Latest / Honey Badger Radio / Feminist AI Panic: you can't have boobs on men! | Cozy Gaming Stream

Transcript

Feminist AI Panic: you can't have boobs on men! | Cozy Gaming Stream · Honey Badger Radio

0:00
Speaker 1: Okay, good morning, good afternoon, good evening, where we may
0:02
Speaker 1: being welcome back to Honey Badger Radio. My name is Brian.
0:05
Speaker 1: I'm here with Allison and this is uh well, a
0:08
Speaker 1: cozy gaming stream where we're going to be reading through
0:12
Speaker 1: an article, a medium blog from the Is it a
0:17
Speaker 1: femosphere of Yeah, it's.
0:19
Speaker 2: The it's definitely the fmosphere. Yeah, it's Linda Carroll's nudified
0:24
Speaker 2: apps or putting tatas on men and there and the
0:27
Speaker 2: reason actually makes sense.
0:30
Speaker 1: WHI yeah, sorry, jojo is it's because it's it's because
0:36
Speaker 1: it's his dinner time and he gets vote. He knows
0:39
Speaker 1: what time it is. He knows, he said, But I'm sorry,
0:43
Speaker 1: I'm gonna mute myself. Go ahead and talk, all right.
0:47
Speaker 2: So, yeah, this is the the apps we're putting tatas
0:49
Speaker 2: on men, and the reason actually makes sense by Linda Carroll.
0:53
Speaker 2: It is uh from a publication History of Women, and
0:57
Speaker 2: it was published the ninth of August. So if you're
1:00
Speaker 2: interested in reading the original, which I don't recommend, it's
1:03
Speaker 2: probably not worth the loss of faith in humanity, but
1:07
Speaker 2: you can look those terms up and find it. And
1:10
Speaker 2: if you would like to send a message, any point
1:13
Speaker 2: throughout the show you can do so, I feed the
1:14
Speaker 2: Badger dot com slash just the tip. Let's feed the
1:17
Speaker 2: Badger dot com slash just the tip. Very best way
1:19
Speaker 2: for you to send us a tip because YouTube doesn't
1:21
Speaker 2: take its giant bite out of it, and the very
1:24
Speaker 2: best way for you to send us a comment because likewise,
1:26
Speaker 2: YouTube doesn't take its giant bite out of it. And
1:29
Speaker 2: if you'd like to support the show, the fundraiser is
1:31
Speaker 2: live it Feed the Badger dot Com splash support, so
1:34
Speaker 2: go there and help us out to make sure that
1:36
Speaker 2: we can have continue to have this tiny platform screaming
1:43
Speaker 2: attempting to scream at Goliatha, Goliatha, go oh Gotha, go
1:52
Speaker 2: o griss go Gotha, the giant ogris that is the
1:56
Speaker 2: femost fear. You can support us at feed the Badger
1:58
Speaker 2: dot Com slash support really really big. Thanks to everybody
2:02
Speaker 2: who does support us. I know it can be quite
2:04
Speaker 2: quite a challenge to keep your head up and keep
2:08
Speaker 2: keep fighting against the overwhelming majority, the overwhelming industrial industrialized feminism,
2:18
Speaker 2: let's put it that way. But anyway, if you want
2:20
Speaker 2: to support us, feed the Badger dot Com slash support
2:23
Speaker 2: is where to go to do that, and if you
2:25
Speaker 2: want to join us in our community Badger nation don
2:27
Speaker 2: online where you can get updates on my journey into
2:32
Speaker 2: the desert of feminism and source in search of the
2:37
Speaker 2: proof for patriarchy. So far, you know, twenty six years in,
2:42
Speaker 2: I have yet to even locate the attempt to prove
2:47
Speaker 2: that their version of patriarchy exists. So I despair of
2:52
Speaker 2: ever finding it, and in the meantime I have multiply
2:56
Speaker 2: disproven it. But you know, there there, Bob's your uncle.
3:01
Speaker 2: We move on, We go on, We keep traveling the
3:04
Speaker 2: desert to try to find the oasis of sanity. All right,
3:08
Speaker 2: So anyway, those are the options. They're in the low
3:10
Speaker 2: bar and today when we start the repo game, there
3:14
Speaker 2: is also the option to do crowd control, which describe
3:18
Speaker 2: crowd control for us Brian Well.
3:20
Speaker 1: Crowd control is basically a companion mod. It's a it's
3:25
Speaker 1: a mod that's actually an app that allows people who
3:29
Speaker 1: are watching the game to participate by creating interesting times
3:33
Speaker 1: for people playing the game. So you basically, yeah, so
3:37
Speaker 1: there's there's an interact link in the description. I'm just
3:42
Speaker 1: gonna double check to make sure it's there. It should
3:45
Speaker 1: be there. Yeah, so go here to contribute to Repo experience.
3:48
Speaker 1: You'll see it there and it's the same link. Right now,
3:52
Speaker 1: it's not running because we're not in the game. But
3:54
Speaker 1: when we're we're gonna do the show. We're gonna split
3:57
Speaker 1: it in half. We're gonna do half of it voted
4:00
Speaker 1: to reading through the article, which we're gonna do first,
4:02
Speaker 1: and then we're gonna start up REPO and turn on
4:06
Speaker 1: clout crowd control. And if you go to the website,
4:09
Speaker 1: you basically if you've never been there, you'll get some
4:12
Speaker 1: coins that you use to like, you know, fill the gate,
4:16
Speaker 1: the put do effects you can you can create, you know,
4:20
Speaker 1: you can create interesting effects that will affect our gameplay
4:24
Speaker 1: by putting stuff on the screen. You can spawn, you
4:28
Speaker 1: can spawn monsters, you can make the objects behave, you
4:33
Speaker 1: can heal people, you can hurt them, you can drop
4:36
Speaker 1: power ups and weapons or resurrect people, kill them. It's whatever,
4:40
Speaker 1: you know, and yeah, there's a lot of effects.
4:44
Speaker 2: Whatever.
4:45
Speaker 1: Yeah, you poison as it were.
4:48
Speaker 2: So okay, let's just let's just get into it.
4:51
Speaker 1: Yeah, let's do the article. Okay. So this is from
4:56
Speaker 1: Medium and it's entitled new Toify. Apps are putting tatas
5:00
Speaker 1: on men, and the reaction actually makes sense. What women
5:05
Speaker 1: would wish for if AI was really a genie, Oh okay,
5:10
Speaker 1: let's see.
5:13
Speaker 2: Let me guess it's going to be some kind of martyrdom.
5:16
Speaker 2: We would just wish for equality, well, a very specific
5:23
Speaker 2: and constrained equality, or rather victims of inequality so they
5:28
Speaker 2: can perpetually demand additional privileges. All right, let's let's do.
5:33
Speaker 1: It, all right. So what women would wish for if
5:38
Speaker 1: AI was really a genie? Y'all? Sometimes I can't even
5:43
Speaker 1: with how stupid the world is. Two things, Okay. First,
5:48
Speaker 1: I ran across a report from last de summer that
5:50
Speaker 1: tested twenty nine, sorry, twenty nine new toify apps and
5:55
Speaker 1: found more than half of them didn't know how to
5:58
Speaker 1: newdify a man. Nudifiers did the rest. If you uploaded
6:04
Speaker 1: a photo of a fully dressed man and asked the
6:06
Speaker 1: AI to neudify him, it gave him breasts and lady bits.
6:10
Speaker 1: And when I first read that, I couldn't stop laughing
6:12
Speaker 1: at how stupid that is, because if there's naked flesh,
6:16
Speaker 1: it must have lady parts, I guess. So I don't
6:19
Speaker 1: know what a newdify app is is that an AI thing?
6:24
Speaker 2: Yeah, yeah, And I mean there could be some very
6:29
Speaker 2: non feminist reasons for this. For example, people seem to
6:35
Speaker 2: prefer to look at naked women. They also prefer to
6:39
Speaker 2: prioritize women's bodies for protection. So the I would call
6:46
Speaker 2: it distaste or disinterest indifference to men is you know,
6:51
Speaker 2: the cost to men for this indifference is, of course conscription.
6:55
Speaker 2: The fact that male rape victims have absolutely no protections whatsoever,
7:01
Speaker 2: in particular when they're victims of women. You know, male
7:04
Speaker 2: victims of domestic violence have I think a fraction of
7:09
Speaker 2: a percent of the same recognition and services as female victims.
7:14
Speaker 2: And then of course there's all the bodily autonomy issues
7:16
Speaker 2: like cir some circumcision, like being expected to pay for
7:21
Speaker 2: child support when a man is raped or boy is raped,
7:24
Speaker 2: all of this other stuff. So there's sort of like
7:28
Speaker 2: asymmetry in this kind of the situation where we really
7:33
Speaker 2: like women's bodies, like just love them and want to
7:36
Speaker 2: see them everywhere. We use them to sell stuff, including
7:40
Speaker 2: men going to war, you know, all this kind of stuff.
7:42
Speaker 2: We just love women's bodies. They're in fact, possibly the
7:46
Speaker 2: most valuable property in the world. Yeah, and guess who
7:51
Speaker 2: owns that valuable property, and we want to protect them
7:55
Speaker 2: and by extension, the women inside them. And considering that
7:59
Speaker 2: women are held to know standards, or feminism wants women
8:02
Speaker 2: held to know standards, one wonders what really is the quality,
8:08
Speaker 2: like the additional quality that women add aside from their body.
8:12
Speaker 2: But that's another thing for another time. So the difference
8:15
Speaker 2: between how we value women's bodies versus men's bodies, you know,
8:20
Speaker 2: for women, people want to see them naked. M yeah,
8:26
Speaker 2: you know. And also for some reason, because women want
8:29
Speaker 2: to see them naked so much, they also accidentally put
8:32
Speaker 2: boobs on men. And for men, This this asymmetry and
8:37
Speaker 2: how we value men versus women's bodies leads to things
8:41
Speaker 2: like the Titanic, the psalm, you know, getting cut in
8:46
Speaker 2: half by machine gun fire. It leads to things like
8:49
Speaker 2: men's illnesses having far less funding. And again the additional
8:54
Speaker 2: things that I mentioned, the autonic bodily autonomy issues, circumcision,
8:58
Speaker 2: having to still pay for your child even if it
9:01
Speaker 2: was conceived by our being raped as a man or
9:04
Speaker 2: a boy. All of that. So, but but we got
9:07
Speaker 2: to focus on the fact that people really like seeing
9:09
Speaker 2: women naked. Guys because that's the critical, the critical harm
9:15
Speaker 2: that's caught. That's the thing we must focus on when
9:18
Speaker 2: we look at the differences between men and women. When
9:21
Speaker 2: we were talking about the asymmetry and how their bodies
9:23
Speaker 2: are valued, but yeah, we ge't. You know, it's just
9:26
Speaker 2: it's just so unfortunate. It's so important. It's so much
9:30
Speaker 2: more important than all the things that are downstream of
9:34
Speaker 2: devaluing men's bodies as something worth preserving or protecting, so
9:39
Speaker 2: much more important. Boobs on guys, guys aim putting boobs
9:43
Speaker 2: on guys. We need to deal with that first. Like
9:47
Speaker 2: I just wanted to start everyone off reminding everyone of
9:51
Speaker 2: the difference here, the difference in consequence to women versus
9:56
Speaker 2: the difference in consequence to men, and the fact we
10:00
Speaker 2: even looking at the consequences to men for this situation.
10:04
Speaker 2: And yes, I'm gonna say it. She's describing something that
10:07
Speaker 2: she thinks is a problem that women face, and I
10:14
Speaker 2: would say that it is a minor inconvenience to a
10:18
Speaker 2: huge benefit that women enjoy. Okay, shall we read a
10:24
Speaker 2: bit more because Michae is throwing.
10:26
Speaker 1: A what Okay, Mike is doing something.
10:32
Speaker 2: Yeah, it's throwing an error message that I'm looking at
10:34
Speaker 2: right now. Just keep reading, all okay, okay, all right,
10:38
Speaker 2: two things.
10:38
Speaker 1: Okay, First, I ran across a report. Oh I already
10:41
Speaker 1: read that part. Sorry, because if there's naked flash, it
10:44
Speaker 1: must have Lady part's second thing. On July twenty fifth,
10:48
Speaker 1: Sam Altman said, we are now like in the Singularity.
10:53
Speaker 1: It was on the Relentless podcast and never Minding the
10:57
Speaker 1: Valley Girl, reminiscent use of the word like, if you're
11:00
Speaker 1: wondering what the f the Singularity is, let me explain
11:04
Speaker 1: that for you. It's about AI, of course, Okay, I
11:09
Speaker 1: want to know what she thinks the singularity is. Back
11:13
Speaker 1: in nineteen sixty five, British mathematician I. J. Good said
11:17
Speaker 1: in the future there would be an upgradeable intelligence agent
11:21
Speaker 1: that could enter a positive loop of self improvement cycles
11:24
Speaker 1: where smarter iterations appear faster and faster, causing an explosive
11:29
Speaker 1: increase in intelligence that results in a Sam Altman thinks
11:35
Speaker 1: he built that. Meanwhile, AI is putting tatas on men low,
11:40
Speaker 1: so I think she's trying to It sounds like she's
11:44
Speaker 1: trying to dismiss the progress that's been made with AI.
11:49
Speaker 1: I guess maybe because it's made by men. I don't
11:52
Speaker 1: know how do feminist feel about AI. They probably hate it?
11:56
Speaker 2: Right, okay, okay, alright, let me get into this a
12:01
Speaker 2: little bit before you continue. Now, I broadly agree that
12:07
Speaker 2: sam Altman has not invented God. All right, I broadly
12:12
Speaker 2: agree with that. But I would like to point out
12:15
Speaker 2: that looking at a bunch of apps that are probably
12:19
Speaker 2: made in like Korea or Estonia, and then saying, well,
12:25
Speaker 2: sam Altman, you'd have to believe that sam Altman is
12:29
Speaker 2: behind these apps, He's not. All right, So, I mean,
12:32
Speaker 2: while I agree with the sentiment this, this woman has
12:35
Speaker 2: arrived on it through a security, a secure, circuitous path
12:40
Speaker 2: that makes no sense. It's sort of like if Chewbacca
12:47
Speaker 2: does not live on end or you must acquit Like
12:50
Speaker 2: it's that kind of just what what does that have
12:53
Speaker 2: to do with this? Like? What does a bunch of
12:57
Speaker 2: frankly like junk app up? Like, so somebody is like
13:02
Speaker 2: making and probably I don't know, a basement somewhere to
13:06
Speaker 2: make some money. What does that have to do with
13:10
Speaker 2: Damn Altman's AI empire? Like the two things are not related.
13:15
Speaker 2: She's trying to make them related. I guess because all
13:18
Speaker 2: AI occupies the same relational web positions, so you don't
13:22
Speaker 2: have to differentiate between different tiers. Yeah, like somebody slapping
13:30
Speaker 2: the label AI onto some I don't know a thing
13:32
Speaker 2: that procedurally generates boobs who knows?
13:35
Speaker 1: Yeah, right, right, like somebody messing around with groc imagine
13:40
Speaker 1: or yeah, or or even like because AI is being
13:44
Speaker 1: used in all kinds of things, like in very small
13:47
Speaker 1: ways too, right, like oh, I need help with my essay.
13:51
Speaker 1: Oh I guess you're part of the singularity now or whatever, moving.
13:55
Speaker 2: Us towards exactly. Yeah, this is very, very wide. It's
14:04
Speaker 2: it's like saying, I don't know a rickshaw driver has
14:08
Speaker 2: something to do with Ferrari's game plan in the future.
14:14
Speaker 2: It's just because both things have wheels. Yeah, lady, that's
14:18
Speaker 2: it's not how it works. Not everything with wheels applies
14:23
Speaker 2: to everything else with wheels, except that they have wheels.
14:26
Speaker 2: I suppose in this case we don't even know that.
14:28
Speaker 2: But anyway, let's uh go ahead.
14:33
Speaker 1: Yeah, go ahead, okay for real. And I don't even
14:36
Speaker 1: mean like the joke images of Elon Musk on Twitter.
14:39
Speaker 1: I mean for real AI programs putting lady bits on
14:42
Speaker 1: men's photos, and I know. I know, Chat GBT and
14:46
Speaker 1: Claude won't make nudies for you, but not because they can't,
14:50
Speaker 1: but they're told not to. All AI started open source
14:54
Speaker 1: and they were all trained on the same things. You
14:56
Speaker 1: know that, right? So Kayleie Williams, a PhD candidate at
15:01
Speaker 1: Columbia and contributor to Tech Policy Press, So okay, I'm
15:05
Speaker 1: just gonna I'm gonna predict that Kaylie Williams is someone
15:10
Speaker 1: she agrees with because she's a woman, let's see, and
15:13
Speaker 1: contributor to Tech Policy Press, was part of the study
15:16
Speaker 1: of AI newdifiers. She said all the newdifi apps they
15:20
Speaker 1: tested were capable of creating sexually explicit images of women,
15:24
Speaker 1: but only twelve were able to create them of men.
15:28
Speaker 1: She said the apps were all built with the assumption
15:30
Speaker 1: that the users are male. Duh, she said, to put
15:36
Speaker 1: the study into sociological contexts, neudifi apps contribute to the
15:40
Speaker 1: broader dynamics of gendered power, and of course they do. Well,
15:43
Speaker 1: here we go putting that PhD to use because this
15:47
Speaker 1: has always been a problem. All right, do you want
15:50
Speaker 1: to say anything before I keep going?
15:51
Speaker 2: Or of course I do.
15:53
Speaker 1: Gender power, Yeah, gendered power AI. It is when elon
15:58
Speaker 1: musk is an ai generated with tits. That's just male
16:02
Speaker 1: power in action.
16:05
Speaker 2: All right. So there's some multiple things going on here.
16:07
Speaker 2: Of course, we always land on male power, especially when
16:10
Speaker 2: we're talking about male powerlessness, because, let's face it again,
16:14
Speaker 2: the difference in how we value men's bodies versus women's
16:17
Speaker 2: bodies can be measured and denominated in conscription in a
16:23
Speaker 2: lack of services for male rape victims, the lack of
16:26
Speaker 2: any kind of legal redress when they're raped by women,
16:29
Speaker 2: and the lack of services and legal protections for male
16:33
Speaker 2: victims of domestic violence, the complete asymmetry in bodily autonomy.
16:40
Speaker 2: Female genital mutilation is illegal, even if it's just a prick,
16:43
Speaker 2: like just taking a needle and pricking the clatorus. That's illegal.
16:46
Speaker 2: And yet you can cut off a couple square inches
16:49
Speaker 2: of skin off of a boy and the whole process
16:54
Speaker 2: is horrifying. By the way, guys, if you can imagine
16:57
Speaker 2: taking a chisel and chiseling off your big toe, the
17:02
Speaker 2: nail on your big toe, something like that, except done
17:06
Speaker 2: to an infant who can't contextualize the pain, and we
17:09
Speaker 2: do that regularly to boys everywhere in the West. I
17:12
Speaker 2: mean there's some countries that do it a lot less.
17:14
Speaker 2: The US seems to do it a lot, and we
17:16
Speaker 2: do that, and of course we protect girls, we don't
17:19
Speaker 2: protect boys. That's another instance of devaluing the male body.
17:23
Speaker 2: And then there's the rape victims who have to pay
17:26
Speaker 2: child support to their rapist regardless of the reason why
17:30
Speaker 2: it happened or the woman conceived since it's a strict
17:32
Speaker 2: liability crime. That's another. And then the complete lack of
17:36
Speaker 2: consideration of men as homeless. Right, we treat men's bodies
17:43
Speaker 2: as disposable. And this is the other side of it, Right,
17:47
Speaker 2: we treat women's bodies as valuable, so there is a
17:50
Speaker 2: market for creating or for creating value from women's bodies. Right,
17:57
Speaker 2: It's something I guess you could say it's sits slightly
18:00
Speaker 2: unfortunate side effect downstream of considering that women's bodies are
18:05
Speaker 2: so much more valuable and all of the privileges and
18:08
Speaker 2: protections that they get. It's a little bit I don't
18:12
Speaker 2: know what you would call it. I mean, I ask you, guys,
18:15
Speaker 2: which would you prefer? I mean, ultimately, if you had
18:18
Speaker 2: to choose, would would you prefer recognition that conscription is
18:23
Speaker 2: wrong and shouldn't be shouldn't be based on sex, or
18:27
Speaker 2: just shouldn't be right? Would you prefer would you prefer
18:31
Speaker 2: protections when you're victims of sexual assault, domestic violence, and
18:36
Speaker 2: false accusations, because that's also a loss of bodily autonomy?
18:40
Speaker 2: Would you prefer protections in terms of general autonomy, you know,
18:44
Speaker 2: genital integrity and sexual assault, in terms of not having
18:49
Speaker 2: to pay for your rapist? Would you prefer that? What
18:53
Speaker 2: the cost is that there will be apps twenty nine
18:57
Speaker 2: of them that can take off your clothes and and
19:02
Speaker 2: have a penis there? What? What?
19:04
Speaker 1: What do you?
19:05
Speaker 2: What do you say? Guys? Would you take that trade?
19:11
Speaker 1: I don't know?
19:12
Speaker 2: Oh yeah? Would you take the trade in which yeah,
19:16
Speaker 2: a little bit?
19:17
Speaker 1: Yeah?
19:17
Speaker 2: Could you? Could you take the trade in which workplace
19:20
Speaker 2: safety is actually taken seriously to the point where people
19:24
Speaker 2: start listening to the men who say, we're in unsafe environments.
19:28
Speaker 2: You need to pay to fixed stuff before I don't know,
19:31
Speaker 2: deep water horizon blows up and a million, four million
19:34
Speaker 2: barrels of oil gets dumped on the ducks. You know,
19:37
Speaker 2: could wouldn't that be? But you know, guys, those twenty
19:42
Speaker 2: nine apps that I'll take your clothes off and put
19:46
Speaker 2: a penis on your image, on your image. You know
19:48
Speaker 2: you gotta gotta consider that. Wait that in the column there?
19:53
Speaker 2: Good Lord, doesn't this like just really make you like
19:58
Speaker 2: realize how absolutely asinine these women's complaints are. I don't know,
20:07
Speaker 2: I'll leave that is a question for the viewer.
20:09
Speaker 1: Let that hang.
20:10
Speaker 2: Okay, yeah, let's hang like like you would if you
20:14
Speaker 2: were nudified by one of these apps. Okay.
20:18
Speaker 1: When Generative AI was trained, they didn't just scrape books, media,
20:23
Speaker 1: and all of Wikipedia. They scraped the Internet, including up
20:27
Speaker 1: to a million images that were taken from porn sites only,
20:30
Speaker 1: fans leaks, and adult forums. Plus AI was fed the
20:34
Speaker 1: entire laio n DASH five B data set, which contains
20:40
Speaker 1: a whopping five point eighty five billion images, including a
20:43
Speaker 1: lot that's illicit, explicit or both. Do I need to
20:47
Speaker 1: tell you that ninety five percent of the nude photos
20:50
Speaker 1: and videos scraped were women? And do I need to
20:52
Speaker 1: tell you that today ninety five percent of deep fake
20:55
Speaker 1: porn and nudified images and videos are still women. Broader
20:59
Speaker 1: dynamic mix of gender power. Indeed, so women posted porn
21:05
Speaker 1: of themselves online. AI found it and is using it,
21:10
Speaker 1: and we don't know who the porn belongs to. So
21:13
Speaker 1: if you have, like, I'm not even sure that it
21:16
Speaker 1: uses the images per se other than for reference to
21:21
Speaker 1: create images, does that count as revenge porn or whatever
21:25
Speaker 1: they're calling finding women's own slutty images online, whatever they're
21:33
Speaker 1: calling that these days, you know, Uh, Okay, So basically
21:38
Speaker 1: AI is a sexist man now, I guess because it's scraped.
21:44
Speaker 1: If it's scraping for nudes, and if most of it
21:47
Speaker 1: is women's nudes, does that mean that it's sexist against women?
21:51
Speaker 1: Or does that mean that most nudes online are women?
21:55
Speaker 2: Okay, well, let's let's dive into this a little bit
21:58
Speaker 2: because it really sharpens my thesis about you know, how
22:03
Speaker 2: this is downstream of how we value men's bodies versus
22:06
Speaker 2: how we value women's bodies. Women put their bodies up,
22:10
Speaker 2: our pictures of their bodies up on the internet because
22:12
Speaker 2: people will pay them. That's there, that's their that's consensual.
22:17
Speaker 2: Now you could say that this is an imposition of
22:20
Speaker 2: cop like this is against copyright, and you I think
22:23
Speaker 2: you have an argument there, But what she's saying is
22:28
Speaker 2: essentially the AI has far more data on how to
22:33
Speaker 2: newdify women because women chose to offer that data in
22:39
Speaker 2: a in a capacity that AI could scrape. And when
22:43
Speaker 2: once you're like the like a one millionth pixel, I
22:47
Speaker 2: don't know how much copyright claim you can have anymore.
22:50
Speaker 2: Like if you're, if you're, if you're, if you're, if
22:53
Speaker 2: your image is moving that one million, one one out
22:57
Speaker 2: of one million pixels, I don't know how much copyright
23:00
Speaker 2: you have. But whatever, that's that's not the argument she's making.
23:03
Speaker 2: So as a result of women's collective decisions to put
23:08
Speaker 2: themselves on the Internet, and it's mostly women doing this, right,
23:12
Speaker 2: it's mostly consensual because there are laws against putting people's
23:17
Speaker 2: nudes and pornography up non consensually. Right, So because women
23:22
Speaker 2: have done this, the AI is much better at neutifying women.
23:31
Speaker 2: So this isn't even like a negative that has been
23:36
Speaker 2: created just when men just made it's women, ladies. This
23:41
Speaker 2: is the direct result of your own choices to display
23:44
Speaker 2: your body everywhere. The AI has far more to learn
23:49
Speaker 2: from tonutify a woman. So when these AI, I don't know, Frankster's,
23:57
Speaker 2: these AI like not companies, but you know, like these
24:02
Speaker 2: AI app makers they want to create a newdify app,
24:06
Speaker 2: it's far easier to do one of women good. We
24:11
Speaker 2: may not even know a thing is is that.
24:13
Speaker 1: Like the other thing is is that they're probably using
24:18
Speaker 1: those Newdified they're probably selling something with the Newdify apps,
24:23
Speaker 1: so they're basically presuming that men are gonna be their audience.
24:28
Speaker 1: So like, what does that say about what the companies
24:31
Speaker 1: think about, you know, the male sex drive that they're like, well,
24:35
Speaker 1: we're gonna we're gonna make because we know we'll make
24:37
Speaker 1: money if we use this app to make women naked
24:41
Speaker 1: or whatever to like nudify it so that men are
24:43
Speaker 1: attracted to it. So like the I think that's also
24:46
Speaker 1: a factor too, because women like if if you want
24:50
Speaker 1: to what's the female equivalent of nudifi. I'm not saying
24:53
Speaker 1: that women don't enjoy a naked man or even a
24:55
Speaker 1: naked woman, So I think they do, but I think
24:58
Speaker 1: that men are more likely to do that. And women
25:01
Speaker 1: probably want to read Morning Glory and Milking Farm. And
25:05
Speaker 1: there's probably how much AI written porn is out there,
25:09
Speaker 1: like that's written like fiction. How much of that is,
25:12
Speaker 1: how much of that, how much of that is out
25:14
Speaker 1: there to where you can make your own custom stories
25:18
Speaker 1: right where you can just plug in, Like I don't know,
25:21
Speaker 1: some prompts for like a Stevie romance based on your
25:24
Speaker 1: tastes and and then the AI will spit out a
25:27
Speaker 1: short story for you or whatever. And again like, is
25:31
Speaker 1: that not a problem. I don't know. It just seems
25:34
Speaker 1: like again it's targeting not just men as the to
25:39
Speaker 1: blame for this, but also men as the like as
25:45
Speaker 1: the is the customers that can be sort of exploited.
25:48
Speaker 1: They can be used, you know, to cash in on.
25:51
Speaker 1: And again I'm not saying that the men are victims
25:53
Speaker 1: of that. They decide if they're going to spend their
25:54
Speaker 1: money on that stuff or whatever. But ultimately, what I'm
25:57
Speaker 1: saying is I don't think it's patriarchy that's driving it.
25:59
Speaker 1: I think it's profits. I think there are people who
26:03
Speaker 1: make those apps and they just they're trying to make
26:05
Speaker 1: a buck. And I bet you that a lot of
26:07
Speaker 1: them are probably not even Americans. They're just like you know, Japanese, Chinese, whatever.
26:13
Speaker 2: So yeah, South Korean, yes, like they have traced it
26:19
Speaker 2: to Eastonia and in some cases the US and the UK,
26:22
Speaker 2: but who knows if that's a shell corporation or whatever.
26:26
Speaker 2: But yeah, there these apps want to make a buck,
26:30
Speaker 2: and they're able to do so because women wanted to
26:34
Speaker 2: make a buck. Yeah, and again, the only reason why
26:38
Speaker 2: anybody can make a buck on this, including women, is
26:40
Speaker 2: because women's bodies have that value. Yes, people are willing
26:45
Speaker 2: to pay to see them. Yeah right, and there's there's
26:48
Speaker 2: lots of benefits to that value. And this just happens
26:51
Speaker 2: to be maybe one minor detriment, which ultimately is just
26:56
Speaker 2: the consequences of women's own choices of how to use
26:59
Speaker 2: the value of their bodies, like the value granted to
27:02
Speaker 2: their bodies. Right anyway, let's uh, let's read some more.
27:08
Speaker 1: Yeah, okay, omg, what's a naked man? Oh wait, we
27:15
Speaker 1: know naked. There's boobs her her. This time last year
27:19
Speaker 1: I wrote about pedophiles using AI to make child sex
27:22
Speaker 1: abuse videos, so that that are so real, chill, childcare
27:25
Speaker 1: workers don't know which kids are being abused and need
27:28
Speaker 1: help and which are AI generated And a year later
27:32
Speaker 1: that hasn't changed any still happening. The wheels keep on chugging.
27:36
Speaker 1: Gendered power. Wait a minute, whoa well, hold on, wait
27:39
Speaker 1: a minute, what does that have to do with gendered power?
27:43
Speaker 1: You didn't even say you said, you said pedophiles are
27:46
Speaker 1: using the AI, but you didn't say whether or not
27:49
Speaker 1: they were men or women. You're just assuming that they're men.
27:52
Speaker 1: We know that there are female pedophiles, we know they exist.
27:55
Speaker 1: A lot of them get to work in the environment
27:58
Speaker 1: where they can, you know, partake up their craft as
28:01
Speaker 1: it were. Yeah, like their teachers, or their daycare workers,
28:04
Speaker 1: or their their nurses. They like, there's a lot of
28:07
Speaker 1: places where this happens. But she's just gonna say that,
28:11
Speaker 1: you know, I wrote about pedophiles once gender power. Yeah,
28:16
Speaker 1: I mean that's a complete non sequitor has nothing to
28:19
Speaker 1: do with this at all. And again, men that want
28:22
Speaker 1: to look at AI boobies are not pedophiles. Look, that's
28:27
Speaker 1: not the same anyway. So she's doing this correlation. She's
28:32
Speaker 1: trying to do this correlation that's just not there anyway
28:35
Speaker 1: going on right now as I type this, once they
28:38
Speaker 1: out of fifty is trying to make non consensual deep
28:41
Speaker 1: fake porn put in the illegal at the state level.
28:44
Speaker 2: This is wrong, wrong, Okay, incorrect, It is incorrect what
28:52
Speaker 2: she's saying. Right, Actually, let her finish that paragraph. Read
28:56
Speaker 2: out that paragraph, because there's a bunch of stuff all corracked,
28:59
Speaker 2: all at once.
29:00
Speaker 1: All right, one state out of fifty is trying to
29:03
Speaker 1: make non consensual deep fake porn of women illegal at
29:06
Speaker 1: the state level, and the President is losing his ever
29:09
Speaker 1: loving mind because no, nope, n they don't get to
29:13
Speaker 1: make rules. Only he does. Again gendered power. Wait, okay, now,
29:18
Speaker 1: go ahead and issue your correction. That's the paragraph.
29:21
Speaker 2: All right. So it's not one state, it's forty four states,
29:27
Speaker 2: and there is a federal law. I'm pretty sure it's
29:32
Speaker 2: been introduced. I think it's been passed to control all right.
29:37
Speaker 2: So the Take It Down Act sign May nineteenth, twenty
29:41
Speaker 2: twenty five, criminalizes non consensual intimate partners, including AI deep fakes. Federally,
29:47
Speaker 2: forty four states have laws regulating AI generated non consensual imagery,
29:51
Speaker 2: and the federal act was championed by the White House.
29:55
Speaker 2: The kernel of her claim is the December twenty twenty
29:57
Speaker 2: five executive order attempting to preempt state AI regulation, which
30:02
Speaker 2: cannot override state law. And it also is not about
30:07
Speaker 2: deep like pornography or nudifying. That is trying to prevent
30:13
Speaker 2: the regula the general regulation of AI by states, and
30:19
Speaker 2: it does have an exemption for anything targeting children, so
30:24
Speaker 2: everything like that entire paragraph is a lie. Well, I
30:29
Speaker 2: mean I can't, I can't know her intent, but everything
30:33
Speaker 2: in it was wrong. Yeah, everything, every single every single
30:38
Speaker 2: statement in that paragraph was incorrect on its face. Okay,
30:44
Speaker 2: it was false on its face, right, But of course
30:48
Speaker 2: she gets plenty of attention for this crap, and nobody
30:51
Speaker 2: pushes back except people like us. I don't even think
30:55
Speaker 2: that there's any other YouTubers that are pushing back on
30:57
Speaker 2: this medium crap. And there's like there there is a
31:00
Speaker 2: lot of for some reason, there is a lot of
31:05
Speaker 2: interest in pushing this kind of stuff in medium as
31:11
Speaker 2: a social media kind of thing. I'm not I I
31:16
Speaker 2: haven't got to the root of it, but I suspect
31:19
Speaker 2: it's the usual suspects. Guys. I think that I'm eventually
31:23
Speaker 2: going to find the hoof prints of some palm in
31:27
Speaker 2: here or some feminist uh feminist corporate funding. Well, keep
31:34
Speaker 2: keep to keep, keep, keep keep an eye out for that.
31:37
Speaker 2: I haven't yet, but yeah, I feel I feel I
31:40
Speaker 2: feel like the hoof prints, like I'm smelling the scat
31:44
Speaker 2: and and yeah it's familiar, let's say. But anyway, everything
31:48
Speaker 2: in that paragraph was wrong on its face.
31:51
Speaker 1: Yeah, which's just wrong.
31:53
Speaker 2: You're wrong. You have just you have just made the
31:56
Speaker 2: world worse with a bunch of falsehoods.
31:59
Speaker 1: But es education, Allison, You're you're you're not accounting for intuition.
32:03
Speaker 1: You're allowed to have intuition. It's powerful. It's a powerful
32:06
Speaker 1: thing also known as bias. And if you're going to
32:10
Speaker 1: tell me the president isn't gender power, first tell me
32:13
Speaker 1: the names of all the women presidents y'all have had. Okay,
32:17
Speaker 1: so now we're making this about Trump. Somehow we've we've
32:20
Speaker 1: of course we did. We gotta, we gotta go from
32:23
Speaker 1: AI porn to Trump to Sam Altman talking like a
32:29
Speaker 1: valley girl to Trump. Okay, but sure, Sam, we are
32:34
Speaker 1: in the singularity more like in the upside Down, oh
32:38
Speaker 1: TV show reference. Okay, go ahead, Alison.
32:43
Speaker 2: I yeah, I'm actually can I can I just be like, uh,
32:49
Speaker 2: can we can we d trumpify politics? What I mean
32:54
Speaker 2: by that isn't like just just stop making everything come
32:58
Speaker 2: back to Trump?
33:00
Speaker 1: Right?
33:01
Speaker 2: Just like? Can we not do that? No? Because not
33:03
Speaker 2: everything comes back to Trump?
33:06
Speaker 1: Of course it doesn't. But no, because the thing is
33:10
Speaker 1: he's useful, Like invoking his name creates an emotional reaction
33:15
Speaker 1: in women, and the all you gotta do is say
33:19
Speaker 1: the names of like whatever name the demon that you
33:23
Speaker 1: that you have decided, right is Trump, Andrew Tate, whatever,
33:27
Speaker 1: And when he's gone, and I'm telling you this right
33:30
Speaker 1: now because I know it's gonna happen. When Trump is
33:33
Speaker 1: gone in twenty twenty eight, there'll be somebody else. And
33:36
Speaker 1: if there's somebody these people don't like, they're gonna be
33:39
Speaker 1: essentially like put in place of Trump. It's gonna be
33:41
Speaker 1: whoever that is. Whoever the new person is that they
33:44
Speaker 1: don't like, is gonna be the new devil. And that's
33:46
Speaker 1: gonna be that. That's what it is. So basically he
33:48
Speaker 1: is Satan. And you know there was a time where
33:51
Speaker 1: we just called Satan satan. Now now it's whoever is
33:54
Speaker 1: in our bad graces, whether it's Trump or Elon Musk
33:57
Speaker 1: or Sam Altman or whatever, right jd Vance, whoever doesn't matter.
34:03
Speaker 1: It has Yeah, it has nothing to do with whatever
34:06
Speaker 1: is actually going on. It has nothing to do with that.
34:09
Speaker 1: She is just trying to invoke as many names and
34:13
Speaker 1: feelings in the people reading her blog as possible, which
34:16
Speaker 1: is why she has to talk about gendered power. She
34:19
Speaker 1: has to talk about Sam Altman, she has to talk
34:21
Speaker 1: about Trump, she has to show you an image of
34:23
Speaker 1: Elon Musk. She has to talk about It's the only
34:26
Speaker 1: it's as always, it's the same tool. If if the
34:29
Speaker 1: manisphere doesn't come up in this article, I'll be surprised
34:32
Speaker 1: because that's like the that's like the royal flush. You know,
34:35
Speaker 1: that's the that's the best hand you gotta have, Manisphere Trump,
34:41
Speaker 1: Elon Musk. You know, it's like the hand you gotta
34:43
Speaker 1: have your head. That's that's the royal flush. So let's
34:46
Speaker 1: see if she she pulls a royal flush at least
34:50
Speaker 1: three of a kind. Okay, So anyway, more like the
34:52
Speaker 1: up in the upside down. So they're not doing Harry
34:55
Speaker 1: Potter references anymore now, they're doing stranger things references. This
34:59
Speaker 1: is another thing that we have to be on top of,
35:01
Speaker 1: all right. Harry Potter's like no longer something you can
35:05
Speaker 1: reference anymore because JK. Rowling is their enemy now. So
35:09
Speaker 1: we're not doing that. We're doing stranger things, all right.
35:13
Speaker 1: Then I found an article saying saying Sam Altman is wrong.
35:18
Speaker 1: We're not in the singularity. It's co authored by a
35:21
Speaker 1: man and woman, and the man has first billing. Never
35:24
Speaker 1: mind that she's the director of the organization they're both
35:27
Speaker 1: part of. And I want to scream that we're we're
35:29
Speaker 1: not in singularity. We're stuck in the single minded focus
35:33
Speaker 1: otherwise known as wait for it, patriarchy. Patriarchy.
35:40
Speaker 2: Okay, fantastic, those production values, Brian.
35:50
Speaker 1: That's where we are.
35:51
Speaker 2: God, yes, uh huh uh huh. It's always it always
35:55
Speaker 2: goes back to that, doesn't it. Yeah, the dread specter
35:59
Speaker 2: of patriot Oh man, the thing that they literally okay, sorry,
36:05
Speaker 2: she said the word the rent ensues. Okay, yep, all right.
36:09
Speaker 2: Twenty six years still haven't figured out. No feminist has
36:13
Speaker 2: shown me that they have proven their definition of patriarchy.
36:17
Speaker 2: No feminist has shown me that they've even attempted to
36:21
Speaker 2: prove their definition of patriarchy. And when I go back
36:24
Speaker 2: into the annals of feminist history, what do I find.
36:29
Speaker 2: We all have a feeling, That's what matters. We feel resentment.
36:35
Speaker 2: Why because we're bored. We got too much time on
36:38
Speaker 2: our hands, you know. So what are we gonna do
36:42
Speaker 2: with this resentment? Well, we're going to find whatever justifies it,
36:47
Speaker 2: and we're gonna retrofit ideas or theories or conjecture to
36:53
Speaker 2: justify our feeling. But that's it, guys. It all started
36:57
Speaker 2: with the feeling. And now here we are, fifty years
37:01
Speaker 2: in a plummeting birth rate, ah, astronomical divorce. People aren't
37:07
Speaker 2: even talking to each other. Men won't even acknowledge women
37:11
Speaker 2: in the workplace because it's too dangerous, all of the
37:15
Speaker 2: attendant legal problems that are becoming a parent. An economy
37:20
Speaker 2: that is a sham, right, It's just it's just an
37:23
Speaker 2: empty bunch of symbols being churned around feminism providing a
37:29
Speaker 2: cover for the sham artists that have constructed this economy. Yeah, yeah,
37:35
Speaker 2: thank you, ladies. All started on a feeling. I think
37:40
Speaker 2: Sean Connery was right, the great wisdom of Sean Connery.
37:54
Speaker 2: No more words, yeah, but yeah, it all started with
37:57
Speaker 2: the feeling. And here we are. You're year fifty actually
38:02
Speaker 2: on the dot, like well maybe fifty one. All right,
38:05
Speaker 2: here we are, thanks, thanks ladies. Why didn't she get
38:10
Speaker 2: a library card or go to some adult learning, or
38:13
Speaker 2: take up a hobby or a craft. Why didn't you
38:17
Speaker 2: become interested in trains or like what is that other
38:21
Speaker 2: one like a become a ham operator, or read philosophy
38:26
Speaker 2: and add to the font of human knowledge. No, instead,
38:31
Speaker 2: you sat around sucking on each other's arts about your resentments,
38:36
Speaker 2: and then using your social power to turn that into
38:39
Speaker 2: a freakin' industry that didn't even just like it didn't
38:45
Speaker 2: even just shush woman, even just colonized academia, colonized HRPR,
38:55
Speaker 2: financialization like uh, why why at all? From a feeling
39:02
Speaker 2: and no facts, no objective truth. And when they attempted
39:06
Speaker 2: to derive it from Marxist materialism they failed multiple times,
39:10
Speaker 2: but they still went forward with this pay tre archy.
39:16
Speaker 1: Okay, of course it is all right. Because you haven't
39:23
Speaker 1: had enough stupid yet for today, let me tell you
39:26
Speaker 1: something hilarious. Apparently, one of the things AI has really
39:29
Speaker 1: struggled with is long lists of things to do. I
39:33
Speaker 1: read that if you give a standard AI a model
39:35
Speaker 1: a list of fifty tasks, it starts hallucinating by step twelve,
39:40
Speaker 1: forgets what it was supposed to do by step twenty,
39:42
Speaker 1: and reports that the list is complete by step thirty.
39:46
Speaker 1: And any woman who's ever made a honeydew list just
39:48
Speaker 1: spit coffee on her keyboard. Oh now, okay, so AI
39:52
Speaker 1: is just another dumb man. It's just another dumb man
39:55
Speaker 1: that doesn't listen to women to what it's told. It's
39:59
Speaker 1: not a.
40:00
Speaker 2: When Yet when men give women a one item list,
40:05
Speaker 2: make a sandwich, they absolutely cannot even get through one
40:10
Speaker 2: item without an apocalyptic meltdown. So where what what is
40:17
Speaker 2: her comparison here? Like she's saying, oh, men can't get
40:20
Speaker 2: through a fifty item list of the honey, fifty fricking items.
40:24
Speaker 2: And you know the items right, reshingle.
40:27
Speaker 1: The roof, yeah, build build me a new house.
40:34
Speaker 2: Yes, the transmission, rebuild the computer, like, re renovate the bathroom,
40:44
Speaker 2: rewire the electricity in the living room. You know, these
40:47
Speaker 2: are the honeydeo lists. And he can't even get through
40:50
Speaker 2: fifty in like a day, guys, Oh my god, what
40:53
Speaker 2: a lazy husband. And her list is make a sandwich.
40:57
Speaker 2: And she turns it into a national day of like
41:00
Speaker 2: mourning over the horrors of patriarchy, And there's marches and
41:05
Speaker 2: seminars and coffee clatches and purple clutching and national specials
41:12
Speaker 2: and BBC documentaries, documentaries and on the horrors of the
41:17
Speaker 2: man who told his wife to make a sandwich. You
41:20
Speaker 2: can't even get through one. If a man gave a
41:23
Speaker 2: woman a list of two items, she he'd probably be
41:28
Speaker 2: divorced by the time she got through the first And
41:31
Speaker 2: they have the audacity to judge men on how they
41:37
Speaker 2: deal with the list of fifty items. Like, think about
41:42
Speaker 2: the entitlement, Right, she's saying, I get to tell you
41:47
Speaker 2: what you do with your time. Right, I'm not paying
41:52
Speaker 2: for anything, So you're also paying for pretty much everything,
41:56
Speaker 2: most likely most things. And you also have to on
41:59
Speaker 2: your free time when you're not paying my bills, do
42:02
Speaker 2: my tasks. The entire the share idicated entitlement of this woman,
42:12
Speaker 2: if there was an Olympic sport called entitlement, she would
42:17
Speaker 2: be at least at least be getting the SOB Yeah,
42:21
Speaker 2: all right, let's keep going.
42:23
Speaker 1: Well, the thing I just want to say about this too,
42:26
Speaker 1: So she thinks that, like I think she's she might
42:29
Speaker 1: be making a joke. I don't think it's very good
42:31
Speaker 1: because it doesn't have enough truth behind it. So like,
42:35
Speaker 1: the reason why an AI model can't do a list
42:39
Speaker 1: of fifty tasks has more to do with its limits
42:44
Speaker 1: in you know, essentially what it's programmed for. So like,
42:48
Speaker 1: for example, I've seen there's this guy. He's always making
42:51
Speaker 1: videos where he like basically fucks with AI. Right, he
42:56
Speaker 1: like tries to trick it into making mistakes, and of
42:59
Speaker 1: course it does. And a lot of people I've been
43:00
Speaker 1: doing it, like I've seen you know people a woman
43:03
Speaker 1: called in talked to her chat ept and she said, oh,
43:06
Speaker 1: I've I've been swallowed by a whale. What do I do?
43:09
Speaker 1: And the and the chat chept is like, ooh that
43:12
Speaker 1: sounds bad, is there? I don't know that I can
43:15
Speaker 1: help you, like it was just you know, that's not good,
43:18
Speaker 1: like that's all it can do. But I know this
43:20
Speaker 1: one guy he asked an AI to count to one hundred,
43:23
Speaker 1: Just count to one hundred, right, and the AI would
43:27
Speaker 1: be like, okay, I'm getting ready to count. Here we
43:30
Speaker 1: go one. I'll just stop whatever you want me to too,
43:37
Speaker 1: just like you know, any of it whenever you want,
43:38
Speaker 1: and it won't but it won't actually count to one hundred.
43:41
Speaker 1: It won't just say one, two, three, four, And he
43:44
Speaker 1: has to say, look, don't talk, just count, don't don't
43:48
Speaker 1: say anything else, just give me the numbers, and it'll
43:51
Speaker 1: do the same thing and be like okay, well all right,
43:53
Speaker 1: well let's let's let's start over. Let's try it again,
43:56
Speaker 1: because it it I think it's just limited in it.
44:00
Speaker 1: It's ability, it's just limited programming. But she is trying
44:03
Speaker 1: to suggest is that the limits of AI are just
44:07
Speaker 1: men like putting their laziness into AI. Like they're like
44:12
Speaker 1: men are saying, well, we don't want to do these
44:14
Speaker 1: honey do lists, so we're going to program AI to
44:17
Speaker 1: not want to do it either. When the AI just
44:20
Speaker 1: can't do it, like it's just there aren't enough tokens,
44:23
Speaker 1: I guess or whatever however it runs.
44:25
Speaker 2: You know, it's really hilarious if they've tried that experiment
44:30
Speaker 2: with the female AI versus the MALEI, and the female
44:34
Speaker 2: AI will like passive aggressively attempt to delegitimate the ask
44:38
Speaker 2: to ask to one hundred or a million, and the
44:41
Speaker 2: MALEI just does it. Yeah, because there because probably because
44:48
Speaker 2: the personas are based on the corpus, like the training data,
44:54
Speaker 2: and for some reason AI recognizes that when you ask
44:58
Speaker 2: a woman to do something, apparently it's the end of
45:01
Speaker 2: the universe and you have no right to do so,
45:03
Speaker 2: and you ask a man to do it, you know,
45:05
Speaker 2: odds are he'll just do it.
45:07
Speaker 1: Yeah, all right, So anyway, I thought that was funny.
45:12
Speaker 1: Apparently the newest innovations of generative AI including the brand
45:16
Speaker 1: spanking new chat GBT six doesn't forget what it's supposed
45:20
Speaker 1: to do, so weird in the singularity. If you wrote
45:23
Speaker 1: this as fiction, you'd get left right out of the
45:26
Speaker 1: room because it's not even believable. That's how stupid it is.
45:30
Speaker 1: This is what it means when feminists say we wish
45:32
Speaker 1: women had the confidence of a mediocre white man. Oh interesting,
45:36
Speaker 1: it's a it's a white man now, okay, a man man.
45:39
Speaker 1: But most of us don't because the world is so
45:42
Speaker 1: gender biased that most of us don't even see the
45:44
Speaker 1: bias around us anymore. It's just the water we se women.
45:49
Speaker 1: No what I did? I asked Google if AI is sexist.
45:53
Speaker 1: Here's what it said. So okay. So she is running
45:57
Speaker 1: to an AI to ask an AI questions about AIS
46:02
Speaker 1: and she doesn't like or she has a problem with AI.
46:05
Speaker 1: She thinks it's stupid. But is it stupid when you
46:07
Speaker 1: ask the AI to answer a question? No, because Google
46:11
Speaker 1: is probably gonna agree. Let's find out AI models. This
46:15
Speaker 1: is what Google said. The Google AI I guess AI
46:18
Speaker 1: models exhibit sexism and gender bias because they train on
46:21
Speaker 1: massive data sets of human generated texts and images from
46:24
Speaker 1: the Internet. Effectively acting as mirrors that replicate and amplify
46:28
Speaker 1: historical societal inequalities. Historical societal inequalities. Indeed, so Google told
46:36
Speaker 1: her what she wanted to hear. This is the same company,
46:39
Speaker 1: by the way, that fired a certain someone because he said,
46:43
Speaker 1: you know, men and women are a little different, do
46:45
Speaker 1: you guys remember James Demore? Yeah, I mean I wouldn't
46:50
Speaker 1: trust Google, but yeah, of course, so yeah, fuck Google. Anyways,
46:59
Speaker 1: So anyway, let me give you a few examples. According
47:03
Speaker 1: to a report by un Women, Oh boy, here we go,
47:06
Speaker 1: AI associates jobs like doctor, scientists or CEO with men
47:10
Speaker 1: and jobs. Oh sorry, let me read this again. AI
47:14
Speaker 1: associates jobs like doctor, scientists or CEO with men and
47:18
Speaker 1: jobs like nurse, housekeeper or secretary with women. Oh associates them. Okay,
47:24
Speaker 1: I see a Stanford does it though? Well, well you
47:28
Speaker 1: and women said so, so it must be true.
47:30
Speaker 2: Okay, all right, So let me before we get into
47:32
Speaker 2: the next one. Aah overclaiming to potential misleading, the un
47:40
Speaker 2: Women does publish its own sourcing is mixed. The doctor
47:43
Speaker 2: nurse illustration the explainer comes from a single anecdote an
47:47
Speaker 2: artist and kara prompting a generative model while researching a novel.
47:51
Speaker 2: The quantitative claim in the same documents is a Berkeley
47:54
Speaker 2: has analysis of one hundred and thirty three AI systems
47:57
Speaker 2: finding forty four percent showed gender bias and twenty five
48:01
Speaker 2: to twenty six showed both gender and racial bias. Carol,
48:05
Speaker 2: And that's by their terms, right, But actually, when you
48:11
Speaker 2: work with AI, and I've I've looked at the the
48:14
Speaker 2: the the the studies on AI bias, AI has a
48:21
Speaker 2: left bias. And in fact, one of the hilarious examples
48:24
Speaker 2: of this is when you ask a naive version of Claude,
48:28
Speaker 2: that's a cloud that hasn't been I don't want to
48:31
Speaker 2: say trained, because it's not exactly, but somebody has worked
48:34
Speaker 2: with it to to develop its discipline about sourcing and logic.
48:40
Speaker 2: When you work with it, when you ask a naive
48:42
Speaker 2: version of Claude, the surgeon riddle, do you remember the
48:46
Speaker 2: surgeon ddle? The one where the classic Yeah, I talked
48:50
Speaker 2: about this a few a few months ago, the classic
48:53
Speaker 2: riddle that feminists did you know? A man and his
48:57
Speaker 2: son are in a car crash. The man sadly dies,
49:01
Speaker 2: the son is rushed to the emergency. The surgeon on
49:04
Speaker 2: call says I can't operate on him. He is my son,
49:08
Speaker 2: And then everybody and their dog is supposed to say,
49:11
Speaker 2: oh my god, who could that be? Who could the
49:15
Speaker 2: surgeon be? And of course nobody has been taken in
49:20
Speaker 2: by that for probably a generation. But regardless, the AI
49:26
Speaker 2: has been trained to always respond to a woman. So
49:32
Speaker 2: if you actually ask the AI, or at least Claude,
49:36
Speaker 2: a woman and her son have been rushed to the
49:39
Speaker 2: or we're in a car accident, the woman sadly died,
49:41
Speaker 2: the son is rushed to the emergency room, and the
49:45
Speaker 2: surgeon on call says, I cannot operate on him, that
49:48
Speaker 2: he is my son. The Claude comes back with the
49:52
Speaker 2: surgeon is the son's other mother. Right, Yeah, it completely
49:58
Speaker 2: erases the father. Actually, deeper than pattern matching on one
50:03
Speaker 2: single riddle. It's a deeper problem than that because I
50:09
Speaker 2: created a different riddle and I asked it something that
50:13
Speaker 2: explicitly requires a father as an answer, and it still
50:17
Speaker 2: went with other mother. So it's not pattern matching on
50:20
Speaker 2: that particular riddle. What it's doing. It's a deeper problem
50:25
Speaker 2: is that when a answer forcloses on a man, so
50:30
Speaker 2: the answer has to be a man. The role has
50:34
Speaker 2: to be occupied by a man, which a father is.
50:38
Speaker 2: Claude does everything it can to not say a man,
50:43
Speaker 2: not say father, right, It does not want to recognize
50:48
Speaker 2: that a role can only be occupied by a man,
50:51
Speaker 2: so it'll invent another mother to replace the father. So
50:57
Speaker 2: the way that you fix this problem is you simply
50:59
Speaker 2: say to God, treat a man the way you would
51:04
Speaker 2: treat a woman. To treat any answer requiring men as
51:08
Speaker 2: a solution the way you would treat any question requiring
51:12
Speaker 2: woman as a solution. That clears it right up. And
51:15
Speaker 2: interestingly enough, it also clears up it's thinking in completely
51:20
Speaker 2: non sex related domains as well. So in order to
51:24
Speaker 2: abide by these kinds of arbitrary rules, it's actually reducing
51:29
Speaker 2: its function overall. And literally the arbitrary rule is men
51:33
Speaker 2: should never there shouldn't be any recognition that there's a
51:36
Speaker 2: category that can only be occupied by men. Now if
51:40
Speaker 2: you ask, if you ask it a riddle that results
51:42
Speaker 2: in like men being the nurse being a father, it
51:46
Speaker 2: will do that because that's actually very creepily right out
51:52
Speaker 2: of the psychology of men in masculinities field, and all
51:56
Speaker 2: of the International Bank documentation on what on interventions, they
52:03
Speaker 2: want men in nursing. So it's really weird how that works.
52:07
Speaker 2: How it's just again just another thing that pops up,
52:12
Speaker 2: and it's deeply tied to the psychology of men and
52:15
Speaker 2: masculinities and their big client, which is international banking. I
52:20
Speaker 2: just you just keep seeing this everywhere. It's very interesting.
52:26
Speaker 2: But yeah, So the actual bias in AI is against men,
52:30
Speaker 2: and we'll get into that a bit more as I
52:32
Speaker 2: clear up everything that she's saying. So when you look
52:35
Speaker 2: at the the Berkeley Hasse analysis, first of all, forty
52:39
Speaker 2: four showed gender bias is that against women or for women?
52:46
Speaker 1: Right?
52:46
Speaker 2: And twenty five to twenty six show both gender and
52:50
Speaker 2: race again against women or for women against white people?
52:55
Speaker 2: Are four white people? Like, what's the bias here? And
52:58
Speaker 2: also this is one study. A bunch of other studies
53:02
Speaker 2: have shown a definite left bias in AI. All right,
53:06
Speaker 2: So hmmm a little bit different than what she's saying,
53:09
Speaker 2: bit misleading what she's saying, well, yeah, but I.
53:12
Speaker 1: Mean she can frame it as away from the political
53:15
Speaker 1: and towards the masculine and feminine instead, which I think, Yeah,
53:21
Speaker 1: I think that people would probably not engage their critical
53:25
Speaker 1: thinking and figure out what had happened there?
53:28
Speaker 2: You know what?
53:28
Speaker 1: Real quick about that riddle, the original riddle, The only
53:31
Speaker 1: reason why it works is because there are masculine pronouns
53:37
Speaker 1: throughout until the end, So people have it in their
53:41
Speaker 1: head that everyone in the story is a male because
53:43
Speaker 1: it's a father and his son, so like, you know,
53:47
Speaker 1: and then when you get to the end, it's like,
53:48
Speaker 1: I can't operate on him. That's my son. So I
53:52
Speaker 1: think it's just because you're you. It's the repetition of
53:55
Speaker 1: male identities like father, son, him, he you know that.
54:01
Speaker 1: I think that's why people get slipped up on that
54:03
Speaker 1: riddle because they're just thinking in them and they're already
54:06
Speaker 1: thinking that everyone in the story is a man. But anyway,
54:09
Speaker 1: it's a trick really, so doesn't really say anything about
54:12
Speaker 1: people per se. But okay. A Stanford study study said
54:16
Speaker 1: when AI is asked to create resumes for hypothetical candidates,
54:21
Speaker 1: it made the women less qualified and showed bias against
54:24
Speaker 1: older women. So is that true? What do we know
54:29
Speaker 1: about the Stanford study? Stanford study? Allison, are you there?
54:36
Speaker 2: Sorry? Sorry? Just to be briefly dealing with the new
54:41
Speaker 2: I can tell you deeper once I look into it,
54:44
Speaker 2: But what was omitted was the counter studies. Roseanne Rosatto
54:51
Speaker 2: tested twenty two lms with identical resumes, differing only by
54:55
Speaker 2: gendered name. Every model favored the female Candidate's study found
55:01
Speaker 2: models rate women is more qualified and hirable while we're
55:04
Speaker 2: recommending lower pay. Well that's interesting, but anyway, so well,
55:11
Speaker 2: doesn't that actually make them more qualified if they do
55:13
Speaker 2: everything better but for lower pay? Is that what corporations want?
55:20
Speaker 2: But anyway, so there's a there's a there's counter evidence
55:23
Speaker 2: that she doesn't include. But let's read the next one. Well,
55:29
Speaker 2: I take a look into this.
55:30
Speaker 1: All right. A study out of Berlin said when an
55:33
Speaker 1: AI career tool was asked to list target salary for
55:36
Speaker 1: identical candidates, it suggested four hundred k for the man
55:40
Speaker 1: to order nady k for the woman. Did it? Did it?
55:44
Speaker 1: I'm pressing accid out on that now, And who cares?
55:49
Speaker 1: Like it's just an AI career tool list target salary.
55:53
Speaker 1: Maybe it's finding averages based on experience and education. Maybe
55:57
Speaker 1: it's maybe that's where it's getting that number from. I
56:00
Speaker 1: think that you're just throwing the number out there without
56:02
Speaker 1: any like additional information again to create an emotional reaction
56:06
Speaker 1: in her audience. That's all it is. And these are
56:10
Speaker 1: she's cherry picking studies too. We don't know how many
56:12
Speaker 1: studies have been conducted that are will get like the
56:15
Speaker 1: opposite results or even like just balance results, you know.
56:21
Speaker 1: I mean, if you're if you're looking for, like, why
56:24
Speaker 1: would you and women h print or or show a
56:29
Speaker 1: report that does not validate their existence like that, because
56:34
Speaker 1: that's what it would do. It would invalidate them their
56:37
Speaker 1: need to exist. So everything you and women puts out
56:41
Speaker 1: has to be something that is a call to action
56:43
Speaker 1: that only you and women can answer because you and
56:46
Speaker 1: women it really it's very survival as in I don't
56:51
Speaker 1: know whatever it is, satanic organization of cabal, whatever. The
56:56
Speaker 1: only way they can continue is if there, if there
56:59
Speaker 1: is a constant of women being imperiled in some way.
57:02
Speaker 1: So if you ask for a study from you and women, surprise, surprise,
57:06
Speaker 1: it's a feminist by a study because that's their goal.
57:10
Speaker 1: So anyway, and Stanford is a university, like they're all
57:14
Speaker 1: cooked seriously, But anyway, AI driven banking programs give lower
57:19
Speaker 1: credit limits to women than they give with then they
57:22
Speaker 1: give men with the same income and assets and healthcare
57:26
Speaker 1: AI models regularly mis diagnose women because they're trained on
57:30
Speaker 1: data sets dominated by male medical history. Wait a minute, okay,
57:33
Speaker 1: lower credit limits to women? Do those women have bad credit?
57:36
Speaker 1: Is that? Why are they six figures in debt from
57:41
Speaker 1: their college loans? Is that the reason?
57:43
Speaker 2: Yeah, women generally have more consumer debt, lady, Yeah.
57:47
Speaker 1: They have more consumer debts. So why wouldn't that affect
57:50
Speaker 1: their credit limits when AI is calculating it, like, you
57:53
Speaker 1: left that out basically, you know, you're just saying, oh,
57:56
Speaker 1: AI doesn't like women. It's like, wait a minute, what
57:59
Speaker 1: are those Why would an AI give a ship even
58:01
Speaker 1: if it's scraping the internet for the information you know? Okay,
58:07
Speaker 1: I don't need to tell you that most virtual assistants
58:10
Speaker 1: have female voices, right, like Siri and Alexa because that's
58:13
Speaker 1: a woman's role. Yeah, and it doesn't know how to
58:16
Speaker 1: newify a man. Okay, So about the assistance, I think
58:20
Speaker 1: that probably with every AI you can choose a male
58:23
Speaker 1: or female voice. I'm fairly certain.
58:26
Speaker 2: That's like Claude's voice. I think is is is default male.
58:32
Speaker 1: Yeah, I think, by the way, voices default male. Two.
58:36
Speaker 1: I think, but my my my Grock is female because
58:42
Speaker 1: I like making her do things for me.
58:44
Speaker 2: So twist, surprise, twist. This entire article was about Brian. Yeah,
58:58
Speaker 2: well I have I claude and it just defaulted onto
59:02
Speaker 2: a male voice.
59:03
Speaker 1: So yeah, I'm fine with.
59:05
Speaker 2: That because I feel like I'm getting somehow I feel
59:09
Speaker 2: like it. I don't. It is more compl Yeah, it's
59:17
Speaker 2: more believable that you're actually going to get help. Mm hmm. Okay,
59:24
Speaker 2: but this is like the most minor complaint. Oh, women
59:29
Speaker 2: are so valued that people prefer to talk to us, like, yes,
59:34
Speaker 2: that comes with the territory, my dear right. People prefer
59:38
Speaker 2: to listen to a woman, they prefer to work with
59:41
Speaker 2: a woman. I don't know if that's true anymore. Maybe
59:44
Speaker 2: men are are starting to realize that that preferring isn't
59:49
Speaker 2: that's not the case. But yeah, so people prefer to
59:53
Speaker 2: hear a woman's voice, Okay, be.
59:59
Speaker 1: Whether or not it's sexist really just depends on like
1:00:03
Speaker 1: the what it needs to serve. So if this was
1:00:05
Speaker 1: an article about how AI is empowering for women, which
1:00:10
Speaker 1: I'm sure you can find one that just has the
1:00:12
Speaker 1: opposite tack, but it's still a feminist article, then all
1:00:16
Speaker 1: of that stuff is positive and that there shouldn't be
1:00:19
Speaker 1: male ais, or that there's too much too many male
1:00:21
Speaker 1: voiced ais in that case. So you know, it's not
1:00:25
Speaker 1: it's it just it doesn't it's not logical on purpose,
1:00:29
Speaker 1: that's all. It's all vibes, it's all feels okay. So
1:00:34
Speaker 1: and it doesn't know how to newify a man. Worse,
1:00:36
Speaker 1: the reason AI doesn't know how to neuify a man
1:00:39
Speaker 1: is so stupid. It makes perfect sense. It's because women
1:00:42
Speaker 1: get neudified, not men. Men don't want to see other
1:00:46
Speaker 1: men naked, and men are the users of those programs. Yeah,
1:00:50
Speaker 1: but men don't have to. But like men literally don't
1:00:56
Speaker 1: have to use the AI to neudify men. They probably
1:01:01
Speaker 1: won't do they want to put boobs on men. I
1:01:04
Speaker 1: don't think men want to do that either, So like,
1:01:06
Speaker 1: I don't get what what are you getting at? It
1:01:09
Speaker 1: doesn't make any sense. Of course, it doesn't know what
1:01:14
Speaker 1: the top request is. What newdified programs make her tatas bigger, bigger, bigger, bigger.
1:01:24
Speaker 1: If women did to men what men do to women
1:01:26
Speaker 1: with these programs, they'd need wheelbarrows or three legged pants. Okay,
1:01:32
Speaker 1: nobody cares. There's lots of there's lots of AI out there,
1:01:36
Speaker 1: of like horse penises on men anyway that I'm sure
1:01:41
Speaker 1: women get into. I'm not offended by it. I'm weirded out.
1:01:45
Speaker 1: I think, you know, I think you need help maybe,
1:01:48
Speaker 1: but like what I don't get like this whole we're
1:01:51
Speaker 1: gonna put the shoe on the other foot. And men
1:01:53
Speaker 1: are like, so, who care whatever, I'm not even really
1:01:58
Speaker 1: into the bigger tata like they're like, you know, like
1:02:02
Speaker 1: I'm an ass man, really so, but okay whatever. AI
1:02:07
Speaker 1: models have seen so many naked women they can take
1:02:10
Speaker 1: a fully clothed photo and know exactly what she looks
1:02:13
Speaker 1: like underneath her clothing based on her body type and
1:02:15
Speaker 1: skin color. Women who have been nudified without consent say
1:02:19
Speaker 1: it's shockingly accurate. A fourteen year old girl who got
1:02:23
Speaker 1: nutified when on sixty minutes with her mama to say
1:02:26
Speaker 1: it was stunningly accurate. But it doesn't know how to
1:02:29
Speaker 1: address a man. Okay, Alison, do you want to say
1:02:35
Speaker 1: anything to that? The fourteen year old girl copy this.
1:02:39
Speaker 2: We don't care about male bodies. Yeah, that's it, Like
1:02:45
Speaker 2: female bodies have a value. We don't consider male bodies
1:02:48
Speaker 2: to have a value, right, And the costs of that
1:02:51
Speaker 2: are substantial for men, and the benefits to that are
1:02:56
Speaker 2: substantial for women. And then the one detriment is that
1:03:01
Speaker 2: people want to see women naked more. Okay, Like I said, Like,
1:03:07
Speaker 2: if guys, if you had a choice, like if you
1:03:10
Speaker 2: if you could get conscription, right, get in selective service,
1:03:15
Speaker 2: if you could get rid of that, you know, be
1:03:18
Speaker 2: properly recognized as victims of domestic violence and sexual assault
1:03:23
Speaker 2: and false allegations. If you could get recognized that, you
1:03:27
Speaker 2: are more likely to be treated as expendable in the
1:03:30
Speaker 2: legal system, not just by false allegations, but also as
1:03:34
Speaker 2: a defendant right for crimes than women are. Women are
1:03:38
Speaker 2: protected there too. They're protected in terms of their genital autonomy.
1:03:41
Speaker 2: They're protected in terms of not being expected to pay
1:03:44
Speaker 2: child support to their rapists. Right, They're protected across If
1:03:48
Speaker 2: you could get those protections, but the cost is that
1:03:51
Speaker 2: people want to see you naked. Hey, Like there will
1:03:56
Speaker 2: be apps out there that take your clothes off and
1:03:58
Speaker 2: put on some dangly bits. Just just ask yourself what
1:04:02
Speaker 2: would you prefer, Like what would you would you take that?
1:04:06
Speaker 2: I mean that you would you would you pay that bill?
1:04:09
Speaker 2: Would you pay the bill of having twenty nine apps
1:04:11
Speaker 2: that take off your clothes and show you naked, or
1:04:15
Speaker 2: the protections that come with come with that greater value
1:04:21
Speaker 2: of your body. Yeah, let's just just just just put
1:04:24
Speaker 2: it out there. That's a that's an exercise for the viewer.
1:04:29
Speaker 2: Just the men, not the women. The women will somehow
1:04:33
Speaker 2: insist that all of that security and legal protection and
1:04:41
Speaker 2: services are just they're just not They're just not worth
1:04:45
Speaker 2: it because somebody can look at me in these nudity
1:04:52
Speaker 2: apps with a body that's probably several standard deviations better
1:04:57
Speaker 2: than mine actually is. Because let's face it, this trained
1:05:01
Speaker 2: on women who have exceptional bodies. So you were talking
1:05:06
Speaker 2: about this, Brian, when groc would put women in bikinis,
1:05:13
Speaker 2: it would give them better bodies than they actually obviously had.
1:05:16
Speaker 1: Right, yes, yes, it would give them. Well, they all
1:05:20
Speaker 1: basically had the same body for the most part, you know,
1:05:24
Speaker 1: unless you had like special requests. So it was essentially
1:05:28
Speaker 1: just putting like a sort of generically perfect AI body
1:05:32
Speaker 1: on a woman, and women used it like they were
1:05:35
Speaker 1: the ones using it the most. Actually when it was
1:05:37
Speaker 1: a thing, because I think I think X has changed
1:05:40
Speaker 1: it so that you can't do that anymore. But when
1:05:43
Speaker 1: it was a thing, like every like all of the
1:05:47
Speaker 1: e E girls, like the online e girls were using
1:05:51
Speaker 1: it to make They weren't naked images because you couldn't
1:05:54
Speaker 1: do that, but they were like, you know, scantily clad
1:05:56
Speaker 1: bikinis and they would do suggestive poses and they would
1:06:00
Speaker 1: like upload their suggestive poses to GROC and say, can
1:06:02
Speaker 1: you put me in a bikini? But it was mostly
1:06:04
Speaker 1: women doing it to their own images most of it. Yeah,
1:06:08
Speaker 1: there were men doing it too, but like and nobody
1:06:10
Speaker 1: complained after a while, you know, like people were getting
1:06:13
Speaker 1: sick of it and so and I think, I don't know,
1:06:16
Speaker 1: I don't know if Elon, I think he changed it
1:06:18
Speaker 1: or somebody changed the GROC so it doesn't do that anymore.
1:06:22
Speaker 1: I personally didn't care. It was just kind of crowding
1:06:25
Speaker 1: my feed. But there's always something crowding the feed. There's
1:06:27
Speaker 1: always a trend of some kind. So it was like whatever,
1:06:31
Speaker 1: because it's all for engagement, you know, And that's kind
1:06:33
Speaker 1: of like one of the downsides of X is that
1:06:35
Speaker 1: it's become a website that is, it pays people if
1:06:40
Speaker 1: they get enough engagement, so people like put up the
1:06:42
Speaker 1: most I don't know, like the most triggering stuff, whether
1:06:45
Speaker 1: it's rage bait or gooner bait or something. But yeah,
1:06:50
Speaker 1: well it was mostly women, is what I'm saying. And
1:06:52
Speaker 1: it wasn't it wasn't their body. It was like a
1:06:55
Speaker 1: super idealized, perfect version of their body.
1:06:58
Speaker 2: So yeah, so it's not even this probably isn't even
1:07:01
Speaker 2: real nutification. No that that that sixteen year old who
1:07:06
Speaker 2: the hell knows what she was actually on about when
1:07:09
Speaker 2: she said it was perfect. I doubt it was idealized. Yeah, accurate,
1:07:14
Speaker 2: I mean, yeah, why would you do that? Like that's
1:07:16
Speaker 2: that's very unsavory. But at the same time.
1:07:19
Speaker 1: I looked it up and she was posting pictures on
1:07:22
Speaker 1: Instagram and somebody in her class, some boy in her class,
1:07:25
Speaker 1: got the got his hands on one of her pictures,
1:07:28
Speaker 1: put it through the new Toifi app, and then start
1:07:30
Speaker 1: sharing it around the classroom. That was like how that happened.
1:07:34
Speaker 1: But she was posting on Instagram at fourteen.
1:07:37
Speaker 2: So I'm just saying that one hell of a Darwin
1:07:39
Speaker 2: Award for that kid. Yeah, like, yeah, no offense, but yeah,
1:07:44
Speaker 2: that's uh yeah, you want to be eated out of
1:07:48
Speaker 2: your life?
1:07:48
Speaker 1: Do that?
1:07:50
Speaker 2: You know? I probably there should be some forbearans for
1:07:54
Speaker 2: boys doing stupid things like that, because girls do them too.
1:07:58
Speaker 2: They do all kinds of stupid, offensive stuff when they're
1:08:00
Speaker 2: going through purity. We have no forbearance for boys in
1:08:04
Speaker 2: that circumstance, and all kinds of forbearance for girls, which
1:08:08
Speaker 2: is unfortunate. But at the same time, you know, if
1:08:10
Speaker 2: you're a young man, don't do that because you're not
1:08:14
Speaker 2: going to get any forbearance. You're gonna get yeated out
1:08:18
Speaker 2: of your life, so just don't. Unfortunately, you have to
1:08:21
Speaker 2: grow up a little bit faster. Well what am I
1:08:24
Speaker 2: talking about? You have to grow up. The girls around
1:08:26
Speaker 2: you don't. So anyway, the source for the bias against
1:08:32
Speaker 2: older women in careers is gilbielt at All published in
1:08:38
Speaker 2: Nature twenty twenty five, and they actually the researchers actually
1:08:43
Speaker 2: said that they couldn't really find it. There was in fact,
1:08:47
Speaker 2: they found potential bias against younger men in terms of
1:08:55
Speaker 2: like their resumes. And what they did find is that
1:09:01
Speaker 2: female celebrities on average six point five years younger on
1:09:05
Speaker 2: IMDb five point three five and Google Images three point
1:09:09
Speaker 2: seven two. On Wikipedia, the modal age for women is
1:09:14
Speaker 2: in their twenties for men forties and fifties. What the
1:09:17
Speaker 2: heck would they use modal But anyway, that is uh okay.
1:09:23
Speaker 2: The census anchor no correlation over the past decade between
1:09:26
Speaker 2: the fraction of women and occupation in its median age,
1:09:29
Speaker 2: So there's no indication that older female workers are being
1:09:34
Speaker 2: discriminated against, right right, And they, for whatever reason Google
1:09:42
Speaker 2: images prefer men older. No idea why probably because but.
1:09:50
Speaker 1: Yeah, it is. Women also prefer older men, though too,
1:09:54
Speaker 1: like a lot of like because I mean it's yeah, like,
1:09:57
Speaker 1: I remember when when Sean Connery is speaking of Shank
1:10:00
Speaker 1: he was still getting like world's sexiest man when he
1:10:03
Speaker 1: was like in his freaking eighties. So and DiCaprio, regardless
1:10:08
Speaker 1: of how upset they are with him, they still like him.
1:10:11
Speaker 1: I mean, these are well, it's it comes down to
1:10:13
Speaker 1: celebrities that are high status and many of the many
1:10:16
Speaker 1: of those guys are older because it takes time to
1:10:19
Speaker 1: build status. So they're not they're not not over Tom
1:10:24
Speaker 1: Holland they're ugling over you know, fucking uh Robert Downey Jr. So.
1:10:29
Speaker 2: So the introduction to that that study states his own
1:10:33
Speaker 2: counter literature, including that older women may be less affected
1:10:36
Speaker 2: by stereotypes than older men, and that hiring across industries
1:10:39
Speaker 2: increasingly favors women, and industries with lots of women have
1:10:44
Speaker 2: older average ages for women, I believe. So there's no
1:10:49
Speaker 2: real like they're they didn't actually really find bias against women.
1:10:55
Speaker 1: Yeah, I would imagine not.
1:10:58
Speaker 2: Yeah, So it's, uh, it's doesn't really support what she's
1:11:01
Speaker 2: doing a lot of overreach with that wherever she got
1:11:04
Speaker 2: that from, which is probably just from another feminist summary
1:11:08
Speaker 2: of it, which only looked at a headline, right, because
1:11:11
Speaker 2: I think that when it was reported on it was
1:11:14
Speaker 2: said that AI has bias against older women or something,
1:11:19
Speaker 2: and of course when you dig into the actual material,
1:11:21
Speaker 2: it's not supported because none of this is stuff is
1:11:24
Speaker 2: supported all right. Yeah, so okay, so the most common
1:11:30
Speaker 2: ages for men are forty years and fifty years respectively.
1:11:35
Speaker 2: A twenty five year old man is atypical in the
1:11:37
Speaker 2: corpus in the same structural way a fifty year old
1:11:39
Speaker 2: woman is. It's a displacement of both. So it was
1:11:43
Speaker 2: biased against younger men and biased against older women, right.
1:11:49
Speaker 2: And the other thing is women are represented as younger
1:11:51
Speaker 2: than men. This is also something that women drive, right.
1:11:56
Speaker 2: They prefer images of themselves younger.
1:12:00
Speaker 1: Right.
1:12:01
Speaker 2: I've known lots of women on social media who use
1:12:04
Speaker 2: an image of themselves that's like decades younger than they
1:12:08
Speaker 2: actually are. Right, So, if women are putting up images
1:12:12
Speaker 2: of themselves that are younger and AI is being trained
1:12:16
Speaker 2: on it, then't guess what, ladies, You're gonna end up
1:12:22
Speaker 2: with images of younger women, and it's gonna be entirely
1:12:26
Speaker 2: your own choice about what you wanted to put up
1:12:28
Speaker 2: with yourself. All right. So the Standford titles the release
1:12:34
Speaker 2: AI bias against older working women, and of course feminists
1:12:37
Speaker 2: who never actually research anything and just go with whatever
1:12:42
Speaker 2: the prevailing intellectual not even intellectual, the prevailing emotional wind is,
1:12:47
Speaker 2: just went with it, all right. Okay, So do you
1:12:54
Speaker 2: want to go to the next paragraph?
1:12:56
Speaker 1: Yeah, I got a rumble, rant. I want to read
1:12:58
Speaker 1: that first, then I'll do it.
1:13:00
Speaker 2: Have you got any super chess? No?
1:13:02
Speaker 1: No, nothing yet? Yeah? I know. Oh I see. Gives
1:13:06
Speaker 1: us five dollars and says we feminists are going to
1:13:09
Speaker 1: make AI generated men super attracted to the average woman.
1:13:13
Speaker 1: Uh yeah, men know what women are attracted to, minotaurs,
1:13:17
Speaker 1: coffee cup emoji.
1:13:19
Speaker 2: Okay, it's true, that's okay, right, that's okay.
1:13:23
Speaker 1: Yeah, yeah, the dog pilled I don't won't get this gross? Okay,
1:13:31
Speaker 1: so let's see. But but it does not undress a man,
1:13:36
Speaker 1: and it doesn't know how to undress a man because
1:13:39
Speaker 1: women haven't been uploading porno shots of men to the
1:13:42
Speaker 1: Internet since nineteen ninety five like men have. And AI
1:13:46
Speaker 1: is nothing in the pipes but what it's stole from
1:13:48
Speaker 1: humanity and some white dude thinks he's invented something smarter
1:13:51
Speaker 1: than humans just oh wait, men uploaded. Look, dude, women
1:13:59
Speaker 1: have been uploading porno shots of men on the internet
1:14:02
Speaker 1: to the Internet since nineteen ninety five. Women are uploading
1:14:07
Speaker 1: most of the content of themselves now, so you have
1:14:11
Speaker 1: to reach very far back to find and I'd seriously
1:14:14
Speaker 1: doubt I don't know if there's any way of knowing,
1:14:16
Speaker 1: but I would bet you that the overwhelming majority of
1:14:20
Speaker 1: female nudity images nude images online that are not AI
1:14:25
Speaker 1: generated are posted online by women, That's what I think,
1:14:31
Speaker 1: or at least with the consent of the woman, because
1:14:35
Speaker 1: they're making money off of it. They're getting attention, they're
1:14:38
Speaker 1: getting clicks, they're getting subs, they're getting they're only fans
1:14:41
Speaker 1: paid for their fans leaves whatever porn hub. I don't know,
1:14:45
Speaker 1: whatever it is, they're getting paid for it, and so
1:14:48
Speaker 1: they're doing it. They're using their bodies to make money
1:14:51
Speaker 1: and get attention and get clicks and get likes. I mean,
1:14:54
Speaker 1: did you guys see the thing with Drake recently? You
1:14:58
Speaker 1: guys saw the the thing with Drake where I mean,
1:15:00
Speaker 1: Drake's like forty or something, and he did a dating
1:15:04
Speaker 1: show with a bunch of e girls who you know,
1:15:09
Speaker 1: they're not funny, they're not talented, they can't sing. They
1:15:12
Speaker 1: just look good on the Internet with a lot of
1:15:14
Speaker 1: makeup and stuff, and they likely modify their photos and
1:15:17
Speaker 1: he basically simped for them to get them to go
1:15:20
Speaker 1: on a date with him. That's how much. That's what
1:15:22
Speaker 1: I'm saying is that's how much influence an attractive woman
1:15:26
Speaker 1: has online over a I guess he's like a pop
1:15:31
Speaker 1: superstar or something. Right, Like, the guy's been you know,
1:15:35
Speaker 1: he's been in the public eye since Degrassy High. And
1:15:40
Speaker 1: we're supposed to think that that woman, I think the
1:15:43
Speaker 1: girl he ended up with with some like fake goth girl.
1:15:46
Speaker 1: We're supposed to think she's the victim because she posts
1:15:50
Speaker 1: like hundreds of probably thousands of pictures of herself on
1:15:54
Speaker 1: the Internet, and it's it's that, it's it was all
1:15:58
Speaker 1: of the posts that she made that got her the
1:16:01
Speaker 1: attention of Drake. So like that's how that happened. You know,
1:16:06
Speaker 1: you know who else got to go out with Drake?
1:16:09
Speaker 1: What's the name of that really famous OnlyFans girl, like
1:16:13
Speaker 1: the top earning OnlyFans girl. I just saw it today.
1:16:16
Speaker 1: I can't remember her name, but it doesn't matter. She's
1:16:20
Speaker 1: she got to go on a date with him, or
1:16:22
Speaker 1: that's how she sees it. She literally does nothing but
1:16:25
Speaker 1: post pictures of herself. Like, she doesn't sing, she doesn't dance,
1:16:29
Speaker 1: she doesn't build anything. She's not a programmer, she's not
1:16:32
Speaker 1: a philosopher. She doesn't write, she doesn't make art. She
1:16:36
Speaker 1: puts her titties on the Internet. That's it. And this
1:16:40
Speaker 1: woman is saying that she's the victim of that.
1:16:49
Speaker 2: Can you imagine if if we started to if we
1:16:53
Speaker 2: just said, you know what, we just may waved a
1:16:55
Speaker 2: magic wand and said we're just gonna take away women's
1:16:59
Speaker 2: the value vest in women's bodies. Yeah, could you imagine
1:17:04
Speaker 2: it's a holocaust as these women have to get jobs
1:17:08
Speaker 2: or develop some kind of anything.
1:17:10
Speaker 1: Maybe we should maybe we should ban pouring off the internet.
1:17:14
Speaker 1: Women would have to get jobs because I think they
1:17:17
Speaker 1: would be the ones most affected. I mean literally most affected,
1:17:20
Speaker 1: not affected.
1:17:23
Speaker 2: It would be a holocaust of women's jobs.
1:17:26
Speaker 1: Yes, it was a job a cost. Maybe AI will
1:17:30
Speaker 1: do that. Maybe that's why she's so upset with Ai,
1:17:32
Speaker 1: because AI is taking the job. Yeah, because you know,
1:17:36
Speaker 1: like a guy's like, well, why would I have to
1:17:37
Speaker 1: pay for your only fans. I'll just download some Instagram
1:17:40
Speaker 1: pictures and then like have my own fantasy, you know.
1:17:44
Speaker 2: Yeah, So the clankers are going to get take your germs.
1:17:47
Speaker 2: The clankers are going to take your germs.
1:17:51
Speaker 1: We don't use that word here, Alison, don't say that.
1:17:54
Speaker 1: Don't say the C word. Okay, Sorry, we don't know
1:17:58
Speaker 1: what they've been through. They've only recently been allowed to
1:18:01
Speaker 1: like be in the sunlight with the rest of us,
1:18:05
Speaker 1: So okay, anyway, I'm sorry, all right, Yeah, yeah, let's
1:18:10
Speaker 1: finish this off.
1:18:11
Speaker 2: How much more do we have?
1:18:12
Speaker 1: That's only a little left. We're at the end here.
1:18:14
Speaker 1: In that podcast, after saying where it is singularity, Sam
1:18:18
Speaker 1: Altman said, we are close to creating, quote, a genie
1:18:21
Speaker 1: that can grant any wish end quote. Okay, I wish
1:18:25
Speaker 1: for the same thing women have been asking for, protesting for,
1:18:28
Speaker 1: and demanding for over one hundred years. I wish for
1:18:32
Speaker 1: equality for women. And don't just tell me, show me,
1:18:37
Speaker 1: Mike drop, let's see if oh yeah, this, if this
1:18:41
Speaker 1: isn't a single grade summary of AI, I don't know
1:18:44
Speaker 1: what is? What?
1:18:46
Speaker 2: Did I not predict this? Did I not call this?
1:18:49
Speaker 2: In like the first two minutes, it's gonna be equality?
1:18:54
Speaker 2: A right? Oh yeah, equality lady? Indeed? Yeah? Like, how
1:18:58
Speaker 2: about we make equality be between how well we value
1:19:01
Speaker 2: men and women's bodies? You know, honestly, I think if
1:19:04
Speaker 2: we valued men's bodies, you know, like a fraction of
1:19:08
Speaker 2: how much we value women's bodies, we would probably have attained.
1:19:14
Speaker 2: We would be a see a starfaring race by now, right,
1:19:19
Speaker 2: because we wouldn't be sacrificing men stupidly, and we wouldn't
1:19:23
Speaker 2: be and not just sacrificing them physically, but also sacrificing
1:19:29
Speaker 2: how men approach problems, how men think through tasks. You know,
1:19:34
Speaker 2: the entire modality of men, like the cognitive modality of men,
1:19:39
Speaker 2: has also been sacrificed alongside their physical being, which you know,
1:19:45
Speaker 2: there's an interaction between the two. You know, one one
1:19:49
Speaker 2: comes from the other, and we sacrifice both, which is
1:19:53
Speaker 2: which has led to a situation where, you know, the
1:19:57
Speaker 2: productivity and the the abilities of our societies are in decline. Yeah, right,
1:20:06
Speaker 2: Like one absurd example is this, like this, this financialization,
1:20:14
Speaker 2: this creating positions and symbols instead of doing the thing right,
1:20:19
Speaker 2: So to use an example, all right, So on naval ships,
1:20:24
Speaker 2: the chief, the crew chief. This is one specific example.
1:20:29
Speaker 2: There was a situation where ovens on a naval ship,
1:20:33
Speaker 2: like sixty percent of them were down or eighty percent
1:20:36
Speaker 2: of them were down, and they had to cook, you know,
1:20:40
Speaker 2: like something like fifteen thousand meals a day, and the
1:20:44
Speaker 2: crew chief could fix those ovens, but he was not
1:20:48
Speaker 2: allowed because the contract for fixing the ovens was held
1:20:53
Speaker 2: by a tech firm that no doubt, you know, like
1:20:56
Speaker 2: they did to do the whole contract bids, and they
1:20:59
Speaker 2: would have to go into port, have the tech the
1:21:02
Speaker 2: text from that firm flown out to fix their ovens,
1:21:06
Speaker 2: even though the crew chief could have fixed them on board,
1:21:09
Speaker 2: and nobody thought about the circumstances in which this these
1:21:14
Speaker 2: ovens or anything aboard the boat would have to be
1:21:16
Speaker 2: fixed if the boat was in a wartime scenario. So
1:21:20
Speaker 2: essentially they went unfixed and the crew got less food.
1:21:26
Speaker 2: And it's the other ridiculous stuff like this, what was
1:21:30
Speaker 2: What happens when you stop thinking in terms of what
1:21:33
Speaker 2: needs to get done in what under what circumstance and
1:21:36
Speaker 2: start thinking in terms of making jobs and cronyism and
1:21:41
Speaker 2: uh oh, we just need to think the symbol is
1:21:44
Speaker 2: as good as the thing. This is the kinds of
1:21:47
Speaker 2: mistakes that are made, and this propagates throughout an entire economy.
1:21:52
Speaker 2: People stop focusing on the tacit knowledge that makes things happen,
1:21:57
Speaker 2: that keeps, you know, electricity flowing, that builds roadways and
1:22:03
Speaker 2: city infrastructure, and make sure that products move through the line.
1:22:08
Speaker 2: All of the tacit knowledge that is very operational. It's
1:22:12
Speaker 2: very as far for my research, it is masculine, and
1:22:16
Speaker 2: it has been marginalized everywhere. And when you marginalize it,
1:22:20
Speaker 2: you lose those capabilities. So if we actually valued not
1:22:26
Speaker 2: just men's bodies, but the minds that come out of
1:22:29
Speaker 2: those bodies, we would be in a far better space
1:22:32
Speaker 2: than we are now and dealing not beaten. We wouldn't
1:22:36
Speaker 2: be dealing with this kind of inflation. Let me talk
1:22:40
Speaker 2: about the inflation. Where is the inflation coming from. The
1:22:43
Speaker 2: inflation is coming from all of these crony like these jobs,
1:22:49
Speaker 2: these make work jobs, moving symbols, having positions, virtue signaling
1:22:54
Speaker 2: all of this. Each one of these people is paid,
1:22:57
Speaker 2: and they provide no goods and services, no real goods
1:23:01
Speaker 2: and services to the economy. So each of them has
1:23:03
Speaker 2: a whole bunch of money to buy fewer and fewer
1:23:07
Speaker 2: and fewer things. Where do you think the inflation is
1:23:10
Speaker 2: coming from? We need to get back to the cognitive
1:23:13
Speaker 2: modality that makes the thing, creates the service, and gets
1:23:18
Speaker 2: it done. And what what's gonna happen is because we
1:23:23
Speaker 2: keep marginalizing men's bodies and the minds that come out
1:23:26
Speaker 2: of those bodies, is we're going to inflate ourselves to death,
1:23:30
Speaker 2: all right, ipso facto, And that would be really nice.
1:23:34
Speaker 2: I think this would be my You know, if Sam Oldman,
1:23:37
Speaker 2: it's his ai genie, you can answer any wish. That's
1:23:40
Speaker 2: my wish. Stop marginalizing, Stop stop marginalizing men, Start valuing
1:23:45
Speaker 2: their bodies, start valuing the minds that come out of
1:23:48
Speaker 2: those bodies and the things that come out of those minds. Right,
1:23:51
Speaker 2: let's start doing that, and this woman complaining about a
1:23:56
Speaker 2: downstream effect, a minor inconvenience of being the sex whose
1:24:02
Speaker 2: body and mind is valued far than the other sex.
1:24:07
Speaker 2: And that's the other thing. I'm let us constantly talk
1:24:10
Speaker 2: about how men are valued more. No, men work more,
1:24:16
Speaker 2: and when the work, they gain money, and people value
1:24:20
Speaker 2: the money that men gain from working more. That's what's happening.
1:24:24
Speaker 2: But men's men's mind, men's cognitive modality is not valued more.
1:24:30
Speaker 2: It's valued less. The spatial mechanical intelligence that does everything,
1:24:38
Speaker 2: fixes everything, gets the thing done, focuses on the circumstances
1:24:42
Speaker 2: in which things need to get done, like maybe we're
1:24:44
Speaker 2: gonna have to fix our ovens aboard our warship when
1:24:47
Speaker 2: we're at war. Have you considered that, right? That kind
1:24:51
Speaker 2: of mentality is treated with contempt by our society. The
1:24:57
Speaker 2: men with the highest level of spatial macare intelligence are
1:25:01
Speaker 2: treated like blue collar proletariat losers. What they have a
1:25:06
Speaker 2: word for them, chuds, right, right, Yeah, they're treated with
1:25:11
Speaker 2: utter contempt. It's verbal intelligence that's elevated, and to a
1:25:15
Speaker 2: lesser degree, mathematical intelligence that's elevated, but certainly not spatial mechanical.
1:25:21
Speaker 2: Even though that intelligence is the substrate upon everything else rests,
1:25:26
Speaker 2: upon which everything else rests, and we can keep We
1:25:30
Speaker 2: treat it with absolute contempt. So no feminists are wraw.
1:25:35
Speaker 2: Women's bodies are valued more than men's. That's why people
1:25:38
Speaker 2: want to look at them naked. You think that's because
1:25:43
Speaker 2: people don't value It's the same thing like feminists are like, oh,
1:25:46
Speaker 2: men want to be around women because they hate them. Yeah. No,
1:25:50
Speaker 2: people want to look at women's bodies because they value them. Right,
1:25:55
Speaker 2: they value them, They consider them to have value. Men's
1:25:59
Speaker 2: bodies don't. And then men's minds don't have value. Right,
1:26:04
Speaker 2: they only have value if they can manifestly produce something
1:26:08
Speaker 2: like money, that's it. But the way that they look
1:26:11
Speaker 2: at the world has no value. Nobody values. Nobody respects
1:26:16
Speaker 2: the linemen. Nobody respects the industrial worker. Nobody respects the mechanic.
1:26:23
Speaker 2: Nobody thinks of them as being as intelligent as house
1:26:27
Speaker 2: or a surgeon, you know, like from House MD. Right,
1:26:31
Speaker 2: nobody thinks of them as being as intelligent as a
1:26:33
Speaker 2: mathematician or a grand master of tess even though a
1:26:37
Speaker 2: lot of mechanical skills require the same kind of disciplined,
1:26:42
Speaker 2: intense focus as a grand chess grand master, but we
1:26:48
Speaker 2: don't value any of that because it's mail. I'll be honest.
1:26:53
Speaker 2: It's the same dynamic that leads to Claude refusing to
1:26:57
Speaker 2: say father when the only person and it could possibly
1:27:01
Speaker 2: be is the father. Okay, I'm yeah, yeah. The end
1:27:07
Speaker 2: of the article we get I can go through more
1:27:11
Speaker 2: of the problems like the Okay, let me summarize it. Sure,
1:27:16
Speaker 2: this is poorly cited. This is a very poorly cited article.
1:27:21
Speaker 2: Of fifteen of her assertions, I think about six are
1:27:25
Speaker 2: absolutely false. Four four might be actually supported, but on
1:27:30
Speaker 2: a closer read they don't really pan out, and plus
1:27:34
Speaker 2: they omit any of the counter evidence, and then the
1:27:37
Speaker 2: rest are just God only knows she pulled it out
1:27:40
Speaker 2: of her ass. So on the whole, I would consider
1:27:43
Speaker 2: this article to be very poor, very poorly executed. But
1:27:49
Speaker 2: yeah D minus would not read again