1 00:00:00,240 --> 00:00:04,680 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,760 --> 00:00:09,080 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,200 --> 00:00:25,560 Speaker 1: production of I Heart Radio. Hello, welcome back to the show. 5 00:00:25,680 --> 00:00:28,560 Speaker 1: My name is Matt Noel is on an adventure today 6 00:00:28,720 --> 00:00:32,080 Speaker 1: but will be returning soon. They call me Ben. We 7 00:00:32,120 --> 00:00:35,760 Speaker 1: are joined as always with our super producer Alexis code 8 00:00:35,800 --> 00:00:39,599 Speaker 1: named Doc Holiday Jackson. Most importantly, you are you. You 9 00:00:39,720 --> 00:00:43,640 Speaker 1: are here, and that makes this stuff they don't want 10 00:00:43,680 --> 00:00:46,880 Speaker 1: you to know. It is the top of the week, 11 00:00:47,320 --> 00:00:51,159 Speaker 1: right after quite a quite a wonderful long weekend for 12 00:00:51,320 --> 00:00:54,520 Speaker 1: us here in uh, not just in Atlanta, but in 13 00:00:54,680 --> 00:00:57,240 Speaker 1: Detroit and in New York, in the various places that 14 00:00:57,280 --> 00:01:01,600 Speaker 1: we find ourselves scattered. Uh. As usual, Matt, as we 15 00:01:01,760 --> 00:01:06,520 Speaker 1: like to do, Uh, you and I have scoured the internet. 16 00:01:06,880 --> 00:01:11,560 Speaker 1: We have uh we have dug deep into the weirdest 17 00:01:11,600 --> 00:01:16,280 Speaker 1: stories that often don't make it to the mainstream spotlight. 18 00:01:16,440 --> 00:01:19,039 Speaker 1: And one thing that I thought was interesting is you 19 00:01:19,160 --> 00:01:24,080 Speaker 1: and I both went with some we we both went 20 00:01:24,160 --> 00:01:28,520 Speaker 1: with some scary tech futurism, some scary tech stories. But 21 00:01:28,680 --> 00:01:32,000 Speaker 1: before we go, you know, I was thinking, because it's 22 00:01:32,040 --> 00:01:35,880 Speaker 1: just the four of us. Oh no, no, that's fine, Yeah, 23 00:01:35,959 --> 00:01:38,360 Speaker 1: go for it. I would feel like I was being 24 00:01:38,480 --> 00:01:40,600 Speaker 1: rude if I didn't back you up on that. We 25 00:01:40,640 --> 00:01:44,440 Speaker 1: can make it if we try. Uh, what's the other way? 26 00:01:44,560 --> 00:01:47,720 Speaker 1: It's just the two of us on Mike currently. Unless 27 00:01:47,880 --> 00:01:51,720 Speaker 1: Doc you know, gets a wild hair m Yeah, if 28 00:01:51,760 --> 00:01:57,480 Speaker 1: we say something that especially infuriates code named Doc Holiday, 29 00:01:57,560 --> 00:02:01,000 Speaker 1: she will not hesitate to put us back in line. 30 00:02:01,600 --> 00:02:03,920 Speaker 1: I said, oh, oh no, wait, we've got video and 31 00:02:04,000 --> 00:02:09,120 Speaker 1: she's gone. That was a close call. Well so and 32 00:02:09,160 --> 00:02:12,519 Speaker 1: that was that was doctor self laughing there. Uh so, 33 00:02:12,960 --> 00:02:15,040 Speaker 1: what I'm thinking we could do, Matt is let's explore 34 00:02:15,040 --> 00:02:17,840 Speaker 1: our stories together and then maybe if we have time, 35 00:02:17,880 --> 00:02:20,840 Speaker 1: if we have sort of a third act, we could 36 00:02:21,200 --> 00:02:24,640 Speaker 1: uh throw some headlines at each other, because I'm sure 37 00:02:24,680 --> 00:02:27,000 Speaker 1: there's a lot of stuff that you see that just 38 00:02:27,560 --> 00:02:30,760 Speaker 1: doesn't make the cut. To be your chosen when almost 39 00:02:30,840 --> 00:02:33,560 Speaker 1: a choice in one, Uh, you're you're you're chosen one 40 00:02:33,639 --> 00:02:35,960 Speaker 1: for the week. How do you feel about that? Sounds great? 41 00:02:36,280 --> 00:02:39,000 Speaker 1: Did you read the one about the sting rays in 42 00:02:39,120 --> 00:02:43,200 Speaker 1: the little touching area of a specific you know, the 43 00:02:43,200 --> 00:02:45,080 Speaker 1: place where you can actually touch the sting rays and 44 00:02:45,080 --> 00:02:48,320 Speaker 1: in aquaria. No, I didn't. Okay, well we'll talk about 45 00:02:48,320 --> 00:02:50,160 Speaker 1: it later. Well, now we have to do it. Did 46 00:02:50,160 --> 00:02:56,040 Speaker 1: you read the one about the rogue AI drone? No? Okay, 47 00:02:56,120 --> 00:02:58,440 Speaker 1: all right, well okay, we'll do the show first and 48 00:02:58,480 --> 00:03:01,880 Speaker 1: then we'll do the rest of this. Okay, So we 49 00:03:01,880 --> 00:03:07,080 Speaker 1: we said we picked two uh pieces of scary tech reporting, 50 00:03:07,520 --> 00:03:10,040 Speaker 1: and Matt, I've got to be honest, mine is going 51 00:03:10,080 --> 00:03:12,680 Speaker 1: to be familiar to quite a few of our fellow 52 00:03:12,720 --> 00:03:17,520 Speaker 1: conspiracy realist on the Here's where it gets crazy Facebook page. Uh. 53 00:03:17,560 --> 00:03:22,680 Speaker 1: This is a story about social media and big data collection, 54 00:03:22,880 --> 00:03:27,280 Speaker 1: and your story is I believe, also about technology that's 55 00:03:27,320 --> 00:03:31,280 Speaker 1: going to become increasingly dangerous and pervasive in the in 56 00:03:31,360 --> 00:03:36,200 Speaker 1: the next few years. And your story also deals specifically 57 00:03:36,280 --> 00:03:39,320 Speaker 1: with some problems that we have pointed out in uh 58 00:03:39,640 --> 00:03:43,960 Speaker 1: emergent AI tech. Right, I. I don't want to go 59 00:03:44,000 --> 00:03:47,280 Speaker 1: too much further without spoiling the surprise, my old friend, 60 00:03:47,280 --> 00:03:50,000 Speaker 1: Do you have a preference for what terrible thing we 61 00:03:50,080 --> 00:03:53,800 Speaker 1: learned about first? Let's can may I go first? And 62 00:03:53,880 --> 00:03:58,600 Speaker 1: it's only because it's a specific hardware software combo that's 63 00:03:58,640 --> 00:04:02,800 Speaker 1: kind of one piece of the larger story. That is 64 00:04:02,840 --> 00:04:05,520 Speaker 1: what you are going to talk about. I think let's 65 00:04:05,520 --> 00:04:08,640 Speaker 1: do it, okay, so we'll build up. Well, I'll read 66 00:04:08,680 --> 00:04:11,960 Speaker 1: you what I've got here. This is the title of 67 00:04:11,960 --> 00:04:17,080 Speaker 1: the article AI emotion detection software tested on weakers. This 68 00:04:17,120 --> 00:04:20,599 Speaker 1: is from BBC News, written by Jane Wakefield. I believe 69 00:04:20,640 --> 00:04:25,320 Speaker 1: it's it's May twenty of this year, and what I 70 00:04:25,320 --> 00:04:28,080 Speaker 1: want to talk about today isn't necessarily about the weaker 71 00:04:28,120 --> 00:04:32,680 Speaker 1: population and their treatment um by Chinese authorities, and you 72 00:04:32,720 --> 00:04:35,679 Speaker 1: know the government there that this is a very important 73 00:04:35,680 --> 00:04:38,479 Speaker 1: aspect of this story and it is worth going into 74 00:04:38,480 --> 00:04:40,760 Speaker 1: and diving into. What I really want to talk about 75 00:04:41,080 --> 00:04:45,919 Speaker 1: is the tech that is emerging here UM and and 76 00:04:46,240 --> 00:04:50,000 Speaker 1: the implications that it could have. So this is what 77 00:04:50,080 --> 00:04:52,599 Speaker 1: I will I'll read you directly from that BBC article. 78 00:04:53,040 --> 00:04:55,599 Speaker 1: This is what it says. A camera system that uses 79 00:04:55,680 --> 00:04:59,920 Speaker 1: AI and facial recognition intended to reveal states of emotion 80 00:05:00,040 --> 00:05:03,320 Speaker 1: and has been tested on weakers in jin Yang. The 81 00:05:03,320 --> 00:05:06,400 Speaker 1: BBC has been told. Now this is important. The BBC 82 00:05:06,680 --> 00:05:10,240 Speaker 1: was told this, and they're not revealing their sources. It 83 00:05:10,320 --> 00:05:13,719 Speaker 1: says a software engineer claimed to have installed such systems 84 00:05:13,720 --> 00:05:16,839 Speaker 1: and police stations in the province, and the software engineer 85 00:05:16,880 --> 00:05:19,919 Speaker 1: agreed to talk to the BBC's Panorama program under the 86 00:05:19,960 --> 00:05:24,240 Speaker 1: condition of anonymity, and this is because he fears for 87 00:05:24,320 --> 00:05:27,600 Speaker 1: his own safety and the company for which he worked 88 00:05:27,720 --> 00:05:29,719 Speaker 1: is not being revealed. It was not revealed in that 89 00:05:29,880 --> 00:05:33,960 Speaker 1: entire article, but he did show Panorama five photographs of 90 00:05:34,240 --> 00:05:38,920 Speaker 1: specifically weaker detainees he claimed had the emotion recognition system 91 00:05:39,040 --> 00:05:42,920 Speaker 1: tested upon them. So, first of all, before we even 92 00:05:43,000 --> 00:05:47,599 Speaker 1: jump into this, this specific article and story that's being 93 00:05:47,600 --> 00:05:52,240 Speaker 1: told to the BBC Panorama is being told by, you know, 94 00:05:52,279 --> 00:05:55,560 Speaker 1: a source that the BBC has chosen to trust with 95 00:05:55,640 --> 00:05:59,000 Speaker 1: that information. But as you know, a listener, as a viewer, 96 00:05:59,200 --> 00:06:02,120 Speaker 1: as a re or, we have to then trust the 97 00:06:02,160 --> 00:06:05,040 Speaker 1: BBC as an outlet to have made that decision that, Okay, 98 00:06:05,120 --> 00:06:10,159 Speaker 1: this information may be legitimate, but we you know, we 99 00:06:10,200 --> 00:06:12,640 Speaker 1: don't have a specific name that can be cited, a 100 00:06:12,640 --> 00:06:15,120 Speaker 1: source that can be cited, so we have to take it, 101 00:06:15,680 --> 00:06:19,080 Speaker 1: I don't know, as a little less than absolutely true. 102 00:06:19,640 --> 00:06:22,200 Speaker 1: Does that make sense? Sure, we have to at least 103 00:06:22,279 --> 00:06:28,400 Speaker 1: be cautious about being overly credulous, right, That's that's something 104 00:06:28,440 --> 00:06:32,320 Speaker 1: that's tricky with any kind of sensitive reporting, because you know, 105 00:06:32,400 --> 00:06:35,919 Speaker 1: if you're a reporter, whether you're Seymour Hurst or a 106 00:06:35,960 --> 00:06:38,159 Speaker 1: cub just starting out. The last thing you want to 107 00:06:38,200 --> 00:06:41,360 Speaker 1: do is compromise a source, because it means other people 108 00:06:41,400 --> 00:06:44,080 Speaker 1: won't trust you in the future, and in this case, 109 00:06:44,240 --> 00:06:50,120 Speaker 1: it means the source could be detained, uh, incarcerated, tortured, murdered. 110 00:06:50,320 --> 00:06:53,720 Speaker 1: These are all options that are on the table. There. 111 00:06:53,720 --> 00:06:56,120 Speaker 1: Those are all options on the table. And simultaneously, we 112 00:06:56,160 --> 00:07:00,320 Speaker 1: cannot rule out that maybe and again I to even 113 00:07:00,320 --> 00:07:03,159 Speaker 1: say this, but we cannot completely rule out that it's 114 00:07:03,160 --> 00:07:07,960 Speaker 1: some kind of propaganda story. You you just can't so 115 00:07:08,320 --> 00:07:11,440 Speaker 1: and again that doesn't I guess you could assign probabilities 116 00:07:11,480 --> 00:07:14,120 Speaker 1: to either of those scenarios, but in this case we 117 00:07:14,200 --> 00:07:16,880 Speaker 1: just have to be aware that they're all possibilities. But 118 00:07:16,960 --> 00:07:19,920 Speaker 1: let's jump into what the article says. What information was 119 00:07:20,000 --> 00:07:23,720 Speaker 1: given to BBC Panorama by this anonymous source, this person, 120 00:07:23,800 --> 00:07:27,400 Speaker 1: the source showed Panorama like it, like we said, five 121 00:07:27,400 --> 00:07:33,720 Speaker 1: photographs of these detainees that supposedly underwent UM experimentation in 122 00:07:33,720 --> 00:07:37,800 Speaker 1: some way or testing with this new software hardware combo. 123 00:07:38,280 --> 00:07:42,120 Speaker 1: And what was displayed, at least for the folks, there 124 00:07:42,360 --> 00:07:45,800 Speaker 1: were these pie charts that represented what the system was 125 00:07:45,840 --> 00:07:50,280 Speaker 1: seeing when it was using this new facial emotion recognition 126 00:07:50,320 --> 00:07:53,800 Speaker 1: software on each individual. And it's a pie chart, and 127 00:07:53,880 --> 00:07:57,600 Speaker 1: what it represents essentially is this person's state of mind, 128 00:07:57,960 --> 00:08:02,160 Speaker 1: at least that's what it purports to represent, and what 129 00:08:02,280 --> 00:08:09,840 Speaker 1: it's looking for our negative emotions, things like anxiety, stress, anger, 130 00:08:10,600 --> 00:08:15,680 Speaker 1: anything that could be considered negative. They are searching this 131 00:08:15,720 --> 00:08:20,440 Speaker 1: person's face, their temperature, all kinds of bio data on 132 00:08:20,600 --> 00:08:23,280 Speaker 1: this person to see whether whether or not they could 133 00:08:23,320 --> 00:08:26,640 Speaker 1: be trouble right, whether or not they're suspicious. And that's 134 00:08:26,680 --> 00:08:30,680 Speaker 1: specifically what's looking for, is this person suspicious? Right? And 135 00:08:30,760 --> 00:08:35,480 Speaker 1: this is going beyond your typical facial recognition match, which 136 00:08:35,559 --> 00:08:39,800 Speaker 1: uses still photos, because when you're using UM, when you're 137 00:08:39,840 --> 00:08:45,240 Speaker 1: capturing video like this, you greatly expand the number of 138 00:08:45,280 --> 00:08:48,920 Speaker 1: things that you can measures. So we're also talking vocal tone, 139 00:08:49,000 --> 00:08:51,040 Speaker 1: we're talking you know, you and I were talking about 140 00:08:51,040 --> 00:08:55,280 Speaker 1: micro expressions off air earlier today. It also will be 141 00:08:55,320 --> 00:08:58,719 Speaker 1: able to check micro expressions. They're a real thing that 142 00:08:58,840 --> 00:09:02,000 Speaker 1: most people have unless they've been trained to try to 143 00:09:02,559 --> 00:09:06,760 Speaker 1: suppress those expressions, those reactions, those little facial ticks. But 144 00:09:06,920 --> 00:09:09,360 Speaker 1: even if you have that kind of training, which again 145 00:09:09,520 --> 00:09:12,800 Speaker 1: is a real thing, uh, you probably wouldn't be able 146 00:09:12,880 --> 00:09:15,920 Speaker 1: to fool a system like this if it had enough 147 00:09:15,960 --> 00:09:19,320 Speaker 1: stuff to measure and if it had enough other faces 148 00:09:19,360 --> 00:09:23,839 Speaker 1: to reference. Yeah, you're right. Uh. Essentially, what you need 149 00:09:23,840 --> 00:09:26,920 Speaker 1: for this to function is time in front of the system. 150 00:09:26,960 --> 00:09:29,360 Speaker 1: You need to have a human like face and be 151 00:09:29,440 --> 00:09:32,640 Speaker 1: in front of the system for you know, longer than 152 00:09:32,679 --> 00:09:35,440 Speaker 1: a few seconds. And this anonymous source is saying that 153 00:09:35,559 --> 00:09:40,280 Speaker 1: they installed this system at prisons with an s and 154 00:09:40,559 --> 00:09:44,080 Speaker 1: that it was specifically being used on the weaker population. 155 00:09:44,880 --> 00:09:46,760 Speaker 1: And and again, as I said before, I don't want 156 00:09:46,760 --> 00:09:50,600 Speaker 1: to jump too deep into to that situation between you know, 157 00:09:50,640 --> 00:09:53,120 Speaker 1: the Chinese government and the weaker population, but we just 158 00:09:53,480 --> 00:09:56,960 Speaker 1: I would just say that it's pretty obvious that multiple 159 00:09:57,000 --> 00:10:00,800 Speaker 1: sources have claimed and seemed to have conferred armed the 160 00:10:00,920 --> 00:10:04,720 Speaker 1: mistreatment of the weaker population by the Chinese government. That's 161 00:10:04,720 --> 00:10:07,920 Speaker 1: at least the way it appears at this time. Yeah, Yeah, 162 00:10:07,960 --> 00:10:11,600 Speaker 1: you're right, Matt. Uh. The quick and dirty version of 163 00:10:11,640 --> 00:10:16,440 Speaker 1: it is that the weaker population is considered one of 164 00:10:16,600 --> 00:10:22,240 Speaker 1: China's fifty five officially recognized ethnic minorities. There are culturally 165 00:10:22,360 --> 00:10:26,360 Speaker 1: distinct in many ways. You know, they have lived in 166 00:10:26,360 --> 00:10:29,200 Speaker 1: the region for a long time, they're technically a Turkic 167 00:10:29,400 --> 00:10:32,959 Speaker 1: ethnic group. But when we say culturally distinct, we mean 168 00:10:33,080 --> 00:10:38,400 Speaker 1: like a majority Muslim. Uh. The cuisine is different. They're 169 00:10:38,960 --> 00:10:44,040 Speaker 1: they're not Han Chinese. And since about the Chinese government 170 00:10:44,120 --> 00:10:48,480 Speaker 1: has been if for being extremely diplomatic. Uh, they've been 171 00:10:48,520 --> 00:10:55,480 Speaker 1: engaging in a policy of total non consensual cultural assimilation. 172 00:10:55,760 --> 00:11:00,200 Speaker 1: So things like secretive internment camps, lack of legal process us. 173 00:11:00,559 --> 00:11:03,760 Speaker 1: You know, you and I have explored various aspects of 174 00:11:03,800 --> 00:11:10,000 Speaker 1: this story in the past, everything from allegations of Oregon harvesting, 175 00:11:10,200 --> 00:11:12,440 Speaker 1: which goes back to your is this how much can 176 00:11:12,480 --> 00:11:18,000 Speaker 1: we trust question right? Allegations of Oregon harvesting, the detainment camps. Uh, 177 00:11:18,480 --> 00:11:23,760 Speaker 1: those have improven. Those are real threats against journalists, forcing 178 00:11:23,880 --> 00:11:27,319 Speaker 1: people to forcing children to learn the language as well 179 00:11:27,360 --> 00:11:30,640 Speaker 1: as adults learned Mandarin at least and then um even 180 00:11:30,679 --> 00:11:36,880 Speaker 1: to the point of sending Chinese military members to live 181 00:11:37,160 --> 00:11:41,560 Speaker 1: in a weaker families home after one of their parents 182 00:11:41,640 --> 00:11:45,040 Speaker 1: has been detained, just to keep an eye on people. 183 00:11:45,120 --> 00:11:49,640 Speaker 1: So it's it's a very it is an unsustainable situation. 184 00:11:50,040 --> 00:11:54,000 Speaker 1: The majority of the weaker population feels that the overall 185 00:11:54,120 --> 00:11:59,360 Speaker 1: Chinese government is attempting to erase them from history, from 186 00:11:59,400 --> 00:12:02,840 Speaker 1: the president, and from the future. Not The Chinese government 187 00:12:02,880 --> 00:12:07,440 Speaker 1: of course does not agree with that with that description, 188 00:12:07,559 --> 00:12:11,720 Speaker 1: but they are outnumbered by the multiple agencies who alleged 189 00:12:11,760 --> 00:12:14,440 Speaker 1: that at least some of those practices are going on. 190 00:12:14,600 --> 00:12:17,280 Speaker 1: So you can understand how people were already concerned about 191 00:12:17,280 --> 00:12:22,280 Speaker 1: the weaker population would be uh, pretty terrified, pretty spooped 192 00:12:22,280 --> 00:12:27,760 Speaker 1: by the idea of adding this naissent, already problematic technology 193 00:12:27,960 --> 00:12:30,840 Speaker 1: to the mix. And that's that's something I think a 194 00:12:30,840 --> 00:12:33,040 Speaker 1: lot of people explore. You know, when you come to 195 00:12:33,120 --> 00:12:37,960 Speaker 1: the concept of what is race to an AI or 196 00:12:38,000 --> 00:12:41,760 Speaker 1: facial recognition algorithm, right, and we know there are major 197 00:12:41,880 --> 00:12:44,760 Speaker 1: issues with a lot of the existing technology for facial 198 00:12:44,800 --> 00:12:49,880 Speaker 1: recognition because of those very things. And there are specifically 199 00:12:49,960 --> 00:12:55,160 Speaker 1: major issues with this version of emotion facial recognition or 200 00:12:55,280 --> 00:12:59,080 Speaker 1: emotion recognition software um and it has to do with 201 00:12:59,120 --> 00:13:01,439 Speaker 1: how it was tested, at least according to this anonymous 202 00:13:01,440 --> 00:13:04,360 Speaker 1: source who spoke with the BBC. This person said that 203 00:13:04,920 --> 00:13:10,040 Speaker 1: test subjects were placed into restraint chairs, which sounds very 204 00:13:10,080 --> 00:13:14,200 Speaker 1: stress free already, just calling it a restraint right, which 205 00:13:14,240 --> 00:13:17,280 Speaker 1: are widely installed in police stations across China. According to 206 00:13:17,280 --> 00:13:20,920 Speaker 1: the BBC, uh quote your risks are locked in place 207 00:13:20,960 --> 00:13:24,480 Speaker 1: by metal restraints, and the same applies to your ankles. Again, 208 00:13:25,120 --> 00:13:26,960 Speaker 1: this is like a day at the spot. Sounds the 209 00:13:27,000 --> 00:13:31,200 Speaker 1: same to me. Uh wow. Then what they do is 210 00:13:31,200 --> 00:13:33,640 Speaker 1: they use the AI system, you know, pointed at your 211 00:13:33,679 --> 00:13:38,200 Speaker 1: face and detect everything from your facial micro expressions as 212 00:13:38,240 --> 00:13:41,959 Speaker 1: you said, Ben, to the way your poores adjust because 213 00:13:41,960 --> 00:13:45,720 Speaker 1: it can see that deeply into you um and you know, 214 00:13:45,800 --> 00:13:48,120 Speaker 1: like you said, temperature, all these other things. And it's 215 00:13:48,120 --> 00:13:52,120 Speaker 1: really tough to even imagine this being real because according 216 00:13:52,160 --> 00:13:55,080 Speaker 1: to the source, the whole reason for this is to 217 00:13:55,120 --> 00:13:58,760 Speaker 1: be able to provide quote pre judgment without any credible 218 00:13:58,800 --> 00:14:02,760 Speaker 1: evidence or without evidence, so to be able to uh 219 00:14:02,960 --> 00:14:07,240 Speaker 1: pre cog right pre cog crime and or threats to 220 00:14:07,880 --> 00:14:11,000 Speaker 1: a location or individuals. That's what this whole thing is about. 221 00:14:11,360 --> 00:14:16,680 Speaker 1: Or to determine it will probably start with determining evidence 222 00:14:17,000 --> 00:14:19,920 Speaker 1: right of a past crime. You know. The lie detector 223 00:14:20,160 --> 00:14:23,320 Speaker 1: idea is I think where they're going. They're definitely going 224 00:14:23,440 --> 00:14:27,440 Speaker 1: first right, show you a picture of something, an explosion maybe, 225 00:14:27,800 --> 00:14:30,680 Speaker 1: and then ask how you feel about it. It's also 226 00:14:30,760 --> 00:14:35,000 Speaker 1: insisting this line and then but that's certainly one way 227 00:14:35,000 --> 00:14:37,600 Speaker 1: that it's being used. I think the other issue is 228 00:14:37,640 --> 00:14:40,720 Speaker 1: that's not just police stations. It's also being used in 229 00:14:41,440 --> 00:14:46,560 Speaker 1: UH assisted living facilities, in some schools. It's being used 230 00:14:46,920 --> 00:14:50,840 Speaker 1: UH just at random police checkpoints you can set one 231 00:14:50,840 --> 00:14:53,640 Speaker 1: of these up or when entering a large corporate building. 232 00:14:54,280 --> 00:14:57,880 Speaker 1: These systems have been tested in several different places. Again, 233 00:14:58,000 --> 00:15:00,000 Speaker 1: it's not all the same systems, not all the same 234 00:15:00,520 --> 00:15:04,520 Speaker 1: software and or hardware, but there are various systems like 235 00:15:04,560 --> 00:15:06,000 Speaker 1: that that are meant to do the same thing and 236 00:15:06,120 --> 00:15:08,960 Speaker 1: just see how you're feeling. Yeah, you know. One of 237 00:15:09,040 --> 00:15:12,400 Speaker 1: the interesting things there to me was that there's a 238 00:15:12,440 --> 00:15:15,920 Speaker 1: list of emotions that they're looking for, right beyond just 239 00:15:16,000 --> 00:15:20,600 Speaker 1: the piagraphs stuff. There's a project. Managers speaks on record 240 00:15:20,840 --> 00:15:24,360 Speaker 1: in the in an article quota by The Guardian and 241 00:15:24,400 --> 00:15:27,960 Speaker 1: they talk about they have literally a list of emotions 242 00:15:28,040 --> 00:15:30,680 Speaker 1: and one of them really stood out to me man 243 00:15:31,160 --> 00:15:35,400 Speaker 1: was boredom. I don't think boredom should ever be listed 244 00:15:35,440 --> 00:15:38,080 Speaker 1: as a crime. I think it's okay to be bored. 245 00:15:38,120 --> 00:15:40,960 Speaker 1: I think there I don't think anybody should ever be 246 00:15:41,160 --> 00:15:44,880 Speaker 1: bored because there's always something interesting to do, right but 247 00:15:45,480 --> 00:15:48,280 Speaker 1: h but I think it's okay if that's like the 248 00:15:48,360 --> 00:15:50,920 Speaker 1: choice you make or that's what you're going through out 249 00:15:50,920 --> 00:15:53,960 Speaker 1: of like, none of this is happening in a vacuum. 250 00:15:54,000 --> 00:15:58,720 Speaker 1: There are multiple reports that weaker populations already have to 251 00:15:58,960 --> 00:16:02,960 Speaker 1: give d n A, have to get facial scans out 252 00:16:02,960 --> 00:16:06,720 Speaker 1: the wazoo, and then also have to download an app 253 00:16:06,840 --> 00:16:09,720 Speaker 1: on their phone and if they don't, Now, if they 254 00:16:09,760 --> 00:16:12,960 Speaker 1: don't carry a smartphone, they may be seen as suspicious 255 00:16:13,040 --> 00:16:15,920 Speaker 1: because they may be seen as trying to avoid that 256 00:16:16,040 --> 00:16:19,240 Speaker 1: app and that tracking. So this is like, if there's 257 00:16:19,240 --> 00:16:22,880 Speaker 1: an emotion that's not terror that these folks are experiencing, 258 00:16:22,960 --> 00:16:24,720 Speaker 1: I say, let them have it, you know, let them 259 00:16:24,720 --> 00:16:28,080 Speaker 1: be bored if you have that luxury. People don't realize 260 00:16:28,160 --> 00:16:31,880 Speaker 1: boredom is a tremendous luxury. Imagine the other implications if 261 00:16:31,880 --> 00:16:34,360 Speaker 1: it's not just bored, if it really is anger that 262 00:16:34,440 --> 00:16:38,760 Speaker 1: they're looking for, or just in general stress and you're 263 00:16:38,800 --> 00:16:41,880 Speaker 1: having something. Let's say you're a Tokyo and you've got 264 00:16:41,920 --> 00:16:44,440 Speaker 1: an Olympics planned and you want to have a way 265 00:16:44,480 --> 00:16:49,000 Speaker 1: to possibly check and see if anyone means other people 266 00:16:49,040 --> 00:16:52,120 Speaker 1: harm when they're entering a large you know, stadium or 267 00:16:52,160 --> 00:16:55,720 Speaker 1: facility or something like that, and you potentially could I 268 00:16:55,760 --> 00:16:59,920 Speaker 1: can imagine in a you know, an authority really liking 269 00:17:00,120 --> 00:17:02,920 Speaker 1: the possibilities that the software would provide, or this the 270 00:17:03,000 --> 00:17:06,520 Speaker 1: system would provide. But then you imagine it in train stations, 271 00:17:06,520 --> 00:17:09,439 Speaker 1: and then you imagine it in airports, and you know, 272 00:17:09,520 --> 00:17:12,359 Speaker 1: at your job and at your kids school, and you 273 00:17:12,400 --> 00:17:16,320 Speaker 1: just imagine the world looking at you and judging if 274 00:17:16,359 --> 00:17:19,320 Speaker 1: you're having a bad day, and possibly if you are 275 00:17:19,400 --> 00:17:22,400 Speaker 1: having a bad day, maybe you're a terrorist, maybe you're 276 00:17:22,600 --> 00:17:27,200 Speaker 1: you know, a potential threat to somebody else. And that's absolutely, 277 00:17:27,480 --> 00:17:30,120 Speaker 1: I mean, that's absolutely what's happening. And I just want 278 00:17:30,119 --> 00:17:31,640 Speaker 1: to bring this up with you because I know you've 279 00:17:31,680 --> 00:17:36,520 Speaker 1: probably thought of this too. Cultural expressions of things are 280 00:17:36,640 --> 00:17:41,879 Speaker 1: not universal across the human population, uh, nor within the 281 00:17:41,960 --> 00:17:47,320 Speaker 1: human population. Right, So what about what about misidentifying people? Right? 282 00:17:47,320 --> 00:17:50,280 Speaker 1: What if you got the wrong angry bird? And then 283 00:17:50,960 --> 00:17:56,679 Speaker 1: what what about people who are neuro atypical or for 284 00:17:56,760 --> 00:18:02,960 Speaker 1: some reason don't display emotions in you know, in what 285 00:18:03,040 --> 00:18:06,480 Speaker 1: the in a way the AI would expect. And I'll 286 00:18:06,480 --> 00:18:08,639 Speaker 1: say it just so I could be a bit a 287 00:18:08,720 --> 00:18:12,840 Speaker 1: bit lighthearted about something that is terrifying and dystopian and 288 00:18:13,080 --> 00:18:15,320 Speaker 1: very much on the way to you. By the way, 289 00:18:15,320 --> 00:18:17,480 Speaker 1: if you live in the US and it's this, what 290 00:18:17,640 --> 00:18:20,000 Speaker 1: if you have the good old RBF, you don't. I'm 291 00:18:20,000 --> 00:18:27,320 Speaker 1: talking about the resting face, but face sure, resting belligerent face. 292 00:18:27,520 --> 00:18:31,679 Speaker 1: We'll say so, like how you know, I'm sure this 293 00:18:31,680 --> 00:18:35,480 Speaker 1: stuff is supposed to have in theory safeguards that might 294 00:18:35,520 --> 00:18:39,960 Speaker 1: just say, oh, that person is not furious and angry. 295 00:18:40,240 --> 00:18:43,879 Speaker 1: They're just like this, Mrs Robert de Niro. You know, 296 00:18:44,119 --> 00:18:47,920 Speaker 1: I'm joking a little bit, but their mouth just goes down. No, 297 00:18:48,080 --> 00:18:50,719 Speaker 1: for sure. And again, if I'm if I'm in an 298 00:18:50,720 --> 00:18:54,399 Speaker 1: off mood and I'm walking by a random kiosk uh, 299 00:18:54,600 --> 00:19:01,760 Speaker 1: I don't want to get tackled by some security force anyway. Um, 300 00:19:01,840 --> 00:19:04,720 Speaker 1: it's a it's a weird it's a weird situation. Right. 301 00:19:05,080 --> 00:19:07,399 Speaker 1: The last thing I want to say here, Ben, is 302 00:19:07,480 --> 00:19:10,880 Speaker 1: that this tech, at least according to the Guardian, and 303 00:19:10,960 --> 00:19:14,280 Speaker 1: you can read this in their article Smile for the Camera, 304 00:19:14,440 --> 00:19:17,720 Speaker 1: The dark Side of China's Emotion recognition tech. You can 305 00:19:17,760 --> 00:19:23,119 Speaker 1: read about how the markets, the global markets view this industry, 306 00:19:24,000 --> 00:19:27,840 Speaker 1: and there's a quote down here that it's being forecast 307 00:19:28,240 --> 00:19:33,040 Speaker 1: globally to be worth nearly thirty six billion dollars by 308 00:19:35,119 --> 00:19:41,920 Speaker 1: this This whole facial recognition, emotion recognition industry, big, big 309 00:19:41,960 --> 00:19:46,600 Speaker 1: emotion detection, and you can actually you can read a 310 00:19:46,640 --> 00:19:48,560 Speaker 1: ton more about that as well if you head over 311 00:19:48,640 --> 00:19:55,040 Speaker 1: to Globe whereas it's in Toronto Globe news Wire. They've 312 00:19:55,080 --> 00:19:58,000 Speaker 1: got an article called Emotion Detection and Recognition e d 313 00:19:58,240 --> 00:20:03,320 Speaker 1: R market to reach thirty three point nine billion, and 314 00:20:03,359 --> 00:20:05,800 Speaker 1: you can read all about it in various pieces of 315 00:20:05,800 --> 00:20:10,119 Speaker 1: information about specific well it's all Allied market Research, a 316 00:20:10,119 --> 00:20:12,359 Speaker 1: lot of it is anyway, It's just that's a lot 317 00:20:12,359 --> 00:20:16,680 Speaker 1: of money because I think the world, in the markets 318 00:20:16,880 --> 00:20:22,199 Speaker 1: at large understand how excited security firms would be to 319 00:20:22,200 --> 00:20:25,399 Speaker 1: get ahold of this. And there are other uses to 320 00:20:25,760 --> 00:20:30,040 Speaker 1: not all of them damaging. You know, I'm I'm fastinated 321 00:20:30,080 --> 00:20:32,919 Speaker 1: to learn about this technology. You know, I think you 322 00:20:32,960 --> 00:20:35,480 Speaker 1: and I talked about it earlier. One of the big 323 00:20:36,000 --> 00:20:40,280 Speaker 1: problems is that we're we're building these things that are 324 00:20:40,320 --> 00:20:46,080 Speaker 1: increasingly robust and intelligent, but we're building them too often 325 00:20:46,320 --> 00:20:51,040 Speaker 1: for what critics would call specifically warlike or punitive purposes. 326 00:20:51,240 --> 00:20:53,640 Speaker 1: You know, I'd like to point out that in another 327 00:20:53,800 --> 00:20:58,520 Speaker 1: world and another reality, uh, this kind of technology could 328 00:20:58,520 --> 00:21:02,600 Speaker 1: be very useful for in ocuous reasons for an acting 329 00:21:02,680 --> 00:21:07,080 Speaker 1: class for example, right, because don't think about it that 330 00:21:07,240 --> 00:21:11,120 Speaker 1: I am reaching, I am free. Um. It's a shame 331 00:21:11,119 --> 00:21:12,800 Speaker 1: it won't be used for that, but you could make 332 00:21:12,960 --> 00:21:17,600 Speaker 1: you could help some people become pretty pretty talented actors 333 00:21:18,080 --> 00:21:23,360 Speaker 1: by training them to recognize and then later to mimic uh, 334 00:21:23,680 --> 00:21:27,600 Speaker 1: you know the proper human emotions, which a lot of 335 00:21:27,680 --> 00:21:31,040 Speaker 1: us do that any like, try in the mirror anyway. 336 00:21:31,200 --> 00:21:33,719 Speaker 1: So I think that would accelerate it, because it's kind 337 00:21:33,720 --> 00:21:36,080 Speaker 1: of a bio feedback, right, like the way you can 338 00:21:36,160 --> 00:21:39,040 Speaker 1: learn to sing um. But that's not what it's going 339 00:21:39,080 --> 00:21:43,280 Speaker 1: to be used for for a while, in my opinion. Yeah, no, 340 00:21:43,320 --> 00:21:46,520 Speaker 1: I agree right now, at least according to this one source, 341 00:21:46,640 --> 00:21:51,560 Speaker 1: it seems to be used to keep control over a 342 00:21:51,600 --> 00:21:54,760 Speaker 1: minority group inside mainland China. That's what it feels like, 343 00:21:55,320 --> 00:21:58,919 Speaker 1: that's what it seems to be used for. Anyway. Uh, 344 00:21:59,240 --> 00:22:01,520 Speaker 1: let's move on, let's talk about something else. I'm done 345 00:22:01,520 --> 00:22:03,840 Speaker 1: with this, you know, saying all of this while a 346 00:22:03,880 --> 00:22:07,359 Speaker 1: camera is just continually taking pictures of my face and 347 00:22:07,440 --> 00:22:11,119 Speaker 1: of your face and doc Holiday's blank screen. It's just 348 00:22:11,600 --> 00:22:13,639 Speaker 1: I don't like it. It's not done with you, Matt, 349 00:22:14,000 --> 00:22:17,560 Speaker 1: not by a long shot. All right, here's our sponsor 350 00:22:26,680 --> 00:22:32,480 Speaker 1: and we have returned. So who has not had this experience? 351 00:22:32,520 --> 00:22:34,840 Speaker 1: If you have social media, you have a smartphone, or 352 00:22:34,840 --> 00:22:37,679 Speaker 1: your friends of social media, your friends have a smartphone, 353 00:22:38,040 --> 00:22:42,600 Speaker 1: you have likely encountered some kind of ad that seemed 354 00:22:42,640 --> 00:22:45,560 Speaker 1: like it came out of the blue but knew too 355 00:22:45,640 --> 00:22:48,440 Speaker 1: much about you. We can make up the anecdotes. It's 356 00:22:48,480 --> 00:22:52,360 Speaker 1: a mad lip at this point. Everybody has a story 357 00:22:52,640 --> 00:22:57,960 Speaker 1: like this. Let's say you're over at your matt give 358 00:22:58,000 --> 00:23:06,479 Speaker 1: me an interesting name, please, Richard's Variety Store. Okay, that's perfect, 359 00:23:06,480 --> 00:23:10,639 Speaker 1: that's perfect. Uh So you're with your cousin Richard, and 360 00:23:10,680 --> 00:23:13,520 Speaker 1: you're at Richard's Variety Store, which is a neat little 361 00:23:13,520 --> 00:23:18,800 Speaker 1: store here in Atlanta, and uh Richard is let's say, 362 00:23:19,119 --> 00:23:23,320 Speaker 1: shopping for a surprise for their partner, right, and they say, 363 00:23:23,520 --> 00:23:26,720 Speaker 1: you know, things are getting pretty serious. Uh so I'm 364 00:23:26,720 --> 00:23:31,680 Speaker 1: gonna get I'm gonna get my sweetheart a stone sculpture. 365 00:23:32,320 --> 00:23:34,320 Speaker 1: You were killing it with the props city a mini 366 00:23:34,359 --> 00:23:37,439 Speaker 1: it's your stone sculpture. There's an ad for it on 367 00:23:37,480 --> 00:23:40,600 Speaker 1: the website I'm looking at and a whoopee cushion, so 368 00:23:40,640 --> 00:23:43,280 Speaker 1: she knows I'm also into the lighter side of life. Uh, 369 00:23:43,320 --> 00:23:45,440 Speaker 1: and let me say, Okay, that's awesome. So you buy 370 00:23:45,480 --> 00:23:48,600 Speaker 1: these little gifts with your pal Richard, and then you 371 00:23:48,840 --> 00:23:53,840 Speaker 1: log onto your Facebook or you're probably statistically more like 372 00:23:54,040 --> 00:23:57,200 Speaker 1: more and more likely your Instagram or TikTok, and then 373 00:23:57,400 --> 00:24:02,919 Speaker 1: you start seeing sponsored content for miniature stone castles or 374 00:24:03,119 --> 00:24:06,680 Speaker 1: whoopee two point oh the last word and whoopee cushions. 375 00:24:06,760 --> 00:24:10,400 Speaker 1: And then you also start seeing recommended things that tell 376 00:24:10,480 --> 00:24:14,040 Speaker 1: you and some it almost seems like urgency. You know, 377 00:24:14,080 --> 00:24:17,000 Speaker 1: you're on Facebook and it says Richard like shake shack. 378 00:24:17,480 --> 00:24:19,960 Speaker 1: There's a picture of a burger and it's snitching on 379 00:24:20,119 --> 00:24:24,640 Speaker 1: Richard's diet to you. Uh, and you don't care. Maybe 380 00:24:24,640 --> 00:24:27,119 Speaker 1: you guys just went to shake Shack. Who knows. Most 381 00:24:27,160 --> 00:24:32,480 Speaker 1: people have pretty reasonably assumed that this was happening because 382 00:24:32,920 --> 00:24:38,200 Speaker 1: social media companies and data aggregators were less than honest 383 00:24:38,400 --> 00:24:41,560 Speaker 1: about what they were collecting, how they were collecting it, 384 00:24:41,680 --> 00:24:44,280 Speaker 1: and where they were moving it or where they were 385 00:24:44,320 --> 00:24:47,959 Speaker 1: sellied it. And we're being candid. And so today's story, 386 00:24:48,160 --> 00:24:50,720 Speaker 1: with help from our conspiracy realist over it, here's where 387 00:24:50,720 --> 00:24:54,879 Speaker 1: it gets crazy, comes from a privacy tech advocate named 388 00:24:55,119 --> 00:25:00,720 Speaker 1: Robert g Reeve, who had one of these experience, and 389 00:25:01,240 --> 00:25:03,880 Speaker 1: he's one of those guys who uh living in that 390 00:25:03,920 --> 00:25:07,000 Speaker 1: text space. He hears stories like this all the time, 391 00:25:07,480 --> 00:25:10,000 Speaker 1: you know, reported the same way somebody might report a 392 00:25:10,040 --> 00:25:13,879 Speaker 1: story about seeing Bigfoot. And I get the feeling that 393 00:25:13,960 --> 00:25:17,760 Speaker 1: he was he was pretty often the voice of reason 394 00:25:17,960 --> 00:25:20,920 Speaker 1: in the room, saying something like, well, I don't know 395 00:25:21,000 --> 00:25:24,359 Speaker 1: if I don't know if your device can hear you 396 00:25:24,480 --> 00:25:27,800 Speaker 1: the way that you seem to think it can. So 397 00:25:28,640 --> 00:25:32,879 Speaker 1: he recently went on Twitter, Matt, and he unraveled the mystery. 398 00:25:32,880 --> 00:25:35,199 Speaker 1: And what I'm hoping we can do with our with 399 00:25:35,280 --> 00:25:39,159 Speaker 1: our time in this segment is to walk through some 400 00:25:39,320 --> 00:25:42,280 Speaker 1: of his story and then stop and and check in. 401 00:25:42,400 --> 00:25:47,040 Speaker 1: So we'll start going through some of the tweets. Awesome, Robert. 402 00:25:47,320 --> 00:25:49,439 Speaker 1: Here's in case you are listening, sir, thank you for 403 00:25:49,440 --> 00:25:52,920 Speaker 1: the fantastic work. Here's what you had to say. I'm 404 00:25:52,960 --> 00:25:55,040 Speaker 1: back from a week in my mom's house and now 405 00:25:55,080 --> 00:25:58,160 Speaker 1: I'm getting ads for her toothpaste brand, the brand I've 406 00:25:58,160 --> 00:26:00,480 Speaker 1: been putting in my mouth for a week. We never 407 00:26:00,520 --> 00:26:04,000 Speaker 1: talked about this brand or googled it or anything like that. 408 00:26:04,359 --> 00:26:07,119 Speaker 1: As a privacy tech worker, let me explain why this 409 00:26:07,240 --> 00:26:11,160 Speaker 1: is happening. And this for a long time, by the way, 410 00:26:11,280 --> 00:26:15,240 Speaker 1: I just assumed the apps were actively listening unless you 411 00:26:15,400 --> 00:26:20,240 Speaker 1: explicitly denied microphone access, and even then I thought, oh, well, 412 00:26:20,280 --> 00:26:22,240 Speaker 1: how much do I you know? How much do I 413 00:26:22,280 --> 00:26:25,520 Speaker 1: trust it? The answer is zero, obviously. Yeah. Well, we'll 414 00:26:25,640 --> 00:26:28,399 Speaker 1: just to be clear, Robert, and I know you're a 415 00:26:28,440 --> 00:26:30,600 Speaker 1: privacy tech worker. Now I'm just I'm not talking down. 416 00:26:30,640 --> 00:26:32,000 Speaker 1: Do you like that, Robert? We we know you know 417 00:26:32,080 --> 00:26:34,760 Speaker 1: more than us. But you know, Siri and Alexa and 418 00:26:34,800 --> 00:26:38,280 Speaker 1: those things are always listening. That doesn't mean they're always 419 00:26:38,280 --> 00:26:41,720 Speaker 1: collecting data, right, They're not always sending data away. But 420 00:26:41,760 --> 00:26:45,680 Speaker 1: we also do know that sometimes they are sending random 421 00:26:45,800 --> 00:26:51,720 Speaker 1: sentences that are not you know, officially, Hey Alex's or whatever. Okay, 422 00:26:51,720 --> 00:26:54,440 Speaker 1: Googles and all that. We've talked about it, but but 423 00:26:54,600 --> 00:26:57,399 Speaker 1: we get what you're saying. Sorry, Yeah, I can you 424 00:26:57,480 --> 00:27:00,600 Speaker 1: change the settings on those I'm someone of a somewhat 425 00:27:00,600 --> 00:27:04,120 Speaker 1: technophobic in that regard, just because the track. I wonder 426 00:27:04,119 --> 00:27:07,520 Speaker 1: if you changed the settings too. Instead of like saying 427 00:27:08,119 --> 00:27:12,480 Speaker 1: Alex so whatever, you could say something like I summon you, 428 00:27:12,760 --> 00:27:15,400 Speaker 1: I bet you can. I'm sure you can that's way 429 00:27:15,400 --> 00:27:18,280 Speaker 1: too much fun for that not to be a thing. Yeah. 430 00:27:18,359 --> 00:27:21,040 Speaker 1: My my favorite way to make changes with our Google 431 00:27:21,119 --> 00:27:23,640 Speaker 1: Home is to just turn it off and then unplug 432 00:27:23,640 --> 00:27:28,159 Speaker 1: our our WiFi. Yeah, same, same, That's what I do 433 00:27:28,240 --> 00:27:32,119 Speaker 1: when I'm in your house. Uh. So to you, welcome man. 434 00:27:32,280 --> 00:27:35,399 Speaker 1: I'm looking out for you because you're already asleep, you 435 00:27:35,400 --> 00:27:37,680 Speaker 1: know what I mean. So it's it's not me, It's 436 00:27:37,680 --> 00:27:40,199 Speaker 1: an issue of responsibility at that point. I appreciate you 437 00:27:40,240 --> 00:27:42,600 Speaker 1: letting me get so much rest. Thank you. Uh just 438 00:27:42,640 --> 00:27:46,159 Speaker 1: for the record, I have not broken into mass house. Uh. 439 00:27:46,200 --> 00:27:51,919 Speaker 1: I left the door open. All back to uh factor Robert, 440 00:27:51,920 --> 00:27:54,800 Speaker 1: and he says, first of all, he he does a 441 00:27:54,800 --> 00:27:56,639 Speaker 1: bit of myth busting here, and I'm grateful for it. 442 00:27:56,680 --> 00:27:59,040 Speaker 1: He says. First of all, your social media apps are 443 00:27:59,080 --> 00:28:02,560 Speaker 1: not listening to you. This is a conspiracy theory. It's 444 00:28:02,560 --> 00:28:06,040 Speaker 1: been debunked over and over again, which you know, as 445 00:28:06,040 --> 00:28:08,560 Speaker 1: you point out, Matt, it's not the same as um 446 00:28:08,680 --> 00:28:13,840 Speaker 1: the voice activated surveillance devices or you know, fund cylinders 447 00:28:13,920 --> 00:28:16,280 Speaker 1: or whatever you're supposed to call him. He continues with 448 00:28:16,320 --> 00:28:18,600 Speaker 1: his tweet, and he says, but frankly, they don't need 449 00:28:18,680 --> 00:28:21,520 Speaker 1: to meaning they don't need to listen to you, because 450 00:28:21,520 --> 00:28:25,720 Speaker 1: everything else you give them unthinkingly is way cheaper and 451 00:28:25,840 --> 00:28:29,280 Speaker 1: way more powerful. Your apps collect a ton of data 452 00:28:29,320 --> 00:28:32,960 Speaker 1: from your phone, your unique device i D your location, 453 00:28:33,240 --> 00:28:37,399 Speaker 1: your demographics. We know this. Data aggregators pay for to 454 00:28:37,560 --> 00:28:40,760 Speaker 1: pull in data from everywhere. When I use my discount 455 00:28:40,760 --> 00:28:43,920 Speaker 1: card at the grocery store, every purchase, that's a data 456 00:28:43,960 --> 00:28:47,480 Speaker 1: set for sale. They can match my Harris Teeter purchases 457 00:28:47,480 --> 00:28:50,160 Speaker 1: to my Twitter account because I gave both of those 458 00:28:50,200 --> 00:28:53,600 Speaker 1: companies my email address and phone number, and I agreed 459 00:28:53,640 --> 00:28:56,120 Speaker 1: to all that data sharing when I accepted those terms 460 00:28:56,120 --> 00:28:59,760 Speaker 1: of service and the privacy policy. Here's where it gets 461 00:29:00,040 --> 00:29:02,840 Speaker 1: really nuts, though, whoa whoa, whoa whoa. Here's where I 462 00:29:02,880 --> 00:29:06,880 Speaker 1: gets truly nuts. Though. I think Robert Roberts trying not 463 00:29:06,960 --> 00:29:10,480 Speaker 1: to like use our phrase nobody knows what he's doing. 464 00:29:11,120 --> 00:29:14,640 Speaker 1: I think that's very that would make our days. You 465 00:29:14,680 --> 00:29:18,200 Speaker 1: know what I mean that, according to that Chinese facial 466 00:29:18,240 --> 00:29:21,480 Speaker 1: recognition software, I have like three emotions a year, and 467 00:29:21,600 --> 00:29:24,440 Speaker 1: I would be it would be it would be awesome 468 00:29:24,640 --> 00:29:26,960 Speaker 1: to being pleasantly surprised that way. It was one of 469 00:29:27,000 --> 00:29:30,520 Speaker 1: those emotions. But I'm saving I like, you know me 470 00:29:30,640 --> 00:29:34,000 Speaker 1: I like to saving towards the end. Then is allowed 471 00:29:34,040 --> 00:29:38,120 Speaker 1: to be bored three times a year? Three is bored 472 00:29:38,160 --> 00:29:42,640 Speaker 1: and emotion I guess. So, I guess. I wonder if 473 00:29:42,680 --> 00:29:46,400 Speaker 1: you can live. I wonder how long someone can be bored. 474 00:29:46,640 --> 00:29:50,000 Speaker 1: It's such a foreign concept to me. I don't know, 475 00:29:50,160 --> 00:29:53,360 Speaker 1: because at some point doesn't just become like list listeness 476 00:29:53,480 --> 00:29:57,400 Speaker 1: or a symptom of something larger, like you can't You 477 00:29:57,440 --> 00:30:01,320 Speaker 1: probably aren't going to go to a psychologist therapists and say, hey, 478 00:30:01,360 --> 00:30:04,800 Speaker 1: I've been feeling bored for five years, I wonder what 479 00:30:04,880 --> 00:30:08,080 Speaker 1: it is, and have them come back with a diagnosis. 480 00:30:08,120 --> 00:30:12,240 Speaker 1: That's just like due to our super board. Yeah, and 481 00:30:12,280 --> 00:30:16,719 Speaker 1: you know, depression and hopelessness and all those things can 482 00:30:16,800 --> 00:30:20,600 Speaker 1: lead one down. I bet paths of boredom, And I 483 00:30:20,640 --> 00:30:23,360 Speaker 1: bet that's why boredom as actually I just thought about it. 484 00:30:23,400 --> 00:30:26,600 Speaker 1: Then that's why boredom is tested because in some of 485 00:30:26,640 --> 00:30:30,400 Speaker 1: the prison systems, not to jump back to hard it's great, great, 486 00:30:30,600 --> 00:30:34,080 Speaker 1: but yeah, they really do. And some of the prison 487 00:30:34,160 --> 00:30:37,360 Speaker 1: systems they're specifically looking to see if an inmate has 488 00:30:37,400 --> 00:30:41,280 Speaker 1: suicidal thoughts or self harm, thoughts of self harm or 489 00:30:41,360 --> 00:30:45,800 Speaker 1: harmy others, but specifically self harm, because the rates of 490 00:30:45,800 --> 00:30:48,480 Speaker 1: self harm in some of the prisons in China are 491 00:30:48,760 --> 00:30:51,480 Speaker 1: so high they're attempting to just make sure nobody is 492 00:30:51,480 --> 00:30:54,800 Speaker 1: planning to hurt themselves that day. Yes, I see, that 493 00:30:55,000 --> 00:30:59,440 Speaker 1: is an astute, an excellent point at And you know, 494 00:30:59,520 --> 00:31:02,880 Speaker 1: both of these stories are really about the erosion of 495 00:31:03,160 --> 00:31:08,880 Speaker 1: privacy and the obsessive tracking of people with the ultimate 496 00:31:08,960 --> 00:31:12,600 Speaker 1: goal of predicting their actions. You know, you're not far 497 00:31:12,680 --> 00:31:15,240 Speaker 1: off at all if you were thinking like Matt did 498 00:31:15,280 --> 00:31:19,880 Speaker 1: about pre crime. This is absolutely the the both of 499 00:31:19,920 --> 00:31:24,280 Speaker 1: these things absolutely lead to that path. But this one, 500 00:31:24,680 --> 00:31:27,480 Speaker 1: this story now from Robert, is primarily at this point 501 00:31:27,520 --> 00:31:31,880 Speaker 1: about messing with your head and priming you to buy stuff. 502 00:31:32,360 --> 00:31:38,920 Speaker 1: Not an Amazon Prime reference. But yeah, I guess yeah, sorry, Jeff, 503 00:31:38,920 --> 00:31:41,000 Speaker 1: I'm not paying you for that one. I just call 504 00:31:41,080 --> 00:31:44,280 Speaker 1: him Jeff. So Robert goes on. He says, here's where 505 00:31:44,280 --> 00:31:48,040 Speaker 1: it gets truly nuts. Though. If my phone is regularly 506 00:31:48,200 --> 00:31:51,800 Speaker 1: in the same GPS location as another phone, they take 507 00:31:51,880 --> 00:31:55,440 Speaker 1: note of that. They start reconstructing the web of people 508 00:31:55,480 --> 00:32:00,520 Speaker 1: I'm in regular contact with. The advertisers can cross reference 509 00:32:00,600 --> 00:32:04,680 Speaker 1: my interests and browsing history and purchase history to those 510 00:32:04,720 --> 00:32:08,160 Speaker 1: around me. It starts showing me different stuff based on 511 00:32:08,320 --> 00:32:15,080 Speaker 1: the people around me, so family, friends, co workers. Knowing this, 512 00:32:15,520 --> 00:32:17,760 Speaker 1: this is just a pause in his tweets. Knowing this, man, 513 00:32:17,880 --> 00:32:22,960 Speaker 1: just think about how sticky and strange those events can get. 514 00:32:23,440 --> 00:32:27,560 Speaker 1: Like let's say, oh, let's take Richard. Okay, let's say 515 00:32:27,680 --> 00:32:34,200 Speaker 1: richards mini tour stone sculpture and whoopee cushion were a success. 516 00:32:35,040 --> 00:32:38,240 Speaker 1: And uh, let's say Richard got a ton of boyfriend 517 00:32:38,240 --> 00:32:41,240 Speaker 1: points for it. He's living high on the hog and 518 00:32:42,320 --> 00:32:46,360 Speaker 1: he is scrolling through some social media app or something, 519 00:32:46,720 --> 00:32:50,520 Speaker 1: and he starts seeing ads for wedding rings and he thinks, well, 520 00:32:50,560 --> 00:32:54,840 Speaker 1: I'm against the idea of matrimony. I am never gonna 521 00:32:54,880 --> 00:32:57,800 Speaker 1: get married. Why the hell is this showing up for me? 522 00:32:58,600 --> 00:33:01,960 Speaker 1: Then your significant other really really wants to get married, 523 00:33:02,200 --> 00:33:04,640 Speaker 1: and that puts you, That puts you in a weird situation. 524 00:33:04,680 --> 00:33:08,760 Speaker 1: That's possible, and and Robert, that's where we continue Robert's 525 00:33:08,880 --> 00:33:11,600 Speaker 1: tweets here. It will serve me ads, he says, for 526 00:33:11,720 --> 00:33:14,480 Speaker 1: things I don't want. But it knows someone I'm in 527 00:33:14,560 --> 00:33:19,120 Speaker 1: regular contact with might want to subliminally get me to 528 00:33:19,280 --> 00:33:23,240 Speaker 1: start a conversation about I don't know, beat me on 529 00:33:23,280 --> 00:33:27,719 Speaker 1: this doctor in toothpaste. It never needed to listen to 530 00:33:27,760 --> 00:33:32,560 Speaker 1: me for this. It's just comparing aggregated metadata. And then 531 00:33:32,600 --> 00:33:34,560 Speaker 1: he also says, look, this is out in the open, 532 00:33:34,720 --> 00:33:37,560 Speaker 1: tons of people report on this. It's just no one cares. 533 00:33:37,960 --> 00:33:41,000 Speaker 1: We decided privacy isn't worth it. It's a losing battle. 534 00:33:41,440 --> 00:33:44,840 Speaker 1: We've already given way too much of ourselves. Uh. And 535 00:33:44,920 --> 00:33:49,080 Speaker 1: then he goes on to share some fantastic resources that 536 00:33:49,160 --> 00:33:53,680 Speaker 1: I highly recommend. Reply all excellent podcasts. They have a 537 00:33:53,680 --> 00:33:56,480 Speaker 1: great episode is Facebook Spying on You? We have a 538 00:33:56,480 --> 00:33:59,360 Speaker 1: pretty solid episode on it too, that could definitely use 539 00:33:59,400 --> 00:34:02,040 Speaker 1: an update because it's several years old, but most of 540 00:34:02,080 --> 00:34:05,920 Speaker 1: the information in there is still relevant and correct. And 541 00:34:05,960 --> 00:34:07,720 Speaker 1: then one that I thought would be great for everyone 542 00:34:07,760 --> 00:34:11,080 Speaker 1: to read is uh. New York Times op ed twelve 543 00:34:11,080 --> 00:34:17,040 Speaker 1: million phones, one data set, zero privacy. And so Robert 544 00:34:17,320 --> 00:34:19,480 Speaker 1: walks through this. He says, so they know my mom's 545 00:34:19,480 --> 00:34:21,359 Speaker 1: tooth baste, they know I was at my mom's, they 546 00:34:21,400 --> 00:34:24,960 Speaker 1: know my Twitter. Now I get Twitter ads for mom's toothpaste. 547 00:34:25,280 --> 00:34:27,560 Speaker 1: Your dad isn't just about you. It's about how can 548 00:34:27,600 --> 00:34:30,520 Speaker 1: be used against every person you know or people you don't, 549 00:34:30,880 --> 00:34:37,000 Speaker 1: to shape behavior. Unconsciously. Yeah, I know, and it's not 550 00:34:37,760 --> 00:34:40,719 Speaker 1: It's not science fiction anymore. It's not the realm of 551 00:34:40,920 --> 00:34:46,839 Speaker 1: speculative novels or screenplays. This is happening and it's going 552 00:34:46,920 --> 00:34:51,480 Speaker 1: to continue. And you know, you can read more of 553 00:34:52,440 --> 00:34:57,040 Speaker 1: Robert's writing and some of the responses to this, But 554 00:34:57,600 --> 00:35:00,799 Speaker 1: I I want to this is what I ke thinking about, Matt, 555 00:35:00,840 --> 00:35:03,800 Speaker 1: and I'd love to have your thoughts on this. How 556 00:35:03,840 --> 00:35:09,560 Speaker 1: close does this get us to a system of sesame credit? 557 00:35:09,840 --> 00:35:13,000 Speaker 1: By this, I mean like, um, when we were all 558 00:35:13,080 --> 00:35:16,080 Speaker 1: going into the office every day and we're you know, 559 00:35:16,320 --> 00:35:18,440 Speaker 1: we work on any number of shows who were recording 560 00:35:18,480 --> 00:35:22,759 Speaker 1: all the time, how much of our data was inter linked? Right? 561 00:35:22,880 --> 00:35:28,480 Speaker 1: Would you or Null get an ad for you know, 562 00:35:28,920 --> 00:35:32,480 Speaker 1: like tactical gear or something because we hang out and 563 00:35:32,520 --> 00:35:35,320 Speaker 1: I was looking at it, or would I get would 564 00:35:35,719 --> 00:35:39,680 Speaker 1: would uh you know like Haull Mission Control or Alexis 565 00:35:39,760 --> 00:35:43,120 Speaker 1: dot Holiday get and add for Magic the Gathering because 566 00:35:43,160 --> 00:35:46,200 Speaker 1: you were looking at new sets, right, And I would think, well, 567 00:35:46,239 --> 00:35:50,000 Speaker 1: that's weird. I wonder why Mark or whoever wants me 568 00:35:50,160 --> 00:35:52,719 Speaker 1: to play Magic the Gathering. That's strange. I never got 569 00:35:52,719 --> 00:35:55,239 Speaker 1: into it. You know, I'm a ghost of Sushima guy 570 00:35:55,440 --> 00:35:57,920 Speaker 1: or whatever. Well, and I would just be like, well, 571 00:35:57,960 --> 00:36:00,399 Speaker 1: you should really try strick shaven because you know, it's 572 00:36:00,400 --> 00:36:02,840 Speaker 1: got like this Harry Potter vibe with all these different 573 00:36:03,080 --> 00:36:06,880 Speaker 1: you know, uh clans within a school, they all study 574 00:36:06,920 --> 00:36:10,480 Speaker 1: different things. You know, I think you'd really be into it. Yeah, yeah, 575 00:36:10,640 --> 00:36:16,080 Speaker 1: like like you just recommended anyways, trying to you just 576 00:36:16,280 --> 00:36:19,840 Speaker 1: did so. Side note, I used to collect Magic the 577 00:36:19,880 --> 00:36:22,120 Speaker 1: Gathering cars when I was little, so it actually wouldn't 578 00:36:22,160 --> 00:36:25,080 Speaker 1: be strange at that par awesome, awesome, Wait do you 579 00:36:25,120 --> 00:36:27,040 Speaker 1: still have them? I was trying to find them and 580 00:36:27,080 --> 00:36:29,560 Speaker 1: I couldn't because I was thinking they might actually be 581 00:36:29,600 --> 00:36:32,600 Speaker 1: worth a lot of mone can find them, they well 582 00:36:32,640 --> 00:36:35,080 Speaker 1: be Well, according to Robert, it doesn't matter if you 583 00:36:35,160 --> 00:36:38,560 Speaker 1: say magic the Gathering out loud again seven more times 584 00:36:38,600 --> 00:36:40,880 Speaker 1: because of your social media isn't listening to you. But 585 00:36:42,120 --> 00:36:44,120 Speaker 1: you have been on a zoom call with me a lot, 586 00:36:44,360 --> 00:36:48,840 Speaker 1: and I've been searching magic a lot, so could be happening. Well. Also, 587 00:36:48,880 --> 00:36:52,440 Speaker 1: the algorithms could get it wrong. I mean, how hilarious 588 00:36:52,440 --> 00:36:55,440 Speaker 1: would it be if we were talking about magic the 589 00:36:55,440 --> 00:36:57,680 Speaker 1: Gathering But the algorithm just picked up the idea of 590 00:36:57,760 --> 00:37:01,160 Speaker 1: magic and we started getting ads for like to become 591 00:37:01,239 --> 00:37:06,239 Speaker 1: stage magicians or something like that. We have a conversation 592 00:37:06,560 --> 00:37:09,200 Speaker 1: like it's like, oh, how is your weekend? H Matt, 593 00:37:09,280 --> 00:37:12,560 Speaker 1: I got really into magic, like, no way, what cards 594 00:37:12,560 --> 00:37:15,120 Speaker 1: do you have? And then I just, uh pull out 595 00:37:15,200 --> 00:37:18,160 Speaker 1: like a deck of fifty two and I'm like, one 596 00:37:18,200 --> 00:37:22,279 Speaker 1: of these is yours. Yeah, I don't know if they're 597 00:37:22,320 --> 00:37:26,400 Speaker 1: that concerned. That terrible choke. But but the reason bringing 598 00:37:26,400 --> 00:37:30,239 Speaker 1: this up is because having your your data and your 599 00:37:30,360 --> 00:37:34,160 Speaker 1: life be bound to the company the software believes you 600 00:37:34,280 --> 00:37:38,239 Speaker 1: keep could in the future have a negative impact on you. 601 00:37:38,920 --> 00:37:43,560 Speaker 1: What if, for instance, as the surveillance net titans, UM, 602 00:37:45,000 --> 00:37:48,600 Speaker 1: you are seeing hang like you hang out with people 603 00:37:48,640 --> 00:37:53,000 Speaker 1: who are considered to have bad credit, right, Uh, will 604 00:37:53,120 --> 00:37:57,239 Speaker 1: you find your credit impacted in the future by that 605 00:37:57,360 --> 00:38:00,520 Speaker 1: just because you are associated with those people? You know, 606 00:38:00,640 --> 00:38:05,040 Speaker 1: It's it's possible. And I don't know what laws have 607 00:38:05,239 --> 00:38:08,520 Speaker 1: been written that might restrict that. They're certainly not applied 608 00:38:08,560 --> 00:38:10,719 Speaker 1: at a federal level as far as I can find. 609 00:38:10,760 --> 00:38:13,360 Speaker 1: But I'd love I'd love to learn more, maybe in 610 00:38:13,360 --> 00:38:17,040 Speaker 1: a full episode. Um, I think we can. We can 611 00:38:17,120 --> 00:38:21,680 Speaker 1: pause here for another word from our sponsor. Wouldn't it 612 00:38:21,719 --> 00:38:25,800 Speaker 1: be funny? It was stage, magicians and magic the gathering. Uh, 613 00:38:26,080 --> 00:38:28,560 Speaker 1: we can hope, We can hope, Matt. But while we're 614 00:38:28,560 --> 00:38:30,680 Speaker 1: on this brief break, before we come back and throw 615 00:38:30,760 --> 00:38:33,560 Speaker 1: headlines at each other, folks, we want to hear from you. 616 00:38:34,760 --> 00:38:39,400 Speaker 1: What are some of the strangest things that you have 617 00:38:39,600 --> 00:38:45,400 Speaker 1: run into? What social media ads have mystified you? And 618 00:38:45,800 --> 00:38:49,719 Speaker 1: did you ever find an explanation for them? Also, what 619 00:38:49,760 --> 00:38:52,719 Speaker 1: do you think about Robert g Reeve? He seems on 620 00:38:52,760 --> 00:38:55,200 Speaker 1: the up and up to us. He looks like he 621 00:38:55,400 --> 00:38:59,120 Speaker 1: is uh acting in good faith. He's relaying events the 622 00:38:59,200 --> 00:39:02,680 Speaker 1: way he under stands them. Do you believe him? Do 623 00:39:02,719 --> 00:39:06,560 Speaker 1: you believe him when he says Instagram isn't tracking you 624 00:39:07,000 --> 00:39:10,280 Speaker 1: through the microphone because they already have so much other stuff. 625 00:39:10,840 --> 00:39:14,000 Speaker 1: Um and if not, what's the alternative? I cannot wait 626 00:39:14,000 --> 00:39:16,880 Speaker 1: to hear these stories one three, three, std w y 627 00:39:16,960 --> 00:39:20,160 Speaker 1: t K conspiracy and I heeart media dot com. Uh, 628 00:39:20,200 --> 00:39:23,040 Speaker 1: stick with us, will be right back to try something 629 00:39:23,120 --> 00:39:34,960 Speaker 1: new and knowing us strange and we're back everyone. I 630 00:39:35,000 --> 00:39:37,160 Speaker 1: have to tell you this and then I do not 631 00:39:37,520 --> 00:39:40,840 Speaker 1: want to embarrass you with this. I need everyone to 632 00:39:40,880 --> 00:39:47,240 Speaker 1: know that Ben reverted into his UM, his extraterrestrial language 633 00:39:47,239 --> 00:39:49,600 Speaker 1: that he sometimes uses. It's something he's learned. It's not 634 00:39:49,719 --> 00:39:52,520 Speaker 1: like inside of him. But he he jumped into it 635 00:39:52,600 --> 00:39:54,640 Speaker 1: for just a moment right before he went to break 636 00:39:55,040 --> 00:39:57,560 Speaker 1: and just played it off like it was nothing, and 637 00:39:57,680 --> 00:40:01,160 Speaker 1: the emotion recognition software didn't pick anything up. So just 638 00:40:01,239 --> 00:40:05,799 Speaker 1: an update. Uh, yeah, that was my bad, you know, 639 00:40:07,200 --> 00:40:10,000 Speaker 1: that was my was my bad. My accents slipped for 640 00:40:10,040 --> 00:40:13,560 Speaker 1: just a second, you know. But luckily we've got each 641 00:40:13,560 --> 00:40:16,680 Speaker 1: other's backs, the three of us here and you listening 642 00:40:16,719 --> 00:40:19,600 Speaker 1: at home, or in the car, or in the spaceship 643 00:40:19,840 --> 00:40:23,640 Speaker 1: or wherever the wide world finds you today. Uh, and 644 00:40:23,719 --> 00:40:28,560 Speaker 1: today Matt speaking of terrible segues. Uh, you and I 645 00:40:28,640 --> 00:40:31,920 Speaker 1: have decided live on air while we were recording this 646 00:40:32,000 --> 00:40:34,799 Speaker 1: show that we were just gonna spend the last few 647 00:40:34,800 --> 00:40:38,359 Speaker 1: minutes throwing some headlines at one another. And I think 648 00:40:38,400 --> 00:40:42,160 Speaker 1: we both just in the real quick check in the 649 00:40:42,239 --> 00:40:45,080 Speaker 1: folks from the beginning of this episode, what Matt and 650 00:40:45,160 --> 00:40:48,160 Speaker 1: I were doing where throat When we each through a headline, 651 00:40:48,239 --> 00:40:51,200 Speaker 1: the other person is honestly just checking to see if 652 00:40:51,200 --> 00:40:53,640 Speaker 1: this could work. And if we had both heard of 653 00:40:53,680 --> 00:40:55,759 Speaker 1: those things that we talked about, then we might not 654 00:40:55,880 --> 00:40:59,040 Speaker 1: have done this. So it's quite fortuitous. We're working live 655 00:40:59,760 --> 00:41:02,120 Speaker 1: mat hit me with it. What's going on? Man? What'd 656 00:41:02,120 --> 00:41:05,000 Speaker 1: you see? Oh? Sure, let me make sure the Chicago 657 00:41:05,160 --> 00:41:08,120 Speaker 1: Tribune will load for me, Yes it will. Uh. This 658 00:41:08,239 --> 00:41:12,600 Speaker 1: was this just popped up on the subreddit news and 659 00:41:12,719 --> 00:41:16,320 Speaker 1: here's here's the title from Chicago Tribune. More than thirty 660 00:41:16,400 --> 00:41:19,920 Speaker 1: sting rays died at the Shed Aquarium over the winter, 661 00:41:20,239 --> 00:41:24,400 Speaker 1: and officials still don't know why. Now. The reason, the 662 00:41:24,400 --> 00:41:26,480 Speaker 1: reason how I was bringing this up is because anytime 663 00:41:26,480 --> 00:41:30,080 Speaker 1: there's a mass die off in any population, Uh, first 664 00:41:30,080 --> 00:41:33,320 Speaker 1: of all, it stinks. Uh. Second of all, it's puzzling 665 00:41:33,360 --> 00:41:38,840 Speaker 1: because this species of sting ray are specifically the kind 666 00:41:38,920 --> 00:41:40,839 Speaker 1: that if you've ever been to an aquarium, you will 667 00:41:40,960 --> 00:41:45,480 Speaker 1: sometimes see a small area where anybody can walk up 668 00:41:45,520 --> 00:41:48,040 Speaker 1: and touch some of the wildlife. So we actually put 669 00:41:48,040 --> 00:41:51,480 Speaker 1: your hands into the water and interact physically with the wildlife. 670 00:41:51,719 --> 00:41:56,960 Speaker 1: And that's what these these sting rays were. And to me, 671 00:41:57,040 --> 00:41:58,960 Speaker 1: I was just wondering, is this some kind of pathogen 672 00:41:59,200 --> 00:42:02,160 Speaker 1: that went from human too sting ray? Is it is 673 00:42:02,200 --> 00:42:04,960 Speaker 1: that even possible that that could happen, that you could 674 00:42:05,000 --> 00:42:07,640 Speaker 1: go across species like that, or you know, is it 675 00:42:07,719 --> 00:42:11,320 Speaker 1: something with the water quality. And this is the Shed 676 00:42:11,440 --> 00:42:15,520 Speaker 1: Aquarium in Chicago, Illinois. It reads as strange to me 677 00:42:15,560 --> 00:42:18,440 Speaker 1: because I they still didn't know what the heck killed 678 00:42:18,560 --> 00:42:20,799 Speaker 1: all these thirty sting rays. They noticed that they were 679 00:42:20,800 --> 00:42:23,279 Speaker 1: acting really strangely in January, and then all of a 680 00:42:23,320 --> 00:42:25,600 Speaker 1: sudden they just all went to put You know what's 681 00:42:25,600 --> 00:42:29,240 Speaker 1: really weird about this, man, is that when I guessed Florida, 682 00:42:30,000 --> 00:42:32,480 Speaker 1: I was wrong maybe about your zoo, but I was 683 00:42:32,600 --> 00:42:38,040 Speaker 1: right about another one. Just four days ago. Uh, all 684 00:42:38,200 --> 00:42:41,160 Speaker 1: all the sting rays in a tank at of a 685 00:42:41,320 --> 00:42:46,200 Speaker 1: zoo Tampa died. What. Oh, that's the that's the one, Ben, 686 00:42:46,400 --> 00:42:50,000 Speaker 1: That is the one. The Chicago article is from twenty nine. 687 00:42:50,719 --> 00:42:56,000 Speaker 1: But still coincidence. There's a stingy killer. Yeah, I wonder. 688 00:42:56,120 --> 00:42:58,719 Speaker 1: And it's different because this mystery maybe a little bit 689 00:42:58,760 --> 00:43:01,879 Speaker 1: easier to solve, give that the stink rays are located 690 00:43:02,560 --> 00:43:07,520 Speaker 1: in a very well defined, smaller environment, right than the ocean. 691 00:43:08,640 --> 00:43:10,640 Speaker 1: All right, Matt, here's here's what I wanted to throw 692 00:43:10,680 --> 00:43:16,040 Speaker 1: at you. This is partially about the terrors of building 693 00:43:16,320 --> 00:43:20,680 Speaker 1: thinking machines as weapons of war, and partially about how 694 00:43:20,680 --> 00:43:26,000 Speaker 1: tricky headlines can be. Business Insider South Africa reports a 695 00:43:26,120 --> 00:43:30,200 Speaker 1: rogue killer drone quote hunted down a human target without 696 00:43:30,280 --> 00:43:33,840 Speaker 1: being instructed to. This is according to a u N report. 697 00:43:34,880 --> 00:43:37,920 Speaker 1: Here's the problem. This thing they're talking about specifically, is 698 00:43:37,960 --> 00:43:43,279 Speaker 1: what's called a cargo to quad copter. It autonomously attacked 699 00:43:43,280 --> 00:43:47,520 Speaker 1: a human being during a battle between Libyan government forces 700 00:43:47,600 --> 00:43:52,359 Speaker 1: and a breakaway military faction. It's built in Turkey. It's 701 00:43:52,400 --> 00:43:57,919 Speaker 1: designed for asymmetric warfare anti terrorism operations, and it's designed 702 00:43:58,040 --> 00:44:03,719 Speaker 1: to it's designed not always need data connectivity between the 703 00:44:03,800 --> 00:44:08,359 Speaker 1: operator and the drone, so it works such that like 704 00:44:08,440 --> 00:44:12,200 Speaker 1: your imagine you're flying the drone. Uh, doc, we're gonna 705 00:44:12,239 --> 00:44:14,640 Speaker 1: pick on you on this one. So your Doc Holiday, 706 00:44:14,760 --> 00:44:19,080 Speaker 1: you're flying the drone and you're the best drone pilot 707 00:44:19,520 --> 00:44:23,160 Speaker 1: in insert military here, right you are. You are the 708 00:44:23,239 --> 00:44:26,840 Speaker 1: Doc Holiday of of this drone program. But there's a 709 00:44:26,880 --> 00:44:31,120 Speaker 1: spotty connection and all of a sudden, your screen freezes 710 00:44:31,760 --> 00:44:36,880 Speaker 1: and you cannot steer or direct your cargo to quad copture. 711 00:44:37,320 --> 00:44:42,319 Speaker 1: What the cargo two does instead is just continue on 712 00:44:42,440 --> 00:44:46,080 Speaker 1: its own with the instructions of the programs it believed 713 00:44:46,520 --> 00:44:49,520 Speaker 1: it was given. So I think it's a little unfair 714 00:44:49,600 --> 00:44:53,839 Speaker 1: to say this thing went rogue but I do think 715 00:44:53,880 --> 00:44:58,880 Speaker 1: it's fair to say this means there are autonomous, uh 716 00:44:58,920 --> 00:45:02,560 Speaker 1: flying killing machine beans out there and there with networking 717 00:45:03,160 --> 00:45:08,360 Speaker 1: with network get shoes. Yes, yeah, absolutely, Uh that's a 718 00:45:08,480 --> 00:45:10,959 Speaker 1: bad one for us to end on, Mett. No, it's okay. 719 00:45:11,000 --> 00:45:13,520 Speaker 1: The situation you described, Ben feels very similar to this 720 00:45:13,600 --> 00:45:16,680 Speaker 1: zoom call where Ben, you you've got a narrative that 721 00:45:16,719 --> 00:45:19,520 Speaker 1: you can that you're speaking to, and then you have 722 00:45:19,560 --> 00:45:23,920 Speaker 1: a co host who has a terrible connection that here's 723 00:45:23,920 --> 00:45:25,800 Speaker 1: you most of the time, then tries to say something 724 00:45:25,800 --> 00:45:29,160 Speaker 1: and then it comes the the information comes in a 725 00:45:29,160 --> 00:45:35,439 Speaker 1: way later and that was too late. It's just really bad. Well, well, 726 00:45:35,520 --> 00:45:38,920 Speaker 1: how twenty one is this, Matt, Uh, You and I 727 00:45:39,040 --> 00:45:43,560 Speaker 1: are in the same building right now, We're like two 728 00:45:43,640 --> 00:45:47,239 Speaker 1: creepy rooms away. People must think our office is the 729 00:45:47,239 --> 00:45:49,880 Speaker 1: creepiest place if they haven't seen our YouTube videos on 730 00:45:50,000 --> 00:45:55,320 Speaker 1: the same network somehow uh so true. I think that 731 00:45:55,320 --> 00:46:00,600 Speaker 1: that is illustrative of just how finnicky networks can be. 732 00:46:01,120 --> 00:46:06,000 Speaker 1: But I would argue this makes this could make drone 733 00:46:06,040 --> 00:46:13,000 Speaker 1: warfare even more dangerous as as these entities become increasingly autonomous. 734 00:46:13,040 --> 00:46:15,920 Speaker 1: You know, what kind of safeguards can we build in 735 00:46:16,600 --> 00:46:20,640 Speaker 1: should we try. Well, I I don't know how hot 736 00:46:20,640 --> 00:46:22,399 Speaker 1: of it take this is, but should we try not 737 00:46:22,520 --> 00:46:29,319 Speaker 1: to specifically design the earliest ancestors of artificial intelligence to 738 00:46:29,480 --> 00:46:33,560 Speaker 1: be war machines? Can't we just make them? We're really 739 00:46:33,840 --> 00:46:37,720 Speaker 1: happy to play magic the gathering. You know, I'm sure 740 00:46:38,120 --> 00:46:40,600 Speaker 1: there's an algorithm that would be amazing at it. We 741 00:46:40,960 --> 00:46:44,880 Speaker 1: tried that already. That AI's name is Sparky. You can 742 00:46:44,920 --> 00:46:47,240 Speaker 1: play against Sparky as much as you want. Sparky even 743 00:46:47,280 --> 00:46:49,200 Speaker 1: says like, hey, let's play again. Oh man, that was 744 00:46:49,239 --> 00:46:52,440 Speaker 1: a great play you. Oh dude, you're good at this. Ah, 745 00:46:52,920 --> 00:46:56,600 Speaker 1: let's try. Let's meet up again soon after you defeat Sparky. 746 00:46:56,640 --> 00:47:00,359 Speaker 1: So they're already working on mt g AI. Wow, that's 747 00:47:00,360 --> 00:47:03,879 Speaker 1: fun to say. Oh I like that. Okay, well let's 748 00:47:04,040 --> 00:47:07,200 Speaker 1: end it there today. Uh, folks, people will do this again. 749 00:47:07,280 --> 00:47:10,520 Speaker 1: We'll throw some random headlines at each other. But you 750 00:47:10,520 --> 00:47:13,959 Speaker 1: know what we'll do, Matt, I think, well, um, let's 751 00:47:14,040 --> 00:47:16,080 Speaker 1: each get like eight that we don't know about and 752 00:47:16,080 --> 00:47:20,120 Speaker 1: then just throw the rapid fire and maybe maybe we 753 00:47:20,160 --> 00:47:21,880 Speaker 1: can even maybe I'm doing like the end of a 754 00:47:21,960 --> 00:47:24,560 Speaker 1: Rick and Morty episode. I'm sorry, maybe we can even 755 00:47:24,600 --> 00:47:26,759 Speaker 1: make it like one of the headlines is fake, and 756 00:47:26,800 --> 00:47:28,719 Speaker 1: you have to guess which one is fake. I'm kind 757 00:47:28,719 --> 00:47:31,520 Speaker 1: of ripping off Jonathan Strickland. So we'll think of something different. 758 00:47:31,520 --> 00:47:33,400 Speaker 1: Maybe we could put some steaks on it. Maybe we 759 00:47:33,400 --> 00:47:38,720 Speaker 1: could gamble gamble steaks. I really thought. Okay, I'm gamble snakes. 760 00:47:38,840 --> 00:47:41,000 Speaker 1: Why not? So I'm gonna read. I'm gonna read you 761 00:47:41,000 --> 00:47:44,000 Speaker 1: to really fast, rapid fire. Jeff Bezos will step down 762 00:47:44,040 --> 00:47:47,879 Speaker 1: as Amazon CEO, so you're gonna have to name a 763 00:47:47,920 --> 00:47:51,000 Speaker 1: different guy next time you make a reference to the 764 00:47:51,000 --> 00:47:56,000 Speaker 1: CEO of a Amazon. Last one. All right, this is 765 00:47:56,000 --> 00:47:59,880 Speaker 1: an old one. But Amazon to buy MGM Studios for 766 00:48:00,040 --> 00:48:03,960 Speaker 1: eight point four or five billion? Oh okay, that none 767 00:48:03,960 --> 00:48:06,640 Speaker 1: of those matter. They're not conspiratorial. Amazon wants to get 768 00:48:06,640 --> 00:48:11,840 Speaker 1: into healthcare. That's conspiratorial. MGM. Dude, we're talking about Mayor 769 00:48:12,000 --> 00:48:20,120 Speaker 1: goldswin m Yeah, nailed it. Metro Golden Mayor. I believe 770 00:48:20,440 --> 00:48:24,600 Speaker 1: there it is Metro Golden Mayor. Uh. We We would 771 00:48:24,680 --> 00:48:29,560 Speaker 1: love to hear more random headlines from you, fellow conspiracy realists. 772 00:48:30,080 --> 00:48:32,480 Speaker 1: We've been a bit light on this when there is 773 00:48:32,520 --> 00:48:37,040 Speaker 1: some incredibly disturbing, heavy stuff going on in the world. 774 00:48:37,160 --> 00:48:41,120 Speaker 1: As we record once. It's been the case forever since 775 00:48:41,200 --> 00:48:44,040 Speaker 1: the creation of this show, and we typically are going 776 00:48:44,080 --> 00:48:47,799 Speaker 1: to return to those in the form of full episodes. 777 00:48:48,200 --> 00:48:51,200 Speaker 1: For now, we need your help. We want to hear 778 00:48:51,280 --> 00:48:54,879 Speaker 1: your stories. What's your experience with facial recognition with all 779 00:48:54,920 --> 00:48:58,680 Speaker 1: this near future tech that is bleeding over the edge 780 00:48:58,719 --> 00:49:03,160 Speaker 1: from fiction into fact. Do you think private company like 781 00:49:03,200 --> 00:49:06,279 Speaker 1: Amazon would be great for healthcare? What do you think 782 00:49:06,840 --> 00:49:11,040 Speaker 1: which do about rogue drones? And what are your weird 783 00:49:11,160 --> 00:49:15,960 Speaker 1: social media stalking stories when the algorithm does it not 784 00:49:15,960 --> 00:49:19,040 Speaker 1: not some weird Oh you met on the internet? That's great. Yes, 785 00:49:19,080 --> 00:49:21,320 Speaker 1: you can find us all over the place on Twitter 786 00:49:21,440 --> 00:49:25,279 Speaker 1: and Facebook. We are at conspiracy Stuff on Instagram, Conspiracy 787 00:49:25,280 --> 00:49:29,040 Speaker 1: Stuff show on YouTube. We are also conspiracy stuff. Check 788 00:49:29,040 --> 00:49:32,000 Speaker 1: out all of our videos. There are so many of them, 789 00:49:32,080 --> 00:49:35,040 Speaker 1: even videos of these conversations. This one might be a 790 00:49:35,040 --> 00:49:39,480 Speaker 1: little choppy considering the network situation in which we currently 791 00:49:39,680 --> 00:49:42,719 Speaker 1: find ourselves. Um, but hey, if you don't want to 792 00:49:42,840 --> 00:49:45,800 Speaker 1: use social media, because you know, we just talked about 793 00:49:45,800 --> 00:49:48,560 Speaker 1: how that's kind of a weird thing, you can always 794 00:49:48,600 --> 00:49:50,560 Speaker 1: give us a call. We have a phone number. Yes, 795 00:49:50,680 --> 00:49:53,560 Speaker 1: we do have a phone number. It is one eight 796 00:49:53,640 --> 00:49:57,640 Speaker 1: three three STD w y t K three minutes. Those 797 00:49:57,680 --> 00:50:00,560 Speaker 1: three minutes belong to you. You'll hear a bree message 798 00:50:00,600 --> 00:50:03,520 Speaker 1: from me, and then you are off to the races 799 00:50:03,520 --> 00:50:06,640 Speaker 1: by friends. Just tell us your name, give yourself a 800 00:50:06,760 --> 00:50:09,480 Speaker 1: nickname if you prefer. We always love those. Tell us 801 00:50:09,520 --> 00:50:12,840 Speaker 1: what's on your mind. If there's anything private that you 802 00:50:12,840 --> 00:50:16,200 Speaker 1: would rather not be stated on air, let us know 803 00:50:16,400 --> 00:50:20,320 Speaker 1: that in the message. I'm suggesting Matt that going forward 804 00:50:20,360 --> 00:50:23,440 Speaker 1: from now on, we just tell people. If you're calling 805 00:50:23,560 --> 00:50:26,120 Speaker 1: us and you do not want us to use your 806 00:50:26,200 --> 00:50:29,560 Speaker 1: name or voice, tell us that you don't. We're switching 807 00:50:29,640 --> 00:50:32,080 Speaker 1: from an opt in to opt out, which is a 808 00:50:32,719 --> 00:50:35,560 Speaker 1: pretty weird move. We'll see how it works out. And 809 00:50:35,640 --> 00:50:40,359 Speaker 1: if you like many of us hate being on the 810 00:50:40,400 --> 00:50:44,320 Speaker 1: phone for any non emergency reason, and you hate social 811 00:50:44,360 --> 00:50:47,319 Speaker 1: media because you heard our earlier episodes on it, never fear. 812 00:50:47,840 --> 00:50:50,799 Speaker 1: We have one more way for you to contact us 813 00:50:50,840 --> 00:50:53,279 Speaker 1: anywhere in the world, any time of day. That's our 814 00:50:53,320 --> 00:50:56,759 Speaker 1: good old fashioned email address where we are conspiracy at 815 00:50:56,760 --> 00:51:17,840 Speaker 1: i heart radio dot com. Stuff they don't want you 816 00:51:17,880 --> 00:51:20,520 Speaker 1: to know is a production of I heart Radio. For 817 00:51:20,600 --> 00:51:22,960 Speaker 1: more podcasts from my heart Radio, visit the i heart 818 00:51:23,040 --> 00:51:25,839 Speaker 1: Radio app Apple Podcasts or wherever you listen to your 819 00:51:25,840 --> 00:51:26,520 Speaker 1: favorite shows.