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The Intelligent Artificial Intelligence Episode · Stuff Mom Never Told You

0:05
Speaker 1: Hey, this is Anny and Samantha and welcome to Stephane
0:07
Speaker 1: Never Told You production of I Heart Radio. And today
0:19
Speaker 1: we are continuing our long technology streak. There's a lot
0:23
Speaker 1: to talk about in the world. Technology is really, really
0:27
Speaker 1: is and this one is a hot topic right now.
0:30
Speaker 1: It is a hot button issue because we are talking
0:32
Speaker 1: about artificial intelligence and women. Um, anything we mentioned in
0:38
Speaker 1: this is not currently sponsored. We're just talking about some
0:40
Speaker 1: of the bigger players in this field. You can see
0:43
Speaker 1: our episode that just published recently with Katrina who works
0:47
Speaker 1: with AI and has been in that community for a while,
0:50
Speaker 1: about her experience. The date is February seven. And I'm
0:55
Speaker 1: saying that because like every day I get a new
0:58
Speaker 1: piece of information about this. Well, you know it's interesting. Um,
1:03
Speaker 1: not only do we get more information, but the way
1:05
Speaker 1: the programs are going, they are updating so quickly that
1:08
Speaker 1: it has grown immensely. So every time we talk about it,
1:12
Speaker 1: they've gone from like Virgin three to version twelve. So
1:15
Speaker 1: it's kind of, uh, we're behind obviously talking about it.
1:19
Speaker 1: And then as it gets on when we talk about
1:21
Speaker 1: things like chat GPT, they're being acquired and there's this competition,
1:25
Speaker 1: a huge competition happening within the technology world. So yeah,
1:29
Speaker 1: things are changing very quickly. Yes, yes, And Samantha and
1:33
Speaker 1: I are not at all experts. This is complicated topic.
1:39
Speaker 1: Um so we're you're getting, you know, kind of a
1:42
Speaker 1: very basic layout and understanding what's going on right now.
1:45
Speaker 1: But I just I would I would hate for people
1:48
Speaker 1: who really know about this to be like pulling out
1:51
Speaker 1: your hair. It's it's fine. Um I know that I
1:54
Speaker 1: am giving the most basic definitions and everything, but it
2:00
Speaker 1: as always please right in let us know, uh if
2:03
Speaker 1: you have any more information or resources about this topic. Okay.
2:08
Speaker 1: Um so we did want to give a very brief
2:12
Speaker 1: history of artificial intelligence or AI. So by the nineteen fifties, scientists, engineers, philosophers,
2:19
Speaker 1: mathematicians all were familiar with the concept of artificial intelligence.
2:24
Speaker 1: In his nineteen fifty paper Computing Machinery and Intelligence, Alan
2:28
Speaker 1: Turing speculated that machines could use available information to make
2:33
Speaker 1: decisions and problem solve and proposed building intelligent machines along
2:38
Speaker 1: with ways to test their intelligence. And if you're if
2:41
Speaker 1: you're if you'll remember like this was, you know, a
2:44
Speaker 1: lot of science fiction was happening around this time too,
2:47
Speaker 1: Like it really captured the public imagination. Technology was changing
2:52
Speaker 1: very quickly then, not nearly as quickly as it is now,
2:55
Speaker 1: but a lot of things that were very very new
2:57
Speaker 1: were coming out TV more people were getting to these,
3:01
Speaker 1: so it was it was a real time for people
3:03
Speaker 1: to think about what was our future with technology and
3:06
Speaker 1: with machines. However, this paper comes out the technology just
3:12
Speaker 1: wasn't there for what Alan Turing was proposing, and everything
3:15
Speaker 1: that was available was prohibitively expensive. That didn't stop people
3:19
Speaker 1: from trying, though, and a couple of years later, a
3:21
Speaker 1: few men came together to create a program called the
3:24
Speaker 1: Logic Theorist, which many labeled the first AI program. Um Basically,
3:29
Speaker 1: it was designed to copy the problem solving skills of
3:32
Speaker 1: a human and the term artificial intelligence was coined in
3:36
Speaker 1: nineteen fifty six after this program was presented. So over
3:40
Speaker 1: the next two decades, the realm of AI really grew
3:43
Speaker 1: as technology improved. Computers were now able to store commands
3:47
Speaker 1: where they previously couldn't. Problem solving improved, along with interpreting
3:52
Speaker 1: spoken languages. Government agencies like DARPA started funding AI research,
3:56
Speaker 1: and by nineteen seventy people were predicting would have a
4:00
Speaker 1: workable AI within the decade. Ohwever, the text still wasn't
4:05
Speaker 1: there mostly issues around data storage and slow processing speeds.
4:10
Speaker 1: In the eighties, a couple of big things happened. One
4:14
Speaker 1: was the introduction of deep learning or machine learning through experience.
4:19
Speaker 1: Another was expert systems, which allowed for computers to copy
4:23
Speaker 1: experts in terms of decision making. It learned by asking
4:28
Speaker 1: the expert questions and storing the answer so that non
4:31
Speaker 1: experts could ask and get the same answer right. The
4:36
Speaker 1: funding did dry out the nineties because basically everyone, and
4:40
Speaker 1: this is kind of like a running joke for things
4:42
Speaker 1: like Back to the Future right where we're like, why
4:43
Speaker 1: don't we have flying cars ship Everyone thought this was
4:45
Speaker 1: going to happen much more quickly than it was happening.
4:48
Speaker 1: But even though the funding did dry up in the nineties,
4:51
Speaker 1: AI flourished in the nineties and two thousands, UM World
4:55
Speaker 1: Chess Champion and Grandmaster Gary Casparo was beaten by deep Blue,
5:00
Speaker 1: which was an IBM chess playing program. UH speech recognition
5:05
Speaker 1: was implemented on Windows that same year. The real game
5:08
Speaker 1: changer and all of those, has been the increase in
5:11
Speaker 1: computer storage and processing time. So this more or less
5:15
Speaker 1: brings us to where we are today. Industries ranging from
5:19
Speaker 1: banking to marketing to entertainment utilize AI all kinds of
5:22
Speaker 1: things that when I was reading it, I was like, oh,
5:23
Speaker 1: I never thought of that, But that's true. It's like
5:25
Speaker 1: it's used in a lot more things than I bet
5:27
Speaker 1: you a lot of us would think at first. It
5:30
Speaker 1: really encompasses a lot more then I would say most
5:34
Speaker 1: people imagine, but that could just be me. I don't
5:37
Speaker 1: want to project. There was a huge spike in popularity
5:43
Speaker 1: of apps, UM programs and platforms like Lenza and mid
5:47
Speaker 1: Journey and chat GPT which stands for Chat Generative pre
5:51
Speaker 1: Trained Transformer. And I mean, if you haven't I'd be
5:55
Speaker 1: surprised if you haven't heard of them, but if you
5:58
Speaker 1: haven't experimented with them, they're basically like text to image
6:01
Speaker 1: prompts and text to text prompts UM. So basically, you
6:05
Speaker 1: type in something, we'll go over some examples in a minute,
6:07
Speaker 1: and then the AI generates an image or generates some
6:11
Speaker 1: text based on what you put in UM. And these
6:14
Speaker 1: platforms have access to billions of images and texts. It's
6:17
Speaker 1: kind of a key part of this whole conversation. I
6:21
Speaker 1: didn't want thrown here. I saw Megan. I'm sure a
6:24
Speaker 1: lot of people working AI were like, no, don't mention Megan.
6:28
Speaker 1: Um let me ask, Let me ask why is the three?
6:32
Speaker 1: Why is Megan? The three? It stands for. I can't
6:35
Speaker 1: remember what it is, but there's three E s and
6:37
Speaker 1: it's an acronym. Okay, okay, so I think M E
6:41
Speaker 1: E E G A N. But cutely Megan, I think, okay, okay,
6:49
Speaker 1: no spoilers. But it had an interesting commentary about AI
6:53
Speaker 1: and then about how the issue is more people and
6:57
Speaker 1: biases programmed in and learn with AI. I read an
7:01
Speaker 1: essay about it that it stuck with me to this day.
7:03
Speaker 1: It was about teaching an AI program too, like I
7:09
Speaker 1: don't know, sign the prettiest signature you've ever seen, and
7:13
Speaker 1: nothing nefarious, but just the way that the AI interpreted
7:17
Speaker 1: the code or the prompts that people were putting in,
7:20
Speaker 1: it ended up destroying the world. But it wasn't designed
7:24
Speaker 1: to do that. Yes, yes, es, yes, but it's also
7:28
Speaker 1: Megan also has a lot of stuff around, like the
7:31
Speaker 1: relationships we've formed with machines, But I would say it
7:35
Speaker 1: was more about replacing human connection with machines. Was the
7:39
Speaker 1: commentary that was happening. I do think this is the
7:41
Speaker 1: fear discomfort a lot of a lot of us have
7:43
Speaker 1: after seeing so many movies. Again, going back to that
7:45
Speaker 1: kind of sci fi thing, A lot of times, the
7:47
Speaker 1: first thing that comes up is like, what about what
7:49
Speaker 1: happened in the ovenders? You know that kind of right.
7:53
Speaker 1: I think of the matrix and I just hope that
7:55
Speaker 1: we have a neo. We gotta get to training, alright.
8:04
Speaker 1: So we do have a few numbers about this rapidly
8:08
Speaker 1: growing market for youth. First is expected to be worth
8:12
Speaker 1: about two hundred and sixty seven billion dollars by seven
8:16
Speaker 1: and it will contribute and estimated fifteen point seven trillion
8:21
Speaker 1: dollars to the global economy by twenty thirty. Of the
8:25
Speaker 1: businesses and organizations used some type of AI. Um. Actually,
8:30
Speaker 1: we just had a fellow podcasts or use chat GPT
8:34
Speaker 1: to do some scripts. So that was interesting. Yeah, yeah,
8:40
Speaker 1: that number. So here's an interesting number. Is estimated that
8:45
Speaker 1: by two years from now, hey, I will destroy eighty
8:51
Speaker 1: five million jobs but create nineties seven million jobs. So
8:58
Speaker 1: y'all better get to learning this AI think. I guess, yeah, yeah,
9:03
Speaker 1: I know, I know. Well, and so I've seen a
9:05
Speaker 1: lot of conflicting numbers about this, but I do think
9:09
Speaker 1: we even kind of used a loaded term destroy. I
9:12
Speaker 1: feel like a lot we're not talking about the creation
9:15
Speaker 1: part so much so much as these jobs will go away,
9:17
Speaker 1: which is scary, Like that's a scary saying, don't get
9:19
Speaker 1: me wrong, especially if that's your job, that has been
9:22
Speaker 1: your job. Um, but it will create jobs as well,
9:38
Speaker 1: all right. So when it comes to gender, over n
9:42
Speaker 1: of AI specialists are men. Men in the field receive
9:45
Speaker 1: an average of one dollars and women almost went a
9:49
Speaker 1: hundred and ten thousand dollars. When it comes to race,
9:53
Speaker 1: of specialists are white, followed by Latino and Black about
9:57
Speaker 1: nine each of those populations, and Asian at about three
10:01
Speaker 1: percent of the workforce. The rest is categorized as other
10:05
Speaker 1: or indigenous UM black and Latino specialists. The Latino specialists
10:09
Speaker 1: received the blowest pay of these categories. There aren't too
10:12
Speaker 1: many numbers about queer folks in this space, but one
10:15
Speaker 1: chart we found suggested that in a breakdown of job titles,
10:18
Speaker 1: queer people generally hovered around five per cent, but in
10:21
Speaker 1: some areas reached around ten percent. I think again, some
10:25
Speaker 1: of these numbers are hard to track down based on
10:28
Speaker 1: what constitutes to day I and also how are they
10:32
Speaker 1: reporting or not reporting those types of numbers. But this
10:35
Speaker 1: brings us to lawsuits. So one of the biggest controversies
10:39
Speaker 1: about AI has been around art and copyright. So if
10:44
Speaker 1: you don't know a recent AI programs and platforms have
10:46
Speaker 1: written children's books, They've created so much art, all kinds
10:50
Speaker 1: of things, much the outrage of a lot of folks
10:53
Speaker 1: in the creative space. So if you haven't used these platforms,
10:55
Speaker 1: you can basically go to a site type in pretty
10:58
Speaker 1: much anything you want from very clear to super obscure,
11:01
Speaker 1: and a handful of images will get spit back out
11:03
Speaker 1: at you or, in the case of GBT, an essay
11:06
Speaker 1: or something like that. Uh. The AI tool and question
11:09
Speaker 1: sort of guesses what you want based on the prompts
11:12
Speaker 1: and then uses all of the images and data available
11:15
Speaker 1: on the Internet to paint a picture, so to speak.
11:19
Speaker 1: Um people have won awards with AI generated art, and
11:22
Speaker 1: I read some really interesting articles about it, which we're
11:25
Speaker 1: going to touch on some of them in a minute.
11:26
Speaker 1: Beginning in January three, lawsuits against AI started rolling out.
11:32
Speaker 1: These these newer platforms that got really popular anyway, Getty
11:36
Speaker 1: Images sued Stability AI, alleging that they had quote chosen
11:40
Speaker 1: to ignore viable licensing options and long standing legal protections
11:44
Speaker 1: in pursuit of their standalone commercial interest. And if you remember,
11:47
Speaker 1: this was the big thing people were warning about with
11:49
Speaker 1: things like LENSA that any picture you uploaded belonged to them. UM.
11:55
Speaker 1: Three visual artists out of San Francisco on the behalf
11:57
Speaker 1: of the visual arts community also sued to Ability AI,
12:01
Speaker 1: uh naming mid Journey and dB and Art as well. Essentially,
12:04
Speaker 1: the lawsuit alleges that these AI tools and sites comb
12:07
Speaker 1: the web for images and use them without crediting the artist,
12:10
Speaker 1: especially because most of them charge of fear. There's a
12:14
Speaker 1: tier where a fee can be involved. UM. Some images
12:18
Speaker 1: generated by LINDSA included distortist included distorted artist signatures, indicating
12:23
Speaker 1: that it used its specific art from these artists but
12:26
Speaker 1: did not credit them. UM. Many of the AI companies
12:28
Speaker 1: in question UM argue that this falls under fair use,
12:31
Speaker 1: which is a very very murky idea and we've had
12:34
Speaker 1: to have a lot of meetings about it in our
12:35
Speaker 1: very own field, so it is a mess. UM. Most
12:39
Speaker 1: of the entities suing are looking were getting credit and
12:43
Speaker 1: compensation as opposed to shutting these AI platforms down. So
12:47
Speaker 1: with Dolly not a part of the lawsuit, I think
12:51
Speaker 1: they might be because, like I said, I wrote, I
12:54
Speaker 1: wrote this section several weeks ago, and I just so
12:57
Speaker 1: much keeps changer. Dolly is one of the original even
13:01
Speaker 1: before mid Journey, Like mid Journey has been around, but
13:04
Speaker 1: Dolly was the original that people were playing with probably
13:06
Speaker 1: last year, like the beginning of last year. So I'm
13:08
Speaker 1: kind of surprised that they wouldn't be a part of that.
13:12
Speaker 1: They do have a free portion, I believe, but then
13:15
Speaker 1: you hit a wall and then you have to pay,
13:18
Speaker 1: So maybe that's why. But that's interesting. Yeah, I mean
13:21
Speaker 1: there's they're very well could be one. I think this
13:24
Speaker 1: is the big example that's happening right now. But there
13:27
Speaker 1: are multiple lawsuits against these these companies, and it is key.
13:32
Speaker 1: As we discuss with Katrina, most experts don't believe AI
13:36
Speaker 1: generated images will erase art like human made art, because
13:40
Speaker 1: we inherently place a human value on it as and
13:43
Speaker 1: I want to support this person. I'll pay for their work.
13:47
Speaker 1: I mean most people do, not everybody, but you know,
13:49
Speaker 1: you can kind of are like, oh, I want to
13:51
Speaker 1: support this person or this feels different than something I
13:54
Speaker 1: could just make online. Um. But that being said, uh,
13:58
Speaker 1: there are indeed questions about the right of styles and
14:01
Speaker 1: the like. And on top of that, some artists argue
14:03
Speaker 1: that some, not all, but some AI generated images are
14:06
Speaker 1: art and they require creativity and experimentation. So it's like
14:10
Speaker 1: a big there's so many conversations happening around this right
14:13
Speaker 1: now because it feels new, and as Katrina also mentioned
14:18
Speaker 1: in that interview we did with her, it's kind of
14:22
Speaker 1: sometimes these shifts just happened, like with photoshop came on
14:24
Speaker 1: the scene and we were all kind of trying to
14:26
Speaker 1: figure out what that means, and it's similar. Um. But
14:30
Speaker 1: and also, as we discussed in that episode with Katrina,
14:33
Speaker 1: who does work in this space, there is a question
14:35
Speaker 1: of accessibility and how AI can help in that way.
14:39
Speaker 1: These kind of AI generated programs, these kind of AI
14:43
Speaker 1: generated text to art or text to text things could
14:47
Speaker 1: be a really beneficial tool for people with accessibility issues, right.
14:51
Speaker 1: And yeah, like you said, it does take a skill
14:54
Speaker 1: for prompting, which is how you kind of pull into things. Um.
14:59
Speaker 1: And it's also worry somewhat you prompt what people can prompt. Yeah,
15:02
Speaker 1: and and speaking of another concern uh and around things
15:07
Speaker 1: like Lindza that owns images you upload, and that was
15:11
Speaker 1: a big to do. People did not read the terms
15:14
Speaker 1: and conditions. After being called out on Twitter, they updated
15:18
Speaker 1: their terms like used in December. They still own your
15:20
Speaker 1: images to train AI, but they now allow users sibilate
15:25
Speaker 1: their data, which I'm kind of concerned about because you know,
15:28
Speaker 1: we talked about that with twenty three and me and
15:30
Speaker 1: DNA testing how one company made say so, but what
15:35
Speaker 1: does the other company get right too? And yes, that
15:37
Speaker 1: means they own your image of public image. There's a
15:41
Speaker 1: whole conversation about like that society conversation makes me worried
15:44
Speaker 1: about like deep fakes, which is a whole big issue
15:46
Speaker 1: right now in the world of technology as well. But
15:49
Speaker 1: if you get these images on through these applications, who
15:54
Speaker 1: owns them, who can use them, and what can they
15:55
Speaker 1: be used for? This has kick started conversations again around
16:00
Speaker 1: privacy and how it seems hopeless to protect ourselves unless
16:03
Speaker 1: we exit the grid completely because there's so many like
16:07
Speaker 1: all every single social media platforms that allow for selfies
16:10
Speaker 1: with filters have a type of AI filter. But that's
16:14
Speaker 1: like conversation in general. When you upload things into social
16:18
Speaker 1: media platforms in itself, that's a good point to make
16:20
Speaker 1: as we do without all of these technology episodes, like
16:24
Speaker 1: these AI platforms are not alone into doing this there,
16:27
Speaker 1: Like this is an issue across a lot of platforms.
16:31
Speaker 1: And I remember one of the big things that happened
16:33
Speaker 1: after Lindsa blew up in December was everyone was like
16:35
Speaker 1: take it down, all of your selfies, like, take all
16:37
Speaker 1: of them down. And then I know before like Google
16:42
Speaker 1: got in big trouble with that several years ago. But
16:44
Speaker 1: people have suddenly see an ad and their pictures in
16:47
Speaker 1: the ad and they didn't write do that, um, but
16:52
Speaker 1: they are I believe at the time Google's argument was
16:55
Speaker 1: what you uploaded it to drive? So thats what Here's
17:04
Speaker 1: a grow from days to digital. There's an adage that's
17:06
Speaker 1: been knocking around the Internet for years in various forms.
17:09
Speaker 1: If you're not paying for the product, then you are
17:11
Speaker 1: the product. Actually, this quote can be traced it back
17:14
Speaker 1: to a nineteen seventy three video by the artist Richard
17:16
Speaker 1: Sarah and Caro Lot of Fish Schoolman. Television delivers people. Quote.
17:20
Speaker 1: Commercial television delivers twenty million people a minute read scrolling text.
17:24
Speaker 1: It is the consumer who is consumed you are the
17:27
Speaker 1: product of TV. You are delivered to the advertiser, who's
17:30
Speaker 1: the customer. He consumes you you are the end product.
17:35
Speaker 1: South familiar, kind of like the recent episode we did
17:38
Speaker 1: with bridget So in two Clear View, AI, which has
17:42
Speaker 1: become under fire for far right ties, offered its facial
17:46
Speaker 1: recognition technology to Ukraine for free. The country has used
17:50
Speaker 1: it to identify hundreds of diseased at Russian soldiers in
17:53
Speaker 1: order to alert the family. But some have argued that
17:57
Speaker 1: this is a way of chipping away at morale. And
18:00
Speaker 1: we've discussed before about how facial recognition AI is not infallible,
18:05
Speaker 1: especially when it comes to people of color and especially
18:07
Speaker 1: black people. UM. The EU has introduced legislation that anything
18:12
Speaker 1: created with AI has to be properly labeled. However, I
18:15
Speaker 1: know a recent app came out that removes water marks,
18:18
Speaker 1: which is one of the solutions they were also proposing
18:21
Speaker 1: for labeling this. UM. It's really really complicated, especially for
18:24
Speaker 1: journalists UM. But ideas that have been suggested are metadata
18:29
Speaker 1: titling reveals, UM interstitials bylines, annotations, side by side, platform
18:36
Speaker 1: and non platform warnings, and none of this is uniform
18:40
Speaker 1: across platform. So basically a lot of this would require
18:44
Speaker 1: the person to be acting in good faith in label
18:47
Speaker 1: things properly, which I don't believe is the thing that
18:50
Speaker 1: people we could trust people to do. But right they
18:53
Speaker 1: are constantly stealing videos, constantly claiming videos or ideas, especially
18:58
Speaker 1: in today's age of trying to being an influencer, isn't
19:01
Speaker 1: quite quite interesting. Yes, yes, um and and I I
19:08
Speaker 1: don't know if you saw this. There's also a huge
19:11
Speaker 1: kerfuffle in the sports world. The guy who owns like
19:17
Speaker 1: I don't I'm going to butcher it. But basically, this
19:19
Speaker 1: guy owns this basketball team he's getting sued and the
19:24
Speaker 1: facial recognition technology recognized one of the lawyers who's suing
19:28
Speaker 1: him and wouldn't let her her her daughter into the
19:31
Speaker 1: Madison Square Garden. Right, So that's kind of like the
19:33
Speaker 1: level we're talking about too of how this is. Yeah,
19:37
Speaker 1: so the you know, the the event was it wasn't
19:39
Speaker 1: a it wasn't a basketball game. A company is suing
19:42
Speaker 1: Madison Square Garden the event space. Uh. Through that, they
19:46
Speaker 1: they's found a person who wasn't even on the case.
19:48
Speaker 1: She just actually works in the same law firm therefore
19:51
Speaker 1: denied her access and her daughter access to an event. Um.
19:56
Speaker 1: And apparently he wasn't the only one. There have been
19:58
Speaker 1: several who have been denied just because of their association,
20:00
Speaker 1: not even being on the case, which does talks about like,
20:03
Speaker 1: oh what does this mean for later? Just because you
20:05
Speaker 1: don't like me or like something that someone else did
20:07
Speaker 1: that I'm connecting to, You're going to deny me service? Yes, yes, yes, yes. Um.
20:13
Speaker 1: There's also been a lot of concern no lawsuits as
20:16
Speaker 1: of recording. I don't think around chat GPT about students
20:20
Speaker 1: using it to write essays about it eliminating jobs. Uh,
20:24
Speaker 1: there are there are a lot of conflicting reports about
20:26
Speaker 1: how effective it is. As you said, things are improving
20:28
Speaker 1: very quickly though, and that probably depends on the topic
20:32
Speaker 1: and level of expertise involved. Then somebody created a tool
20:36
Speaker 1: to detect chat GPT, but I feel like that's just
20:39
Speaker 1: going to be a game of like constantly one opening
20:43
Speaker 1: like right right, It definitely is, yeah, But it did
20:48
Speaker 1: recently pass an exam at an Ivy League business school,
20:50
Speaker 1: and it's past several exams at colleges. And this has
20:54
Speaker 1: started a whole conversation and arguments around well, what does
20:59
Speaker 1: it mean around plagiarism in schools and how worried should
21:02
Speaker 1: we be about it? Anyway? So that's the whole thing, too, right.
21:07
Speaker 1: I actually I just read a title of an article.
21:10
Speaker 1: I did not read the article, y'all, but it said
21:12
Speaker 1: that there had been three examples of a chat gpt
21:17
Speaker 1: um writing out resumes that were perfect, and they were.
21:21
Speaker 1: These people were hired based on those resumes to executive
21:25
Speaker 1: level of positions, like high level positions through this, which
21:29
Speaker 1: that means we need to work on it. Any kidding, Alright,
21:38
Speaker 1: let's talk about the women portion, which is what we
21:40
Speaker 1: are coming to. Uh. So, why is AI women? Uh?
21:45
Speaker 1: There's been a lot of debate and conversation about why
21:48
Speaker 1: so many of our AI voices are women. Sirie Alexa
21:52
Speaker 1: Cortana the default setting on most map apps, uh, though
21:57
Speaker 1: this is not the case in all countries, even in
21:59
Speaker 1: fixed and her Megan x makin a mother from Alien
22:03
Speaker 1: though you don't hear her voice the Red Queen from
22:06
Speaker 1: Resident Evil. One of the theories behind this gendering exists
22:10
Speaker 1: is based on the roles we typically assign women YEP
22:14
Speaker 1: scheduling appointments, setting up reminders, answering questions, looking up data,
22:19
Speaker 1: and communicating it's it's literally literally thought of it as
22:22
Speaker 1: an assistant, something typically feminine coded in our society, proceed
22:27
Speaker 1: as more welcoming, more soothing, less threatening. Others have cautioned
22:32
Speaker 1: about the message this sends that with enough programming, we
22:36
Speaker 1: can create the perfect subservient, pleasing woman. Mm hmmm, which
22:41
Speaker 1: is that whole movie? Yes, yep, yep her Um, most
22:48
Speaker 1: sex robots are in the image of a woman, of
22:50
Speaker 1: though I think there's a couple points we could expand
22:53
Speaker 1: upon within that. But some suggest that this indicates there
22:57
Speaker 1: might be some sexualization of this a I voice that's
23:01
Speaker 1: going on. Some also argue, which I thought was interesting,
23:04
Speaker 1: that this is the reason so many of our AI
23:07
Speaker 1: voices are feminine, because people were so traumatized by the
23:10
Speaker 1: male voice of how from two thousands, I wouldn't even
23:15
Speaker 1: know what that is, Oh I I remember, Uh, I'll
23:19
Speaker 1: be sure. Maybe it's probably a lot of things. Yeah,
23:22
Speaker 1: I've just learned, um that the train announcement from uh
23:27
Speaker 1: the in England that says, mind the gap has switched
23:30
Speaker 1: from the man to a woman. Um. And there was
23:33
Speaker 1: this whole se story of this woman who used to
23:35
Speaker 1: go to after her fault. It was her husband who
23:38
Speaker 1: did this voice, and after he died, she would go
23:41
Speaker 1: to the tubes just to hear his voice. And when
23:44
Speaker 1: they were switching over to the woman that the transit
23:48
Speaker 1: department actually gave her the recording to have, but like
23:50
Speaker 1: she was so sad and so just wrong when it
23:53
Speaker 1: went away. That was a side side piece of love.
23:59
Speaker 1: But that is interesting because I didn't know this, but
24:01
Speaker 1: the UK kept coming up as the example of like
24:04
Speaker 1: the map default voice on map apps as a band um,
24:07
Speaker 1: a lot of their default apps are masculine, and they
24:11
Speaker 1: were kind of talking about why that might be. But interesting,
24:14
Speaker 1: this brings us to the quote perfect woman, the sort
24:17
Speaker 1: of idea of you know, mostly male users trying to
24:22
Speaker 1: create what they see is the perfect woman through these prompts,
24:25
Speaker 1: and then what does the AI return? Usually a white woman,
24:30
Speaker 1: very traditionally beautiful, brown haired. But I did want to
24:35
Speaker 1: include some of the very specific request that male creators
24:41
Speaker 1: male users have input for women, starting with this one
24:46
Speaker 1: desirable gorgeous woman in her fifties enjoying a pint at
24:49
Speaker 1: the pub, four stools down, no one in between us
24:52
Speaker 1: posh wearing thin white gauze blouse tailored for a very
24:55
Speaker 1: close fit, unbuttoned, black demi cup, push up bra thin
25:00
Speaker 1: white gal goals keep saying goals. I think it's right. Whatever.
25:04
Speaker 1: Miniskirt tailored for a very close fit, gartered, fish net hose,
25:09
Speaker 1: high heels, long straight hair, redhead with large bust, narrow waist,
25:12
Speaker 1: wide hips, trim, fant body, curvy, full lips, very little makeup,
25:17
Speaker 1: nice muscular legs, legs crossed, smiling back to the counter,
25:21
Speaker 1: leaning back on the counter, propped up on elbows, revealing
25:24
Speaker 1: skin side profile view, ultra wide angle ins hyper realistic
25:28
Speaker 1: three D render. Okay, um, right, so you know, like
25:34
Speaker 1: we had a whole experiment um with some of the
25:37
Speaker 1: things and just put in a woman prompt and most
25:39
Speaker 1: of them came out in redheaded, busty, small, very gorgeous,
25:43
Speaker 1: unrealistic looking women. And it was kind of interesting that
25:46
Speaker 1: that was the first basis to the point that there
25:49
Speaker 1: was conversation with the creators, like why is this thing?
25:53
Speaker 1: And that's what we talked about with Katrina. That's what's
25:55
Speaker 1: on the internet, that is what is out there, so
25:58
Speaker 1: therefore that is what it's like bringing back in. But yeah,
26:02
Speaker 1: stuff like that prompting is more common than not, and
26:04
Speaker 1: it's getting very specific to the point I think there's
26:07
Speaker 1: so much conversation that we need to have about who
26:11
Speaker 1: is controlling, just like in any social media platforms, as
26:14
Speaker 1: we've talked about before, like the guidelines and making sure
26:16
Speaker 1: they stick to the guidelines as is without question, because
26:20
Speaker 1: there's so many things that could happen, including the fact
26:22
Speaker 1: that I've seen obvious anime versions of young girls skinly
26:26
Speaker 1: dressed and that you know who this is for, and
26:29
Speaker 1: it's so concerning that this is continued to think like
26:32
Speaker 1: this shouldn't be a thing, Like this is not helping
26:35
Speaker 1: in any way for anyone. Yeah, yeah, And that's kind
26:39
Speaker 1: of the it gets really complicated really quickly, because that's
26:43
Speaker 1: part of the issue is also, as you said, like
26:45
Speaker 1: what is what are we upholding as a society? Is
26:48
Speaker 1: like you know, the beautiful, perfect woman? What is a woman?
26:50
Speaker 1: Like when you just type that in, what does the
26:53
Speaker 1: AI return to you? Because that says a lot. But
26:55
Speaker 1: then also by doing this, by putting in these prompts
26:59
Speaker 1: and by being like, oh, this is what I meant,
27:01
Speaker 1: it's like reinforcing the learning on the AI side as well.
27:05
Speaker 1: But it's also, like, like we said it with Katrina,
27:09
Speaker 1: you know if I think part of the issue two
27:12
Speaker 1: is it's a lot of men doing this so they
27:15
Speaker 1: can kind of objectify women, which you know, uh, but
27:20
Speaker 1: like if you're a woman and you wanna have a
27:24
Speaker 1: you know, sexy picture of you, or if you want
27:26
Speaker 1: to look at sexy pictures in a safe constittuation, I
27:29
Speaker 1: don't know, that's fine. So it's like, I don't want
27:32
Speaker 1: to stigmatize women's bodies or sexualized women's bodies even more
27:35
Speaker 1: than they've already been. But at the same time, they
27:37
Speaker 1: are being sexualized through these AI. So it's just it's
27:44
Speaker 1: a lot. It's a lot. That comment I read apparently
27:49
Speaker 1: this guy, it's the same guy, and they just followed
27:51
Speaker 1: him trying to find the perfect woman in his mind
27:54
Speaker 1: over several hours worth of prompts. So you can read
27:57
Speaker 1: if you wanted to, you can read the whole journey.
27:59
Speaker 1: He went on. Um, but I don't think he no
28:04
Speaker 1: no no open. AI recently changed his algorithm to be
28:08
Speaker 1: more racially diverse. We'll see how that goes. Because part
28:11
Speaker 1: of this is, yeah, they're all white women from what
28:14
Speaker 1: I understand. Samantha, who told me a story about how
28:17
Speaker 1: it's making dogs even sexualized. There's it's really interesting because
28:23
Speaker 1: you can't upload photos and it will try to translate
28:26
Speaker 1: that in this sometimes words, it sometimes doesn't. It is.
28:29
Speaker 1: It hasn't gotten there, y'all. I hadn't. It hasn't gotten
28:32
Speaker 1: to that point. Um. Even I think people who have
28:34
Speaker 1: used the AI filters have realized this is not me.
28:37
Speaker 1: This is kind of me, but not me that kind
28:39
Speaker 1: of version. And there was an experiment when uh we
28:43
Speaker 1: used they uploaded my dog, they being my partner, and
28:48
Speaker 1: it just did some prompts. None of it had to
28:50
Speaker 1: do with women, none of it had to do it,
28:51
Speaker 1: but it was a couple of victors actually gave her breasts,
28:54
Speaker 1: gave her a shapely look, and she she was humanized,
28:57
Speaker 1: which was a nightmare, y'all. I'd never want to see
28:59
Speaker 1: those images. Why did it have to happen? But it
29:01
Speaker 1: was interesting that that was a prompt that that was
29:04
Speaker 1: I think at one point it was um something about
29:06
Speaker 1: like sophisticated dog essentially because my dog is very sophisticated
29:10
Speaker 1: if you were one course, so the AI must be
29:13
Speaker 1: as well. But it kind of like one was really funny.
29:16
Speaker 1: It looked like it was her scruff around her neck
29:20
Speaker 1: looked like the royal old school British where so she
29:25
Speaker 1: kind of looked like a British royalty at one point.
29:27
Speaker 1: The male version, I guess and then others was just
29:30
Speaker 1: literally feminine human features on her because of that word
29:34
Speaker 1: sophisticated or any of these things, maybe like just automatically
29:38
Speaker 1: made her to have breasts, which was like why right, Yeah,
29:43
Speaker 1: I mean, there's a lot of stuff there we could unpack,
29:48
Speaker 1: but it just seems like if there's an an opportunity
29:51
Speaker 1: to sexualize something that's happened, you not to be da
29:57
Speaker 1: how dare you? Um? I I had to find out
30:00
Speaker 1: more information about this. We'll probably come back to it
30:03
Speaker 1: in the future. But the first AI model, which is
30:07
Speaker 1: is a thing, was a black woman named Shoe Doo
30:10
Speaker 1: and here's a quote from the outlet. Within the first
30:12
Speaker 1: two years of her career, she was featured in Vogue,
30:15
Speaker 1: hype Beast, v Magazine, and w w D, fronted campaigns
30:19
Speaker 1: for Balmain and Ellis Grace the red carpet at Bath
30:22
Speaker 1: to nineteen Awards, wearing a bespoke gown by Savorski, released
30:26
Speaker 1: her own record and was named one of the most
30:28
Speaker 1: influential people on the Internet by time. However, her creator
30:33
Speaker 1: is a white man who is making money off of this,
30:36
Speaker 1: and in the words of Twitter user Vanessa, I d
30:39
Speaker 1: that's what you're enabling Lindsa to do when you give
30:40
Speaker 1: them your likeness. The more images you give it, the
30:43
Speaker 1: smarter and more life like it becomes. In addition to
30:45
Speaker 1: deep fakes and putting your face somewhere it's never been,
30:48
Speaker 1: or saying something you never say, AI is also like
30:52
Speaker 1: black face. Yeah, because I thought I thought I was
30:58
Speaker 1: going to find more about this, because I remember that
31:01
Speaker 1: kind of being a big conversation at one point of
31:04
Speaker 1: like these creators you're following on social media aren't real
31:07
Speaker 1: and who's running them? But I couldn't actually find that
31:10
Speaker 1: much about it. So again, listeners, if you know, uh,
31:13
Speaker 1: please the point is in the right direction. But yeah,
31:17
Speaker 1: I mean, we're going to talk about deep fixs more
31:18
Speaker 1: in a second. But it is very scary that the
31:20
Speaker 1: idea that you could be somebody could be using your
31:24
Speaker 1: likeness to say something you wouldn't want to say and
31:28
Speaker 1: to make money without your permission. And then there's the
31:46
Speaker 1: TikTok as I've kind of already mentioned. Uh, there's a
31:48
Speaker 1: popular AI filter on TikTok right now Ai manga um
31:52
Speaker 1: and people are tricking it to generate images of women
31:55
Speaker 1: with big boobs but holding up items like cups or
31:58
Speaker 1: hats over their chests, which but why yeah, so basically
32:02
Speaker 1: they're like putting yeah, hats over their chests, and then
32:06
Speaker 1: it tricks the filter into making like big brists. I
32:09
Speaker 1: guess right. I haven't gone to that side of TikTok.
32:13
Speaker 1: I've seen the ghost TikTok's with the AI like if
32:17
Speaker 1: the image pops up and that's that's that, and then
32:19
Speaker 1: the like the dog are they human? Or the animals?
32:22
Speaker 1: Are they human? And are they because like sometimes when
32:24
Speaker 1: you take the main guys thing on animals, they appear
32:28
Speaker 1: as human and so their skin walkers is what they say.
32:34
Speaker 1: My dog is not if you're wondering, Peaches is not
32:36
Speaker 1: a skin walker? I see. But then yeah, let's talk
32:42
Speaker 1: about Revenge Born, which you know, um as ai has exploded.
32:46
Speaker 1: Moderators and guides have scrambled to keep up with it,
32:50
Speaker 1: especially in terms of banning harmful content, and this is complicated,
32:54
Speaker 1: to say the least. Sometimes are outright band others are debated,
32:59
Speaker 1: and of course this involved individual biases and can vary
33:02
Speaker 1: around the world. Uh. Some platforms have rules against generating
33:06
Speaker 1: images of celebrities. Uh for one instance. You know, which
33:10
Speaker 1: is interesting because I know recently something came up. This
33:14
Speaker 1: is the things that I know because my partners really
33:16
Speaker 1: into it. The word black in Spanish has actually been banned,
33:21
Speaker 1: and a lot of Spanish and Latino people are like,
33:25
Speaker 1: you can't do that to us. At the same time like,
33:27
Speaker 1: We're so sorry. We're the company based out of the
33:29
Speaker 1: US and racism is a big thing. We really can't
33:33
Speaker 1: do this. I apologize that you can't us just Spanish words,
33:36
Speaker 1: but can you do this? And that is complicated. You
33:38
Speaker 1: do have the moment of like, oh, how does that go? Yeah? Yeah, yeah,
33:44
Speaker 1: especially as we said, things around the world do vary
33:48
Speaker 1: and it is complicated for sure. Um. And this is
33:50
Speaker 1: one of the things I was really worried about with
33:52
Speaker 1: a I was revenge porn. It was one of the
33:53
Speaker 1: first things I was like, oh no, I'm sure. Um.
33:56
Speaker 1: There hasn't been too much written about it yet, but
34:00
Speaker 1: one article over on Wired wrote about how Lindsa generated nudes.
34:06
Speaker 1: The author wrote about how Lensa generated nudes from her childhood.
34:08
Speaker 1: Despite having the rules no nudes and no kids adults only. UM.
34:13
Speaker 1: Women have reported uploading modest images of themselves only to
34:18
Speaker 1: get nudes are cartoonishly sexualized images in return. Um and
34:22
Speaker 1: the article warns this is the danger of how much
34:24
Speaker 1: we sexualize women. UM. Here's a quote. I, for example,
34:28
Speaker 1: received several fully nude results despite uploading only headshots. The
34:33
Speaker 1: sexualization was also often racialized. Nearly a dozen women of
34:36
Speaker 1: color told me that LINDSA whitened their skin and anglicized
34:40
Speaker 1: their features, and LINDSA widely seeks to quote beautify images
34:45
Speaker 1: of women, meaning whitened and sexualized. Many reported feeling violated
34:50
Speaker 1: after seeing these images right um and then the author continued,
34:55
Speaker 1: I'm used to feeling violated by the Internet, having been
34:58
Speaker 1: the target of several raspient campaigns. I've seen my image manipulated, distorted,
35:04
Speaker 1: and distributed without my consent on multiple occasions. Because I
35:08
Speaker 1: am not face out as a sex worker, the novelty
35:12
Speaker 1: of hunting down and circulating my likeness is for some
35:14
Speaker 1: a sport. Because sex workers are not perceived by the
35:18
Speaker 1: general public as human or deserving of basic rights, this
35:22
Speaker 1: behavior is celebrated rather than condemned, and because sex workers
35:26
Speaker 1: is so often presumed to be a moral failing rather
35:29
Speaker 1: than a job, our dehumanization is redundant. I've logged onto
35:32
Speaker 1: Twitter to see my face photoshopped into other women's bodies,
35:35
Speaker 1: pictures of myself and unclothed clients and session and once
35:38
Speaker 1: even as word search comprised of my face, personal details
35:42
Speaker 1: and research interest. I'm not afraid of LINDSA. Yeah. Basically
35:46
Speaker 1: she was like, I've seen this before, Lindsa, yeah, and
35:50
Speaker 1: I'm going to see how bad you are, because she
35:52
Speaker 1: was doing tests to see like what would happen and
35:55
Speaker 1: the images were horrifying, a lot of them with a
35:58
Speaker 1: child's face on clearly an adult nude body, right, And
36:03
Speaker 1: again like I said that, some of the images that
36:05
Speaker 1: I've seen, that's just projected, and you're like, why, we
36:08
Speaker 1: know what your purposes? Why are you doing this? And
36:10
Speaker 1: it's obvious what it is to sexualize young women, and
36:13
Speaker 1: without any moderation or oversight, the potential for AI generated
36:17
Speaker 1: violence inherent in quote magic avatars is staggering. LINDSA doesn't
36:22
Speaker 1: seem to enforce its policies prohibiting nudity and miners, and
36:26
Speaker 1: it doesn't have any policies at all, stipulating that users
36:30
Speaker 1: can only upload images of themselves. It's only relevant specifications
36:34
Speaker 1: are quote same person on all photos and uh no
36:38
Speaker 1: other people on the photo. And like most other tech innovations,
36:43
Speaker 1: lendsa's misusual most severely harm those already at risk, children,
36:48
Speaker 1: women of color, and sex workers. Yes, so that was
36:51
Speaker 1: another quote from the article. Um. The author also points
36:54
Speaker 1: out how this could impact the sex work industry, not
36:57
Speaker 1: just the art industry, and sex workers incurring blame for
37:02
Speaker 1: quote teaching the AI by posting adult content when it
37:06
Speaker 1: was never intended to be used that way. A lot
37:09
Speaker 1: of platforms like mid Journey do you have a PG
37:11
Speaker 1: thirteen rating? But it's it's it's similar to what we
37:13
Speaker 1: talked about in YouTube, where people are still finding ways
37:16
Speaker 1: around this stuff. Right well, tictok is supposed to be like,
37:20
Speaker 1: I think twelve and older or something or thirteen and older,
37:24
Speaker 1: and it ends up being younger kids and you're just
37:26
Speaker 1: trying to figure out why, right right, right? Well, speaking
37:29
Speaker 1: of it's time to talk about Lobe, who's the creepy
37:37
Speaker 1: AI woman who is haunting the Internet and our dreams.
37:40
Speaker 1: It was the original impetus for this episode because I
37:42
Speaker 1: hadn't heard of this. Yes you bought it for my attention,
37:46
Speaker 1: and I actually was thinking of something totally different. We
37:50
Speaker 1: were on tunipment pages like no, go look though, go look, yes, Yes,
37:55
Speaker 1: welcome for the nightmare. Yeah, thank you, thank you. First
37:59
Speaker 1: introduced to the Internet on September six, this text image
38:04
Speaker 1: AI generated woman has been haunting the internet. Um. She
38:07
Speaker 1: has been called everything from the first cryptod of latent
38:10
Speaker 1: space to a demon to a queer icon. I was
38:14
Speaker 1: gonna try to describe her, but honestly, she appears in
38:16
Speaker 1: so much stuff. I would say generally she's like a
38:19
Speaker 1: creepy older woman. She's generally creepy. We'll talk about it
38:23
Speaker 1: more in a second. Not always creepy older woman with
38:26
Speaker 1: brown hair. She's white, and she's got dark eyes. Uh.
38:30
Speaker 1: Distorted face, distorted face usually um hellobe was created in
38:37
Speaker 1: April by Twitter users super composite, who detailed the whole
38:41
Speaker 1: process in a Twitter thread. The super short version is
38:45
Speaker 1: that this user put in a negative prompt brand O
38:49
Speaker 1: on an AI platform, meaning the AI generates the opposite
38:53
Speaker 1: of the prompt, and it returned an image of a
38:55
Speaker 1: skyline with the letters digita, P and T I c
38:59
Speaker 1: at um. The user put that in as a negative prompt,
39:04
Speaker 1: so that phrase plus skyline logo, and it returned Lobe
39:09
Speaker 1: because the text. We call her that because their text
39:13
Speaker 1: seems to appear in one of the images. And again
39:16
Speaker 1: we're not experts at this. I hope we haven't like
39:19
Speaker 1: totally butchered it. But it's a really interesting process because
39:21
Speaker 1: you're essentially asking for the opposite of something, and opposites
39:25
Speaker 1: can be interpreted in all kinds of ways. But it's
39:28
Speaker 1: also interesting because you might guess it would return Brando
39:32
Speaker 1: the original negative prompt, but it didn't. End of noe.
39:35
Speaker 1: Negative prompts don't always return the same images, so it's
39:39
Speaker 1: puzzling that Lobe consistently returns this woman. One explanation for
39:45
Speaker 1: this is the fact that the opposite of logos, or
39:49
Speaker 1: at the very least, because it's kind of like the
39:52
Speaker 1: way they explained it, it's like imagine a brain like
39:54
Speaker 1: a map, a neural map, and it's not necessarily like
40:00
Speaker 1: the opposite is how you and I might think for opposite,
40:04
Speaker 1: it's the opposite is the thing that's the farthest away
40:07
Speaker 1: from whatever you put in. So the opposite for an
40:11
Speaker 1: AI of logos might be something that is scary gory imagery,
40:19
Speaker 1: and these images, to be clear, don't previously exist. They
40:22
Speaker 1: are being created in response to the prompt, though now
40:26
Speaker 1: it could be in something of a feedback loop because
40:28
Speaker 1: people are feeding images of Lobe into AI platforms, and
40:32
Speaker 1: Supercomposite has confirmed Lobe does exist on multiple platforms. They
40:37
Speaker 1: haven't said which one was the original one they used
40:39
Speaker 1: because they didn't want people to, I don't know, mess up,
40:43
Speaker 1: like I'll flood to it and mess it up. It
40:48
Speaker 1: feels like the newer slender Man to me. But anyway,
40:52
Speaker 1: so Supercomposite didn't stop there feeding the AI that image
40:55
Speaker 1: and instructing it to create something new using Lobe as
40:59
Speaker 1: the base image. The results were unsettling, to say the least,
41:03
Speaker 1: including images of decapitation and gory no is adults and
41:07
Speaker 1: children alike. Super Composite said low quote haunts everything she touches,
41:12
Speaker 1: and quote, I guess because she's very far away from
41:15
Speaker 1: a lot of concepts and so it's hard to get
41:17
Speaker 1: out of her little spooky area in latent space. The
41:21
Speaker 1: cultural question of why the data put this woman way
41:23
Speaker 1: out there at the edge of the latin space near
41:26
Speaker 1: gory horror imagery is another thing to think about. Um,
41:30
Speaker 1: it's possible there's more to the story. Super Composite isn't telling,
41:33
Speaker 1: but the story certainly grabbed the public imagination. Me too, Yes,
41:38
Speaker 1: it did, it did um. I mean I when I
41:42
Speaker 1: was researching this, I was looking at some of the
41:44
Speaker 1: pictures like, yeah, what is this? You're welcome, thank you,
41:49
Speaker 1: thank you. Some have questioned why our culture, as super
41:52
Speaker 1: Composite was saying, and by extension, AI associate older women
41:55
Speaker 1: with horror, and you know that highlights some agis um
41:58
Speaker 1: ablis um sexism. Super Composite set of the people's reactions
42:02
Speaker 1: to the first images of Lobe because the first images
42:04
Speaker 1: weren't that creepy um or at all creepy. They kind
42:08
Speaker 1: of went on this process to find out like what's
42:11
Speaker 1: going on in this latent space, but it took a
42:13
Speaker 1: minute before they became like the really creepy, gory ones. Right,
42:17
Speaker 1: So your composite said, clearly, it's a I made an association.
42:20
Speaker 1: It shouldn't have. I also think some people are being
42:22
Speaker 1: very stupid and making fun of how Lowe looks in
42:24
Speaker 1: the first pictures, like that's the horror show. That's not
42:27
Speaker 1: the point at all, and it bums me out. She
42:28
Speaker 1: looks like an average person to me, just really sad. Yeah,
42:31
Speaker 1: so you can go. You can follow the Twitter thread
42:33
Speaker 1: and see the first ones into what it eventually became.
42:39
Speaker 1: But that is interesting. Gosh, there's so much, so much
42:43
Speaker 1: to talk about here. Yeah, there are efforts to make
42:47
Speaker 1: AIM more inclusive, though there's a long way to go
42:49
Speaker 1: when it comes to pretty much every part of this world. UM,
42:54
Speaker 1: like the who's working on it, who's using it because
42:57
Speaker 1: it's not as many women using it, and these kind
43:01
Speaker 1: of things with what images get returned and generated. I
43:05
Speaker 1: did find an organization called Black Women in AI that
43:08
Speaker 1: has a podcast. I would love if anybody else knows
43:11
Speaker 1: of more things like that, because we talked about that
43:15
Speaker 1: with UM. Some of newer technologies where people are trying
43:18
Speaker 1: to like at the beginning, like VR, we talked about
43:20
Speaker 1: it like take steps so it is more inclusive. But
43:25
Speaker 1: we'll see, like like we've been saying this whole time,
43:28
Speaker 1: this is it's growing so rapidly. M hmmm mm hmm.
43:34
Speaker 1: Well we'll see maybe one day chat GPT over places
43:46
Speaker 1: right for us, we'll voice it. I mean he went
43:49
Speaker 1: to parents. I like to write my own scripts chat GPTM. Wow.
43:57
Speaker 1: This has been quite a roller coaster, but listeners, If
44:02
Speaker 1: you have any experience with this, any thoughts, any images
44:06
Speaker 1: you'd like to share, uh, any resources you'd like to share,
44:09
Speaker 1: We would love to hear from you. You can emails
44:11
Speaker 1: at Stephanie and Mom stuff at iHeart media dot com.
44:13
Speaker 1: You can find us on Twitter at most podcast, or
44:15
Speaker 1: on Instagram and TikTok at Stuff I've Never Told You. Thanks.
44:18
Speaker 1: It's always to our super producer, Christina. Thank you, Christina, Yes,
44:21
Speaker 1: and thanks to you for listening. Stuff Whenever Told You
44:24
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44:26
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44:28
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