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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 Todd and this

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

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Speaker 1: another episode of There Are No Girls on the Internet.

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Speaker 1: This is another rendition of our weekly news roundup where

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Speaker 1: we summarize all the stories on the Internet that you

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Speaker 1: might have missed if you don't have to. And I

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Speaker 1: cannot tell you how much of a fan I am

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Speaker 1: of the person that we're great who's present we are

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Speaker 1: graced with as this week's co host Chapter T SHOWDIA scientist,

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Speaker 1: engineer and co host of the Fantastic Dope Labs podcast.

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Speaker 1: Welcome to the show, Bridget.

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Speaker 2: I have dreamed about this moment. I am so excited

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Speaker 2: to be here. Thank you so much for having me.

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Speaker 1: I mean, I'm such a big fan of what you're

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Speaker 1: doing with Dope Labs. For folks who don't listen to

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Speaker 1: the podcast, you should be listening, but give us a

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Speaker 1: summary of what do you all do at Dope Labs.

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Speaker 2: So at Dope Labs I co hosted with my one

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Speaker 2: of my best friends. Her name is Zakiah Wattley. She

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Speaker 2: also has a PhD hers in genetics and genomics. Mine

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Speaker 2: is in mechanical engineering and material science, and we show

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Speaker 2: how science intersects with pop culture. So it's a really

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Speaker 2: fun show that is rooted in our friendship because we've

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Speaker 2: known each other since grad school.

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Speaker 1: We bonded in the struggle.

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Speaker 2: And we just take things that folks are talking about

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Speaker 2: in their group chats, things that are tumbling down your timeline,

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Speaker 2: and show that there's a little bit of science behind

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Speaker 2: it all.

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Speaker 1: I still remember the very first episode. It might have

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Speaker 1: been the episode that the first episode I heard. It

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Speaker 1: might have been the first one you did about cuffing season.

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Speaker 2: Oh yes, I absolutely loved that episode and that was

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Speaker 2: such a great introduction for us to the world because

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Speaker 2: it I feel like it just encapsulated everything that we're about,

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Speaker 2: you know, the science, the clownery, and you know, talking

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Speaker 2: to strangers and hearing all these really funny, amazing stories

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Speaker 2: and talking about you know, the ecology behind all of it.

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Speaker 1: It's it was really fun. Science, clowning and talking of

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Speaker 1: strangers is like the hat trick of things that I enjoy. Yes,

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Speaker 1: and I'm want to I mean, I'm excited that you're

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Speaker 1: here familiar reasons, but one of the reasons is that

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Speaker 1: you all just did a your most recent episode was

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Speaker 1: all about how folks should be thinking about AI in

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Speaker 1: their own lives.

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Speaker 2: Was what was Can give us a little bit a

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Speaker 2: little taste of that conversation, Yeah, I think the reason

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Speaker 2: why we wanted to talk about AI. This was kind

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Speaker 2: of like our second episode that we talk about AI.

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Speaker 2: We talked about the impacts and the cost of AI,

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Speaker 2: and then this most recent one we kind of talked

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Speaker 2: about how we can engage in AI in ways that

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Speaker 2: make sense, that fit into our lives and we don't

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Speaker 2: have to feel, you know, bad about it because AI

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Speaker 2: is coming whether we like it or not. And you know,

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Speaker 2: there are some things that are really really bad about AI.

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Speaker 2: I mean, there's things that are really really bad about

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Speaker 2: a lot of technology. But we just want people to

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Speaker 2: arm theirselves with knowledge so that as we move forward,

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Speaker 2: because this is going to move forward with or without us,

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Speaker 2: that we can you know, step into our workspace, we

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Speaker 2: can step into the rest of the world and know

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Speaker 2: one what is out there, make and make an intelligent

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Speaker 2: decision about whether or not we want to engage for

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Speaker 2: whatever reason, whether it's oh, I'm not going to upload

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Speaker 2: photos of me and my kids and my family into

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Speaker 2: an AI large language model or Okay, this is how

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Speaker 2: I'm going to engage with it. I'm only going to

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Speaker 2: use this for work. I'm going to make sure that

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Speaker 2: I toggle these certain things off so that you can

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Speaker 2: use it in ways that you feel comfortable with and

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Speaker 2: you don't feel like your privacy is being violated.

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Speaker 1: And so much of what we do on this show

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Speaker 1: is just sort of I guess I'll, for lack of

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Speaker 1: a better phrase, arm people with the truth, arm people

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Speaker 1: and the facts. And so I talk to people who

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Speaker 1: are really skittish about using AI and in any capacity. And

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Speaker 1: you know, I think like for folks who are like

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Speaker 1: I would never use AI, I would never use tash GPT,

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Speaker 1: I totally get it. But I want people to be

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Speaker 1: able to make informed decisions have a sense of it.

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Speaker 1: So just so don't have a sense that is rooted

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Speaker 1: in AI hype of like it can do all of

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Speaker 1: these things that it absolutely get off to you. It's

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Speaker 1: a good These use cases are great when they're not

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Speaker 1: actually good use cases.

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Speaker 3: Right.

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Speaker 1: Similarly, don't fall into this trap of like you need

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Speaker 1: to be afraid of it. You can't use it right.

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Speaker 2: I mean, because the way that I always talk about

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Speaker 2: AI is AI is a new technology. If you think

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Speaker 2: back in the history of this world, every time a

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Speaker 2: new technology came into the forefront, there was always this

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Speaker 2: stage where people were terrified. When you think about mathematicians,

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Speaker 2: when the advocus was invented, there was this whole big

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Speaker 2: thing where it's like, you are not truly intelligent if

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Speaker 2: you have to use an advocus. Fast forward, when the

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Speaker 2: calculator first came out, everyone was saying, you should not

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Speaker 2: be using a calculator. No one's going to know how

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Speaker 2: to use their brains anymore. Then you have you know,

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Speaker 2: the advanced calculators TI eighty nine, We have computers and

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Speaker 2: everything like that. Then the Internet comes and everybody's like, wow, no,

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Speaker 2: this is not something we should be working with. And

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Speaker 2: now the Internet is ubiquitous, it's everywhere that our fingertips.

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Speaker 2: And then we he had Google. And when Google first started,

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Speaker 2: everyone was very very confused and saying, it's just like

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Speaker 2: this cycle with technology where it's like something comes out,

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Speaker 2: there's an innovation there, people are like, don't use it.

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Speaker 2: You should be scared, and then everybody realizes, oh, it's

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Speaker 2: not that bad.

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Speaker 1: We can live with this, and it actually is making

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Speaker 1: our lives better. So it's just important to understand how

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Speaker 1: these things work, understand how you can interact with them

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Speaker 1: or not interact with them if you don't want to.

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Speaker 1: I know some people who haven't used the tid In

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Speaker 1: not calculator, and that is totally fine. That doesn't make

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Speaker 1: them less smart or more smart or whatever. It's just

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Speaker 1: if you don't need it, don't use it. So I

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Speaker 1: want to talk about the executive orders the Trump administration

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Speaker 1: put out about AI, because I'm sure you have thoughts

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Speaker 1: I have thus, but before we jump into that, I

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Speaker 1: just wanted to really quickly talk about this thing that

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Speaker 1: I saw floating all over the internet, and that is

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Speaker 1: this Jubilee Media. I guess they're calling it a debate,

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Speaker 1: but I'm not even comfortable really using that word. Where

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Speaker 1: this journalists medi Hassan, who is like a progressive journalists, right,

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Speaker 1: I really like respect, do you do? You know what's work? Yes?

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Speaker 2: I do really respect him. Loved all of everything that

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Speaker 2: he's put out unto same.

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Speaker 1: So this clip of him debating twenty far right conservatives

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Speaker 1: viral and Jubilee Media. For folks who don't know Jubilee Media.

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Speaker 1: They started their founder. I looked into them. Their founders

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Speaker 1: said that they started because they were so set up

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Speaker 1: with how partisan politics have gotten and they wanted to

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Speaker 1: create what he called the Disney Channel for empathy. That

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Speaker 1: was a long time ago, because basically what they're more

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Speaker 1: known for now is platforming extremists. So you know the

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Speaker 1: far right conservatives quote unquotes that they had on the

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Speaker 1: show that were debating medi Hassan. They say things like, oh,

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Speaker 1: I don't mind being called a Nazi, and yes I

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Speaker 1: am a fascist and things like that, And it's really

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Speaker 1: clever how this platform has been able to really normalize

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Speaker 1: and launder these very extreme positions like aligning yourself like

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Speaker 1: like explicitly aligning yourself with Nazis is extreme to say

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Speaker 1: the least, But then they phrase it as, oh, these

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Speaker 1: are conservatives. I mean, I am not a conservative. I

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Speaker 1: would not say that it's fair to say, like, oh,

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Speaker 1: somebody who aligns themselves with Nazis, that's merely a conservative perspective, right. No. No,

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Speaker 1: The viral moment that I want to talk about was

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Speaker 1: this guy named Connor. You're a little bit more than

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Speaker 1: a far right Republican. Okay, what can I say? I

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Speaker 1: think you say I'm a fascist, Yeah, I am so,

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Speaker 1: basically Connor and like, of course his name is Connor

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Speaker 1: proudly proclaims, yeah, that's right, I'm a fascist. So this

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Speaker 1: interview goes viral and Connor he goes on the crowdfunding platform,

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Speaker 1: gives sen Goo and says I lost my job. I'm

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Speaker 1: trying to cut crowdfund twenty thousand dollars. We actually talked

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Speaker 1: about give Seno on our first episode of this season,

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Speaker 1: that it's kind of become the platform for people who

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Speaker 1: have done something bad in public and then a big

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Speaker 1: payday from it. Our earlier episode was about this woman

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Speaker 1: who calls a black child a slur on a playground

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Speaker 1: and raised almost a million dollars on Gibson Go afterward.

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Speaker 1: If you missed that episode, we will put it in

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Speaker 1: the show notes. But that woman kind of became a

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Speaker 1: right wing celebrity, and I think Connor from Jubilee is

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Speaker 1: also on his way there too. He already raised quite

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Speaker 1: a bit more than the fifteen thousand dollars he was

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Speaker 1: initially asking for, and so you know, there's a lot

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Speaker 1: to be said about Jubilee Media and like what they're doing.

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Speaker 1: But I mean, I just I I wanted to talk

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Speaker 1: about them briefly because I don't think this is going

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Speaker 1: to be the last we see of Connor, right, Like, no,

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Speaker 1: it's definitely not going to be the last we see

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Speaker 1: of him, because you know how they say a watch

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Speaker 1: pot never boils. It's like a watched fascist never shress

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Speaker 1: the fuck up, you know what I mean?

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Speaker 2: And that's T shirt please, it's like you will not

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Speaker 2: shut up. And I'm just like, that's why I refuse

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Speaker 2: to click on certain things. If I see the clip,

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Speaker 2: I scroll pass. I said, I don't want to contribute

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Speaker 2: to any of those metrics because I feel like it's

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Speaker 2: mostly you know, people who are not conservative that are

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Speaker 2: running up the numbers because we're so outraged, and so

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Speaker 2: they're using outrage as currency. And I'm just like, no,

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Speaker 2: I don't want to contribute to this guy's notoriety.

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Speaker 1: I don't want to.

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Speaker 2: Contribute to, you know, it being pushed more and more

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Speaker 2: onto people's fees, Like we have to stop giving them

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Speaker 2: a microphone. I'm so sick of it. Who else did that?

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Speaker 2: It was what was his name, otto Acho or whatever?

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Speaker 2: Who had that show where he was talking to racist.

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Speaker 2: I was like, this is the dumbest idea ever. Why

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Speaker 2: not use that microphone to talk to somebody who actually

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Speaker 2: has brains in their head and is willing to have

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Speaker 2: smart conversations, not these really old, outdated conversations. If we

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Speaker 2: stop giving these folks microphones and turning up the volume

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Speaker 2: just so that we can get clicks.

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Speaker 1: They will eventually shut up. And it will they won't

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Speaker 1: be able to give all of these other people out

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Speaker 1: in the world who are looking for, you know, verbiage

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Speaker 1: to use in their little racist conversations. They won't have

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Speaker 1: anything to say.

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Speaker 2: They'll have nothing to have in their mouths to then

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Speaker 2: regurgitate onto the rest of us exactly.

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Speaker 1: And my thing about people like Connor is that if

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Speaker 1: it wasn't for Jubilee Media giving him a platform, he'd

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Speaker 1: probably just be like typing his opinions on our Reddit threat. Honest,

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Speaker 1: he would not be our problem. And giving him a microphone,

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Speaker 1: giving him a platform, giving him a way to to

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Speaker 1: make a little bit of coin for himself, or the

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Speaker 1: abhorrent views, I think really is the problem. And you

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Speaker 1: put it so well, I do think this is We're

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Speaker 1: not in twenty sixteen anymore, right, There was a time

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Speaker 1: where people wanted to talk about like, oh, defeat the

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Speaker 1: al alt right in the marketplace of ideas, just debate them,

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Speaker 1: debate them. No, in twenty sixteen, this style of debate

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Speaker 1: content is a scam. It's not informing anybody. Yes, And

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Speaker 1: I guess ultimately I strongly feel like there is no

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Speaker 1: debating people, Like, how do you debate somebody who with

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Speaker 1: their full chest says yes, I am a fascist. When

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Speaker 1: you don't have a shared reality, there is no way

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Speaker 1: to have a debate. And I think that we should

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Speaker 1: stop acting like this is anything other than a spectacle

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Speaker 1: and right wing a starmaker machine.

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Speaker 2: You can't call it a debate because it's you're not

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Speaker 2: talking to somebody reasonable. You know, you have to have reason,

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Speaker 2: you have to have something, you have to have intelligent

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Speaker 2: thought in order to have a debate with someone. You

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Speaker 2: can't just say I'm a fascist and cross your arms

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Speaker 2: and say, yeah, that's what it is.

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Speaker 1: That's what it is.

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Speaker 2: That's not someone that you can debate. That is just

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Speaker 2: a that is it's like talking to a brick wall. Honestly,

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Speaker 2: like that's they don't.

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Speaker 1: Have any because they will.

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Speaker 2: What they will try and do is make you become

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Speaker 2: the intellectual and they they will you will have to

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Speaker 2: intellectualize their fascism. There is no intellectual roots to racism

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Speaker 2: and fascism. There's nothing intellectual about it. But this whole

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Speaker 2: conversation that that folks are having around all of this,

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Speaker 2: because we keep giving these people microphones is intellectualizing something.

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Speaker 1: That is at its core not intellectual exactly. And the

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Speaker 1: way that you put that makes it so clear that

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Speaker 1: like if you say, like when it's like when black

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Speaker 1: lives matter. In the height of the Black Lives Matter movement,

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Speaker 1: there was a lot of that debate style content, and

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Speaker 1: it's like, how do I debate somebody who doesn't feel

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Speaker 1: that I deserve rights? How do I abate somebody that

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Speaker 1: doesn't think I'm human? Like what, like what can I say?

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Speaker 1: That would be like, oh, you got me. We're not

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Speaker 1: having that, We're not speaking the same language at a

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Speaker 1: certain point, right, And there's no conversation to be had

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Speaker 1: period exactly. And I mean I definitely have gotten offers

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Speaker 1: from not from a Jubilee, but from similar platforms that

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Speaker 1: are like, oh, come on, and then you can be

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Speaker 1: the sort of like progressive voice or a feminist voice.

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Speaker 1: And I just don't think in twenty twenty five, any

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Speaker 1: serious person all to be participating in this, Like I

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Speaker 1: already it doesn't matter. I could, I could go on

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Speaker 1: those platforms and make the most well formed argument, but

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Speaker 1: I still know regardless, it's gonna be you know, conservative

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Speaker 1: Republican takes down feminists like because that's really what they're after, right.

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Speaker 1: They're not after any kind of like actual moment of

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Speaker 1: clarity or empathy or understanding each other. It's just a

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Speaker 1: ridge debate with a foregone conclusion.

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Speaker 2: Right, And even if you go in there and you

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Speaker 2: cut them up to shreds, they're gonna clip it up

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Speaker 2: so that it looks like you got tongue tied, like

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Speaker 2: you were stumped. And no, you're not gonna let me

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Speaker 2: fall victim to the editing. This is not love and

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Speaker 2: hip hop. Okay, hey, this is not Love Island.

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Speaker 1: You're not gonna have me looking like a fool just

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Speaker 1: because you wanted to clip me up and now I'm

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Speaker 1: looking crazy. Yes, exactly. Let's take a quick break at

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Speaker 1: our back, Okay, Gezi, we have to talk about somebody

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Speaker 1: who I am a little bit I have a weird

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Speaker 1: obsession with Maybe obsession is the wrong word, Okay, that

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Speaker 1: is Candice Owens. I have people who listen to the show.

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Speaker 1: I have described her as my shadow self because I

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Speaker 1: feel that she's almost like a fun house mirror version

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Speaker 1: of myself. I know that sounds well, but we did

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Speaker 1: an episode about her rise that I see so much

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Speaker 1: of myself In her early days, we were both, you know,

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Speaker 1: people who like to talk about politics on the early

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Speaker 1: days of the internet. She back in the day when

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Speaker 1: we were both doing this at the same time, she

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Speaker 1: was actually a progressive voice, and she had a very

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Speaker 1: specific switch where she went onto some of her more

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Speaker 1: like noxious extremist views. So she also is somebody who

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Speaker 1: I think is prone to hyperfixation, which I also am,

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Speaker 1: although her hyperfixations are very different than mine. Hers are

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Speaker 1: things like Blake Lively is lying about everything, or Harvey

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Speaker 1: Weinstein is actually a victim, the victim of me too.

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Speaker 1: And one of her most recent hyper fixations has been

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Speaker 1: the First Lady of France, Bridget Macrone, the wife of

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Speaker 1: the French President Emmanuel Mark Macrone. Owens spent months months

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Speaker 1: building out this increasingly elaborate, completely unfounded conspiracy that the

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Speaker 1: First Lady of France is trans. She does it before

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Speaker 1: you ask. She has no proof of this. This is

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Speaker 1: just something she's like. It's like she she initially started

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Speaker 1: calling it a gut feeling, and it's so fucked up

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Speaker 1: because initially she was it was like clearly like she

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Speaker 1: basically has never had any proof, and she's gone from

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Speaker 1: being like, this is a gut feeling. I have to

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Speaker 1: I have the proof of this, I have the receipts,

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Speaker 1: and so I have quietly been wondering where this all

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Speaker 1: ends for her for a while, and we may have

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Speaker 1: her answer because this week the French president and his

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Speaker 1: wife file the defamation lawsuit in the US against Owen's

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Speaker 1: centered on her claim that the First Lady is trans.

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Speaker 1: So they filed this complaint in the Delaware Superior Court,

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Speaker 1: saying that Owens has waged a lie filled campaign of

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Speaker 1: global humiliation to promote her podcast and expand her frenzied

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Speaker 1: fan base. As somebody who keeps tabs on what she's

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Speaker 1: up to, I wholeheartedly agree with how they're characterizing this,

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Speaker 1: because I've honestly seen I don't call it missed reporting,

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Speaker 1: but I've seen it reported like as if Candice Owens

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Speaker 1: just said this at a party or something that she

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Speaker 1: just casually said this once or twice, and that's actually

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Speaker 1: not what's going on in reality. She has made multiple

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Speaker 1: podcasts and videos cleaning who have found smoking guns and

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Speaker 1: claiming to have all this evidence, like she's got like

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Speaker 1: a string board and she's connecting the DUTs on this

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Speaker 1: claim that she's made like very concrete claims, and she's

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Speaker 1: gotten millions of viewers, which translates to cash to her

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Speaker 1: for this lie. So it's not like she's just like

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Speaker 1: saying this and they're coming after for saying this like

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Speaker 1: casually once or twice, as I feel like some of

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Speaker 1: the reportings suggest she has really monetized this unfounded lie

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Speaker 1: and builken into a whole conspiracy theory with her millions

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Speaker 1: of viewers. Yeah.

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Speaker 2: I mean that's the bread and butter for folks like

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Speaker 2: Candice Owens because what their base it fails to grasp

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Speaker 2: onto is fact and so then they have to think

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Speaker 2: very very like dark meta. Yeah you know what I mean,

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Speaker 2: where it's like no, no, no, y'all are so blinded

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Speaker 2: by facts that you're missing all of this mystery and

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Speaker 2: nothing over here. And it's like that's how she that's

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Speaker 2: how she makes her money. She just makes something up

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Speaker 2: and then runs with it and it's like, oh, but

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Speaker 2: look at this, Oh why did she cross her legs

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Speaker 2: like this? Instead of like it's ridiculous, it's honestly ridiculous.

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Speaker 2: And anytime somebody is doing something like this, I'm always

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Speaker 2: just like, this is Olympic level like jumping, Like you're

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Speaker 2: going from one thing, you're doing an absolute triple jump.

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Speaker 2: You gold medalist in this jump that you're doing trying

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Speaker 2: to get to whatever conclusion and the other part of

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Speaker 2: it that I'm which is my issue with everyone that

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Speaker 2: has issue with transgender folks, why do we care? Like

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Speaker 2: I'm just like, and if she is, like, why do

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Speaker 2: you care?

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Speaker 1: Who's doing abandon.

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Speaker 3: Who cares?

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Speaker 1: I'm not worried about what Matt Cralin is doing with

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Speaker 1: his partner, his wife. I don't care what they do

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Speaker 1: in their bedroom. I care about his policy. How is

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Speaker 1: that going to affect his people? The folks in the

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Speaker 1: United States the world don't. I don't even know what

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Speaker 1: his wife looks like.

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Speaker 2: Like the fact that she's putting so much energy into this,

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Speaker 2: I'm like, can this girlfriend, now imagine if you put

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Speaker 2: this same level of intensity into anything else, Yes, you

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Speaker 2: would be able to do anything you could take over

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Speaker 2: this world, but you choose to hyper focus.

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Speaker 1: On these really ridiculous things.

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Speaker 2: I'm like, use your powers for good, not evil, and

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Speaker 2: don't get yourself in prison or sue to the point

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Speaker 2: where you can't even afford a microphone because you want

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Speaker 2: to do this. It's crazy.

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Speaker 1: You put it so perfectly, the way that she has

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Speaker 1: picked apart every little iteration of their appearance, their marriage,

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Speaker 1: their friends, their family, their personal history, the way that

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Speaker 1: the way that Brigitte's hair will fall, like, oh, that

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Speaker 1: should tell tales. I'm like, like, she is dead. And

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Speaker 1: then sometimes it'll be like the absence of evidence is

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Speaker 1: what the evidence is. It's like, well, if she wasn't,

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Speaker 1: if she wasn't actually trends, don't you think, like it's

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Speaker 1: it is such a rabbit hole. And keep it in

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Speaker 1: mind that Brigitte and Emmanuel Macrone, they've been in a

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Speaker 1: romantic relationship since for a very long time. Like their

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Speaker 1: relationship is its own weird thing, Like they met when

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Speaker 1: Brigitte was a teacher at his school when he was

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Speaker 1: fifteen and she was in her thirties, And they met

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Speaker 1: in drama club at a school that like he was

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Speaker 1: a student and and she was a teacher into like

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Speaker 1: now that I know, I mean like exactly, so like

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Speaker 1: they're actually focused on the wrong thing a thousand percent.

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Speaker 1: There actually is stuff where it's like, well, who to

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Speaker 1: talk about how we're their marriage is? That's right there,

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Speaker 1: Like that's like Lagitimately we should be talking about screw

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Speaker 1: he focus on the wrong thing, like talk about the

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Speaker 1: real stuff. I did not know that no side eye

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Speaker 1: and the whole thing. If you wanted to make podcast

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Speaker 1: episodes about them, there's a lot to say that is

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Speaker 1: a mertle ground for discussion, and so you don't actually

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Speaker 1: need to make up these completely unfounded claims. Yeah. So

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Speaker 1: Owens responded to the lawsuit. She says, this lawsuit is

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Speaker 1: a littered with factual inaccuracies and part of an obvious

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Speaker 1: and desperate public relations strategy to smear my character. And

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Speaker 1: my question is this, why would the president of France

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Speaker 1: want to smear this random American podcaster other than the

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Speaker 1: fact that she keeps telling these outlandish lies about her,

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Speaker 1: like like like why why he's busy doing French president stuff?

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Speaker 1: You think he can even want to like target you

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Speaker 1: like a random right wing podcaster.

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Speaker 2: Absolutely not right, It's it's really ridiculous. And I also

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Speaker 2: saw that she says something like, oh, you're just suing

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Speaker 2: me so you can have it on record that you

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Speaker 2: sued me.

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Speaker 1: But I'll see you in court. Girl. This ain't what

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Speaker 1: you want. No, ain't what you gonna is not gonna say, oh,

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Speaker 1: you have to prove that you are biologically female.

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Speaker 2: They're not gonna do that. They're gonna say that you

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Speaker 2: have no reason to believe that you. It is baseless,

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Speaker 2: and so you're in trouble, and sister, those dollars ain't

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Speaker 2: set up the right way, especially now that you haven't

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Speaker 2: always been aligning yourself with everything that Trump's doing. So

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Speaker 2: I feel like you've lost a little bit of your base.

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Speaker 2: They're not gonna say, sister, they're not gonna save you

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Speaker 2: in this instance.

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Speaker 1: You're just gonna be broke. Okay. Yeah, this is a mistake,

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Speaker 1: and it is something that we we we've talked about

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Speaker 1: this on the show, but it's I think it's something

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Speaker 1: that we've seen more and more of where someone, often

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Speaker 1: like an extremist, will go on a smear campaign and

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Speaker 1: the only recourse is suing them for defamation. And we've

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Speaker 1: seen it work. So it's a business model. Now, yeah,

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Speaker 1: it is. It kind of is, and like I I mean,

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Speaker 1: it's it sounds like from reading the lawsuit that they're like,

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Speaker 1: we gave her ample opportunity to just stop this. We

433
00:22:33,600 --> 00:22:35,560
Speaker 1: were like, we wouldn't. And so I do think like

434
00:22:36,359 --> 00:22:38,439
Speaker 1: they would not have pursued this if they did not

435
00:22:38,560 --> 00:22:41,320
Speaker 1: feel like they had to. And I think the fact

436
00:22:41,320 --> 00:22:45,520
Speaker 1: that she has shown no signs of planning on reversing

437
00:22:45,640 --> 00:22:48,479
Speaker 1: course on this and just shutting up and saving herself

438
00:22:48,480 --> 00:22:50,960
Speaker 1: the trouble makes me think that they're that that's true.

439
00:22:51,040 --> 00:22:52,120
Speaker 1: They had to do this.

440
00:22:52,960 --> 00:22:57,320
Speaker 2: Listen, if I had that level intensity about anything, I

441
00:22:57,400 --> 00:22:59,040
Speaker 2: just want to bottle what she has and.

442
00:22:59,119 --> 00:23:02,040
Speaker 1: Used it in the right way. Yahsi your energy girl.

443
00:23:02,680 --> 00:23:03,320
Speaker 1: My god.

444
00:23:03,680 --> 00:23:06,280
Speaker 2: I was gonna say, get you somebody who is so

445
00:23:06,400 --> 00:23:09,040
Speaker 2: fixed sated on you like that. But you don't want

446
00:23:09,080 --> 00:23:14,440
Speaker 2: that type ofation. That's too much stalking talk to that.

447
00:23:15,080 --> 00:23:23,119
Speaker 1: Okay. So should we talk about Trump's woke AI executive orders? Huh?

448
00:23:23,160 --> 00:23:24,960
Speaker 1: I mean it would be wild to do a podcast

449
00:23:24,960 --> 00:23:28,760
Speaker 1: episode and not, so let's just get into it. So

450
00:23:28,840 --> 00:23:33,520
Speaker 1: Trump signed three AI focused executive orders on Wednesday aimed

451
00:23:33,560 --> 00:23:37,800
Speaker 1: at keeping woke AI models that's his word out of

452
00:23:37,920 --> 00:23:42,520
Speaker 1: Washington and hopefully this idea of turning America into an

453
00:23:42,520 --> 00:23:46,280
Speaker 1: AI export powerhouse. So there are three executive orders of this.

454
00:23:46,720 --> 00:23:49,040
Speaker 1: I will quickly summarize two of them, and I want

455
00:23:49,040 --> 00:23:51,679
Speaker 1: to focus on one of them kind of exclusively, but

456
00:23:51,720 --> 00:23:53,199
Speaker 1: I'll give you the sense of like what's going on

457
00:23:53,240 --> 00:23:57,160
Speaker 1: with everything so perfect. One is called Promoting the Export

458
00:23:57,240 --> 00:24:00,440
Speaker 1: of the American AI Technology Stack, which aims to support

459
00:24:00,480 --> 00:24:04,480
Speaker 1: the export of American developed AI systems. Its main mechanism

460
00:24:04,600 --> 00:24:08,199
Speaker 1: seem to be financing support for American AI companies to

461
00:24:08,240 --> 00:24:12,440
Speaker 1: facilitate them and licensing them to allied nations. It honestly

462
00:24:12,520 --> 00:24:16,600
Speaker 1: is pretty light on specifics, which, like surprise, surprise, an

463
00:24:16,640 --> 00:24:19,480
Speaker 1: EO is like light on specific side of this administration.

464
00:24:19,520 --> 00:24:23,439
Speaker 1: Who could have thought? But all the stated goal anyway

465
00:24:23,560 --> 00:24:26,000
Speaker 1: is to establish the US as like the leader of

466
00:24:26,040 --> 00:24:29,159
Speaker 1: the global leader in AI. I will say, like, to me,

467
00:24:29,440 --> 00:24:31,760
Speaker 1: it really does seem like it's a good opportunity for

468
00:24:32,200 --> 00:24:36,000
Speaker 1: American tax dollars to subsidize big tech companies even further,

469
00:24:36,200 --> 00:24:39,680
Speaker 1: particularly those companies that the Trump administration favors. Like mm hmm.

470
00:24:39,800 --> 00:24:42,719
Speaker 1: I'm expert in this, but reading what they put out

471
00:24:42,760 --> 00:24:45,280
Speaker 1: of all this just seems like a big gift to

472
00:24:45,880 --> 00:24:50,919
Speaker 1: Trump tech cronies. So something to know. Mm hm. The

473
00:24:50,960 --> 00:24:55,520
Speaker 1: next EO is called Accelerating Federal Permitting of Data Center Infrastructure,

474
00:24:55,600 --> 00:24:59,440
Speaker 1: which exclusively focuses on building new large data center projects.

475
00:24:59,560 --> 00:25:02,080
Speaker 1: Those were acquiring at least one hundred megawats of power.

476
00:25:02,160 --> 00:25:05,040
Speaker 1: So I am not a scientist or an engineer, so

477
00:25:05,119 --> 00:25:06,800
Speaker 1: you can let me know if I have this right.

478
00:25:08,280 --> 00:25:10,320
Speaker 1: I did a little bit of research, and how does

479
00:25:10,480 --> 00:25:13,960
Speaker 1: seem like one hundred megawatts is what you might think

480
00:25:14,000 --> 00:25:16,280
Speaker 1: of as like at the larger end of what is

481
00:25:16,400 --> 00:25:19,120
Speaker 1: normal for a data center? Is that puviny cent of that?

482
00:25:19,920 --> 00:25:23,440
Speaker 1: I think? So? Yeah, okay, And this Executive Order calls

483
00:25:23,480 --> 00:25:27,479
Speaker 1: on agencies to remove environmental regulatory hurdles so that the

484
00:25:27,520 --> 00:25:31,560
Speaker 1: negative impacts of the environment or water resources are not

485
00:25:31,680 --> 00:25:34,440
Speaker 1: a barrier to what everybody wants, which is the construction

486
00:25:34,600 --> 00:25:39,560
Speaker 1: of new large data centers, which okay, I mean there

487
00:25:39,560 --> 00:25:42,399
Speaker 1: are literally people who do not have water in their

488
00:25:42,400 --> 00:25:45,080
Speaker 1: households because of data centers. There are communities like the

489
00:25:45,119 --> 00:25:48,359
Speaker 1: neighborhood in Tennessee where Elon Musk built a massive data center,

490
00:25:49,040 --> 00:25:52,199
Speaker 1: no longer have clean air. This Executive Order basically is

491
00:25:52,240 --> 00:25:56,760
Speaker 1: like fuck them Maribobi, Fine, we need this technology. Yeah,

492
00:25:56,760 --> 00:25:57,240
Speaker 1: And it's a.

493
00:25:58,800 --> 00:26:00,880
Speaker 2: In my opinion, it's a ruse because it's like, it's

494
00:26:00,880 --> 00:26:03,560
Speaker 2: not really about the technology. It's about the people who

495
00:26:03,560 --> 00:26:06,200
Speaker 2: are going to be making money from building these data

496
00:26:06,200 --> 00:26:11,560
Speaker 2: centers and expanding their empires. It's really not about And

497
00:26:11,880 --> 00:26:14,760
Speaker 2: that's what I also want people to understand about like

498
00:26:14,880 --> 00:26:17,120
Speaker 2: AI and these technologies that are coming down the pike.

499
00:26:17,200 --> 00:26:20,960
Speaker 2: It's like the reason why the government is so hyper

500
00:26:20,960 --> 00:26:23,560
Speaker 2: focused on it. Anytime the government is doing all of this,

501
00:26:23,720 --> 00:26:26,600
Speaker 2: or you have a president that is like just so

502
00:26:26,880 --> 00:26:30,480
Speaker 2: laser focused on something, you have to start really thinking

503
00:26:30,840 --> 00:26:34,840
Speaker 2: where is the money going? That should always be the

504
00:26:34,880 --> 00:26:38,280
Speaker 2: first question, because if you're thinking about the greater good

505
00:26:38,280 --> 00:26:43,280
Speaker 2: of America and how do we make ourselves more technologically

506
00:26:43,320 --> 00:26:45,760
Speaker 2: advanced and all these things like that, you would invest

507
00:26:45,800 --> 00:26:49,639
Speaker 2: in education first and foremost. You would make sure that

508
00:26:49,720 --> 00:26:53,960
Speaker 2: people that everybody in the like everybody that is coming

509
00:26:54,040 --> 00:26:57,040
Speaker 2: up in the US education system, understands how to code,

510
00:26:57,320 --> 00:27:01,200
Speaker 2: that they understand like how a semic conductor chip works,

511
00:27:01,400 --> 00:27:03,639
Speaker 2: that they know how these models work.

512
00:27:04,080 --> 00:27:06,400
Speaker 1: We're not doing that. We're not investing in education. They're

513
00:27:06,480 --> 00:27:08,920
Speaker 1: like build the things, and that's because when you build

514
00:27:08,920 --> 00:27:11,360
Speaker 1: the things and you start to make product, you can

515
00:27:11,400 --> 00:27:12,680
Speaker 1: sell it all over the world.

516
00:27:12,800 --> 00:27:15,879
Speaker 2: That means people are making money. And so I'm just

517
00:27:15,920 --> 00:27:18,480
Speaker 2: like all of these executive orders, all of these it's

518
00:27:18,560 --> 00:27:23,480
Speaker 2: just a formal way of lining their own pockets. Because Trump,

519
00:27:23,560 --> 00:27:26,399
Speaker 2: he's thinking, after this presidency is over, I need to

520
00:27:26,440 --> 00:27:27,919
Speaker 2: make sure that I'm setting things up so I make

521
00:27:27,920 --> 00:27:30,160
Speaker 2: the most amount of money possible and make my rich

522
00:27:30,200 --> 00:27:32,280
Speaker 2: friends happy so that just in case I need them,

523
00:27:32,560 --> 00:27:34,400
Speaker 2: you know, they can you know, slim me a little,

524
00:27:34,480 --> 00:27:37,920
Speaker 2: a little some something. And so that's always what I

525
00:27:37,960 --> 00:27:40,840
Speaker 2: think about it. I'm like, it doesn't make sense for

526
00:27:41,240 --> 00:27:44,400
Speaker 2: you to say we want to be leaders in technology

527
00:27:44,560 --> 00:27:48,679
Speaker 2: but not investing in education. Who gonna do it.

528
00:27:48,880 --> 00:27:51,919
Speaker 1: Who's gonna do it, Who's gonna advance us if we

529
00:27:52,080 --> 00:27:56,639
Speaker 1: are not making sure that college is affordable, that K

530
00:27:56,800 --> 00:27:59,520
Speaker 1: through twelve schools, that they have the resources that they need,

531
00:27:59,640 --> 00:28:02,400
Speaker 1: that teachers are being paid in a that a teacher

532
00:28:02,480 --> 00:28:04,919
Speaker 1: is being paid the amount that a living wage, that

533
00:28:04,960 --> 00:28:07,000
Speaker 1: they can go to these schools and be able to

534
00:28:07,320 --> 00:28:10,560
Speaker 1: arm these children with knowledge, training teachers, like making sure

535
00:28:10,600 --> 00:28:15,440
Speaker 1: that defunding academic institutions like Harvard and all these things

536
00:28:15,440 --> 00:28:18,040
Speaker 1: like that. It doesn't those two things don't go together.

537
00:28:18,280 --> 00:28:20,159
Speaker 1: You can't say we need to be the leaders in

538
00:28:20,160 --> 00:28:24,760
Speaker 1: this technology. But then also, Department of Education, you're going

539
00:28:24,840 --> 00:28:27,879
Speaker 1: to the dumps, and I think we're doing exactly the

540
00:28:27,960 --> 00:28:30,480
Speaker 1: opposite of what we should be doing. It's like we've

541
00:28:30,720 --> 00:28:36,240
Speaker 1: It's like this administration has clocked that an educated, critical,

542
00:28:36,560 --> 00:28:39,600
Speaker 1: upwardly mobile scenery is not is not going to be

543
00:28:39,640 --> 00:28:41,600
Speaker 1: conducive to the way that they're trying to do business.

544
00:28:41,640 --> 00:28:43,440
Speaker 1: I think I think all of all of the way

545
00:28:43,440 --> 00:28:46,120
Speaker 1: that they're moving tells me that that that's what they've deduced.

546
00:28:46,200 --> 00:28:49,200
Speaker 2: Yes, absolutely, that's exactly it. I feel like you hit

547
00:28:49,200 --> 00:28:50,240
Speaker 2: the nail right on the head.

548
00:28:53,800 --> 00:29:07,560
Speaker 1: More after a quick break, let's get right back into it.

549
00:29:11,840 --> 00:29:15,800
Speaker 1: So let's talk about the woke AI bit of this.

550
00:29:16,040 --> 00:29:18,440
Speaker 1: That's not me. I mean, I don't know, I don't

551
00:29:18,520 --> 00:29:21,040
Speaker 1: understand what that is same And so here's the thing.

552
00:29:21,080 --> 00:29:23,840
Speaker 1: When I was seeing woke AI everywhere in all the headlines,

553
00:29:23,880 --> 00:29:26,960
Speaker 1: I was like, well, certainly this is like they're extrapolating

554
00:29:27,080 --> 00:29:29,360
Speaker 1: or they're using that as a stand in. No, that

555
00:29:29,480 --> 00:29:32,040
Speaker 1: is actually the name of the executive order, the Executive

556
00:29:32,080 --> 00:29:36,840
Speaker 1: Order US called the preventing woke AI in the federal government. Hi,

557
00:29:37,120 --> 00:29:41,040
Speaker 1: when I tell you that, I would pay good money

558
00:29:41,080 --> 00:29:43,560
Speaker 1: to have Trump like sit down and explain to me, like,

559
00:29:44,200 --> 00:29:47,960
Speaker 1: what do you when you say WHOAOKAI? What do you mean?

560
00:29:48,120 --> 00:29:52,280
Speaker 1: Give me some specifics. And he probably couldn't.

561
00:29:53,600 --> 00:29:56,040
Speaker 2: No, he probably couldn't because I think it's just like

562
00:29:56,640 --> 00:30:01,320
Speaker 2: anything any AI that would say anything negative about him

563
00:30:01,360 --> 00:30:02,320
Speaker 2: and his administration.

564
00:30:02,600 --> 00:30:04,360
Speaker 1: Basically that's basically it.

565
00:30:04,720 --> 00:30:08,920
Speaker 2: Yeah, and so now we're talking about another hit to

566
00:30:09,400 --> 00:30:13,440
Speaker 2: freedom of speech and things like that. And when people

567
00:30:13,520 --> 00:30:15,320
Speaker 2: are thinking about AI, they're like, oh, well, AI is

568
00:30:15,360 --> 00:30:20,320
Speaker 2: not a human. It's not AI is not built from nothing.

569
00:30:21,240 --> 00:30:24,000
Speaker 2: It The way that AI works is that it take

570
00:30:24,160 --> 00:30:28,400
Speaker 2: it crawls the entire Internet and grabs everything that's out there,

571
00:30:28,880 --> 00:30:33,160
Speaker 2: every single corner, every single piece of the Internet. That

572
00:30:33,360 --> 00:30:36,520
Speaker 2: is what it's working from. So if he's saying I

573
00:30:36,560 --> 00:30:40,320
Speaker 2: don't want AI to use to have anything in it

574
00:30:40,760 --> 00:30:45,120
Speaker 2: that says these specific things, that's basically putting a blackout

575
00:30:45,160 --> 00:30:47,760
Speaker 2: on parts of the Internet. When it comes to AI,

576
00:30:48,520 --> 00:30:52,200
Speaker 2: AI people think of AI as like this robot that's

577
00:30:52,320 --> 00:30:57,400
Speaker 2: just super fast at learning, and I'm like to learn something.

578
00:30:57,640 --> 00:31:02,280
Speaker 2: Things have to exist, and so like Yes, there are

579
00:31:02,320 --> 00:31:05,400
Speaker 2: issues with you know, creativity and using people's image and likeness.

580
00:31:05,480 --> 00:31:08,719
Speaker 1: Yes, I get all of that. But when we're talking

581
00:31:08,760 --> 00:31:13,640
Speaker 1: about censoring AI, he's censoring us.

582
00:31:14,040 --> 00:31:17,160
Speaker 2: He's ansoring the people. And that's what people need to understand.

583
00:31:17,200 --> 00:31:20,760
Speaker 2: It's like, oh yeah, sure, woki no wo ki. That

584
00:31:20,920 --> 00:31:26,520
Speaker 2: is words, that is images that are there that the

585
00:31:26,840 --> 00:31:30,160
Speaker 2: AI is learning from that will not be used. So

586
00:31:30,200 --> 00:31:32,720
Speaker 2: it's the same thing as burning down a library or

587
00:31:32,720 --> 00:31:35,160
Speaker 2: a whole section of a library. It's the same thing

588
00:31:35,360 --> 00:31:39,280
Speaker 2: as taking away access to certain parts of the Internet

589
00:31:40,080 --> 00:31:40,520
Speaker 2: like they.

590
00:31:40,400 --> 00:31:43,440
Speaker 1: Do in China. You know, they don't allow them to

591
00:31:43,520 --> 00:31:46,600
Speaker 1: have things like TikTok, and they like the way that

592
00:31:46,640 --> 00:31:49,200
Speaker 1: their internet works is not the way our internet works.

593
00:31:49,560 --> 00:31:51,680
Speaker 1: And that's essentially what he's trying to do.

594
00:31:51,800 --> 00:31:55,400
Speaker 2: And we have to really keep our eyes open because

595
00:31:55,960 --> 00:31:58,840
Speaker 2: you can come up with a lot of like fancy

596
00:31:59,520 --> 00:32:04,360
Speaker 2: white house house legal ways legal quote unquote to censor

597
00:32:04,920 --> 00:32:06,680
Speaker 2: whole populations of people.

598
00:32:07,040 --> 00:32:09,280
Speaker 1: I mean, I had never even thought about it that way,

599
00:32:09,320 --> 00:32:11,280
Speaker 1: but that is something that's a drum that we beat

600
00:32:11,360 --> 00:32:13,920
Speaker 1: a lot on the show that AI is people, right,

601
00:32:13,920 --> 00:32:16,920
Speaker 1: it's yeah, by people trained by people. It's not you know,

602
00:32:17,000 --> 00:32:20,200
Speaker 1: it's it's not a computer brain or something right for us.

603
00:32:20,280 --> 00:32:23,120
Speaker 1: And so what you're saying is that this sort of

604
00:32:23,160 --> 00:32:27,200
Speaker 1: preventing woke AI executive order, that's like, no, AI must

605
00:32:27,240 --> 00:32:30,360
Speaker 1: not have anything that have Messa must not have any

606
00:32:30,400 --> 00:32:34,040
Speaker 1: kind of ideological agenda. That's just another way of censoring

607
00:32:34,080 --> 00:32:37,640
Speaker 1: the Internet and ultimately censoring us expression.

608
00:32:38,120 --> 00:32:42,880
Speaker 2: Exactly because if I'm if I'm using uh, the Internet

609
00:32:42,880 --> 00:32:46,880
Speaker 2: and I'm creating blog posts that I'm talking about my

610
00:32:46,960 --> 00:32:49,840
Speaker 2: experience as a black woman and stem my experience as

611
00:32:49,840 --> 00:32:52,560
Speaker 2: a black woman in America, and I'm saying I feel

612
00:32:52,600 --> 00:32:56,719
Speaker 2: like this certain legislation is not right, or that i

613
00:32:56,760 --> 00:32:59,640
Speaker 2: feel like I'm being disenfranchised, and all these things like that,

614
00:33:00,080 --> 00:33:03,120
Speaker 2: the AI models will not be able to pull from

615
00:33:03,160 --> 00:33:06,520
Speaker 2: my blog in order to answer someone's question that might

616
00:33:06,560 --> 00:33:09,880
Speaker 2: be like, in twenty twenty five, what was it like

617
00:33:09,960 --> 00:33:14,560
Speaker 2: to live in America for a black woman? I'm silenced.

618
00:33:14,800 --> 00:33:19,800
Speaker 2: It's quiet, everybody on mute. And that's dangerous because you

619
00:33:20,000 --> 00:33:23,680
Speaker 2: essentially can rewrite history. Imagine if we could go into

620
00:33:23,840 --> 00:33:26,960
Speaker 2: every single library, take books and just rip out whole

621
00:33:27,000 --> 00:33:29,640
Speaker 2: sections and say, Nope, that didn't happen.

622
00:33:30,080 --> 00:33:32,680
Speaker 1: That is what is happening. That's what the technology is.

623
00:33:32,760 --> 00:33:37,520
Speaker 2: The Internet is something that we has is boundless, but

624
00:33:37,600 --> 00:33:42,160
Speaker 2: there is absolutely the ability to censor, and we have

625
00:33:42,240 --> 00:33:46,360
Speaker 2: to really be careful because these are the stories. These

626
00:33:46,400 --> 00:33:50,720
Speaker 2: are the stories that tell what history was. Our experiences.

627
00:33:50,960 --> 00:33:53,520
Speaker 2: People might you know, pooh pooh doing a blog or

628
00:33:53,720 --> 00:33:57,360
Speaker 2: you know, vlogging your life. These are archives that people

629
00:33:57,440 --> 00:33:59,680
Speaker 2: one hundred years from now will watch, Like when we

630
00:33:59,720 --> 00:34:03,120
Speaker 2: see videos from those old black and white videos that

631
00:34:03,160 --> 00:34:04,840
Speaker 2: they restore and we're like, wow, look at those.

632
00:34:04,760 --> 00:34:07,160
Speaker 1: People walking around. Oh my goodness, that's so cool.

633
00:34:07,760 --> 00:34:09,600
Speaker 2: Are you trying to tell me that we will that's

634
00:34:09,960 --> 00:34:13,920
Speaker 2: our whole experience as black women won't be there. We

635
00:34:13,960 --> 00:34:16,919
Speaker 2: won't like your podcast won't be my podcast won't be there.

636
00:34:17,600 --> 00:34:21,400
Speaker 2: Like images of us at marches, images of our family

637
00:34:22,040 --> 00:34:25,239
Speaker 2: like having like having community won't be there because they

638
00:34:25,320 --> 00:34:28,480
Speaker 2: might say, oh that that's woke. Black family reunions is

639
00:34:28,520 --> 00:34:31,880
Speaker 2: too woke for the internet. We really have to be careful.

640
00:34:31,920 --> 00:34:35,560
Speaker 2: We really have to like sit up and take notice

641
00:34:35,600 --> 00:34:38,120
Speaker 2: of these of these ways that we're also being censored

642
00:34:38,120 --> 00:34:41,520
Speaker 2: and that they're trying to rewrite history and silence us.

643
00:34:42,000 --> 00:34:43,879
Speaker 1: Because if they silence us in this.

644
00:34:43,920 --> 00:34:47,800
Speaker 2: Technology, we're in a really dangerous spot for the history

645
00:34:48,440 --> 00:34:52,120
Speaker 2: being told. And as we know, history always repeats itself,

646
00:34:52,120 --> 00:34:56,239
Speaker 2: and especially if that that narrative is not there. I mean,

647
00:34:56,320 --> 00:34:59,080
Speaker 2: look at how long we went. I feel like how

648
00:34:59,120 --> 00:35:03,640
Speaker 2: long America went with Native Americans and understanding the history

649
00:35:04,120 --> 00:35:08,760
Speaker 2: and the plight of Native Americans, like the indigenous people

650
00:35:08,880 --> 00:35:10,640
Speaker 2: of America.

651
00:35:10,920 --> 00:35:13,280
Speaker 1: It's they should want it back in blood.

652
00:35:13,440 --> 00:35:17,160
Speaker 2: Honestly, it's pretty sick that we have, you know, sports

653
00:35:17,200 --> 00:35:18,439
Speaker 2: teams that are like.

654
00:35:18,560 --> 00:35:22,480
Speaker 1: The chiefs the Indians, like they should want it back

655
00:35:22,520 --> 00:35:25,040
Speaker 1: in blood. And that's what happens, That's what will happen

656
00:35:25,120 --> 00:35:27,200
Speaker 1: to whole populations.

657
00:35:27,560 --> 00:35:31,600
Speaker 2: And some people who are part of Trump's space might

658
00:35:31,640 --> 00:35:34,440
Speaker 2: be like, oh, well, I don't care because that isn't me.

659
00:35:35,360 --> 00:35:36,399
Speaker 1: It's gonna be you too.

660
00:35:36,480 --> 00:35:36,840
Speaker 3: Yeah.

661
00:35:36,920 --> 00:35:39,239
Speaker 2: You think you live in Appalachia and you think that

662
00:35:39,280 --> 00:35:40,880
Speaker 2: they want to hear about your struggles.

663
00:35:41,040 --> 00:35:42,839
Speaker 1: No, they don't want to hear about your struggles either.

664
00:35:42,880 --> 00:35:44,600
Speaker 1: They don't want to hear that you can't afford to

665
00:35:45,360 --> 00:35:48,440
Speaker 1: pay to get groceries for your your family this week.

666
00:35:48,480 --> 00:35:50,080
Speaker 1: You want food stamps. They don't want to hear that either.

667
00:35:50,120 --> 00:35:55,160
Speaker 1: That's woke. We're all in danger. We are in danger,

668
00:35:55,200 --> 00:35:57,480
Speaker 1: and we really need to take notice. I mean, I

669
00:35:58,239 --> 00:36:01,319
Speaker 1: completely agree you put that so well, and I do

670
00:36:01,440 --> 00:36:04,280
Speaker 1: think when you look at the words and this executive

671
00:36:04,360 --> 00:36:07,360
Speaker 1: order under the like with the lens that you just

672
00:36:07,440 --> 00:36:10,120
Speaker 1: laid out, it's clear that's exactly what they're doing. They're right,

673
00:36:10,200 --> 00:36:14,480
Speaker 1: They're right there carving out any part of the experience

674
00:36:14,880 --> 00:36:17,880
Speaker 1: of being a human that they don't align with and

675
00:36:17,920 --> 00:36:21,960
Speaker 1: don't like. And what I find so fucked up is

676
00:36:22,000 --> 00:36:24,680
Speaker 1: that it relies on this idea that there is sort

677
00:36:24,680 --> 00:36:28,440
Speaker 1: of one objective truth and who gets to decide that

678
00:36:28,800 --> 00:36:32,399
Speaker 1: the Trump administration so exactly the truth of like, even

679
00:36:32,400 --> 00:36:35,440
Speaker 1: if you're a white person in Appalachia who can't afford groceries,

680
00:36:35,960 --> 00:36:38,279
Speaker 1: the truth that the Trump administration wants to give is

681
00:36:38,320 --> 00:36:41,879
Speaker 1: that groceries have never been cheaper and everybody. So that's

682
00:36:41,920 --> 00:36:44,360
Speaker 1: so get that right out of there. And yeah, I

683
00:36:44,360 --> 00:36:48,160
Speaker 1: mean the way that they in this executive Order, they

684
00:36:48,200 --> 00:36:52,200
Speaker 1: basically spell out that we get to decide what is

685
00:36:52,239 --> 00:36:55,200
Speaker 1: and is not objective, what is it is not truth?

686
00:36:55,280 --> 00:36:57,239
Speaker 1: What is it is not They've come up with this

687
00:36:57,520 --> 00:37:01,040
Speaker 1: term that they basically disinvented, that of whole cloth unbiased

688
00:37:01,080 --> 00:37:04,360
Speaker 1: AI principles, And essentially that means that they are only

689
00:37:04,640 --> 00:37:09,359
Speaker 1: supporting AI models that meet this term of unbiased that

690
00:37:09,480 --> 00:37:12,880
Speaker 1: they have just decided themselves.

691
00:37:13,360 --> 00:37:17,400
Speaker 2: Yeah, the when you talk about anything that is man

692
00:37:17,440 --> 00:37:23,480
Speaker 2: made and saying that it is unbiased, it's just not possible.

693
00:37:23,920 --> 00:37:25,719
Speaker 2: There was a study done a while ago, I don't

694
00:37:25,719 --> 00:37:27,840
Speaker 2: I don't remember what year it was, but they had

695
00:37:28,160 --> 00:37:31,480
Speaker 2: an AI model crawl the entire Internet and basically create

696
00:37:31,520 --> 00:37:34,240
Speaker 2: a personality based on what they found on the Internet.

697
00:37:34,760 --> 00:37:39,640
Speaker 2: And that model was racist. It was sexist, and it

698
00:37:39,680 --> 00:37:43,640
Speaker 2: was xenophobic, and like, that's just based off of what's

699
00:37:43,680 --> 00:37:47,560
Speaker 2: on the Internet. And so it started out with nothing

700
00:37:47,680 --> 00:37:50,439
Speaker 2: and then said they said build from here. Just crawl

701
00:37:50,480 --> 00:37:53,480
Speaker 2: the internet, read as much as you can. And it

702
00:37:53,560 --> 00:37:55,600
Speaker 2: crawled the entire Internet. And that's what the result was.

703
00:37:56,239 --> 00:38:01,920
Speaker 2: Everything that is man made is biased. Everything. Like the

704
00:38:02,040 --> 00:38:05,600
Speaker 2: term biased, I think people always think of it in

705
00:38:05,640 --> 00:38:08,000
Speaker 2: one direction. If I were to make something, it's gonna

706
00:38:08,040 --> 00:38:11,080
Speaker 2: be biased. You know, if I make something for if

707
00:38:11,120 --> 00:38:14,320
Speaker 2: I make a product that I use for my hair, well,

708
00:38:14,360 --> 00:38:18,480
Speaker 2: my hair is very specific type. Okay, I have afro

709
00:38:18,680 --> 00:38:22,239
Speaker 2: hair that I love and that product is going to

710
00:38:22,320 --> 00:38:26,279
Speaker 2: work in my hair. Now you go next door to them,

711
00:38:26,280 --> 00:38:30,200
Speaker 2: white folks, they might not enjoy that product because I

712
00:38:30,280 --> 00:38:35,680
Speaker 2: made it and it was biased, absolutely, And so this

713
00:38:35,719 --> 00:38:40,480
Speaker 2: whole idea of something being unbiased, it is just again

714
00:38:40,600 --> 00:38:43,400
Speaker 2: a ruse, like it's just not possible. Anything made by

715
00:38:43,520 --> 00:38:46,959
Speaker 2: man is biased. You now, you can go to great

716
00:38:47,040 --> 00:38:51,640
Speaker 2: lengths to make sure that lots of biases are represented

717
00:38:51,760 --> 00:38:54,680
Speaker 2: in the making of a product. So if I again,

718
00:38:54,719 --> 00:38:57,719
Speaker 2: if I'm using that same example, let's say I want

719
00:38:57,719 --> 00:39:00,160
Speaker 2: to make a hair product, I can say, Okay, I'm

720
00:39:00,160 --> 00:39:03,080
Speaker 2: going to go talk to my my one of my

721
00:39:03,120 --> 00:39:05,239
Speaker 2: best friends who're from the Dominican Republic. I'm going to

722
00:39:05,280 --> 00:39:07,320
Speaker 2: talk to another one of my best friends who's black

723
00:39:07,480 --> 00:39:09,440
Speaker 2: but has a really loose curl pattern.

724
00:39:09,600 --> 00:39:13,399
Speaker 1: I'm going to talk to one of my friends who

725
00:39:13,440 --> 00:39:13,840
Speaker 1: is white.

726
00:39:14,120 --> 00:39:15,879
Speaker 2: I'm going to talk to one of my friends who

727
00:39:15,920 --> 00:39:19,120
Speaker 2: has alopecia and figure out, like, what are you looking

728
00:39:19,120 --> 00:39:22,040
Speaker 2: for in products? That is a way to take a

729
00:39:22,080 --> 00:39:26,239
Speaker 2: lot of biases into.

730
00:39:25,040 --> 00:39:26,480
Speaker 1: Account when you're creating something.

731
00:39:26,600 --> 00:39:29,799
Speaker 2: But to say something has no bias, it almost does

732
00:39:29,880 --> 00:39:32,040
Speaker 2: not make sense to me at all.

733
00:39:35,480 --> 00:39:47,439
Speaker 3: More after a quick break, let's get right back into it.

734
00:39:50,920 --> 00:39:53,320
Speaker 1: And I think one of the most frustrating things about

735
00:39:53,360 --> 00:39:56,719
Speaker 1: this e EO is that it's specifically names that it

736
00:39:56,800 --> 00:40:00,719
Speaker 1: does not want any kind of quote DEI in the

737
00:40:01,080 --> 00:40:04,439
Speaker 1: in AI, which already it's like, I mean, I hate

738
00:40:04,480 --> 00:40:07,880
Speaker 1: when Trump kind of makes me think through the logic

739
00:40:07,920 --> 00:40:09,160
Speaker 1: of the things they put out. I'm like, what do

740
00:40:09,160 --> 00:40:11,920
Speaker 1: they mean by that exactly? But in the real world,

741
00:40:12,440 --> 00:40:16,200
Speaker 1: having more voices at the table, having a diversity of

742
00:40:16,239 --> 00:40:19,680
Speaker 1: folks educated on AI, working in AI, building models, training models,

743
00:40:19,719 --> 00:40:22,600
Speaker 1: all of that that can only help us, Like that

744
00:40:22,719 --> 00:40:24,960
Speaker 1: is that is like one of the ways, one of

745
00:40:24,960 --> 00:40:28,720
Speaker 1: the tools in our toolbox to combat bias in AI AI.

746
00:40:28,760 --> 00:40:31,640
Speaker 1: Then it's harmful AI that doesn't recognize us for the

747
00:40:31,719 --> 00:40:34,480
Speaker 1: like multi faceted people that we are. Is having a

748
00:40:34,520 --> 00:40:37,759
Speaker 1: plurality of people and perspectives at the table, and this

749
00:40:37,880 --> 00:40:41,319
Speaker 1: executive order, it says. They say DEI and AI can

750
00:40:41,400 --> 00:40:45,200
Speaker 1: lead to discriminatory outcomes, distort and manipulate AI model outputs

751
00:40:45,200 --> 00:40:48,440
Speaker 1: in regard to race and sex, incorporate concepts like critical

752
00:40:48,520 --> 00:40:54,560
Speaker 1: race theory, transgenderism, unconscious bias, intersectionality, and systemic racism. DEI

753
00:40:54,760 --> 00:40:58,399
Speaker 1: displaces the commitment to truth in favor of preferred outcomes, and,

754
00:40:58,600 --> 00:41:02,040
Speaker 1: as recent history illustrate, it poses an existential threat to

755
00:41:02,080 --> 00:41:05,840
Speaker 1: reliable AI and that's what I think kills me is

756
00:41:05,840 --> 00:41:11,960
Speaker 1: that we know AI can be biased, can be sexist,

757
00:41:12,040 --> 00:41:13,960
Speaker 1: can be racist, all of this stuff, Like we know

758
00:41:14,040 --> 00:41:17,319
Speaker 1: that because it's trained on us, and like humans, those

759
00:41:17,320 --> 00:41:20,560
Speaker 1: are all pitfalls that humans unfortunately struggle with. And so

760
00:41:21,200 --> 00:41:25,240
Speaker 1: this idea that, oh, the way that we make AI

761
00:41:25,400 --> 00:41:29,600
Speaker 1: good is making sure that there there's no even whiff

762
00:41:29,640 --> 00:41:33,440
Speaker 1: of inclusion around AI. I mean, it's it's I shouldn't

763
00:41:33,440 --> 00:41:35,080
Speaker 1: even try to find any kind of logic in the

764
00:41:35,080 --> 00:41:36,520
Speaker 1: things that they put out. But it doesn't. It just

765
00:41:36,640 --> 00:41:39,319
Speaker 1: it just doesn't make any sense. And I think when

766
00:41:39,320 --> 00:41:43,759
Speaker 1: we're talking about something as critical as AI, that we're

767
00:41:43,800 --> 00:41:46,120
Speaker 1: all having this big conversation about how AIM is going

768
00:41:46,120 --> 00:41:48,719
Speaker 1: to change all of our lives, right, you know the

769
00:41:48,760 --> 00:41:50,920
Speaker 1: fact that we can we're not having like these are

770
00:41:50,920 --> 00:41:53,719
Speaker 1: not serious people, We're not there's a there are serious

771
00:41:53,719 --> 00:41:56,719
Speaker 1: conversations because you're having. But like in lieu of that,

772
00:41:56,800 --> 00:41:59,600
Speaker 1: we're putting out nonsense that doesn't even make any fucking

773
00:41:59,680 --> 00:42:01,080
Speaker 1: sense exactly.

774
00:42:01,120 --> 00:42:02,640
Speaker 2: And that, I mean, that just goes back to what

775
00:42:02,680 --> 00:42:05,839
Speaker 2: I was saying earlier, where it's just like they will

776
00:42:05,920 --> 00:42:10,120
Speaker 2: try and make you intellectualize things that are at their

777
00:42:10,160 --> 00:42:12,200
Speaker 2: core not intellectual at all.

778
00:42:12,560 --> 00:42:13,480
Speaker 1: It's just racist.

779
00:42:13,960 --> 00:42:17,560
Speaker 2: Like when you think of the history of racism, there's

780
00:42:17,600 --> 00:42:23,000
Speaker 2: this really brilliant researcher, her name is Angela Saini, who

781
00:42:23,040 --> 00:42:26,040
Speaker 2: does a lot of research on race and the history

782
00:42:26,080 --> 00:42:28,800
Speaker 2: of racism, and I learned so much from her books.

783
00:42:29,320 --> 00:42:34,839
Speaker 1: But racism is not like racism made up thing.

784
00:42:34,880 --> 00:42:39,080
Speaker 2: We all know that, and racism was a science as

785
00:42:39,280 --> 00:42:43,240
Speaker 2: at one point, like the research that went into trying

786
00:42:43,280 --> 00:42:46,560
Speaker 2: to prove that there was a more superior race. But

787
00:42:46,600 --> 00:42:50,920
Speaker 2: then all of that research was racist, impious and not true,

788
00:42:51,000 --> 00:42:54,640
Speaker 2: not rooted in anything that was true. And so I

789
00:42:54,760 --> 00:42:59,440
Speaker 2: just feel like the more we don't call this what

790
00:42:59,520 --> 00:43:03,480
Speaker 2: it is when we see it, it makes it an

791
00:43:03,520 --> 00:43:06,960
Speaker 2: intellectual conversation and it's truly not. And so then when

792
00:43:07,080 --> 00:43:10,839
Speaker 2: people like you and I start to try and talk

793
00:43:10,880 --> 00:43:14,520
Speaker 2: about it, it makes it really difficult because and that's

794
00:43:14,560 --> 00:43:16,640
Speaker 2: why it's hard to have conversations with people like that,

795
00:43:16,760 --> 00:43:17,759
Speaker 2: because you just you.

796
00:43:18,080 --> 00:43:20,680
Speaker 1: Don't even know what to say, because it's just like.

797
00:43:21,040 --> 00:43:25,160
Speaker 2: If someone says I believe in unicorns, and you're like,

798
00:43:25,239 --> 00:43:26,000
Speaker 2: have you ever seen one?

799
00:43:26,120 --> 00:43:31,480
Speaker 1: No, but they're they're real. It's like, okay, but you've

800
00:43:31,480 --> 00:43:36,200
Speaker 1: seen horses, right, Yes, but unicorns are the best. Do

801
00:43:36,239 --> 00:43:39,360
Speaker 1: you where are they? Does it matter? You tell me

802
00:43:39,400 --> 00:43:39,960
Speaker 1: where they are?

803
00:43:40,920 --> 00:43:41,000
Speaker 3: What?

804
00:43:42,160 --> 00:43:45,080
Speaker 2: These types of conversations just they are fruitless, and so

805
00:43:45,760 --> 00:43:48,320
Speaker 2: it's always just so frustrating. There's a quote from Maya

806
00:43:48,360 --> 00:43:51,640
Speaker 2: Angelou that Zakiah says all the time when it comes

807
00:43:51,680 --> 00:43:55,120
Speaker 2: to like talking to racists, and she says, somebody says

808
00:43:55,120 --> 00:43:57,280
Speaker 2: you have no language, and so you spend twenty years

809
00:43:57,280 --> 00:44:00,080
Speaker 2: proving that you do. Somebody says your head is and

810
00:44:00,160 --> 00:44:02,560
Speaker 2: shape properly, so you have scientists working on the fact

811
00:44:02,600 --> 00:44:05,160
Speaker 2: that it is. Someone says you have no art, so

812
00:44:05,200 --> 00:44:08,239
Speaker 2: you dredge that up. Somebody says you have no kingdoms,

813
00:44:08,360 --> 00:44:11,640
Speaker 2: and so you dredge that up. None of that is necessary.

814
00:44:11,640 --> 00:44:13,680
Speaker 2: There will always be one more thing.

815
00:44:15,280 --> 00:44:18,520
Speaker 1: We I That's a quote that I find a lot

816
00:44:18,560 --> 00:44:21,400
Speaker 1: of wisdom in and I'm recurring to and I guess

817
00:44:21,440 --> 00:44:24,560
Speaker 1: that's my main point, is the distraction of it all.

818
00:44:24,760 --> 00:44:27,040
Speaker 1: And that's the thing. It's like from listening to your

819
00:44:27,040 --> 00:44:30,520
Speaker 1: episode on the on AI like AI is come, but

820
00:44:30,600 --> 00:44:34,359
Speaker 1: there there are lots of reasons to be critical of

821
00:44:34,400 --> 00:44:36,920
Speaker 1: AI and syptical of AI and absolute talk about the

822
00:44:36,920 --> 00:44:39,680
Speaker 1: ways that it's flawed and biased. I am right there,

823
00:44:39,920 --> 00:44:44,640
Speaker 1: but when we are when the dominant conversation that the

824
00:44:44,680 --> 00:44:48,000
Speaker 1: president puts out is something that is such a distraction,

825
00:44:48,400 --> 00:44:52,440
Speaker 1: and it's it's such a like culture war, like just

826
00:44:52,560 --> 00:44:57,560
Speaker 1: stoking the flames of division, when along the lines that's

827
00:44:57,560 --> 00:45:01,160
Speaker 1: something that is so important. I just I mean, there's

828
00:45:01,200 --> 00:45:02,560
Speaker 1: not I mean I should be used to it from

829
00:45:02,560 --> 00:45:05,560
Speaker 1: this administration by now, but I just think, what a miss.

830
00:45:05,560 --> 00:45:08,440
Speaker 1: And it takes me back to we did an episode

831
00:45:08,480 --> 00:45:12,600
Speaker 1: with doctor Joe Bolamwini, who was one of the people

832
00:45:12,640 --> 00:45:15,759
Speaker 1: who worked on the Biden administration's Executive Order on AI

833
00:45:15,920 --> 00:45:18,840
Speaker 1: right that EO. It wasn't perfect, but it definitely was like,

834
00:45:18,920 --> 00:45:20,960
Speaker 1: let's put a few guarbrails on this, let's get a

835
00:45:20,960 --> 00:45:23,120
Speaker 1: sense of like how this should be developed. Let's make

836
00:45:23,120 --> 00:45:25,279
Speaker 1: sure that the voices who are talking about this and

837
00:45:25,400 --> 00:45:28,400
Speaker 1: thinking about this are a little more diverse. The fact

838
00:45:28,480 --> 00:45:32,760
Speaker 1: that we went from that like like certainly not perfect,

839
00:45:32,840 --> 00:45:37,040
Speaker 1: but like someone who had some goddamn sense was talking

840
00:45:37,080 --> 00:45:40,040
Speaker 1: fucking sense on the issue to this in just a

841
00:45:40,080 --> 00:45:42,360
Speaker 1: few months. It really it makes my head.

842
00:45:42,280 --> 00:45:45,200
Speaker 2: Spin, honestly, and I think that that's part of the tactic,

843
00:45:45,360 --> 00:45:49,880
Speaker 2: is to make us so tired that we will just

844
00:45:49,960 --> 00:45:53,560
Speaker 2: basically lie down and just be like enough, I like,

845
00:45:53,760 --> 00:45:55,560
Speaker 2: just let whatever happen happen.

846
00:45:56,239 --> 00:46:00,600
Speaker 1: And so I think, like we recently interviewed Chelsea.

847
00:46:00,360 --> 00:46:02,640
Speaker 2: Clinton on the show, and that was one of her

848
00:46:02,680 --> 00:46:04,840
Speaker 2: parting messages that she left with us, and it was

849
00:46:04,920 --> 00:46:08,839
Speaker 2: that we should not stop because that's like they're trying

850
00:46:08,840 --> 00:46:11,600
Speaker 2: to disorient us, They're trying to fatigue us, They're trying

851
00:46:11,640 --> 00:46:15,320
Speaker 2: to make us be quiet, and we should keep calling

852
00:46:15,360 --> 00:46:19,000
Speaker 2: things out. We should keep researching and asking questions and

853
00:46:19,040 --> 00:46:24,399
Speaker 2: asking hard questions and pushing our local lawmakers and our

854
00:46:24,719 --> 00:46:28,160
Speaker 2: state lawmakers and the president and his cabinet and making

855
00:46:28,160 --> 00:46:31,680
Speaker 2: sure that we don't let these like diversion tactics and

856
00:46:31,760 --> 00:46:35,640
Speaker 2: these tactics that they're using to make us be quiet work.

857
00:46:36,120 --> 00:46:41,359
Speaker 2: Because the first thirty days of this administration was just

858
00:46:41,960 --> 00:46:45,879
Speaker 2: everything was just coming so fast and curious, and I

859
00:46:45,920 --> 00:46:49,240
Speaker 2: was just like, oh my gosh, like I was having

860
00:46:49,280 --> 00:46:50,960
Speaker 2: to like in the morning, I would wake up and

861
00:46:51,000 --> 00:46:53,200
Speaker 2: be like, maybe I should just not look at my

862
00:46:53,280 --> 00:46:55,880
Speaker 2: phone for like an hour, have a cup of coffee,

863
00:46:56,239 --> 00:46:58,120
Speaker 2: like pretend like all is right in the world.

864
00:46:58,719 --> 00:47:01,120
Speaker 1: But now I'm like, no, I need.

865
00:47:01,320 --> 00:47:03,000
Speaker 2: Like I might give myself a little bit of time,

866
00:47:03,160 --> 00:47:06,120
Speaker 2: but I'm getting right back into it because being on

867
00:47:06,160 --> 00:47:08,839
Speaker 2: shows like this talking about these things and putting more

868
00:47:08,880 --> 00:47:12,480
Speaker 2: out there is so so important. And that's why I

869
00:47:12,520 --> 00:47:16,040
Speaker 2: absolutely love the work that you do, Bridget, because you

870
00:47:16,080 --> 00:47:18,680
Speaker 2: shine a light in so many on show, so many

871
00:47:18,719 --> 00:47:21,759
Speaker 2: areas that I feel like people they might not have

872
00:47:21,800 --> 00:47:24,640
Speaker 2: the words, but they come to you to find the words,

873
00:47:24,920 --> 00:47:26,920
Speaker 2: and you help them think through some of.

874
00:47:27,040 --> 00:47:29,720
Speaker 1: A lot of these things that they might be.

875
00:47:30,200 --> 00:47:32,520
Speaker 2: Like too tired to do the research on their own,

876
00:47:33,160 --> 00:47:37,360
Speaker 2: or like feel like they aren't whatever smart enough to

877
00:47:37,400 --> 00:47:39,600
Speaker 2: be able to get to the facts on their own.

878
00:47:39,880 --> 00:47:43,480
Speaker 2: You create these safe spaces for people to find facts

879
00:47:43,880 --> 00:47:47,080
Speaker 2: and to you know, laugh a little bit, but also

880
00:47:47,200 --> 00:47:48,840
Speaker 2: be like, Okay, now I know what I gotta do,

881
00:47:49,080 --> 00:47:51,760
Speaker 2: because you come to every single episode with this certain

882
00:47:52,080 --> 00:47:56,160
Speaker 2: energy that makes me be like, turn on my microphone,

883
00:47:56,200 --> 00:48:00,560
Speaker 2: I'm ready to go. You're so inspirational to so many

884
00:48:00,640 --> 00:48:02,640
Speaker 2: other podcasters that out there. I know, I know that's

885
00:48:02,680 --> 00:48:04,080
Speaker 2: not what we're talking about right now, but I just

886
00:48:04,120 --> 00:48:06,480
Speaker 2: want to say, because it came to my mind how

887
00:48:06,600 --> 00:48:08,720
Speaker 2: important you are to this space.

888
00:48:09,239 --> 00:48:13,000
Speaker 1: You are not nobody. You are somebody to a lot

889
00:48:13,000 --> 00:48:16,200
Speaker 1: of people. You are somebody to me and like you,

890
00:48:16,760 --> 00:48:19,399
Speaker 1: I hope you never ever ever stop. Like I've been

891
00:48:19,440 --> 00:48:23,480
Speaker 1: following you for so long, and it's what you do

892
00:48:23,600 --> 00:48:28,319
Speaker 1: is amazing. I can only aspire t I'm gonna cry.

893
00:48:29,680 --> 00:48:32,800
Speaker 1: You're so important, You're so so important to the culture.

894
00:48:33,080 --> 00:48:36,040
Speaker 1: Feel the same way about what you do at Dope Labs,

895
00:48:36,040 --> 00:48:38,759
Speaker 1: and I just I think it's so important. I mean,

896
00:48:39,360 --> 00:48:42,440
Speaker 1: how many people that listen to Dope Labs tell you, like, oh,

897
00:48:42,480 --> 00:48:44,640
Speaker 1: it's so nice to have a show about science that

898
00:48:44,760 --> 00:48:47,719
Speaker 1: makes me feel like included and mean and welcomed into

899
00:48:47,760 --> 00:48:51,000
Speaker 1: the conversation, right that, Like what you're doing, Like you

900
00:48:51,040 --> 00:48:54,000
Speaker 1: do that so well, that's like what I want to

901
00:48:54,000 --> 00:48:57,040
Speaker 1: do here on the podcast. Like that's absolutely what you're doing.

902
00:48:57,960 --> 00:48:58,680
Speaker 1: You've no idea.

903
00:48:58,719 --> 00:49:04,520
Speaker 2: I mean, I'm hi, You're you are perfect, like honestly,

904
00:49:05,360 --> 00:49:06,440
Speaker 2: oh my gosh.

905
00:49:06,480 --> 00:49:07,799
Speaker 1: And it's I mean, I think we I think it

906
00:49:07,880 --> 00:49:10,640
Speaker 1: just goes to show that we for so long. I

907
00:49:10,960 --> 00:49:13,480
Speaker 1: feel like when it comes to stem, whether you're doing

908
00:49:13,520 --> 00:49:16,640
Speaker 1: a show about science or tech, like it's just very

909
00:49:16,680 --> 00:49:20,799
Speaker 1: easy for people to feel unseen and left out and

910
00:49:20,840 --> 00:49:22,759
Speaker 1: that and then when you feel unseen and left out,

911
00:49:22,760 --> 00:49:25,200
Speaker 1: you're checked out. And so when the president goes on

912
00:49:25,280 --> 00:49:28,799
Speaker 1: TV and it's talking about something AI related, and there

913
00:49:28,840 --> 00:49:31,040
Speaker 1: is nobody who is who can hear the sound of

914
00:49:31,080 --> 00:49:32,960
Speaker 1: my voice right now. There's nobody who's listening to this

915
00:49:33,000 --> 00:49:36,960
Speaker 1: podcast who is not smarter than Trump about it about

916
00:49:37,239 --> 00:49:40,200
Speaker 1: full stop, end of sentence, but also specifically when it

917
00:49:40,239 --> 00:49:41,920
Speaker 1: comes to tech and AI. If you were insen in

918
00:49:41,960 --> 00:49:44,360
Speaker 1: this podcast, you know more than him. End of sentence,

919
00:49:44,480 --> 00:49:48,040
Speaker 1: full stop, I guarantee you autely. It's just this very

920
00:49:49,520 --> 00:49:52,440
Speaker 1: I just hate this culture that can be so alienating

921
00:49:52,880 --> 00:49:55,360
Speaker 1: and can can prompt people to do exactly what you

922
00:49:55,440 --> 00:49:57,600
Speaker 1: just talked about, Like just check out and be like, Okay,

923
00:49:57,640 --> 00:50:00,279
Speaker 1: well they're gonna do what they're gonna do. I don't

924
00:50:00,280 --> 00:50:03,520
Speaker 1: need I'm not gonna. I'm not gonna try to like

925
00:50:03,680 --> 00:50:06,280
Speaker 1: arm myself with information. I'm not gonna. I'm not gonna

926
00:50:06,360 --> 00:50:08,280
Speaker 1: What difference does that mean? Right, you're going to pass

927
00:50:08,719 --> 00:50:12,240
Speaker 1: executive orders, are gonna sneak umperrate into the privacy policy.

928
00:50:12,280 --> 00:50:14,600
Speaker 1: I'm gonna use it anyway, Like, and I really want

929
00:50:14,640 --> 00:50:17,680
Speaker 1: folks to feel a culture shipt around that that's like, No,

930
00:50:17,920 --> 00:50:19,880
Speaker 1: you can arm yourself with information. You don't have to

931
00:50:19,920 --> 00:50:22,239
Speaker 1: be afraid. You don't have to be you know, you

932
00:50:22,239 --> 00:50:24,600
Speaker 1: don't have to be trepidacious about about being part of

933
00:50:24,600 --> 00:50:27,680
Speaker 1: the conversation because this the science and technology. It really

934
00:50:27,680 --> 00:50:28,600
Speaker 1: does unpack all of.

935
00:50:28,520 --> 00:50:31,759
Speaker 2: Our lives and it's it's so true, and it's like

936
00:50:31,880 --> 00:50:33,760
Speaker 2: that's one of the things that Key and I always

937
00:50:33,760 --> 00:50:35,480
Speaker 2: try and do with the show is just trying to

938
00:50:35,560 --> 00:50:38,080
Speaker 2: create a space where people don't feel like we're preaching

939
00:50:38,080 --> 00:50:40,799
Speaker 2: to them, like we're learning. We are always learning with

940
00:50:41,120 --> 00:50:44,359
Speaker 2: the people who are listening to our show, and it's

941
00:50:44,400 --> 00:50:47,880
Speaker 2: so important for folks to know like the scientific process,

942
00:50:47,880 --> 00:50:50,960
Speaker 2: and we go through the scientific process in every single episode.

943
00:50:51,000 --> 00:50:53,279
Speaker 2: You do it here on your podcast as well. The

944
00:50:53,280 --> 00:50:57,200
Speaker 2: amount of research that goes into it. That is the

945
00:50:57,280 --> 00:51:01,279
Speaker 2: scientific method, and you get to a conclusion and that

946
00:51:01,400 --> 00:51:04,200
Speaker 2: is not you just pulling anything you know.

947
00:51:04,200 --> 00:51:04,879
Speaker 1: Out of your head.

948
00:51:04,920 --> 00:51:06,400
Speaker 2: You do a lot of research. They get to the

949
00:51:06,440 --> 00:51:09,640
Speaker 2: conclusions that you do that is scientific. You are a scientist,

950
00:51:09,719 --> 00:51:14,760
Speaker 2: you're a cultural scientist, and people need to start feeling

951
00:51:14,800 --> 00:51:17,760
Speaker 2: empowered to be to do that, to take the extra

952
00:51:17,800 --> 00:51:21,480
Speaker 2: step to do the research, ask an additional question, like

953
00:51:21,680 --> 00:51:24,920
Speaker 2: really not just read the headline, but read the full article,

954
00:51:25,000 --> 00:51:28,480
Speaker 2: see what the sources are, see what that study was, Oh, they.

955
00:51:28,360 --> 00:51:30,440
Speaker 1: Only talk to ten people that night.

956
00:51:30,560 --> 00:51:32,920
Speaker 2: Might not be enough to say, oh ninety percent of

957
00:51:32,960 --> 00:51:36,080
Speaker 2: people do this or do that. You probably need a

958
00:51:36,160 --> 00:51:38,440
Speaker 2: larger sample size. Those are the things that you should

959
00:51:38,480 --> 00:51:40,719
Speaker 2: be doing. And that's what we're trying to teach people.

960
00:51:40,800 --> 00:51:42,960
Speaker 2: We don't want anybody to feel like, oh, I'm not

961
00:51:43,000 --> 00:51:44,799
Speaker 2: smart enough, or I can't do this, or that.

962
00:51:45,239 --> 00:51:45,840
Speaker 1: We want to.

963
00:51:45,840 --> 00:51:49,759
Speaker 2: Empower folks to ask those questions and know that there

964
00:51:49,840 --> 00:51:52,640
Speaker 2: is no such thing as a silly question. And you know,

965
00:51:52,840 --> 00:51:55,080
Speaker 2: in some instances, like when we were talking about the

966
00:51:55,160 --> 00:52:00,960
Speaker 2: vaccine back in twenty twenty, like we really sounded the

967
00:52:01,000 --> 00:52:04,560
Speaker 2: alarm on why people are vaccine hesitant, why people of

968
00:52:04,600 --> 00:52:07,240
Speaker 2: color are vaccine hesitant, And it's not because they're stupid.

969
00:52:07,560 --> 00:52:09,279
Speaker 1: It's because it's rooted in real.

970
00:52:10,520 --> 00:52:15,120
Speaker 2: History, like medical history, where black folks were experimented on

971
00:52:15,200 --> 00:52:17,680
Speaker 2: and sterilized and all these things like that. So the

972
00:52:17,680 --> 00:52:20,839
Speaker 2: distrust of the medical community is not baseless. So there's

973
00:52:20,880 --> 00:52:23,400
Speaker 2: a certain level of care you need to come you

974
00:52:23,480 --> 00:52:27,200
Speaker 2: need to come into those conversations with because like people

975
00:52:27,239 --> 00:52:31,239
Speaker 2: of color are not just are not stupid, they're operating

976
00:52:31,600 --> 00:52:35,040
Speaker 2: within the framework of our history in this country, and

977
00:52:35,120 --> 00:52:37,760
Speaker 2: so When we talk about the vaccine, we say, listen,

978
00:52:37,880 --> 00:52:40,000
Speaker 2: I get it, but these are the things that we

979
00:52:40,080 --> 00:52:42,680
Speaker 2: know about vaccines. We break the vaccine down, we talk

980
00:52:42,719 --> 00:52:45,399
Speaker 2: about the different elements of it and why they should

981
00:52:45,800 --> 00:52:47,840
Speaker 2: they should feel like they can trust it. You know,

982
00:52:47,960 --> 00:52:50,400
Speaker 2: the creator of one of the vaccines is a black woman,

983
00:52:50,800 --> 00:52:53,040
Speaker 2: and we talk to her. And so these are the

984
00:52:53,040 --> 00:52:54,759
Speaker 2: things that we just try and arm people with. It's

985
00:52:54,840 --> 00:52:58,879
Speaker 2: like the tools necessary in order to get to what

986
00:52:58,920 --> 00:53:00,759
Speaker 2: the facts are. And then you can decide what you

987
00:53:00,760 --> 00:53:02,560
Speaker 2: want to do, what you do with the facts, that's

988
00:53:02,640 --> 00:53:04,319
Speaker 2: up to you. But I just want you to know

989
00:53:04,360 --> 00:53:06,840
Speaker 2: what the facts are. You know, because what I always

990
00:53:06,880 --> 00:53:10,240
Speaker 2: say is truth is subjective. Like your truth, my truth,

991
00:53:10,440 --> 00:53:14,680
Speaker 2: that person's truth. It's all your perspective. But fact it's

992
00:53:14,719 --> 00:53:17,759
Speaker 2: the same no matter who's looking at it. Do you

993
00:53:17,840 --> 00:53:21,160
Speaker 2: feel that in twenty twenty five, I don't know.

994
00:53:21,280 --> 00:53:25,359
Speaker 1: I feel that we've gotten to a place where we

995
00:53:25,400 --> 00:53:29,080
Speaker 1: are so quick to just read the headline or throw

996
00:53:29,120 --> 00:53:31,200
Speaker 1: it into Google and then read the AI summary. I

997
00:53:31,239 --> 00:53:35,440
Speaker 1: mean what I like it's under It's so like and

998
00:53:35,480 --> 00:53:39,279
Speaker 1: I just worry that the art of fundamentally, the reason

999
00:53:39,280 --> 00:53:41,120
Speaker 1: why I do what I do is because, like at

1000
00:53:41,160 --> 00:53:43,000
Speaker 1: my core, I am a nosy bitch and I want

1001
00:53:43,000 --> 00:53:44,680
Speaker 1: to find out what I want to find out. So

1002
00:53:44,719 --> 00:53:47,240
Speaker 1: when I call you a scientist, not a nosy listen,

1003
00:53:47,480 --> 00:53:49,520
Speaker 1: I think I think it's one and the same. I

1004
00:53:49,640 --> 00:53:53,279
Speaker 1: think there's more, you know, you know, like it's like

1005
00:53:53,280 --> 00:53:56,520
Speaker 1: because it's about yeah, but when you read something and

1006
00:53:56,560 --> 00:53:59,080
Speaker 1: you're like, huh, that's interesting, let me read more, let

1007
00:53:59,120 --> 00:54:00,880
Speaker 1: me look into that way, let me read the study.

1008
00:54:01,080 --> 00:54:04,120
Speaker 1: Oh the PI on the study, let me google them.

1009
00:54:04,160 --> 00:54:06,400
Speaker 1: What do we have that like you? I can only

1010
00:54:06,480 --> 00:54:09,480
Speaker 1: I'm just a nosy bitch. Yeah, I just want to know.

1011
00:54:09,640 --> 00:54:12,680
Speaker 1: And I wonder like, are we losing the art of

1012
00:54:12,800 --> 00:54:15,200
Speaker 1: just wanting to know? Are we losing the art of

1013
00:54:15,280 --> 00:54:19,359
Speaker 1: like I worry that in twenty twenty five, it's it's

1014
00:54:19,360 --> 00:54:21,400
Speaker 1: so trendy to be like media literacy is dead. But

1015
00:54:21,760 --> 00:54:24,000
Speaker 1: I do think you can. You can, You truly can

1016
00:54:24,160 --> 00:54:26,480
Speaker 1: go on the internet and say anything and people will

1017
00:54:26,480 --> 00:54:28,480
Speaker 1: not check. People will not check. You could say anything

1018
00:54:28,480 --> 00:54:29,680
Speaker 1: and people won't say anything.

1019
00:54:29,840 --> 00:54:33,040
Speaker 2: Listen, I'm not gonna throw nobody under the bus, but

1020
00:54:33,120 --> 00:54:34,760
Speaker 2: let me tell you there's a lot of things happening

1021
00:54:34,800 --> 00:54:38,799
Speaker 2: on whats that Yes, with some of these older people.

1022
00:54:38,520 --> 00:54:40,640
Speaker 1: That are parent age.

1023
00:54:42,040 --> 00:54:44,719
Speaker 2: And that is I'm just like, hey, where did you

1024
00:54:44,760 --> 00:54:48,560
Speaker 2: find this information? Like did you did you try and

1025
00:54:48,600 --> 00:54:51,040
Speaker 2: do some research about it? Like they're telling you never

1026
00:54:51,120 --> 00:54:51,960
Speaker 2: drink ice water.

1027
00:54:52,920 --> 00:54:55,440
Speaker 1: Why I don't think that that's how they're like, oh,

1028
00:54:55,520 --> 00:54:57,520
Speaker 1: it'll freeze your your goot.

1029
00:54:57,840 --> 00:55:00,840
Speaker 2: I don't think that that's how ice water works, truly.

1030
00:55:01,800 --> 00:55:04,759
Speaker 2: It's just it's crazy. But I mean, yes, I do

1031
00:55:04,840 --> 00:55:05,400
Speaker 2: agree with you.

1032
00:55:05,440 --> 00:55:05,879
Speaker 1: I think that.

1033
00:55:06,320 --> 00:55:09,480
Speaker 2: I think that with the way the Internet is now,

1034
00:55:09,600 --> 00:55:13,279
Speaker 2: because like we're carrying it around with us constantly, it's

1035
00:55:13,400 --> 00:55:18,280
Speaker 2: always right there, people are like losing their ability to wonder.

1036
00:55:19,880 --> 00:55:26,160
Speaker 2: Like if I say, how tall do you think? How

1037
00:55:26,160 --> 00:55:30,320
Speaker 2: tall do you think Lebron James is? Somebody's just gonna go, Siri,

1038
00:55:30,400 --> 00:55:34,160
Speaker 2: how tall is Lebron James? Like nobody's gonna say, hm, well,

1039
00:55:34,160 --> 00:55:39,000
Speaker 2: he was standing next to did you hear that?

1040
00:55:39,800 --> 00:55:44,719
Speaker 1: Next meet nine inches? Siri? Please, I'm recording an episode

1041
00:55:44,760 --> 00:55:46,960
Speaker 1: with the bridget Todd Thank you. Oh my god. I

1042
00:55:46,960 --> 00:55:48,279
Speaker 1: didn't even mean to be talking to her. I didn't

1043
00:55:48,280 --> 00:55:49,799
Speaker 1: even press the button. I guess she heard her name.

1044
00:55:50,040 --> 00:55:54,120
Speaker 2: But like you won't say, oh, I saw him standing

1045
00:55:54,120 --> 00:55:56,040
Speaker 2: next to this thing, and that's probably about this height.

1046
00:55:56,400 --> 00:55:59,560
Speaker 2: Nobody wonders anymore, and I think that that is part

1047
00:55:59,600 --> 00:56:02,719
Speaker 2: of the problem, part of the problem, and we just

1048
00:56:02,760 --> 00:56:05,840
Speaker 2: go straight to our phones. And then because we're not wondering,

1049
00:56:05,880 --> 00:56:11,080
Speaker 2: we're not doing any like, we're not creating these pathways

1050
00:56:11,080 --> 00:56:14,279
Speaker 2: in our brain that are reinforced. So Lebron James is

1051
00:56:14,320 --> 00:56:16,680
Speaker 2: six foot nine. This just told me I didn't do

1052
00:56:16,760 --> 00:56:19,400
Speaker 2: any work to get there. So that piece of information,

1053
00:56:19,480 --> 00:56:20,319
Speaker 2: I'm gonna forget it.

1054
00:56:21,360 --> 00:56:21,520
Speaker 3: Now.

1055
00:56:21,560 --> 00:56:24,680
Speaker 2: If I did a lot of research and took time

1056
00:56:24,719 --> 00:56:27,280
Speaker 2: and tried to figure out how tall is see exactly

1057
00:56:27,600 --> 00:56:30,480
Speaker 2: and do did measurements, I won't forget because that's just

1058
00:56:30,520 --> 00:56:31,920
Speaker 2: how our brains are set up.

1059
00:56:32,200 --> 00:56:32,400
Speaker 1: You know.

1060
00:56:32,400 --> 00:56:34,480
Speaker 2: There are some people who are very, very intelligent that

1061
00:56:34,520 --> 00:56:36,440
Speaker 2: I only have to see things one time or hear

1062
00:56:36,480 --> 00:56:38,959
Speaker 2: things one time and they'll remember forever. But the way

1063
00:56:39,000 --> 00:56:42,640
Speaker 2: that the average brain works is that it needs reinforcement

1064
00:56:42,719 --> 00:56:46,319
Speaker 2: and in order to like, information needs reinforcement in order

1065
00:56:46,320 --> 00:56:49,879
Speaker 2: for it to stick. And part of that is the

1066
00:56:50,320 --> 00:56:53,680
Speaker 2: wondering and thinking about something for a long time, and

1067
00:56:53,719 --> 00:56:56,360
Speaker 2: that's what helps us to remember and to recall. So

1068
00:56:56,480 --> 00:56:59,719
Speaker 2: without the ability to wonder and like have curiosity and

1069
00:56:59,760 --> 00:57:04,000
Speaker 2: try to get to the facts, without just doing the

1070
00:57:04,120 --> 00:57:07,440
Speaker 2: Google or talking to Siri. Yeah, it makes It affects

1071
00:57:07,480 --> 00:57:10,120
Speaker 2: your attention span, it affects your ability to remember things,

1072
00:57:10,120 --> 00:57:13,480
Speaker 2: it affects your ability to be able to connect dots

1073
00:57:14,239 --> 00:57:17,280
Speaker 2: in other aspects of your life, and you know, everything

1074
00:57:17,360 --> 00:57:21,680
Speaker 2: becomes very very difficult. Like I mean, yeah, I'm guilty

1075
00:57:21,720 --> 00:57:23,360
Speaker 2: of that myself. Like I'll be like, Okay, I'm gonna

1076
00:57:23,360 --> 00:57:25,720
Speaker 2: go to the roasty store. It's like that sesame street

1077
00:57:25,760 --> 00:57:30,520
Speaker 2: thing that stick a buttick of butter, milk, some bread,

1078
00:57:30,840 --> 00:57:33,200
Speaker 2: and then I get there. I'm not like that little

1079
00:57:33,200 --> 00:57:33,600
Speaker 2: black girl.

1080
00:57:33,600 --> 00:57:36,680
Speaker 1: I'm like, you don't get the little flash on top

1081
00:57:36,720 --> 00:57:40,360
Speaker 1: of her head that's like the butter, the milk, the bread. No,

1082
00:57:40,440 --> 00:57:43,920
Speaker 1: I'm like, did I need I think I need a

1083
00:57:44,000 --> 00:57:45,800
Speaker 1: chicken nuggets. That's what I came out here for.

1084
00:57:46,800 --> 00:57:49,080
Speaker 2: And then I get back home and I'm like, I

1085
00:57:49,120 --> 00:57:51,760
Speaker 2: did need butter and I did not get it. Like

1086
00:57:51,840 --> 00:57:53,960
Speaker 2: I'm guilty of it too, because I use my phone

1087
00:57:54,000 --> 00:57:56,720
Speaker 2: as a crutch for a lot of things. But exercising

1088
00:57:56,760 --> 00:58:00,240
Speaker 2: your brain and exercising those muscles is so important to

1089
00:58:00,440 --> 00:58:03,920
Speaker 2: like being able to understand what we're looking at and

1090
00:58:03,960 --> 00:58:07,080
Speaker 2: not just reading the headlines, because when you exercise those muscles,

1091
00:58:07,120 --> 00:58:08,600
Speaker 2: the headline will never be enough.

1092
00:58:09,040 --> 00:58:11,640
Speaker 1: It won't be a thousand percent. And when I I mean,

1093
00:58:11,680 --> 00:58:14,280
Speaker 1: when I hear about young people in college who are

1094
00:58:15,280 --> 00:58:18,000
Speaker 1: really using chaship et to get them through college. On

1095
00:58:18,080 --> 00:58:20,080
Speaker 1: the one hand, I get it, because it's like, you

1096
00:58:20,120 --> 00:58:22,040
Speaker 1: have a million classes, you have a lot of course work.

1097
00:58:22,080 --> 00:58:23,880
Speaker 1: Da da da da. But I think back to when

1098
00:58:23,880 --> 00:58:25,960
Speaker 1: I was in college a million years ago, and it's like,

1099
00:58:26,160 --> 00:58:31,000
Speaker 1: I definitely had nights where if chad sheep Et existed,

1100
00:58:31,040 --> 00:58:33,480
Speaker 1: I would have totally used chaship bet to test to

1101
00:58:33,520 --> 00:58:37,320
Speaker 1: help me get through coursework. However, I also had nights

1102
00:58:37,320 --> 00:58:41,360
Speaker 1: of discovery where I learned something that turns something on

1103
00:58:41,440 --> 00:58:43,800
Speaker 1: in my brain or ignited a curiosity in me that

1104
00:58:43,840 --> 00:58:47,920
Speaker 1: I still have today years later. And you know, I

1105
00:58:48,040 --> 00:58:50,680
Speaker 1: don't think that we should be thinking about all of

1106
00:58:50,720 --> 00:58:56,280
Speaker 1: this as just tasks to be offloaded or automated, because

1107
00:58:56,840 --> 00:58:59,400
Speaker 1: that's I mean. I can't I mean, I cannot even

1108
00:58:59,520 --> 00:59:03,920
Speaker 1: express how many times I would encounter something and it

1109
00:59:04,040 --> 00:59:07,400
Speaker 1: unlocked a lifelong passion or interest or curiosity. And we

1110
00:59:07,480 --> 00:59:12,120
Speaker 1: do our in our hyper connected world where we it's

1111
00:59:12,160 --> 00:59:15,480
Speaker 1: so easy to offload things to technology, we do have

1112
00:59:15,520 --> 00:59:18,680
Speaker 1: to make room for like our brain getting the tingles

1113
00:59:18,720 --> 00:59:20,680
Speaker 1: because we read something cool and we want to learn

1114
00:59:20,720 --> 00:59:23,520
Speaker 1: more about it, Like that stuff happens by happenstance, and

1115
00:59:23,560 --> 00:59:26,080
Speaker 1: if you are not giving yourself experience, like it's almost

1116
00:59:26,080 --> 00:59:28,920
Speaker 1: a like I worry that people are robbing themselves of

1117
00:59:28,960 --> 00:59:32,840
Speaker 1: the experience of that happenstance encountering something that like turns

1118
00:59:32,840 --> 00:59:34,840
Speaker 1: something on for you or unlock something for you.

1119
00:59:35,160 --> 00:59:38,080
Speaker 2: Yeah, I mean, and I totally agree, And I think

1120
00:59:38,120 --> 00:59:42,280
Speaker 2: that folks can do that in a chat ept or

1121
00:59:42,480 --> 00:59:46,960
Speaker 2: Claude or whatever, Gemini or copilot, whatever your AI model

1122
00:59:47,000 --> 00:59:50,760
Speaker 2: that you want to use is because those the output.

1123
00:59:50,320 --> 00:59:51,720
Speaker 1: Is only as good as the input.

1124
00:59:51,880 --> 00:59:56,720
Speaker 2: If you're curious and you and you can ask the

1125
00:59:56,800 --> 01:00:00,640
Speaker 2: right questions and follow up questions and like you're learning

1126
01:00:01,240 --> 01:00:05,920
Speaker 2: as them, as the AI agent or whoever is giving

1127
01:00:05,920 --> 01:00:10,440
Speaker 2: you this information, you can still discover Like chat GBT

1128
01:00:10,600 --> 01:00:12,600
Speaker 2: has deep research and sometimes I'll put in there, it'll

1129
01:00:12,640 --> 01:00:14,600
Speaker 2: spit out like a twenty page thing. Now this is

1130
01:00:14,600 --> 01:00:16,600
Speaker 2: not something I'm copy pasting. That is something that I

1131
01:00:16,720 --> 01:00:19,720
Speaker 2: use to research. Like I'm reading it and I'm like,

1132
01:00:19,760 --> 01:00:21,680
Speaker 2: oh my gosh, and I'm highlighting things. I'm like, oh,

1133
01:00:21,680 --> 01:00:23,600
Speaker 2: what's this source, I'll click on it, It'll take me

1134
01:00:23,680 --> 01:00:26,560
Speaker 2: to whatever that source is. I'll read that and I'll say, huh,

1135
01:00:26,640 --> 01:00:29,760
Speaker 2: well based on that, do you think that? H And

1136
01:00:29,920 --> 01:00:32,280
Speaker 2: what about if you were to blah blah blah blah blah,

1137
01:00:32,400 --> 01:00:35,000
Speaker 2: like you can still get there with AI.

1138
01:00:35,400 --> 01:00:39,480
Speaker 1: It's it's like AI is. It's like going to the library,

1139
01:00:39,640 --> 01:00:42,320
Speaker 1: but instead of having to get the little car. Did

1140
01:00:42,360 --> 01:00:44,280
Speaker 1: they still do that with the little cards and everything?

1141
01:00:44,480 --> 01:00:46,280
Speaker 1: I hope? I doubt it. O.

1142
01:00:46,760 --> 01:00:51,240
Speaker 2: Man, Well, it's like going to the library and instead

1143
01:00:51,240 --> 01:00:56,360
Speaker 2: of having to check out fifty books, AI can can

1144
01:00:56,360 --> 01:00:58,160
Speaker 2: tell you what the books are. It can help you

1145
01:00:58,240 --> 01:01:01,160
Speaker 2: get to the exact pair that you would need that

1146
01:01:01,240 --> 01:01:04,600
Speaker 2: information from, and then from there you can build from there.

1147
01:01:05,080 --> 01:01:07,240
Speaker 2: Like sometimes people are like, oh, well, I put in

1148
01:01:07,400 --> 01:01:10,240
Speaker 2: I put in chat GBT tell me I'm gonna be

1149
01:01:10,400 --> 01:01:13,920
Speaker 2: on a show with Bridgetod and what questions? Uh do

1150
01:01:13,960 --> 01:01:15,560
Speaker 2: you think she's gonna ask me? And it gave me

1151
01:01:15,600 --> 01:01:19,360
Speaker 2: all the wrong answers. I'm like, well, you could have also.

1152
01:01:19,120 --> 01:01:28,640
Speaker 1: Said Bridgetad is a cultural scientist AKA who loves some drama,

1153
01:01:28,800 --> 01:01:31,760
Speaker 1: loves some t and just wants to know and and

1154
01:01:32,360 --> 01:01:35,920
Speaker 1: you know she is talking about current events. She is

1155
01:01:36,040 --> 01:01:38,360
Speaker 1: very funny. I would also like to be funny on

1156
01:01:38,400 --> 01:01:42,400
Speaker 1: this show. Can you like get me up to speed

1157
01:01:42,480 --> 01:01:45,600
Speaker 1: with what's going on in current events that she might

1158
01:01:45,640 --> 01:01:48,760
Speaker 1: be talking about, and it will give you all of

1159
01:01:48,800 --> 01:01:52,840
Speaker 1: these different responses and help you understand things more deeply,

1160
01:01:53,240 --> 01:01:56,160
Speaker 1: like honestly, just like I was saying with the calculator,

1161
01:01:56,360 --> 01:01:58,840
Speaker 1: a calculator is only as good as as you are.

1162
01:01:59,600 --> 01:02:02,680
Speaker 2: Like somebody can give you the most advanced calculator, the

1163
01:02:02,720 --> 01:02:06,200
Speaker 2: most advanced software. They can give you the most advanced

1164
01:02:06,200 --> 01:02:09,880
Speaker 2: coding software. If you don't know how to use it,

1165
01:02:09,880 --> 01:02:11,920
Speaker 2: it's useless. Like you could put stuff in there, you'd

1166
01:02:11,920 --> 01:02:16,160
Speaker 2: be like one plus one is two D already knew that. Okay, Well,

1167
01:02:16,240 --> 01:02:18,080
Speaker 2: do you know how to do the area under curve?

1168
01:02:18,400 --> 01:02:20,640
Speaker 2: Do you know how to do can you do differential

1169
01:02:20,640 --> 01:02:23,840
Speaker 2: equations like you have? Like you have to know how

1170
01:02:23,880 --> 01:02:26,320
Speaker 2: to use these things in order for to maximize your

1171
01:02:26,360 --> 01:02:28,640
Speaker 2: potential when you're using it. And so that's what I

1172
01:02:28,640 --> 01:02:30,440
Speaker 2: always say to people. I'm like, if you're interested in

1173
01:02:30,520 --> 01:02:34,120
Speaker 2: using AI to like advance your life, really sit down

1174
01:02:34,320 --> 01:02:37,800
Speaker 2: and spend time like going back and forth with it,

1175
01:02:38,440 --> 01:02:41,280
Speaker 2: and like try not to be too much of a

1176
01:02:41,320 --> 01:02:45,200
Speaker 2: Senate and see how it can help you, because a

1177
01:02:45,200 --> 01:02:46,720
Speaker 2: lot of a lot of jobs now.

1178
01:02:47,000 --> 01:02:50,960
Speaker 1: So Morgan to mom, who's the owner of Blavity. She

1179
01:02:51,240 --> 01:02:53,320
Speaker 1: was she had made a post where she said that

1180
01:02:53,400 --> 01:02:56,400
Speaker 1: she was looking for looking through resumes for folks to hire,

1181
01:02:57,000 --> 01:02:59,800
Speaker 1: and she was like, it's a shame because a lot

1182
01:02:59,800 --> 01:03:02,880
Speaker 1: of these folks are not saying that they are proficient

1183
01:03:02,960 --> 01:03:07,120
Speaker 1: in like catch ept in using AI and that is

1184
01:03:07,240 --> 01:03:11,040
Speaker 1: mandatory to work in my company. And that's the path

1185
01:03:11,080 --> 01:03:11,880
Speaker 1: we're on now.

1186
01:03:12,320 --> 01:03:14,720
Speaker 2: And so for folks to be like, I will never,

1187
01:03:15,280 --> 01:03:19,720
Speaker 2: like know where we're headed before you say I will never,

1188
01:03:19,880 --> 01:03:22,919
Speaker 2: because you really don't want to be left behind when

1189
01:03:22,920 --> 01:03:26,000
Speaker 2: it comes to knowing how to use this technology, like

1190
01:03:26,640 --> 01:03:29,240
Speaker 2: you we if you work in corporate America, you a

1191
01:03:29,720 --> 01:03:32,040
Speaker 2: we all know, like an old person who is still

1192
01:03:32,080 --> 01:03:35,000
Speaker 2: struggling with emails and turning their camera on when they're

1193
01:03:35,040 --> 01:03:37,720
Speaker 2: on zoom. And you're just like, if you don't just

1194
01:03:37,880 --> 01:03:41,880
Speaker 2: learn this stuff, don't be that person when you still

1195
01:03:41,920 --> 01:03:44,080
Speaker 2: have the ability to learn how to use these things

1196
01:03:44,360 --> 01:03:44,959
Speaker 2: at your job.

1197
01:03:45,400 --> 01:03:47,360
Speaker 1: And it's interesting that you say this because one of

1198
01:03:47,400 --> 01:03:49,440
Speaker 1: the things I was looking at to prepare for this

1199
01:03:49,480 --> 01:03:52,560
Speaker 1: conversation was this study in Harvard Business Review about how

1200
01:03:52,920 --> 01:03:56,480
Speaker 1: women are a bit slower to adopt AI in their

1201
01:03:56,520 --> 01:03:58,880
Speaker 1: work than then, and they they actually gave a lot

1202
01:03:58,880 --> 01:04:00,880
Speaker 1: of like interesting expl day for why that might be

1203
01:04:01,440 --> 01:04:04,200
Speaker 1: women caring more than men about some of the ethical

1204
01:04:04,240 --> 01:04:07,680
Speaker 1: considerations of AI. Well, that makes sense, but also one

1205
01:04:07,680 --> 01:04:11,160
Speaker 1: of the things they pointed out was women being feeling

1206
01:04:11,200 --> 01:04:14,960
Speaker 1: like they're under more scrutiny for using AI and feeling

1207
01:04:15,200 --> 01:04:17,440
Speaker 1: they might face more judgment than men if they do

1208
01:04:17,640 --> 01:04:20,560
Speaker 1: use AI in their work. And I really felt like

1209
01:04:20,560 --> 01:04:23,640
Speaker 1: that was so interesting because yeah, it is like, on

1210
01:04:23,680 --> 01:04:26,680
Speaker 1: the one hand, I totally agree, but in twenty twenty five,

1211
01:04:26,800 --> 01:04:29,440
Speaker 1: you want to make yourself seem like ative, a competitive candidate,

1212
01:04:29,760 --> 01:04:34,200
Speaker 1: you like you should be able to speak articulately about AI. Absolutely. Also,

1213
01:04:34,240 --> 01:04:38,000
Speaker 1: I wonder like, are is are are women who are

1214
01:04:38,040 --> 01:04:39,680
Speaker 1: self reporting like, well, I don't want to use it

1215
01:04:39,680 --> 01:04:41,960
Speaker 1: too much because I don't want to looking at me crazy, Right,

1216
01:04:42,200 --> 01:04:44,000
Speaker 1: What do you say to somebody who feels that way?

1217
01:04:44,840 --> 01:04:48,800
Speaker 1: I would say, uh, don't box yourself out in that way,

1218
01:04:48,920 --> 01:04:53,600
Speaker 1: because the men are using it, and they're using it proudly, honestly,

1219
01:04:54,080 --> 01:04:56,200
Speaker 1: And if you are in an industry where you feel

1220
01:04:56,200 --> 01:04:59,400
Speaker 1: like it will help you be more efficient. You should

1221
01:04:59,520 --> 01:05:01,640
Speaker 1: use it, and you should use it proudly, because even

1222
01:05:01,680 --> 01:05:04,720
Speaker 1: in job interviews, they'll they'll they'll give you a scenario,

1223
01:05:05,120 --> 01:05:07,760
Speaker 1: and if you don't say I would use AI for

1224
01:05:07,800 --> 01:05:11,400
Speaker 1: this part of it, they're like that too slow is

1225
01:05:11,520 --> 01:05:12,840
Speaker 1: you're gonna be way too slow.

1226
01:05:12,920 --> 01:05:16,240
Speaker 2: Like oh I would research, No, say I would plug

1227
01:05:16,280 --> 01:05:18,840
Speaker 2: that into chat GBT Chat GBT will tell me this

1228
01:05:19,000 --> 01:05:21,400
Speaker 2: and then from there I'll be able to pinpoint the

1229
01:05:21,480 --> 01:05:24,480
Speaker 2: right people within the organization to blah blah blah, like

1230
01:05:25,320 --> 01:05:26,960
Speaker 2: you don't want to box yourself out.

1231
01:05:27,040 --> 01:05:31,200
Speaker 1: Don't don't let these don't let these men. Don't let

1232
01:05:31,240 --> 01:05:35,480
Speaker 1: me putting pro that they are, that they are prompt

1233
01:05:35,520 --> 01:05:38,440
Speaker 1: engineers on their resumeation, I know how to ask cha.

1234
01:05:39,800 --> 01:05:41,680
Speaker 1: I have seen it. I can confirm it with my

1235
01:05:41,800 --> 01:05:42,760
Speaker 1: own eyes.

1236
01:05:43,040 --> 01:05:45,160
Speaker 2: And I remember I can't remember what book I read

1237
01:05:45,200 --> 01:05:48,160
Speaker 2: it in, But it was like when a man applies

1238
01:05:48,200 --> 01:05:50,800
Speaker 2: for a job, if there are five qualifications and he

1239
01:05:50,880 --> 01:05:53,520
Speaker 2: only has one, he's he's more likely to apply for

1240
01:05:53,560 --> 01:05:55,640
Speaker 2: it than a woman who has three or four of

1241
01:05:55,640 --> 01:05:59,040
Speaker 2: those qualifications, like she will she will deny herself if

1242
01:05:59,040 --> 01:06:02,520
Speaker 2: she has only four of the five qualifications.

1243
01:06:02,560 --> 01:06:04,520
Speaker 1: When a man will apply when he just has one.

1244
01:06:06,480 --> 01:06:07,720
Speaker 1: We need to move with the.

1245
01:06:07,680 --> 01:06:12,560
Speaker 2: Boldness of unqualified white men.

1246
01:06:13,560 --> 01:06:18,000
Speaker 1: Yes, and I've said this on the show before. Is

1247
01:06:18,000 --> 01:06:21,240
Speaker 1: that my first ever job in podcasting? It was back

1248
01:06:21,280 --> 01:06:24,919
Speaker 1: in like twenty twelve, before Cereal was a thing, before

1249
01:06:24,920 --> 01:06:27,960
Speaker 1: anybody knew what a podcast was. I applied. I was

1250
01:06:28,000 --> 01:06:29,760
Speaker 1: just somebody who listened to podcasts and I need to

1251
01:06:29,800 --> 01:06:32,240
Speaker 1: work on a podcast, and I put out on my

1252
01:06:32,320 --> 01:06:33,920
Speaker 1: resume or I think I said in the interview that

1253
01:06:33,960 --> 01:06:36,960
Speaker 1: I knew how to do final cut pro. In reality,

1254
01:06:36,960 --> 01:06:38,440
Speaker 1: I had never I did not know how to do

1255
01:06:38,480 --> 01:06:39,880
Speaker 1: final cut pro. It is tho. This was just a

1256
01:06:39,920 --> 01:06:43,840
Speaker 1: fucking whole lie. And then I got the job, and

1257
01:06:43,840 --> 01:06:48,040
Speaker 1: then I learned on YouTube. So have confidence. I am

1258
01:06:48,040 --> 01:06:50,560
Speaker 1: an advocate for people lying themselves into the job that

1259
01:06:50,600 --> 01:06:53,720
Speaker 1: they want to. Why not? You know, like you can

1260
01:06:53,840 --> 01:06:54,200
Speaker 1: learn it.

1261
01:06:54,360 --> 01:06:56,040
Speaker 2: And that's the thing is that in any job you

1262
01:06:56,080 --> 01:06:58,720
Speaker 2: can learn. You can learn any of the stuff if

1263
01:06:58,760 --> 01:07:00,880
Speaker 2: they if you have enough of the quasis, you're a

1264
01:07:00,920 --> 01:07:02,800
Speaker 2: fast learner and you feel like you could pick stuff up.

1265
01:07:03,680 --> 01:07:04,960
Speaker 1: Just do what you gotta do to get in there.

1266
01:07:05,000 --> 01:07:07,960
Speaker 1: You learn when you get there. Okay, Gezi, I have

1267
01:07:08,120 --> 01:07:08,840
Speaker 1: one last question.

1268
01:07:08,920 --> 01:07:09,240
Speaker 3: For you.

1269
01:07:09,360 --> 01:07:12,520
Speaker 1: Yeah, you know in your in your episode about AI,

1270
01:07:13,360 --> 01:07:15,680
Speaker 1: you really I will put it in the Showwes because

1271
01:07:15,680 --> 01:07:18,520
Speaker 1: people really should hear it. I am often asked, So

1272
01:07:18,640 --> 01:07:21,240
Speaker 1: I am I feel similarly to you that people should

1273
01:07:21,320 --> 01:07:23,880
Speaker 1: be testing out how AI might fit into their own

1274
01:07:24,000 --> 01:07:26,920
Speaker 1: lives and their own work. People often say, what about

1275
01:07:27,000 --> 01:07:31,800
Speaker 1: the like environmental and ethical considerations which are real? I

1276
01:07:31,880 --> 01:07:34,400
Speaker 1: was gonna talk recently about the use of AI and

1277
01:07:34,440 --> 01:07:36,480
Speaker 1: podcasting and how it actually does show up in some

1278
01:07:36,520 --> 01:07:38,480
Speaker 1: podcasts even though peole don't want to admit it. What

1279
01:07:38,600 --> 01:07:40,280
Speaker 1: do you say to that? People are like, well, how

1280
01:07:40,280 --> 01:07:43,400
Speaker 1: do you square that with all the ethical and environmental considerations?

1281
01:07:43,680 --> 01:07:48,280
Speaker 2: I always say that there are ethical and environmental considerations

1282
01:07:48,320 --> 01:07:52,120
Speaker 2: for all things, and if you want to hyper focus

1283
01:07:52,200 --> 01:07:55,200
Speaker 2: on AI, then sure, But I don't know if that's

1284
01:07:55,240 --> 01:07:59,080
Speaker 2: the hill you want to die on, because if folks

1285
01:07:59,120 --> 01:08:00,560
Speaker 2: want to like if you can.

1286
01:08:00,480 --> 01:08:02,040
Speaker 1: So sometimes people talk about water.

1287
01:08:02,960 --> 01:08:05,400
Speaker 2: To make a serving of rice, the amount of water

1288
01:08:05,440 --> 01:08:09,960
Speaker 2: that's consumed is seventy two liters. One hamburger patty is

1289
01:08:10,000 --> 01:08:12,880
Speaker 2: two thousand, four hundred liters. And it's not just to

1290
01:08:13,040 --> 01:08:16,840
Speaker 2: cook it. It's like from the cow, the slaughter all

1291
01:08:16,920 --> 01:08:21,280
Speaker 2: the way into your happy meal twenty four hundred liters

1292
01:08:21,320 --> 01:08:27,000
Speaker 2: of water and one chat gbtique query is point zero five. Leaders, Like,

1293
01:08:27,680 --> 01:08:30,120
Speaker 2: there are a lot of things people have talked about

1294
01:08:30,200 --> 01:08:33,120
Speaker 2: almonds and how much water it uses, rice and how

1295
01:08:33,200 --> 01:08:35,640
Speaker 2: much water it uses. It's like, I don't want to

1296
01:08:35,960 --> 01:08:41,080
Speaker 2: discourage people from taking AI to task and saying, hey,

1297
01:08:41,120 --> 01:08:44,320
Speaker 2: we need to make sure that as we're developing this

1298
01:08:44,400 --> 01:08:46,920
Speaker 2: technology that we are considering the environment. We all got

1299
01:08:46,920 --> 01:08:49,240
Speaker 2: to live here, and I don't want this earth to

1300
01:08:49,280 --> 01:08:49,720
Speaker 2: burn up.

1301
01:08:49,760 --> 01:08:51,800
Speaker 1: I want it to last for a long time. I

1302
01:08:51,840 --> 01:08:55,719
Speaker 1: love you Earth. But we also have.

1303
01:08:55,600 --> 01:09:00,960
Speaker 2: To think about things holistically and the costs for a

1304
01:09:01,000 --> 01:09:04,160
Speaker 2: lot of other things as well. Like we also need

1305
01:09:04,200 --> 01:09:08,160
Speaker 2: to we haven't figured out the whole emissions thing, we

1306
01:09:08,200 --> 01:09:11,080
Speaker 2: haven't figured out the fossil fuels thing. But it's like

1307
01:09:11,120 --> 01:09:14,240
Speaker 2: people aren't talking about it anymore, and I'm just like

1308
01:09:14,520 --> 01:09:19,320
Speaker 2: everybody's hyper focused on AI and which, you know, when

1309
01:09:19,320 --> 01:09:21,280
Speaker 2: you think about some of the awful things that have

1310
01:09:21,320 --> 01:09:23,400
Speaker 2: been done with some of these data centers and people,

1311
01:09:23,479 --> 01:09:25,400
Speaker 2: you know, losing the access to clean air and things

1312
01:09:25,439 --> 01:09:28,599
Speaker 2: like that, Yes, but there are so many things where

1313
01:09:28,680 --> 01:09:33,120
Speaker 2: we're just not there yet, and we've kind of lost

1314
01:09:33,120 --> 01:09:35,920
Speaker 2: the plot a little bit and we've lost focus. And

1315
01:09:36,680 --> 01:09:39,519
Speaker 2: I think that we have to just continue to beat

1316
01:09:39,520 --> 01:09:44,640
Speaker 2: the drum about doing things in a sustainable way and

1317
01:09:44,960 --> 01:09:49,360
Speaker 2: doing things that don't harm our environment, but also advancing

1318
01:09:49,400 --> 01:09:52,040
Speaker 2: ourselves because AI might be able to get us to

1319
01:09:52,080 --> 01:09:55,559
Speaker 2: a place where we could find alternatives to fossil fuels.

1320
01:09:55,800 --> 01:09:58,639
Speaker 2: There are scientists that are actively working to make sure

1321
01:09:58,720 --> 01:10:01,240
Speaker 2: that AI is more sustainable. But I'll tell you this

1322
01:10:01,400 --> 01:10:04,639
Speaker 2: right now, those the fossil fuel folks, I won't say

1323
01:10:04,680 --> 01:10:06,120
Speaker 2: no names, I won't be specific.

1324
01:10:06,520 --> 01:10:11,360
Speaker 1: They're not trying to do that. No, they're not trying

1325
01:10:11,400 --> 01:10:12,120
Speaker 1: to do that.

1326
01:10:13,360 --> 01:10:15,360
Speaker 2: But I know that the folks that are that are

1327
01:10:15,400 --> 01:10:18,719
Speaker 2: researching and trying to get us to a better spot

1328
01:10:18,760 --> 01:10:22,439
Speaker 2: when it comes to AI and its environmental costs, they're

1329
01:10:22,720 --> 01:10:26,160
Speaker 2: they're absolutely taking it, taking it into consideration, and it's

1330
01:10:26,200 --> 01:10:28,840
Speaker 2: gonna take some time and it's gonna take some innovation.

1331
01:10:29,640 --> 01:10:32,880
Speaker 2: But uh yeah, I think that folks really have to

1332
01:10:33,240 --> 01:10:36,000
Speaker 2: ask themselves other questions as well when they're thinking about

1333
01:10:36,040 --> 01:10:40,040
Speaker 2: the impacts. I'm like, it's not like, oh, everything is bad,

1334
01:10:40,160 --> 01:10:41,559
Speaker 2: so might as well just do it.

1335
01:10:41,560 --> 01:10:45,280
Speaker 1: It's like, yes, there are some bad aspects to AI

1336
01:10:45,479 --> 01:10:49,840
Speaker 1: that is bad. For the environment. Let's let's tackle that.

1337
01:10:50,200 --> 01:10:54,120
Speaker 1: And let's also not forget about fossil fuels. And let's

1338
01:10:54,160 --> 01:10:59,439
Speaker 1: also not forget about fast fashion. Like, let's also not

1339
01:10:59,439 --> 01:11:03,519
Speaker 1: forget about the landfill. Let's also not forget about the

1340
01:11:03,520 --> 01:11:06,759
Speaker 1: trash that's floating in the ocean. Let's also not forget

1341
01:11:06,840 --> 01:11:11,360
Speaker 1: about vaping. Hello, let's also not forget about.

1342
01:11:11,280 --> 01:11:13,960
Speaker 2: Like there's just I just want us to always like

1343
01:11:14,800 --> 01:11:17,479
Speaker 2: a lot of these things are you know, taking our

1344
01:11:17,479 --> 01:11:20,280
Speaker 2: attention away? I just want us to always like remember

1345
01:11:20,360 --> 01:11:22,760
Speaker 2: that there are so many things that we have to

1346
01:11:24,040 --> 01:11:25,120
Speaker 2: tackle as.

1347
01:11:24,960 --> 01:11:28,360
Speaker 1: A global community, and we can't lose sight of that

1348
01:11:29,320 --> 01:11:33,479
Speaker 1: t ty beautiful closing word, thank you so much for

1349
01:11:33,520 --> 01:11:35,519
Speaker 1: being here. You're such a breadth of fresh air. Oh

1350
01:11:35,600 --> 01:11:38,639
Speaker 1: my goodness, I'm so happy to be here. Are you kidding?

1351
01:11:38,680 --> 01:11:43,400
Speaker 1: I mean, this is like a mutual podcast love affair. Honestly,

1352
01:11:43,760 --> 01:11:47,000
Speaker 1: I'm I'm sased. Where can folks listen to the podcast?

1353
01:11:47,120 --> 01:11:49,639
Speaker 1: What should they know about the podcast? Follow? You give

1354
01:11:49,720 --> 01:11:50,360
Speaker 1: us all the deeps?

1355
01:11:50,960 --> 01:11:53,519
Speaker 2: Yes, so you can listen to Dope Labs wherever you

1356
01:11:53,600 --> 01:11:56,000
Speaker 2: get your podcasts, So you just look for a Dope

1357
01:11:56,080 --> 01:11:59,400
Speaker 2: Labs podcast. We have a really cool black and yellow

1358
01:11:59,720 --> 01:12:03,360
Speaker 2: low it's so good. You can find us on Instagram

1359
01:12:03,439 --> 01:12:06,479
Speaker 2: mainly at Dope Labs podcast. You can find me at

1360
01:12:06,640 --> 01:12:09,560
Speaker 2: d R Underscore t Sho, and if you want to

1361
01:12:09,600 --> 01:12:12,040
Speaker 2: follow my co host Sakia, you can find her at

1362
01:12:12,240 --> 01:12:14,760
Speaker 2: z set So. You can also go to dope blabpodcast

1363
01:12:14,760 --> 01:12:16,519
Speaker 2: dot com where we put a whole bunch of stuff

1364
01:12:16,720 --> 01:12:19,520
Speaker 2: on fun stuff, and you could subscribe to our newsletter

1365
01:12:19,520 --> 01:12:22,720
Speaker 2: where we put a lot of little tidbits about ourselves

1366
01:12:22,760 --> 01:12:24,280
Speaker 2: and like what we're up to and the things that

1367
01:12:24,320 --> 01:12:24,800
Speaker 2: we're into.

1368
01:12:25,320 --> 01:12:28,120
Speaker 1: We will hopefully be hearing Zakiya on the pod very

1369
01:12:28,160 --> 01:12:30,519
Speaker 1: soon as well. Thank you so much for being here.

1370
01:12:30,680 --> 01:12:32,519
Speaker 1: I mean, it's been a pleasure and thank you for

1371
01:12:32,560 --> 01:12:34,840
Speaker 1: the work that you're doing. I have ever since I

1372
01:12:35,000 --> 01:12:37,559
Speaker 1: the first time I heard about the concept for Dope

1373
01:12:37,640 --> 01:12:39,679
Speaker 1: Lab is, I was like, this is it? We need

1374
01:12:39,680 --> 01:12:42,280
Speaker 1: this in the world. It's just I'm so glad that

1375
01:12:42,320 --> 01:12:45,599
Speaker 1: you all made this beautiful podcast and that it exists

1376
01:12:45,600 --> 01:12:48,240
Speaker 1: in the world. Thank you, Thank you may so much

1377
01:12:48,280 --> 01:12:51,679
Speaker 1: for being a shining example for us to follow. I mean,

1378
01:12:52,240 --> 01:12:55,160
Speaker 1: it's hard out here in these podcast streets, but seeing

1379
01:12:55,200 --> 01:12:57,719
Speaker 1: you do this and do this so well for so long,

1380
01:12:57,880 --> 01:13:01,519
Speaker 1: it's always so encouraging and it's really an honor to

1381
01:13:01,560 --> 01:13:05,840
Speaker 1: be here. Like when the email came in, I was like, I,

1382
01:13:06,000 --> 01:13:12,120
Speaker 1: this clearly is a phishing was any whatever link is

1383
01:13:12,160 --> 01:13:12,320
Speaker 1: in this?

1384
01:13:12,560 --> 01:13:15,320
Speaker 2: I know it's fishing because what is going on? I

1385
01:13:15,320 --> 01:13:18,240
Speaker 2: cannot go oh my gosh, well, thank you so many time.

1386
01:13:18,479 --> 01:13:20,080
Speaker 2: We love having you come back.

1387
01:13:20,120 --> 01:13:22,439
Speaker 1: Anytime I hope to be back.

1388
01:13:22,520 --> 01:13:26,920
Speaker 2: Please have me back whenever I'm ready.

1389
01:13:30,920 --> 01:13:32,960
Speaker 1: Got a story about an interesting thing in tech, or

1390
01:13:33,040 --> 01:13:34,840
Speaker 1: just want to say hi, you can be just said

1391
01:13:34,880 --> 01:13:37,599
Speaker 1: hello at tengody dot com. You can also find transcripts

1392
01:13:37,600 --> 01:13:40,080
Speaker 1: for today's episode at tengody dot com. There Are No

1393
01:13:40,120 --> 01:13:42,200
Speaker 1: Girls on the Internet was created by me Bridget Tod.

1394
01:13:42,560 --> 01:13:46,000
Speaker 1: It's a production of iHeartRadio, an unbossed creative. Jonathan Strickland

1395
01:13:46,040 --> 01:13:48,760
Speaker 1: is our executive producer. Terry Harrison is our producer and

1396
01:13:48,840 --> 01:13:52,599
Speaker 1: sound engineer. Michael Almato is our contributing producer. I'm your host,

1397
01:13:52,600 --> 01:13:55,400
Speaker 1: bridget Todd. If you want to help us grow, rate

1398
01:13:55,439 --> 01:13:56,000
Speaker 1: and review us.

1399
01:13:55,960 --> 01:13:57,160
Speaker 3: On Apple Podcasts.

1400
01:13:57,640 --> 01:14:00,439
Speaker 1: For more podcasts from iHeartRadio, check out the iHeart You app,

1401
01:14:00,520 --> 01:14:02,560
Speaker 1: Apple Podcasts, or wherever you get your podcasts.