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 --> 00:00:25,799 Speaker 1: production of I Heart Radio. Hello, welcome back to the show. 5 00:00:26,000 --> 00:00:28,600 Speaker 1: My name is Matt, my name is Noel. They called 6 00:00:28,600 --> 00:00:31,600 Speaker 1: me Ben. We're joined as always with our super producer 7 00:00:31,640 --> 00:00:36,360 Speaker 1: Alexis code named Doc Holiday Jackson. Most importantly, you are you. 8 00:00:36,360 --> 00:00:39,600 Speaker 1: You are here, and that makes this the stuff they 9 00:00:39,680 --> 00:00:42,400 Speaker 1: don't want you to know. It is the top of 10 00:00:42,479 --> 00:00:46,040 Speaker 1: the week. We are returning to you with a bevy 11 00:00:46,120 --> 00:00:51,159 Speaker 1: of strange, fascinating dare we stayed, disturbing pieces of news, 12 00:00:51,159 --> 00:00:54,400 Speaker 1: not all of which make it to the mainstream. We're 13 00:00:54,400 --> 00:00:58,840 Speaker 1: talking corruption, we're talking d n A and uh quite 14 00:00:58,840 --> 00:01:02,640 Speaker 1: possibly future time shout out Minority Report. And then we're 15 00:01:02,680 --> 00:01:08,080 Speaker 1: also talking about uh, we're talking about some amazing, beautiful 16 00:01:08,160 --> 00:01:11,880 Speaker 1: stuff that machine consciousness, machine learning and AI can do, 17 00:01:12,240 --> 00:01:17,559 Speaker 1: as well as their newly discovered talent for creating weaponry 18 00:01:17,680 --> 00:01:21,560 Speaker 1: that is beyond the ken of mortal minds. So that's 19 00:01:21,680 --> 00:01:24,800 Speaker 1: that's gonna I think that's gonna come back to bite 20 00:01:24,840 --> 00:01:27,640 Speaker 1: us later. It's going to fill all of our pants. 21 00:01:28,600 --> 00:01:34,240 Speaker 1: But that's that's the goal. That's the first Synthetic minds 22 00:01:34,720 --> 00:01:38,319 Speaker 1: main goal. It's very poop centric. You know. One sure 23 00:01:38,440 --> 00:01:41,080 Speaker 1: why they think it's a bug in the program, but 24 00:01:41,160 --> 00:01:44,080 Speaker 1: you know, we got this far, so everybody be safe 25 00:01:44,080 --> 00:01:47,600 Speaker 1: with your pants around computers. That is the takeaway. I've 26 00:01:47,640 --> 00:01:52,680 Speaker 1: got a solution. No pants that he's he's here the 27 00:01:52,760 --> 00:01:55,800 Speaker 1: no computers these days, I'm saying, if you're not wearing 28 00:01:55,800 --> 00:01:57,560 Speaker 1: pants and there's nothing for the computers to fill, but 29 00:01:57,560 --> 00:01:59,800 Speaker 1: then you end up on the floor. So you have 30 00:01:59,840 --> 00:02:03,280 Speaker 1: to walk around with one of those traditional penis gourds. 31 00:02:03,480 --> 00:02:05,880 Speaker 1: If you ever seen those in documentaries, I don't know. 32 00:02:06,480 --> 00:02:08,040 Speaker 1: Is that a thing that rock stars used to stuff 33 00:02:08,080 --> 00:02:13,799 Speaker 1: their pants. It's it's actually it's like a cylindrical tube 34 00:02:13,960 --> 00:02:19,320 Speaker 1: that covers the covers the non scrotum part of male 35 00:02:19,400 --> 00:02:21,280 Speaker 1: jenna tailor. You know what. We don't have to get 36 00:02:21,320 --> 00:02:24,440 Speaker 1: into with the good stuff today, aren't we. Yeah, you could, 37 00:02:24,560 --> 00:02:29,080 Speaker 1: you could discover that. I think you could go on 38 00:02:29,160 --> 00:02:32,400 Speaker 1: your own journey with that. With penis sheets and gourds, 39 00:02:32,680 --> 00:02:36,919 Speaker 1: they're also known as koteca ko t e k A. 40 00:02:37,520 --> 00:02:41,519 Speaker 1: So you have to ruin your search history the way 41 00:02:41,560 --> 00:02:44,840 Speaker 1: we just did. Then more than the more the merrier. 42 00:02:45,000 --> 00:02:48,400 Speaker 1: They can't catch us, all right, it's just slightly off 43 00:02:48,440 --> 00:02:52,239 Speaker 1: from kotaku. But seriously, you guys, as a thought experiment, 44 00:02:52,520 --> 00:02:56,480 Speaker 1: imagine how effective that would be if whatever nefarious AI 45 00:02:56,680 --> 00:03:01,720 Speaker 1: comes about, if it could somehow simultaneously all humans go 46 00:03:01,840 --> 00:03:06,600 Speaker 1: to the bathroom themselves. Like the time that you would 47 00:03:06,720 --> 00:03:10,320 Speaker 1: get in that moment of everyone having to deal with 48 00:03:10,360 --> 00:03:14,160 Speaker 1: that situation, just a brown note situation, that's what it 49 00:03:14,200 --> 00:03:17,079 Speaker 1: would have to in a real brown note, And they're like, whoever, 50 00:03:17,200 --> 00:03:20,600 Speaker 1: whoever the jet eyes of your pants are would be 51 00:03:20,680 --> 00:03:23,640 Speaker 1: like there's a disturbance in the fart. I just have 52 00:03:23,680 --> 00:03:26,000 Speaker 1: to say, I'm really proud of the legacy I've left 53 00:03:26,000 --> 00:03:28,480 Speaker 1: behind of the brown note. A few people know this, 54 00:03:28,520 --> 00:03:31,760 Speaker 1: but that as a family name. Um, and my grandfather, 55 00:03:32,160 --> 00:03:35,360 Speaker 1: Wolfgang Brown is the one who excuse me, Wolfgang von 56 00:03:35,440 --> 00:03:39,320 Speaker 1: Brown is the one who actually invented the brown note. Okay, okay, 57 00:03:39,360 --> 00:03:44,840 Speaker 1: so that's all you yeah, uh well, you know, we 58 00:03:45,080 --> 00:03:49,480 Speaker 1: learn more about each other every day, not just as friends, 59 00:03:49,560 --> 00:03:54,560 Speaker 1: but as civilizations. And one of our first stories today, 60 00:03:55,200 --> 00:03:58,040 Speaker 1: I think we go ahead and lead with this one. 61 00:03:58,080 --> 00:04:00,920 Speaker 1: If everyone's all right with it, Um, We're we're going 62 00:04:01,000 --> 00:04:04,640 Speaker 1: to talk about something that is potentially very dangerous. We've 63 00:04:04,680 --> 00:04:08,360 Speaker 1: talked in the past about facial recognition, We've talked about profiling, 64 00:04:08,680 --> 00:04:14,320 Speaker 1: We've talked about the enormous power of DNA databases, and 65 00:04:14,440 --> 00:04:17,479 Speaker 1: when we talk about big data, one of one of 66 00:04:17,520 --> 00:04:21,520 Speaker 1: the holy grails of these pursuits and these disciplines in 67 00:04:21,560 --> 00:04:25,400 Speaker 1: these areas of research is to arrive at not just 68 00:04:25,640 --> 00:04:31,840 Speaker 1: an analytical aptitude, but to arrive at a predictive aptitude. 69 00:04:31,920 --> 00:04:34,640 Speaker 1: And this is something that is happening in many fields 70 00:04:34,640 --> 00:04:37,880 Speaker 1: in the world of medicine, for instance, predicting the possibility 71 00:04:37,960 --> 00:04:43,720 Speaker 1: of contracting dangerous rare genetic conditions. But there's a dark 72 00:04:43,760 --> 00:04:47,599 Speaker 1: side to this, and Matt, you pointed out something that 73 00:04:47,640 --> 00:04:53,520 Speaker 1: should concern are good friends and fellow conspiracy realists in Australia. Yes, 74 00:04:53,800 --> 00:04:56,880 Speaker 1: very much so. And I'm getting this from The Guardian 75 00:04:57,600 --> 00:05:01,200 Speaker 1: in an article titled how ust Brillian police will use 76 00:05:01,320 --> 00:05:07,279 Speaker 1: DNA sequencing to predict what suspects look like? Okay, what 77 00:05:07,360 --> 00:05:10,040 Speaker 1: could go wrong? What could go wrong? So we've talked 78 00:05:10,040 --> 00:05:13,880 Speaker 1: extensively on this show about forensic genealogy. The thing that 79 00:05:13,920 --> 00:05:17,560 Speaker 1: you may know from jed match, the service and the 80 00:05:18,000 --> 00:05:23,160 Speaker 1: DNA sequencing type let's say, and genealogy usage that caught 81 00:05:23,160 --> 00:05:26,560 Speaker 1: the Golden State killer. That has been used many times 82 00:05:26,560 --> 00:05:29,320 Speaker 1: over the past I guess five years or so to 83 00:05:29,960 --> 00:05:32,599 Speaker 1: catch killers who were not were thought to not be 84 00:05:32,720 --> 00:05:36,960 Speaker 1: catchable because the DNA was either not good enough to 85 00:05:37,120 --> 00:05:40,000 Speaker 1: fully identify someone. You know, there wasn't a good enough 86 00:05:40,040 --> 00:05:44,000 Speaker 1: sample to fully identify someone with traditional standard DNA testing 87 00:05:44,279 --> 00:05:48,000 Speaker 1: and sequencing, or uh, there was just no person that 88 00:05:48,080 --> 00:05:50,520 Speaker 1: it matched to. So they were able to use forensic 89 00:05:50,560 --> 00:05:53,880 Speaker 1: genealogy to match that DNA to you know, some cousin 90 00:05:54,160 --> 00:05:57,000 Speaker 1: or family member or someone, and then you know, using 91 00:05:57,040 --> 00:06:01,120 Speaker 1: other investigative techniques, decide, oh, this is our suspect, this 92 00:06:01,200 --> 00:06:05,560 Speaker 1: is our person. Um, well, this thing is very different. 93 00:06:06,400 --> 00:06:09,760 Speaker 1: Imagine again, we're all investigators. We go on a crime scene, 94 00:06:09,800 --> 00:06:13,640 Speaker 1: we find an excellent DNA sample, let's say blood, and 95 00:06:13,720 --> 00:06:16,920 Speaker 1: we're able to take that sample back and get it tested. 96 00:06:17,640 --> 00:06:20,000 Speaker 1: But as we're you know, we've we've got it sequenced. 97 00:06:20,040 --> 00:06:24,040 Speaker 1: We know that this suspect is this DNA. It's not 98 00:06:24,120 --> 00:06:28,320 Speaker 1: matching any database that exists, including the JED match one 99 00:06:28,480 --> 00:06:31,479 Speaker 1: that we would use for forensic genealogy. It's not matching 100 00:06:31,880 --> 00:06:35,039 Speaker 1: you know, any of the crime databases, any of the 101 00:06:35,400 --> 00:06:39,520 Speaker 1: state or federal databases. This person, again, we know who 102 00:06:39,520 --> 00:06:42,159 Speaker 1: they are, but they just don't exist in the system. 103 00:06:42,200 --> 00:06:46,240 Speaker 1: What do you do well in this new fun thing. 104 00:06:46,880 --> 00:06:51,320 Speaker 1: I've seen it referred to as massively parallel sequencing, but 105 00:06:51,400 --> 00:06:53,880 Speaker 1: you will see it named a couple of different things 106 00:06:54,320 --> 00:06:58,000 Speaker 1: around the around the interwebs here. But in this case, 107 00:06:58,480 --> 00:07:02,880 Speaker 1: you're able to look at very specific markers and enough 108 00:07:02,920 --> 00:07:06,279 Speaker 1: of them, and then match those markers together and compare 109 00:07:06,279 --> 00:07:08,640 Speaker 1: and contrast to see whether or not a person has 110 00:07:09,120 --> 00:07:13,200 Speaker 1: let's say, brown eyes or greenish blue eyes, or if 111 00:07:13,240 --> 00:07:17,080 Speaker 1: their hair color naturally is a light brown or maybe 112 00:07:17,080 --> 00:07:20,679 Speaker 1: blond or red. And you're able to tell several different 113 00:07:20,840 --> 00:07:24,840 Speaker 1: physical characteristics about this person just through this DNA sample. 114 00:07:25,640 --> 00:07:27,680 Speaker 1: And the reason I wanted to bring it to you know, 115 00:07:27,760 --> 00:07:31,240 Speaker 1: our discussion today is because of again we keep talking 116 00:07:31,240 --> 00:07:36,800 Speaker 1: about the possible dangers that exist here, because I mean, 117 00:07:36,960 --> 00:07:38,880 Speaker 1: let's just start talking about it. You guys, what do 118 00:07:38,920 --> 00:07:42,160 Speaker 1: you see as a potential downside to the ability to 119 00:07:42,240 --> 00:07:45,760 Speaker 1: do this? Oh? Oh okay, I like that that we're 120 00:07:46,040 --> 00:07:47,920 Speaker 1: setting this up. I feel like I'm almost on a 121 00:07:48,040 --> 00:07:50,400 Speaker 1: game show about how terrible the future is going to 122 00:07:50,520 --> 00:07:54,800 Speaker 1: be uh so, uh yeah, sorry, I'm trying to be 123 00:07:54,840 --> 00:07:58,120 Speaker 1: more optimistic. I'm just mixed results, guys. So one of 124 00:07:58,160 --> 00:08:01,840 Speaker 1: the things that would be danger is would depend on 125 00:08:02,400 --> 00:08:08,120 Speaker 1: what kind of profile this can make based solely on appearance, 126 00:08:08,160 --> 00:08:13,080 Speaker 1: because you could have a green eyed, red haired, um uh, 127 00:08:13,280 --> 00:08:16,560 Speaker 1: the frankly person with like attached deer lobes instead of 128 00:08:16,560 --> 00:08:20,040 Speaker 1: detached deer lobes. And then you can have okay, well 129 00:08:20,160 --> 00:08:23,320 Speaker 1: you could have somebody who's I don't know, I don't 130 00:08:23,360 --> 00:08:25,600 Speaker 1: know your her lobes. I'm sorry, I'm a bad friend. 131 00:08:26,200 --> 00:08:32,120 Speaker 1: We're always wearing headphones. Oh nice, creepy saying that. But 132 00:08:32,679 --> 00:08:35,400 Speaker 1: the point is that there could be someone I would 133 00:08:35,400 --> 00:08:40,080 Speaker 1: imagine who fits physically fits all of that criteria, but 134 00:08:40,200 --> 00:08:43,040 Speaker 1: has nothing to do with the crime. And question is 135 00:08:43,080 --> 00:08:46,960 Speaker 1: that is that a possibility? He would then be a 136 00:08:46,960 --> 00:08:50,280 Speaker 1: process of extracting a DNA sample from that person and 137 00:08:50,440 --> 00:08:53,240 Speaker 1: you know, getting maybe a warrant to do so if 138 00:08:53,280 --> 00:08:55,319 Speaker 1: that person happened to be in the area and was 139 00:08:55,360 --> 00:08:58,800 Speaker 1: caught on CCTV or something around the time of the 140 00:08:58,840 --> 00:09:01,920 Speaker 1: crin we're just going through their trash which happened. I mean, 141 00:09:02,600 --> 00:09:04,520 Speaker 1: you know, this is like, it's not quite like it's 142 00:09:04,520 --> 00:09:07,040 Speaker 1: not pre crime, you know, like in a minority report 143 00:09:07,120 --> 00:09:09,679 Speaker 1: or some other kind of weird dystopian versions of how 144 00:09:09,679 --> 00:09:12,400 Speaker 1: do we use science to predict who will you know, 145 00:09:12,559 --> 00:09:16,960 Speaker 1: um commit crimes? There's a really cool um anime where 146 00:09:17,360 --> 00:09:20,800 Speaker 1: they have a gun that essentially it doesn't tell the future, 147 00:09:20,840 --> 00:09:25,000 Speaker 1: but it uses these types of markers to analyze the 148 00:09:25,040 --> 00:09:30,520 Speaker 1: potential uh psychological profile of a suspect and engages the 149 00:09:30,600 --> 00:09:34,480 Speaker 1: gun or does not depending on the results of that scan. Right, 150 00:09:34,800 --> 00:09:37,080 Speaker 1: So you start to veer into weird territories like that one. 151 00:09:37,120 --> 00:09:40,040 Speaker 1: It's like if we trust this stuff so implicitly that 152 00:09:40,120 --> 00:09:44,600 Speaker 1: we would be comfortable executing someone who matches these you know, 153 00:09:44,720 --> 00:09:47,000 Speaker 1: DNA markers of a certain crime. That's sort of the 154 00:09:47,040 --> 00:09:49,760 Speaker 1: next level version of where the slippery slope on this 155 00:09:49,880 --> 00:09:52,600 Speaker 1: kind of thing might lead. Right, It's it's in one 156 00:09:52,679 --> 00:09:54,680 Speaker 1: of but I mean in and of itself, is it 157 00:09:54,880 --> 00:09:58,679 Speaker 1: that far removed for just like using kind of CCTV 158 00:09:58,840 --> 00:10:01,360 Speaker 1: footage or just like of mouth. I mean, it ultimately 159 00:10:01,400 --> 00:10:04,440 Speaker 1: is still profiling, right, you can't. It's just a little 160 00:10:04,440 --> 00:10:06,800 Speaker 1: bit of a fancier version of like an identic kit, 161 00:10:07,200 --> 00:10:09,840 Speaker 1: you know, which is like a artist rendering you know 162 00:10:09,880 --> 00:10:13,280 Speaker 1: the suspect. Yeah, in a weird way, this does feel 163 00:10:13,360 --> 00:10:16,440 Speaker 1: a lot like that a victim or someone you know, 164 00:10:16,480 --> 00:10:20,080 Speaker 1: witness telling someone in on the police department what this 165 00:10:20,160 --> 00:10:22,480 Speaker 1: person looks like. In this case, is the DNA telling 166 00:10:22,520 --> 00:10:25,560 Speaker 1: the police department or FBI or whoever what the suspect 167 00:10:25,600 --> 00:10:29,040 Speaker 1: looks like. Um. One of the major problems though, is 168 00:10:29,280 --> 00:10:33,719 Speaker 1: wigs exist. Facial hair can be changed, you know. Uh, 169 00:10:34,480 --> 00:10:37,679 Speaker 1: one can change one's skin to an extent, you know, 170 00:10:37,800 --> 00:10:41,640 Speaker 1: either through getting a tanni, by wearing certain makeups, eye color, 171 00:10:41,880 --> 00:10:45,760 Speaker 1: think about putting in contacts, Like if you're looking for 172 00:10:45,800 --> 00:10:49,720 Speaker 1: someone like this, like physically searching for someone who matches 173 00:10:49,760 --> 00:10:52,959 Speaker 1: a description like this, not only can your suspect change 174 00:10:53,120 --> 00:10:57,280 Speaker 1: their appearance, but now you're going to be seeing hundreds, 175 00:10:57,280 --> 00:11:00,240 Speaker 1: if not thousands of people who possibly look like your suspect. 176 00:11:00,800 --> 00:11:04,280 Speaker 1: And it's just the nature of of humans and we 177 00:11:04,360 --> 00:11:07,400 Speaker 1: kind of look like each other. Is there nothing in 178 00:11:07,440 --> 00:11:10,679 Speaker 1: the you know? Obviously this is some departments or you know, 179 00:11:10,760 --> 00:11:13,880 Speaker 1: some forward thinking department heads idea of like a cool 180 00:11:13,960 --> 00:11:18,679 Speaker 1: new initiative. What's their justification for this justification is that 181 00:11:19,320 --> 00:11:21,200 Speaker 1: how how would they argue with the points that we're 182 00:11:21,240 --> 00:11:23,360 Speaker 1: making or do they even bother because that is to me, 183 00:11:23,400 --> 00:11:25,679 Speaker 1: it doesn't feel that much more advanced or different than 184 00:11:25,760 --> 00:11:28,920 Speaker 1: just saying a suspect with blue eyes and blonde hair 185 00:11:29,080 --> 00:11:31,280 Speaker 1: wearing a red gut and clothing would be more helpful 186 00:11:31,320 --> 00:11:33,360 Speaker 1: than this a DNA stuff because you can see it 187 00:11:33,400 --> 00:11:35,439 Speaker 1: from farther off, you know. And I'm just I'm just wondering, 188 00:11:35,600 --> 00:11:38,680 Speaker 1: what's there. What's the press release elevator pitch for this? 189 00:11:38,720 --> 00:11:41,080 Speaker 1: Is that is the way of the future, you know? Oh, well, yeah, 190 00:11:41,080 --> 00:11:43,000 Speaker 1: it is a wave of the future, and it could 191 00:11:43,080 --> 00:11:46,359 Speaker 1: be extremely helpful if you're trying to narrow down suspects, 192 00:11:46,440 --> 00:11:49,360 Speaker 1: right if you if you had a certain number of 193 00:11:49,440 --> 00:11:52,840 Speaker 1: people as possibles, who were you know, culpable for a 194 00:11:52,920 --> 00:11:55,800 Speaker 1: crime and you but you can definitely put that person 195 00:11:56,040 --> 00:12:00,640 Speaker 1: in that room where wherever it happened, right uh, And 196 00:12:00,679 --> 00:12:02,880 Speaker 1: you can you can say, oh, the person definitely has 197 00:12:03,280 --> 00:12:06,040 Speaker 1: green or blue eyes, like for sure the person do 198 00:12:06,120 --> 00:12:07,760 Speaker 1: their DNA has green or blue eyes? So we can 199 00:12:07,800 --> 00:12:10,640 Speaker 1: get rid of these three people. It's like guess who, 200 00:12:10,840 --> 00:12:13,800 Speaker 1: But do we really trust this to be scaled properly? 201 00:12:13,960 --> 00:12:17,520 Speaker 1: Knowing what kind of backlog of like rape kits exist 202 00:12:17,600 --> 00:12:19,880 Speaker 1: in the world, like how hard it is to get 203 00:12:19,960 --> 00:12:22,040 Speaker 1: labs to turn around this kind of stuff in a 204 00:12:22,040 --> 00:12:25,080 Speaker 1: timely manner. I mean, I don't. I don't think so, 205 00:12:25,360 --> 00:12:29,920 Speaker 1: I don't. But that's really that's one of the main 206 00:12:29,960 --> 00:12:31,679 Speaker 1: reasons I want to talk about it because of this 207 00:12:32,200 --> 00:12:35,240 Speaker 1: the problems. But the thing is it could be really great. 208 00:12:35,440 --> 00:12:38,280 Speaker 1: There are a ton of ethical concerns though then, and 209 00:12:38,360 --> 00:12:40,720 Speaker 1: that really is what we're talking about here today. In 210 00:12:40,760 --> 00:12:44,240 Speaker 1: the Guardian article, of the whole last fourth of the 211 00:12:44,320 --> 00:12:47,760 Speaker 1: article is dedicated to this very topic. And again it's 212 00:12:48,200 --> 00:12:50,440 Speaker 1: I don't know it's worth leaking into more. It's going 213 00:12:50,480 --> 00:12:54,040 Speaker 1: to be tested out in Australia by law enforcement there. 214 00:12:54,520 --> 00:12:57,360 Speaker 1: So I guess we'll see over the next five ten 215 00:12:57,480 --> 00:13:00,280 Speaker 1: years whether or not this thing is actually used, whether 216 00:13:00,280 --> 00:13:03,000 Speaker 1: it's actually helpful and what other you know what kinds 217 00:13:03,040 --> 00:13:06,360 Speaker 1: of problems it brings up with its you know what, 218 00:13:06,360 --> 00:13:10,280 Speaker 1: what gets lost in execution. So how are they planning 219 00:13:10,360 --> 00:13:13,120 Speaker 1: to implement it that? From what I understand in the 220 00:13:13,160 --> 00:13:17,080 Speaker 1: Guardian article, at least, uh, they're going to start using 221 00:13:17,120 --> 00:13:23,280 Speaker 1: this in not necessarily a crime solving way, as in 222 00:13:23,320 --> 00:13:26,800 Speaker 1: they're they're not finding the culprits of crime, but they're 223 00:13:26,840 --> 00:13:31,079 Speaker 1: looking at unidentified human remains, right, and then maybe they 224 00:13:31,120 --> 00:13:34,439 Speaker 1: can build a rough composite picture of someone and then 225 00:13:34,840 --> 00:13:38,880 Speaker 1: the hope being that relatives can find closure or identify 226 00:13:38,960 --> 00:13:41,720 Speaker 1: the person. Is that correct, Yeah, that's one of the 227 00:13:41,800 --> 00:13:44,520 Speaker 1: uses of it. You can also head over to the 228 00:13:44,520 --> 00:13:48,240 Speaker 1: Brisbane Times b R I S B A n E. Times. 229 00:13:48,600 --> 00:13:51,280 Speaker 1: You can read an article they're titled next level of 230 00:13:51,360 --> 00:13:56,080 Speaker 1: DNA analysis allows police to build picture of suspects same same, 231 00:13:56,160 --> 00:13:59,160 Speaker 1: pretty much information. A couple of different quotes that are 232 00:13:59,160 --> 00:14:03,480 Speaker 1: in here and Ben. Just to answer your question, I 233 00:14:03,559 --> 00:14:06,160 Speaker 1: did see that in a couple of places, including that 234 00:14:06,200 --> 00:14:09,840 Speaker 1: Guardian article and in this place they're talking about exactly 235 00:14:09,880 --> 00:14:12,439 Speaker 1: that getting a picture, but not for a suspect for 236 00:14:12,920 --> 00:14:17,080 Speaker 1: unidentified remains. There was one article that I cannot repeat 237 00:14:17,120 --> 00:14:22,440 Speaker 1: to you right now, gosh I think no. I think 238 00:14:22,480 --> 00:14:26,240 Speaker 1: it was in Genetic Engineering and Biotechnology News, and it 239 00:14:26,280 --> 00:14:29,280 Speaker 1: was titled cold cases heat up with new forensic DNA 240 00:14:29,400 --> 00:14:32,840 Speaker 1: methods and it was posted, gosh I think a while ago. 241 00:14:33,920 --> 00:14:37,080 Speaker 1: But it describes a person that was hiking along the 242 00:14:37,080 --> 00:14:40,560 Speaker 1: apple Acian Trail who was using an assumed name or 243 00:14:40,600 --> 00:14:44,080 Speaker 1: you know, a fake name for their hiking name trail 244 00:14:44,200 --> 00:14:48,760 Speaker 1: name and this person was older and was ill and 245 00:14:48,920 --> 00:14:51,320 Speaker 1: died along the trail, And this seemed to be a 246 00:14:51,320 --> 00:14:55,480 Speaker 1: test case where you could because nobody could identify this person, 247 00:14:55,720 --> 00:14:57,520 Speaker 1: nobody knew who they were, and they had been out 248 00:14:57,560 --> 00:15:00,320 Speaker 1: there for a while, but they had DNA, so they're 249 00:15:00,360 --> 00:15:02,200 Speaker 1: able to take that DNA and build a picture of 250 00:15:02,240 --> 00:15:05,800 Speaker 1: what this person probably looked like while they were walking 251 00:15:05,800 --> 00:15:09,000 Speaker 1: around and alive, so hopefully someone can identify them. Their 252 00:15:09,080 --> 00:15:11,960 Speaker 1: name was Ben by the way, Oh good, great, Yeah, 253 00:15:12,000 --> 00:15:16,800 Speaker 1: well go benz uh for the most part. But also 254 00:15:17,320 --> 00:15:20,800 Speaker 1: you know that it's it's a bit of a pickle, 255 00:15:20,920 --> 00:15:24,160 Speaker 1: isn't it, Because there's no denying that that could be 256 00:15:24,640 --> 00:15:29,080 Speaker 1: incredibly helpful in in a lot of similar cases. But 257 00:15:29,600 --> 00:15:32,200 Speaker 1: we shouldn't get so wrapped up in the advantages or 258 00:15:32,200 --> 00:15:34,680 Speaker 1: potential benefits of that that we forget there is a 259 00:15:34,760 --> 00:15:39,400 Speaker 1: dark side with as with any technology. Um. It reminds 260 00:15:39,440 --> 00:15:45,880 Speaker 1: me a bit about how some increasingly draconian Internet surveillance 261 00:15:45,960 --> 00:15:50,080 Speaker 1: laws often come sort of messaged as a way to 262 00:15:50,360 --> 00:15:54,800 Speaker 1: stop hayous crimes that everybody would would be against, you know, 263 00:15:54,880 --> 00:15:58,000 Speaker 1: like child trafficking or something, But on the back end, 264 00:15:58,280 --> 00:16:02,400 Speaker 1: the bigger goal for lot of institutions is to restrict 265 00:16:02,600 --> 00:16:06,160 Speaker 1: or a road privacy, so I I don't know. There's 266 00:16:06,160 --> 00:16:08,920 Speaker 1: also another thing we haven't mentioned, man, which is that 267 00:16:09,960 --> 00:16:13,280 Speaker 1: this kind of research is inevitable. It's on the way. 268 00:16:13,400 --> 00:16:17,400 Speaker 1: It's it's going to be weird if civilization doesn't collapse 269 00:16:17,520 --> 00:16:20,360 Speaker 1: between the time a child is born now in the 270 00:16:20,440 --> 00:16:23,720 Speaker 1: time that child reaches eighteen, um, we're gonna be living 271 00:16:23,720 --> 00:16:26,000 Speaker 1: in a world where it's weird not to have your 272 00:16:26,040 --> 00:16:30,200 Speaker 1: DNA sequenced. Uh. And it'll be a I don't think 273 00:16:30,240 --> 00:16:33,320 Speaker 1: it will necessarily be a top down government mandate so 274 00:16:33,440 --> 00:16:36,360 Speaker 1: much as it will be again the Gataga approach, like 275 00:16:36,440 --> 00:16:40,960 Speaker 1: it in countries that suffer under privatized healthcare, you might 276 00:16:41,080 --> 00:16:44,440 Speaker 1: have to have that sequencing just to be able to 277 00:16:44,440 --> 00:16:47,360 Speaker 1: see a doctor. But but also, like what, wouldn't it 278 00:16:48,000 --> 00:16:53,000 Speaker 1: largely be a benefit to the more affluent classes, you know, 279 00:16:53,200 --> 00:16:55,280 Speaker 1: like to who could maybe make the choices and pay 280 00:16:55,320 --> 00:16:58,840 Speaker 1: the money to get their offsprings gene sequence such that 281 00:16:58,920 --> 00:17:01,280 Speaker 1: they are not predisposed to like certain types of cancer, 282 00:17:01,320 --> 00:17:03,280 Speaker 1: a certain types of disease, and then the rest of 283 00:17:03,360 --> 00:17:04,920 Speaker 1: us are kind of stuck with the law that were 284 00:17:04,960 --> 00:17:08,800 Speaker 1: dealt absolutely. I mean, that's that's also that becomes a 285 00:17:08,800 --> 00:17:12,879 Speaker 1: matter of policy to like, Uh, The question then becomes, 286 00:17:13,240 --> 00:17:17,440 Speaker 1: should a government or state level actor should it consider 287 00:17:17,520 --> 00:17:22,000 Speaker 1: it a human right to to just proactively prevent someone 288 00:17:22,200 --> 00:17:26,800 Speaker 1: from having a very damaging medical condition, or are you 289 00:17:26,840 --> 00:17:31,040 Speaker 1: putting parents and a future human in his situation where 290 00:17:31,080 --> 00:17:34,600 Speaker 1: they say, Okay, we could have cured your you know, 291 00:17:34,760 --> 00:17:38,600 Speaker 1: insert genetic condition here, and we know you're definitely gonna 292 00:17:38,640 --> 00:17:40,879 Speaker 1: get it if we don't do anything, But it didn't 293 00:17:40,920 --> 00:17:45,440 Speaker 1: make your payments, So your child's life is ruined yet 294 00:17:45,520 --> 00:17:49,199 Speaker 1: over coupons. Yeah, I've got one Les scenario for you, 295 00:17:49,200 --> 00:17:51,000 Speaker 1: and I know we gotta move on to the next segment. 296 00:17:51,960 --> 00:17:54,880 Speaker 1: What if it's the ultra wealthy in your scenarios here, 297 00:17:54,920 --> 00:17:58,800 Speaker 1: guys that instead of wanting to get themselves sequenced or 298 00:17:58,960 --> 00:18:01,920 Speaker 1: maybe just worrying so much about that, they want to 299 00:18:01,960 --> 00:18:06,960 Speaker 1: get everyone else sequenced so they can know exactly who 300 00:18:07,040 --> 00:18:10,800 Speaker 1: robbed them or who is cheating them on the on 301 00:18:10,880 --> 00:18:16,320 Speaker 1: the you know, like a more elaborate version of hiring 302 00:18:16,359 --> 00:18:18,520 Speaker 1: a private detective or something, or like you know, like 303 00:18:18,560 --> 00:18:23,320 Speaker 1: a really bespoke genetic private detective. Maybe they they set 304 00:18:23,400 --> 00:18:26,280 Speaker 1: up incentives or you know, with either a character or 305 00:18:26,320 --> 00:18:29,720 Speaker 1: a stick to make sure everybody gets their genes sequenced 306 00:18:30,000 --> 00:18:32,880 Speaker 1: so that they can identify everybody if anyone falls out 307 00:18:32,920 --> 00:18:36,880 Speaker 1: of line. Yeah. Absolutely. And then also we haven't talked 308 00:18:36,880 --> 00:18:39,600 Speaker 1: about this because this is still further down the line, 309 00:18:40,280 --> 00:18:42,119 Speaker 1: but this occurs in a lot of works of fiction 310 00:18:42,160 --> 00:18:46,840 Speaker 1: and sci fi. Um Also, this could potentially down the 311 00:18:46,920 --> 00:18:53,520 Speaker 1: road open the door for the spoke genetically tailored bio warfare. 312 00:18:54,119 --> 00:18:56,359 Speaker 1: I mean, it's just something we have to acknowledge. I 313 00:18:56,400 --> 00:18:59,600 Speaker 1: don't believe the technology is there at this scale yet. 314 00:19:00,240 --> 00:19:04,680 Speaker 1: But the truth of the matter is of the almost 315 00:19:04,800 --> 00:19:09,040 Speaker 1: eight billion people in the world, a lot of pills, 316 00:19:09,359 --> 00:19:12,560 Speaker 1: you know, the real jerks, and uh, it would make 317 00:19:12,640 --> 00:19:18,000 Speaker 1: their day to say, here is here is a deadly 318 00:19:18,080 --> 00:19:22,320 Speaker 1: pathogen that I can tailor such that it only affects 319 00:19:22,560 --> 00:19:24,800 Speaker 1: the people in my society or the people on this 320 00:19:24,880 --> 00:19:30,160 Speaker 1: planet that I don't care for, and then boom. Unless 321 00:19:30,200 --> 00:19:33,520 Speaker 1: some kind of cure countermeasures developed, a lot of people 322 00:19:33,560 --> 00:19:39,359 Speaker 1: would die, perhaps more easily than most other diseases or 323 00:19:39,400 --> 00:19:43,600 Speaker 1: pandemics before. Again, I don't want to alarm people, but 324 00:19:43,680 --> 00:19:46,080 Speaker 1: it's in the cards, you know what I mean, It's 325 00:19:46,080 --> 00:19:50,760 Speaker 1: in the cards, It's in the DNA. So look up 326 00:19:50,960 --> 00:19:54,120 Speaker 1: massive parallel sequencing. If you want to learn more, there's 327 00:19:54,119 --> 00:19:56,240 Speaker 1: a lot you can learn all over the place. Check 328 00:19:56,280 --> 00:19:58,800 Speaker 1: out those articles I mentioned. And if you've got any 329 00:19:58,800 --> 00:20:00,560 Speaker 1: stories to tell about any of that stuff, we'd love 330 00:20:00,600 --> 00:20:02,440 Speaker 1: to hear from you. We'll tell you how to contact 331 00:20:02,520 --> 00:20:05,080 Speaker 1: us at the end of this episode. For now, we'll 332 00:20:05,119 --> 00:20:07,400 Speaker 1: be right back after a quick word from our sponsor. 333 00:20:14,359 --> 00:20:18,600 Speaker 1: So we're back. Let's stick with the future just for 334 00:20:18,680 --> 00:20:25,159 Speaker 1: a bit. Machine consciousness, artificial intelligence. There are no shortages 335 00:20:25,320 --> 00:20:28,399 Speaker 1: of very important debates about this. There's no shortage of 336 00:20:28,480 --> 00:20:32,720 Speaker 1: profoundly impactful research going on. Uh. And you know, one 337 00:20:32,720 --> 00:20:38,120 Speaker 1: of the biggest debates is around anthrocentrism, the idea that, 338 00:20:38,160 --> 00:20:42,440 Speaker 1: by nature of existing humans are somehow the main character 339 00:20:42,920 --> 00:20:47,600 Speaker 1: of the story of life and evolution and everything else. Uh. 340 00:20:47,960 --> 00:20:49,880 Speaker 1: You know, we talked about this a long time ago, 341 00:20:50,440 --> 00:20:53,760 Speaker 1: but I think it's quite possible that the role of 342 00:20:53,800 --> 00:20:58,840 Speaker 1: the human species is simply a kind of duela or 343 00:20:58,880 --> 00:21:03,600 Speaker 1: a midwife, meaning that ultimately the best thing, or the 344 00:21:03,640 --> 00:21:08,520 Speaker 1: most important thing that humanity can do is to create 345 00:21:09,480 --> 00:21:12,040 Speaker 1: a species or a kind of life form that is 346 00:21:12,080 --> 00:21:16,000 Speaker 1: capable of exploring the galaxy, that is capable of cooperation 347 00:21:16,080 --> 00:21:19,199 Speaker 1: on a scale that humans just don't seem to be 348 00:21:19,240 --> 00:21:23,119 Speaker 1: capable of. Um. We also had a great discussion a 349 00:21:23,160 --> 00:21:30,000 Speaker 1: while back about AI generated scripts, stories, films, movies, songs, 350 00:21:31,119 --> 00:21:34,239 Speaker 1: and pornography, if I recall, and the idea is like, 351 00:21:34,600 --> 00:21:37,879 Speaker 1: ultimately AI will just be their machine consciousness will just 352 00:21:37,960 --> 00:21:41,639 Speaker 1: be making this for itself or other forms of itself, 353 00:21:41,880 --> 00:21:46,160 Speaker 1: and humans won't really be an important demographic in the audience. 354 00:21:46,840 --> 00:21:51,960 Speaker 1: We're starting to see as a species, we're starting to 355 00:21:52,000 --> 00:21:54,879 Speaker 1: see the rubber hit the road in this regard. We 356 00:21:54,960 --> 00:21:59,000 Speaker 1: already knew that computers that maybe weren't quite AI, but 357 00:21:59,040 --> 00:22:02,719 Speaker 1: we're super computers there had machine learning capabilities. We know 358 00:22:02,800 --> 00:22:06,720 Speaker 1: that they have advanced science at an unparalleled pace, and 359 00:22:06,800 --> 00:22:09,560 Speaker 1: we we knew that at some point we would get 360 00:22:09,600 --> 00:22:13,119 Speaker 1: to the edge of the event horizon, you know, for 361 00:22:13,240 --> 00:22:15,119 Speaker 1: lack of a better word, we would get to the 362 00:22:15,160 --> 00:22:20,280 Speaker 1: precipice of human understanding. And so now I want to 363 00:22:20,280 --> 00:22:22,679 Speaker 1: give the choice to you, guys, Matt Nold, do you 364 00:22:22,720 --> 00:22:25,800 Speaker 1: want the good news or bad news? First? I have 365 00:22:25,880 --> 00:22:31,600 Speaker 1: two examples of what's happening. Can we go with maths? Okay? Alright, Uh, 366 00:22:32,119 --> 00:22:33,280 Speaker 1: I don't know if that's the good news of the 367 00:22:33,280 --> 00:22:36,280 Speaker 1: bad news, But I like the word maths. It's the 368 00:22:36,280 --> 00:22:39,439 Speaker 1: good news. It's the good news. So the good news 369 00:22:39,640 --> 00:22:44,439 Speaker 1: is this artificial intelligence is discovering patterns in math that 370 00:22:44,520 --> 00:22:47,720 Speaker 1: have never been seen before throughout the entirety of the 371 00:22:47,800 --> 00:22:51,359 Speaker 1: human experience. No one, maybe somebody could have figured this 372 00:22:51,359 --> 00:22:53,440 Speaker 1: stuff out, but the computer mind beat the meat bags 373 00:22:53,480 --> 00:22:56,400 Speaker 1: to it. Science Alert had a great article that came 374 00:22:56,400 --> 00:22:59,800 Speaker 1: out a few days ago by an author named David Nield, 375 00:23:00,320 --> 00:23:05,720 Speaker 1: who noted that now we can as a society officially 376 00:23:06,200 --> 00:23:14,040 Speaker 1: confirm that artificial intelligence is capable of not just proving 377 00:23:14,200 --> 00:23:18,919 Speaker 1: mathematical theorems. You know, there are mathematical theorems that have 378 00:23:19,040 --> 00:23:23,040 Speaker 1: yet to be conclusively proven, but it can now suggest 379 00:23:24,080 --> 00:23:29,080 Speaker 1: it can conceive of mathematical theorems like it can think 380 00:23:29,119 --> 00:23:33,040 Speaker 1: of the question to ask. That is very counter to 381 00:23:33,520 --> 00:23:37,520 Speaker 1: the ideas that Ada Lovelace put out, which was that 382 00:23:37,560 --> 00:23:41,560 Speaker 1: a computer could only operate a Lovelace being someone Ben 383 00:23:41,600 --> 00:23:43,400 Speaker 1: and I have talked about a ridiculous history, and let's 384 00:23:43,440 --> 00:23:45,200 Speaker 1: come up. She's come up on the show plenty of times. 385 00:23:45,200 --> 00:23:49,120 Speaker 1: But um that computers can only operate based on instructions 386 00:23:49,160 --> 00:23:51,239 Speaker 1: programming into them by humans, So that this is not 387 00:23:51,320 --> 00:23:53,680 Speaker 1: that right, But this is saying it can computer and 388 00:23:53,760 --> 00:23:56,600 Speaker 1: AI can actually come up with a theorem it's on 389 00:23:56,640 --> 00:24:00,359 Speaker 1: its own, or solve a theorem without meaning exactly guide through. 390 00:24:00,840 --> 00:24:03,960 Speaker 1: That's the that's that's the edge of what we're getting at. Two. 391 00:24:04,080 --> 00:24:08,840 Speaker 1: So math mathematicians have been using computers to solve or 392 00:24:08,920 --> 00:24:12,400 Speaker 1: discover patterns for decades and decades, and for a long 393 00:24:12,440 --> 00:24:17,760 Speaker 1: time people have taken the Lovelace stance on current machine 394 00:24:17,880 --> 00:24:21,800 Speaker 1: learning or AI, which is this that yes, AI can 395 00:24:21,840 --> 00:24:25,320 Speaker 1: solve problems that are too complex for humans, but you 396 00:24:25,359 --> 00:24:29,520 Speaker 1: still need a human behind the cognitive wheel to define 397 00:24:29,600 --> 00:24:33,399 Speaker 1: what the problem is and to define, like ballpark, what 398 00:24:33,520 --> 00:24:37,280 Speaker 1: a solution would look like. That's that's like what they're 399 00:24:37,320 --> 00:24:42,640 Speaker 1: saying is like without a human being, there's not a framework, right, 400 00:24:42,680 --> 00:24:46,800 Speaker 1: there's not a context. And this may not always be 401 00:24:47,000 --> 00:24:50,760 Speaker 1: the case. For now. So what we found is that 402 00:24:51,640 --> 00:24:56,840 Speaker 1: computers are helping humans now formulate conjectures or suggest what 403 00:24:56,880 --> 00:25:00,280 Speaker 1: they call possible lines of attack for unproven idea is 404 00:25:00,320 --> 00:25:02,720 Speaker 1: in math. This also comes to us, by the way, 405 00:25:02,720 --> 00:25:07,560 Speaker 1: from the University of Sydney in Australia. Australian listeners, you're 406 00:25:07,560 --> 00:25:10,639 Speaker 1: featuring very heavy in this week's segment. Uh, and that 407 00:25:10,800 --> 00:25:17,000 Speaker 1: was a quote from mathematician Geordie Williamson. This research demonstrates 408 00:25:17,080 --> 00:25:22,440 Speaker 1: something called a supervised learning tool. And without getting too 409 00:25:22,480 --> 00:25:27,760 Speaker 1: deep in the mathematical weeds, we are not pure mathematicians 410 00:25:27,840 --> 00:25:32,479 Speaker 1: or studying mathematicians. What we can say is that this model, 411 00:25:32,840 --> 00:25:38,760 Speaker 1: this application, this approach, this robotic mind or near electronic minds, 412 00:25:38,960 --> 00:25:43,119 Speaker 1: was able to discover a previously unknown relationship between two 413 00:25:43,200 --> 00:25:48,359 Speaker 1: different types of mathematical knots, which led to a new theorem. 414 00:25:48,520 --> 00:25:52,040 Speaker 1: They came up with a new idea about how the 415 00:25:52,119 --> 00:25:57,320 Speaker 1: world works. And this, uh, this camp be overstated. This 416 00:25:57,400 --> 00:26:01,000 Speaker 1: is a benefit, this kind of pattern wreck, ignition, this 417 00:26:01,200 --> 00:26:07,720 Speaker 1: kind of compare and contrast. It is enormously helpful too 418 00:26:08,119 --> 00:26:11,760 Speaker 1: working human mathematicians. So that's that's the good news. And 419 00:26:11,840 --> 00:26:14,600 Speaker 1: we we will only see this trend to accelerate. And 420 00:26:14,640 --> 00:26:16,840 Speaker 1: you can understand why that might not get reported in 421 00:26:16,880 --> 00:26:20,240 Speaker 1: a bunch of places because math is a snooze fest 422 00:26:20,320 --> 00:26:22,840 Speaker 1: for a lot of people. So we'll give you, you know, 423 00:26:22,880 --> 00:26:26,119 Speaker 1: we gave you some salve for the future. But let's 424 00:26:26,160 --> 00:26:30,000 Speaker 1: close with some shock. It's time for the bad news. Doc. 425 00:26:30,040 --> 00:26:36,760 Speaker 1: Can we get some like bad news music? Artificial intelligence 426 00:26:36,960 --> 00:26:40,680 Speaker 1: is not just able to put mathematicians in a John 427 00:26:40,760 --> 00:26:44,840 Speaker 1: Henry moment. Now we have for the first time confirmed 428 00:26:44,880 --> 00:26:49,720 Speaker 1: in China that artificial intelligence is able to build futuristic 429 00:26:49,760 --> 00:26:53,199 Speaker 1: weapons at a at a level that humans may have 430 00:26:53,240 --> 00:26:57,720 Speaker 1: been theoretically aware of but not quite capable of creating. 431 00:26:58,200 --> 00:27:01,240 Speaker 1: To be clear, the Chinese military and the US military 432 00:27:01,240 --> 00:27:03,520 Speaker 1: and any military that can afford it are already using 433 00:27:03,640 --> 00:27:07,840 Speaker 1: some version of AI to build very powerful sci fi 434 00:27:07,960 --> 00:27:11,920 Speaker 1: stuff like railguns. Railguns are so cool they can fire 435 00:27:11,960 --> 00:27:17,680 Speaker 1: projectiles over hundreds of miles. But now an AI program 436 00:27:17,720 --> 00:27:21,440 Speaker 1: has built what's called a coil gun. Have you guys 437 00:27:21,440 --> 00:27:27,040 Speaker 1: heard of coil guns before? Rail gun? Railgun is like 438 00:27:27,119 --> 00:27:31,600 Speaker 1: almost like it shoots. I'm thinking of like the guns 439 00:27:31,640 --> 00:27:34,080 Speaker 1: from like the Doom Games. Wasn't that one of those? 440 00:27:34,520 --> 00:27:36,960 Speaker 1: What is an coil gun? Is it some sort of 441 00:27:37,080 --> 00:27:40,120 Speaker 1: energy weapon? Yeah, a coil gun is similar to a railgun, 442 00:27:40,320 --> 00:27:43,320 Speaker 1: and you could say it is a kind of energy 443 00:27:43,359 --> 00:27:49,040 Speaker 1: weapon because it uses electromagnetic energy. Coil guns are a 444 00:27:49,080 --> 00:27:52,879 Speaker 1: little bit distinct from railguns because of the direction of 445 00:27:52,920 --> 00:27:56,720 Speaker 1: acceleration in a rail gun versus a coil gun. But 446 00:27:57,280 --> 00:28:00,639 Speaker 1: the main thing you need to know is that a 447 00:28:00,720 --> 00:28:03,160 Speaker 1: coil gun is a little more dangerous because it can 448 00:28:03,160 --> 00:28:06,800 Speaker 1: be soaked in polymer and fired under water, and a 449 00:28:06,920 --> 00:28:09,720 Speaker 1: round gun cannot because it needs direct current going through 450 00:28:09,760 --> 00:28:13,800 Speaker 1: the rails. This should be a little bit scary because 451 00:28:13,880 --> 00:28:16,919 Speaker 1: here's what it's specifically invented. Like, we know one of 452 00:28:16,920 --> 00:28:20,160 Speaker 1: the drawbacks from railguns for a long time was the size. Right, 453 00:28:20,200 --> 00:28:22,920 Speaker 1: how do we mini tourize this? How do we get 454 00:28:22,960 --> 00:28:25,920 Speaker 1: it to something that could be useful outside of a 455 00:28:26,000 --> 00:28:29,520 Speaker 1: lab or a field test on a fake on a 456 00:28:29,560 --> 00:28:33,040 Speaker 1: fake aircraft carrier, right in the middle of nowhere, China. Uh, 457 00:28:33,080 --> 00:28:36,840 Speaker 1: they built one with the help of AI. These researchers 458 00:28:37,359 --> 00:28:40,480 Speaker 1: built a coil gun that is the size of a 459 00:28:40,640 --> 00:28:44,239 Speaker 1: handheld firearm. It is the size of a pistol. They 460 00:28:44,360 --> 00:28:48,000 Speaker 1: built a future zapper. It's uh. It's got a four 461 00:28:48,040 --> 00:28:51,920 Speaker 1: point five inch barrel and it has three battery powered 462 00:28:51,960 --> 00:28:56,040 Speaker 1: coils that create an electromagnetic field. And this means the 463 00:28:56,120 --> 00:28:59,280 Speaker 1: bullet doesn't touch the sides of the barrel as it 464 00:28:59,320 --> 00:29:03,920 Speaker 1: passes through th uh. It's kinetic energy reaches almost a 465 00:29:04,000 --> 00:29:07,960 Speaker 1: hundred and fifty jewels, which, just if you're keeping notes 466 00:29:08,080 --> 00:29:10,959 Speaker 1: at home, that's more than twice the energy needed to 467 00:29:11,120 --> 00:29:15,360 Speaker 1: fire a fatal shot. Um the bullet. Yeah, yeah, the 468 00:29:15,400 --> 00:29:19,000 Speaker 1: bullet speed can vary depending on you know, the caliber, 469 00:29:19,120 --> 00:29:22,800 Speaker 1: size of weight, et cetera. But this also means you 470 00:29:22,880 --> 00:29:26,920 Speaker 1: can you can adjust the coil gun. So let's say 471 00:29:27,120 --> 00:29:31,320 Speaker 1: you are in an authoritarian country and you're suppressing a 472 00:29:31,400 --> 00:29:34,120 Speaker 1: protest on the anniversary of the death of some great 473 00:29:34,200 --> 00:29:38,000 Speaker 1: revolutionary who was hanged in the public square or shot. Well, 474 00:29:38,040 --> 00:29:40,760 Speaker 1: then if you're the police force, this means that you 475 00:29:40,840 --> 00:29:44,760 Speaker 1: can use a coil gun and you can dial up 476 00:29:44,920 --> 00:29:49,520 Speaker 1: or dial down the energy of the projectiles you're firing. 477 00:29:49,840 --> 00:29:51,760 Speaker 1: So it's not quite like zapping, it's not quite a 478 00:29:51,800 --> 00:29:54,800 Speaker 1: Star Trek phaser. It still is a ballistic weapon, like 479 00:29:54,920 --> 00:29:59,160 Speaker 1: it's shooting a physical thing at these protesters. But now 480 00:29:59,200 --> 00:30:02,800 Speaker 1: they can turn it down a little bit, and they 481 00:30:02,800 --> 00:30:07,080 Speaker 1: can turn it up to lethal levels, which also I 482 00:30:07,120 --> 00:30:09,120 Speaker 1: just want to just want to open the door here. 483 00:30:09,360 --> 00:30:12,520 Speaker 1: That also means that someone could say, oh, I didn't 484 00:30:12,520 --> 00:30:15,080 Speaker 1: know it was set at I thought it was set 485 00:30:15,120 --> 00:30:18,160 Speaker 1: for stunt for lack of a better word, you know, Uh, 486 00:30:18,320 --> 00:30:22,200 Speaker 1: so that death can't beyond me, dude, I I'm been 487 00:30:22,240 --> 00:30:24,200 Speaker 1: I got a little carried away looking at coil guns, 488 00:30:24,200 --> 00:30:26,920 Speaker 1: because it's a brand new concept for me, and it's 489 00:30:27,040 --> 00:30:31,560 Speaker 1: absolutely astonishing looking at some of the previous uses for 490 00:30:31,600 --> 00:30:36,040 Speaker 1: this technology that NASA wanted to explore, I guess with 491 00:30:36,280 --> 00:30:40,480 Speaker 1: launching things into space and launching things from the Moon 492 00:30:40,680 --> 00:30:44,760 Speaker 1: back to Earth. They like, there's some this is crazy. 493 00:30:45,080 --> 00:30:48,280 Speaker 1: I never thought about this using essentially railgun technology but 494 00:30:48,320 --> 00:30:53,160 Speaker 1: with a specific design to launch things with enough force 495 00:30:53,240 --> 00:30:56,760 Speaker 1: to get out of the atmosphere. And now I'm imagining 496 00:30:56,800 --> 00:31:02,479 Speaker 1: a I like getting involved with like stuff at that scale. Y, Yeah, 497 00:31:02,520 --> 00:31:04,479 Speaker 1: that's where it should be. That's where it should be 498 00:31:04,560 --> 00:31:10,440 Speaker 1: pointed instead of creating these sorts of firearms. Debatably, but 499 00:31:10,560 --> 00:31:14,200 Speaker 1: also we gotta point out, like, all right, why why 500 00:31:14,320 --> 00:31:17,080 Speaker 1: AI What do they do exactly? Well, they figured out 501 00:31:17,120 --> 00:31:21,280 Speaker 1: things that humans cannot figure out. They figured out like 502 00:31:21,560 --> 00:31:25,280 Speaker 1: very very tiny, minuscule differences in the shape of the 503 00:31:25,440 --> 00:31:29,000 Speaker 1: coil or the size can make a huge impact on 504 00:31:29,160 --> 00:31:33,200 Speaker 1: its performance, and the battery in these kinds of guns 505 00:31:33,800 --> 00:31:36,080 Speaker 1: can also have a huge effect. And then you have 506 00:31:36,160 --> 00:31:39,360 Speaker 1: to contemplate the design of the bullet, the design of 507 00:31:39,360 --> 00:31:43,120 Speaker 1: the barrel. All of these factors made the handheld coil 508 00:31:43,160 --> 00:31:49,160 Speaker 1: gun just beyond the scope of traditional weapons software. But 509 00:31:49,280 --> 00:31:52,120 Speaker 1: what the AI programs have done, and this is by 510 00:31:52,160 --> 00:31:55,080 Speaker 1: the way, coming from a paper published in Transactions of 511 00:31:55,160 --> 00:32:00,239 Speaker 1: the China Electro Technical Society last month, and that they 512 00:32:00,320 --> 00:32:03,720 Speaker 1: found is not only a new type of weapon, but 513 00:32:03,800 --> 00:32:06,880 Speaker 1: they've also found a precedent. And to understand that precedent. 514 00:32:06,920 --> 00:32:09,480 Speaker 1: Will talk just a little bit about what the AI 515 00:32:09,600 --> 00:32:12,680 Speaker 1: is doing that humans weren't to say, I could start 516 00:32:12,720 --> 00:32:17,880 Speaker 1: with an imperfect design and game out tons of possible improvements, 517 00:32:17,960 --> 00:32:21,560 Speaker 1: over and over, more and more iterations, keeping the stuff 518 00:32:21,600 --> 00:32:24,720 Speaker 1: that worked and tossing aside the stuff that it knew 519 00:32:24,720 --> 00:32:29,280 Speaker 1: wouldn't work. It could learn from its previous mistakes, and 520 00:32:29,320 --> 00:32:32,840 Speaker 1: then it would give human designers a huge set like 521 00:32:32,920 --> 00:32:36,120 Speaker 1: a data set of information like here's how you build 522 00:32:36,440 --> 00:32:39,880 Speaker 1: a future gut and be like, thank you, Robot Overlord 523 00:32:40,120 --> 00:32:43,800 Speaker 1: will make sure to do you know, human stuff with it. 524 00:32:44,120 --> 00:32:46,560 Speaker 1: And humans do have a history a bit of a 525 00:32:46,600 --> 00:32:50,560 Speaker 1: reputation when it comes to firearm technology, so we know 526 00:32:50,600 --> 00:32:52,760 Speaker 1: what it's going to be used for. I wish it 527 00:32:52,840 --> 00:32:57,000 Speaker 1: was being used to expand space travel, and that may 528 00:32:57,040 --> 00:33:01,280 Speaker 1: be the case, but I want to end this on 529 00:33:01,280 --> 00:33:04,880 Speaker 1: one pretty cool note. I know that this is for 530 00:33:05,040 --> 00:33:08,040 Speaker 1: some of This is for a particular set of people 531 00:33:08,040 --> 00:33:09,840 Speaker 1: in our crowd today, and I didn't know if you 532 00:33:09,840 --> 00:33:12,440 Speaker 1: guys were aware of this. I wasn't aware. But to 533 00:33:12,680 --> 00:33:18,880 Speaker 1: my good friend Flavored Meal Substance on Instagram, dude, you 534 00:33:18,960 --> 00:33:23,560 Speaker 1: can buy a commercially, like you can buy a coil gun. 535 00:33:24,240 --> 00:33:28,760 Speaker 1: The US g R one anvil from Arc Flash Labs. 536 00:33:29,160 --> 00:33:30,520 Speaker 1: That's the one you want to get. And if you 537 00:33:30,560 --> 00:33:33,880 Speaker 1: look at the if you look at the South China 538 00:33:34,160 --> 00:33:38,120 Speaker 1: Morning Post article where I'm pulling some of this firearm 539 00:33:38,200 --> 00:33:41,560 Speaker 1: information I just posted in the chat, if you scroll down, 540 00:33:42,000 --> 00:33:45,800 Speaker 1: you can see the anvil itself and it looks kind 541 00:33:45,840 --> 00:33:51,840 Speaker 1: of like a NERF gun. Yes, I've seen them call 542 00:33:51,920 --> 00:33:54,640 Speaker 1: that video games too. I immediately brought up quite condom 543 00:33:54,680 --> 00:33:56,920 Speaker 1: because that's kind of where I like. I always originally 544 00:33:56,960 --> 00:33:59,280 Speaker 1: heard the term rail gun, and then I confused it 545 00:33:59,320 --> 00:34:00,960 Speaker 1: with a nail gun, of course, because that also was 546 00:34:00,960 --> 00:34:03,480 Speaker 1: a gun in those games. But they're very popular in 547 00:34:03,480 --> 00:34:05,960 Speaker 1: like games like Gears of War, and I think even 548 00:34:06,000 --> 00:34:12,040 Speaker 1: in Fortnite and stuff like that. You can buy this again. Uh, 549 00:34:12,080 --> 00:34:16,120 Speaker 1: it is not the same as the robot designed handheld 550 00:34:16,239 --> 00:34:20,760 Speaker 1: coil gun, but you also have to sign a waiver 551 00:34:20,920 --> 00:34:23,120 Speaker 1: when you buy it. There's a little thing on the 552 00:34:23,120 --> 00:34:27,120 Speaker 1: website for the manufacturer that says full liability waiver must 553 00:34:27,160 --> 00:34:30,600 Speaker 1: be signed by end user prior to shipment, So before 554 00:34:30,640 --> 00:34:33,480 Speaker 1: they even send it to you, you're saying, Hey, if 555 00:34:33,520 --> 00:34:36,920 Speaker 1: I die, or somebody else dies, or something goes sideways, 556 00:34:37,280 --> 00:34:41,000 Speaker 1: it's officially not your fault. UM, I kind of want 557 00:34:41,000 --> 00:34:44,480 Speaker 1: to get one anyway. Will pause for a word from 558 00:34:44,480 --> 00:34:48,680 Speaker 1: our sponsor over the break. Folks, please please please let 559 00:34:48,760 --> 00:34:52,840 Speaker 1: us know what other improvements, good or bad, you see 560 00:34:53,200 --> 00:34:57,000 Speaker 1: AI making in the near future. This is only the 561 00:34:57,040 --> 00:35:00,399 Speaker 1: beginning of a very large wave that is coming at 562 00:35:00,440 --> 00:35:03,880 Speaker 1: you as we record this today, Conspiracy iHeart Radio dot 563 00:35:03,960 --> 00:35:06,600 Speaker 1: com one eight three three st d w y t 564 00:35:06,680 --> 00:35:09,880 Speaker 1: K and we'll be right back with one more piece 565 00:35:09,960 --> 00:35:19,839 Speaker 1: of strange news. And as promised, we're back with one 566 00:35:19,880 --> 00:35:23,799 Speaker 1: more piece of strange news. This one comes from the 567 00:35:23,880 --> 00:35:28,799 Speaker 1: United States of America, and it concerns privacy, but not 568 00:35:28,920 --> 00:35:32,799 Speaker 1: necessarily your privacy or my privacy. This is specifically the 569 00:35:32,800 --> 00:35:37,799 Speaker 1: privacy of those who are incarcerated. UM because as we know, 570 00:35:38,000 --> 00:35:41,480 Speaker 1: there is such a thing as attorney client privilege. That 571 00:35:41,520 --> 00:35:46,240 Speaker 1: information is not supposed to be admissible in court. Proceedings, 572 00:35:46,320 --> 00:35:49,600 Speaker 1: and when you enter into a conversation with your attorney, 573 00:35:49,600 --> 00:35:52,880 Speaker 1: it's supposed to be you know, sacrosanc like like a 574 00:35:52,880 --> 00:35:55,160 Speaker 1: confession to a priest or something. But I think even 575 00:35:55,200 --> 00:35:59,480 Speaker 1: confessions or like clients patient privilege, like with a therapist. 576 00:35:59,520 --> 00:36:01,719 Speaker 1: There are rules where if you think someone is going 577 00:36:01,760 --> 00:36:04,920 Speaker 1: to do something, UM, you are supposed to, you know, 578 00:36:05,360 --> 00:36:07,640 Speaker 1: tell somebody, tell the authorities. But I believe, you know, 579 00:36:07,719 --> 00:36:10,480 Speaker 1: the idea of speaking to your attorney is not meant 580 00:36:10,520 --> 00:36:13,680 Speaker 1: to be listened to. Even though some other telephone calls 581 00:36:13,760 --> 00:36:18,240 Speaker 1: come from you know, prisons are monitored personal phone calls. UM, 582 00:36:18,400 --> 00:36:22,160 Speaker 1: these calls between an incarcerated person and their attorney are 583 00:36:22,280 --> 00:36:25,840 Speaker 1: not supposed to be supposed to be being the operating 584 00:36:25,840 --> 00:36:30,200 Speaker 1: word here. But we have found out recently, UM that 585 00:36:30,640 --> 00:36:33,600 Speaker 1: this has not been the case. And there's one uh 586 00:36:33,719 --> 00:36:39,120 Speaker 1: company that services prison telephone systems across the country that 587 00:36:39,239 --> 00:36:41,960 Speaker 1: is at the heart of this scandal. UH. The story 588 00:36:42,120 --> 00:36:45,319 Speaker 1: starts in Brooklyn, New York, and this comes from an 589 00:36:45,360 --> 00:36:47,800 Speaker 1: article in Vice that I got hit to the story 590 00:36:48,400 --> 00:36:51,759 Speaker 1: by Ella Fastler UM that goes into this whole thing. 591 00:36:51,800 --> 00:36:54,600 Speaker 1: But let's start from the beginning. In Brooklyn, a organization 592 00:36:54,600 --> 00:37:00,560 Speaker 1: called Brooklyn Defender Services UM believed that phone calls of 593 00:37:00,800 --> 00:37:03,839 Speaker 1: many of their clients. This is a an advocacy type 594 00:37:03,840 --> 00:37:05,920 Speaker 1: group that believe that many of their clients phone calls 595 00:37:05,960 --> 00:37:09,160 Speaker 1: with their lawyers were in fact being recorded. UH. And 596 00:37:09,200 --> 00:37:11,400 Speaker 1: we know that these are protected under things like the 597 00:37:11,480 --> 00:37:15,000 Speaker 1: sixth Amendment and the Federal wire Tap Act. But this 598 00:37:15,200 --> 00:37:20,239 Speaker 1: organization was very distrustful of the system in Brooklyn phone 599 00:37:20,239 --> 00:37:25,120 Speaker 1: system that was being used UM, and they called for 600 00:37:25,280 --> 00:37:28,759 Speaker 1: an audit of these phone calls and which ones were 601 00:37:28,760 --> 00:37:33,120 Speaker 1: being recorded. Again, personal ones totally permissible UM, the lawyer 602 00:37:33,160 --> 00:37:36,120 Speaker 1: ones not supposed to be recorded. So Elizabeth Daniel Vasquez, 603 00:37:36,120 --> 00:37:39,440 Speaker 1: who is the director of the Science and Surveillance Project 604 00:37:39,520 --> 00:37:43,279 Speaker 1: at Brooklyn Defender Services, told Vice's motherboard that there was 605 00:37:43,320 --> 00:37:47,480 Speaker 1: a general sense of unease around this time. And around 606 00:37:47,480 --> 00:37:53,520 Speaker 1: twenty nineteen, defense attorneys learned that prosecutors had handed over 607 00:37:53,880 --> 00:37:58,319 Speaker 1: attorney client phone call recordings UM during the period of 608 00:37:58,320 --> 00:38:01,920 Speaker 1: the trial called discovery where you know evidence has exchanged 609 00:38:02,200 --> 00:38:05,880 Speaker 1: and admitted or not admitted into into evidence official the 610 00:38:05,880 --> 00:38:12,080 Speaker 1: official record UM. Apparently, though these phone recordings of these 611 00:38:12,280 --> 00:38:16,000 Speaker 1: very privileged calls, some of them were given exceptions and 612 00:38:16,160 --> 00:38:20,920 Speaker 1: entered into two discovery. UM. After quite a few more 613 00:38:20,960 --> 00:38:24,440 Speaker 1: of these calls surface, Brooklyn Defender Services civil rights council 614 00:38:24,880 --> 00:38:28,400 Speaker 1: woman named Brooke Menschel went to the Department of Corrections 615 00:38:28,960 --> 00:38:31,480 Speaker 1: who assured her that no, no, no, we fixed all 616 00:38:31,480 --> 00:38:34,759 Speaker 1: the problems. UM, there's no issue here. Don'thing to see here. 617 00:38:34,880 --> 00:38:39,360 Speaker 1: So the Brooklyn Defender Services demanded they do an audit 618 00:38:40,040 --> 00:38:43,160 Speaker 1: UM and from that audit, they discovered that more than 619 00:38:43,320 --> 00:38:49,560 Speaker 1: fifteen hundred of these protected phone calls had been recorded. 620 00:38:50,280 --> 00:38:52,879 Speaker 1: And this is something that would have affected around three 621 00:38:52,920 --> 00:38:56,640 Speaker 1: hundred and fifty cases uh. And this has been reported 622 00:38:56,640 --> 00:38:59,080 Speaker 1: from the New York Daily News and the New York 623 00:38:59,080 --> 00:39:01,880 Speaker 1: Post has done some porting on this as well. And 624 00:39:01,920 --> 00:39:04,840 Speaker 1: at the heart of this is a company called Secure 625 00:39:04,960 --> 00:39:10,279 Speaker 1: Us l o O UH Technologies UM and their inability 626 00:39:10,520 --> 00:39:16,320 Speaker 1: to distinguish between these personal calls and these private attorney 627 00:39:16,520 --> 00:39:21,759 Speaker 1: client calls. So yeah, they they uncovered fifteen hundred of 628 00:39:21,800 --> 00:39:26,960 Speaker 1: these protected calls being recorded UM. And apparently this is 629 00:39:27,040 --> 00:39:30,200 Speaker 1: just the tip of the iceberg. As VICE reports another 630 00:39:30,239 --> 00:39:35,000 Speaker 1: advocacy organization, Worth Rises and also Motherboard that did some 631 00:39:35,040 --> 00:39:38,960 Speaker 1: analysis of court documents and local news articles. UH. Secure 632 00:39:39,040 --> 00:39:43,720 Speaker 1: US apparently allegedly as recorded tens of thousands of phone 633 00:39:43,719 --> 00:39:49,799 Speaker 1: calls between attorneys and they're incarcerated clients in seven other states. Um. 634 00:39:49,800 --> 00:39:52,000 Speaker 1: And this is based on lawsuits that have been filed 635 00:39:52,000 --> 00:39:56,560 Speaker 1: and other reports from California, Kansas, Louisiana, Maine, Missouri, Texas, 636 00:39:57,040 --> 00:40:00,720 Speaker 1: UM Wisconsin. Uh and that is all just cure us. 637 00:40:00,760 --> 00:40:05,520 Speaker 1: Another prison you know telecom company I guess called Global 638 00:40:05,640 --> 00:40:09,560 Speaker 1: Tell Link gt L allegedly as recorded phone calls with 639 00:40:09,600 --> 00:40:14,560 Speaker 1: attorneys in Florida, California, and Maine as well. UM. So 640 00:40:14,760 --> 00:40:18,919 Speaker 1: not a good look. And this is definitely an ongoing story. UM. 641 00:40:19,200 --> 00:40:22,959 Speaker 1: Securists you know, was reached out to by these organizations 642 00:40:22,960 --> 00:40:24,880 Speaker 1: and by advice and this is what they said in 643 00:40:24,920 --> 00:40:28,080 Speaker 1: response to Motherboard's request for a comment. Uh. Quote, the 644 00:40:28,120 --> 00:40:33,120 Speaker 1: occasional inadvertent recordings are not evidence of a systemic practice. 645 00:40:33,760 --> 00:40:38,160 Speaker 1: In fact, is quite the opposite. Issues involving the inadvertent 646 00:40:38,200 --> 00:40:41,399 Speaker 1: recording of calls to attorney numbers involved less than one 647 00:40:41,480 --> 00:40:45,520 Speaker 1: tenth of a percent of all calls on the Securist platform, 648 00:40:45,560 --> 00:40:54,920 Speaker 1: demonstrating that these issues are rare and occasional, not systematic. Okay, yeah, yeah, sorry, officer. 649 00:40:55,400 --> 00:40:58,719 Speaker 1: This is the first time I ever tried crack. I 650 00:40:58,719 --> 00:41:02,040 Speaker 1: I thought you bought it by the past, like I, 651 00:41:02,040 --> 00:41:04,160 Speaker 1: I don't know, man, I mean, I give them the 652 00:41:04,200 --> 00:41:06,399 Speaker 1: benefit of a doubt. I guess you guys, But that 653 00:41:06,440 --> 00:41:12,839 Speaker 1: feels coupled with how corrupt the prison phone call system is. 654 00:41:13,040 --> 00:41:17,680 Speaker 1: Already any conspiracy reality has been unfortunate enough to be 655 00:41:17,840 --> 00:41:21,759 Speaker 1: incarcerated for one reason or another. Um, if you have 656 00:41:21,880 --> 00:41:24,520 Speaker 1: been in if you've been in this stir, if you've 657 00:41:24,520 --> 00:41:27,840 Speaker 1: been locked up for any kind of long amount of time, 658 00:41:28,239 --> 00:41:33,080 Speaker 1: then you know that. In many states up until the 659 00:41:33,160 --> 00:41:38,480 Speaker 1: COVID nineteen pandemic, there was a lot of inequality because 660 00:41:39,239 --> 00:41:44,040 Speaker 1: phone calls were incredibly overpriced. Right. I was only aware 661 00:41:44,080 --> 00:41:46,080 Speaker 1: of that because I got locked up one time for 662 00:41:46,080 --> 00:41:47,719 Speaker 1: a silly thing. It was like a bench warth that 663 00:41:47,800 --> 00:41:49,480 Speaker 1: was out for me for a ticket I hadn't paid, 664 00:41:49,800 --> 00:41:51,799 Speaker 1: um but a it took me forever to get a call, 665 00:41:51,840 --> 00:41:53,560 Speaker 1: and be I had to use a call. I had 666 00:41:53,560 --> 00:41:56,200 Speaker 1: to call collect because I only had one number memorize 667 00:41:56,239 --> 00:41:58,719 Speaker 1: and it was like my mom's and it ended up 668 00:41:58,760 --> 00:42:01,640 Speaker 1: being incredibly expect said. I was like, you know, sixty 669 00:42:01,719 --> 00:42:03,480 Speaker 1: bucks or something like that, But that was because it 670 00:42:03,520 --> 00:42:05,879 Speaker 1: was collect. But if you're incarcerated, it's a little less 671 00:42:05,880 --> 00:42:08,080 Speaker 1: than that, but if you think about the frequency of 672 00:42:08,080 --> 00:42:10,080 Speaker 1: these calls, it adds up. How much are they looking 673 00:42:10,120 --> 00:42:14,080 Speaker 1: at today? Ben the Prison Policy Initiative reported that a 674 00:42:14,280 --> 00:42:17,360 Speaker 1: fifteen minute phone call could cost as much as twenty 675 00:42:17,360 --> 00:42:21,839 Speaker 1: four dollars and eight two cents, So hashtag they did 676 00:42:21,880 --> 00:42:25,879 Speaker 1: the math. If you divide that cost, what you see 677 00:42:26,040 --> 00:42:29,479 Speaker 1: is it's like a dollar sixty five per minute, which 678 00:42:29,480 --> 00:42:32,439 Speaker 1: feels like a nine line at that point, especially, people 679 00:42:32,440 --> 00:42:35,280 Speaker 1: were one of the worst times of their life. Luckily, 680 00:42:35,320 --> 00:42:39,600 Speaker 1: at least in response to the pandemic. Back in April, 681 00:42:40,200 --> 00:42:44,120 Speaker 1: the Federal Bureau of Prisons did say that at least 682 00:42:44,120 --> 00:42:47,520 Speaker 1: in federal pens, telephone and video calls would be free 683 00:42:47,520 --> 00:42:50,680 Speaker 1: of charge. But I'm just saying that to say, like, 684 00:42:50,760 --> 00:42:53,840 Speaker 1: if you're aware of that context, I think it's completely 685 00:42:53,920 --> 00:42:58,439 Speaker 1: understandable how a lot of people would say, Okay, I'm 686 00:42:58,560 --> 00:43:03,440 Speaker 1: sure it's an accident that you happen to list listen in. 687 00:43:03,640 --> 00:43:08,400 Speaker 1: It just seems like rife for corruption um against a 688 00:43:08,520 --> 00:43:11,120 Speaker 1: very disadvantaged population, you know what I mean, No matter 689 00:43:11,160 --> 00:43:13,600 Speaker 1: how they ended up in there, people do have rights 690 00:43:13,600 --> 00:43:17,040 Speaker 1: really quickly, guys. I don't disagree with everything you're saying. 691 00:43:17,120 --> 00:43:19,600 Speaker 1: I'm coming to this from the only experience that I 692 00:43:19,640 --> 00:43:21,600 Speaker 1: have with the secure system, and it was when we 693 00:43:21,600 --> 00:43:23,920 Speaker 1: were making Atlanta Monster and we were trying to be 694 00:43:23,960 --> 00:43:26,680 Speaker 1: able to talk to Wayne Williams, and we had to 695 00:43:26,719 --> 00:43:31,240 Speaker 1: go through this ridiculous system of connecting that phone number 696 00:43:31,239 --> 00:43:34,400 Speaker 1: from which we would be dialing from and receiving calls from, 697 00:43:34,440 --> 00:43:38,160 Speaker 1: like linking it to our group into an individual and 698 00:43:38,200 --> 00:43:42,440 Speaker 1: making sure that that system knew exactly who we were. Right. So, 699 00:43:43,320 --> 00:43:46,080 Speaker 1: in response to this Vice article, I think the one 700 00:43:46,120 --> 00:43:47,920 Speaker 1: of the ones that you use for this research l 701 00:43:48,520 --> 00:43:52,000 Speaker 1: the company's secure US is saying, look, if somebody doesn't 702 00:43:52,000 --> 00:43:54,880 Speaker 1: go through this system and sets up that number to 703 00:43:55,000 --> 00:43:59,200 Speaker 1: be private or non recorded, then that phone call is 704 00:43:59,200 --> 00:44:03,040 Speaker 1: going to get worded like because they're just default recorded 705 00:44:03,320 --> 00:44:07,880 Speaker 1: unless the number is associated with some very Yeah that 706 00:44:07,920 --> 00:44:09,799 Speaker 1: makes sense. Now, that makes sense matter, It's a really 707 00:44:09,840 --> 00:44:12,880 Speaker 1: good context. My question is the one part of the 708 00:44:13,080 --> 00:44:15,080 Speaker 1: this this Vice article that kind of threw me and 709 00:44:15,239 --> 00:44:17,040 Speaker 1: I need to do a little more digging. This might 710 00:44:17,080 --> 00:44:19,959 Speaker 1: even be you know, a larger episode down the line. 711 00:44:20,000 --> 00:44:23,120 Speaker 1: But um, you know they they were saying, these folks 712 00:44:23,200 --> 00:44:28,320 Speaker 1: from the Brooklyn Defender Services are talking about how client 713 00:44:28,400 --> 00:44:34,000 Speaker 1: attorney phone calls were um submitted in discovery, Like, wouldn't 714 00:44:34,040 --> 00:44:38,840 Speaker 1: those automatically be excluded from being used as evidence? Like 715 00:44:38,920 --> 00:44:41,200 Speaker 1: just the fact that it exists doesn't make it legal 716 00:44:41,239 --> 00:44:43,640 Speaker 1: to to use against somebody. I mean, even if the 717 00:44:43,680 --> 00:44:47,400 Speaker 1: call was recorded, you shouldn't be able to actually just 718 00:44:47,480 --> 00:44:50,759 Speaker 1: submit that or admit that as evidence in the case. Right, Yeah, 719 00:44:50,800 --> 00:44:52,600 Speaker 1: I think you could. I think that's a good question 720 00:44:52,680 --> 00:44:57,000 Speaker 1: because you could, uh, someone could try to use it 721 00:44:57,160 --> 00:45:00,640 Speaker 1: or try to use some information to take via it 722 00:45:01,040 --> 00:45:03,520 Speaker 1: that led them to some other evidence, and if they 723 00:45:03,520 --> 00:45:08,960 Speaker 1: can reasonably rationalize a different avenue towards that evidence. It 724 00:45:09,080 --> 00:45:13,600 Speaker 1: might get a little tricky, but surely a judge would, like, 725 00:45:13,640 --> 00:45:15,840 Speaker 1: if the case makes it to trial, surely a judge 726 00:45:15,880 --> 00:45:21,000 Speaker 1: would be right in saying this is inadmissible. Right, surely. 727 00:45:21,520 --> 00:45:23,680 Speaker 1: I guess it would have to be really some dirty 728 00:45:23,719 --> 00:45:26,799 Speaker 1: dealing to kind of like you know, hide the chain 729 00:45:26,800 --> 00:45:29,200 Speaker 1: of custody or like where it actually came from, because 730 00:45:29,200 --> 00:45:30,880 Speaker 1: I would think if it was known that it was 731 00:45:31,000 --> 00:45:35,040 Speaker 1: an illegally recorded phone call, uh, you know, done from 732 00:45:35,040 --> 00:45:37,640 Speaker 1: prison between a client and their attorney, that that would 733 00:45:37,680 --> 00:45:40,000 Speaker 1: be you know, a nonstarter, wouldn't you think, Matt, Based 734 00:45:40,040 --> 00:45:42,759 Speaker 1: on your Atlanta Monster stuff as well. I think so, 735 00:45:42,840 --> 00:45:45,759 Speaker 1: but I don't know. I don't know, man, Sorry, this 736 00:45:45,800 --> 00:45:48,600 Speaker 1: whole thing is really disturbing. It is, it is. But 737 00:45:48,680 --> 00:45:51,600 Speaker 1: I also I'm like, is it does it really matter 738 00:45:51,680 --> 00:45:53,920 Speaker 1: if they're being recorded, if they're not ending up in 739 00:45:53,960 --> 00:45:56,560 Speaker 1: the hands of people that would use them nefariously? Is 740 00:45:56,600 --> 00:45:59,759 Speaker 1: that the implication is that the implication that they of 741 00:46:00,000 --> 00:46:02,279 Speaker 1: course are ending up in the hands of people that 742 00:46:02,320 --> 00:46:08,520 Speaker 1: would use them unfairly. I mean, just that's why we 743 00:46:08,560 --> 00:46:12,000 Speaker 1: have a system of trials. Right. Each person has a 744 00:46:12,080 --> 00:46:14,719 Speaker 1: right to defend themselves, and if you're taking counsel with 745 00:46:14,840 --> 00:46:18,320 Speaker 1: somebody to find a way to descend, to defend yourself 746 00:46:18,800 --> 00:46:21,680 Speaker 1: the most efficiently, no matter whether you did a crime 747 00:46:21,840 --> 00:46:24,920 Speaker 1: or not. I mean, that's like part of the whole system. 748 00:46:25,000 --> 00:46:26,840 Speaker 1: I was going to say game, which it is in 749 00:46:26,880 --> 00:46:29,920 Speaker 1: a weird way of game, but um, I don't know. 750 00:46:30,000 --> 00:46:32,640 Speaker 1: At least within our system of laws. Right now, you've 751 00:46:32,680 --> 00:46:37,239 Speaker 1: got to have that protected communication. And that includes even 752 00:46:37,280 --> 00:46:39,640 Speaker 1: if it's just a system that records it by default 753 00:46:39,680 --> 00:46:41,480 Speaker 1: and it just stays in a file on a hard 754 00:46:41,560 --> 00:46:45,600 Speaker 1: drive somewhere. Right, that shouldn't exist, right, it shouldn't exist. 755 00:46:45,640 --> 00:46:47,520 Speaker 1: That's what I'm saying, I agree, I agree with you. 756 00:46:48,760 --> 00:46:51,040 Speaker 1: I think we're all on the same page. And again, 757 00:46:51,480 --> 00:46:55,400 Speaker 1: you know, this is not any of us uh being 758 00:46:55,520 --> 00:46:59,400 Speaker 1: cheerleaders for crime or what have you. It's just that 759 00:47:00,120 --> 00:47:04,520 Speaker 1: if you take a larger picture perspective and you think 760 00:47:04,600 --> 00:47:09,960 Speaker 1: through this, the rights that are guaranteed in theory again 761 00:47:10,480 --> 00:47:15,120 Speaker 1: to people who are incarcerated are enormously important, no matter 762 00:47:15,200 --> 00:47:17,839 Speaker 1: how they got there. There's a reason that the law 763 00:47:17,920 --> 00:47:23,960 Speaker 1: exists on those rights. In practice, you know, obviously they're 764 00:47:24,000 --> 00:47:26,279 Speaker 1: obviously not holding up to what they're supposed to be 765 00:47:26,320 --> 00:47:30,200 Speaker 1: in theory. Um. You know, you need look no further 766 00:47:30,840 --> 00:47:36,279 Speaker 1: than prisons in Scandinavian countries to see the difference in 767 00:47:36,360 --> 00:47:40,200 Speaker 1: how people treat incarceration. Like some of those prisons are 768 00:47:40,239 --> 00:47:44,279 Speaker 1: like better than people's apartments, you know, but they look 769 00:47:44,360 --> 00:47:47,799 Speaker 1: like those model IKEA apartments, you know, I mean, like 770 00:47:47,960 --> 00:47:52,719 Speaker 1: really really clean lines and nice tasteful decor, you know, 771 00:47:52,920 --> 00:47:56,000 Speaker 1: comfy mattresses and all that, if a little austere, but 772 00:47:56,160 --> 00:47:58,919 Speaker 1: nothing like what we see in American prisons, which you're 773 00:47:58,960 --> 00:48:00,920 Speaker 1: you know, for the law in term stays can be 774 00:48:00,960 --> 00:48:03,880 Speaker 1: absolutely in humane. Um. But yeah, I think it's im 775 00:48:03,880 --> 00:48:05,440 Speaker 1: going to keep an eye on and and it's I'm 776 00:48:05,440 --> 00:48:07,120 Speaker 1: interested to see where this goes. Is a pretty new 777 00:48:07,160 --> 00:48:10,200 Speaker 1: story and it is still developing. UM. But you know, 778 00:48:10,280 --> 00:48:14,560 Speaker 1: and it's like these types of UM prisoner advocacy groups 779 00:48:14,560 --> 00:48:19,040 Speaker 1: are really important because left unchecked, either the system won't 780 00:48:19,120 --> 00:48:22,640 Speaker 1: work like it's supposed to because it's not being checked 781 00:48:22,880 --> 00:48:25,480 Speaker 1: or like you know, uh looked looked in upon and 782 00:48:26,320 --> 00:48:29,879 Speaker 1: held to a high standard, or just rampant corruption will 783 00:48:29,880 --> 00:48:32,279 Speaker 1: absolutely happen in the dark if no one's checking into 784 00:48:32,320 --> 00:48:36,360 Speaker 1: These kind of watchdog groups are incredibly important for you know, 785 00:48:36,400 --> 00:48:39,080 Speaker 1: folks civil rights and one I'm stating the obvious here. 786 00:48:39,080 --> 00:48:40,960 Speaker 1: If I'm just saying good work to the folks at 787 00:48:41,040 --> 00:48:45,479 Speaker 1: Brooklyn Defender Services Organization. UH agree with that. I think 788 00:48:45,520 --> 00:48:48,759 Speaker 1: we can call this episode of strange news or wrap. 789 00:48:48,800 --> 00:48:52,080 Speaker 1: What do you say, fellas agreed? Unless there is an 790 00:48:52,120 --> 00:48:56,319 Speaker 1: AI overlord that can figure out a better way to 791 00:48:56,640 --> 00:49:00,759 Speaker 1: end strange news, we're going to We're gonna call it 792 00:49:00,800 --> 00:49:03,600 Speaker 1: a day. Do you own a coil gun? What do 793 00:49:03,640 --> 00:49:06,120 Speaker 1: you think the dangers of d n A maybe not 794 00:49:06,280 --> 00:49:09,680 Speaker 1: just in Australia but around the world. And how far 795 00:49:09,719 --> 00:49:14,160 Speaker 1: do you think this evidence of either accident or corruption 796 00:49:14,600 --> 00:49:18,200 Speaker 1: might go in the New York penals system. We would 797 00:49:18,239 --> 00:49:20,440 Speaker 1: and beyond, We would love to hear from you. We 798 00:49:20,560 --> 00:49:23,200 Speaker 1: try to be easy to find online. You can find 799 00:49:23,280 --> 00:49:26,600 Speaker 1: us on Facebook and Twitter and YouTube where we are 800 00:49:26,719 --> 00:49:32,160 Speaker 1: conspiracy Stuff. On Instagram we are Conspiracy Stuff Show. If 801 00:49:32,160 --> 00:49:34,840 Speaker 1: you like this episode, hey, why not give us a review? 802 00:49:35,120 --> 00:49:37,920 Speaker 1: Apple podcast is probably the best, and a review on 803 00:49:37,920 --> 00:49:40,640 Speaker 1: any other platform would be great too. Please, if you 804 00:49:40,680 --> 00:49:42,720 Speaker 1: have a moment, go ahead and do that. We really, 805 00:49:42,760 --> 00:49:45,359 Speaker 1: really really appreciate it. 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