course, yes, the lack of

574
00:29:34,000 --> 00:29:37,040
Speaker 5: diversity in the AI industry is like very stark. It's

575
00:29:37,080 --> 00:29:40,640
Speaker 5: also sadly in the uk AN industry, where for example,

576
00:29:40,640 --> 00:29:43,960
Speaker 5: women's representation is actually getting worse, not better. So that's

577
00:29:44,000 --> 00:29:45,840
Speaker 5: a sad slap in the face for a lot of

578
00:29:45,840 --> 00:29:49,240
Speaker 5: the progress narrative that we want to see. But sometimes

579
00:29:49,280 --> 00:29:52,680
Speaker 5: it's not even necessarily that the people creating these tools

580
00:29:52,720 --> 00:29:55,719
Speaker 5: have bad intentions, maybe not even that they're using faulty

581
00:29:55,800 --> 00:29:58,160
Speaker 5: data sets or biased data sets. These are two of

582
00:29:58,200 --> 00:30:00,760
Speaker 5: the really big problems that are flagged, But sometimes the

583
00:30:00,880 --> 00:30:04,800
Speaker 5: underlying idea behind their product is just focus. Like it's

584
00:30:04,920 --> 00:30:07,800
Speaker 5: just a really bad concept, and yet somehow it gets

585
00:30:07,840 --> 00:30:10,480
Speaker 5: brought to market again because of all this hype around AI.

586
00:30:11,000 --> 00:30:13,360
Speaker 5: So with the video interview tools that we look at,

587
00:30:13,400 --> 00:30:16,480
Speaker 5: for example, they basically claim that they can discern a

588
00:30:16,600 --> 00:30:20,320
Speaker 5: candidate's personality from their face and from their words, so

589
00:30:20,360 --> 00:30:22,920
Speaker 5: how they talk, how they move, They can decide how

590
00:30:22,960 --> 00:30:25,800
Speaker 5: extroverted you are, or how you know open you are,

591
00:30:25,880 --> 00:30:28,480
Speaker 5: how neurotic you are, how conscientious you are, all these

592
00:30:28,520 --> 00:30:33,360
Speaker 5: different markets of personality, to which I would say, firstly, no,

593
00:30:33,840 --> 00:30:36,360
Speaker 5: there's absolutely no when AI can do that. This is

594
00:30:36,560 --> 00:30:40,160
Speaker 5: just a very old kind of racial pseudoscience making its

595
00:30:40,160 --> 00:30:43,040
Speaker 5: way back into the mainstream, saying Okay, we can totally

596
00:30:43,040 --> 00:30:46,200
Speaker 5: guess your personality from your face. Like it's like your

597
00:30:46,240 --> 00:30:49,400
Speaker 5: friends looking at someone's profile picture on like Pinder or

598
00:30:49,440 --> 00:30:52,120
Speaker 5: whatever and being like they look like they'd be really

599
00:30:52,200 --> 00:30:55,000
Speaker 5: fun at a party. Like it's about that level of accuracy,

600
00:30:55,600 --> 00:30:58,560
Speaker 5: you know. And then second, is that even a good

601
00:30:58,560 --> 00:31:00,720
Speaker 5: way of judging if someone's gonna be good for a job?

602
00:31:00,920 --> 00:31:03,720
Speaker 5: Like how extra vetted do you want to person in

603
00:31:03,760 --> 00:31:06,080
Speaker 5: a job to be? Maybe in your job that's really

604
00:31:06,160 --> 00:31:09,040
Speaker 5: really helpful In my job, I don't know how helpful

605
00:31:09,080 --> 00:31:10,880
Speaker 5: it is. So there's just kind of a lot of

606
00:31:11,200 --> 00:31:14,320
Speaker 5: flaws at the very you know, bottom of these products

607
00:31:14,320 --> 00:31:16,000
Speaker 5: that we should be worried about.

608
00:31:16,360 --> 00:31:21,239
Speaker 2: Just like a C minus level job hiring process. Like

609
00:31:21,280 --> 00:31:23,640
Speaker 2: that's what I feel like so many of the things,

610
00:31:23,680 --> 00:31:26,200
Speaker 2: like when you get down to them and see them

611
00:31:26,240 --> 00:31:30,240
Speaker 2: in action, they're like not that good. Like it does

612
00:31:30,320 --> 00:31:33,400
Speaker 2: feel like the whole thing is being hyped to a

613
00:31:33,440 --> 00:31:36,600
Speaker 2: large degree, And like that's something I heard from somebody

614
00:31:37,000 --> 00:31:40,640
Speaker 2: I know who like works in you know, like all

615
00:31:40,680 --> 00:31:43,360
Speaker 2: of my friends who work in finance, or like any

616
00:31:43,400 --> 00:31:45,760
Speaker 2: of those things, Like my brain shuts off when he

617
00:31:45,800 --> 00:31:49,160
Speaker 2: starts talking about what he does, but he was saying,

618
00:31:49,680 --> 00:31:52,000
Speaker 2: like he pays attention to the market, and he was saying,

619
00:31:52,040 --> 00:31:55,720
Speaker 2: there's like a big thing propping up the stock market

620
00:31:55,840 --> 00:31:59,000
Speaker 2: right now is AI. And it really is like that,

621
00:31:59,000 --> 00:32:02,320
Speaker 2: that's where so much of people's wealth is tied up,

622
00:32:02,320 --> 00:32:04,920
Speaker 2: as in like the stock market, and it's just tied

623
00:32:05,000 --> 00:32:08,800
Speaker 2: to like what you can get people excited about in

624
00:32:08,840 --> 00:32:11,760
Speaker 2: a lot of cases. So it really like that from

625
00:32:11,800 --> 00:32:16,520
Speaker 2: that perspective, the incentive structure makes sense, Like you want

626
00:32:16,560 --> 00:32:20,960
Speaker 2: people talking about how your AI technology can do all

627
00:32:21,000 --> 00:32:25,680
Speaker 2: these amazing things because that literally makes you have more

628
00:32:25,720 --> 00:32:28,440
Speaker 2: money than you would have if they knew the truth.

629
00:32:28,240 --> 00:32:31,760
Speaker 3: About your your product without being like yeah, wait, how

630
00:32:31,760 --> 00:32:35,160
Speaker 3: many seven fingered Trump pictures can we create?

631
00:32:35,320 --> 00:32:39,280
Speaker 1: And right be like yeah, man, fucking millions into this.

632
00:32:39,720 --> 00:32:40,640
Speaker 2: Yeah yeah.

633
00:32:40,720 --> 00:32:43,440
Speaker 5: I mean that's kind of something that's really come out

634
00:32:43,480 --> 00:32:45,560
Speaker 5: of the last few years is how many fans just

635
00:32:45,680 --> 00:32:47,920
Speaker 5: use the label of AI to get funding. Like I

636
00:32:47,920 --> 00:32:49,560
Speaker 5: think there was a study a couple of years ago

637
00:32:49,600 --> 00:32:54,320
Speaker 5: that said of European AI startups didn't use AI, which

638
00:32:54,360 --> 00:32:55,720
Speaker 5: I point, you're like, well, what are you doing?

639
00:32:58,520 --> 00:33:01,520
Speaker 2: Well, we could though, this podcast is actually an AI

640
00:33:01,600 --> 00:33:05,880
Speaker 2: podcast because eventually it could use AI. And before we

641
00:33:05,880 --> 00:33:09,560
Speaker 2: were recording, Miles was actually putting in. He asked an

642
00:33:09,560 --> 00:33:14,360
Speaker 2: AI to pitch him an episode of Friends in which

643
00:33:14,400 --> 00:33:17,760
Speaker 2: the cast and you know, the people on the the

644
00:33:17,840 --> 00:33:21,080
Speaker 2: friends on the show deal with the fallout from the

645
00:33:21,120 --> 00:33:22,120
Speaker 2: events of nine to eleven.

646
00:33:22,600 --> 00:33:25,160
Speaker 1: And it wouldn't do it. So we can't.

647
00:33:24,960 --> 00:33:28,640
Speaker 2: Quite claim that we are an AI podcast yet, but

648
00:33:28,640 --> 00:33:29,760
Speaker 2: but we did it.

649
00:33:29,840 --> 00:33:32,080
Speaker 3: Did do it when I said, do it, pitch me

650
00:33:32,120 --> 00:33:36,040
Speaker 3: an episode where Joey and Monica drive Uber from Yeah,

651
00:33:36,080 --> 00:33:36,840
Speaker 3: and it did.

652
00:33:36,800 --> 00:33:39,600
Speaker 1: So clearly it because you can see where these guardrails out.

653
00:33:39,600 --> 00:33:42,240
Speaker 1: They're like, don't do nine to eleven stuff, though that's

654
00:33:43,000 --> 00:33:43,520
Speaker 1: right now.

655
00:33:44,000 --> 00:33:46,840
Speaker 2: But I mean, so yeah, I think there's two things

656
00:33:46,880 --> 00:33:51,880
Speaker 2: we're talking about here, Like from from one perspective, like, yes,

657
00:33:51,920 --> 00:33:55,040
Speaker 2: you could put it in the category of like, well, yes,

658
00:33:55,120 --> 00:34:01,360
Speaker 2: the wheel makes racism or colonialism more frictionless is a

659
00:34:01,360 --> 00:34:04,120
Speaker 2: word that gets used a lot, but like literally in

660
00:34:04,120 --> 00:34:07,000
Speaker 2: the case of the wheel, frictionless. But AI and like

661
00:34:07,040 --> 00:34:11,040
Speaker 2: a lot of technology is designed to make groups of

662
00:34:11,120 --> 00:34:13,799
Speaker 2: people and like our interactions and the things that make

663
00:34:13,840 --> 00:34:16,680
Speaker 2: people money more frictionless. And that's something that you guys

664
00:34:16,719 --> 00:34:21,520
Speaker 2: have talked about on recent episodes of Good Robot, Like,

665
00:34:22,120 --> 00:34:24,279
Speaker 2: there's this one example that really jumped out to me

666
00:34:24,440 --> 00:34:26,759
Speaker 2: that I think was from your most recent episode, or

667
00:34:26,760 --> 00:34:30,720
Speaker 2: at least the one that's up most recently right now

668
00:34:31,080 --> 00:34:34,759
Speaker 2: as we're recording this, where you guys were talking about

669
00:34:35,520 --> 00:34:39,440
Speaker 2: a company that asked a regulating body to make an

670
00:34:39,440 --> 00:34:43,080
Speaker 2: exception to a law around like a high risk use

671
00:34:43,080 --> 00:34:46,200
Speaker 2: of AI, and the law said that people had to

672
00:34:46,320 --> 00:34:51,200
Speaker 2: supervise the use of AI like just because it seemed dangerous,

673
00:34:51,239 --> 00:34:55,080
Speaker 2: and the company appealed to the regulating body by saying, well,

674
00:34:55,120 --> 00:34:57,799
Speaker 2: we just like that would cost too much and we

675
00:34:57,800 --> 00:35:00,720
Speaker 2: would never be able to like scale this make a profit.

676
00:35:01,440 --> 00:35:04,200
Speaker 2: And it feels to me like our answer as a

677
00:35:04,200 --> 00:35:09,440
Speaker 2: civilization to that complaint needs to be that's not our problem,

678
00:35:09,760 --> 00:35:13,880
Speaker 2: Like that's then you shouldn't be doing it. But instead,

679
00:35:13,920 --> 00:35:17,280
Speaker 2: it seems like the answer too often, not just in AI,

680
00:35:17,520 --> 00:35:21,960
Speaker 2: but just across the board, especially in the US, is like, Okay,

681
00:35:22,000 --> 00:35:24,279
Speaker 2: well we have to make an exception so that they

682
00:35:24,320 --> 00:35:26,719
Speaker 2: can make a profit around this technology, or else the

683
00:35:26,760 --> 00:35:31,920
Speaker 2: technology won't get developed because the only thing that drives

684
00:35:32,120 --> 00:35:36,680
Speaker 2: technological progress is like the profit motive. But that's you know,

685
00:35:37,000 --> 00:35:39,200
Speaker 2: as I think you guys talked about in that episode,

686
00:35:39,239 --> 00:35:41,880
Speaker 2: that's never been the best way to develop technology, Like

687
00:35:42,239 --> 00:35:47,200
Speaker 2: it's it's been a good way sometimes to democratize existing technology,

688
00:35:47,200 --> 00:35:49,120
Speaker 2: but like that's I don't know.

689
00:35:49,160 --> 00:35:49,920
Speaker 1: I feel like that.

690
00:35:50,040 --> 00:35:53,880
Speaker 2: Idea of you have to make it profitable, you have

691
00:35:53,960 --> 00:35:56,400
Speaker 2: to make it easy on these companies to keep trying

692
00:35:56,440 --> 00:35:59,880
Speaker 2: different things for AI to become profitable is baked in

693
00:36:00,400 --> 00:36:04,760
Speaker 2: at a like cellular level at this point in how

694
00:36:05,000 --> 00:36:09,320
Speaker 2: a lot of you know, Western colonial civilizations operate.

695
00:36:09,840 --> 00:36:11,480
Speaker 5: Yeah, I mean, I think too often a lot of

696
00:36:11,480 --> 00:36:14,279
Speaker 5: the technologies that shape our daily lives are made by

697
00:36:14,320 --> 00:36:17,240
Speaker 5: a very narrow set of people who ultimately aren't beholden

698
00:36:17,320 --> 00:36:19,879
Speaker 5: to us. They're beholden to their shareholders or to their

699
00:36:19,920 --> 00:36:21,920
Speaker 5: boss and right, so they don't really have our best

700
00:36:22,520 --> 00:36:25,279
Speaker 5: interests at heart, right, Like, for example, take this whole

701
00:36:25,320 --> 00:36:28,640
Speaker 5: rebranding of like Twitter to Eggs by Musk. I remember

702
00:36:28,680 --> 00:36:31,759
Speaker 5: waking up and finding my little Twitter bird replaced with

703
00:36:31,800 --> 00:36:34,840
Speaker 5: this huge X and just being like, oh, con firstly

704
00:36:34,880 --> 00:36:38,040
Speaker 5: because it was part of you know, Twitter's low decline,

705
00:36:38,320 --> 00:36:41,960
Speaker 5: but secondly it maybe you feel pretty disappointed or really

706
00:36:42,000 --> 00:36:44,799
Speaker 5: aware of the fact that one guy could have such

707
00:36:44,800 --> 00:36:48,279
Speaker 5: a huge impact on how literally millions of people use

708
00:36:48,400 --> 00:36:51,920
Speaker 5: a social networking platform that's actually super important, you know,

709
00:36:52,040 --> 00:36:53,960
Speaker 5: to their daily lives and has played a huge role

710
00:36:54,000 --> 00:36:57,040
Speaker 5: in activist movements and fostering different communities. And I think

711
00:36:57,040 --> 00:36:58,960
Speaker 5: that's a story we see time and time again with

712
00:36:59,040 --> 00:37:01,680
Speaker 5: some of these big tech com which is not only

713
00:37:01,680 --> 00:37:04,560
Speaker 5: do they have their own profit motive at heart, they

714
00:37:04,640 --> 00:37:07,200
Speaker 5: also they're not beholden in any way to the public,

715
00:37:07,200 --> 00:37:10,399
Speaker 5: and they're not being compelled by regulation to make good

716
00:37:10,440 --> 00:37:14,120
Speaker 5: decisions that necessarily benefit the public. So I think a

717
00:37:14,160 --> 00:37:17,600
Speaker 5: really important question going forward is how do we support

718
00:37:17,719 --> 00:37:20,880
Speaker 5: kinds of technology development that are very much based in

719
00:37:20,920 --> 00:37:23,719
Speaker 5: the communities that the technology is for. I think one

720
00:37:23,800 --> 00:37:26,680
Speaker 5: really big part of that is recognizing that so many

721
00:37:26,760 --> 00:37:30,040
Speaker 5: AI models, as you mentioned right, they're designed to be

722
00:37:30,120 --> 00:37:32,279
Speaker 5: scalable and that's how they make money. This idea that

723
00:37:32,280 --> 00:37:35,279
Speaker 5: you can apply them globally and universally, and I think

724
00:37:35,320 --> 00:37:38,800
Speaker 5: that's a big problem, partly because it often is really homogenizing.

725
00:37:38,960 --> 00:37:41,600
Speaker 5: It can involve like explitting a model from the US

726
00:37:41,719 --> 00:37:44,360
Speaker 5: usually out to the rest of the world. It's probably

727
00:37:44,400 --> 00:37:47,600
Speaker 5: not actually appropriate to using those contexts. But also a

728
00:37:47,600 --> 00:37:50,040
Speaker 5: lot of really exciting and good uses of technology I

729
00:37:50,080 --> 00:37:53,720
Speaker 5: think come from these really localized, specific, community based levels.

730
00:37:53,840 --> 00:37:56,320
Speaker 5: So sometimes I think it can be about thinking smaller

731
00:37:56,400 --> 00:37:57,160
Speaker 5: rather than bigger.

732
00:37:58,320 --> 00:38:01,080
Speaker 3: Yeah, that was like another thing that struck me about

733
00:38:01,120 --> 00:38:03,840
Speaker 3: like just all the warnings and even in that pause

734
00:38:03,920 --> 00:38:07,279
Speaker 3: letters sort of like the presumption that it's like, well,

735
00:38:07,320 --> 00:38:10,040
Speaker 3: all you motherfuckers are going to use this, so we

736
00:38:10,120 --> 00:38:11,960
Speaker 3: got to talk about it where it's like I don't know,

737
00:38:12,000 --> 00:38:13,799
Speaker 3: I don't even fucking know what it is, Like a

738
00:38:13,800 --> 00:38:16,440
Speaker 3: second ago, I thought it was Skynet, and now, like

739
00:38:17,000 --> 00:38:18,640
Speaker 3: you know, you have your company being like, yeah, we

740
00:38:18,680 --> 00:38:21,320
Speaker 3: now have enterprise AI tools like welcome.

741
00:38:21,400 --> 00:38:23,319
Speaker 1: You're like, but what am I huh like?

742
00:38:23,840 --> 00:38:26,040
Speaker 3: And I think that's what's an really interesting thing about this,

743
00:38:26,120 --> 00:38:29,399
Speaker 3: as like a sort of technological advancement is before people

744
00:38:29,560 --> 00:38:32,520
Speaker 3: really understand what it is, there is like from the

745
00:38:32,800 --> 00:38:35,239
Speaker 3: higher the powers that be sort of going into it

746
00:38:35,280 --> 00:38:38,040
Speaker 3: being like, well, this is it. Like everyone's using it,

747
00:38:38,280 --> 00:38:40,799
Speaker 3: but I'm still not sure how. And I guess that

748
00:38:40,920 --> 00:38:44,000
Speaker 3: probably feeds into this whole model of generating as much

749
00:38:44,400 --> 00:38:48,040
Speaker 3: you know, excitement market excitement about AI is by taking

750
00:38:48,080 --> 00:38:51,680
Speaker 3: the angle of like everything's different because everyone is going

751
00:38:51,760 --> 00:38:54,560
Speaker 3: to be using AI. Most of y'all don't know what

752
00:38:54,640 --> 00:38:57,160
Speaker 3: that is, but get ready. And I think that's what

753
00:38:57,200 --> 00:38:59,239
Speaker 3: also makes it very confusing for me, as just like

754
00:38:59,560 --> 00:39:02,759
Speaker 3: a lay person outside of the text sphere to just

755
00:39:02,760 --> 00:39:03,880
Speaker 3: be like, wait, so are.

756
00:39:03,760 --> 00:39:04,680
Speaker 1: We all using it?

757
00:39:04,719 --> 00:39:08,000
Speaker 3: And even now, I really I still can't see what

758
00:39:08,200 --> 00:39:11,239
Speaker 3: that is and how that benefits me. And I think

759
00:39:11,480 --> 00:39:13,880
Speaker 3: that's a big part of I'm sure your work too,

760
00:39:14,040 --> 00:39:17,080
Speaker 3: or even like any ethicist, is to understand like, well,

761
00:39:17,080 --> 00:39:20,000
Speaker 3: who does it? Ben Like, first, we're making this because

762
00:39:20,000 --> 00:39:22,200
Speaker 3: it benefits who and how?

763
00:39:22,440 --> 00:39:22,680
Speaker 5: Yeah?

764
00:39:22,719 --> 00:39:25,520
Speaker 1: And I think yeah, I mean right now, it benefits

765
00:39:25,640 --> 00:39:27,440
Speaker 1: the companies that are making it. And it sort of

766
00:39:27,440 --> 00:39:27,840
Speaker 1: feels like.

767
00:39:27,880 --> 00:39:30,520
Speaker 3: That's the way it's being present or slightly being like, yeah,

768
00:39:30,560 --> 00:39:32,440
Speaker 3: you guys are gonna love this, but really we're going

769
00:39:32,520 --> 00:39:34,520
Speaker 3: to benefit from the adoption of this technology.

770
00:39:35,320 --> 00:39:37,400
Speaker 5: Yeah, I mean, I think that's this crucial question, is

771
00:39:37,440 --> 00:39:40,480
Speaker 5: this stepping back and saying, actually, is this inevitable? And

772
00:39:40,520 --> 00:39:42,400
Speaker 5: do we even want this in the first place? And

773
00:39:42,440 --> 00:39:45,320
Speaker 5: I think that's what really frustrated me about the Pause

774
00:39:45,360 --> 00:39:47,200
Speaker 5: letter and about a number of kind of big text

775
00:39:47,239 --> 00:39:49,640
Speaker 5: figures signing on to it, is that they're very much

776
00:39:49,680 --> 00:39:53,600
Speaker 5: pushing this narrative of like, oh, this is like unstoppable

777
00:39:53,719 --> 00:39:56,000
Speaker 5: and it's inevitable and it's happening. We've got a fine

778
00:39:56,040 --> 00:39:58,680
Speaker 5: way to deal with it. And it's like you're making

779
00:39:58,719 --> 00:40:01,839
Speaker 5: it like you are the people really making these technologies,

780
00:40:01,840 --> 00:40:04,040
Speaker 5: and a lot of cases, so if you really think

781
00:40:04,040 --> 00:40:09,120
Speaker 5: it's an existential risk to humanity, stop it. Honestly could

782
00:40:09,320 --> 00:40:12,719
Speaker 5: even be that simple, but that's you know, what makes

783
00:40:12,719 --> 00:40:15,000
Speaker 5: me really then question their motives and sort of coming

784
00:40:15,040 --> 00:40:17,239
Speaker 5: forward with a lot of this kind of very doom

785
00:40:17,239 --> 00:40:20,120
Speaker 5: and gloom language. I think it's also interesting if you

786
00:40:20,160 --> 00:40:23,880
Speaker 5: look at, for example, countries as national AI strategies. So

787
00:40:23,920 --> 00:40:25,879
Speaker 5: if you look at say like China and the UK

788
00:40:26,080 --> 00:40:28,160
Speaker 5: and the US and these countries that are now thinking

789
00:40:28,200 --> 00:40:30,759
Speaker 5: about what their national long term AI strategy is going

790
00:40:30,840 --> 00:40:33,759
Speaker 5: to be, they also very much frame it around the

791
00:40:33,800 --> 00:40:36,480
Speaker 5: idea that AI is completely inevitable, that this is going

792
00:40:36,560 --> 00:40:40,400
Speaker 5: to be the transformative technology for imaging the future, for

793
00:40:40,480 --> 00:40:44,759
Speaker 5: geopolitical dominance, for economic supremacy. And again, I think as

794
00:40:44,760 --> 00:40:46,880
Speaker 5: an ethesis, what I really want people to do is

795
00:40:46,880 --> 00:40:49,480
Speaker 5: step back and say, I think we're actually at a crossroad,

796
00:40:49,560 --> 00:40:51,279
Speaker 5: or we can decide whether or not, don't we think

797
00:40:51,280 --> 00:40:55,399
Speaker 5: these technologies are good for us, and whether they are sustainable,

798
00:40:55,400 --> 00:40:58,879
Speaker 5: whether they are a useful long term thing for our societies,

799
00:40:59,360 --> 00:41:02,560
Speaker 5: or actually whether the benefits of these technologies are going

800
00:41:02,640 --> 00:41:05,840
Speaker 5: to be experienced by very few people and the costs

801
00:41:05,840 --> 00:41:07,080
Speaker 5: are going to be borne by many.

802
00:41:08,080 --> 00:41:13,000
Speaker 2: Right, we talked last week about the scientific application that

803
00:41:13,640 --> 00:41:17,000
Speaker 2: you know, used deep learning to figure out the shapes

804
00:41:17,040 --> 00:41:21,479
Speaker 2: of proteins, the structures of proteins, and that could have

805
00:41:22,080 --> 00:41:26,560
Speaker 2: some beneficial uses, will probably have some beneficial uses for

806
00:41:26,920 --> 00:41:29,719
Speaker 2: you know, how we understand disease and medicine and how

807
00:41:29,719 --> 00:41:34,400
Speaker 2: we treat that. But there are ways to probably differentiate

808
00:41:34,440 --> 00:41:38,440
Speaker 2: and think about these things, Like it's not you don't

809
00:41:38,480 --> 00:41:43,240
Speaker 2: just have to be either luddite or like AI pedal

810
00:41:43,320 --> 00:41:46,759
Speaker 2: to the floor. You know, let's just get out of

811
00:41:46,760 --> 00:41:50,040
Speaker 2: the way of the big companies. You know, it feels like,

812
00:41:50,600 --> 00:41:55,640
Speaker 2: but it is such a complicated technology that I think

813
00:41:55,680 --> 00:41:59,520
Speaker 2: there's going to be inherent cloudiness around how people understand

814
00:41:59,600 --> 00:42:04,799
Speaker 2: it and also manufactured cloudiness because it is in the

815
00:42:04,840 --> 00:42:10,480
Speaker 2: overall system's benefit. The overall system being like capitalism, it's

816
00:42:10,520 --> 00:42:14,600
Speaker 2: in their benefit to generate like market excitement where there

817
00:42:14,680 --> 00:42:16,080
Speaker 2: shouldn't be any.

818
00:42:16,360 --> 00:42:19,160
Speaker 5: Basically, yeah, I mean, I think it's easy to generate

819
00:42:19,200 --> 00:42:21,760
Speaker 5: this kind of nebulousness around AI because to some extent

820
00:42:21,800 --> 00:42:23,919
Speaker 5: we still don't really know what it is. It's still

821
00:42:23,960 --> 00:42:26,560
Speaker 5: more of a concept than anything else because the term

822
00:42:26,560 --> 00:42:29,800
Speaker 5: AI is like stretched and used to describe so many applications.

823
00:42:30,239 --> 00:42:33,480
Speaker 5: Like I spent two years interviewing engineers and data scientists.

824
00:42:33,520 --> 00:42:36,080
Speaker 5: Is a big tech firm, and they would sort of grumble, well,

825
00:42:36,160 --> 00:42:38,319
Speaker 5: fifteen or twenty years ago, you know, we didn't even

826
00:42:38,360 --> 00:42:40,560
Speaker 5: call this AI, and we were already doing it. It's

827
00:42:40,640 --> 00:42:42,520
Speaker 5: just a decision tree, you know. Again, it's kind of

828
00:42:42,560 --> 00:42:45,200
Speaker 5: part of that branding. But also we have these again

829
00:42:45,360 --> 00:42:48,279
Speaker 5: thousands of years of stories and thinking about what an

830
00:42:48,280 --> 00:42:51,160
Speaker 5: intelligent machine is, and that means we can get super

831
00:42:51,200 --> 00:42:55,360
Speaker 5: invested in super cloudy very very quickly. And yeah, I

832
00:42:55,360 --> 00:42:57,520
Speaker 5: don't want people to feel bad for being scared of

833
00:42:57,600 --> 00:43:01,920
Speaker 5: being cloudy about these technologies. It is dense and confusing,

834
00:43:02,200 --> 00:43:05,160
Speaker 5: But at the same time, I do think that it

835
00:43:05,239 --> 00:43:07,279
Speaker 5: is really important to come back to that question of

836
00:43:07,280 --> 00:43:09,680
Speaker 5: what does this do for us? So, you know, the

837
00:43:09,760 --> 00:43:12,360
Speaker 5: question of like Luddism or being a lud die I

838
00:43:12,400 --> 00:43:15,279
Speaker 5: think is really interesting because I you know, personally I

839
00:43:15,360 --> 00:43:18,360
Speaker 5: do use AI applications. There's certain things about these technologies

840
00:43:18,400 --> 00:43:21,319
Speaker 5: that really excite me. But I'm really sympathetic to some

841
00:43:21,360 --> 00:43:24,480
Speaker 5: of the kind of old school ludyet who weren't necessarily

842
00:43:24,520 --> 00:43:28,120
Speaker 5: anti technology, but we're really against the kind of impacts

843
00:43:28,120 --> 00:43:30,680
Speaker 5: that technology we're having on their society. So the way

844
00:43:30,680 --> 00:43:33,200
Speaker 5: that new technology is, like I think it would be

845
00:43:33,200 --> 00:43:36,960
Speaker 5: things like spinning and weaving, we're causing mass unemployment and

846
00:43:37,040 --> 00:43:40,600
Speaker 5: the kind of broader rammifications that was having for people

847
00:43:40,600 --> 00:43:43,200
Speaker 5: in the UK socially, and that kind of has quite

848
00:43:43,239 --> 00:43:46,440
Speaker 5: a scary parallel to today in terms of thinking about

849
00:43:46,640 --> 00:43:50,080
Speaker 5: maybe what AI will bring about for the rest of

850
00:43:50,160 --> 00:43:52,839
Speaker 5: us who maybe aren't researchers in a lab, but who

851
00:43:52,840 --> 00:43:55,200
Speaker 5: maybe might be replaced by some of these algorithms in

852
00:43:55,239 --> 00:43:57,200
Speaker 5: terms of our work and our output.

853
00:43:58,320 --> 00:44:02,040
Speaker 2: Yeah, can you talk at all about open source like

854
00:44:02,160 --> 00:44:06,240
Speaker 2: models of because you know what, when we talk about

855
00:44:06,360 --> 00:44:11,000
Speaker 2: this idea that corporations have all this power and are

856
00:44:11,040 --> 00:44:15,080
Speaker 2: incentivized to do whatever is going to make the most money,

857
00:44:15,120 --> 00:44:17,080
Speaker 2: which in a lot of cases is going to be

858
00:44:17,120 --> 00:44:21,880
Speaker 2: the thing that removes the friction from consumption decisions, and

859
00:44:22,239 --> 00:44:25,680
Speaker 2: you know, just how people interact and do these things,

860
00:44:25,719 --> 00:44:28,480
Speaker 2: which well, as you guys talked about in your episode,

861
00:44:28,520 --> 00:44:31,319
Speaker 2: like removing the friction, Like friction can be really good

862
00:44:31,360 --> 00:44:34,960
Speaker 2: sometimes sometimes your system needs friction to stop and correct

863
00:44:34,960 --> 00:44:39,880
Speaker 2: itself and recognize when bad shit, when things are going wrong.

864
00:44:40,360 --> 00:44:45,880
Speaker 2: But you know, there's also a history in even in

865
00:44:45,920 --> 00:44:50,560
Speaker 2: the US, where corporations are racing to get to a

866
00:44:50,640 --> 00:44:56,239
Speaker 2: development and ultimately are beat by open source models of

867
00:44:56,360 --> 00:45:01,640
Speaker 2: technological organizing around like getting a specific solution. Do you

868
00:45:01,640 --> 00:45:05,120
Speaker 2: have any hope for like open source in the future

869
00:45:05,160 --> 00:45:05,600
Speaker 2: of AI.

870
00:45:06,360 --> 00:45:08,880
Speaker 5: Yeah, I mean, I think I'm really interested in community

871
00:45:09,080 --> 00:45:11,480
Speaker 5: forms of development, and I think open source is a

872
00:45:11,520 --> 00:45:15,200
Speaker 5: really interesting example. I think we've seen other interesting examples

873
00:45:15,200 --> 00:45:18,520
Speaker 5: around things like collective data labeling, and I think that

874
00:45:18,600 --> 00:45:21,319
Speaker 5: these kinds of collective movements, on the one hand, seem

875
00:45:21,400 --> 00:45:25,600
Speaker 5: like a really exciting community based alternative to the concentration

876
00:45:26,200 --> 00:45:29,640
Speaker 5: of power in a very very narrow segment of tech companies.

877
00:45:30,040 --> 00:45:32,719
Speaker 5: On the other hand, though, I think community work is

878
00:45:32,840 --> 00:45:36,760
Speaker 5: really hard work. We had doctor David Adlani on our podcast,

879
00:45:36,760 --> 00:45:39,880
Speaker 5: who's very important figure in Mascana, which is a grassroots

880
00:45:39,960 --> 00:45:43,160
Speaker 5: organization that aims to bring some of the over four

881
00:45:43,200 --> 00:45:47,960
Speaker 5: thousand African languages into natural language processing or NLP systems,

882
00:45:48,000 --> 00:45:50,120
Speaker 5: and he talks a lot about how the work he

883
00:45:50,160 --> 00:45:53,839
Speaker 5: does with Masakana is so valuable and so important, but

884
00:45:53,920 --> 00:45:57,600
Speaker 5: it's also really really hard because when you're working in

885
00:45:57,680 --> 00:46:00,640
Speaker 5: that kind of collective, decentralized environment, and it can be

886
00:46:00,800 --> 00:46:02,440
Speaker 5: much slower, and as you said, there can be a

887
00:46:02,440 --> 00:46:06,640
Speaker 5: lot more friction in that process. But counter to this

888
00:46:07,000 --> 00:46:09,640
Speaker 5: move fast, break things kind of culture, sometimes that friction

889
00:46:09,680 --> 00:46:11,560
Speaker 5: can be really productive and it can help us slow

890
00:46:11,600 --> 00:46:14,040
Speaker 5: down and think about, you know, the decisions that we're

891
00:46:14,040 --> 00:46:17,319
Speaker 5: making very intentionally rather than just kind of racing as

892
00:46:17,320 --> 00:46:20,720
Speaker 5: farst as we can to make the next newest, shiniest product.

893
00:46:22,320 --> 00:46:24,920
Speaker 3: Was almost like in your work too, you know you

894
00:46:25,000 --> 00:46:28,360
Speaker 3: talk about how, you know, like looking at these technologies

895
00:46:28,440 --> 00:46:31,759
Speaker 3: especially through a lens of like feminism and intersectionality and

896
00:46:32,040 --> 00:46:34,920
Speaker 3: you know, bipop communities and things like that, and I

897
00:46:35,239 --> 00:46:37,160
Speaker 3: like broadly in science there's like, you know, there's an

898
00:46:37,200 --> 00:46:41,319
Speaker 3: issue of like language hegemony in scientific research, where if

899
00:46:41,320 --> 00:46:45,000
Speaker 3: things aren't written in English a lot, sometimes studies just

900
00:46:45,040 --> 00:46:48,480
Speaker 3: get fucking ignored because like I don't speak Spanish or

901
00:46:48,640 --> 00:46:51,480
Speaker 3: I can't read Chinese, therefore I don't know if this

902
00:46:51,600 --> 00:46:55,080
Speaker 3: research is being done and therefore it just doesn't exist

903
00:46:55,160 --> 00:46:58,040
Speaker 3: because the larger community is like, we all just think

904
00:46:58,080 --> 00:47:00,520
Speaker 3: in English, like, so how do you like, you know

905
00:47:00,560 --> 00:47:04,320
Speaker 3: specifically because you know, in hearing the description of your work,

906
00:47:04,800 --> 00:47:07,319
Speaker 3: help me understand, like and the listeners too, like, of

907
00:47:07,360 --> 00:47:09,200
Speaker 3: how we should be looking at these things from that

908
00:47:09,440 --> 00:47:11,919
Speaker 3: also from that perspective too, because I think right now

909
00:47:11,960 --> 00:47:12,879
Speaker 3: we're all caught up and.

910
00:47:12,920 --> 00:47:16,080
Speaker 1: Like it's talking side at and it's not you.

911
00:47:16,040 --> 00:47:18,320
Speaker 3: Know, hold on, like there are other subtleties that actually

912
00:47:18,400 --> 00:47:21,680
Speaker 3: we should really think deeply about because to your point,

913
00:47:21,920 --> 00:47:23,880
Speaker 3: I feel like those are the dimensions of an emerging

914
00:47:23,880 --> 00:47:27,239
Speaker 3: technology or trend or something that gets ignored because to

915
00:47:27,280 --> 00:47:30,520
Speaker 3: your point, it's like the thing, well, of course it's

916
00:47:30,000 --> 00:47:34,239
Speaker 3: it's it's unequal, of course it's racist or whatever. But

917
00:47:34,280 --> 00:47:36,360
Speaker 3: what are those like, what are those ways that people

918
00:47:36,480 --> 00:47:38,560
Speaker 3: need to really be thinking about this technology?

919
00:47:39,080 --> 00:47:41,279
Speaker 5: Yeah, I mean I think English language hegemony is a

920
00:47:41,320 --> 00:47:43,960
Speaker 5: really good example of this broader problem of the more

921
00:47:44,040 --> 00:47:47,160
Speaker 5: subtle kinds of exclusions that get built into these technologies,

922
00:47:47,239 --> 00:47:50,200
Speaker 5: because I think we've all probably seen the cases of

923
00:47:50,360 --> 00:47:54,160
Speaker 5: AI systems that have been really horrifically and explicitly racist

924
00:47:54,320 --> 00:47:57,960
Speaker 5: or really horrifically sexist from you know, Tay, the chatbot

925
00:47:58,000 --> 00:48:02,200
Speaker 5: that started spousing horrific right racist propaganda and had get

926
00:48:02,200 --> 00:48:06,520
Speaker 5: taken down through to Amazon hiring tool that's systemically discriminated

927
00:48:06,560 --> 00:48:11,279
Speaker 5: against female candidates. These are really I think avert depictions

928
00:48:11,280 --> 00:48:13,080
Speaker 5: of the kinds of harms a I can do. But

929
00:48:13,200 --> 00:48:17,400
Speaker 5: I think things like English language hegemony are also incredibly

930
00:48:17,440 --> 00:48:21,080
Speaker 5: important for showing how existing kinds of exclusions and patterns

931
00:48:21,080 --> 00:48:23,920
Speaker 5: of power get replicated in these tools. Because to an

932
00:48:23,960 --> 00:48:27,719
Speaker 5: English language speaker, very crucially, they might use CHATGPT and

933
00:48:27,760 --> 00:48:30,080
Speaker 5: think this is great, this is what my whole world

934
00:48:30,160 --> 00:48:32,880
Speaker 5: looks like if they only speak English. Obvious to anyone

935
00:48:32,920 --> 00:48:35,279
Speaker 5: who is not a native English speaker who doesn't only

936
00:48:35,280 --> 00:48:38,279
Speaker 5: speak English, it's going to be an incredibly different experience.

937
00:48:38,440 --> 00:48:40,440
Speaker 5: And that's where I think we see the benefits of

938
00:48:40,480 --> 00:48:43,640
Speaker 5: these tools being really unequally distributed. I think it's also

939
00:48:43,640 --> 00:48:47,600
Speaker 5: important because there's such exclusions in which kinds of languages

940
00:48:47,600 --> 00:48:51,239
Speaker 5: and forms of communication can get translated into these systems. So,

941
00:48:51,360 --> 00:48:54,000
Speaker 5: for example, I work with a linguists at the University

942
00:48:54,000 --> 00:48:56,000
Speaker 5: of Newcastle and she talks about the fact that there's

943
00:48:56,040 --> 00:48:59,480
Speaker 5: so many languages like signed languages and languages that don't

944
00:48:59,480 --> 00:49:01,759
Speaker 5: have a written language, but they're never going to be

945
00:49:01,840 --> 00:49:04,839
Speaker 5: translated into these tools and never going to benefit from them.

946
00:49:05,200 --> 00:49:08,640
Speaker 5: You might think, Okay, well, do these communities want those

947
00:49:09,200 --> 00:49:13,399
Speaker 5: languages translated into an AI tool? Maybe maybe not. I'd argue,

948
00:49:13,480 --> 00:49:15,799
Speaker 5: of course, that's up to them. But those communities are

949
00:49:15,800 --> 00:49:18,279
Speaker 5: still going to experience the negative effects of AI, like

950
00:49:18,320 --> 00:49:21,120
Speaker 5: the climate coth of these tools. And so I think

951
00:49:21,160 --> 00:49:23,520
Speaker 5: it's just really important, like you said, to think about

952
00:49:23,640 --> 00:49:27,120
Speaker 5: what kinds of hegemony are getting further entrenched by AI

953
00:49:27,200 --> 00:49:28,280
Speaker 5: powered technologies.

954
00:49:29,239 --> 00:49:32,399
Speaker 2: All right, great, let's take a quick break and we'll

955
00:49:32,400 --> 00:49:33,120
Speaker 2: come back.

956
00:49:33,000 --> 00:49:36,359
Speaker 1: And finish up with a few questions. We'll be right back.

957
00:49:46,960 --> 00:49:51,719
Speaker 2: And we're back and all right, So, I mean, one

958
00:49:51,719 --> 00:49:55,520
Speaker 2: thing that I want myself to just like get out

959
00:49:55,520 --> 00:49:58,840
Speaker 2: of this conversation is just a sense of like ways

960
00:49:58,960 --> 00:50:04,600
Speaker 2: that we like what we think AI might look like,

961
00:50:04,640 --> 00:50:08,000
Speaker 2: how it might impact what the world around us looks

962
00:50:08,080 --> 00:50:12,120
Speaker 2: like in the not too distant future. And we've we've

963
00:50:12,160 --> 00:50:15,600
Speaker 2: already talked about like some ways that AI is being overrated,

964
00:50:15,760 --> 00:50:21,560
Speaker 2: as like a autonomous killbot like terminator style kilbot. You know,

965
00:50:21,719 --> 00:50:25,360
Speaker 2: there's there's some other examples like On one of the

966
00:50:25,440 --> 00:50:30,360
Speaker 2: episodes of your podcast, an AI expert talks about getting

967
00:50:30,600 --> 00:50:35,880
Speaker 2: an AI enhanced cancer scan and assuming the scan was

968
00:50:35,920 --> 00:50:38,960
Speaker 2: like taking three D video and doing or doing like

969
00:50:39,000 --> 00:50:41,880
Speaker 2: three D modeling, and it was just putting a box

970
00:50:41,920 --> 00:50:44,879
Speaker 2: on a two D image. And I believe the guests

971
00:50:44,920 --> 00:50:48,440
Speaker 2: like admits that they were influenced by like Hollywood movies,

972
00:50:48,960 --> 00:50:53,880
Speaker 2: and it seems like that's what people who are trying

973
00:50:53,880 --> 00:50:55,560
Speaker 2: to make money off of this one. So like that

974
00:50:55,719 --> 00:50:58,799
Speaker 2: we're going to have this steady push to make us

975
00:50:58,920 --> 00:51:04,359
Speaker 2: overrate misunderstand what the actual promise of AI is going

976
00:51:04,400 --> 00:51:07,640
Speaker 2: to look like, how what tools are going to be

977
00:51:07,680 --> 00:51:09,719
Speaker 2: given to us in the near future, and like what

978
00:51:09,760 --> 00:51:12,520
Speaker 2: are those tools actually able to do? So I'd just

979
00:51:12,600 --> 00:51:16,400
Speaker 2: be curious to hear from you, like what do you

980
00:51:16,560 --> 00:51:21,880
Speaker 2: think ways that AI might intervene in our lives in

981
00:51:21,920 --> 00:51:25,759
Speaker 2: the near future might already be intervening in our lives? Well,

982
00:51:25,840 --> 00:51:30,160
Speaker 2: what are those things that we're underrating, like and what

983
00:51:30,200 --> 00:51:34,799
Speaker 2: are the things that you think are probably taking up

984
00:51:34,840 --> 00:51:37,399
Speaker 2: too much of our bandwidth in terms of how we're

985
00:51:37,440 --> 00:51:38,240
Speaker 2: picturing AI.

986
00:51:40,080 --> 00:51:42,480
Speaker 5: Oh, that's a great question. I think to start with

987
00:51:42,520 --> 00:51:44,160
Speaker 5: the second half, to start with what I think is

988
00:51:44,200 --> 00:51:46,520
Speaker 5: maybe a little bit overrated, or maybe we'll take a

989
00:51:46,520 --> 00:51:48,920
Speaker 5: bit longer to pan out. I would say some of

990
00:51:48,920 --> 00:51:53,160
Speaker 5: these really high tech applications, things like AI and medicine,

991
00:51:53,200 --> 00:51:55,960
Speaker 5: for example, I think that we might not see them

992
00:51:56,000 --> 00:51:59,759
Speaker 5: expand beyond a very narrow set of controlled circumstances. I

993
00:51:59,760 --> 00:52:02,640
Speaker 5: think this idea that you know, soon every hospital will

994
00:52:02,640 --> 00:52:05,160
Speaker 5: have this tool, or say AI in education, that every

995
00:52:05,160 --> 00:52:07,560
Speaker 5: classroom is going to have access to this tool, I think,

996
00:52:07,840 --> 00:52:11,560
Speaker 5: unfortun she's just really grossly overestimating the kinds of resources,

997
00:52:11,600 --> 00:52:15,040
Speaker 5: but only in this country that schools and the hospitals have.

998
00:52:15,280 --> 00:52:17,880
Speaker 5: I was talking to a friend who's a doctor here

999
00:52:17,920 --> 00:52:19,319
Speaker 5: and she was saying, oh, well, what do you think

1000
00:52:19,320 --> 00:52:21,600
Speaker 5: about AI in medicine? And then she kind of stopped

1001
00:52:21,600 --> 00:52:24,200
Speaker 5: for a moment and just laughed and said, well, I

1002
00:52:24,200 --> 00:52:26,320
Speaker 5: don't know why I'm asking you that, because my hospital

1003
00:52:26,400 --> 00:52:30,160
Speaker 5: uses paper notes. And I said what, and she said, yeah,

1004
00:52:30,239 --> 00:52:33,719
Speaker 5: our whole hospital is not computerized. And that was a

1005
00:52:33,760 --> 00:52:37,520
Speaker 5: good reality check for me because it made me realize, like, wow, actually,

1006
00:52:37,560 --> 00:52:39,400
Speaker 5: some of the stuff that I'm thinking about is so

1007
00:52:39,600 --> 00:52:42,279
Speaker 5: far away from the reality of what people like my

1008
00:52:42,360 --> 00:52:44,920
Speaker 5: friend the doctor is having to deal with. On the ground,

1009
00:52:44,960 --> 00:52:48,000
Speaker 5: and same with schools. You know, here a whole hundreds

1010
00:52:48,000 --> 00:52:49,640
Speaker 5: of schools I think have had to be closed because

1011
00:52:49,640 --> 00:52:51,239
Speaker 5: there's some kind of issues with the concrete that I

1012
00:52:51,360 --> 00:52:54,439
Speaker 5: might collapse, which is a horrific but B is such

1013
00:52:54,480 --> 00:52:58,600
Speaker 5: a different level of infrastructural problem that thinking about AI

1014
00:52:58,760 --> 00:53:02,359
Speaker 5: in the classroom is really not on the radar that scenario.

1015
00:53:04,239 --> 00:53:04,719
Speaker 1: Exactly.

1016
00:53:05,000 --> 00:53:10,799
Speaker 5: Yeah, autonomous, it's like a low bar. Yeah, people like,

1017
00:53:10,800 --> 00:53:19,279
Speaker 5: what are students cheat? And you're like, look, yeah, you know.

1018
00:53:19,680 --> 00:53:21,840
Speaker 5: So those are things that I think and maybe a

1019
00:53:21,880 --> 00:53:26,359
Speaker 5: little bit on the overrated side. I think things are underrated,

1020
00:53:26,520 --> 00:53:29,960
Speaker 5: Like I'm really interested in how AI can change how

1021
00:53:29,960 --> 00:53:32,200
Speaker 5: we see the world and how we see ourselves, like

1022
00:53:32,320 --> 00:53:34,720
Speaker 5: I think, you know, take for example, something like TikTok.

1023
00:53:35,239 --> 00:53:37,720
Speaker 5: You know, I have to confess I'm a big TikTok user.

1024
00:53:37,840 --> 00:53:40,759
Speaker 5: I love grolling a lot of mind list diarbage. It

1025
00:53:41,000 --> 00:53:44,160
Speaker 5: brings me a lot of joy and peace. But even

1026
00:53:44,160 --> 00:53:46,960
Speaker 5: things like AI powered beauty filters, right, like, I think

1027
00:53:47,000 --> 00:53:49,360
Speaker 5: that has such a profound effect on how you just

1028
00:53:49,440 --> 00:53:52,480
Speaker 5: understand and see yourself and your own faith. They're often

1029
00:53:52,719 --> 00:53:56,200
Speaker 5: really imbued with gendered and racist kinds of assumptions, as well,

1030
00:53:56,239 --> 00:53:58,080
Speaker 5: I often look a lot whiter when I use a

1031
00:53:58,080 --> 00:54:01,480
Speaker 5: beauty filter, even though I'm multiracial. Like all of those assumptions,

1032
00:54:01,520 --> 00:54:03,960
Speaker 5: I think they get baked in that seep into our

1033
00:54:04,000 --> 00:54:06,960
Speaker 5: lives and like really suther ways, but I think collectively

1034
00:54:06,960 --> 00:54:08,880
Speaker 5: it can have quite a big impact.

1035
00:54:09,120 --> 00:54:12,200
Speaker 2: Yeah, I was sorry, I was talking about the I

1036
00:54:12,239 --> 00:54:14,560
Speaker 2: don't know if this is technically AI, but then again,

1037
00:54:14,600 --> 00:54:17,360
Speaker 2: I don't know what AI is or if there's like

1038
00:54:17,400 --> 00:54:21,239
Speaker 2: a specific argument, but like the way that like iPhones

1039
00:54:21,320 --> 00:54:25,239
Speaker 2: take forty pictures like consecutively and then like choose the

1040
00:54:25,280 --> 00:54:28,040
Speaker 2: best one from the forty, and like the live photo

1041
00:54:28,120 --> 00:54:31,680
Speaker 2: setting like feels like a thing and like it's really

1042
00:54:31,719 --> 00:54:34,120
Speaker 2: good at it, and it's something that I had, like

1043
00:54:34,239 --> 00:54:36,920
Speaker 2: just it happened on my phone with like an update

1044
00:54:36,960 --> 00:54:38,359
Speaker 2: and I was like, yeah, this is just how you

1045
00:54:38,360 --> 00:54:41,520
Speaker 2: take pictures now, and like pictures are way better than

1046
00:54:41,560 --> 00:54:44,000
Speaker 2: they used to be. I think I saw somebody speculate

1047
00:54:44,040 --> 00:54:47,120
Speaker 2: that like some of the software like Photoshop and other

1048
00:54:47,239 --> 00:54:52,120
Speaker 2: things that have traditionally had sort of a difficult learning

1049
00:54:52,200 --> 00:54:57,400
Speaker 2: curve will suddenly get much easier for people to use.

1050
00:54:57,920 --> 00:55:02,359
Speaker 2: So yeah, I like those like having actual tools that

1051
00:55:02,440 --> 00:55:06,520
Speaker 2: are like kind of easy to use them, like very simple, straightforward.

1052
00:55:06,560 --> 00:55:11,440
Speaker 2: We know what the goal is here, and we're able

1053
00:55:11,480 --> 00:55:16,960
Speaker 2: to use these enhanced kind of programs to achieve that goal.

1054
00:55:17,280 --> 00:55:20,759
Speaker 2: Like that that feels more possible to me and like

1055
00:55:20,800 --> 00:55:23,000
Speaker 2: something that we we might see in the not too

1056
00:55:23,040 --> 00:55:23,720
Speaker 2: distant future.

1057
00:55:24,040 --> 00:55:26,520
Speaker 3: Totally, that's like a pitch you can understand, like this

1058
00:55:26,560 --> 00:55:29,440
Speaker 3: will make it easier for you to photoshop a friend

1059
00:55:29,480 --> 00:55:32,360
Speaker 3: out and just put someone else in. You're like, okay,

1060
00:55:32,440 --> 00:55:35,520
Speaker 3: rather than right now it's like this shit will end

1061
00:55:35,560 --> 00:55:38,080
Speaker 3: the world and you're like, well, what is it? I

1062
00:55:38,080 --> 00:55:40,960
Speaker 3: don't know, I don't know, I don't know, but.

1063
00:55:41,000 --> 00:55:43,680
Speaker 1: It will fuck you up. And you're like, huh, and

1064
00:55:43,760 --> 00:55:47,280
Speaker 1: I guess that's such a different proposition up front and earlier,

1065
00:55:47,320 --> 00:55:50,560
Speaker 1: doctor Carry was saying like there are things that genuinely.

1066
00:55:50,040 --> 00:55:53,040
Speaker 3: Excite you about like this sort of like these everging

1067
00:55:53,080 --> 00:55:55,839
Speaker 3: technologies from I, and I would love to hear from

1068
00:55:55,840 --> 00:55:58,600
Speaker 3: your vantage point because you are looking at this and

1069
00:55:58,719 --> 00:56:01,000
Speaker 3: a like like what is ethical and what is going

1070
00:56:01,040 --> 00:56:03,839
Speaker 3: to bring meaningful value to people? Like what are those

1071
00:56:03,880 --> 00:56:05,960
Speaker 3: things that I can feel like, oh yeah, yeah, I

1072
00:56:05,960 --> 00:56:07,120
Speaker 3: can get down with that future.

1073
00:56:08,000 --> 00:56:10,319
Speaker 5: Yeah. I mean I recognize that I sound very doom

1074
00:56:10,320 --> 00:56:12,160
Speaker 5: and gloom a lot of the time that I speak,

1075
00:56:12,280 --> 00:56:14,480
Speaker 5: and all my friends have to deal with my sort

1076
00:56:14,480 --> 00:56:19,319
Speaker 5: of constant existential anxiety about technologies. But at the same time,

1077
00:56:19,400 --> 00:56:21,480
Speaker 5: you know, I'm an active user of a lot of them,

1078
00:56:21,560 --> 00:56:24,640
Speaker 5: Like I use a lot of voice dictation software, voice

1079
00:56:24,719 --> 00:56:27,600
Speaker 5: editing software, you know, for when we record our podcasts,

1080
00:56:27,600 --> 00:56:31,520
Speaker 5: and these things that genuinely make my life so much

1081
00:56:31,520 --> 00:56:34,040
Speaker 5: better and easier. I love being able to transcribe, you know,

1082
00:56:34,120 --> 00:56:35,960
Speaker 5: what I'm saying and have it appear on the screen

1083
00:56:36,080 --> 00:56:38,160
Speaker 5: and not have to type out my emails and have

1084
00:56:38,200 --> 00:56:40,440
Speaker 5: them a pair. I recognize you, they're like, all very boring.

1085
00:56:40,480 --> 00:56:44,040
Speaker 5: I should have said something like I DJ no, not

1086
00:56:44,160 --> 00:56:48,880
Speaker 5: at all, I write email using voice. It's very you know,

1087
00:56:49,080 --> 00:56:52,399
Speaker 5: but I think kind of, you know, extrapolating out from that,

1088
00:56:52,480 --> 00:56:56,239
Speaker 5: you know, there's really amazing applications accessibility, and when it

1089
00:56:56,280 --> 00:56:58,799
Speaker 5: comes to the kinds of access people can now have

1090
00:56:58,920 --> 00:57:03,800
Speaker 5: online because of advancements in aipowered tools, and so I think, though,

1091
00:57:04,160 --> 00:57:06,279
Speaker 5: you know, what's really central there is kind of that

1092
00:57:06,360 --> 00:57:08,680
Speaker 5: broader vision of you know, what is the kind of

1093
00:57:08,680 --> 00:57:12,680
Speaker 5: benefit this is meaningfully bringing to society, What problem is

1094
00:57:12,719 --> 00:57:15,120
Speaker 5: being solved? And are the people who are like most

1095
00:57:15,160 --> 00:57:17,920
Speaker 5: affected by that problem, the ones leading the conversation and

1096
00:57:17,960 --> 00:57:20,600
Speaker 5: saying what they need, because too often I think we

1097
00:57:20,640 --> 00:57:23,880
Speaker 5: see tech developers creating stuff that actually no one really

1098
00:57:23,920 --> 00:57:26,000
Speaker 5: asked for. I'm sure you have like all seen that

1099
00:57:26,080 --> 00:57:29,040
Speaker 5: thing on Amazon when you're like, you're asked for that

1100
00:57:29,160 --> 00:57:34,280
Speaker 5: banana holder or avocado peeler or something. And too often

1101
00:57:34,320 --> 00:57:35,960
Speaker 5: I think tech add on to be a little bit

1102
00:57:36,040 --> 00:57:38,360
Speaker 5: like that, Whereas I think when we have like really

1103
00:57:38,400 --> 00:57:42,240
Speaker 5: interesting conscious development of say, for example, feminist tools that

1104
00:57:42,280 --> 00:57:46,240
Speaker 5: are designed to encourage good conversations things like that, that

1105
00:57:46,400 --> 00:57:48,760
Speaker 5: really makes me excited about the kinds of futures we

1106
00:57:48,800 --> 00:57:49,880
Speaker 5: could have with technology.

1107
00:57:50,520 --> 00:57:50,720
Speaker 1: Right.

1108
00:57:51,360 --> 00:57:54,040
Speaker 3: It almost feels like the danger here is when someone

1109
00:57:54,080 --> 00:57:55,080
Speaker 3: has like a technology and.

1110
00:57:55,080 --> 00:57:57,560
Speaker 1: Go and this is going to be in every home,

1111
00:57:57,640 --> 00:58:00,160
Speaker 1: and you're like, this is a sales pitch. Actually yeah, because.

1112
00:58:00,040 --> 00:58:02,560
Speaker 3: If you're not saying this is how we will work

1113
00:58:02,680 --> 00:58:06,880
Speaker 3: less and have more time to frolic, to enjoy our

1114
00:58:06,960 --> 00:58:11,080
Speaker 3: human existence, to connect with our families, then then like

1115
00:58:11,160 --> 00:58:13,200
Speaker 3: miss me with that because it reminds me of Crypto,

1116
00:58:13,320 --> 00:58:15,360
Speaker 3: it reminds me of the metaverse, and it reminds me

1117
00:58:15,440 --> 00:58:19,960
Speaker 3: of Zuckerberg being like every worker will have this fucking

1118
00:58:20,080 --> 00:58:25,440
Speaker 3: headset on no no, but nice fucking triasshole. And I

1119
00:58:25,480 --> 00:58:27,000
Speaker 3: feel like this is kind of like it has a

1120
00:58:27,040 --> 00:58:31,160
Speaker 3: similar tone of like, get ready, folks for this thing.

1121
00:58:31,440 --> 00:58:33,920
Speaker 3: And granted they have some a shiny toy in the

1122
00:58:33,920 --> 00:58:36,680
Speaker 3: form of these large language models that are fun to do,

1123
00:58:37,400 --> 00:58:40,600
Speaker 3: and really they're just skimming the Internet and just giving

1124
00:58:40,600 --> 00:58:43,240
Speaker 3: it to you in a nice, tidy sentence. But yeah,

1125
00:58:43,280 --> 00:58:44,960
Speaker 3: like I feel like that is just kind of like

1126
00:58:45,760 --> 00:58:48,440
Speaker 3: I'm seeing that dimension when you see the hype around it,

1127
00:58:48,440 --> 00:58:50,840
Speaker 3: which feels a little more like y'all are talking about

1128
00:58:50,840 --> 00:58:53,600
Speaker 3: this to make money, whereas the other things that you're

1129
00:58:53,600 --> 00:58:57,680
Speaker 3: talking about, like accessibility and trying to democratize certain applications.

1130
00:58:57,720 --> 00:59:02,200
Speaker 3: With things like that less there's less scale.

1131
00:59:01,880 --> 00:59:04,640
Speaker 1: Involved with something like that. So men, Yeah, hasn't.

1132
00:59:04,440 --> 00:59:07,560
Speaker 5: Talked about exactly, And I just think it's really important

1133
00:59:07,600 --> 00:59:09,640
Speaker 5: to recognize that, you know, I'm not saying that say

1134
00:59:09,680 --> 00:59:13,040
Speaker 5: things like spart home technology is like Amazon Alexa and

1135
00:59:13,080 --> 00:59:15,760
Speaker 5: things are like inherently bad products. I'm sure gips of

1136
00:59:15,800 --> 00:59:18,320
Speaker 5: people like love having those technologies in their home, they

1137
00:59:18,400 --> 00:59:20,800
Speaker 5: find them useful, But I just think whenever you bring

1138
00:59:20,840 --> 00:59:24,040
Speaker 5: in a new technology, you also bring a new vulnerabilities

1139
00:59:24,120 --> 00:59:26,720
Speaker 5: and you bring a new costs, and sometimes the hype

1140
00:59:26,760 --> 00:59:29,200
Speaker 5: wave means we focus on the benefits. You know, Oh,

1141
00:59:29,200 --> 00:59:31,720
Speaker 5: if you bring this to every home, everyone's life will

1142
00:59:31,760 --> 00:59:35,160
Speaker 5: be easier, if everyone will have a more seamless home experience,

1143
00:59:35,360 --> 00:59:37,640
Speaker 5: when actually that's not the case. There's real costs for

1144
00:59:37,760 --> 00:59:40,959
Speaker 5: bringing those tools into a lot of people's homes, everything from,

1145
00:59:40,960 --> 00:59:43,320
Speaker 5: for example, the way that certain kinds of elder care

1146
00:59:43,400 --> 00:59:46,840
Speaker 5: is getting replaced by technological tools in the UK care system,

1147
00:59:47,320 --> 00:59:49,560
Speaker 5: through to the fact that those tools can really easily

1148
00:59:49,560 --> 00:59:52,440
Speaker 5: be used for the purposes of domestic abuse, into a

1149
00:59:52,560 --> 00:59:54,720
Speaker 5: partner violence, It can be used to control or trap

1150
00:59:54,760 --> 00:59:57,360
Speaker 5: someone in their home. And these are really ugly truths

1151
00:59:57,360 --> 00:59:59,720
Speaker 5: about those technologies, and they're often not the ones that

1152
00:59:59,720 --> 01:00:03,120
Speaker 5: are at the heart of technology development. And so that's

1153
01:00:03,120 --> 01:00:05,760
Speaker 5: a lot of my job, I guess, is just to say,

1154
01:00:06,000 --> 01:00:08,920
Speaker 5: how do we put those vulnerabilities in, those costs at

1155
01:00:08,920 --> 01:00:11,760
Speaker 5: the forefront, and then judge holistically whether or not we

1156
01:00:11,880 --> 01:00:14,080
Speaker 5: really want this product to exist.

1157
01:00:14,280 --> 01:00:16,440
Speaker 3: Yeah, but here's the thing, I got a lot of

1158
01:00:16,480 --> 01:00:18,960
Speaker 3: my stock portfolio tied up in these companies.

1159
01:00:20,600 --> 01:00:23,320
Speaker 1: I need these I need these fucking things to moon.

1160
01:00:23,680 --> 01:00:24,520
Speaker 1: If you know, what I mean.

1161
01:00:25,000 --> 01:00:27,560
Speaker 2: So I want to end the episode just doing something

1162
01:00:27,560 --> 01:00:30,480
Speaker 2: that you guys do on The Good Robot, which is

1163
01:00:30,960 --> 01:00:35,400
Speaker 2: talk talk about some like books that are recommended or

1164
01:00:35,640 --> 01:00:37,640
Speaker 2: you know what works you recommend. You know, you talk

1165
01:00:37,720 --> 01:00:40,160
Speaker 2: on an episode earlier, I think it was in the

1166
01:00:40,200 --> 01:00:44,280
Speaker 2: summer about how a lot of the ideas and fears

1167
01:00:44,320 --> 01:00:48,440
Speaker 2: and hopes that we have currently are very similar to

1168
01:00:48,480 --> 01:00:54,040
Speaker 2: what we've seen in movies and like those like when

1169
01:00:54,040 --> 01:00:57,440
Speaker 2: you look at Elon Musk's ten favorite books, they're all

1170
01:00:57,480 --> 01:01:00,160
Speaker 2: like fed by this same like Isaac aha as I

1171
01:01:00,160 --> 01:01:03,200
Speaker 2: am of like I robot thing I'd had a lot

1172
01:01:03,200 --> 01:01:08,120
Speaker 2: of Hubrick's ideas are around AI are go back to

1173
01:01:08,280 --> 01:01:15,080
Speaker 2: this very specific version of technology where it inevitably turns

1174
01:01:15,120 --> 01:01:19,240
Speaker 2: into a mean psychopath who is set on dominating all

1175
01:01:19,280 --> 01:01:20,000
Speaker 2: of humanity.

1176
01:01:20,440 --> 01:01:21,200
Speaker 1: So as an.

1177
01:01:21,120 --> 01:01:24,320
Speaker 2: Alternative, because we do like to talk about, you know,

1178
01:01:24,640 --> 01:01:28,560
Speaker 2: the importance of expanding our imagination, Like you know, with

1179
01:01:28,600 --> 01:01:30,680
Speaker 2: regards to climate, a lot of it is like either

1180
01:01:31,040 --> 01:01:35,240
Speaker 2: it's either apocalypse or business as usual capitalism. There's not

1181
01:01:35,600 --> 01:01:40,440
Speaker 2: like people have a hard time imagining the alternative on AI.

1182
01:01:41,000 --> 01:01:45,840
Speaker 1: On the subject of AI, like what are is there?

1183
01:01:46,000 --> 01:01:48,400
Speaker 2: First of all, is there any like mainstream kind of

1184
01:01:48,440 --> 01:01:52,400
Speaker 2: popular movie that you feel like, actually like that example

1185
01:01:52,440 --> 01:01:55,520
Speaker 2: of AI, like got it right? And then are there

1186
01:01:55,560 --> 01:01:59,120
Speaker 2: other kind of more obscure works that you would send

1187
01:01:59,200 --> 01:02:02,080
Speaker 2: people to in order to sort of feed their imagination

1188
01:02:02,240 --> 01:02:06,400
Speaker 2: of like what what a world with AI technology could

1189
01:02:06,400 --> 01:02:06,760
Speaker 2: look like?

1190
01:02:07,360 --> 01:02:10,040
Speaker 5: Yeah? I mean that question of the imagination and how

1191
01:02:10,160 --> 01:02:14,160
Speaker 5: our current imagination of AI is really narrow is super key,

1192
01:02:14,280 --> 01:02:16,800
Speaker 5: And you know, I think it's fascinating that, yes, you know,

1193
01:02:16,920 --> 01:02:19,080
Speaker 5: Musk and these folks have like a very narrow set

1194
01:02:19,160 --> 01:02:21,800
Speaker 5: of stories that they refer to. In those stories, it's

1195
01:02:21,840 --> 01:02:24,920
Speaker 5: always like, oh, the type of masculine take Bro makes

1196
01:02:24,920 --> 01:02:27,400
Speaker 5: an AI and then it looks like him and it

1197
01:02:27,440 --> 01:02:30,000
Speaker 5: takes over the world, and you're like, are you okay

1198
01:02:30,760 --> 01:02:32,880
Speaker 5: that you're like your bedtime story?

1199
01:02:33,160 --> 01:02:36,800
Speaker 2: Weird? The mean sociopath bent on world domination thinks that

1200
01:02:37,040 --> 01:02:39,919
Speaker 2: all AI is going to be a mean sociopath bent

1201
01:02:40,040 --> 01:02:40,960
Speaker 2: on world domination.

1202
01:02:41,120 --> 01:02:43,600
Speaker 1: That's so weird, right, Yeah?

1203
01:02:43,640 --> 01:02:44,760
Speaker 5: Who would have guessed that?

1204
01:02:45,080 --> 01:02:45,360
Speaker 1: Yeah?

1205
01:02:45,400 --> 01:02:49,280
Speaker 5: But yeah, I mean, and something that I really like

1206
01:02:49,440 --> 01:02:52,560
Speaker 5: our stories, particularly science fiction stories that I love sci

1207
01:02:52,640 --> 01:02:55,200
Speaker 5: fi and fantasy. I'm like a huge believer in like

1208
01:02:55,600 --> 01:02:57,760
Speaker 5: the way that it can help us imagine different worlds.

1209
01:02:58,120 --> 01:03:01,160
Speaker 5: In terms of like mainstream films, I guess the one

1210
01:03:01,160 --> 01:03:02,680
Speaker 5: that I comes to the top of my head is

1211
01:03:02,720 --> 01:03:05,600
Speaker 5: Big Hero six. So the Disney Pixar film where you

1212
01:03:05,640 --> 01:03:09,240
Speaker 5: have an AI powered kind of robot healthcare buddy called

1213
01:03:09,280 --> 01:03:11,800
Speaker 5: bay Max, And I think that's a really interesting example

1214
01:03:11,880 --> 01:03:14,040
Speaker 5: of an AI that you know, at one point in

1215
01:03:14,040 --> 01:03:16,400
Speaker 5: the film, he gets dressed up in armor and you know,

1216
01:03:16,960 --> 01:03:18,960
Speaker 5: the kid here to starts to try and use him

1217
01:03:19,000 --> 01:03:20,800
Speaker 5: as a kind of weapon. But like, this is an

1218
01:03:20,800 --> 01:03:23,040
Speaker 5: AI that so hasn't been designed to be a weapon,

1219
01:03:23,440 --> 01:03:26,760
Speaker 5: that kind of resists being weaponized a lot of ways,

1220
01:03:26,920 --> 01:03:29,200
Speaker 5: And in that sense, it's really countercultural to a lot

1221
01:03:29,200 --> 01:03:31,640
Speaker 5: of the AI we see in Western film and cinema,

1222
01:03:31,680 --> 01:03:34,960
Speaker 5: which is often very weaponized. It's often either this like

1223
01:03:35,080 --> 01:03:38,520
Speaker 5: kind of sexy cyborg figure or it's this hyper masculine

1224
01:03:38,640 --> 01:03:41,880
Speaker 5: terminator figure. Yeah, so I find bay Max is kind

1225
01:03:41,880 --> 01:03:43,400
Speaker 5: of gender lid and like.

1226
01:03:43,480 --> 01:03:49,280
Speaker 2: Just puffy balloon person. But it's also so accurate of

1227
01:03:49,360 --> 01:03:53,120
Speaker 2: what a young boy would do immediately with that is

1228
01:03:53,160 --> 01:03:57,520
Speaker 2: like weapon. It's like the tweet I can tell you

1229
01:03:57,560 --> 01:03:59,800
Speaker 2: me and my friends would have killed et with hammers.

1230
01:04:00,000 --> 01:04:00,360
Speaker 1: It's like.

1231
01:04:01,920 --> 01:04:06,000
Speaker 2: Little boys are monsters, but that's a great example.

1232
01:04:06,240 --> 01:04:09,920
Speaker 5: Yeah, in terms of like some alternative like stories and ideas,

1233
01:04:10,200 --> 01:04:12,800
Speaker 5: try to do a really shameless self plug But we

1234
01:04:12,880 --> 01:04:15,640
Speaker 5: have a book coming out called The Robot Why Technology

1235
01:04:15,680 --> 01:04:19,520
Speaker 5: Needs Feminism, and I'm plugging it because it's really beautiful.

1236
01:04:19,520 --> 01:04:22,120
Speaker 5: We worked with a science fiction illustrator because Sin John Lee,

1237
01:04:22,120 --> 01:04:25,000
Speaker 5: and there was myself and doctor Elinoaldrage, my co host.

1238
01:04:25,080 --> 01:04:27,240
Speaker 5: And the whole thing is these two thousand word essays.

1239
01:04:27,320 --> 01:04:30,160
Speaker 5: They're really short and really punchy by lots of different

1240
01:04:30,160 --> 01:04:32,520
Speaker 5: guests with had on the podcast, and they all respond

1241
01:04:32,560 --> 01:04:35,240
Speaker 5: to this idea of good technology dot dot dot. So

1242
01:04:35,240 --> 01:04:38,760
Speaker 5: it might be good technology is free or good technology,

1243
01:04:38,920 --> 01:04:41,440
Speaker 5: you know, challenges power, and the idea is you can

1244
01:04:41,520 --> 01:04:44,600
Speaker 5: just dip in read one. They're also illustrated, and then

1245
01:04:44,720 --> 01:04:46,520
Speaker 5: just dip out when you need a break or a moment.

1246
01:04:47,040 --> 01:04:49,400
Speaker 5: But yeah, I really would encourage you to pick that

1247
01:04:49,520 --> 01:04:53,040
Speaker 5: up because it just contains the most incredible feminist philosophers,

1248
01:04:53,080 --> 01:04:57,320
Speaker 5: technologists and vendors, activists who are really pushing forward different

1249
01:04:57,360 --> 01:04:59,800
Speaker 5: ideas of the kinds of technological futures we could have.

1250
01:05:00,360 --> 01:05:01,840
Speaker 5: I mean, apart from that, I read a lot of

1251
01:05:01,880 --> 01:05:06,240
Speaker 5: like Chinese diaspora Asian American sci fi as well, particularly

1252
01:05:06,360 --> 01:05:08,480
Speaker 5: sci fi that's like thinking a lot about the climate

1253
01:05:08,520 --> 01:05:10,960
Speaker 5: crisis and AI and the intersections of that, and so

1254
01:05:11,040 --> 01:05:13,800
Speaker 5: I think a lot of those stories have really interesting

1255
01:05:13,840 --> 01:05:17,040
Speaker 5: and different perspectives on what AI is. It can be

1256
01:05:17,400 --> 01:05:20,440
Speaker 5: from like Elliott de Badad's work, which explores, you know,

1257
01:05:20,520 --> 01:05:23,840
Speaker 5: the relationships between like humans and sentient mind shifts in

1258
01:05:23,880 --> 01:05:27,600
Speaker 5: this base opera universe, through the like lyrical lized Fultfish Girl,

1259
01:05:27,640 --> 01:05:30,720
Speaker 5: which is like a very dystopian imagining of like a

1260
01:05:30,720 --> 01:05:34,080
Speaker 5: future society based on labor from clones. Like all of

1261
01:05:34,120 --> 01:05:36,760
Speaker 5: these novels, I think just as they're like a lot

1262
01:05:36,800 --> 01:05:38,600
Speaker 5: more love and a lot more could ask, they're just

1263
01:05:39,040 --> 01:05:43,760
Speaker 5: absolutely interesting and imaginative, been gorgeous ideas about the future.

1264
01:05:44,480 --> 01:05:44,960
Speaker 1: Amazing.

1265
01:05:45,320 --> 01:05:48,040
Speaker 2: Well, this has been such a fascinating conversation. Thank you

1266
01:05:48,080 --> 01:05:50,160
Speaker 2: so much for taking the time. At what I have

1267
01:05:50,240 --> 01:05:51,960
Speaker 2: to assume is midnight where you.

1268
01:05:51,960 --> 01:05:54,120
Speaker 1: Are right now? It's almost nine?

1269
01:05:54,600 --> 01:05:57,600
Speaker 2: Almost nine, Okay, yeah, that's too late. Well, thank you

1270
01:05:57,680 --> 01:06:01,800
Speaker 2: doctor McNerney for doing this show. Where can people find you?

1271
01:06:01,960 --> 01:06:04,360
Speaker 1: Follow you? Hear you all that good stuff?

1272
01:06:04,840 --> 01:06:06,640
Speaker 5: Oh yeah, thanks so much for having me on. It's

1273
01:06:06,640 --> 01:06:08,800
Speaker 5: been a bluff. Yeah, check out the Good Robot. We're

1274
01:06:08,800 --> 01:06:11,760
Speaker 5: on YouTube, Apple, Spotify, and then keep an eye out

1275
01:06:11,800 --> 01:06:14,000
Speaker 5: for the book coming in February twenty twenty four.

1276
01:06:14,880 --> 01:06:17,320
Speaker 1: Amazing. Yeah, we'll have to have you back for that. Yeah.

1277
01:06:17,520 --> 01:06:20,080
Speaker 2: Yeah, absolutely. And is there a work of media that

1278
01:06:20,120 --> 01:06:20,520
Speaker 2: you've been.

1279
01:06:20,520 --> 01:06:24,400
Speaker 5: Enjoying ooh, media in terms of like a show.

1280
01:06:24,360 --> 01:06:28,680
Speaker 2: Like a pot show, a podcast, book and tweet.

1281
01:06:28,840 --> 01:06:31,720
Speaker 5: Oh. I feel like I shoul choose something like really intellectual.

1282
01:06:31,720 --> 01:06:33,880
Speaker 5: But I actually just sent my husband this tweet that

1283
01:06:33,920 --> 01:06:37,760
Speaker 5: really got me because very me, which is it was

1284
01:06:37,920 --> 01:06:39,800
Speaker 5: it's actually a bit dark, but with this news headline

1285
01:06:39,800 --> 01:06:42,640
Speaker 5: about this hiker had been missing and the rescuers couldn't

1286
01:06:42,640 --> 01:06:44,680
Speaker 5: get in contact with them and they didn't get rescued

1287
01:06:44,680 --> 01:06:47,560
Speaker 5: for twenty four hours because they wouldn't answer their phone.

1288
01:06:47,760 --> 01:06:51,840
Speaker 5: It was an unknown number. And this is just exactly me,

1289
01:06:52,080 --> 01:06:56,600
Speaker 5: like my husband like please fuck you know what, I'm like,

1290
01:06:57,280 --> 01:06:59,760
Speaker 5: I will not I will not answer it under a number.

1291
01:07:00,120 --> 01:07:02,360
Speaker 5: So that brought me a little bit of joy today.

1292
01:07:03,120 --> 01:07:05,680
Speaker 2: Absolutely, And I feel like that's a great way the

1293
01:07:06,200 --> 01:07:09,760
Speaker 2: great example of how technology has marched forward and completely

1294
01:07:09,880 --> 01:07:13,880
Speaker 2: ruined like something that used to work, phone communication, but

1295
01:07:13,960 --> 01:07:19,240
Speaker 2: we can't answer our phones anymore because the fucking spots, Miles,

1296
01:07:19,320 --> 01:07:21,280
Speaker 2: where can people find you? What's a work media you've

1297
01:07:21,280 --> 01:07:21,920
Speaker 2: been enjoying?

1298
01:07:22,040 --> 01:07:25,840
Speaker 3: Yeah at Miles of Gray wherever they got at. I'm

1299
01:07:25,880 --> 01:07:32,120
Speaker 3: there soon to Blue Sky, shout out Ill Will Christy Main. Yeah,

1300
01:07:32,400 --> 01:07:34,840
Speaker 3: you and you and I were hopping in okay codes

1301
01:07:34,840 --> 01:07:36,120
Speaker 3: we got we got the invite.

1302
01:07:36,200 --> 01:07:38,840
Speaker 1: Just check out the Blue Sky. So yeah there shortly.

1303
01:07:38,920 --> 01:07:41,880
Speaker 1: I'll also check Jack and Eye out on our basketball podcast.

1304
01:07:41,600 --> 01:07:44,400
Speaker 3: Miles and jackot Man Maps. Please check me out on

1305
01:07:44,440 --> 01:07:49,000
Speaker 3: my ninety Day Fiance podcast for twenty Day Fiance and

1306
01:07:49,040 --> 01:07:51,760
Speaker 3: also the true crime show The Good Thief, which has

1307
01:07:51,800 --> 01:07:53,480
Speaker 3: all episode eight episodes out, so.

1308
01:07:53,440 --> 01:07:57,280
Speaker 1: Please stream those binge those talking about the Greek robin Hood,

1309
01:07:57,680 --> 01:08:01,680
Speaker 1: the man who legitimately was kidnapped millionaires and doing some

1310
01:08:01,720 --> 01:08:05,840
Speaker 1: old fashioned wealth redistribution in a positive way, never hurting

1311
01:08:05,880 --> 01:08:09,760
Speaker 1: people either, never hurting people, like again, an ethical criminal,

1312
01:08:09,800 --> 01:08:12,280
Speaker 1: if there is such a thing. Some tweets I like.

1313
01:08:12,360 --> 01:08:14,360
Speaker 3: First one is from you know, shout out to the

1314
01:08:14,440 --> 01:08:17,559
Speaker 3: WGA and all the negotiators, because it looks like the

1315
01:08:17,640 --> 01:08:21,240
Speaker 3: WGA strike is they're close to getting something ratified, so

1316
01:08:21,320 --> 01:08:24,439
Speaker 3: we like that. This tweet is from Jeff Yang at

1317
01:08:24,479 --> 01:08:28,559
Speaker 3: Original Spin tweeted just underscoring how WGA negotiators told the

1318
01:08:28,600 --> 01:08:32,080
Speaker 3: studios the union wouldn't go back to work until SAG

1319
01:08:32,160 --> 01:08:34,920
Speaker 3: also has a deal, because if the last five months

1320
01:08:35,000 --> 01:08:38,439
Speaker 3: proved anything, it's in together, win together. And there's this

1321
01:08:39,000 --> 01:08:41,400
Speaker 3: snippet from it might be a deadline, but it says quote.

1322
01:08:41,479 --> 01:08:42,599
Speaker 1: The studios also.

1323
01:08:42,400 --> 01:08:45,320
Speaker 3: Inquired if once a tentative agreement is ratified by the scribes,

1324
01:08:45,400 --> 01:08:47,120
Speaker 3: if the writers would pick up their pens and hit

1325
01:08:47,160 --> 01:08:50,080
Speaker 3: their keyboards again. Very soon afterwards, the guild, from what

1326
01:08:50,120 --> 01:08:52,720
Speaker 3: we understand, made it clear that they would not be

1327
01:08:52,840 --> 01:08:56,200
Speaker 3: going back to work until SAG Aftra also had a

1328
01:08:56,280 --> 01:09:00,599
Speaker 3: new agreement with the AMPTP. Reflective of the w w

1329
01:09:00,760 --> 01:09:04,479
Speaker 3: GA's feeling of solidarity between the two unions that has

1330
01:09:04,560 --> 01:09:08,320
Speaker 3: characterized their first mutual strikes in nineteen sixty love the

1331
01:09:08,400 --> 01:09:10,920
Speaker 3: solidarity and I know AYATSI is also going to have

1332
01:09:11,000 --> 01:09:13,440
Speaker 3: a contract that is going to need to be renegotiated

1333
01:09:13,720 --> 01:09:17,760
Speaker 3: next year. And it looks like guess what, folks, they did.

1334
01:09:17,760 --> 01:09:19,200
Speaker 1: It only listen to one thing.

1335
01:09:19,760 --> 01:09:22,479
Speaker 3: Yeah, putting your fucking putting the tools down. Put the

1336
01:09:22,560 --> 01:09:24,760
Speaker 3: tools down to pick them other tools up, which is

1337
01:09:24,760 --> 01:09:27,559
Speaker 3: getting in your solidarity back, so we'd love to see that.

1338
01:09:27,760 --> 01:09:30,360
Speaker 3: And another tweet I like is from t Pain at

1339
01:09:30,400 --> 01:09:34,679
Speaker 3: t Pain just put bartender just doing her job.

1340
01:09:35,280 --> 01:09:38,719
Speaker 1: Me just this prudo of Kevin James at the butt

1341
01:09:38,800 --> 01:09:42,960
Speaker 1: of Kevin James. That's going fucking rugged, but it is drugging,

1342
01:09:43,160 --> 01:09:46,280
Speaker 1: like trying to be like, hey, hey, can I get

1343
01:09:46,280 --> 01:09:46,960
Speaker 1: your eye content?

1344
01:09:47,280 --> 01:09:47,479
Speaker 2: Yeah?

1345
01:09:47,680 --> 01:09:50,840
Speaker 1: Just a just a ginger beer if you can. Those

1346
01:09:50,840 --> 01:09:52,160
Speaker 1: are my tools.

1347
01:09:52,360 --> 01:09:56,920
Speaker 2: Tweet I've been enjoying at BRNBNE. I don't know how

1348
01:09:56,960 --> 01:10:00,640
Speaker 2: to pronounce that, but their display name is bbb bbbb

1349
01:10:01,200 --> 01:10:03,639
Speaker 2: space BBBBBBB, which I have.

1350
01:10:03,640 --> 01:10:04,760
Speaker 1: To assume stands for.

1351
01:10:05,000 --> 01:10:09,240
Speaker 2: Bone bone bone bone bone boom bone boom boom. But

1352
01:10:09,479 --> 01:10:13,200
Speaker 2: the tweet I usually don't use for my tweet, the

1353
01:10:13,240 --> 01:10:17,679
Speaker 2: ones that things that I've retweeted, but this one got

1354
01:10:17,720 --> 01:10:20,280
Speaker 2: me so much I had to reshare it. The tweet

1355
01:10:20,360 --> 01:10:22,639
Speaker 2: is what if you went to ET's planet and all

1356
01:10:22,680 --> 01:10:25,200
Speaker 2: of the other ets were wearing clothes.

1357
01:10:26,920 --> 01:10:31,320
Speaker 1: That really, oh, really fucked up? How I thought about Et? Yeah?

1358
01:10:32,000 --> 01:10:32,920
Speaker 1: Yo what?

1359
01:10:36,720 --> 01:10:41,240
Speaker 2: You can find me on Twitter at Jack Underscore O'Brien,

1360
01:10:41,600 --> 01:10:44,840
Speaker 2: and on threads at Jack Underscore, Oh Underscore, Brian, and

1361
01:10:45,080 --> 01:10:49,800
Speaker 2: on Blue Skill. I'll have a username there eventually soon

1362
01:10:50,320 --> 01:10:53,120
Speaker 2: and you can find us on Twitter at daily szeikeist

1363
01:10:53,160 --> 01:10:54,559
Speaker 2: d daily Zekeist on Instagram.

1364
01:10:54,600 --> 01:10:56,000
Speaker 1: We have a Facebook fan page.

1365
01:10:55,720 --> 01:10:58,040
Speaker 2: On a website daily zeikes dot com where we post

1366
01:10:58,040 --> 01:11:01,639
Speaker 2: our episodes and our footnote. We off to the information

1367
01:11:01,640 --> 01:11:03,479
Speaker 2: that we talked about in today's episode, as well as

1368
01:11:03,479 --> 01:11:05,800
Speaker 2: a song that we think you might enjoy. Myles, what's

1369
01:11:05,840 --> 01:11:07,240
Speaker 2: the song people might enjoy?

1370
01:11:07,720 --> 01:11:07,920
Speaker 1: Uh?

1371
01:11:07,960 --> 01:11:12,439
Speaker 3: This is an artist Detroit's very own John FM, and

1372
01:11:12,560 --> 01:11:14,840
Speaker 3: I just figure out what an appropriate title. It's called

1373
01:11:14,920 --> 01:11:18,600
Speaker 3: White Science h And the track is like very It

1374
01:11:18,640 --> 01:11:22,040
Speaker 3: feels like if, like like I don't know, like if

1375
01:11:22,080 --> 01:11:24,240
Speaker 3: Prince was making shit in like the late nights. It

1376
01:11:24,320 --> 01:11:27,080
Speaker 3: has like a frinsy vibe like it's frincy and it's

1377
01:11:27,120 --> 01:11:30,160
Speaker 3: kind of got this like vocal modulator on it that

1378
01:11:30,200 --> 01:11:33,240
Speaker 3: feels a little bit princey. So and also it's just

1379
01:11:33,240 --> 01:11:36,000
Speaker 3: a really good track. So this is John FM with

1380
01:11:36,240 --> 01:11:37,080
Speaker 3: White Science.

1381
01:11:37,479 --> 01:11:41,160
Speaker 2: That's what Jack FM's mom calls him when he's in trouble.

1382
01:11:42,080 --> 01:11:43,599
Speaker 1: John FM, get in here now.

1383
01:11:43,840 --> 01:11:46,000
Speaker 2: The Daily Zeitge is in production by Heart Radio. For

1384
01:11:46,080 --> 01:11:48,320
Speaker 2: more podcasts from my heart Radio is the iHeartRadio app

1385
01:11:48,320 --> 01:11:50,680
Speaker 2: Apple podcast wherever you listen to your favorite shows. That

1386
01:11:50,840 --> 01:11:53,479
Speaker 2: is gonna do it for us this morning, back this

1387
01:11:53,520 --> 01:11:56,240
Speaker 2: afternoon to tell you what is trending, and we'll talk

1388
01:11:56,280 --> 01:11:56,760
Speaker 2: to you all then.

1389
01:11:56,960 --> 01:12:01,600
Speaker 1: Bye bye,