1 00:00:00,520 --> 00:00:03,240 Speaker 1: Hello the Internet, and welcome to season three, twenty two, 2 00:00:03,240 --> 00:00:08,240 Speaker 1: episode two of Dirt Daily's Guys. It's a production of iHeartRadio. 3 00:00:08,520 --> 00:00:12,160 Speaker 1: It's a podcast where we take a deep dive into 4 00:00:12,440 --> 00:00:16,520 Speaker 1: America's shared consciousness. And it is Tuesday, January twenty third, 5 00:00:16,520 --> 00:00:17,279 Speaker 1: twenty twenty four. 6 00:00:17,920 --> 00:00:21,680 Speaker 2: Yeah you know what that two three two one two 7 00:00:21,760 --> 00:00:26,120 Speaker 2: three two three, Well guess what? It's National National Handwriting Day, 8 00:00:26,239 --> 00:00:28,280 Speaker 2: shout out to everybody who's still nice with the pen 9 00:00:28,360 --> 00:00:32,960 Speaker 2: and pencil or quill, and also National Pie Day. But yeah, handwriting. 10 00:00:33,400 --> 00:00:36,400 Speaker 2: I realize they're not teaching cursive anymore out here in 11 00:00:36,440 --> 00:00:37,320 Speaker 2: the US. 12 00:00:37,080 --> 00:00:41,760 Speaker 1: So I yeah, I haven't written cursive since like sixth 13 00:00:41,880 --> 00:00:43,920 Speaker 1: seventh grade. I think, do you have a pen and paper? 14 00:00:44,000 --> 00:00:47,440 Speaker 1: Handwriting Day is discriminatory against me as a left handed person. 15 00:00:47,440 --> 00:00:48,880 Speaker 2: Wait do you do you have a pen and paper 16 00:00:48,880 --> 00:00:49,680 Speaker 2: in front of you right now? 17 00:00:50,560 --> 00:00:50,720 Speaker 1: No? 18 00:00:51,040 --> 00:00:52,760 Speaker 2: Oh, I was gonna say, would you write your name 19 00:00:52,840 --> 00:00:53,600 Speaker 2: right now? In cursive? 20 00:00:55,960 --> 00:00:59,319 Speaker 1: I mean that one I can probably do right right 21 00:00:59,360 --> 00:01:03,240 Speaker 1: my signature, but like, yeah, pizzeria pizzeria would be a 22 00:01:03,480 --> 00:01:10,640 Speaker 1: real Billy Madison style problem. Yeah yeah, those z's are 23 00:01:10,640 --> 00:01:11,360 Speaker 1: you fucking kicking? 24 00:01:11,600 --> 00:01:11,800 Speaker 3: Yeah? 25 00:01:12,760 --> 00:01:15,400 Speaker 1: Those are those? Don't don't look like any letter I 26 00:01:15,440 --> 00:01:18,959 Speaker 1: ever seen anyways. By the way, I was just being 27 00:01:19,200 --> 00:01:22,080 Speaker 1: a type of person I hate. Who's like really calling 28 00:01:22,120 --> 00:01:24,040 Speaker 1: you out for calling it twenty twenty three? 29 00:01:24,560 --> 00:01:26,360 Speaker 2: Oh shit, did I say? 30 00:01:26,440 --> 00:01:29,000 Speaker 1: It's like, oh yeah, yeah, you said one, two three, 31 00:01:29,080 --> 00:01:33,679 Speaker 1: two three, And that's fine for I think we have 32 00:01:33,720 --> 00:01:37,880 Speaker 1: a grace period until June. Personally, after the pandemic, it's 33 00:01:38,000 --> 00:01:41,479 Speaker 1: it's it's weird time has time has gone in a 34 00:01:41,520 --> 00:01:45,319 Speaker 1: weird direction. And I think it's fine if somebody writes 35 00:01:45,400 --> 00:01:48,360 Speaker 1: the year like as twenty twenty two, that's a fun 36 00:01:48,400 --> 00:01:50,520 Speaker 1: thing to like laugh with them about. Yeah, but twenty 37 00:01:50,520 --> 00:01:53,280 Speaker 1: twenty three. Give them a fucking break. Yeah, Like you're 38 00:01:53,320 --> 00:01:56,800 Speaker 1: being like the person at the sleepover who's like, you 39 00:01:56,880 --> 00:02:00,400 Speaker 1: mean this morning because it just hit midnight? Yeah, and 40 00:02:00,480 --> 00:02:02,320 Speaker 1: twelve correcting people. 41 00:02:02,280 --> 00:02:05,960 Speaker 2: You mean yesterday. Yeah. I feel like with that, I'm 42 00:02:06,000 --> 00:02:10,320 Speaker 2: finally experiencing that form of time dilation our parents experience 43 00:02:10,400 --> 00:02:12,800 Speaker 2: when they say the other day and it could mean 44 00:02:13,280 --> 00:02:17,240 Speaker 2: three weeks to thirteen years ago. Oh yeah, yeah, yeah, 45 00:02:17,280 --> 00:02:20,559 Speaker 2: so yeah, yeah, the other day, it's twenty twenty three. 46 00:02:20,840 --> 00:02:24,360 Speaker 1: With movies that like came out I think recently, and 47 00:02:24,400 --> 00:02:26,920 Speaker 1: it turns out it's over a decade ago. That really 48 00:02:26,919 --> 00:02:30,040 Speaker 1: fucks me up, and it happens to me all the time. Anyways. 49 00:02:30,080 --> 00:02:32,720 Speaker 1: My name is Jack O'Brien aka the rich don't give 50 00:02:32,760 --> 00:02:35,639 Speaker 1: a fuck about us, The rich don't give a fuck 51 00:02:35,720 --> 00:02:38,800 Speaker 1: about us. The rich don't give a fuck about us. 52 00:02:38,919 --> 00:02:42,320 Speaker 1: Eat them on the halfshell, Lambeau power. That is courtesy 53 00:02:42,360 --> 00:02:47,120 Speaker 1: of Malacaroni on the Discord, who I was just fucking 54 00:02:47,160 --> 00:02:49,280 Speaker 1: around when I said, you can't write songs about me 55 00:02:49,320 --> 00:02:51,520 Speaker 1: because of my pants anymore? Man, You can. You can 56 00:02:51,560 --> 00:02:54,440 Speaker 1: totally write songs about it. That's that's fine. I'm the 57 00:02:54,480 --> 00:02:56,000 Speaker 1: one who's choosing to sing men. 58 00:02:56,360 --> 00:03:00,560 Speaker 2: Yeah, yeah, but through clenched teeth wiping away tears every Yeah. 59 00:03:00,560 --> 00:03:03,239 Speaker 1: But it's a it's something that my therapist has said 60 00:03:03,280 --> 00:03:07,320 Speaker 1: I need to work through musically, So you're doing the work. 61 00:03:07,600 --> 00:03:10,120 Speaker 1: I'm thrilled to be joined as always by my co 62 00:03:10,240 --> 00:03:12,320 Speaker 1: host mister Miles Great. 63 00:03:12,360 --> 00:03:19,639 Speaker 2: Miles Gray aka how about a deal to make you fail? Okay, 64 00:03:20,320 --> 00:03:24,720 Speaker 2: we know you failed, depressed shut how about a quick 65 00:03:24,760 --> 00:03:28,839 Speaker 2: holiday shout out to Andrew bubb on the Discord because 66 00:03:28,840 --> 00:03:31,919 Speaker 2: we were talking about Blue Monday, the most depressed day 67 00:03:32,120 --> 00:03:34,280 Speaker 2: ever in history. But we don't really get that so 68 00:03:34,360 --> 00:03:36,120 Speaker 2: much in the US, and just how it's really just 69 00:03:36,160 --> 00:03:38,920 Speaker 2: a scheme to sell people vacations or McDonald's. 70 00:03:38,920 --> 00:03:41,160 Speaker 1: Oh I missed that one. Well, which day is blue Monday? 71 00:03:41,200 --> 00:03:41,640 Speaker 1: Is it today? 72 00:03:41,680 --> 00:03:45,360 Speaker 2: It was last Monday? It was, yes, yes, exactly. 73 00:03:45,440 --> 00:03:47,480 Speaker 1: Oh, but they gave us a day off so that 74 00:03:47,520 --> 00:03:49,760 Speaker 1: we wouldn't like be so said. 75 00:03:49,600 --> 00:03:51,160 Speaker 2: Well, just like a thing that was started in the 76 00:03:51,240 --> 00:03:54,120 Speaker 2: UK with sky Travel, where they were basically had a 77 00:03:54,120 --> 00:03:57,520 Speaker 2: guy be like I have out like mathematically calculated the 78 00:03:57,600 --> 00:04:01,120 Speaker 2: most depressed day as like a function of time that 79 00:04:01,280 --> 00:04:05,200 Speaker 2: is a lapsed since Christmas, sadness and motivation, and it 80 00:04:05,280 --> 00:04:08,160 Speaker 2: was all just bullshit and you know, yeah, but hey, 81 00:04:08,360 --> 00:04:10,040 Speaker 2: that's a great way to sell I respect it. 82 00:04:10,040 --> 00:04:14,200 Speaker 1: It's Touch Todays, Yeah, Miles, Yeah, we are thrilled to 83 00:04:14,240 --> 00:04:17,800 Speaker 1: be joined in our third seat, yes by an expert guest, 84 00:04:18,080 --> 00:04:23,120 Speaker 1: a returning champion guests on our most popular episode of 85 00:04:23,160 --> 00:04:23,960 Speaker 1: twenty twenty three. 86 00:04:24,640 --> 00:04:25,120 Speaker 2: Oh. 87 00:04:25,640 --> 00:04:29,400 Speaker 1: She's a research associate at the Leverholmes Center for the 88 00:04:29,440 --> 00:04:33,480 Speaker 1: Future of Intelligence. I don't think I pronounced that word 89 00:04:33,560 --> 00:04:39,080 Speaker 1: right few, where she researches AI from the perspective of 90 00:04:39,120 --> 00:04:42,440 Speaker 1: gender studies, critical race theory, and Asian Diasporas Studies, also 91 00:04:42,520 --> 00:04:45,720 Speaker 1: a research fellow at the AI Now Institute, the co 92 00:04:45,880 --> 00:04:49,599 Speaker 1: editor of the new book The Good Robot, Why Technology 93 00:04:49,640 --> 00:04:52,960 Speaker 1: Needs Feminism, and a co host of The Good Robot podcast. 94 00:04:53,160 --> 00:04:57,599 Speaker 1: Please welcome back to the show, the Brilliant doctor Kerrie mcinnarn. 95 00:05:00,000 --> 00:05:01,320 Speaker 4: Oh, thanks for having me back. 96 00:05:01,760 --> 00:05:03,880 Speaker 2: Oh hey, thank you so much for accepting our invitation. 97 00:05:04,240 --> 00:05:05,240 Speaker 2: So good to have you back. 98 00:05:05,360 --> 00:05:07,000 Speaker 1: And what time is it where you are right now? 99 00:05:07,839 --> 00:05:11,040 Speaker 3: It is about ten to eight pm and I am 100 00:05:11,120 --> 00:05:14,120 Speaker 3: an early bird, so this is near my bedtime. But 101 00:05:14,440 --> 00:05:16,000 Speaker 3: that just shows how keen I am to get to 102 00:05:16,040 --> 00:05:18,000 Speaker 3: come back on and talk to you. But that I know, 103 00:05:18,560 --> 00:05:21,080 Speaker 3: usually after sort of nine pm. Well my friends know, 104 00:05:21,160 --> 00:05:23,880 Speaker 3: they like there's just going to be no response, no 105 00:05:24,240 --> 00:05:25,360 Speaker 3: trying to plan anything. 106 00:05:26,240 --> 00:05:27,719 Speaker 4: Yeah, it's a special occasion. 107 00:05:28,240 --> 00:05:32,360 Speaker 1: Yeah, you're such an impressive guest that we actually opened 108 00:05:32,480 --> 00:05:35,520 Speaker 1: our call with you by asking you, like why you 109 00:05:35,600 --> 00:05:38,320 Speaker 1: came back. We were like, so, like you're you're like 110 00:05:38,320 --> 00:05:43,320 Speaker 1: like us or something like you're cool, You're so we're 111 00:05:43,360 --> 00:05:47,680 Speaker 1: like idiots, Right, why are you back? And yeah, we're 112 00:05:47,720 --> 00:05:51,479 Speaker 1: thrilled to have you, regardless of the questionable decision by 113 00:05:51,560 --> 00:05:54,279 Speaker 1: you to come hang out with us. This late in 114 00:05:54,360 --> 00:05:55,280 Speaker 1: the evening. 115 00:05:54,880 --> 00:05:57,840 Speaker 2: I'm learned. Yes, yeah, because so much has happened. I 116 00:05:57,880 --> 00:06:00,359 Speaker 2: feel like everything, there's always something happening with A and 117 00:06:00,400 --> 00:06:02,280 Speaker 2: we're like, once we hit we got to ask doctor 118 00:06:02,320 --> 00:06:04,080 Speaker 2: carry like what about this? What about this? And then 119 00:06:04,400 --> 00:06:06,440 Speaker 2: now we've got the perfect opportunity to ask you a 120 00:06:06,440 --> 00:06:10,480 Speaker 2: bunch of questions and also let you rain your knowledge 121 00:06:10,520 --> 00:06:12,680 Speaker 2: down upon us and the listeners, because yeah, last time 122 00:06:12,800 --> 00:06:14,880 Speaker 2: was really really good talking to you and gave us 123 00:06:14,920 --> 00:06:17,960 Speaker 2: so much perspective because we were definitely like we like 124 00:06:18,240 --> 00:06:22,040 Speaker 2: AI end of world right, Like it's like so powerful. 125 00:06:22,600 --> 00:06:25,680 Speaker 2: Oh no, no, just okay, okay, just these language models 126 00:06:25,680 --> 00:06:27,480 Speaker 2: are doing a lot of the lifting right now. 127 00:06:27,760 --> 00:06:30,960 Speaker 1: We've seen T two. We get it. It's wild how 128 00:06:31,080 --> 00:06:34,080 Speaker 1: much that movie has a whole Like it's still raised 129 00:06:34,200 --> 00:06:39,160 Speaker 1: in so many articles. Like yeah, journalists are like as 130 00:06:39,200 --> 00:06:42,720 Speaker 1: in the movie Terminator too, like they're still they're like, 131 00:06:42,720 --> 00:06:45,120 Speaker 1: that's the one I remember. So that's what we're going 132 00:06:45,200 --> 00:06:48,760 Speaker 1: with for this model of understanding. But we're gonna so 133 00:06:48,800 --> 00:06:51,440 Speaker 1: we're gonna get into all sorts of AI questions. Your 134 00:06:51,480 --> 00:06:57,160 Speaker 1: new book talks about the question of is there good 135 00:06:57,320 --> 00:07:01,120 Speaker 1: technology is that possible? Which is a real question for 136 00:07:01,279 --> 00:07:04,640 Speaker 1: me at this point, because I've been been so long 137 00:07:04,720 --> 00:07:09,440 Speaker 1: at sea in this hyper capitalist paradigm. So I'm curious, 138 00:07:09,560 --> 00:07:12,680 Speaker 1: very interested to hear your answer to that, unless it's 139 00:07:12,720 --> 00:07:17,000 Speaker 1: just like, nah, it's not possible. Yeah, super short book. 140 00:07:17,400 --> 00:07:21,000 Speaker 1: But before we get into all of that, we do 141 00:07:21,440 --> 00:07:24,320 Speaker 1: like to ask our guest, what is something from your 142 00:07:24,600 --> 00:07:26,800 Speaker 1: search history that's revealing about who you are? 143 00:07:27,240 --> 00:07:29,240 Speaker 3: Oh gosh, I don't really know if I like what 144 00:07:29,280 --> 00:07:31,720 Speaker 3: this reveals about me, But you know, we need to 145 00:07:31,720 --> 00:07:35,040 Speaker 3: get like really obsessed with something. And it's not even recent, 146 00:07:35,120 --> 00:07:38,360 Speaker 3: it's not trending, like there's no reason why, but for 147 00:07:38,520 --> 00:07:40,360 Speaker 3: all my search history for the last two days just 148 00:07:40,760 --> 00:07:44,800 Speaker 3: really wild deep dives on the twenty nineteen film Cats, 149 00:07:44,840 --> 00:07:48,400 Speaker 3: you know, the awful one, the Cgifer and you know, 150 00:07:48,440 --> 00:07:50,920 Speaker 3: all the superstars kind of crawling around it all fours 151 00:07:51,200 --> 00:07:52,840 Speaker 3: And I don't know why, but for some reason, like 152 00:07:52,880 --> 00:07:55,280 Speaker 3: a specter just came back to want me, and I 153 00:07:55,360 --> 00:07:57,840 Speaker 3: was like, today, I need to like listen to an 154 00:07:57,960 --> 00:07:59,920 Speaker 3: hour and a half YouTube video on the making of 155 00:08:00,320 --> 00:08:02,760 Speaker 3: the movie twenty nineteen and why it was a disaster. 156 00:08:03,320 --> 00:08:06,360 Speaker 3: And so I'm not busy enough. I think it's I 157 00:08:06,400 --> 00:08:09,840 Speaker 3: need more hobbies. And I need more productive uses of 158 00:08:09,880 --> 00:08:10,320 Speaker 3: my time. 159 00:08:10,440 --> 00:08:15,000 Speaker 4: But I am now an encyclopedia on terrible CGI slash 160 00:08:15,120 --> 00:08:18,400 Speaker 4: why you shouldn't try and make very weird Broadway musicals 161 00:08:18,400 --> 00:08:19,200 Speaker 4: into films. 162 00:08:19,440 --> 00:08:23,000 Speaker 1: Yeah, I was immediately like trying to connect the dots. 163 00:08:23,000 --> 00:08:25,640 Speaker 1: I was like, oh, well, I think I remember that 164 00:08:25,720 --> 00:08:29,640 Speaker 1: they used AI technology to remove the cat assholes from 165 00:08:30,000 --> 00:08:31,960 Speaker 1: like was that one of the things where it was 166 00:08:31,960 --> 00:08:34,240 Speaker 1: about to come out and they did a test screening 167 00:08:34,280 --> 00:08:38,319 Speaker 1: and everyone's like, they're pink cat assholes are in our 168 00:08:38,360 --> 00:08:42,000 Speaker 1: face the entire movie? Or are we going to just 169 00:08:42,080 --> 00:08:45,440 Speaker 1: do that? And they went back and digitally removed them. 170 00:08:45,559 --> 00:08:49,120 Speaker 1: But now, were you into the movie when it came out? 171 00:08:49,320 --> 00:08:53,160 Speaker 1: Were you anticipating it or you just kind of have it? 172 00:08:53,960 --> 00:08:56,720 Speaker 3: So I love I'm a dancer, so like I love musicals, 173 00:08:56,720 --> 00:08:58,079 Speaker 3: and like you know, I was one of the only 174 00:08:58,120 --> 00:09:00,880 Speaker 3: people that was like an ironically ex that thought. 175 00:09:00,760 --> 00:09:01,679 Speaker 4: Of the Cat's musicals. 176 00:09:01,720 --> 00:09:04,839 Speaker 3: I realized that's like four people who like actually would 177 00:09:04,840 --> 00:09:05,760 Speaker 3: have wanted to see this. 178 00:09:06,040 --> 00:09:08,160 Speaker 4: And then the trailer came out and it was just 179 00:09:08,200 --> 00:09:08,880 Speaker 4: so frightening. 180 00:09:08,920 --> 00:09:10,920 Speaker 3: What it was a really good example of is like 181 00:09:11,200 --> 00:09:13,880 Speaker 3: in like AI and robotics, either term the uncanny Valley. 182 00:09:13,920 --> 00:09:15,800 Speaker 3: This might be something you've heard of, this idea of 183 00:09:15,920 --> 00:09:18,480 Speaker 3: like the closest something looks like to a human or 184 00:09:18,520 --> 00:09:21,520 Speaker 3: to like a living creature, but like being different enough 185 00:09:21,559 --> 00:09:23,640 Speaker 3: that you can tell, like it's not quite human, like 186 00:09:23,800 --> 00:09:26,360 Speaker 3: the creepy era it is. And so this original robotics 187 00:09:26,400 --> 00:09:29,720 Speaker 3: experiment Matito Mari or the person who like writes this 188 00:09:29,880 --> 00:09:33,120 Speaker 3: essay about this, used the example of like a mechanical 189 00:09:33,240 --> 00:09:36,600 Speaker 3: hand that moves, and it's like, that's super creepy, like 190 00:09:36,640 --> 00:09:38,880 Speaker 3: no one wants to see that. And all I could 191 00:09:38,920 --> 00:09:42,600 Speaker 3: think of is this like essay from this Jepanese roboticist. 192 00:09:42,640 --> 00:09:45,840 Speaker 3: When I saw those cat humans things moving, and I 193 00:09:45,960 --> 00:09:48,960 Speaker 3: was like, it's going to be so bad, like no 194 00:09:49,000 --> 00:09:50,840 Speaker 3: one wants to see this, whether or not the pink 195 00:09:50,880 --> 00:09:53,040 Speaker 3: households are there or not, you know, and they just 196 00:09:53,120 --> 00:09:55,920 Speaker 3: end up being all smooth doubt, which is also awful. 197 00:09:56,679 --> 00:10:00,280 Speaker 4: So yes, it was a kind of terrifying disappointment. 198 00:10:00,040 --> 00:10:02,840 Speaker 2: Because yeah, there's kind of a streisand effect. I feel 199 00:10:02,840 --> 00:10:05,600 Speaker 2: like people are like, but where are the assholes? You 200 00:10:05,640 --> 00:10:07,200 Speaker 2: know what I mean? And then people are like, well 201 00:10:07,240 --> 00:10:09,400 Speaker 2: we actually like h I don't know, might have been 202 00:10:09,480 --> 00:10:13,320 Speaker 2: better with them, have you speaking of cats and an interesting, 203 00:10:13,559 --> 00:10:16,160 Speaker 2: uh sort of obsession with them. Have you listened to 204 00:10:16,240 --> 00:10:20,600 Speaker 2: your fellow compatriots podcast Guy Montgomery and Tim Batt's podcast 205 00:10:20,800 --> 00:10:23,800 Speaker 2: called My Week with Cats where they keep watching Cats 206 00:10:23,840 --> 00:10:25,160 Speaker 2: over and over and talking about it. 207 00:10:26,320 --> 00:10:28,080 Speaker 4: But it sounds exactly up my street. 208 00:10:28,200 --> 00:10:31,199 Speaker 2: Yeah, yeah, yeah, yeah, we've had them on the show. 209 00:10:31,480 --> 00:10:34,600 Speaker 2: Guy is one of our favorite guests and fellow Kiwi 210 00:10:34,640 --> 00:10:38,440 Speaker 2: and yeah, like to such an absurd podcast just keep 211 00:10:38,520 --> 00:10:40,520 Speaker 2: revisiting cats over and over. 212 00:10:40,920 --> 00:10:43,400 Speaker 3: Yeah, I mean, have you listened to the iconic KIWI 213 00:10:43,480 --> 00:10:45,880 Speaker 3: podcast who Shout on the Floor at my wedding with 214 00:10:46,400 --> 00:10:48,760 Speaker 3: kind of viral? I still haven't listened to it yet, 215 00:10:48,760 --> 00:10:50,360 Speaker 3: but that's also on my listening list. 216 00:10:50,559 --> 00:10:53,400 Speaker 2: Yeah, that's one that I've heard many times, be like 217 00:10:53,440 --> 00:10:55,760 Speaker 2: have you come on? And all the write ups about 218 00:10:55,800 --> 00:10:59,080 Speaker 2: it too are like it's absolutely the most riveting thing 219 00:10:59,160 --> 00:11:01,240 Speaker 2: that we've listened to this year, is what I feel 220 00:11:01,240 --> 00:11:03,320 Speaker 2: like most people say about that. 221 00:11:03,320 --> 00:11:06,920 Speaker 1: That's amazing. So are you how many viewings deep are 222 00:11:07,000 --> 00:11:09,200 Speaker 1: you of twenty nineteen Cats or is it just like 223 00:11:09,360 --> 00:11:12,840 Speaker 1: you watched it once and then it's all YouTube like explainers? 224 00:11:13,200 --> 00:11:15,040 Speaker 3: Oh yeah, I watched it once. I don't know if 225 00:11:15,080 --> 00:11:16,720 Speaker 3: I could do it again, to be honest, like I 226 00:11:16,720 --> 00:11:20,200 Speaker 3: did it once on like a transatlantic flight and that 227 00:11:20,920 --> 00:11:23,959 Speaker 3: was you know, myself trapped in the metal tube. Had 228 00:11:23,960 --> 00:11:26,600 Speaker 3: this moment with cats when I was like, oh, it 229 00:11:26,640 --> 00:11:29,440 Speaker 3: is as bad as everyone said, And then now it's 230 00:11:29,559 --> 00:11:33,319 Speaker 3: just extremely scathing movie reviews on YouTube and in print. 231 00:11:33,720 --> 00:11:36,880 Speaker 3: So I'm like single handedly keeping the movie YouTube review 232 00:11:36,920 --> 00:11:38,440 Speaker 3: economy alive at this point. 233 00:11:40,200 --> 00:11:42,080 Speaker 1: What is something you think is overrated? 234 00:11:42,840 --> 00:11:43,160 Speaker 4: Hmmm? 235 00:11:44,240 --> 00:11:47,680 Speaker 3: I mean I feel like pretty much anything TikTok tries 236 00:11:47,720 --> 00:11:51,079 Speaker 3: to sell me, but particularly those like gigantic cups. 237 00:11:50,720 --> 00:11:53,040 Speaker 4: Those Stanley cups. I don't know if you've seen this, yeah, 238 00:11:53,080 --> 00:11:54,520 Speaker 4: but they've gone so viral. 239 00:11:54,559 --> 00:11:56,960 Speaker 3: But they're like huge, and my dad used to take 240 00:11:57,240 --> 00:11:59,840 Speaker 3: like a thermoust tumbler of that size out fishing for 241 00:11:59,840 --> 00:12:01,880 Speaker 3: the day so that he would like have enough like 242 00:12:01,960 --> 00:12:04,000 Speaker 3: tea or coffee to keep him going. But now I 243 00:12:04,080 --> 00:12:06,440 Speaker 3: just see like normal people and then normal little walks 244 00:12:06,440 --> 00:12:09,760 Speaker 3: like with a huge flask, and like, I don't get it. 245 00:12:09,800 --> 00:12:12,360 Speaker 3: So if anyone can like sell me, like why why 246 00:12:12,440 --> 00:12:15,280 Speaker 3: what is so attractive about these gigantic cups. 247 00:12:17,000 --> 00:12:17,920 Speaker 4: Around them? 248 00:12:18,840 --> 00:12:20,120 Speaker 1: I mean, that's what's important. 249 00:12:20,360 --> 00:12:20,720 Speaker 2: Doctor. 250 00:12:21,440 --> 00:12:23,360 Speaker 1: Yeah, I've ever heard of it. 251 00:12:24,920 --> 00:12:28,160 Speaker 3: The medical doctors don't come to me on the plane 252 00:12:28,240 --> 00:12:29,200 Speaker 3: anything useful. 253 00:12:29,320 --> 00:12:32,800 Speaker 2: It's not the same screen. All right. I'm sorry I 254 00:12:32,880 --> 00:12:35,880 Speaker 2: sent you so many medical questions earlier this week over email. 255 00:12:36,240 --> 00:12:39,480 Speaker 2: Didn't realize that wasn't your area of expertise. I mean, 256 00:12:39,480 --> 00:12:41,640 Speaker 2: it's like one of those weird It's just one of 257 00:12:41,640 --> 00:12:44,800 Speaker 2: those consumer things that have taken over. It's like the 258 00:12:44,920 --> 00:12:47,679 Speaker 2: era of like when I was a kid, like millennials 259 00:12:47,679 --> 00:12:50,439 Speaker 2: and stuff, when everybody wanted like a slat bracelet or 260 00:12:50,480 --> 00:12:55,400 Speaker 2: a Tamagatchi or a furbie like that or beanie babies. 261 00:12:55,440 --> 00:12:57,560 Speaker 2: Like we're taking that into adulthood. And now it's like, 262 00:12:58,000 --> 00:13:01,880 Speaker 2: do you have all this Stanley fucking quenchers? Yeah, which 263 00:13:01,920 --> 00:13:04,840 Speaker 2: is like a wild thing. But then also it's wild 264 00:13:04,880 --> 00:13:06,880 Speaker 2: when it's done for the SEO, because when you search 265 00:13:06,960 --> 00:13:10,120 Speaker 2: Stanley Cup, which is the like trophy for the National 266 00:13:10,120 --> 00:13:15,319 Speaker 2: Hockey League, it's mostly overtaken by these fucking Stanley insulated bugs. 267 00:13:15,520 --> 00:13:19,720 Speaker 1: Yeah, I mean, it is wild though. Those are fifty 268 00:13:19,760 --> 00:13:22,200 Speaker 1: dollars each of those things. 269 00:13:22,440 --> 00:13:25,559 Speaker 2: Yeah, yeah, they're like at least forty five, right, Yeah, 270 00:13:26,320 --> 00:13:26,920 Speaker 2: get you in the door. 271 00:13:27,480 --> 00:13:31,080 Speaker 1: People are lining up the day before like to get 272 00:13:31,120 --> 00:13:34,480 Speaker 1: an opportunity to pay forty five dollars for this thing that, 273 00:13:34,800 --> 00:13:37,960 Speaker 1: as far as I could tell, is like has been 274 00:13:38,000 --> 00:13:40,719 Speaker 1: available on the market for a long time, like the 275 00:13:41,000 --> 00:13:44,240 Speaker 1: you know, just well insulated cups. 276 00:13:44,000 --> 00:13:46,600 Speaker 2: That thermist like that you're talking about, Doctor Carrey. That's 277 00:13:46,640 --> 00:13:49,920 Speaker 2: like the like O. G. Stanley like item that people 278 00:13:49,920 --> 00:13:51,800 Speaker 2: have that was like sort of like that minty green 279 00:13:52,480 --> 00:13:55,760 Speaker 2: very like every work in person, every work in stiff 280 00:13:55,880 --> 00:13:58,199 Speaker 2: sort of insulated thermist kind of thing. 281 00:13:58,720 --> 00:14:01,160 Speaker 1: We need a stan Lee stay on the show to 282 00:14:01,280 --> 00:14:05,240 Speaker 1: just explain it to us, Like what is the what? What? 283 00:14:05,240 --> 00:14:09,000 Speaker 1: What is so much better about the Stanley mug from 284 00:14:09,040 --> 00:14:13,200 Speaker 1: like all the other insulated water carrying devices out there. 285 00:14:13,559 --> 00:14:16,120 Speaker 2: Yeah, all right, look zeit gang. If you're a Stanley, 286 00:14:16,120 --> 00:14:19,160 Speaker 2: stan hit us up. We were interested hearing from you. 287 00:14:19,400 --> 00:14:21,320 Speaker 2: And also I feel like there's a high likelihood that 288 00:14:21,360 --> 00:14:24,560 Speaker 2: you are also involved in some other like completionist collecting 289 00:14:24,640 --> 00:14:28,280 Speaker 2: hobby in childhood. I'm just guessing, you know, like before 290 00:14:28,320 --> 00:14:31,040 Speaker 2: I was like I had all the fucking Pokemon cars, 291 00:14:31,080 --> 00:14:32,720 Speaker 2: or like I had all the Beanie babies, Like it 292 00:14:32,800 --> 00:14:35,080 Speaker 2: just has that same energy of being like gotta get 293 00:14:35,120 --> 00:14:37,720 Speaker 2: them all, gotta have them. If you're on that wave 294 00:14:37,760 --> 00:14:39,280 Speaker 2: because I know some people just like them, but some 295 00:14:39,280 --> 00:14:41,080 Speaker 2: people have like way too many. 296 00:14:41,480 --> 00:14:44,640 Speaker 1: Yeah, I'm like over here laughing at my wife for 297 00:14:44,760 --> 00:14:49,400 Speaker 1: like be her most value valued object right now is 298 00:14:49,400 --> 00:14:51,480 Speaker 1: a Stanley tumbler. She got it like a week and 299 00:14:51,520 --> 00:14:53,680 Speaker 1: a half ago. She like wrote her name and was like, 300 00:14:53,760 --> 00:14:58,280 Speaker 1: I will reward if if found because like she's really 301 00:14:58,320 --> 00:15:00,360 Speaker 1: into it. And I was like wow, like what is 302 00:15:00,440 --> 00:15:02,000 Speaker 1: why are you so obsessed? Just like I got in 303 00:15:02,080 --> 00:15:05,440 Speaker 1: like right before, like I got it before everything sold out, 304 00:15:05,600 --> 00:15:07,800 Speaker 1: Like I really like this color way. And when she 305 00:15:07,880 --> 00:15:11,400 Speaker 1: was like talking about the color, like the colorway of it, 306 00:15:11,440 --> 00:15:13,560 Speaker 1: I was like, Oh, it's exactly how I am with shoes, 307 00:15:13,800 --> 00:15:17,640 Speaker 1: Like I have absolutely no room to look down. 308 00:15:17,920 --> 00:15:18,880 Speaker 2: We all have things. 309 00:15:19,320 --> 00:15:24,280 Speaker 1: Yeah, shoes are more expensive. So yeah, Jordan's which were 310 00:15:24,320 --> 00:15:25,760 Speaker 1: a thing that I was into when I was a kid. 311 00:15:26,320 --> 00:15:31,280 Speaker 1: What is doctor McNerney? What is something you think is underrated? 312 00:15:31,760 --> 00:15:31,960 Speaker 4: Oh? 313 00:15:32,000 --> 00:15:35,320 Speaker 3: Well, I just finished watching with my husband season four 314 00:15:35,440 --> 00:15:37,480 Speaker 3: of a show that I think is so underrated. 315 00:15:37,640 --> 00:15:40,600 Speaker 4: It's called for All Mankind. It's on Apple. Have you 316 00:15:40,640 --> 00:15:41,120 Speaker 4: seen it? 317 00:15:41,480 --> 00:15:43,040 Speaker 2: I've heard of it, but I have not seen it. 318 00:15:43,160 --> 00:15:45,400 Speaker 4: Yeah, okay, well now I can pitch it. 319 00:15:45,480 --> 00:15:48,440 Speaker 3: I don't work for Apple for this show, just to 320 00:15:48,480 --> 00:15:50,880 Speaker 3: be clear, although I should get commissioned now because I've 321 00:15:50,920 --> 00:15:51,720 Speaker 3: pitched it so hard. 322 00:15:51,920 --> 00:15:54,840 Speaker 4: But it's like an alternative history of the space rate. 323 00:15:54,960 --> 00:15:57,640 Speaker 3: So the idea is like what happened if Russia got 324 00:15:57,680 --> 00:16:00,400 Speaker 3: to the Moon first, and so space express and became 325 00:16:00,440 --> 00:16:03,960 Speaker 3: this like major site of like investment and travel, And like, 326 00:16:04,000 --> 00:16:06,720 Speaker 3: I'm like not one of those people who's super into space, 327 00:16:06,800 --> 00:16:08,840 Speaker 3: Like I'm not someone who sneaks out a lot of 328 00:16:08,880 --> 00:16:10,040 Speaker 3: space related sci fi. 329 00:16:10,080 --> 00:16:11,280 Speaker 4: But this was riveting. 330 00:16:11,400 --> 00:16:15,120 Speaker 3: It's so well acted, and every season it's a different decade, 331 00:16:15,160 --> 00:16:18,280 Speaker 3: so it like starts in the nineteen sixties and the seventies, eighties, 332 00:16:18,400 --> 00:16:21,320 Speaker 3: nineties and two thousands kind of mapping this alternate history 333 00:16:21,360 --> 00:16:23,040 Speaker 3: of the world. And they've got lots of little like 334 00:16:23,520 --> 00:16:26,400 Speaker 3: kind of like eat eggs, little plot twists and things 335 00:16:26,440 --> 00:16:29,040 Speaker 3: like that. So in this like alternate history, for example, 336 00:16:29,120 --> 00:16:31,440 Speaker 3: this is like not a big spoiler, say, like the 337 00:16:31,800 --> 00:16:34,080 Speaker 3: John Lennon is shot, but he doesn't die, so he's 338 00:16:34,160 --> 00:16:36,720 Speaker 3: kind of still alive and like featuring a little plot 339 00:16:36,760 --> 00:16:39,560 Speaker 3: point or like I think Margaret Thatcher gets assassinated by 340 00:16:39,560 --> 00:16:41,360 Speaker 3: the IRA, or like it's like a lot of these 341 00:16:41,360 --> 00:16:44,600 Speaker 3: like little turning points in history, they go differently, So 342 00:16:44,920 --> 00:16:48,160 Speaker 3: I would recommend checking that out is a really great watch. 343 00:16:48,680 --> 00:16:51,240 Speaker 1: Okay, And this all happens because Russia gets to the moon. 344 00:16:51,360 --> 00:16:53,960 Speaker 1: For like, as far as I can tell, the race 345 00:16:54,000 --> 00:16:56,760 Speaker 1: to the Moon, the America got up there and there 346 00:16:56,840 --> 00:17:00,000 Speaker 1: was like, ah, man, there ain't shit here. Turns out 347 00:17:00,080 --> 00:17:04,320 Speaker 1: out there's a lot of dust. We discovered nothing. Is it? 348 00:17:04,440 --> 00:17:06,639 Speaker 1: Just like the bragging rights kind of turns everything on 349 00:17:06,680 --> 00:17:07,040 Speaker 1: its head. 350 00:17:07,440 --> 00:17:10,000 Speaker 3: Yeah, I think that they kind of portray that, like, 351 00:17:10,040 --> 00:17:11,320 Speaker 3: not only I mean a lot of it is the 352 00:17:11,359 --> 00:17:13,920 Speaker 3: bragging rights and the international politics of like, oh, we've 353 00:17:13,920 --> 00:17:16,199 Speaker 3: got to be competing to show that we're more like 354 00:17:16,240 --> 00:17:19,199 Speaker 3: technologically advanced, but also it shows like, yeah, how the 355 00:17:19,280 --> 00:17:22,840 Speaker 3: technological advances that came from continuing to invest in space 356 00:17:22,920 --> 00:17:25,400 Speaker 3: as well as the discoveries they make out they're part 357 00:17:25,440 --> 00:17:28,480 Speaker 3: a slightly different course in human history, from like new 358 00:17:28,600 --> 00:17:32,520 Speaker 3: energy sources through to yeah, like new technologies for flight 359 00:17:32,760 --> 00:17:35,480 Speaker 3: or for you know, cultivating life in different places. So 360 00:17:35,680 --> 00:17:38,120 Speaker 3: it's just quite an interesting, like speculative future. 361 00:17:38,880 --> 00:17:41,240 Speaker 1: Yeah, I see, I love a speculative future. 362 00:17:41,359 --> 00:17:46,040 Speaker 2: I've I had Apple TV plus plus TV or whatever, 363 00:17:46,119 --> 00:17:49,640 Speaker 2: and then I've I let the thing lapse, so then 364 00:17:49,680 --> 00:17:51,760 Speaker 2: I'm like my I turn it on my like TV 365 00:17:51,840 --> 00:17:53,439 Speaker 2: and they're like, oh, you don't got this anymore. But 366 00:17:53,440 --> 00:17:56,159 Speaker 2: then I realized you basically get it for free if 367 00:17:56,200 --> 00:17:58,720 Speaker 2: you buy like any Apple product anymore. And I recently 368 00:17:59,040 --> 00:18:01,240 Speaker 2: like replaced my phone, so i'd be like, oh, yeah, 369 00:18:01,240 --> 00:18:04,880 Speaker 2: I'll cash in my whatever three month period of freeness. 370 00:18:04,920 --> 00:18:07,080 Speaker 2: I get to maybe check this one out because I 371 00:18:07,119 --> 00:18:10,080 Speaker 2: just wasn't watching a lot of stuff on Apple TV. 372 00:18:10,520 --> 00:18:13,000 Speaker 2: But it's also because we talk about this all the time. 373 00:18:13,000 --> 00:18:15,639 Speaker 2: There's just too many things to watch now. So I need, like, 374 00:18:16,080 --> 00:18:18,800 Speaker 2: if people like you, who's opinion I respect, to come 375 00:18:18,840 --> 00:18:20,320 Speaker 2: on and be like you gotta check this out. And 376 00:18:20,359 --> 00:18:23,000 Speaker 2: I'm like, see that. I need something like that rather 377 00:18:23,080 --> 00:18:25,280 Speaker 2: than seeing some Twitter account being. 378 00:18:25,200 --> 00:18:26,640 Speaker 1: Like this is the fucking dope as shit. 379 00:18:26,720 --> 00:18:29,200 Speaker 2: Ever, I need more human recommendations. 380 00:18:29,320 --> 00:18:34,480 Speaker 1: Yeah, human recommendation. Yeah. All right, Well we're going to 381 00:18:34,520 --> 00:18:38,400 Speaker 1: talk about how AI stacks up to humans when we 382 00:18:38,640 --> 00:18:39,160 Speaker 1: get back. 383 00:18:39,200 --> 00:18:39,960 Speaker 2: We'll be right back. 384 00:18:40,240 --> 00:18:41,919 Speaker 1: It's not really what we're going to talk about, but 385 00:18:42,400 --> 00:18:44,720 Speaker 1: I wanted it to sound like I was good at 386 00:18:44,720 --> 00:18:46,960 Speaker 1: my job, so I did like a transition. 387 00:18:46,520 --> 00:18:48,600 Speaker 2: That that sounded like yeah, that sounded like a show 388 00:18:48,600 --> 00:19:03,680 Speaker 2: that I would listen to and we're back, We're back, 389 00:19:03,880 --> 00:19:09,720 Speaker 2: and all right. So I had something happened over the weekend. 390 00:19:10,760 --> 00:19:14,960 Speaker 1: I just wanted to remember medic I just want you 391 00:19:15,000 --> 00:19:16,280 Speaker 1: to look at this thing on my back. 392 00:19:16,520 --> 00:19:17,000 Speaker 2: Yeah. 393 00:19:17,040 --> 00:19:19,119 Speaker 1: I know you're not a medical doctor, but it's just 394 00:19:20,600 --> 00:19:25,800 Speaker 1: I could just use your no. Uh. So I had 395 00:19:26,520 --> 00:19:29,200 Speaker 1: one of my son's friend. My son is really too chess, 396 00:19:29,680 --> 00:19:35,040 Speaker 1: and his friend was playing chess on his iPad and 397 00:19:35,400 --> 00:19:37,680 Speaker 1: he my son asked who he was playing. He said, 398 00:19:37,680 --> 00:19:40,640 Speaker 1: I'm playing AI instead of just like playing the computer. 399 00:19:40,720 --> 00:19:42,919 Speaker 1: It was the first time I'd heard somebody refer to 400 00:19:42,960 --> 00:19:48,159 Speaker 1: it as AI instead of playing the computer. And as 401 00:19:48,240 --> 00:19:50,800 Speaker 1: you know, we knew you're coming on. I was like 402 00:19:50,840 --> 00:19:53,320 Speaker 1: looking at all the headlines and it felt like a 403 00:19:53,400 --> 00:19:58,280 Speaker 1: lot of the stories that are are coming up are 404 00:19:58,320 --> 00:20:03,439 Speaker 1: basically on the same continuum of progress of like things 405 00:20:03,480 --> 00:20:07,280 Speaker 1: that the tech industry has been advancing towards for a 406 00:20:07,359 --> 00:20:11,920 Speaker 1: long time. And I'm I'm like starting to wonder if 407 00:20:12,840 --> 00:20:14,919 Speaker 1: because because I think a lot of people have a 408 00:20:14,920 --> 00:20:18,960 Speaker 1: hard time conceptualizing, like, so what exactly is AI though, 409 00:20:19,440 --> 00:20:22,120 Speaker 1: And I think there's a lot of people like capitalizing 410 00:20:22,119 --> 00:20:25,200 Speaker 1: off of that, because now it's like it's no longer 411 00:20:25,359 --> 00:20:29,040 Speaker 1: just algorithms are making it hard to tell what is 412 00:20:29,080 --> 00:20:31,920 Speaker 1: true and what isn't in on social media. Now it's 413 00:20:31,920 --> 00:20:35,879 Speaker 1: like AI is gonna fuck up this election. Everybody. You know. 414 00:20:35,920 --> 00:20:39,199 Speaker 1: Miles was talking about how like CS was, you know, 415 00:20:39,240 --> 00:20:41,439 Speaker 1: full of all these products that are like, yeah, but 416 00:20:41,480 --> 00:20:46,280 Speaker 1: there's Ai in the rapid rehydration sports drink. 417 00:20:46,520 --> 00:20:50,040 Speaker 2: Everything said was posted in this weird way to be like, yeah, 418 00:20:50,080 --> 00:20:52,359 Speaker 2: this thing and now it's got Ai in it, and 419 00:20:52,400 --> 00:20:54,800 Speaker 2: you're like AI in it? What do you mean we're 420 00:20:54,800 --> 00:20:57,200 Speaker 2: talking about. A coffee maker is like yeah, dude, he's 421 00:20:57,200 --> 00:21:00,879 Speaker 2: got Ai in there, and everyone's like bathlessly being like 422 00:21:00,960 --> 00:21:03,760 Speaker 2: oh my god, this new fucking thing has Ai in it. 423 00:21:03,800 --> 00:21:07,600 Speaker 2: But again it just sounds like algorithms. But also people 424 00:21:07,640 --> 00:21:10,680 Speaker 2: are also I think also confusing like large, large language 425 00:21:10,720 --> 00:21:13,639 Speaker 2: models for being like, oh, so the computer is gonna 426 00:21:13,640 --> 00:21:16,639 Speaker 2: think like a person, and then are my coffee machine's 427 00:21:16,680 --> 00:21:19,040 Speaker 2: gonna think like a human and problem solve like a 428 00:21:19,119 --> 00:21:21,639 Speaker 2: barista versus like so I just stick a little brain 429 00:21:21,720 --> 00:21:25,399 Speaker 2: in there, right, So I guess like the glibway of 430 00:21:25,400 --> 00:21:29,199 Speaker 2: summarizing it would be that, like, you know, there were 431 00:21:29,240 --> 00:21:31,840 Speaker 2: these large language models that impress people quite a bit, 432 00:21:32,440 --> 00:21:36,600 Speaker 2: and they've used that to change the word for like 433 00:21:36,720 --> 00:21:40,399 Speaker 2: all tech advances to be just like AI for like 434 00:21:40,480 --> 00:21:41,800 Speaker 2: branding purposes. Kind of. 435 00:21:42,040 --> 00:21:44,199 Speaker 1: I don't think that's true about all of it. And 436 00:21:44,240 --> 00:21:48,080 Speaker 1: there's definitely some things that seem to be advancing more 437 00:21:48,160 --> 00:21:50,960 Speaker 1: quickly than I'm comfortable with, and that I think people 438 00:21:50,960 --> 00:21:53,320 Speaker 1: are comfortable with. But like, I guess just I'm curious 439 00:21:53,320 --> 00:21:55,479 Speaker 1: to hear like your thoughts on that. As you're seeing 440 00:21:55,560 --> 00:21:58,560 Speaker 1: so many stories and products and all these things, and 441 00:21:58,680 --> 00:22:02,719 Speaker 1: children playing chess misusing the phrase AI, what are your thoughts. 442 00:22:03,680 --> 00:22:05,199 Speaker 3: I mean, my first thought when you were saying that 443 00:22:05,240 --> 00:22:08,040 Speaker 3: about the sports drink, I was like, maybe the Stanley Cup. 444 00:22:07,880 --> 00:22:08,679 Speaker 4: Has AI in it. 445 00:22:08,800 --> 00:22:13,600 Speaker 3: That's why the virus plugging in the technical markets stargning 446 00:22:13,600 --> 00:22:17,240 Speaker 3: its quest for world domination. Yeah, I mean, I think 447 00:22:17,280 --> 00:22:19,399 Speaker 3: this is one of those cases where two things can 448 00:22:19,440 --> 00:22:21,239 Speaker 3: be true at once, Like it can be true that 449 00:22:21,240 --> 00:22:25,200 Speaker 3: we've seen masive developments in large language models and generator 450 00:22:25,200 --> 00:22:27,680 Speaker 3: of AI and other kinds of like you know, sort 451 00:22:27,680 --> 00:22:31,080 Speaker 3: of big data projects, and at the same time, lots 452 00:22:31,080 --> 00:22:33,560 Speaker 3: of firms are jumping on the AI train and marketing 453 00:22:33,600 --> 00:22:36,000 Speaker 3: a lot of things is AI that you know, even 454 00:22:36,080 --> 00:22:39,159 Speaker 3: if you somehow degree a decide on one definition of 455 00:22:39,160 --> 00:22:41,880 Speaker 3: what AI is, we would probably say that these things 456 00:22:41,920 --> 00:22:45,080 Speaker 3: definitely aren't AI enabled in any way. And actually I 457 00:22:45,080 --> 00:22:48,919 Speaker 3: spent like a couple of years interviewing scientists and engineers 458 00:22:48,960 --> 00:22:51,160 Speaker 3: at this big tech firm, and one of the things 459 00:22:51,160 --> 00:22:53,720 Speaker 3: that they were super concerned about was this exact problem. 460 00:22:53,800 --> 00:22:55,760 Speaker 4: So saying, you know, oh well. 461 00:22:55,480 --> 00:22:57,680 Speaker 3: Ten or fifteen years ago, we would have just called 462 00:22:57,680 --> 00:23:00,560 Speaker 3: this a decision tree. Now we're calling it a That 463 00:23:00,600 --> 00:23:03,080 Speaker 3: doesn't mean there's something wrong with that technology, but it 464 00:23:03,080 --> 00:23:05,480 Speaker 3: does mean that maybe the way AI is being used 465 00:23:05,560 --> 00:23:07,639 Speaker 3: is more to do with branding, more to do with 466 00:23:07,720 --> 00:23:09,639 Speaker 3: selling it as a product, and less to do with 467 00:23:10,000 --> 00:23:13,320 Speaker 3: its actual technical functions or what the tools can actually do. 468 00:23:13,680 --> 00:23:15,240 Speaker 3: So like a lot of my job as an AI 469 00:23:15,240 --> 00:23:20,000 Speaker 3: ethicis is just like asking customers and people to just 470 00:23:20,040 --> 00:23:22,200 Speaker 3: be a little bit more critical and ask those questions 471 00:23:22,240 --> 00:23:25,159 Speaker 3: around can this tool actually do what it says on 472 00:23:25,200 --> 00:23:28,000 Speaker 3: the ten What does it mean for something to BAI 473 00:23:28,160 --> 00:23:29,200 Speaker 3: or AI powered? 474 00:23:29,440 --> 00:23:31,600 Speaker 4: And it's it going to actually help you get where 475 00:23:31,600 --> 00:23:32,359 Speaker 4: you want to be? 476 00:23:32,359 --> 00:23:34,159 Speaker 3: Because you know, it actually doesn't matter if it has 477 00:23:34,160 --> 00:23:37,639 Speaker 3: an AI function if like that AI function is not 478 00:23:37,680 --> 00:23:39,000 Speaker 3: going to help you achieve your goal. 479 00:23:39,480 --> 00:23:40,640 Speaker 4: So, like an example of. 480 00:23:40,560 --> 00:23:43,160 Speaker 3: This is like a lot of tools, say AI powered 481 00:23:43,240 --> 00:23:46,639 Speaker 3: hiring tools is an example, I know best, Like they'll say, okay, 482 00:23:46,680 --> 00:23:48,880 Speaker 3: we have an AI function, right, so we use AI 483 00:23:48,960 --> 00:23:51,800 Speaker 3: to help analyze say, like the words that the candidate 484 00:23:51,880 --> 00:23:54,080 Speaker 3: uses in the interviews. But like a lot of these 485 00:23:54,080 --> 00:23:56,320 Speaker 3: tools have like a toggle on off function, so you 486 00:23:56,359 --> 00:23:58,720 Speaker 3: can just like turn the AI part off, which is 487 00:23:58,760 --> 00:24:00,880 Speaker 3: what a lot of companies will do. They'll buy in 488 00:24:01,080 --> 00:24:03,520 Speaker 3: an AI powered tool, but then because that opens them 489 00:24:03,600 --> 00:24:05,720 Speaker 3: up to a lot of liabilities if they actually use 490 00:24:05,760 --> 00:24:08,720 Speaker 3: AI to process candidates, they'll just turn that bit off, so. 491 00:24:08,680 --> 00:24:11,959 Speaker 4: They kind of you know, try to have things both ways. 492 00:24:12,000 --> 00:24:14,240 Speaker 3: I think where you're getting a tool that seems really 493 00:24:14,280 --> 00:24:16,960 Speaker 3: high tech, but you're like not actually using that function. 494 00:24:17,480 --> 00:24:19,280 Speaker 3: So yeah, I think this kind of idea of AI 495 00:24:19,320 --> 00:24:22,800 Speaker 3: as branding is also something that we should all really 496 00:24:22,800 --> 00:24:24,280 Speaker 3: be a bit concerned about. 497 00:24:24,520 --> 00:24:26,919 Speaker 2: Yeah. Yeah, well, and also just seems like there's this 498 00:24:26,960 --> 00:24:29,119 Speaker 2: weird thing too where companies are like, yeah, we use 499 00:24:29,160 --> 00:24:31,960 Speaker 2: AI and other times don't fucking tell them we're using AI, 500 00:24:32,280 --> 00:24:33,719 Speaker 2: Like you know what I mean, It's like what the 501 00:24:33,800 --> 00:24:36,920 Speaker 2: fuck is it? Because recently there's this video game Apex 502 00:24:37,000 --> 00:24:40,000 Speaker 2: Legends that there was like some art artwork that came 503 00:24:40,040 --> 00:24:42,480 Speaker 2: out for like a new thing evolving Final Fantasy, and 504 00:24:42,520 --> 00:24:45,880 Speaker 2: people were like, wait, I think this is like generative 505 00:24:45,880 --> 00:24:48,120 Speaker 2: AI was used for some of these like models because 506 00:24:48,160 --> 00:24:50,280 Speaker 2: like the fingers don't look right, et cetera. And it 507 00:24:50,400 --> 00:24:52,359 Speaker 2: was like a whole thing where it's like embarrassed to 508 00:24:52,440 --> 00:24:55,879 Speaker 2: admit or shouldn't have used AI. So there's there's like 509 00:24:55,920 --> 00:24:59,680 Speaker 2: this interesting balancing act where people like, don't use it there, Yes, 510 00:25:00,000 --> 00:25:03,520 Speaker 2: I'm glad my insulated mug has AI in it, and 511 00:25:03,960 --> 00:25:06,399 Speaker 2: but we're but like we're still kind of finding like 512 00:25:06,440 --> 00:25:10,760 Speaker 2: what is useful, what is acceptable, what is embarrassing or whatever. 513 00:25:10,840 --> 00:25:12,879 Speaker 2: So yeah, it just feels like so many things are 514 00:25:12,880 --> 00:25:14,880 Speaker 2: still up in the air in regards to its use. 515 00:25:15,680 --> 00:25:15,920 Speaker 1: Yeah. 516 00:25:16,000 --> 00:25:16,960 Speaker 4: Yeah, And I think you're right. 517 00:25:17,000 --> 00:25:19,359 Speaker 3: There's this interesting dynamic where like, on the one hand, 518 00:25:19,400 --> 00:25:21,399 Speaker 3: I saw a lot of critiques on TikTok, which is 519 00:25:21,440 --> 00:25:23,399 Speaker 3: again how I spend most of my time sort of 520 00:25:23,760 --> 00:25:27,440 Speaker 3: mindlessly scrolling past mugs and other various bits and pieces. 521 00:25:27,560 --> 00:25:28,960 Speaker 3: But there were a lot of critiques of that new 522 00:25:29,000 --> 00:25:31,960 Speaker 3: Disney film, right, wish, which is kind of blending two 523 00:25:32,040 --> 00:25:35,120 Speaker 3: D animation three D animation. And one of the critiques 524 00:25:35,160 --> 00:25:37,760 Speaker 3: people kept saying was like the song sounds like it's 525 00:25:37,800 --> 00:25:40,840 Speaker 3: AI generated, or the art looks AI generated. It was 526 00:25:41,080 --> 00:25:44,040 Speaker 3: being used differently in this like quite derogatory way, Whereas 527 00:25:44,080 --> 00:25:46,760 Speaker 3: you're right, like a lot of AI startups sell themselves 528 00:25:46,800 --> 00:25:49,400 Speaker 3: in the idea that like wear an AI first company. 529 00:25:49,720 --> 00:25:51,840 Speaker 3: I think there was a study that was done with it, 530 00:25:51,920 --> 00:25:54,440 Speaker 3: like a few years ago, with like European based AI 531 00:25:54,480 --> 00:25:57,520 Speaker 3: startups which showed i think, you know, like something like 532 00:25:57,600 --> 00:25:59,840 Speaker 3: up to forty percent of those AI startups like didn't 533 00:25:59,840 --> 00:26:00,840 Speaker 3: that use AI. 534 00:26:01,000 --> 00:26:03,520 Speaker 4: They were just marketing themselves as AI startups. 535 00:26:03,600 --> 00:26:05,119 Speaker 3: So there's a lot of kind of you know, like 536 00:26:05,920 --> 00:26:10,439 Speaker 3: you know, weird sort of misleading advertising going on. But 537 00:26:10,480 --> 00:26:12,919 Speaker 3: then it can also lead to some like really strange 538 00:26:13,119 --> 00:26:17,240 Speaker 3: other phenomena like the Wizard of Odds phenomenon, which is 539 00:26:17,240 --> 00:26:20,359 Speaker 3: when you actually have companies having humans pretending to be AI, 540 00:26:20,640 --> 00:26:22,880 Speaker 3: not the other way around, so that they look more 541 00:26:22,920 --> 00:26:24,800 Speaker 3: tiped forward. So you think you're talking to an AI 542 00:26:24,880 --> 00:26:27,440 Speaker 3: chatpot and you're not. You're actually talking to a human 543 00:26:27,560 --> 00:26:29,840 Speaker 3: pretending to be an AI. So it's kind of just 544 00:26:30,359 --> 00:26:31,200 Speaker 3: viraled down. 545 00:26:31,280 --> 00:26:36,200 Speaker 1: So also, isn't one of Amazon's big companies called the 546 00:26:36,240 --> 00:26:40,600 Speaker 1: Mechanical Turk, Like based on the there was like an 547 00:26:40,600 --> 00:26:44,159 Speaker 1: early instance where a robot was like really good at 548 00:26:44,160 --> 00:26:46,160 Speaker 1: playing chess and woe like beating all these people at chests, 549 00:26:46,160 --> 00:26:47,840 Speaker 1: and it turned out they just put a chess master 550 00:26:48,040 --> 00:26:52,879 Speaker 1: inside a like robot suit and that's who was beating everybody. 551 00:26:53,080 --> 00:26:57,080 Speaker 1: Like it feels like they're knowingly doing that. They're you know, yeah, 552 00:26:57,080 --> 00:27:00,280 Speaker 1: they're they're trying to make it seem like they are 553 00:27:00,359 --> 00:27:03,520 Speaker 1: using advanced technology when they're not. I think it like that. 554 00:27:03,680 --> 00:27:05,840 Speaker 1: One of the big questions that I've had all along 555 00:27:06,400 --> 00:27:11,000 Speaker 1: is like in reading articles interviewing like Sam Altman or 556 00:27:11,680 --> 00:27:15,760 Speaker 1: these people who are at the forefront in these massive 557 00:27:15,760 --> 00:27:21,760 Speaker 1: positions of power in the tech industry and specifically around AI, 558 00:27:22,480 --> 00:27:26,680 Speaker 1: like they really seem to be willing to sell the 559 00:27:26,760 --> 00:27:32,000 Speaker 1: threat and like evoke like terminator to type imagery when 560 00:27:32,000 --> 00:27:34,120 Speaker 1: they're talking about like, you know, I think the last 561 00:27:34,119 --> 00:27:36,120 Speaker 1: time You're on we talked about how Sam Altman, the 562 00:27:36,200 --> 00:27:39,680 Speaker 1: head of Open Eie what like in a New Yorker 563 00:27:39,680 --> 00:27:43,120 Speaker 1: interview is like, yeah, keep like keep a cyanide capsule 564 00:27:43,160 --> 00:27:45,760 Speaker 1: on me, you know, just in case the robots take 565 00:27:45,800 --> 00:27:48,120 Speaker 1: over and I gotta I gotta go because I don't 566 00:27:48,119 --> 00:27:49,119 Speaker 1: want to see what that looks like. 567 00:27:49,200 --> 00:27:51,520 Speaker 2: Yeah, I don't want my Stanley Cup to revolt against me. 568 00:27:52,880 --> 00:27:57,119 Speaker 1: And it's like, well, he of and of all people 569 00:27:57,320 --> 00:28:01,439 Speaker 1: knows that that's like not a threat right now, like 570 00:28:01,520 --> 00:28:06,520 Speaker 1: at this moment. But he is doing this dance that 571 00:28:06,560 --> 00:28:10,960 Speaker 1: we're talking about right where he it is driving major 572 00:28:11,359 --> 00:28:15,280 Speaker 1: massive volume for him to convince people with money that 573 00:28:15,880 --> 00:28:19,240 Speaker 1: like the thing they're doing is not part of is 574 00:28:19,280 --> 00:28:23,400 Speaker 1: not on this continuum with like past technological advances. It's 575 00:28:23,440 --> 00:28:25,920 Speaker 1: this new thing that's going to like come in and 576 00:28:26,520 --> 00:28:29,439 Speaker 1: like you know, be a magical HyperIntelligence. 577 00:28:30,080 --> 00:28:30,280 Speaker 4: Yeah. 578 00:28:30,320 --> 00:28:32,720 Speaker 3: And I think there's something particularly ironic when it is 579 00:28:32,800 --> 00:28:35,560 Speaker 3: people like sam Oltman, who you know, may very well 580 00:28:35,600 --> 00:28:39,040 Speaker 3: actually believe all of these things, right, I don't know personally, 581 00:28:39,160 --> 00:28:40,760 Speaker 3: I mean, I don't want to say, oh, he's just 582 00:28:40,800 --> 00:28:43,800 Speaker 3: sort of trying to deliberately create this hype like in 583 00:28:43,840 --> 00:28:44,840 Speaker 3: a very cynical way. 584 00:28:45,120 --> 00:28:49,160 Speaker 4: Maybe he really does believe that. But surely if you really. 585 00:28:48,880 --> 00:28:51,240 Speaker 3: Thought, like, oh, there's actually like a meaningful risk that 586 00:28:51,360 --> 00:28:53,000 Speaker 3: robots could take over and I'm gonna have to. 587 00:28:52,920 --> 00:28:54,200 Speaker 4: Take my site an eye tablet. 588 00:28:54,440 --> 00:28:58,400 Speaker 3: When you just stop investing and making these things, you know, 589 00:28:58,480 --> 00:29:01,080 Speaker 3: these are people who have, like actually do have the 590 00:29:01,200 --> 00:29:04,360 Speaker 3: power to shape the direction of tech development. And I 591 00:29:04,360 --> 00:29:08,000 Speaker 3: think there's something particularly ironic, you know, quite hypocritical about 592 00:29:08,400 --> 00:29:12,760 Speaker 3: scaremongering about AI and AGI while also actively orienting your 593 00:29:12,760 --> 00:29:17,320 Speaker 3: company towards building it and also simultaneously like undercutting regulation. 594 00:29:17,600 --> 00:29:19,600 Speaker 3: So like, at the same time that Oltman was going 595 00:29:19,640 --> 00:29:23,360 Speaker 3: really public about the risks of artificial intelligence, open AI 596 00:29:23,480 --> 00:29:27,280 Speaker 3: was like simultaneously lobbying for weaker regulation with the EUAI Act. 597 00:29:27,960 --> 00:29:30,040 Speaker 3: And I think we see this a lot with Elon Musk, 598 00:29:30,080 --> 00:29:33,720 Speaker 3: for example, warning around the dangers of AI based simultaneously 599 00:29:33,800 --> 00:29:37,760 Speaker 3: firing toward this whole ethical AI team. So to some extent, 600 00:29:38,000 --> 00:29:40,960 Speaker 3: you know, I'm not saying that they deliberately trying to 601 00:29:41,040 --> 00:29:44,120 Speaker 3: weaponize this narrative, but there's just a real mismatch between 602 00:29:44,400 --> 00:29:46,719 Speaker 3: what they're saying and like what they're actually doing and 603 00:29:46,800 --> 00:29:48,320 Speaker 3: the domains that they can control. 604 00:29:48,800 --> 00:29:50,840 Speaker 1: Yeah, at the same time, the people at the forefront 605 00:29:50,960 --> 00:29:54,640 Speaker 1: of you know, understanding what climate change was going to 606 00:29:54,680 --> 00:29:58,320 Speaker 1: look like, We're at Exxon and Chevron, and you know, 607 00:29:58,600 --> 00:30:02,400 Speaker 1: they they were perfectly willing to be like this is 608 00:30:02,480 --> 00:30:06,440 Speaker 1: fucking bad, you guys. It's not our fault, it's your fault. 609 00:30:06,480 --> 00:30:10,920 Speaker 1: Maybe you should switch to paper straws. I think so. Yeah, 610 00:30:11,400 --> 00:30:15,120 Speaker 1: the consensus among people like that, Like I was in 611 00:30:15,240 --> 00:30:16,840 Speaker 1: Davos this past week, and I don't know if you 612 00:30:16,840 --> 00:30:18,800 Speaker 1: guys were there and just talking to some of the 613 00:30:18,840 --> 00:30:21,840 Speaker 1: people there, and I was not in Davos. 614 00:30:21,480 --> 00:30:22,400 Speaker 3: But the. 615 00:30:23,960 --> 00:30:27,640 Speaker 1: Like the idea that sure, we can have these ethical concerns, 616 00:30:28,240 --> 00:30:32,239 Speaker 1: but that's the progress and like what capitalism tells us 617 00:30:32,240 --> 00:30:35,760 Speaker 1: to do is inevitable, Like there's no there's no arguing 618 00:30:35,840 --> 00:30:38,000 Speaker 1: with that. It's just like we're going to always do 619 00:30:38,080 --> 00:30:41,400 Speaker 1: what delivers the most shareholder value. Yeah, and like anything 620 00:30:41,400 --> 00:30:42,240 Speaker 1: else is naive. 621 00:30:42,640 --> 00:30:46,000 Speaker 2: On the World Economic Forums website, like four takeaways from 622 00:30:46,040 --> 00:30:48,600 Speaker 2: you know, like the gathering in Davos, Like one of 623 00:30:48,640 --> 00:30:50,600 Speaker 2: them was talking about like AI is all the buzz, 624 00:30:50,640 --> 00:30:52,440 Speaker 2: and a lot of it was definitely focused on like 625 00:30:52,840 --> 00:30:55,680 Speaker 2: this is how like we're gonna fucking boost global GDP, 626 00:30:56,240 --> 00:30:58,440 Speaker 2: this is how we can close the gaps in like 627 00:30:58,480 --> 00:31:01,280 Speaker 2: the lack of skilled labor, Like it's all gonna happen. 628 00:31:01,400 --> 00:31:03,640 Speaker 2: Then this philosopher spoke like on something about like what 629 00:31:03,760 --> 00:31:05,520 Speaker 2: we completely lose our touch to know what it means 630 00:31:05,640 --> 00:31:07,120 Speaker 2: human anyway, that was like a whole other thing. But 631 00:31:07,160 --> 00:31:09,000 Speaker 2: then this other person came on to talk about like 632 00:31:09,200 --> 00:31:12,719 Speaker 2: they really like sort of nodded at maybe there's something 633 00:31:12,760 --> 00:31:14,240 Speaker 2: to contend with here, but I feel like more of 634 00:31:14,240 --> 00:31:16,600 Speaker 2: the takeaway was like this is y'all, this is how 635 00:31:16,640 --> 00:31:20,680 Speaker 2: we're also gonna get more richer, please, So it's yeah, 636 00:31:20,720 --> 00:31:22,840 Speaker 2: like it's they're definitely trying to do many things at 637 00:31:22,840 --> 00:31:25,880 Speaker 2: the same time, which is like boosting in our in 638 00:31:25,920 --> 00:31:29,160 Speaker 2: our sort of like Zeitgeist. They consciousness that like AI 639 00:31:29,360 --> 00:31:32,280 Speaker 2: is like the fucking like the new pluton like uranium 640 00:31:32,360 --> 00:31:36,320 Speaker 2: plutonium rod that can power everything and also destroy us, 641 00:31:36,320 --> 00:31:38,840 Speaker 2: but also like good for profits. So it feels like 642 00:31:38,880 --> 00:31:42,400 Speaker 2: this omni issue and it just depends on your own 643 00:31:42,480 --> 00:31:44,680 Speaker 2: sort of like your sort of dispositions to sort of 644 00:31:44,800 --> 00:31:47,360 Speaker 2: figure out like what the which version of it you're seeing. 645 00:31:47,840 --> 00:31:50,600 Speaker 2: And then recently, I feel like the latest thing I 646 00:31:50,600 --> 00:31:52,520 Speaker 2: saw as it relates to like politics, because that's where 647 00:31:52,520 --> 00:31:55,280 Speaker 2: I see like it having the most impact right now. 648 00:31:55,320 --> 00:31:58,760 Speaker 2: Like I've seen on YouTube, so many like mid Journey 649 00:31:58,800 --> 00:32:01,520 Speaker 2: created thumbnails to like make it look like certain things 650 00:32:01,560 --> 00:32:03,120 Speaker 2: are happening in the world that aren't just as a 651 00:32:03,120 --> 00:32:05,320 Speaker 2: way to get people to click, but also like in 652 00:32:05,320 --> 00:32:08,120 Speaker 2: New Hampshire, where there's a primary apparently there was like 653 00:32:08,200 --> 00:32:11,440 Speaker 2: a fake Joe Biden robo call that people were trying 654 00:32:11,440 --> 00:32:14,080 Speaker 2: to suspect whether it was like used through some kind 655 00:32:14,120 --> 00:32:19,000 Speaker 2: of AI voice tool or an impressionist or whatever. That 656 00:32:19,000 --> 00:32:21,840 Speaker 2: that's where I feel like we're starting to see the 657 00:32:22,000 --> 00:32:25,320 Speaker 2: real kind of stuff impact our real world rather than 658 00:32:25,360 --> 00:32:27,880 Speaker 2: being like no one works at McDonald's anymore. I guess 659 00:32:27,920 --> 00:32:30,400 Speaker 2: all that to say is is that you know, from 660 00:32:30,440 --> 00:32:32,720 Speaker 2: your perspective, that's why I'm like, okay, doctor carry Like 661 00:32:33,400 --> 00:32:36,959 Speaker 2: I get it. AI is in our toasters now or whatever. 662 00:32:37,120 --> 00:32:39,920 Speaker 2: I can put that aside. But you know, like when 663 00:32:40,080 --> 00:32:42,680 Speaker 2: I think that's the for I think for usespecially in 664 00:32:42,680 --> 00:32:45,520 Speaker 2: the US with this election you're happening, that's where I 665 00:32:45,560 --> 00:32:48,360 Speaker 2: see like a lot of potential for like just it's 666 00:32:48,400 --> 00:32:50,760 Speaker 2: just really these tools that allow people to add to 667 00:32:50,920 --> 00:32:54,360 Speaker 2: like a media narrative very easily in an un compelling way. 668 00:32:54,360 --> 00:32:56,960 Speaker 2: That feels like one of the most potent things we're 669 00:32:56,960 --> 00:33:00,479 Speaker 2: going to see this year. But I'm am, I off, like, 670 00:33:00,520 --> 00:33:03,040 Speaker 2: what from your perspective, what are the things we should 671 00:33:03,040 --> 00:33:05,880 Speaker 2: be looking at, or maybe some things that are distractions. 672 00:33:06,400 --> 00:33:08,480 Speaker 3: No, No, I mean I think you're definitely on there 673 00:33:08,520 --> 00:33:11,960 Speaker 3: when it comes to the meaningful risks posed by AI 674 00:33:11,960 --> 00:33:16,440 Speaker 3: and its ability to help generate disinformation. And because we've 675 00:33:16,440 --> 00:33:18,800 Speaker 3: got I think, over seventy elections this year, so it's 676 00:33:18,800 --> 00:33:22,240 Speaker 3: a huge year internationally for elections, including as you mentioned, 677 00:33:22,520 --> 00:33:25,680 Speaker 3: the US election, and we've seen kind of the past 678 00:33:25,720 --> 00:33:29,080 Speaker 3: few elections in the US and also elsewhere, like the 679 00:33:29,160 --> 00:33:32,480 Speaker 3: way that disinformation and kind of the mass spreading of 680 00:33:32,800 --> 00:33:37,680 Speaker 3: forms of political ideology has been hugely damaging I think 681 00:33:37,720 --> 00:33:41,000 Speaker 3: in many ways to our information society and the way 682 00:33:41,000 --> 00:33:45,040 Speaker 3: that we conduct political elections. And we've seen unfortunately examples 683 00:33:45,040 --> 00:33:48,720 Speaker 3: of specifically AI generated misinformation. But I think actually it 684 00:33:48,760 --> 00:33:52,760 Speaker 3: was in the Slovakian elections a few weeks ago where 685 00:33:53,040 --> 00:33:56,640 Speaker 3: there were a number of AI generated clips and deep fakes, 686 00:33:56,800 --> 00:33:59,960 Speaker 3: including one where one of the candidates was I think 687 00:34:00,040 --> 00:34:02,760 Speaker 3: the audio was edited in a way, but it appeared 688 00:34:02,760 --> 00:34:04,400 Speaker 3: that they said they were going to double the price 689 00:34:04,440 --> 00:34:07,560 Speaker 3: of beer, which in that context was like a huge 690 00:34:08,080 --> 00:34:11,440 Speaker 3: which kind of outcry, like really very very damaging to things. 691 00:34:12,000 --> 00:34:14,719 Speaker 4: President's so important. Yeah, and so people were like. 692 00:34:14,719 --> 00:34:16,919 Speaker 3: Whoa you know, and so, you know, we do see 693 00:34:16,920 --> 00:34:19,719 Speaker 3: examples of this At the same time, I think, you know, 694 00:34:20,280 --> 00:34:24,239 Speaker 3: for me, this is one of those classic examples where 695 00:34:24,560 --> 00:34:28,000 Speaker 3: tech illuminates a problem that's already there, right, so AI 696 00:34:28,040 --> 00:34:31,000 Speaker 3: can provide people the tools to make more convincing misinformation, 697 00:34:31,400 --> 00:34:34,600 Speaker 3: maybe to make that more scale. These things are both bad, 698 00:34:35,040 --> 00:34:36,960 Speaker 3: but I think we need to say, actually, what makes 699 00:34:36,960 --> 00:34:40,040 Speaker 3: people vulnerable the misinformation in the first place? What makes 700 00:34:40,080 --> 00:34:43,040 Speaker 3: people vulnerable to fake news? Why do people still choose 701 00:34:43,080 --> 00:34:45,520 Speaker 3: to consume and share fake news even when they know 702 00:34:45,600 --> 00:34:48,200 Speaker 3: it might not be factually true? Like these are all 703 00:34:48,320 --> 00:34:51,600 Speaker 3: things where again, you know, I think technology is often 704 00:34:51,600 --> 00:34:54,840 Speaker 3: treated as a domain just for computer scientists or mathematicians. 705 00:34:55,000 --> 00:34:57,239 Speaker 3: But this is why we need psychologists, and we need 706 00:34:57,280 --> 00:35:00,520 Speaker 3: people from the humanities and the social scientists, we need 707 00:35:00,760 --> 00:35:03,839 Speaker 3: political scientists to try and understand, you know, what makes 708 00:35:03,880 --> 00:35:08,080 Speaker 3: in communities vulnerable to these kinds of narratives and ideas. 709 00:35:08,120 --> 00:35:10,399 Speaker 3: And I think until we do that and really prioritize 710 00:35:10,400 --> 00:35:14,080 Speaker 3: this kind of research, building bitter media literacy and bitter 711 00:35:14,200 --> 00:35:16,960 Speaker 3: resilience to these narratives, like, we're not going to necessarily 712 00:35:17,000 --> 00:35:18,680 Speaker 3: be able to find a quick tech fix. 713 00:35:19,719 --> 00:35:22,840 Speaker 1: Yeah, I would just stand. I would just like accept 714 00:35:23,120 --> 00:35:25,560 Speaker 1: going eight years into the past in terms of like 715 00:35:25,640 --> 00:35:28,799 Speaker 1: how much media there was, like how much money was 716 00:35:28,840 --> 00:35:31,480 Speaker 1: being spent on media, because it's just such a fucking 717 00:35:32,400 --> 00:35:35,560 Speaker 1: just waste land out there these days, like all all 718 00:35:35,600 --> 00:35:39,480 Speaker 1: of the legacy media outlets are just gone or not 719 00:35:39,600 --> 00:35:43,120 Speaker 1: spending money they are they seem as likely to be 720 00:35:43,280 --> 00:35:47,600 Speaker 1: using AI tools. Like I got this headline from the 721 00:35:47,719 --> 00:35:52,160 Speaker 1: La Times this weekend sent through to my phone. The 722 00:35:52,200 --> 00:35:55,480 Speaker 1: headline was could your life could use more? Wonder question 723 00:35:55,560 --> 00:35:59,920 Speaker 1: mark from the La Times, Like it's I don't know 724 00:36:00,160 --> 00:36:02,600 Speaker 1: that they're using AI, Like maybe it's just a typo, 725 00:36:02,840 --> 00:36:06,479 Speaker 1: but like I don't know, Sports Illustrated like we found 726 00:36:06,520 --> 00:36:11,279 Speaker 1: out about them getting caught using AI, uh and it 727 00:36:11,360 --> 00:36:14,120 Speaker 1: was you know, the articles are just there and like 728 00:36:14,239 --> 00:36:17,399 Speaker 1: saying nothing. So like it's just this weird situation where 729 00:36:17,400 --> 00:36:20,120 Speaker 1: the people who I think typically we would be counting 730 00:36:20,160 --> 00:36:23,680 Speaker 1: on to be like gatekeepers for this sort of like 731 00:36:24,200 --> 00:36:27,319 Speaker 1: misinformation are also the ones who are like trying to 732 00:36:27,719 --> 00:36:32,360 Speaker 1: get by using the technology to replace humans. 733 00:36:32,800 --> 00:36:33,280 Speaker 2: Yeah. 734 00:36:33,360 --> 00:36:37,239 Speaker 1: So it's just a such a weird environment and it 735 00:36:37,360 --> 00:36:41,160 Speaker 1: just feels like we're facing a blizzard of of misinformation 736 00:36:41,600 --> 00:36:44,200 Speaker 1: now instead of you know, something that you can like 737 00:36:44,280 --> 00:36:47,439 Speaker 1: point to and be like, okay, here's one thing, one 738 00:36:47,600 --> 00:36:50,040 Speaker 1: lie that they're trying to spread. Now it's like there's 739 00:36:50,080 --> 00:36:53,319 Speaker 1: forty a day. Basically could you life use more? 740 00:36:53,440 --> 00:36:57,439 Speaker 2: Wonder could And you're like, mom, maybe, but like it's 741 00:36:57,480 --> 00:37:01,319 Speaker 2: also I feel like it's just accelerated just degradation of 742 00:37:01,600 --> 00:37:04,440 Speaker 2: like journalism in general, and like, you know, look at 743 00:37:04,440 --> 00:37:06,960 Speaker 2: what happened to Pitchfork in other places, or like I 744 00:37:07,040 --> 00:37:09,440 Speaker 2: think a lot of media companies do a look at 745 00:37:09,440 --> 00:37:12,640 Speaker 2: it is like, yeah, sure, this website that used to 746 00:37:12,719 --> 00:37:15,520 Speaker 2: have all these great journalists, people don't come there for 747 00:37:15,640 --> 00:37:18,960 Speaker 2: like their human writing anymore. It's just a brand that 748 00:37:19,000 --> 00:37:21,839 Speaker 2: brings people to click on things. And if I can 749 00:37:21,920 --> 00:37:25,799 Speaker 2: see that sort of tent with all this fake tent 750 00:37:26,080 --> 00:37:29,279 Speaker 2: or content as y'all call it, that people will just 751 00:37:29,320 --> 00:37:31,000 Speaker 2: go continue to click, it is like, and that's how 752 00:37:31,040 --> 00:37:32,920 Speaker 2: we make money. It's not even that what's written there 753 00:37:32,960 --> 00:37:34,839 Speaker 2: is important. It's just that I can create a little 754 00:37:34,840 --> 00:37:37,839 Speaker 2: click website that people do clickies on all day and 755 00:37:37,960 --> 00:37:41,799 Speaker 2: fuck all the human like contribution aspect to it. And yeah, 756 00:37:41,840 --> 00:37:43,839 Speaker 2: then we're left with more and more people getting laid 757 00:37:43,840 --> 00:37:47,480 Speaker 2: off and just these like really bizarre like you can 758 00:37:47,520 --> 00:37:50,040 Speaker 2: tell it's just wild that you can tell pretty quickly. Now, 759 00:37:50,080 --> 00:37:51,480 Speaker 2: I feel like when I'm starting to read something with 760 00:37:51,560 --> 00:37:54,480 Speaker 2: a I'm like you're leaning into these like very specific 761 00:37:54,560 --> 00:37:56,759 Speaker 2: details so much in the writing or it's like it 762 00:37:56,840 --> 00:37:59,120 Speaker 2: reminds me again of like when I used to bullshit 763 00:37:59,160 --> 00:38:02,080 Speaker 2: on an essay and high school and be like George Washington, 764 00:38:02,280 --> 00:38:06,239 Speaker 2: who was also known as our nation's first president, and 765 00:38:06,400 --> 00:38:09,440 Speaker 2: like just adding all this detail about George Washington rather 766 00:38:09,480 --> 00:38:11,759 Speaker 2: than like getting to the point or commentary about what 767 00:38:11,880 --> 00:38:15,040 Speaker 2: he did. And so yeah, like it's just I feel 768 00:38:15,040 --> 00:38:18,399 Speaker 2: like those are the other meaningful ways that we see 769 00:38:18,400 --> 00:38:19,960 Speaker 2: things going downhill. 770 00:38:20,320 --> 00:38:26,319 Speaker 3: But it's yeah, yeah, I mean I feel like just 771 00:38:26,360 --> 00:38:28,880 Speaker 3: the sad way you said, yeah, it was like a 772 00:38:28,920 --> 00:38:31,960 Speaker 3: pretty good summary. So I feel going on Twitter nowadays, 773 00:38:32,040 --> 00:38:34,480 Speaker 3: like I just, you know, all my joy from that 774 00:38:34,680 --> 00:38:37,680 Speaker 3: platform has gone. But I think you point out something 775 00:38:37,719 --> 00:38:40,760 Speaker 3: really important, which is while the revenue model for news 776 00:38:40,800 --> 00:38:44,799 Speaker 3: sites is clickspace and advertising base, like I think we're 777 00:38:44,800 --> 00:38:47,200 Speaker 3: not necessarily also going to see a solution to this 778 00:38:47,280 --> 00:38:50,319 Speaker 3: problem because unfortunately, you know, and I know this from 779 00:38:50,320 --> 00:38:53,880 Speaker 3: doing like my own social media promotion with the podcast 780 00:38:53,920 --> 00:38:57,640 Speaker 3: I do the Good Robot. The thing that generates money 781 00:38:57,800 --> 00:39:01,799 Speaker 3: is controversy effectively, like nothing does as well as the 782 00:39:01,800 --> 00:39:04,440 Speaker 3: post that I do on TikTok where someone has, you know, 783 00:39:04,520 --> 00:39:07,040 Speaker 3: a go at me because they don't like my particular 784 00:39:07,080 --> 00:39:09,560 Speaker 3: interpretation of Star Trek or whatever. It can be pretty 785 00:39:09,560 --> 00:39:12,080 Speaker 3: good null, but that as soon as a fight starts 786 00:39:12,120 --> 00:39:14,600 Speaker 3: in your comments, that's when a video starts to get views. 787 00:39:15,080 --> 00:39:17,480 Speaker 3: And so, you know, Kathy O'Neil, the AI ethicist, has 788 00:39:17,520 --> 00:39:19,960 Speaker 3: a really interesting book on this called The Shame Machine, 789 00:39:20,160 --> 00:39:23,200 Speaker 3: which he talks about who profits from public shaming, who 790 00:39:23,239 --> 00:39:26,560 Speaker 3: profits from like these big Twitter pilons, Like it's big tech, 791 00:39:26,680 --> 00:39:30,319 Speaker 3: it's firms like Twitter or x or whatever that will 792 00:39:30,320 --> 00:39:33,080 Speaker 3: inevitably be called next year. But you know, it's the 793 00:39:33,080 --> 00:39:35,880 Speaker 3: big platforms, and it's not the people who are doing 794 00:39:35,960 --> 00:39:38,320 Speaker 3: the piling on or the people getting piled on. And 795 00:39:38,480 --> 00:39:39,960 Speaker 3: so I think we see kind of a similar thing 796 00:39:39,960 --> 00:39:42,719 Speaker 3: with a lot of these news media organizations, which is 797 00:39:43,000 --> 00:39:46,520 Speaker 3: when the incentive is just to get as much visibility 798 00:39:46,520 --> 00:39:47,480 Speaker 3: as possible. 799 00:39:47,200 --> 00:39:49,240 Speaker 2: Rather than to be informing. 800 00:39:48,840 --> 00:39:52,880 Speaker 3: You know, providing you know, yeah, true information. You know, 801 00:39:52,920 --> 00:39:55,200 Speaker 3: I think we're not going to find a solution. 802 00:39:56,320 --> 00:39:59,080 Speaker 1: Yeah, all right, we're going to take a quick break 803 00:39:59,120 --> 00:40:01,720 Speaker 1: and we're going to come back and talk about maybe 804 00:40:01,760 --> 00:40:07,600 Speaker 1: some good things, some good possibilities, like is good technology possible? Still, 805 00:40:08,040 --> 00:40:22,640 Speaker 1: we will be right back, and we're back. And one 806 00:40:22,680 --> 00:40:27,560 Speaker 1: of the things that your new book, doctor mcnernie, you 807 00:40:27,600 --> 00:40:30,480 Speaker 1: talk about is what is good technology and is it possible? 808 00:40:30,719 --> 00:40:33,960 Speaker 1: And I feel like I'm so used to this hyper 809 00:40:34,120 --> 00:40:39,359 Speaker 1: capitalism paradigm that I don't think we can have technology 810 00:40:39,400 --> 00:40:43,400 Speaker 1: without having like loss of jobs and free will. But 811 00:40:43,800 --> 00:40:46,319 Speaker 1: I don't know, I've seen this like recent reappraisal of 812 00:40:46,360 --> 00:40:49,560 Speaker 1: like the Luddite movement, which is just a phrase that 813 00:40:49,640 --> 00:40:52,560 Speaker 1: I grew up using to be like anybody who didn't 814 00:40:52,920 --> 00:40:56,240 Speaker 1: want to use a computer and you know, was slightly 815 00:40:56,360 --> 00:41:01,400 Speaker 1: resistant to technology technological progress. And now people like pointing out, no, 816 00:41:01,520 --> 00:41:04,080 Speaker 1: they didn't just want to destroy all machines. They were 817 00:41:04,120 --> 00:41:06,680 Speaker 1: focused on the ones that took jobs and led to 818 00:41:06,719 --> 00:41:09,600 Speaker 1: wage losses. But we turned them into like old man 819 00:41:09,680 --> 00:41:13,600 Speaker 1: screaming at cloud because of our paradigm of like, yeah, 820 00:41:13,640 --> 00:41:17,840 Speaker 1: but that's counterprogress, that's unrealistic. Like so what is my 821 00:41:18,280 --> 00:41:23,239 Speaker 1: closed off capitalist mind missing out on when I think 822 00:41:23,280 --> 00:41:26,960 Speaker 1: about like the direction that technology can take like what 823 00:41:27,280 --> 00:41:31,560 Speaker 1: are the good things that aren't just like basically AI 824 00:41:31,719 --> 00:41:32,680 Speaker 1: being McKinsey. 825 00:41:33,800 --> 00:41:36,160 Speaker 3: Yeah, no, no, I mean, first, I do love this 826 00:41:36,239 --> 00:41:39,440 Speaker 3: reappraisal of the Loutist movement. I know Brian Merchant has 827 00:41:39,480 --> 00:41:41,360 Speaker 3: a book out called Blood in the Machine which is 828 00:41:41,400 --> 00:41:45,000 Speaker 3: like specifically try to reframe the Luddites, this movement. It 829 00:41:45,000 --> 00:41:48,239 Speaker 3: gives what a mation in the UK as the origins 830 00:41:48,239 --> 00:41:50,680 Speaker 3: of the revolution against Big Pick. And I do love 831 00:41:50,719 --> 00:41:52,719 Speaker 3: this because I do think the Ludyites have been unfairly 832 00:41:52,760 --> 00:41:56,200 Speaker 3: maligned as these tech haters. But yeah, second, you know 833 00:41:56,719 --> 00:42:00,799 Speaker 3: so myself and my work wife, doctor Eleanol Drage co 834 00:42:01,000 --> 00:42:03,040 Speaker 3: edited this book called The Good Robot, which is the 835 00:42:03,080 --> 00:42:06,239 Speaker 3: same as our podcast on this provocative question, and we 836 00:42:06,520 --> 00:42:08,840 Speaker 3: mean it very much as like a suggestion and idea, 837 00:42:09,000 --> 00:42:12,960 Speaker 3: not like an inevitability that technology, you know, maybe can 838 00:42:13,040 --> 00:42:14,240 Speaker 3: be good in. 839 00:42:14,120 --> 00:42:16,399 Speaker 4: A lot of spaces. That definitely doesn't sound like. 840 00:42:16,320 --> 00:42:19,319 Speaker 3: A particularly radical idea, particularly in the like tech type 841 00:42:19,360 --> 00:42:22,120 Speaker 3: spaces we've discussed, but like for people like us who 842 00:42:22,120 --> 00:42:24,800 Speaker 3: spend our day to day looking at these really awful 843 00:42:24,840 --> 00:42:27,799 Speaker 3: effects technology like either because they have been designed to 844 00:42:27,840 --> 00:42:31,359 Speaker 3: be really awful, like predictive policing, tools, or because they're 845 00:42:31,400 --> 00:42:34,520 Speaker 3: being exploited and used in like really harmful ways, like 846 00:42:34,560 --> 00:42:38,120 Speaker 3: the way that technologies used to perpetrate gender based violence. 847 00:42:38,400 --> 00:42:41,520 Speaker 3: It can be really easy to be unable to see 848 00:42:41,560 --> 00:42:44,200 Speaker 3: any kind of positive possibilities for a lot of these 849 00:42:44,239 --> 00:42:47,560 Speaker 3: new technologies. But while I definitely, you know, think that 850 00:42:47,600 --> 00:42:50,040 Speaker 3: there's a real place for just the total refusal of 851 00:42:50,040 --> 00:42:52,919 Speaker 3: people like the Luodites or the neo Luodite movement, which 852 00:42:52,960 --> 00:42:54,719 Speaker 3: is kind of trying to bring back a lot of 853 00:42:54,760 --> 00:42:57,800 Speaker 3: these ideas. We want to challenge ourselves and all the 854 00:42:57,840 --> 00:43:00,960 Speaker 3: guests we have in our podcasts to say, like, you know, what, 855 00:43:01,040 --> 00:43:02,959 Speaker 3: does what would it mean for technology to be good? 856 00:43:02,960 --> 00:43:05,799 Speaker 3: And so for us that's feminist and pro justice and 857 00:43:05,840 --> 00:43:09,120 Speaker 3: afformed by all these different kinds of ideas about equality 858 00:43:09,160 --> 00:43:12,319 Speaker 3: and fairness and also what would that look like sort 859 00:43:12,360 --> 00:43:15,560 Speaker 3: of grounded in our everyday lives. So for me, for example, 860 00:43:16,320 --> 00:43:18,400 Speaker 3: a lot of thinking about good technology is trying to 861 00:43:18,400 --> 00:43:21,439 Speaker 3: reclaim technologies that we might not think of as being 862 00:43:21,560 --> 00:43:25,040 Speaker 3: very like high tech, often because they've been associated with women. 863 00:43:25,160 --> 00:43:27,120 Speaker 3: So like I knit, and I have a pair of 864 00:43:27,200 --> 00:43:29,840 Speaker 3: knitting needles on the table next to me, and knitting 865 00:43:29,960 --> 00:43:32,040 Speaker 3: is often not understood as being like a very high 866 00:43:32,120 --> 00:43:35,759 Speaker 3: tech practice. But in the nineteen eighties, when people are 867 00:43:35,760 --> 00:43:38,520 Speaker 3: trying to get more girls and women back into computer science, 868 00:43:39,000 --> 00:43:40,600 Speaker 3: there was this idea that if you can knit, you 869 00:43:40,640 --> 00:43:42,800 Speaker 3: can code, because if you can read a knitting pattern, 870 00:43:43,080 --> 00:43:45,480 Speaker 3: then you can use a coding program like and so 871 00:43:45,640 --> 00:43:49,120 Speaker 3: sometimes now computer science conferences you'll see people put a 872 00:43:49,160 --> 00:43:51,680 Speaker 3: knitting pattern up on the screen and just say what 873 00:43:51,760 --> 00:43:54,360 Speaker 3: coding language is this, And then usually only one or 874 00:43:54,360 --> 00:43:58,520 Speaker 3: two people, often one of the few female attendees will say, oh, 875 00:43:58,960 --> 00:44:00,960 Speaker 3: that's a knitting pattern, because they're the only ones that 876 00:44:01,000 --> 00:44:03,480 Speaker 3: can understand that kind of code. So I think there's 877 00:44:03,480 --> 00:44:07,680 Speaker 3: something really beautiful and like reclaiming those particular kinds of 878 00:44:07,760 --> 00:44:10,520 Speaker 3: technologies that have been maybe excluded from the way we 879 00:44:10,560 --> 00:44:13,879 Speaker 3: talk about tech on my again work wife who co 880 00:44:14,000 --> 00:44:16,920 Speaker 3: edited this book, well she edited most of it actually, 881 00:44:17,080 --> 00:44:19,800 Speaker 3: so who really did the heavy lifting on this book. 882 00:44:20,040 --> 00:44:22,480 Speaker 3: Eleanor talks about the whisk as her example of a 883 00:44:22,560 --> 00:44:25,120 Speaker 3: good technology, and she says she loves the way it looks, 884 00:44:25,120 --> 00:44:26,440 Speaker 3: she likes how she can use it in all these 885 00:44:26,440 --> 00:44:29,080 Speaker 3: different ways, and she says, you know, she's sure there's 886 00:44:29,120 --> 00:44:31,399 Speaker 3: ways that you can misuse this but you know, she's 887 00:44:31,440 --> 00:44:33,400 Speaker 3: it's something that just like makes her life better and 888 00:44:33,560 --> 00:44:37,000 Speaker 3: is designed well in a very simple way. I unfortunately 889 00:44:37,000 --> 00:44:40,000 Speaker 3: have already undermined this good technology for her because I 890 00:44:40,040 --> 00:44:42,640 Speaker 3: then told her about when I was about fourteen, my 891 00:44:42,760 --> 00:44:46,239 Speaker 3: school went to a trip at the Technology Museum in Auckland. 892 00:44:46,560 --> 00:44:48,840 Speaker 3: It's good motet if you're from New Zealand. Everyone's in 893 00:44:48,920 --> 00:44:51,799 Speaker 3: Auckland's been to this museum because it's not that much 894 00:44:51,880 --> 00:44:55,560 Speaker 3: to do in Aland for a school trip. And then 895 00:44:55,640 --> 00:44:58,160 Speaker 3: one of the girls in my class wound the hair 896 00:44:58,239 --> 00:45:00,640 Speaker 3: of another girl into like an antique egg beta. 897 00:45:01,040 --> 00:45:03,080 Speaker 4: Then they couldn't get her out. She got stuck. 898 00:45:04,200 --> 00:45:07,480 Speaker 3: You know, children can make full technologies bad. But apart 899 00:45:07,520 --> 00:45:09,600 Speaker 3: from that, you know, I think like trying to find 900 00:45:09,640 --> 00:45:13,360 Speaker 3: these like little examples of technology that aren't about the 901 00:45:13,440 --> 00:45:15,799 Speaker 3: kind of like big hype of AI, but maybe bring 902 00:45:15,880 --> 00:45:18,319 Speaker 3: us back into the ways that we use technology to 903 00:45:18,400 --> 00:45:20,480 Speaker 3: reshape how worlds and make things a bit better. 904 00:45:20,840 --> 00:45:22,440 Speaker 4: Is what I like to do with this question. 905 00:45:24,080 --> 00:45:27,360 Speaker 2: Is when you think of like, you know, I think 906 00:45:27,680 --> 00:45:30,160 Speaker 2: that the one version is like, well, this generative AI, 907 00:45:30,280 --> 00:45:33,000 Speaker 2: like it democratizes certain things, and I think while on 908 00:45:33,000 --> 00:45:35,560 Speaker 2: one hand, it may allow people access to like create 909 00:45:35,640 --> 00:45:39,160 Speaker 2: things that they haven't before. It also makes other Like 910 00:45:39,160 --> 00:45:41,280 Speaker 2: you're saying, it's the use of it that makes things 911 00:45:42,360 --> 00:45:45,200 Speaker 2: that sort of ultimately determines whether or not a technology 912 00:45:45,239 --> 00:45:47,239 Speaker 2: is good or you know, used in a positive or 913 00:45:47,239 --> 00:45:50,399 Speaker 2: negative way? Is there like when you look at all 914 00:45:50,920 --> 00:45:53,440 Speaker 2: like for all the people that are preaching and proclaiming 915 00:45:53,480 --> 00:45:56,479 Speaker 2: about how AI is opening the door to something new, 916 00:45:57,280 --> 00:45:59,960 Speaker 2: what like as it relates to sort of these law 917 00:46:00,040 --> 00:46:03,800 Speaker 2: large language models, what are the ways that that can't 918 00:46:03,840 --> 00:46:05,759 Speaker 2: Like is that more about a use case or we 919 00:46:05,760 --> 00:46:08,160 Speaker 2: need to lean more into the regulations to make sure 920 00:46:08,200 --> 00:46:11,360 Speaker 2: that AI isn't wielded by nefarious powers? Like how do 921 00:46:11,400 --> 00:46:15,480 Speaker 2: you look at that specific technology and think, Okay, while 922 00:46:15,480 --> 00:46:18,240 Speaker 2: there's definitely like a lot of biased or weird uses 923 00:46:18,280 --> 00:46:21,239 Speaker 2: of it, like, there's also another way to look at 924 00:46:21,239 --> 00:46:23,600 Speaker 2: this and not just kind of like lean into the 925 00:46:23,640 --> 00:46:24,600 Speaker 2: skynet version. 926 00:46:25,920 --> 00:46:28,120 Speaker 3: Yeah, I mean I would take this in a lot 927 00:46:28,120 --> 00:46:30,279 Speaker 3: of different ways. Like my first and sort of most 928 00:46:30,320 --> 00:46:32,879 Speaker 3: important route in is I would say who makes these 929 00:46:32,880 --> 00:46:35,319 Speaker 3: models and who has control over them and who can 930 00:46:35,360 --> 00:46:38,560 Speaker 3: afford to because you know, one of the big changes 931 00:46:38,600 --> 00:46:40,719 Speaker 3: I think that's happened in the last few years is 932 00:46:40,760 --> 00:46:43,920 Speaker 3: that language models have gone from much smaller models that 933 00:46:44,040 --> 00:46:47,319 Speaker 3: maybe like one researcher with a reasonable budget could train 934 00:46:47,400 --> 00:46:51,040 Speaker 3: themselves a lab, to being these absolutely huge models that 935 00:46:51,080 --> 00:46:54,200 Speaker 3: you need a massive amount of energy, a massive amount 936 00:46:54,200 --> 00:46:57,320 Speaker 3: of data, and a massive amount of money to create. 937 00:46:57,440 --> 00:46:59,799 Speaker 3: And so what that means is that companies with a 938 00:47:00,239 --> 00:47:04,359 Speaker 3: move advantage like Google, like open ai are the ones 939 00:47:04,360 --> 00:47:06,239 Speaker 3: who can afford to make these models. And I think 940 00:47:06,320 --> 00:47:09,239 Speaker 3: increasingly it's going to be harder and harder as the 941 00:47:09,360 --> 00:47:12,160 Speaker 3: models get bigger for small firms to enter that market. 942 00:47:12,320 --> 00:47:14,400 Speaker 3: So what we end up with them is a monopoly, 943 00:47:14,440 --> 00:47:16,560 Speaker 3: and I think we're starting to see some of the 944 00:47:16,600 --> 00:47:19,080 Speaker 3: effects of that monopoly right now, where you have a 945 00:47:19,239 --> 00:47:22,200 Speaker 3: few big tech firms kind of having a hand on 946 00:47:22,280 --> 00:47:25,640 Speaker 3: like most of the most powerful and effective models. And 947 00:47:25,719 --> 00:47:27,600 Speaker 3: so I think like even though people say, oh, this 948 00:47:27,680 --> 00:47:31,040 Speaker 3: is going to democratize AI because everyone can generate text 949 00:47:31,120 --> 00:47:33,279 Speaker 3: with these models, It's like, yes, but you know, very 950 00:47:33,280 --> 00:47:36,319 Speaker 3: few people are profiting from it. And also, you know, 951 00:47:36,719 --> 00:47:39,719 Speaker 3: I think very few people then have control as well 952 00:47:39,760 --> 00:47:42,160 Speaker 3: over how long we're able to use those models for 953 00:47:42,680 --> 00:47:44,480 Speaker 3: one day, will they just all be turned off or 954 00:47:44,520 --> 00:47:46,520 Speaker 3: will they be shifted in a way, you know, so 955 00:47:46,600 --> 00:47:49,919 Speaker 3: I think there's still kind of a concentration of control. Yeah, 956 00:47:49,920 --> 00:47:52,640 Speaker 3: And I think second, like you mentioned kind of biases 957 00:47:52,680 --> 00:47:55,680 Speaker 3: and like weird stuff in the models super important, Like 958 00:47:55,800 --> 00:48:00,000 Speaker 3: large language models are trained on data scrape from the Internet, 959 00:48:00,000 --> 00:48:02,160 Speaker 3: and that can be not the best place, as. 960 00:48:02,000 --> 00:48:02,480 Speaker 4: We all know. 961 00:48:02,560 --> 00:48:06,080 Speaker 3: It can be right for all kinds of information, but 962 00:48:06,160 --> 00:48:08,799 Speaker 3: it's also full of a lot of exclusions. So like, 963 00:48:09,160 --> 00:48:11,960 Speaker 3: for example, again when I was working talking to these 964 00:48:12,040 --> 00:48:15,000 Speaker 3: data scientists and engineers at this big tech firm with us, 965 00:48:15,120 --> 00:48:17,160 Speaker 3: things like, oh, well, where do you pull your training 966 00:48:17,239 --> 00:48:21,440 Speaker 3: data from? And they'd say Wikipedia, for example, and you know, 967 00:48:21,480 --> 00:48:24,040 Speaker 3: would say, oh, but like Wikipedia is not a very 968 00:48:24,040 --> 00:48:27,719 Speaker 3: equitable place. Like women are really really vaskally underrepresented on 969 00:48:27,760 --> 00:48:31,000 Speaker 3: Wikipedia pages, both in terms of who writes them and 970 00:48:31,080 --> 00:48:34,000 Speaker 3: also in terms of who gets Wikipedia pages written about them. 971 00:48:34,320 --> 00:48:36,839 Speaker 3: So the physicist Jess Waded here in the UK has 972 00:48:36,840 --> 00:48:40,480 Speaker 3: had this long running project where she just adds a 973 00:48:40,560 --> 00:48:44,160 Speaker 3: woman to Wikipedia like every day, and she's done that, 974 00:48:44,200 --> 00:48:46,960 Speaker 3: I think now for like years. But that it kind 975 00:48:46,960 --> 00:48:49,920 Speaker 3: of just shows like how inequitable though that distribution is. 976 00:48:50,280 --> 00:48:53,480 Speaker 3: If you're training a model on data from Wikipedia, implicitly, 977 00:48:53,520 --> 00:48:55,560 Speaker 3: you might not be trying to do this in any way, 978 00:48:55,880 --> 00:48:58,400 Speaker 3: like you're also training that model to believe in a 979 00:48:58,440 --> 00:49:02,000 Speaker 3: world where say, like women make up of the population 980 00:49:02,239 --> 00:49:05,200 Speaker 3: not fifty percent plus. So there's a lot of like 981 00:49:05,360 --> 00:49:08,640 Speaker 3: bio season harms that come just from exclusion. Another example 982 00:49:08,680 --> 00:49:10,839 Speaker 3: of this is, you know, I have a good friend 983 00:49:10,880 --> 00:49:13,200 Speaker 3: who is a linguist, and something she talks about is 984 00:49:13,480 --> 00:49:17,920 Speaker 3: community that don't have written languages are already automatically just 985 00:49:17,960 --> 00:49:20,880 Speaker 3: not going to be able to partake and whatever benefits 986 00:49:20,960 --> 00:49:23,960 Speaker 3: might come from large language models, whether that's signed languages 987 00:49:24,320 --> 00:49:26,719 Speaker 3: or languages that are only oral, and so you know, 988 00:49:26,719 --> 00:49:28,800 Speaker 3: I think there's just a lot of different ways that 989 00:49:29,320 --> 00:49:31,520 Speaker 3: even beyond these kind of immediate harms of like the 990 00:49:31,560 --> 00:49:34,120 Speaker 3: AI has produced something that we think is really offensive 991 00:49:34,280 --> 00:49:37,160 Speaker 3: or gross that we could see the use of large 992 00:49:37,239 --> 00:49:39,720 Speaker 3: language models maybe creating further inequities. 993 00:49:40,440 --> 00:49:45,520 Speaker 1: Now, a lot of the AI like promise, like the 994 00:49:45,600 --> 00:49:49,520 Speaker 1: developments that are being promised. So even at Davos, like 995 00:49:49,600 --> 00:49:54,800 Speaker 1: this past Davos, at this page I had to miss unfortunately, 996 00:49:54,920 --> 00:49:56,880 Speaker 1: and I hate to miss Davos because I learned so 997 00:49:56,960 --> 00:49:57,319 Speaker 1: much there. 998 00:49:57,360 --> 00:49:58,719 Speaker 2: But you've been to four Davi. 999 00:49:59,040 --> 00:50:04,239 Speaker 1: Havn't you Devia my fourth dim was the best man. 1000 00:50:04,320 --> 00:50:07,160 Speaker 1: We all did mally and just had a cuddle puddle. 1001 00:50:07,480 --> 00:50:11,280 Speaker 1: But Sam Altman, I don't know if he's like consciously 1002 00:50:11,320 --> 00:50:14,279 Speaker 1: scaling back people's expectations, but he was like, soon you 1003 00:50:14,360 --> 00:50:16,200 Speaker 1: might just be able to say what are my most 1004 00:50:16,239 --> 00:50:20,480 Speaker 1: important emails today and have AI summarize them. I was 1005 00:50:20,560 --> 00:50:24,960 Speaker 1: just like, all right, Like I doesn't like outlook already 1006 00:50:25,000 --> 00:50:29,000 Speaker 1: have an offer, like offer a shitty version of that already. 1007 00:50:29,040 --> 00:50:32,759 Speaker 1: So it's like it just feels like the versions of 1008 00:50:32,920 --> 00:50:36,800 Speaker 1: AI that I'm hearing, Like there's this older New Yorker 1009 00:50:36,880 --> 00:50:40,120 Speaker 1: article that was like, I'm not that worried about AI. 1010 00:50:40,200 --> 00:50:43,239 Speaker 1: I think it's from like the kill us all perspective. 1011 00:50:43,320 --> 00:50:46,360 Speaker 1: I think it's going to be like a little McKenzie 1012 00:50:46,560 --> 00:50:49,239 Speaker 1: in everyone's pocket, Like it's going to be this like 1013 00:50:49,640 --> 00:50:54,040 Speaker 1: economic optimization tool that like everybody has access to, and 1014 00:50:54,120 --> 00:50:59,000 Speaker 1: that's going to just make everything shitty and boring. So 1015 00:50:59,200 --> 00:51:01,640 Speaker 1: I don't know, like I'm just curious for your thoughts 1016 00:51:01,680 --> 00:51:05,000 Speaker 1: on that, and like if there are examples of just 1017 00:51:05,040 --> 00:51:09,400 Speaker 1: like functionality from AI that actually like capture your imagination 1018 00:51:09,440 --> 00:51:12,160 Speaker 1: where you're like, oh shit, that would be like cool, 1019 00:51:12,480 --> 00:51:15,920 Speaker 1: that's a cool idea of like something that would be 1020 00:51:16,600 --> 00:51:20,319 Speaker 1: fun and you know, improve people's lives, even if it's 1021 00:51:20,400 --> 00:51:22,520 Speaker 1: just like make their video games better or whatever. 1022 00:51:23,040 --> 00:51:26,640 Speaker 3: Yeah, I mean maybe the email thing appeals to some people. Personally, 1023 00:51:27,080 --> 00:51:30,080 Speaker 3: I want fewer emails. I don't want summary of my emails. 1024 00:51:30,120 --> 00:51:33,000 Speaker 3: I just want my inbox to quietly shut down through 1025 00:51:33,080 --> 00:51:36,880 Speaker 3: the hours of like five pm, to like send a 1026 00:51:36,880 --> 00:51:39,760 Speaker 3: AM every day and just be like I'm email free. 1027 00:51:40,000 --> 00:51:42,200 Speaker 3: And then the run at em Bogass I think has 1028 00:51:42,200 --> 00:51:45,359 Speaker 3: this idea of hyper employment, which is like the technologies 1029 00:51:45,360 --> 00:51:47,520 Speaker 3: that say they're going to make our lives easier and 1030 00:51:47,640 --> 00:51:50,680 Speaker 3: more stress free actually make our lives much busier and 1031 00:51:50,719 --> 00:51:52,719 Speaker 3: we now waste a lot more time. So he talks 1032 00:51:52,719 --> 00:51:55,520 Speaker 3: about emails as a way of, you know, saying like, oh, 1033 00:51:55,560 --> 00:51:57,640 Speaker 3: we're gonna have far fewer meetings and we're gonna like 1034 00:51:57,840 --> 00:52:00,960 Speaker 3: have spend less time like sending each other letters or whatever. 1035 00:52:01,040 --> 00:52:04,359 Speaker 3: Sorry I'm from the post internet generation, but then now 1036 00:52:04,360 --> 00:52:07,279 Speaker 3: we spend like so much time like answering emails. And 1037 00:52:07,320 --> 00:52:09,600 Speaker 3: I think AI feels a bit like this, like when 1038 00:52:09,640 --> 00:52:11,560 Speaker 3: people say like AI is going to save you so 1039 00:52:11,640 --> 00:52:14,960 Speaker 3: much time, I'm like, you are not a teacher an educator, 1040 00:52:15,000 --> 00:52:17,480 Speaker 3: because the amount of time we have wasted this year 1041 00:52:17,560 --> 00:52:20,160 Speaker 3: trying to figure out what to do with AI generated 1042 00:52:20,320 --> 00:52:24,600 Speaker 3: essays like absolutely not. Yeah, and so I feel like 1043 00:52:24,680 --> 00:52:27,799 Speaker 3: things like the AI email summarized I since could end 1044 00:52:27,880 --> 00:52:30,399 Speaker 3: up in a very similar appile. But you know, kind 1045 00:52:30,400 --> 00:52:32,400 Speaker 3: of to come to the more positive side of your question, 1046 00:52:32,640 --> 00:52:35,520 Speaker 3: like what makes me excited, I think a couple of things. 1047 00:52:35,560 --> 00:52:39,359 Speaker 3: Like one is like anything where like AI can genuinely 1048 00:52:39,400 --> 00:52:42,480 Speaker 3: scale up in a way that is not too ecologically 1049 00:52:42,840 --> 00:52:46,520 Speaker 3: damaging or costly a process that is already going well, 1050 00:52:46,560 --> 00:52:49,160 Speaker 3: where the statistics and the procedures in place are working 1051 00:52:49,200 --> 00:52:53,200 Speaker 3: for us. Because AI is able to scale things, you know, 1052 00:52:53,280 --> 00:52:56,360 Speaker 3: it's not necessarily able to do new things always. So 1053 00:52:56,440 --> 00:52:58,840 Speaker 3: if we know we have a sorting or categorization process 1054 00:52:58,880 --> 00:53:01,600 Speaker 3: that works, that's I think AI computer vision. 1055 00:53:01,640 --> 00:53:03,560 Speaker 4: These kinds of systems can be really useful. 1056 00:53:03,800 --> 00:53:05,960 Speaker 3: Where it doesn't work is when you're asking AI to 1057 00:53:06,000 --> 00:53:09,040 Speaker 3: do something that like we don't actually have good processes 1058 00:53:09,080 --> 00:53:11,439 Speaker 3: in place to do so, like when a tool says, oh, 1059 00:53:11,560 --> 00:53:15,319 Speaker 3: I can like power candidate's personality from their face, Like. 1060 00:53:15,719 --> 00:53:18,719 Speaker 4: No, you can't do that. That's just a straight up phrenology. 1061 00:53:18,760 --> 00:53:19,600 Speaker 4: Please don't do that. 1062 00:53:19,680 --> 00:53:22,840 Speaker 3: But also Secondly, like, you know, if we had easy 1063 00:53:22,920 --> 00:53:24,480 Speaker 3: ways of telling if someone was going to be good 1064 00:53:24,520 --> 00:53:26,000 Speaker 3: for a job, like humans will be able to do 1065 00:53:26,040 --> 00:53:29,040 Speaker 3: it already, Like this is a much much more complicated 1066 00:53:29,040 --> 00:53:31,640 Speaker 3: than you're making it out to be. A second kind 1067 00:53:31,640 --> 00:53:34,120 Speaker 3: of use I think I find really exciting or makes 1068 00:53:34,160 --> 00:53:37,880 Speaker 3: me like you're really happy. I think it's particularly tools 1069 00:53:37,920 --> 00:53:42,279 Speaker 3: around trying to kind of like support particular community as 1070 00:53:42,400 --> 00:53:45,719 Speaker 3: needs in a way that is really driven by that community. 1071 00:53:45,800 --> 00:53:48,600 Speaker 3: So for example, in New Zealand where I'm from, there's 1072 00:53:48,600 --> 00:53:51,080 Speaker 3: been a lot of effort put into different kinds of 1073 00:53:51,120 --> 00:53:56,880 Speaker 3: like AI powered tools and data sovereignty programs around Maudi traditions, 1074 00:53:57,000 --> 00:54:00,279 Speaker 3: the Maudi language or three a Maudi and like I think, 1075 00:54:00,360 --> 00:54:02,759 Speaker 3: you know, this is an example of where like that's 1076 00:54:02,800 --> 00:54:05,880 Speaker 3: been led by Maudi people and is in a response 1077 00:54:05,960 --> 00:54:08,360 Speaker 3: to kind of the way that in colonial New Zealand, 1078 00:54:08,440 --> 00:54:11,480 Speaker 3: like there a Maudi was like very deliberately stamped out, 1079 00:54:11,520 --> 00:54:13,560 Speaker 3: and there's been a huge movement to try and kind 1080 00:54:13,560 --> 00:54:17,000 Speaker 3: of protect and revive the language. And I think it's 1081 00:54:17,000 --> 00:54:18,920 Speaker 3: like when your projects like this that makes me a 1082 00:54:18,920 --> 00:54:21,360 Speaker 3: bit more hopeful about the way that AI machine learning 1083 00:54:21,400 --> 00:54:25,560 Speaker 3: could be used to you know, promote these pro justice projects. 1084 00:54:26,200 --> 00:54:28,000 Speaker 3: But you know, I think those projects that always have 1085 00:54:28,040 --> 00:54:29,799 Speaker 3: to exist in a little bit of tension with like 1086 00:54:29,880 --> 00:54:33,200 Speaker 3: big tech. And we've seen this like with other organizations, 1087 00:54:33,200 --> 00:54:36,920 Speaker 3: for example like Masacana, which is the amazing grassroots organization 1088 00:54:37,280 --> 00:54:40,680 Speaker 3: which aims to bring the like four thousand different African 1089 00:54:40,760 --> 00:54:45,320 Speaker 3: languages into our natural language processing models or large language models. 1090 00:54:45,600 --> 00:54:45,799 Speaker 4: You know. 1091 00:54:45,800 --> 00:54:47,600 Speaker 3: But I know that these kinds of groups often do 1092 00:54:47,680 --> 00:54:50,440 Speaker 3: struggle with this idea like do we commercialize because then 1093 00:54:50,760 --> 00:54:53,960 Speaker 3: will we be brought into this hyper capitalist world? Do 1094 00:54:54,000 --> 00:54:56,200 Speaker 3: we keep this to ourselves? But yeah, I think it's 1095 00:54:56,239 --> 00:54:58,080 Speaker 3: it is important sometimes to step back and be like, 1096 00:54:58,120 --> 00:55:01,400 Speaker 3: there are really interesting community projects which are trying to 1097 00:55:01,520 --> 00:55:04,640 Speaker 3: use these techniques and these kinds of knowledge in ways 1098 00:55:04,680 --> 00:55:07,600 Speaker 3: that push back against like the email summarizing bot. 1099 00:55:08,280 --> 00:55:13,400 Speaker 1: Right, is there a historical present? Like I was reading 1100 00:55:13,719 --> 00:55:18,880 Speaker 1: some articles about the competition between the United States and 1101 00:55:18,960 --> 00:55:22,360 Speaker 1: China and how the US is like trying to freeze 1102 00:55:22,480 --> 00:55:25,319 Speaker 1: export of like certain chips to China because they think 1103 00:55:25,360 --> 00:55:28,759 Speaker 1: that will allow China to catch up with them, And 1104 00:55:29,120 --> 00:55:33,040 Speaker 1: it feels like we're there's going to be inevitably an 1105 00:55:33,160 --> 00:55:37,000 Speaker 1: argument where they're like, we need to just go pedal 1106 00:55:37,040 --> 00:55:40,840 Speaker 1: to the floor on AI development because this is the 1107 00:55:40,920 --> 00:55:46,040 Speaker 1: new Manhattan Project. Trust us, you talk a lot about 1108 00:55:46,120 --> 00:55:49,680 Speaker 1: just like this, this alternate possibility of like what what 1109 00:55:49,840 --> 00:55:56,280 Speaker 1: if we didn't use this technology for hypercapitalism and militarism. 1110 00:55:56,760 --> 00:56:01,520 Speaker 1: Are there examples where, like from history that you're aware of, 1111 00:56:01,560 --> 00:56:06,160 Speaker 1: where like technology hasn't been has successfully been like protected 1112 00:56:06,239 --> 00:56:09,759 Speaker 1: from those sorts of things. Are there any even a 1113 00:56:09,960 --> 00:56:14,160 Speaker 1: very small examples where people have been able to keep 1114 00:56:14,239 --> 00:56:17,600 Speaker 1: technology like fenced off from that sort of thing. 1115 00:56:18,600 --> 00:56:21,520 Speaker 3: Yeah, no, I mean this is something that really preoccupies me. 1116 00:56:21,600 --> 00:56:23,880 Speaker 3: I spend a lot of time mapping and tracking with 1117 00:56:23,960 --> 00:56:27,040 Speaker 3: the AI now institute this narrative of an AI arms 1118 00:56:27,160 --> 00:56:30,520 Speaker 3: race between the US and China and how that story 1119 00:56:30,640 --> 00:56:33,319 Speaker 3: is super damaging because it can cause us like race 1120 00:56:33,360 --> 00:56:36,720 Speaker 3: to the bottom and leads to us trying to develop 1121 00:56:36,760 --> 00:56:39,880 Speaker 3: AI faster and faster without necessarily trying to make it 1122 00:56:39,920 --> 00:56:42,319 Speaker 3: better or safer. And do you know, I think we've 1123 00:56:42,360 --> 00:56:46,000 Speaker 3: seen some positive movements when it comes to AI regulation recently, 1124 00:56:46,200 --> 00:56:50,439 Speaker 3: from like the US's commitment or declaration around AI through 1125 00:56:50,480 --> 00:56:55,359 Speaker 3: to the Bletchley Declaration in the UK and the EUAI Act. 1126 00:56:55,800 --> 00:56:57,920 Speaker 3: But at the same time, you know, I think that 1127 00:56:58,239 --> 00:57:01,680 Speaker 3: this sort of racing narrative like still looks and it's 1128 00:57:01,719 --> 00:57:04,960 Speaker 3: still sometimes used to try and push back against regulatory measures, 1129 00:57:05,160 --> 00:57:08,439 Speaker 3: particularly by people with investments in big tech. But yeah, 1130 00:57:08,440 --> 00:57:10,799 Speaker 3: at the same time, I also think this question of, like, 1131 00:57:11,280 --> 00:57:13,840 Speaker 3: can we look to history to find ways to tell 1132 00:57:13,960 --> 00:57:17,320 Speaker 3: different stories about AI maybe bring about different futures is 1133 00:57:17,320 --> 00:57:19,960 Speaker 3: something that really interests me. I think the comparison that 1134 00:57:20,040 --> 00:57:23,840 Speaker 3: is most often made between AI and another technology when 1135 00:57:23,880 --> 00:57:26,880 Speaker 3: it comes to like regulation and governance is nuclear and 1136 00:57:26,960 --> 00:57:31,160 Speaker 3: specifically nuclear weapons. Like and like you mentioned, Manhattan Project, 1137 00:57:31,440 --> 00:57:33,720 Speaker 3: suddenly kind of this sort of language of like an 1138 00:57:33,720 --> 00:57:36,800 Speaker 3: Oppenheimer moment when it comes to AI, and this kind 1139 00:57:36,800 --> 00:57:39,000 Speaker 3: of idea of being like the cusp of a new 1140 00:57:39,080 --> 00:57:42,040 Speaker 3: cold water will be AI driven rather than nuclear weapons 1141 00:57:42,120 --> 00:57:45,280 Speaker 3: driven is a very common media narrative. But something I 1142 00:57:45,440 --> 00:57:47,360 Speaker 3: try to do, like with my own research, is to 1143 00:57:47,400 --> 00:57:51,040 Speaker 3: try and look for and support different kinds of historical 1144 00:57:51,080 --> 00:57:56,040 Speaker 3: analogies that maybe offer maybe less kind of less hawkish 1145 00:57:57,120 --> 00:58:00,720 Speaker 3: futures when it comes to international politics. So for example, 1146 00:58:00,760 --> 00:58:03,840 Speaker 3: Maya Indira Ganish, who's a fantastic researcher at Believer Human 1147 00:58:03,880 --> 00:58:07,600 Speaker 3: Center where I'm at, looks to like histories of feminists 1148 00:58:07,600 --> 00:58:10,720 Speaker 3: cyber governance in the early two thousands, as a way 1149 00:58:10,760 --> 00:58:13,000 Speaker 3: of saying, like, actually, we have a lot of precedents 1150 00:58:13,000 --> 00:58:15,760 Speaker 3: for thinking about the ethics of the internet. Why aren't 1151 00:58:15,800 --> 00:58:19,880 Speaker 3: we bringing this into thinking about AI? Or Matischmas, who's 1152 00:58:19,880 --> 00:58:23,680 Speaker 3: a legal researcher, looks the histories of technological restraints, So like, 1153 00:58:23,760 --> 00:58:26,439 Speaker 3: when did we choose not to make a technology even 1154 00:58:26,440 --> 00:58:28,960 Speaker 3: though we could because we thought that it actually wouldn't 1155 00:58:28,960 --> 00:58:31,200 Speaker 3: be good for the world and for societies. And so 1156 00:58:31,360 --> 00:58:33,840 Speaker 3: I think, you know, like making sure we have examples 1157 00:58:33,920 --> 00:58:37,560 Speaker 3: outside of nuclear because wild nuclear can be still useful 1158 00:58:37,560 --> 00:58:40,840 Speaker 3: in some ways, Like it's only one metaphor, and metaphors 1159 00:58:40,840 --> 00:58:43,720 Speaker 3: are inherently limited. They tell us something about the world, 1160 00:58:43,840 --> 00:58:46,200 Speaker 3: but they can't tell us everything. And I thinks having 1161 00:58:46,280 --> 00:58:50,200 Speaker 3: these alternative historical examples can be really useful for thinking 1162 00:58:50,200 --> 00:58:50,960 Speaker 3: a bit differently. 1163 00:58:51,920 --> 00:58:52,200 Speaker 1: Yeah. 1164 00:58:52,680 --> 00:58:55,320 Speaker 2: So, yeah, it's just funny because in the end, it 1165 00:58:55,320 --> 00:58:59,480 Speaker 2: always feels like thing with potential to create like unforeseen 1166 00:58:59,600 --> 00:59:02,360 Speaker 2: levels productivity or power. It's like and then we made 1167 00:59:02,400 --> 00:59:03,360 Speaker 2: it a weapon. 1168 00:59:03,160 --> 00:59:04,800 Speaker 1: Then we like put it in the bomb, you know, 1169 00:59:05,280 --> 00:59:05,640 Speaker 1: kind of. 1170 00:59:05,600 --> 00:59:07,919 Speaker 2: Like and yeah, like we really do have to sort 1171 00:59:07,960 --> 00:59:10,160 Speaker 2: of break out of that thinking. I mean, I wonder 1172 00:59:10,400 --> 00:59:13,720 Speaker 2: precisely because our brains are filled with sky Net and 1173 00:59:13,760 --> 00:59:18,120 Speaker 2: Oppenheimer and the Manhattan Project, that that's we're just We're 1174 00:59:18,160 --> 00:59:21,200 Speaker 2: just in this really weird pattern of always looking at 1175 00:59:21,240 --> 00:59:24,560 Speaker 2: something that has the potential to unlock new levels of 1176 00:59:24,640 --> 00:59:27,680 Speaker 2: something are inherently going to always be like, but how 1177 00:59:27,760 --> 00:59:30,919 Speaker 2: does how do our enemies kill us with it? And 1178 00:59:30,960 --> 00:59:33,120 Speaker 2: then we begin to lose the plot there. So yeah, 1179 00:59:33,200 --> 00:59:36,880 Speaker 2: I'm I'm yeah, look to these other examples to try 1180 00:59:36,880 --> 00:59:39,919 Speaker 2: and again open my mind to looking at it less 1181 00:59:39,920 --> 00:59:41,960 Speaker 2: of like and then how they make and then how 1182 00:59:42,000 --> 00:59:43,640 Speaker 2: they make global domination with that? 1183 00:59:44,440 --> 00:59:46,280 Speaker 3: Right, Yeah, I mean I think it's either it's like 1184 00:59:46,320 --> 00:59:48,720 Speaker 3: how do we kill someone with it? Or as I 1185 00:59:48,720 --> 00:59:51,480 Speaker 3: think the history of like how tech is represented in 1186 00:59:51,520 --> 00:59:53,880 Speaker 3: Hollywood would show us like how do we have sex 1187 00:59:53,920 --> 00:59:55,840 Speaker 3: with it? And this is like a very classic trip 1188 00:59:55,880 --> 00:59:57,800 Speaker 3: in sci fi, right, It's like you get like a 1189 00:59:57,880 --> 00:59:59,880 Speaker 3: dude in his basement who makes like a sex spot. 1190 01:00:00,160 --> 01:00:03,400 Speaker 3: And I remember interviewing Jack Harbuston, who's like this very 1191 01:00:03,640 --> 01:00:07,640 Speaker 3: famous feminist and queer theorist with Eleanor on the podcast, 1192 01:00:08,040 --> 01:00:10,200 Speaker 3: and he was talking about the film X Markina. Have 1193 01:00:10,240 --> 01:00:12,960 Speaker 3: you seen this since from like twenty fourteen. It's like 1194 01:00:13,160 --> 01:00:14,920 Speaker 3: a kind of a kind of an indie film, but 1195 01:00:14,960 --> 01:00:19,000 Speaker 3: had quite a lot of prestige I think success particularly like. 1196 01:00:19,000 --> 01:00:20,680 Speaker 1: Tech Circles, Yeah for sure. 1197 01:00:21,040 --> 01:00:21,480 Speaker 2: Yeah. 1198 01:00:21,680 --> 01:00:24,280 Speaker 3: Yeah, And so I remember we were talking about x 1199 01:00:24,320 --> 01:00:27,160 Speaker 3: Markina and Jack Harbison was saying like it sort of 1200 01:00:27,200 --> 01:00:30,120 Speaker 3: shows like the limits of the imagination, like particularly like 1201 01:00:30,160 --> 01:00:33,120 Speaker 3: the tech pro imagination that he has like all this 1202 01:00:33,280 --> 01:00:36,720 Speaker 3: expertise at his fingertips, all this data and he basically 1203 01:00:36,760 --> 01:00:39,960 Speaker 3: just makes like sex robots and like that's really. 1204 01:00:39,800 --> 01:00:41,040 Speaker 4: Good, really thing to do with it. 1205 01:00:41,160 --> 01:00:43,600 Speaker 3: And I think, you know, to some extent, like we're 1206 01:00:43,640 --> 01:00:46,080 Speaker 3: still a little bit trapped in that imagination, which is 1207 01:00:46,080 --> 01:00:49,520 Speaker 3: why I think like both like different projects to do 1208 01:00:49,640 --> 01:00:54,240 Speaker 3: with AI, but also different stories about AI are really crucial, right. 1209 01:00:54,200 --> 01:00:56,120 Speaker 2: Yeah, Yeah, we have to get out of the fuck 1210 01:00:56,240 --> 01:01:00,160 Speaker 2: or fear paradigm. Right that we have the technology, it's 1211 01:01:00,200 --> 01:01:03,200 Speaker 2: going to do one of the two, man, So yeah, 1212 01:01:03,240 --> 01:01:05,080 Speaker 2: we need a new ways. 1213 01:01:05,400 --> 01:01:08,680 Speaker 1: I'm here to do two things, fuck something or kill 1214 01:01:09,000 --> 01:01:09,960 Speaker 1: because I'm scared of it. 1215 01:01:10,280 --> 01:01:10,840 Speaker 2: Yeah. Yeah. 1216 01:01:12,320 --> 01:01:14,960 Speaker 1: Well, doctor Kerry mcinernie, what a pleasure having you back 1217 01:01:14,960 --> 01:01:18,040 Speaker 1: on the Daily Geist. Where can people find you? 1218 01:01:18,200 --> 01:01:18,560 Speaker 2: Follow? 1219 01:01:18,600 --> 01:01:19,840 Speaker 1: You all that good stuff. 1220 01:01:20,320 --> 01:01:21,520 Speaker 4: You can find me at Carrie A. 1221 01:01:21,720 --> 01:01:26,120 Speaker 3: Mcinernie on Twitter slash x reluctantly still there, I know 1222 01:01:26,200 --> 01:01:29,480 Speaker 3: those are around. That's the Good Robot Book, which you 1223 01:01:29,520 --> 01:01:31,800 Speaker 3: can find at Bloomsbury for pre order. It'll be out 1224 01:01:31,880 --> 01:01:35,360 Speaker 3: next month, I think, also on Amazon, but yes, please 1225 01:01:35,440 --> 01:01:37,880 Speaker 3: take a look if you're interested in all things feminism 1226 01:01:38,000 --> 01:01:38,800 Speaker 3: and technology. 1227 01:01:39,280 --> 01:01:42,400 Speaker 1: Amazing and is there a work of media that you've 1228 01:01:42,440 --> 01:01:43,120 Speaker 1: been enjoying. 1229 01:01:43,480 --> 01:01:47,160 Speaker 3: Oh gosh, all that's coming to mind right now is 1230 01:01:47,200 --> 01:01:49,760 Speaker 3: this tweet that I saw that I just moved to 1231 01:01:49,800 --> 01:01:52,280 Speaker 3: London quite recently, and it was a tweet where someone 1232 01:01:52,320 --> 01:01:54,760 Speaker 3: had said should I like stay in bed or get 1233 01:01:54,760 --> 01:01:57,400 Speaker 3: out of bed and inevitably spend one hundred and forty dollars? 1234 01:01:57,440 --> 01:01:59,760 Speaker 4: And I feel like that's literally my life right now. 1235 01:02:00,480 --> 01:02:02,200 Speaker 3: I just spend all my time deciding like do I 1236 01:02:02,280 --> 01:02:05,520 Speaker 3: risk going outside? And this very beautiful but very expensive 1237 01:02:05,520 --> 01:02:07,000 Speaker 3: city I've been enjoying that. 1238 01:02:07,920 --> 01:02:11,240 Speaker 2: Yeah, it's uh, it's funny too because even comparatively, like 1239 01:02:11,280 --> 01:02:13,560 Speaker 2: coming from la I'm like, man, I wish there was 1240 01:02:13,600 --> 01:02:16,959 Speaker 2: London prices here. Yeah. 1241 01:02:17,120 --> 01:02:20,040 Speaker 1: The US is bad right now right now, and it's 1242 01:02:20,080 --> 01:02:21,760 Speaker 1: gonna get better one day. I kicks in. 1243 01:02:22,120 --> 01:02:26,840 Speaker 2: Yeah, exactly. Can a I bring the prices down? No, unfortunately, 1244 01:02:27,040 --> 01:02:28,520 Speaker 2: but like it's funny too because we were just on 1245 01:02:28,560 --> 01:02:31,280 Speaker 2: the heels of like that story that came out about like, yeah, 1246 01:02:31,320 --> 01:02:33,520 Speaker 2: a lot of the cost of living of ills that 1247 01:02:33,560 --> 01:02:37,040 Speaker 2: we're feeling because of greed flation and really nothing to 1248 01:02:37,080 --> 01:02:39,280 Speaker 2: do with anything else aside from companies being like, yeah, yeah, 1249 01:02:39,280 --> 01:02:40,000 Speaker 2: we can squeeze them. 1250 01:02:40,040 --> 01:02:42,600 Speaker 1: Let's squeeze them. If only there was a daily comedic 1251 01:02:42,640 --> 01:02:45,080 Speaker 1: news podcast that has been saying that for years. 1252 01:02:46,760 --> 01:02:49,040 Speaker 2: That makes me very mad. And if there is, let 1253 01:02:49,080 --> 01:02:52,400 Speaker 2: us know about that podcast. Yeah, that sounds like a 1254 01:02:52,440 --> 01:02:53,520 Speaker 2: good show. Miles. 1255 01:02:53,520 --> 01:02:55,600 Speaker 1: Where can people find you as their working media you've 1256 01:02:55,600 --> 01:02:56,160 Speaker 1: been enjoying? 1257 01:02:56,880 --> 01:03:01,520 Speaker 2: Uh yeah, let's see. You can find me at Miles 1258 01:03:01,520 --> 01:03:04,600 Speaker 2: of Gray, at all the app based platforms. You can 1259 01:03:04,640 --> 01:03:07,720 Speaker 2: find us Jack and Miles on our basketball podcast Miles 1260 01:03:07,720 --> 01:03:10,080 Speaker 2: and Jack Got Mad map please, And if you like 1261 01:03:10,120 --> 01:03:13,320 Speaker 2: a bit of ninety day reality trash, you can check 1262 01:03:13,360 --> 01:03:16,120 Speaker 2: me out on four to twenty day Fiance that I 1263 01:03:16,160 --> 01:03:21,919 Speaker 2: do with Sofia Alexandra. Let's see. Let's see. Let's see 1264 01:03:21,960 --> 01:03:25,120 Speaker 2: a tweet I like this one is from the hype 1265 01:03:25,320 --> 01:03:29,840 Speaker 2: with five four wise tweeted boarding a flight, a guy 1266 01:03:29,920 --> 01:03:32,720 Speaker 2: is trying to sit down in seat sixteen D because 1267 01:03:32,760 --> 01:03:35,280 Speaker 2: that's his seat, but there's a lady already sitting there. 1268 01:03:35,400 --> 01:03:38,200 Speaker 2: He asks if she's in the right seat, and she says, no, 1269 01:03:38,360 --> 01:03:41,480 Speaker 2: I'm actually fifteen D, but sixteen is my lucky number, 1270 01:03:41,560 --> 01:03:45,479 Speaker 2: So do you mind if I stay here? And he goes, what, 1271 01:03:50,000 --> 01:03:52,480 Speaker 2: it's a wild like we always see stories like this 1272 01:03:52,600 --> 01:03:55,880 Speaker 2: or people like just on like just out for vibes purposes, 1273 01:03:55,960 --> 01:03:58,520 Speaker 2: like do you mind if I, like don't sit in 1274 01:03:58,600 --> 01:04:01,400 Speaker 2: my seat and because I just like a window better, 1275 01:04:01,880 --> 01:04:04,240 Speaker 2: Like huh no. 1276 01:04:03,960 --> 01:04:05,320 Speaker 4: No, this is my pet peek. 1277 01:04:05,360 --> 01:04:08,280 Speaker 1: I'm generally a pretty easy switch on the on the plane, 1278 01:04:08,400 --> 01:04:10,320 Speaker 1: I don't I don't really give a fuck. I mean, 1279 01:04:10,360 --> 01:04:12,600 Speaker 1: obviously if I'm sitting next to my kids, but like 1280 01:04:12,800 --> 01:04:15,960 Speaker 1: if if it's just me, I'll switch with you. Yeah, yeah, 1281 01:04:16,000 --> 01:04:19,720 Speaker 1: you can target me weird vibes people, I'll switch with you. 1282 01:04:19,800 --> 01:04:21,760 Speaker 1: I'll be like, oh, you seem attached to it. 1283 01:04:21,840 --> 01:04:23,920 Speaker 2: I mean, I get like, if I'm just sitting in 1284 01:04:23,960 --> 01:04:27,320 Speaker 2: an identical seat just in a row behind, I guess not, 1285 01:04:27,480 --> 01:04:30,439 Speaker 2: but it's just so weird for the person that sort. 1286 01:04:30,440 --> 01:04:32,680 Speaker 2: It just irks me a bit. Is that you feel 1287 01:04:32,720 --> 01:04:35,240 Speaker 2: that you are entitled to your vibe seat. 1288 01:04:35,440 --> 01:04:37,400 Speaker 1: God, Like, what it must be like to live in 1289 01:04:37,400 --> 01:04:40,680 Speaker 1: a head like that though, Yeah, goddamn. 1290 01:04:40,120 --> 01:04:42,560 Speaker 2: I would be like what would happen if I said, no, 1291 01:04:42,760 --> 01:04:45,720 Speaker 2: the plane goes down right, The plane goes down right, 1292 01:04:46,240 --> 01:04:48,080 Speaker 2: the plane goes down right? Yeah, then you know what, 1293 01:04:48,680 --> 01:04:50,520 Speaker 2: I'm not feeling too great, So you know what, I 1294 01:04:50,560 --> 01:04:54,120 Speaker 2: think I want my original seat. Thanks get your asked 1295 01:04:54,120 --> 01:04:56,760 Speaker 2: to fifteen D doctor mcnernie. You were saying that's your 1296 01:04:56,800 --> 01:04:59,200 Speaker 2: pet peeve? Is somebody try really is? 1297 01:04:59,280 --> 01:05:01,320 Speaker 3: Because I'm like a we towel of a person. So 1298 01:05:01,440 --> 01:05:03,160 Speaker 3: someone just like sits in my seat and it's like, 1299 01:05:03,200 --> 01:05:05,320 Speaker 3: this is my seat. Now, I'm like, I resent you 1300 01:05:05,440 --> 01:05:07,440 Speaker 3: so much? But also am I really. 1301 01:05:07,240 --> 01:05:09,160 Speaker 4: Going to pick a fight? But what if you're just like, 1302 01:05:09,360 --> 01:05:13,880 Speaker 4: what sixteen is my lucky number? Oh it's crazy who's 1303 01:05:13,920 --> 01:05:14,840 Speaker 4: going to win on that? 1304 01:05:15,120 --> 01:05:15,320 Speaker 2: Yeah? 1305 01:05:15,400 --> 01:05:19,840 Speaker 3: But no, I resent that the person that tweeted I'm 1306 01:05:19,880 --> 01:05:22,600 Speaker 3: with you, Yeah my seat please? 1307 01:05:23,040 --> 01:05:25,800 Speaker 2: I mean I get to like, like from jack your perspectives, 1308 01:05:25,800 --> 01:05:27,520 Speaker 2: like whatever, it's not that big of a deal for 1309 01:05:27,600 --> 01:05:29,640 Speaker 2: me to like sit there. But there's also just this 1310 01:05:29,720 --> 01:05:33,760 Speaker 2: part of like how quickly the world just owed you everything. 1311 01:05:34,240 --> 01:05:37,560 Speaker 1: Mm hmmm, Oh, I will be sitting in my new 1312 01:05:37,600 --> 01:05:41,600 Speaker 1: seat for the entire flight, sweating with anger at them 1313 01:05:42,200 --> 01:05:45,360 Speaker 1: to keep it inside, and I'll have a summer later on. 1314 01:05:46,280 --> 01:05:48,360 Speaker 2: Like you know what, I'm going to say something, ladies 1315 01:05:48,360 --> 01:05:51,640 Speaker 2: and gentlemen, we are making our descent, so if you'd like, 1316 01:05:51,680 --> 01:05:54,880 Speaker 2: please put your seat back. I should have said something, 1317 01:05:54,920 --> 01:05:56,160 Speaker 2: should have said something next. 1318 01:05:56,280 --> 01:05:59,640 Speaker 1: My seat's twenty seven c right across from the laboratory. 1319 01:06:01,200 --> 01:06:06,560 Speaker 2: Okay, the stink seat over in stinc hut tweet. 1320 01:06:06,600 --> 01:06:09,400 Speaker 1: I've been enjoying. Something that got retweeted a lot was 1321 01:06:09,440 --> 01:06:12,400 Speaker 1: this tweet from Emily K tweeted. The thing about Taylor 1322 01:06:12,440 --> 01:06:15,280 Speaker 1: Swift is that she so perfectly encapsulates through her lyrics 1323 01:06:15,280 --> 01:06:18,439 Speaker 1: the interior lives of women. It's why we all can't 1324 01:06:18,440 --> 01:06:21,560 Speaker 1: stop listening. We're all saying, wait, you felt that way, 1325 01:06:21,640 --> 01:06:24,440 Speaker 1: We were all feeling this way. Do men have someone 1326 01:06:24,520 --> 01:06:29,680 Speaker 1: like that? And then metal dot t xt responded we do, yeah, 1327 01:06:29,840 --> 01:06:33,120 Speaker 1: and screen capped the chorus from the boys are back 1328 01:06:33,160 --> 01:06:36,800 Speaker 1: in town, which God, which reads the boys are back 1329 01:06:36,840 --> 01:06:39,439 Speaker 1: in town. The boys are back in town. I said, 1330 01:06:39,480 --> 01:06:41,840 Speaker 1: the boys are bagging down, the boys are backing down, 1331 01:06:41,960 --> 01:06:45,840 Speaker 1: Like seven times. I didn't realize that said it so 1332 01:06:45,920 --> 01:06:48,920 Speaker 1: many times in a row. So yeah, we got that 1333 01:06:49,080 --> 01:06:52,240 Speaker 1: one cover. Yeah, you want to know how men's minds work. 1334 01:06:53,120 --> 01:06:55,680 Speaker 1: We're just excited that the boys are back in town, 1335 01:06:56,200 --> 01:07:00,680 Speaker 1: and that's basically it. You can find me on Twitter 1336 01:07:00,760 --> 01:07:03,840 Speaker 1: at Jack Underscore O'Brien. You can find us on Twitter 1337 01:07:03,840 --> 01:07:06,600 Speaker 1: at Daily Zeitgeist. We're at the Daily Zeitgeist on Instagram. 1338 01:07:06,600 --> 01:07:09,160 Speaker 1: We have a Facebook fampage and a website, Daily zeiguist 1339 01:07:09,160 --> 01:07:12,360 Speaker 1: dot com, where we post our episodes and our footnotes 1340 01:07:12,720 --> 01:07:14,360 Speaker 1: where we link off to the information that we talked 1341 01:07:14,360 --> 01:07:17,440 Speaker 1: about today's episode, as well as a song that we 1342 01:07:17,480 --> 01:07:21,240 Speaker 1: think you might enjoy. Miles, what song do we think 1343 01:07:21,280 --> 01:07:22,400 Speaker 1: people might enjoy? 1344 01:07:22,880 --> 01:07:25,560 Speaker 2: Uh? It's rainy here in La. I was just I've 1345 01:07:25,560 --> 01:07:28,040 Speaker 2: been listening to some Alex, some jazz, you know, some 1346 01:07:28,080 --> 01:07:32,000 Speaker 2: saxophone from Joe Henderson, who's a great sax player. This 1347 01:07:32,120 --> 01:07:35,080 Speaker 2: is a track called black Narcissus. Uh, and just a 1348 01:07:35,120 --> 01:07:37,600 Speaker 2: good just a nice rainy day song just to have 1349 01:07:37,720 --> 01:07:40,120 Speaker 2: on and just look out your window, do some reading, 1350 01:07:40,200 --> 01:07:42,200 Speaker 2: whatever whatever you want to do. But yeah, this is 1351 01:07:42,240 --> 01:07:44,200 Speaker 2: it Joe Henderson Black Narcissus. 1352 01:07:44,480 --> 01:07:46,760 Speaker 1: All right, we will look off to that footnotes Today. 1353 01:07:46,880 --> 01:07:48,880 Speaker 1: Zaike is a production by Heart Radio. For more podcast 1354 01:07:48,920 --> 01:07:51,360 Speaker 1: from My heart Radio, visit the iHeartRadio, ap Apple podcast, 1355 01:07:51,440 --> 01:07:53,360 Speaker 1: or wherever you listen to your favorite shows. That's gonna 1356 01:07:53,400 --> 01:07:55,440 Speaker 1: do it for us this morning, back this afternoon to 1357 01:07:55,480 --> 01:07:58,000 Speaker 1: tell you what is trending and we'll talk to you 1358 01:07:58,040 --> 01:08:02,160 Speaker 1: all then Bye bye. She drank a chop and pods