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Speaker 1: There Are No Girls on the Internet, as a production

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Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Toad and this

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Speaker 1: is There Are No Girls on the Internet. Welcome to

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Speaker 1: There Are No Girls on the Internet, where we explore

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Speaker 1: the intersection of technology, identity, and social media.

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Speaker 2: Now.

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Speaker 1: Even if you've never seen the iconic image a former

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Speaker 1: model Lenna Forson, if you've ever shared a meme on

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Speaker 1: the Internet or texted a picture to a friend, in

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Speaker 1: some ways, you have her picture to thank for it.

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Speaker 1: I joined my friends Samantha and Annie over at the

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Speaker 1: podcast Stuff. Mom never told you to dig into the

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Speaker 1: Lenna image and why this month Lena is finally retiring

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Speaker 1: as the first Lady of the Internet.

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Speaker 3: Hey, this is Annie and Samantha, I walk to Stuff.

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Speaker 3: I never told you your propictionure of iHeartRadio. Today, we

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Speaker 3: are once again thrilled to be joined by the fabulous,

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Speaker 3: the fantastic Bridget Todd to Welcome Bridget.

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Speaker 2: Thank you so much for having me.

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Speaker 1: It's always such a joy when I get to start

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Speaker 1: my week talking to you all.

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Speaker 4: Yes, I feels like you have an extra glow. Maybe

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Speaker 4: it's because you've been like soaking in all of the

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Speaker 4: sun on the beautiful beaches abroad. I've been stalking you

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Speaker 4: on Instagram and I'm like, how is this woman always traveling?

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Speaker 4: And I missed and I'm sad that I'm not.

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Speaker 2: I'm just kidding. Oh you all should have come.

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Speaker 1: We actually so, I was in Mazatlan, Mexico for a

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Speaker 1: clips to be in a hospitality. We actually it was

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Speaker 1: one of those trips where we'd invited all of our friends.

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Speaker 2: We were like, we're gonna get a big house on

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Speaker 2: the beach. It's gonna be amazing.

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Speaker 1: And then all of our friends are in and then

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Speaker 1: one by one by one by one, I'm.

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Speaker 2: Just there alone, essentially. No enjoyed it.

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Speaker 1: I enjoyed it. I love Mexico. It is one of

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Speaker 1: my favorite places. It was my first time in mazat Lawn.

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Speaker 1: Ten out of ten completely recommend it.

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Speaker 3: Okay, did you see the eclipse and totality?

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Speaker 2: I saw the eclipse of totality.

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Speaker 1: It was my first time ever being in the path

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Speaker 1: of totality of an eclipse.

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Speaker 2: You have either of you ever experienced this?

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Speaker 3: No? Yes? Once?

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Speaker 2: What were your thoughts? Annie, I am dying to know.

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Speaker 3: Oh my gosh. If this was a different podcast, we

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Speaker 3: would go into a whole separate thing because I had

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Speaker 3: like a relationship issue that was happening on this day

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Speaker 3: and kind of a drama situation. So a lot of

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Speaker 3: times when I look back at it, a lot of

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Speaker 3: the pictures I took, I was like, oh, wow, we

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Speaker 3: were fighting, but it was also a work event that

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Speaker 3: I was at, so there was that layer. But it

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Speaker 3: was beautiful. It was so cool. I it sounds silly,

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Speaker 3: but I love space, like I like love the stars

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Speaker 3: are like my favorite thing. So it was really really

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Speaker 3: cool to see. It was not what I quite expected

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Speaker 3: because the glasses. Samanth and I were joking about this recently,

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Speaker 3: but the glasses feel so funny because you're like looking

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Speaker 3: around like they're not working, and then you like find

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Speaker 3: the space you're supposed to look up. I was just like,

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Speaker 3: very very happy to see it, honestly, like all that

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Speaker 3: drama I was working aside, I remember thinking, this is

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Speaker 3: really cool that I get to see this, and I'm

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Speaker 3: really happy that I get to see this.

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Speaker 2: Yes, that was what I remember too.

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Speaker 1: I burst into tears and the next day I woke

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Speaker 1: up in the middle of the night in a panic

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Speaker 1: because I was worried that I will forget what it

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Speaker 1: looked like being in totality like that, Like that's how

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Speaker 1: like I had never seen it, but I'd never say

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Speaker 1: anything like it. Anybody who listens to their no girls

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Speaker 1: on the internet is probably so sick of me talking

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Speaker 1: to me about this eclipse, and I am fully making

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Speaker 1: seeing this total eclipse like my personality. But it's kind

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Speaker 1: But yeah, I'm already planning where I will go for

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Speaker 1: the next one. So I guess I will see y'all

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Speaker 1: in I think, what is it, Spain?

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Speaker 2: So that's that's the one.

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Speaker 1: The next the next one that you can see if

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Speaker 1: you can go to Spain and see I think in

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Speaker 1: twenty twenty six. Okay, so but then the one that

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Speaker 1: you're referring to, Sam is like, don't hote me on

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Speaker 1: any of this.

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Speaker 2: But that's supposed to be like the big one, the

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Speaker 2: big one that we will.

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Speaker 1: Probably be able to see in our lifetimes, and I

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Speaker 1: think it's in parts of the African continent.

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Speaker 2: I want to say, Morocco. Don't houte me on that either.

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Speaker 4: Yeah, I did say at one point like it you

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Speaker 4: would be able to view it in the US. That's

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Speaker 4: the next time you'll be able to it. I don't

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Speaker 4: know if it's like the actual like the totality, as

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Speaker 4: you say, but like, I don't know, because I know

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Speaker 4: nothing about this. That's the only I know the date

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Speaker 4: in no kind for this past eclipse.

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Speaker 1: Earlier this month, we had done so much planning, including

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Speaker 1: like looking at farmer's almanacs to see what the weather

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Speaker 1: and cloud coverage is like this time of year. And

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Speaker 1: that's how we settled on Mazetlan, Mexico, because it was

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Speaker 1: the place that is closest to us on the East

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Speaker 1: Coast in the United States that was most likely to

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Speaker 1: not have cloud coverage in April, because you could see

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Speaker 1: it from Vermont and Upstate New York and Texas, but

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Speaker 1: a lot of those places in April might be cloudy.

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Speaker 1: And so I have friends who were in Vermont and

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Speaker 1: upstate New York who were like, Oh, We're just.

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Speaker 2: Gonna see it from our house, and I'm like, oh,

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Speaker 2: will you.

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Speaker 1: Then on the morning of vietclipse in Mazatlan, Mexico, we'd

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Speaker 1: been there for a week. Every single day it's like

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Speaker 1: a beautiful, cloudless, blue sky day. So I wake up

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Speaker 1: on April eighth, the day of the eclipse, and it's cloudy,

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Speaker 1: the first cloudy day we've had in Mexico for the

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Speaker 1: entire week we have been there. Luckily, during the eclipse time,

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Speaker 1: the clouds did part, so we did get to see it.

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Speaker 1: But there would have been a lot of feelings had

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Speaker 1: we've not been able to see it.

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Speaker 3: It's a lot of pressure to put on a trip

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Speaker 3: like that. Honestly, it's pressure to put on the eclipse.

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Speaker 4: I mean, it exists.

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Speaker 2: It's not their fault. So you can can contact like

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Speaker 2: the manager of.

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Speaker 4: The sky to be like, actually, we didn't get a

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Speaker 4: good view, and we were like, okay, for y'all who

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Speaker 4: are Christians, here tell this God that truly did we

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Speaker 4: get canceled?

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Speaker 1: We were like getting a little superstitious, like the things

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Speaker 1: that we were doing to try to like ensure good sky.

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Speaker 2: It was getting a little a little out there. We'll

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Speaker 2: just sleep it at that.

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Speaker 4: He brought a shaman in like we're going in.

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Speaker 3: Oh my gosh, Bridget, I want to ask so many questions.

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Speaker 2: About this later.

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Speaker 3: Oh well, I'm very glad that you got to see it.

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Speaker 3: It is. It is amazing, like truly, and over on

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Speaker 3: the other podcast I New Savor, we did a episode

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Speaker 3: on like weird companies making money off of the eclipse

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Speaker 3: with their products, and I have heard from so many

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Speaker 3: people about the foods they made for the eclipse, and

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Speaker 3: it's brought me so much story. So oh oh yeah,

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Speaker 3: like totalityea, like.

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Speaker 2: Tea, Oh yeah, that's good.

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Speaker 3: Oh my gosh, so many things like this. So I

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Speaker 3: feel like we have a couple of we have some

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Speaker 3: years to brainstorm things like this.

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Speaker 4: Next giant celebration, but keep that.

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Speaker 3: In the back of your head, you know.

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Speaker 1: Oh, next time, definitely doing eclipse themed at food party

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Speaker 1: or dinner party or something.

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Speaker 2: I love that.

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Speaker 3: Yes, there's a so many puns. I will I will

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Speaker 3: hold myself back for now, but I have to say

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Speaker 3: I am very, very excited to talk about the topic

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Speaker 3: he brought today, Bridget, because it is a thing that

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Speaker 3: I love of, like the history of something I think

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Speaker 3: a lot of people don't question the history of and

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Speaker 3: it's fascinating and I didn't know about it. So can

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Speaker 3: you tell us what we're discussing today?

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Speaker 1: I feel the exact same way, And today we are

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Speaker 1: talking about the Lenna image.

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Speaker 2: Is this something that either of you had ever heard of?

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Speaker 1: I do not know, not known, So even if you're

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Speaker 1: listening and you're like, what is the Lenna image?

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Speaker 2: I've never heard of this image. I've never seen this image.

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Speaker 2: Even if you.

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Speaker 1: Don't know the story and you don't feel like you've

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Speaker 1: ever seen this image before, you kind of do know

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Speaker 1: this image because, as Linda Kinsler puts it in a

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Speaker 1: really meaty piece for Wired that I'll be referencing a

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Speaker 1: few times in this conversation, she writes, whether or not

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Speaker 1: you know her face, you've used the technology it helps

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Speaker 1: create practically every photo you've ever taken, every website you've

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Speaker 1: ever visited, every meme you've ever shared. Owes some small

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Speaker 1: debt to Lenna, and it really is exactly as you

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Speaker 1: were saying, Annie, one of those stories that is foundational

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Speaker 1: to the Internet and technology that you don't necessarily think of,

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Speaker 1: don't necessarily think of how it came to be, and

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Speaker 1: especially I think it's one of those stories that says

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Speaker 1: a lot about technology. On you here on Sminty, We've

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Speaker 1: had plenty of conversations about this. I've had many conversations

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Speaker 1: about this on their No Girls on the Internet, about

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Speaker 1: how things like massogyny are kind of can sort of

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Speaker 1: be baked into the foundation of technology, and I think

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Speaker 1: that is one of the reasons why tech is so

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Speaker 1: often perpetuating misogyny, not because it's some sort of an

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Speaker 1: unfortunate bug, but because this misogyny can be sort of

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Speaker 1: foundational in some ways. And I think this image really

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Speaker 1: is a good example of what I mean. And I think,

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Speaker 1: especially as we're having conversations about the rise of things

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Speaker 1: like new toify apps and AI generated adult content creators,

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Speaker 1: we're seeing what is kind of becoming a marketplace that is,

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Speaker 1: men making money off of the bodies and or labor

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Speaker 1: of women without their consent, certainly without their compensation. And

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Speaker 1: I think this situation in the Lenna image, where this

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Speaker 1: image of a woman went on to create this entire

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Speaker 1: field of technology without her consent, perhaps really tell us

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Speaker 1: something about where we're headed in twenty twenty four.

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Speaker 3: Yes, absolutely, especially when you consider where it comes from,

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Speaker 3: which I know we'll talk about. But also, yeah, these

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Speaker 3: conversations we're having now about like actors perhaps not given

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Speaker 3: their consent to being used in certain ways just and honestly,

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Speaker 3: it ekes sense to all of us if you've posted

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Speaker 3: an image on her line, right, not consenting to using

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Speaker 3: an image to an image getting used in a certain way.

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Speaker 3: But so much about this history is fascinating because it

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Speaker 3: feels so standardized, which is odd. Can you tell us

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Speaker 3: about that?

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Speaker 2: Totally?

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Speaker 1: So for folks who don't know, the Lenna image is

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Speaker 1: literally an image of this woman, Lenna Letna Forsin. She

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Speaker 1: is a woman from Sweden who in the seventies was

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Speaker 1: a model. So this kind of sensual image of her

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Speaker 1: wearing a tan hat with a purple feather flowing down

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Speaker 1: her bare back, staring kind of seductively over one shoulder.

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Speaker 1: That image of her was published in Playboy in nineteen

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Speaker 1: seventy two.

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Speaker 2: She was essentially a playmate.

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Speaker 1: That image would go on to become what's called a

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Speaker 1: standard test image.

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Speaker 2: So big caveat here. I am at an engineer.

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Speaker 1: If I say something that is you're like, if you're

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Speaker 1: an engineer listening, and you're like, that's not totally correct.

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Speaker 1: I am at an engineer. But here is a definition

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Speaker 1: of what a standard test image is that I found

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Speaker 1: from caggle dot com, which is like a developer community site.

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Speaker 1: They say a standard test image is a digital image

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Speaker 1: file used across different institutions to test image processing and

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Speaker 1: image compression algorithms by using the same standard test images,

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Speaker 1: different labs are able to compare results both visually and quantitatively.

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Speaker 1: The images are in many cases chosen to represent natural

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Speaker 1: or typical images that a class of processing techniques would

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Speaker 1: need to deal with. Other test images are chosen because

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Speaker 1: they present a range of challenges to image reconstruction algorithms,

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Speaker 1: such as the reproduction of fine detail and textures, sharp

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Speaker 1: transitions and edges, and uniform reasons. So basically, to put

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Speaker 1: that in Layman's terms, a standard test image is it's

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Speaker 1: like a test image that tests to make sure that

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Speaker 1: the technology is working as.

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Speaker 2: It should be, or like rendering the way that it

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Speaker 2: should be.

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Speaker 1: Lenna's image is not the only common standard test image.

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Speaker 1: There's also one that is like a bunch of different

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Speaker 1: colored jelly beans on a table. There's another one that's

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Speaker 1: called peppers that's just a bunch of different colored like

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Speaker 1: red and green peppers, like callapeno peppers. So this is

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Speaker 1: just a thing that becomes a way for technologists to

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Speaker 1: test that the image generating technology is working correctly.

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Speaker 3: I do think this is very interesting for a lot

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Speaker 3: of reasons. But if you have like jelly beans and peppers,

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Speaker 3: those are things to be consumed, and then when you're

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Speaker 3: thinking about where they got this image from, a Lena like,

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Speaker 3: how did this happen? How did this image become this

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Speaker 3: standard testing thing?

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Speaker 2: So this is actually a pretty interesting story.

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Speaker 1: The story of how Lenna's Playboy picture becomes this standard

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Speaker 1: test image that is everywhere and very ubiquitous starts with

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Speaker 1: computer and a electrical engineer, Alexander Sawchuk. According to the

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Speaker 1: newsletter for the Institute of Electrical and Electronic Engineering, or

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Speaker 1: the I triple E, as I have found out it's

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Speaker 1: sometimes called.

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Speaker 2: I was talking to somebody about this and I was like, oh,

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Speaker 2: the I E E ee.

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Speaker 1: And they were like, it's just the I triple ELL.

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Speaker 3: I want to so I E E.

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Speaker 5: Actually the I E exactly.

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Speaker 2: So it's the summer of nineteen seventy three.

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Speaker 1: Alexander Sauchuk was an assistant professor of electrical engineering at

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Speaker 1: the University of Southern California and also a grad student

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Speaker 1: in the SIPI lab as a manager. As the story goes,

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Speaker 1: he's like frantically searching around the lab for a good

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Speaker 1: image to scan for a colleagues's conference paper. He had

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Speaker 1: just sort of gotten bored with their usual stock test images.

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Speaker 1: Because they mostly had come from like nineteen sixties TV

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Speaker 1: standards and then we're just a little bit dull. He

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Speaker 1: wanted something glossy and sort of like fresh and dynamic,

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Speaker 1: but he also wanted to use a human face specifically

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Speaker 1: just that as the story goes, somebody happens to walk

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Speaker 1: in holding the most recent issue of Playboy magazine.

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Speaker 2: Why this person was.

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Speaker 1: Bringing Playboy Magazine into his workplace, I'm cannot tell you

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Speaker 1: how good you.

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Speaker 4: Just come into your institute. Okay, cool?

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Speaker 1: Yeah, Like, I mean, I do think that that sort

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Speaker 1: of like gives you a sense of like the dynamics

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Speaker 1: that we're dealing with, right, that somebody just happens to

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Speaker 1: walk in with the but the most recent.

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Speaker 2: Playboy under their arm.

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Speaker 1: Right, The engineers tore away the top third of the

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Speaker 1: centerfold so they could wrap it around the drum of

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Speaker 1: their merhead wire photo scanner, which they had outfitted with

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Speaker 1: analog to digital converters, one for each red, green, and

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Speaker 1: blue channels, and an HP twenty one hundred mini computer.

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Speaker 1: So all of that to say is that they effectively

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Speaker 1: cropped this image so that you can't see the models

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Speaker 1: bears in the image, So it's just a picture of

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Speaker 1: her from the shoulders up looking over her shoulder. It's

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Speaker 1: still like quite a seductive photo, but the full photo

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Speaker 1: has her like bare booty in it.

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Speaker 2: She's wearing I look like.

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Speaker 1: A feather boa and like thigh high stockings looking over

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Speaker 1: her shoulder. So back in the seventies and eighties, this

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Speaker 1: image was really sort of like used in very limited cases.

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Speaker 1: You could only really see it on dot org domains.

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Speaker 1: It was pretty limited to like engineers. Then in July

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Speaker 1: of nineteen ninety one, the image was featured on the

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Speaker 1: cover of Optical Engineering, alongside that other test image of

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Speaker 1: the different colored peppers. Funny enough, I took a look

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Speaker 1: at that cover. It's all black and white. So I'm like, oh,

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Speaker 1: I think they're trying to demonstrate that, like these images

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Speaker 1: had all these different dynamic colors, but both of them

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Speaker 1: are rendered in black and white, kind of rendering that meaningless.

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Speaker 1: So this is when Playboy gets wind of this, and

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Speaker 1: they are not happy because it's basically copyright infringement, which

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Speaker 1: this is not related to the story, but I always

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Speaker 1: have to add this whenever it comes up. How litigious

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Speaker 1: Hugh Hefner and Playboy were. I always think this is

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Speaker 1: very rich because, as y'all probably know, Hugh Hefner made

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Speaker 1: an entire lucrative industry off of images of Marilyn Monroe

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Speaker 1: that she did for a calendar company, for which she

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Speaker 1: was only paid fifty dollars. Many years after that photo shoot,

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Speaker 1: Hugh Hefner bought those photographs from the calendar company republished

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Speaker 1: them without Marilyn Monroe's consent or permission in nineteen fifty three.

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Speaker 1: That was the first ever issue of Playboy. Hugh Hefner

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Speaker 1: paid five hundred dollars. She got fifty dollars. Right, So,

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Speaker 1: whenever I read about how litigious Playboy is, which they're

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Speaker 1: very litigious, I always had to chuckle at that that, Oh, like,

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Speaker 1: you don't want somebody profiting off of your intellectual property,

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Speaker 1: but had no problem profiting off of a woman's body

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Speaker 1: without compensating.

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Speaker 2: Her fairly or even her consent.

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Speaker 4: Interesting, this is like par for the course for him.

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Speaker 2: Oh my godness, don't even get me started with Rough Heffner.

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Speaker 2: We will be here all day.

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Speaker 4: The things that came out after he died, which I'm like, Wow,

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Speaker 4: he had a pretty good, like powerful handle on people

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Speaker 4: not talking until he died.

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Speaker 2: Oh my gosh.

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Speaker 1: I was listening to an episode of celebrity memoir book

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Speaker 1: Club where they read a lot of X Playmate and

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Speaker 1: ex Playboy Bunny memoirs. Some of the things that they

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Speaker 1: write about, I'm like, oh my god. Like even even

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Speaker 1: Lenna in an interview, she talks about how in the seventies,

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Speaker 1: after this photo shoot, she was invited to go to

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Speaker 1: the Playboy mansion and the quote is something like, they

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Speaker 1: made it clear in the invites that I would have

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Speaker 1: to spend time with Hugh Hefner while he was in

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Speaker 1: his dressing robe, and I said, no, thanks, I mean.

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Speaker 5: She already knew. Was like, yeah, yeah.

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Speaker 1: So Playboy threatens to suite these engineers and at this point,

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Speaker 1: the engineers, it sounds like, had like grown very fond

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Speaker 1: of using this image that they thought back. Eventually Playboy

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Speaker 1: back down because, as a Playboy vice president put it, quote,

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Speaker 1: we decided we should exploit this because it is a phenomenon.

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Speaker 1: So yeah, by his own words like, oh, let's exploit this.

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Speaker 2: Yeah.

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Speaker 1: No, talk about the fact that this is two groups

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Speaker 1: of men fighting over who owns this image of a woman,

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Speaker 1: in one case being used in a manner that is

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Speaker 1: completely without her consent or control it just it.

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Speaker 2: Already from the beginning.

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Speaker 1: It just feels to me like men fighting over how

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Speaker 1: they can use a woman's representation that I think is

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Speaker 1: so foundational to some of the conversations we're having about

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Speaker 1: technology like AI right here in twenty twenty.

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Speaker 3: Four, absolutely and she did become pretty foundational, right.

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Speaker 1: Oh, absolutely, So this is when the image of Leta

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Speaker 1: really becomes super popular. The whole drama about the cover

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Speaker 1: catapults this image into what you might think of as

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Speaker 1: like early Internet virality or popularity. This was in nineteen

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Speaker 1: eighty five. The use of the photo and electronic imaging

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Speaker 1: has been described as clearly one of the most important

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Speaker 1: events in history. It is truly hard to overstate how

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Speaker 1: ubiquitous this one image is in technology. There is this

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Speaker 1: fascinating interactive piece by Jennifer Ding at the Pudding. The

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Speaker 1: piece is so cool, It's like one of those interactive

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Speaker 1: pieces that has a timeline.

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Speaker 2: Definitely check it out.

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Speaker 1: But in that piece, Ding actually includes a freeze frame

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Speaker 1: of the show Silicon Valley on HBO, where in the

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Speaker 1: background there is a poster with the Lenna image on

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Speaker 1: the wall. Right, So this image is also included in

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Speaker 1: scientific journals just all over the place. Ding found that

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Speaker 1: within the dot edu world, so like websites related to education,

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Speaker 1: the Lenna image continues to appear in homework questions, class slides,

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Speaker 1: and to be hosted on educational and research sites, ensuring

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Speaker 1: that it will be passed down to new generations of engineer.

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Speaker 1: So this became so popular that Lenna herself is often

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Speaker 1: called the first Lady of the Internet.

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Speaker 3: Wow, I kind of like her taking that picture having

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Speaker 3: no idea that this is what would happen, which, yeah,

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Speaker 3: I mean, I guess that speaks to the next question,

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Speaker 3: why did this image take off the way that it did?

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Speaker 2: Well? If you asked David C.

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Speaker 1: Munson, who is the editor in chief of the i

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Speaker 1: E or the Tripoli Transactions on image processing, he said

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Speaker 1: that the image happened to meet all of these requirements

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Speaker 1: for a good test image because of its detail, it's

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Speaker 1: flat regions, shading and texture. But even he will not

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Speaker 1: leave out the obvious fact that it's also a picture

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Speaker 1: of like a seductive, sexy young woman. Duh, right, like that,

402
00:20:38,880 --> 00:20:41,560
Speaker 1: That's definitely part of it. He says, the Lena image

403
00:20:41,600 --> 00:20:44,320
Speaker 1: picture is of an attractive woman. It is not surprising

404
00:20:44,320 --> 00:20:47,240
Speaker 1: to me that the mostly male image processing research community

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Speaker 1: gravitated toward an image that they found attractive. And so

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Speaker 1: I do think there's something about these highly male dominated

407
00:20:56,640 --> 00:20:59,160
Speaker 1: spaces where it's not just that there's a lot of men,

408
00:20:59,560 --> 00:21:04,840
Speaker 1: it's like their worldviews, their interests, their perspectives, their biases

409
00:21:05,200 --> 00:21:07,520
Speaker 1: that are really taking up a lot of space in

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Speaker 1: these in these spaces, I just think that men feel

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Speaker 1: like these spaces are theirs, and that they are free

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Speaker 1: to decorate those spaces with the pretty women that they

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Speaker 1: think they feel like they should be able to use

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Speaker 1: without their consent or compensation. I just think that, like Annie,

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Speaker 1: you mentioned earlier, that the other test images are these

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Speaker 1: things that you consume, right, like peppers or jelly beans.

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00:21:29,240 --> 00:21:32,879
Speaker 1: There's another famous one of a baboon that's has different

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Speaker 1: colors on its face.

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Speaker 2: It's interesting to me that it's these.

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Speaker 1: Things that are not human, things that are like animal

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Speaker 1: or that you consume that like throwing a sexy young

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00:21:42,720 --> 00:21:44,960
Speaker 1: woman into that mix. I don't think maybe seem like

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00:21:45,000 --> 00:21:46,439
Speaker 1: a huge departure for these guys.

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Speaker 3: Yeah, and again, when we think about things in the

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Speaker 3: realm of AI or even I know I've complained about

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00:21:54,400 --> 00:21:58,000
Speaker 3: this many times. But in the worlds of fandom are gaming,

427
00:21:58,240 --> 00:21:59,919
Speaker 3: it's like that, it's like, you can come into our

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00:22:00,080 --> 00:22:01,720
Speaker 3: world on our terms and you wear what we want

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Speaker 3: you to wear. You are here because we let you

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Speaker 3: be here in this male dominated space, but you're gonna

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00:22:07,320 --> 00:22:10,760
Speaker 3: do what we want. It's not up to you, and

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Speaker 3: that's the only way that you can you can be

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Speaker 3: in this world. But that being said, there has been

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Speaker 3: some pushback recently. Ish right, Bridgie.

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Speaker 2: Yeah, So one thing about what you just said.

436
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Speaker 1: When I was researching for this episode, some of the

437
00:22:29,160 --> 00:22:34,400
Speaker 1: different engineers who had contributed to this image's popularity, they

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Speaker 1: were quoted when they actually met the actual real Letta

439
00:22:38,000 --> 00:22:40,320
Speaker 1: at a conference that she was invited to, they were like,

440
00:22:40,840 --> 00:22:42,439
Speaker 1: I can't believe she's a real person.

441
00:22:42,640 --> 00:22:44,040
Speaker 2: And part of me was like, you didn't even see

442
00:22:44,040 --> 00:22:44,879
Speaker 2: her as a real human.

443
00:22:45,200 --> 00:22:47,159
Speaker 1: They just saw her as something that they had got

444
00:22:47,160 --> 00:22:50,160
Speaker 1: an image in a picture that they had been consuming

445
00:22:50,200 --> 00:22:53,560
Speaker 1: for decades, and they had so removed her from being

446
00:22:53,600 --> 00:22:57,240
Speaker 1: a real, breathing human that meeting her in real life

447
00:22:57,560 --> 00:22:58,399
Speaker 1: was like they were.

448
00:22:58,280 --> 00:22:59,680
Speaker 2: Surprised that she was real.

449
00:23:00,000 --> 00:23:02,000
Speaker 1: I think that really speaks to the sort of fandom

450
00:23:02,080 --> 00:23:03,240
Speaker 1: element that you were talking about.

451
00:23:03,240 --> 00:23:03,960
Speaker 2: This idea that like.

452
00:23:04,320 --> 00:23:08,919
Speaker 1: You can come if you are a fantasy and in

453
00:23:08,960 --> 00:23:11,080
Speaker 1: some ways not even a real human.

454
00:23:11,440 --> 00:23:15,320
Speaker 2: You know what I'm saying. You're like, do I ever?

455
00:23:16,000 --> 00:23:19,760
Speaker 3: Yeah, like, don't say anything that I don't like, like,

456
00:23:19,920 --> 00:23:22,080
Speaker 3: keep quiet and look the way I like, then you

457
00:23:22,119 --> 00:23:26,560
Speaker 3: can be here. But oh you're a real person. Oh no,

458
00:23:26,640 --> 00:23:28,560
Speaker 3: I don't want to hear it at all.

459
00:23:29,560 --> 00:23:32,960
Speaker 1: Yeah, so you're exactly right, Annie. All of this happened,

460
00:23:32,960 --> 00:23:36,720
Speaker 1: but it was not without pushback. Around like the twenty tens,

461
00:23:37,480 --> 00:23:41,359
Speaker 1: people started publicly asking whether or not this image of

462
00:23:41,400 --> 00:23:46,040
Speaker 1: a woman from Playboy should be so soundational to technology,

463
00:23:46,200 --> 00:23:49,919
Speaker 1: especially in education settings, you know, given conversations about the

464
00:23:49,920 --> 00:23:52,399
Speaker 1: need for more women in these spaces and how to

465
00:23:52,400 --> 00:23:54,320
Speaker 1: make these spaces more inclusive and more diverse.

466
00:23:54,680 --> 00:23:56,080
Speaker 2: That's really around when you start.

467
00:23:55,880 --> 00:23:58,600
Speaker 1: Hearing like people in public being like, wait a minute,

468
00:23:58,640 --> 00:24:01,919
Speaker 1: maybe this isn't so cool. In twenty fifteen, Mattie Zugg,

469
00:24:01,920 --> 00:24:04,120
Speaker 1: who was then a student at the Thomas Jefferson High

470
00:24:04,119 --> 00:24:06,719
Speaker 1: School for Science and Technology right here in DC, riilive

471
00:24:06,760 --> 00:24:09,080
Speaker 1: who I should say now is a product safety engineer

472
00:24:09,119 --> 00:24:12,159
Speaker 1: at Apple who focuses on preventing tech and abled abuse

473
00:24:12,200 --> 00:24:15,439
Speaker 1: and stalking and harassment on Apple platforms, So like go Maddie.

474
00:24:15,440 --> 00:24:17,760
Speaker 1: Maddie sounds like she was cool in high school and

475
00:24:17,880 --> 00:24:21,600
Speaker 1: is cool now. So Maddie wrote this op ed basically

476
00:24:21,640 --> 00:24:24,480
Speaker 1: asking the question of like, should I, as a high

477
00:24:24,520 --> 00:24:28,720
Speaker 1: school student at at a STEM high school be given

478
00:24:29,200 --> 00:24:32,240
Speaker 1: an image from Playboy as part of my education in

479
00:24:32,320 --> 00:24:35,600
Speaker 1: technology and STEM? She writes, I first saw a picture

480
00:24:35,600 --> 00:24:39,440
Speaker 1: of Playboy magazines Miss November nineteen seventy two, A year ago.

481
00:24:39,520 --> 00:24:43,080
Speaker 1: As a junior at TJ, my artificial intelligence teacher told

482
00:24:43,119 --> 00:24:46,159
Speaker 1: our class to search Google for Lenna Soderbird, not the

483
00:24:46,160 --> 00:24:48,959
Speaker 1: full image, though, and use her picture to test our

484
00:24:49,040 --> 00:24:52,439
Speaker 1: latest coding assignment. At the time, I was sixteen and

485
00:24:52,480 --> 00:24:55,080
Speaker 1: struggling to believe that I belonged in a male dominated

486
00:24:55,119 --> 00:24:57,879
Speaker 1: computer science class. I tried to tune out the boy's

487
00:24:57,920 --> 00:25:01,480
Speaker 1: sexual comments. Why is an advanced science, Technology, Engineering, and

488
00:25:01,520 --> 00:25:05,520
Speaker 1: Mathematics school using a Playboy centerfold in its classrooms? Her

489
00:25:05,560 --> 00:25:08,480
Speaker 1: piece ends was saying it's time for TJ to say

490
00:25:08,520 --> 00:25:12,720
Speaker 1: hello to inclusive computer science education and say goodbye to Lena.

491
00:25:12,840 --> 00:25:15,719
Speaker 1: So Maddie was not the only person who was like,

492
00:25:16,040 --> 00:25:18,240
Speaker 1: maybe this image shouldn't be the thing that all of

493
00:25:18,280 --> 00:25:21,200
Speaker 1: our education is centered around. In that piece for Wired,

494
00:25:21,200 --> 00:25:23,600
Speaker 1: I mentioned they talked to several women in technology. You

495
00:25:23,640 --> 00:25:26,320
Speaker 1: had very similar stories. This one is actually pretty funny.

496
00:25:26,560 --> 00:25:30,200
Speaker 1: Deanna Needle, a math professor at UCLA, had similar memories

497
00:25:30,200 --> 00:25:32,880
Speaker 1: from college. So in twenty thirteen, she and a colleague

498
00:25:32,920 --> 00:25:36,239
Speaker 1: staged a quiet protest. They acquired the rights to a

499
00:25:36,240 --> 00:25:40,400
Speaker 1: headshot of the male model Fabio Lonzoni and used that

500
00:25:40,600 --> 00:25:43,760
Speaker 1: for their imaging research. So they kind of like turned

501
00:25:43,760 --> 00:25:46,320
Speaker 1: it around, like, oh, you're gonna use a sexy woman, Well,

502
00:25:46,400 --> 00:25:47,439
Speaker 1: we'll use a sexy man.

503
00:25:47,480 --> 00:25:48,280
Speaker 2: What do you think about that?

504
00:25:50,280 --> 00:25:50,840
Speaker 3: I love it.

505
00:25:51,760 --> 00:25:55,240
Speaker 1: So in that piece they actually tracked down and speak

506
00:25:55,359 --> 00:25:58,879
Speaker 1: to the real Lenna, who also called for her image

507
00:25:58,880 --> 00:26:01,560
Speaker 1: to be retired. She says, I retired from modeling a

508
00:26:01,560 --> 00:26:04,840
Speaker 1: long time ago. It is time I retired from tech two.

509
00:26:04,920 --> 00:26:06,920
Speaker 1: We can make a simple change today that creates a

510
00:26:07,000 --> 00:26:08,480
Speaker 1: lasting change for tomorrow.

511
00:26:08,720 --> 00:26:10,439
Speaker 2: Let's commit to losing me.

512
00:26:11,320 --> 00:26:14,000
Speaker 1: And there's actually some news on that front, because as

513
00:26:14,080 --> 00:26:17,080
Speaker 1: of April first of this year, that I Triple E

514
00:26:17,320 --> 00:26:20,560
Speaker 1: officially retired the use of the Letna image and announced

515
00:26:20,600 --> 00:26:23,280
Speaker 1: they will no longer be using that image and their publications.

516
00:26:23,720 --> 00:26:25,600
Speaker 1: Ours Technica points out that this is kind of a

517
00:26:25,640 --> 00:26:28,200
Speaker 1: really big deal that will likely have a ripple effect

518
00:26:28,240 --> 00:26:31,280
Speaker 1: in the space. Because the journal has been so historically

519
00:26:31,280 --> 00:26:35,160
Speaker 1: important for computer imaging development, it'll likely set a precedent

520
00:26:35,280 --> 00:26:38,840
Speaker 1: removing this image from common use. In an email, a

521
00:26:38,840 --> 00:26:42,359
Speaker 1: spokesperson for the I Triple E recommended wider sensitivity about

522
00:26:42,359 --> 00:26:45,600
Speaker 1: the issue, writing. In order to raise awareness of and

523
00:26:45,640 --> 00:26:49,399
Speaker 1: increase author compliance with this new policy, program, committee members

524
00:26:49,400 --> 00:26:51,959
Speaker 1: and reviewers should look for inclusion of this image, and

525
00:26:52,040 --> 00:26:55,120
Speaker 1: if present, should ask authors to replace the Letna image

526
00:26:55,160 --> 00:26:56,159
Speaker 1: with an alternative.

527
00:26:56,880 --> 00:27:00,600
Speaker 3: Yeah. I love that from Lenna herself, like, let's commit

528
00:27:00,640 --> 00:27:04,560
Speaker 3: to forgetting me. That's such a great line. But it

529
00:27:04,600 --> 00:27:08,840
Speaker 3: does speak to It speaks volumes, as you've been saying,

530
00:27:08,880 --> 00:27:13,119
Speaker 3: bridget to our attitude towards women on the Internet and

531
00:27:13,200 --> 00:27:17,800
Speaker 3: towards consent on the internet. And so when we're thinking

532
00:27:17,840 --> 00:27:23,760
Speaker 3: about this, which was foundational, what do you think about

533
00:27:23,800 --> 00:27:26,440
Speaker 3: this the legacy of this image?

534
00:27:27,040 --> 00:27:29,720
Speaker 1: Yeah, I love that question. You know what I was

535
00:27:29,840 --> 00:27:32,800
Speaker 1: reading about how this image came to be. I'm imagining

536
00:27:33,040 --> 00:27:36,679
Speaker 1: a very different time, right, It's the seventies. People aren't

537
00:27:36,680 --> 00:27:40,760
Speaker 1: necessarily having a lot of public, loud conversations about the

538
00:27:40,840 --> 00:27:43,320
Speaker 1: power dynamics of who's in the room and who's not

539
00:27:43,400 --> 00:27:44,120
Speaker 1: in the room.

540
00:27:44,280 --> 00:27:46,160
Speaker 2: Where a lot of this technology is getting built.

541
00:27:46,560 --> 00:27:49,560
Speaker 1: And it really made me think of, like, Wow, the seventies,

542
00:27:49,560 --> 00:27:51,919
Speaker 1: that probably was such a different time. But here in

543
00:27:51,920 --> 00:27:56,080
Speaker 1: twenty twenty four, we are having those conversations, loud voices,

544
00:27:56,160 --> 00:27:59,800
Speaker 1: are publicly having those conversations. There are women and people

545
00:27:59,880 --> 00:28:02,560
Speaker 1: of color, and trans folks and queer folks and all

546
00:28:02,760 --> 00:28:05,600
Speaker 1: kinds of folks who are building and making the technology

547
00:28:05,600 --> 00:28:08,440
Speaker 1: that shapes our world today. And so in twenty twenty four,

548
00:28:08,760 --> 00:28:12,560
Speaker 1: it almost feels like we are pretending that we're still

549
00:28:12,680 --> 00:28:16,480
Speaker 1: in this nineteen seventies we didn't really know how who

550
00:28:16,480 --> 00:28:20,280
Speaker 1: could have foreseen world, when in fact we're not really

551
00:28:20,320 --> 00:28:22,760
Speaker 1: in that world. People are asking the questions, people are

552
00:28:22,880 --> 00:28:24,840
Speaker 1: raising the alarm, and I guess I don't think it

553
00:28:24,880 --> 00:28:29,960
Speaker 1: should be several decades after AI technology becomes ubiquitous for

554
00:28:30,000 --> 00:28:34,280
Speaker 1: people to start asking the question about how traditionally marginalized

555
00:28:34,280 --> 00:28:37,480
Speaker 1: people like women are being used and represented and perhaps

556
00:28:37,560 --> 00:28:41,000
Speaker 1: exploited without their consent in these spaces. I think it

557
00:28:41,040 --> 00:28:43,840
Speaker 1: provides a really interesting precedent for what's going on here

558
00:28:43,880 --> 00:28:45,360
Speaker 1: in twenty twenty four and.

559
00:28:45,840 --> 00:28:47,520
Speaker 2: Jennifer Ding put it really well.

560
00:28:47,520 --> 00:28:50,120
Speaker 1: She writes to me, the crux of the Lena story

561
00:28:50,160 --> 00:28:53,120
Speaker 1: is how little power we have over our data and.

562
00:28:53,040 --> 00:28:54,440
Speaker 2: How it is used and abused.

563
00:28:55,200 --> 00:28:59,040
Speaker 1: That threat seems disproportionately higher for women, who are overrepresented

564
00:28:59,080 --> 00:29:03,240
Speaker 1: in Internet content but underrepresented in Internet company leadership and

565
00:29:03,320 --> 00:29:07,600
Speaker 1: decision making. Given this reality, engineering and product decisions will

566
00:29:07,640 --> 00:29:12,560
Speaker 1: continue to consciously and unconsciously exclude our needs and concerns.

567
00:29:12,640 --> 00:29:14,680
Speaker 2: Right, And so I really agree with that.

568
00:29:14,480 --> 00:29:16,960
Speaker 1: That this let us story really is a story about

569
00:29:17,040 --> 00:29:21,480
Speaker 1: power dynamics and who is represented in technology and who

570
00:29:21,520 --> 00:29:24,560
Speaker 1: is just sort of like has their needs exploited or erased?

571
00:29:24,640 --> 00:29:25,040
Speaker 2: Right, Like.

572
00:29:26,880 --> 00:29:30,520
Speaker 1: Men wanting to consume the bodies of women is like

573
00:29:31,160 --> 00:29:34,080
Speaker 1: foundational to the Internet. It's like why we have the

574
00:29:34,080 --> 00:29:36,120
Speaker 1: Internet the way that we have it. And I think

575
00:29:36,320 --> 00:29:40,400
Speaker 1: we know that now. It's like an objective fact about

576
00:29:40,400 --> 00:29:43,320
Speaker 1: the Internet and technology. I don't think we can still

577
00:29:43,880 --> 00:29:47,600
Speaker 1: make technology that does not honest about that, because if

578
00:29:47,640 --> 00:29:50,120
Speaker 1: we're not being honest about that, we can never fix that,

579
00:29:50,200 --> 00:29:52,160
Speaker 1: we can never question that, we can never have that

580
00:29:52,240 --> 00:29:56,040
Speaker 1: be a dynamic that we stop perpetuating with technology.

581
00:29:56,400 --> 00:29:58,560
Speaker 3: Yeah, and I think it's like going back to the

582
00:29:58,600 --> 00:30:02,600
Speaker 3: point about being in a classroom setting and being shown

583
00:30:02,680 --> 00:30:06,000
Speaker 3: explicitly like this is how women are viewed in space.

584
00:30:06,080 --> 00:30:08,640
Speaker 3: This is what built a lot of what we use today,

585
00:30:08,680 --> 00:30:12,040
Speaker 3: and we're still talking about it is telling in itself,

586
00:30:12,080 --> 00:30:16,000
Speaker 3: and especially when we're seeing that perpetuate in all of

587
00:30:16,040 --> 00:30:19,400
Speaker 3: these tech spaces where it still feels in a lot

588
00:30:19,400 --> 00:30:21,640
Speaker 3: of ways even though women in marginalized people have built

589
00:30:21,680 --> 00:30:26,200
Speaker 3: those spaces that like, you're the guest here, and you're

590
00:30:26,320 --> 00:30:30,440
Speaker 3: only here because we're opening our gates a little bit

591
00:30:31,240 --> 00:30:35,160
Speaker 3: to let you in, but otherwise, yes, get out.

592
00:30:35,840 --> 00:30:37,720
Speaker 1: And I just think that's a dynamic we need to

593
00:30:37,760 --> 00:30:40,320
Speaker 1: be questioning in twenty twenty four. And I think so

594
00:30:40,400 --> 00:30:44,160
Speaker 1: like something about the use of this image it's ubiquity

595
00:30:44,200 --> 00:30:48,440
Speaker 1: in education spaces, I find so telling. But also even

596
00:30:48,480 --> 00:30:50,920
Speaker 1: if you're not studying to be an engineer or something,

597
00:30:51,280 --> 00:30:53,320
Speaker 1: I think there is a dynamic that says that if

598
00:30:53,360 --> 00:30:57,040
Speaker 1: you are a person who is traditionally marginalized, you're not

599
00:30:57,080 --> 00:30:59,600
Speaker 1: a decision maker, you're not a power holder, you're not

600
00:30:59,720 --> 00:31:02,600
Speaker 1: doing or making anything that anybody needs to care about.

601
00:31:02,640 --> 00:31:06,840
Speaker 1: And the entire dynamic is that we use you in fact, right,

602
00:31:06,960 --> 00:31:09,360
Speaker 1: So Ding actually points this out on her piece, she says,

603
00:31:09,600 --> 00:31:12,880
Speaker 1: while social norms are changing toward non consensual data collection

604
00:31:12,920 --> 00:31:16,000
Speaker 1: and data exploitation, digital norms seem to be moving in

605
00:31:16,000 --> 00:31:19,520
Speaker 1: the opposite direction. Advancement and machine learning algorithms and data

606
00:31:19,560 --> 00:31:23,600
Speaker 1: storage capabilities are only making data misuse easier, whether the

607
00:31:23,640 --> 00:31:28,160
Speaker 1: outcome is revenge porn, or targeted ads, surveillance, or discriminatory AI.

608
00:31:28,520 --> 00:31:30,920
Speaker 1: If we want a world where our data can retire

609
00:31:30,960 --> 00:31:33,800
Speaker 1: when it's outlived its time or when it's directly harming

610
00:31:33,840 --> 00:31:36,560
Speaker 1: our lives, we must create the tools and policies that

611
00:31:36,640 --> 00:31:39,719
Speaker 1: empower data subjects to have a say in what happens

612
00:31:39,720 --> 00:31:42,600
Speaker 1: to their data, including allowing their data to die. And

613
00:31:42,640 --> 00:31:44,840
Speaker 1: so I think, even if you're not somebody who is

614
00:31:44,840 --> 00:31:48,040
Speaker 1: a techie, that does concern you this dynamic that just

615
00:31:48,040 --> 00:31:52,320
Speaker 1: says we consume, we exploit, we make money from you,

616
00:31:52,600 --> 00:31:53,440
Speaker 1: and you don't get to have a.

617
00:31:53,440 --> 00:31:54,000
Speaker 2: Say about it.

618
00:31:54,120 --> 00:31:56,680
Speaker 1: That's the dynamics that I think this Lenna image really

619
00:31:56,720 --> 00:32:00,640
Speaker 1: did usher in without really even necessarily meaning to.

620
00:32:10,920 --> 00:32:13,719
Speaker 4: I think there's a big conversation here on like the

621
00:32:13,760 --> 00:32:18,160
Speaker 4: power of capitalism within the tech industry and what makes money.

622
00:32:18,760 --> 00:32:21,720
Speaker 4: I can't help but think, like with the Lina image,

623
00:32:21,880 --> 00:32:24,920
Speaker 4: the fact that this toxicity was used to make more

624
00:32:24,960 --> 00:32:28,960
Speaker 4: profit and more power within this industry. It took forty

625
00:32:29,000 --> 00:32:31,200
Speaker 4: to fifty years for it to even have a conversation

626
00:32:31,240 --> 00:32:34,240
Speaker 4: about like let's change it, let's retire it. But the

627
00:32:34,280 --> 00:32:36,440
Speaker 4: fact that it had that much push back because they

628
00:32:36,480 --> 00:32:38,720
Speaker 4: didn't care enough and they wanted to build on this

629
00:32:38,800 --> 00:32:41,360
Speaker 4: toxicity because they knew it could make money is the

630
00:32:41,400 --> 00:32:44,560
Speaker 4: most concerning thing to me. And then the powers that

631
00:32:44,600 --> 00:32:46,520
Speaker 4: be are saying that, yeah, yeah, we're definitely going to

632
00:32:46,560 --> 00:32:49,240
Speaker 4: control this and then just goes after an app instead

633
00:32:49,240 --> 00:32:51,680
Speaker 4: of the root of the problem. Seems like the biggest

634
00:32:51,760 --> 00:32:54,800
Speaker 4: part of the conversation because even in the AI world,

635
00:32:54,920 --> 00:32:57,880
Speaker 4: with new apps coming through, new programs coming through, and

636
00:32:57,920 --> 00:33:00,920
Speaker 4: they're all competing with each other, they don't want to

637
00:33:01,000 --> 00:33:04,000
Speaker 4: let go of the toxicity. But that's what's making the money,

638
00:33:04,080 --> 00:33:05,400
Speaker 4: which is really really concerning.

639
00:33:06,280 --> 00:33:08,600
Speaker 1: Yeah, And I mean like if there was one, so

640
00:33:08,840 --> 00:33:11,600
Speaker 1: what of why I wanted to have this conversation, Sam,

641
00:33:11,640 --> 00:33:14,640
Speaker 1: That is exactly it that it is about money. It

642
00:33:14,720 --> 00:33:17,960
Speaker 1: is about capitalism. It is about making money off of

643
00:33:18,000 --> 00:33:21,720
Speaker 1: people's own exploitation and selling that exploitation back to them

644
00:33:21,920 --> 00:33:25,640
Speaker 1: to make more money. And it's just a really toxic

645
00:33:25,920 --> 00:33:30,160
Speaker 1: dynamic that I believe is harming us and making the

646
00:33:30,200 --> 00:33:34,880
Speaker 1: people who have created that dynamic rich all the while

647
00:33:34,920 --> 00:33:36,200
Speaker 1: they get to be like, Oh, it's not a big

648
00:33:36,240 --> 00:33:36,640
Speaker 1: deal for you.

649
00:33:36,720 --> 00:33:38,120
Speaker 2: Actually, this is going to be really good for you.

650
00:33:38,160 --> 00:33:39,360
Speaker 2: This is going to be convenient for you.

651
00:33:39,760 --> 00:33:42,840
Speaker 1: And I don't know, Like I woke up this morning

652
00:33:43,000 --> 00:33:44,960
Speaker 1: when I was trying to decide, like what I wanted

653
00:33:44,960 --> 00:33:47,440
Speaker 1: to talk to you all today about, and one of

654
00:33:47,480 --> 00:33:49,680
Speaker 1: the ideas that I had that I that I scrapped

655
00:33:50,080 --> 00:33:53,280
Speaker 1: was just this feeling that being on the internet just

656
00:33:53,320 --> 00:33:56,360
Speaker 1: doesn't feel fun anymore. Anytime I go on a website,

657
00:33:56,400 --> 00:33:59,680
Speaker 1: anytime I google something just to find out information, it

658
00:33:59,720 --> 00:34:02,760
Speaker 1: feels feels like a scam. It feels like exploitation. I

659
00:34:02,760 --> 00:34:05,480
Speaker 1: feel like I am one click away from somebody getting.

660
00:34:05,200 --> 00:34:06,280
Speaker 2: My social Security number.

661
00:34:06,520 --> 00:34:11,600
Speaker 1: It feels like AI generated garbage. And I just think

662
00:34:11,640 --> 00:34:14,640
Speaker 1: we have hit the wall of that feeling. I can't

663
00:34:14,640 --> 00:34:17,640
Speaker 1: imagine that I'm alone in this. I think the feeling

664
00:34:17,680 --> 00:34:20,080
Speaker 1: of being showing up online today in twenty twenty four

665
00:34:20,320 --> 00:34:24,600
Speaker 1: feels exhausting, and I think part of it is because

666
00:34:24,800 --> 00:34:27,400
Speaker 1: it feels like we are being bled dry by people

667
00:34:27,440 --> 00:34:30,960
Speaker 1: that we have already made rich from our own exploitation.

668
00:34:31,120 --> 00:34:32,560
Speaker 2: Do you re feel that way? Oh?

669
00:34:32,560 --> 00:34:36,480
Speaker 4: Absolutely, I think with because getting on TikTok, the first

670
00:34:36,520 --> 00:34:39,120
Speaker 4: opening video, I'm sure you've seen it is that content

671
00:34:39,200 --> 00:34:41,880
Speaker 4: manager who's like, I'm here for the safety of TikTok.

672
00:34:41,920 --> 00:34:42,440
Speaker 4: Have you seen this?

673
00:34:42,880 --> 00:34:43,279
Speaker 2: I have not.

674
00:34:43,840 --> 00:34:46,640
Speaker 4: She's been there. She is for safety in something like

675
00:34:46,680 --> 00:34:51,160
Speaker 4: she has a very specific safety Yes, Susie someone she

676
00:34:51,280 --> 00:34:53,800
Speaker 4: is very white and she's very redheaded. It's I was like, okay,

677
00:34:54,000 --> 00:34:55,879
Speaker 4: so we've got played into the xenophobey. She's like, look,

678
00:34:55,880 --> 00:34:58,000
Speaker 4: I'm a white person. I'm gonna help you out here.

679
00:34:58,040 --> 00:34:59,279
Speaker 2: Don't worry about don't worry out.

680
00:34:59,320 --> 00:35:02,759
Speaker 4: I'm get But that's the first thing that I'm seeing, So like,

681
00:35:03,480 --> 00:35:06,640
Speaker 4: you know, urging TikTok users to talk to the government

682
00:35:06,640 --> 00:35:08,440
Speaker 4: because they voted this in and this is real bad

683
00:35:08,480 --> 00:35:10,279
Speaker 4: and all this and not whatnot. And I'm just like,

684
00:35:10,280 --> 00:35:14,399
Speaker 4: all right, it's gonna go away next. This is now

685
00:35:14,480 --> 00:35:17,400
Speaker 4: my attitude because also I'm very tired. But also I

686
00:35:17,520 --> 00:35:20,160
Speaker 4: just got an email saying that AT and d Yes

687
00:35:20,239 --> 00:35:22,759
Speaker 4: has a record that oh that that reached they have

688
00:35:22,840 --> 00:35:24,920
Speaker 4: your stuff. But good news, since you don't have a

689
00:35:24,960 --> 00:35:25,879
Speaker 4: bill with us we don't.

690
00:35:26,080 --> 00:35:26,439
Speaker 2: We didn't.

691
00:35:26,480 --> 00:35:29,880
Speaker 4: You didn't get any perfect information. But I literally think

692
00:35:30,520 --> 00:35:33,480
Speaker 4: every month I have been given seeing an email that

693
00:35:33,600 --> 00:35:37,000
Speaker 4: says something of my information is has been breached, and

694
00:35:37,040 --> 00:35:39,520
Speaker 4: it's nothing that I have done. It is literally everything

695
00:35:39,560 --> 00:35:45,400
Speaker 4: from my insurance, my dental insurance, my healthcare provider, my internet,

696
00:35:45,640 --> 00:35:49,560
Speaker 4: which I'm like, what the hell, my phone subscription, my

697
00:35:50,040 --> 00:35:51,920
Speaker 4: cell phone, which I'm like, I'm starting to get back

698
00:35:51,960 --> 00:35:53,919
Speaker 4: to that. I think I want a landline. I'm gonna

699
00:35:53,920 --> 00:35:56,480
Speaker 4: have this moment, y'all to each of those things popping

700
00:35:56,520 --> 00:35:58,759
Speaker 4: up on things, I'm like, I hadn't. I have to

701
00:35:58,880 --> 00:36:01,360
Speaker 4: use that information in order for me to have healthcare.

702
00:36:01,719 --> 00:36:04,520
Speaker 4: So y'all, let my healthcare information go out and they

703
00:36:04,520 --> 00:36:07,040
Speaker 4: have my social Security number. There's nothing I can do

704
00:36:07,080 --> 00:36:09,960
Speaker 4: about that. As many times as I can change my password,

705
00:36:10,560 --> 00:36:13,799
Speaker 4: the next email I'm getting is telling me that I've

706
00:36:13,800 --> 00:36:16,759
Speaker 4: got a data breach of my information. So what is

707
00:36:16,840 --> 00:36:20,160
Speaker 4: the point. Like, at this point, the only way is

708
00:36:20,239 --> 00:36:24,439
Speaker 4: to rewrite my identity and to never get online again,

709
00:36:24,440 --> 00:36:26,000
Speaker 4: which would be really hard for my job.

710
00:36:26,440 --> 00:36:31,120
Speaker 1: Yes, Like, if you have a phone in your life,

711
00:36:31,160 --> 00:36:35,080
Speaker 1: if you vote, if you drive, these things that we

712
00:36:35,120 --> 00:36:39,240
Speaker 1: are required to do to participate in public life should

713
00:36:39,280 --> 00:36:42,000
Speaker 1: not just be avenues for somebody to make money and

714
00:36:42,040 --> 00:36:42,600
Speaker 1: scam us.

715
00:36:42,760 --> 00:36:44,479
Speaker 2: But yet it feels that way.

716
00:36:44,520 --> 00:36:47,080
Speaker 1: And you know what, Sam, I have actually not seen

717
00:36:47,280 --> 00:36:49,480
Speaker 1: the TikTok that you're referring to, because I have not

718
00:36:49,520 --> 00:36:52,600
Speaker 1: opened my TikTok app in days because it's starting to

719
00:36:52,640 --> 00:36:54,600
Speaker 1: feel like QVC.

720
00:36:54,080 --> 00:36:55,760
Speaker 2: And I cannot take it anymore.

721
00:36:55,920 --> 00:36:58,560
Speaker 1: Like whatever happened to spaces on the Internet that we're

722
00:36:58,560 --> 00:37:02,000
Speaker 1: supposed to feel like safety or exploration or fun or

723
00:37:02,040 --> 00:37:05,480
Speaker 1: community or connection. I'm I hope that somebody out there

724
00:37:05,480 --> 00:37:08,080
Speaker 1: listening is like Bridget you're old and on hip. We

725
00:37:08,200 --> 00:37:11,520
Speaker 1: have those spaces, they are syz tell me about them.

726
00:37:11,560 --> 00:37:13,400
Speaker 1: I want to know about them. But I think that

727
00:37:13,400 --> 00:37:15,480
Speaker 1: we should. We really got to get back to, like

728
00:37:17,000 --> 00:37:20,760
Speaker 1: to those principles of the Internet feeling like something other

729
00:37:20,880 --> 00:37:23,400
Speaker 1: than being taken for a ride.

730
00:37:23,280 --> 00:37:24,640
Speaker 2: On which you are the chump.

731
00:37:25,120 --> 00:37:27,279
Speaker 4: Right, And I will say a lot of people have

732
00:37:27,320 --> 00:37:30,040
Speaker 4: felt like Discord and read it has been like brought

733
00:37:30,040 --> 00:37:32,400
Speaker 4: in but we already know Reddit has god its problems.

734
00:37:32,800 --> 00:37:35,040
Speaker 4: And then I think there's a new lawsuit with Discord

735
00:37:35,239 --> 00:37:38,239
Speaker 4: with his problems and its terms of service changing as well.

736
00:37:38,280 --> 00:37:40,040
Speaker 2: I'm like, what totally it's happened.

737
00:37:40,080 --> 00:37:45,000
Speaker 4: So there's literally no one is protecting the individual to like,

738
00:37:45,040 --> 00:37:47,759
Speaker 4: there's no protection for us at all, but they want

739
00:37:47,840 --> 00:37:50,640
Speaker 4: us to say, they want us to to take away

740
00:37:50,680 --> 00:37:53,160
Speaker 4: things from us, which is like the least of our worries,

741
00:37:53,640 --> 00:37:57,120
Speaker 4: or they're just like sorry, you're like you can't see us.

742
00:37:57,480 --> 00:37:59,879
Speaker 1: Yeah, I think everybody is feeling that, but I think

743
00:37:59,880 --> 00:38:05,640
Speaker 1: it is particularly dangerous for people who are traditionally marginalized because, yeah,

744
00:38:05,680 --> 00:38:08,239
Speaker 1: which it's just the expectation that.

745
00:38:08,320 --> 00:38:10,360
Speaker 2: Oh, it's totally fine.

746
00:38:10,600 --> 00:38:15,080
Speaker 1: If people who make apps that non consensually undress women

747
00:38:15,200 --> 00:38:18,160
Speaker 1: using AI, why wouldn't they be able to advertise on

748
00:38:18,200 --> 00:38:20,040
Speaker 1: Facebook or Instagram or Twitter.

749
00:38:20,160 --> 00:38:21,760
Speaker 2: They got to make money. That's a business.

750
00:38:21,800 --> 00:38:25,280
Speaker 1: Like how how easy it is to erase the human

751
00:38:25,520 --> 00:38:29,200
Speaker 1: people at the heart of this dynamic, erase their concerns,

752
00:38:29,280 --> 00:38:32,759
Speaker 1: erase their needs, erase their harm because men gotta make

753
00:38:32,840 --> 00:38:33,440
Speaker 1: money off of it.

754
00:38:33,640 --> 00:38:35,040
Speaker 2: Right, I'm thick of it, right?

755
00:38:35,239 --> 00:38:38,080
Speaker 4: Or is tradition literally like, yeah, this image has always

756
00:38:38,120 --> 00:38:41,080
Speaker 4: been here, we need to teach it as a historical now.

757
00:38:41,120 --> 00:38:45,040
Speaker 4: It was definitely not exploiting somebody or taking advantage of

758
00:38:45,120 --> 00:38:49,560
Speaker 4: somebody or using humiliating content because she wasn't humiliated, I

759
00:38:49,600 --> 00:38:51,400
Speaker 4: don't think, but like in the ideal of like it

760
00:38:51,480 --> 00:38:55,480
Speaker 4: being forever and ever and ever, of like your seductive

761
00:38:55,840 --> 00:38:59,000
Speaker 4: picture being used for it people, which is a whole

762
00:38:59,000 --> 00:39:01,080
Speaker 4: different conversation in this Yeah, I.

763
00:39:01,080 --> 00:39:03,240
Speaker 2: Mean so Lenna, the real life Lenna.

764
00:39:03,400 --> 00:39:06,040
Speaker 1: And again there's a really interesting Wired article that has

765
00:39:06,080 --> 00:39:06,879
Speaker 1: an interview with her.

766
00:39:07,040 --> 00:39:08,719
Speaker 2: She doesn't feel like she was exploited.

767
00:39:08,800 --> 00:39:10,840
Speaker 1: She's actually really proud of that image, even as she

768
00:39:10,880 --> 00:39:13,800
Speaker 1: recognizes that it's like time for it to be retired. However,

769
00:39:14,040 --> 00:39:18,440
Speaker 1: she does wish that she had been fairly compensated for

770
00:39:18,480 --> 00:39:21,160
Speaker 1: what would go on to be her like non consensual

771
00:39:21,200 --> 00:39:23,719
Speaker 1: contributions to tech when she took that image. There's no

772
00:39:23,800 --> 00:39:26,480
Speaker 1: way that as a young playboy playmate in the night

773
00:39:26,520 --> 00:39:29,120
Speaker 1: in nineteen seventy one or whatever, you would have a

774
00:39:29,160 --> 00:39:30,880
Speaker 1: sense of like, well, if this goes on to be

775
00:39:31,480 --> 00:39:33,480
Speaker 1: to make me the first lady of the Internet, I

776
00:39:33,560 --> 00:39:37,399
Speaker 1: better have compensation and protections. No way, right, So in

777
00:39:37,440 --> 00:39:39,920
Speaker 1: that Wired piece they say it makes sense that she

778
00:39:39,960 --> 00:39:42,560
Speaker 1: would feel this way. Unlike so many women in tech,

779
00:39:42,880 --> 00:39:46,360
Speaker 1: Lenna has at least been acknowledged, even feted, for her contribution.

780
00:39:46,760 --> 00:39:49,040
Speaker 1: She did that work, and the people started using that

781
00:39:49,080 --> 00:39:51,440
Speaker 1: photo in this neat new way, and now she has

782
00:39:51,480 --> 00:39:54,880
Speaker 1: this kind of immortality woven into the design of the machine.

783
00:39:55,280 --> 00:39:57,600
Speaker 1: This is from Marie Hicks, a historian of technology and

784
00:39:57,600 --> 00:39:59,359
Speaker 1: the author of programmed Inequality.

785
00:40:00,040 --> 00:40:01,160
Speaker 2: All of this happened for a reason.

786
00:40:01,239 --> 00:40:04,080
Speaker 1: Hicks writes, if they hadn't used a Playboy centerfold, they

787
00:40:04,120 --> 00:40:06,360
Speaker 1: almost certainly would have used another picture of a pretty

788
00:40:06,360 --> 00:40:09,400
Speaker 1: white woman. The Playboy thing gets our attention, but really

789
00:40:09,440 --> 00:40:11,680
Speaker 1: what it's about is this world building that's gone on

790
00:40:12,000 --> 00:40:14,920
Speaker 1: in computing from the beginning. It's about building worlds for

791
00:40:15,000 --> 00:40:17,360
Speaker 1: certain people and not for others.

792
00:40:17,800 --> 00:40:19,799
Speaker 4: I find it interesting, dude, that they invited her to

793
00:40:19,880 --> 00:40:23,959
Speaker 4: the conference, Like, I'm wondering what the purpose was other

794
00:40:24,040 --> 00:40:27,319
Speaker 4: than two like for because it obviously wasn't to ask

795
00:40:27,360 --> 00:40:31,160
Speaker 4: her questions about tech and how she did this thing,

796
00:40:31,320 --> 00:40:34,400
Speaker 4: because they did not even consider human as we know.

797
00:40:34,680 --> 00:40:37,880
Speaker 4: It was just literally to aggle her in real life.

798
00:40:38,160 --> 00:40:40,719
Speaker 2: Yeah, I was thinking about why they did that.

799
00:40:41,400 --> 00:40:43,520
Speaker 1: I don't know, I have parted me wonders if it

800
00:40:43,560 --> 00:40:45,879
Speaker 1: was like an attempt to be like, oh, we need

801
00:40:45,920 --> 00:40:49,960
Speaker 1: to acknowledge the way that this woman's image was so

802
00:40:50,040 --> 00:40:54,400
Speaker 1: foundational to our technology, but then like not really doing it,

803
00:40:54,440 --> 00:40:56,479
Speaker 1: like still sort of treating her as like a booth

804
00:40:56,560 --> 00:40:57,440
Speaker 1: babe or something like.

805
00:40:57,480 --> 00:41:00,400
Speaker 4: I don't know, right, I just find all of that

806
00:41:00,440 --> 00:41:04,920
Speaker 4: interesting in this level of like not again of not

807
00:41:05,040 --> 00:41:08,160
Speaker 4: what she was doing this for. She came in with,

808
00:41:08,280 --> 00:41:12,080
Speaker 4: like whatever her ambitions were in being this model and whatnot,

809
00:41:12,120 --> 00:41:14,320
Speaker 4: and then all of a sudden being told you're being

810
00:41:14,760 --> 00:41:20,120
Speaker 4: used as an example for computers, like for specially images

811
00:41:20,160 --> 00:41:23,360
Speaker 4: for computers, and not only will you see this, but

812
00:41:23,440 --> 00:41:25,920
Speaker 4: your grandkids will also, like if she has children, like

813
00:41:25,920 --> 00:41:28,480
Speaker 4: any of those things, and your your family members forever.

814
00:41:29,000 --> 00:41:31,359
Speaker 1: And like who would who would have ever thought that

815
00:41:31,360 --> 00:41:32,879
Speaker 1: that's how that image would go on to be used

816
00:41:32,880 --> 00:41:35,719
Speaker 1: in history. And I really think like this is where

817
00:41:35,719 --> 00:41:37,480
Speaker 1: we are today, and this is like why I wanted

818
00:41:37,520 --> 00:41:41,240
Speaker 1: to talk about this is that I think, like the idea,

819
00:41:41,440 --> 00:41:44,920
Speaker 1: the concept of images being shared online, the way we

820
00:41:45,000 --> 00:41:47,239
Speaker 1: understand that in twenty twenty four, the fact that this

821
00:41:47,320 --> 00:41:51,960
Speaker 1: image of Leta became so foundational to that concept without

822
00:41:51,960 --> 00:41:55,719
Speaker 1: her consent, you know, perhaps without like proper contribution to

823
00:41:55,719 --> 00:41:57,960
Speaker 1: the way that she actually was foundational to that and

824
00:41:58,040 --> 00:42:01,440
Speaker 1: building out this entire universe around it that is mostly

825
00:42:01,840 --> 00:42:07,000
Speaker 1: controlled and protected and profited off by men, and nobody's

826
00:42:07,000 --> 00:42:10,359
Speaker 1: stopping to ask about the rampifications of that until decades later.

827
00:42:10,800 --> 00:42:14,680
Speaker 1: I just think it really establishes like a concerning precedent

828
00:42:14,719 --> 00:42:16,560
Speaker 1: for where we're going right now with AI in twenty

829
00:42:16,600 --> 00:42:17,040
Speaker 1: twenty four.

830
00:42:17,080 --> 00:42:17,840
Speaker 2: And it doesn't have to.

831
00:42:18,320 --> 00:42:20,719
Speaker 1: We can learn from what we did with that letter

832
00:42:20,760 --> 00:42:23,480
Speaker 1: image if we ask the right questions, if we center

833
00:42:23,520 --> 00:42:26,120
Speaker 1: the right perspectives and the right voices, and so Yeah,

834
00:42:26,160 --> 00:42:29,520
Speaker 1: I don't want to wait until twenty forty to be like, oh,

835
00:42:29,680 --> 00:42:31,960
Speaker 1: should we have been talking about the ways that women

836
00:42:32,000 --> 00:42:35,400
Speaker 1: and girls and other marginalized people are being exploited and

837
00:42:35,480 --> 00:42:38,480
Speaker 1: used to make technology companies money.

838
00:42:39,040 --> 00:42:40,840
Speaker 2: I don't want to ask that question when it's too.

839
00:42:40,719 --> 00:42:45,680
Speaker 4: Late, Right, And here's like the big conversation is, shouldn't

840
00:42:45,719 --> 00:42:49,239
Speaker 4: we also recognize that big companies and big tech companies

841
00:42:49,280 --> 00:42:52,359
Speaker 4: and big companies that are developing are purposely leaving out

842
00:42:52,400 --> 00:42:55,160
Speaker 4: marginalized people because they like the old ways and that

843
00:42:55,200 --> 00:42:57,520
Speaker 4: it's only making a certain amount of people money.

844
00:42:58,000 --> 00:43:00,239
Speaker 2: Yes, that's exact. I think that I would argue that's

845
00:43:00,239 --> 00:43:02,799
Speaker 2: exactly what's going on. I mean, in twenty twenty four,

846
00:43:02,840 --> 00:43:03,439
Speaker 2: there are so.

847
00:43:03,640 --> 00:43:08,400
Speaker 1: Many loud, thoughtful voices from women and people of color

848
00:43:08,440 --> 00:43:12,000
Speaker 1: who are really talking about AI in some interesting and

849
00:43:12,040 --> 00:43:14,960
Speaker 1: thoughtful ways. So they exist, They are out there. This

850
00:43:15,080 --> 00:43:16,399
Speaker 1: is the tale as the oldest time. When it comes

851
00:43:16,440 --> 00:43:18,920
Speaker 1: to technology. It is not that they are not there.

852
00:43:19,200 --> 00:43:23,800
Speaker 1: It is that they are being, whether intentionally or unintentionally, marginalized,

853
00:43:23,920 --> 00:43:29,600
Speaker 1: sideline silenced, pushed aside to make room for voices who

854
00:43:29,640 --> 00:43:33,400
Speaker 1: are just repeating the status quo, who are just saying like, well,

855
00:43:33,880 --> 00:43:36,240
Speaker 1: I'm trying to get rich, so who cares how this harms?

856
00:43:36,280 --> 00:43:39,520
Speaker 1: Somebody who cares about whether or not this goes on

857
00:43:39,560 --> 00:43:42,200
Speaker 1: to exploit. And I think that's really it's really like

858
00:43:42,280 --> 00:43:45,080
Speaker 1: a it's a little bit of a complicated cultural dynamic

859
00:43:45,120 --> 00:43:46,680
Speaker 1: and cultural shift that I think.

860
00:43:46,520 --> 00:43:47,960
Speaker 2: That we really got a break.

861
00:43:48,880 --> 00:43:51,719
Speaker 3: Yeah. Yeah, And it's really sad, going back to your

862
00:43:51,719 --> 00:43:54,600
Speaker 3: point Bridget of like the Internet not being a place

863
00:43:54,640 --> 00:43:58,200
Speaker 3: of joy anymore, because so many times it was marginalized

864
00:43:58,239 --> 00:44:02,080
Speaker 3: people who made those spaces because they couldn't find them

865
00:44:02,360 --> 00:44:08,760
Speaker 3: anywhere else, and then these companies come in and are like, Okay,

866
00:44:08,760 --> 00:44:12,560
Speaker 3: well we can make money, and then it doesn't become

867
00:44:12,800 --> 00:44:16,920
Speaker 3: a joyous space anymore. It becomes a very toxic, a

868
00:44:17,040 --> 00:44:22,480
Speaker 3: toxic place, and so like hearing this story and seeing

869
00:44:23,239 --> 00:44:26,760
Speaker 3: how so much of what we use still is based

870
00:44:26,840 --> 00:44:30,160
Speaker 3: on something that was a guy walked in with the

871
00:44:30,200 --> 00:44:37,680
Speaker 3: Playboy magazine like it's it's bad when you that doesn't

872
00:44:37,680 --> 00:44:39,880
Speaker 3: feel so out of place and what we're talking about

873
00:44:40,320 --> 00:44:41,239
Speaker 3: in our current time.

874
00:44:42,000 --> 00:44:45,000
Speaker 1: Yeah, and again, I mean, I opened up our conversation

875
00:44:45,120 --> 00:44:46,480
Speaker 1: with this, but I guess, and I guess I'll close

876
00:44:46,480 --> 00:44:49,640
Speaker 1: with that too. I believe people when I say this,

877
00:44:49,680 --> 00:44:52,799
Speaker 1: people think I sound alarmist or extreme, but I mean

878
00:44:52,840 --> 00:44:53,680
Speaker 1: it the way that I mean it.

879
00:44:53,920 --> 00:44:56,640
Speaker 2: I think that these things are features, not bugs.

880
00:44:56,880 --> 00:44:58,600
Speaker 1: I think we got to be honest about the ways

881
00:44:58,640 --> 00:45:02,319
Speaker 1: that things like misogyny and exploitation, particularly when it comes

882
00:45:02,360 --> 00:45:06,279
Speaker 1: to marginalized people has been foundational to technology and the

883
00:45:06,280 --> 00:45:09,560
Speaker 1: Internet from the very beginning. I love the Internet. I

884
00:45:09,600 --> 00:45:11,719
Speaker 1: love technology. It is why I do the work that

885
00:45:11,800 --> 00:45:12,160
Speaker 1: I do.

886
00:45:12,440 --> 00:45:14,719
Speaker 2: But I think that until we are honest about that

887
00:45:15,360 --> 00:45:18,200
Speaker 2: but these things are features and not bugs, we will

888
00:45:18,200 --> 00:45:19,000
Speaker 2: never get anywhere.

889
00:45:19,040 --> 00:45:21,160
Speaker 1: And so I think that it really starts with having

890
00:45:21,239 --> 00:45:24,359
Speaker 1: honest conversations about where we started so that we can

891
00:45:24,400 --> 00:45:26,440
Speaker 1: get to a place that we that actually feels a

892
00:45:26,440 --> 00:45:27,640
Speaker 1: little bit better for everybody.

893
00:45:28,360 --> 00:45:34,320
Speaker 3: Yes, yes, well, thank you so much as always, Virtute

894
00:45:34,360 --> 00:45:36,000
Speaker 3: every time you come on, I'm like, we could talk

895
00:45:36,040 --> 00:45:39,160
Speaker 3: for hours about this and this and this.

896
00:45:39,440 --> 00:45:42,240
Speaker 1: Invite me back for an episode, just dragging Hugh Hefner.

897
00:45:42,680 --> 00:45:44,759
Speaker 2: Yeah, I'll be for it.

898
00:45:44,920 --> 00:45:46,719
Speaker 4: I think we need to do this because I think

899
00:45:47,160 --> 00:45:50,800
Speaker 4: for a minute, back into the magazine world and jumping

900
00:45:50,800 --> 00:45:52,239
Speaker 4: into like all of that.

901
00:45:52,560 --> 00:45:55,560
Speaker 1: Don't even get I mean like this is like spoiler alert.

902
00:45:55,719 --> 00:45:58,000
Speaker 1: I like totally had this wrong. For so long in

903
00:45:58,040 --> 00:46:00,440
Speaker 1: my life, I was like, oh, you have was a

904
00:46:00,520 --> 00:46:02,400
Speaker 1: champion for free speech and some little riots and.

905
00:46:02,360 --> 00:46:04,240
Speaker 2: Blah blah blah. Then I grew.

906
00:46:04,160 --> 00:46:06,719
Speaker 1: Up and learned and I was like, actually, she wasn't

907
00:46:06,719 --> 00:46:07,560
Speaker 1: such a good guy.

908
00:46:08,160 --> 00:46:11,040
Speaker 4: Right, I mean we really fed into the but read

909
00:46:11,080 --> 00:46:12,520
Speaker 4: the articles so so good.

910
00:46:12,600 --> 00:46:20,040
Speaker 3: Oh my god, yes, yes, oh yes, please come back

911
00:46:20,040 --> 00:46:24,480
Speaker 3: for that, Bridget. In the meantime, where can the good

912
00:46:24,480 --> 00:46:25,359
Speaker 3: listeners find you?

913
00:46:25,560 --> 00:46:27,160
Speaker 1: Well, you can listen to my podcast. There are no

914
00:46:27,239 --> 00:46:29,279
Speaker 1: girls on the internet. You can follow me. I'm not

915
00:46:29,320 --> 00:46:31,319
Speaker 1: really on social media that much anymore, but you can

916
00:46:31,360 --> 00:46:33,440
Speaker 1: try to find me there. I'm on Instagram at Bridget

917
00:46:33,480 --> 00:46:37,040
Speaker 1: Marie and DC. I am at Blue Sky at Bridget

918
00:46:37,120 --> 00:46:42,240
Speaker 1: Todd on Threads at Bridget Marie and DC sometimes on TikTok.

919
00:46:42,280 --> 00:46:44,000
Speaker 2: You'll I'm easy to find. You'll find google me. You'll

920
00:46:44,000 --> 00:46:46,520
Speaker 2: find me. Yes, google me.

921
00:46:47,040 --> 00:46:47,680
Speaker 4: That's a flex.

922
00:46:48,960 --> 00:46:52,120
Speaker 3: It's true though. Our listeners are smart. They can find

923
00:46:52,160 --> 00:46:54,440
Speaker 3: you and listeners. If you would like to contact us,

924
00:46:54,480 --> 00:46:57,759
Speaker 3: you can. Our email is stephaniea momstuff at iHeartMedia dot com.

925
00:46:57,760 --> 00:46:59,880
Speaker 3: You can find us on Twitter at most of the podcast,

926
00:47:00,040 --> 00:47:02,319
Speaker 3: on TikTok, and Instagram that Stuff I Never told You.

927
00:47:02,320 --> 00:47:04,240
Speaker 3: We're also on YouTube. We have a tea public store

928
00:47:04,719 --> 00:47:06,560
Speaker 3: and a book you can get wherever you get your books.

929
00:47:06,680 --> 00:47:08,840
Speaker 3: Thanks as always to our super produced Christina, our executive

930
00:47:08,840 --> 00:47:11,680
Speaker 3: producer My and your contributor Joey. Thank you, and thanks

931
00:47:11,719 --> 00:47:13,400
Speaker 3: to you for listening. Stuff Will Never Told You. This

932
00:47:13,360 --> 00:47:15,480
Speaker 3: proction by Heart Radio. For more podcasts on my heart Radio,

933
00:47:15,480 --> 00:47:17,200
Speaker 3: you can check out the heart Radio app Apple Podcasts

934
00:47:17,200 --> 00:47:18,719
Speaker 3: wherever you listen to your favorite shows