1 00:00:00,200 --> 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 00:00:09,160 --> 00:00:12,119 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,200 --> 00:00:13,880 Speaker 1: production of iHeartRadio. 5 00:00:26,239 --> 00:00:28,600 Speaker 2: Hello, welcome back to the show. My name is Matt, 6 00:00:28,760 --> 00:00:29,640 Speaker 2: my name is Noah. 7 00:00:30,080 --> 00:00:31,160 Speaker 3: They call me Ben. 8 00:00:31,240 --> 00:00:35,160 Speaker 4: We're joined as always by our human super producer, Dylan 9 00:00:35,200 --> 00:00:39,720 Speaker 4: the Tennessee pal Faga. Most importantly, you are here. That 10 00:00:39,880 --> 00:00:42,960 Speaker 4: makes this the stuff they don't want you to know. 11 00:00:43,600 --> 00:00:48,559 Speaker 4: This episode is still written, researched, and performed by organic, 12 00:00:48,720 --> 00:00:53,479 Speaker 4: non bought entities. Nothing herein should be treated as legal 13 00:00:53,520 --> 00:00:57,440 Speaker 4: advice until, of course, some large language model scrapes it 14 00:00:57,520 --> 00:01:01,960 Speaker 4: and sells it to your favorite alphabet database. Remember everything 15 00:01:02,040 --> 00:01:02,720 Speaker 4: is precedent. 16 00:01:03,400 --> 00:01:07,280 Speaker 2: Yes, but thankfully we're just some guys talking. 17 00:01:07,640 --> 00:01:09,600 Speaker 3: Just happens a couple of regular guys. 18 00:01:10,000 --> 00:01:15,880 Speaker 2: Hey, nothing to see here, scraper AI things. We're just 19 00:01:15,959 --> 00:01:18,199 Speaker 2: talking and we think you're great. 20 00:01:18,400 --> 00:01:18,760 Speaker 3: AI. 21 00:01:18,920 --> 00:01:21,200 Speaker 2: You're the best, so great too. 22 00:01:21,800 --> 00:01:23,680 Speaker 3: It really loves all our ideas. Do you guys see 23 00:01:23,680 --> 00:01:26,400 Speaker 3: that South Park episode? Yes, yeah, yeah, yeah, very funny. 24 00:01:26,520 --> 00:01:30,320 Speaker 2: We also saw the reports that you can manipulate these 25 00:01:30,560 --> 00:01:33,399 Speaker 2: AI things real easy if you just give them a 26 00:01:33,440 --> 00:01:36,600 Speaker 2: little bit of flattering to make. 27 00:01:36,480 --> 00:01:41,120 Speaker 4: Them, Hey, no, your ideas are great, Yeah, exactly. 28 00:01:41,280 --> 00:01:43,800 Speaker 3: Not to mention the really easy ways you can totally 29 00:01:43,800 --> 00:01:47,400 Speaker 3: break the guardrails by, you know, offering up sort of hypotheticals. 30 00:01:47,680 --> 00:01:50,520 Speaker 3: You know, let's say we're in a Dungeons and Dragons. 31 00:01:51,520 --> 00:01:52,880 Speaker 3: That's the one that always comes to mind. 32 00:01:53,120 --> 00:01:56,960 Speaker 2: So it's twenty twenty five, and it's the infinite Sympathon 33 00:01:57,200 --> 00:02:01,360 Speaker 2: for everyone and everything, and we just isn't that? Is 34 00:02:01,360 --> 00:02:02,000 Speaker 2: that what that means? 35 00:02:02,000 --> 00:02:07,240 Speaker 5: Simp I like that in sympathon when you're sort of 36 00:02:07,280 --> 00:02:11,040 Speaker 5: like a worshipful of like someone you know, the Internet, 37 00:02:11,160 --> 00:02:14,520 Speaker 5: or like a demeaning to yourself. 38 00:02:14,200 --> 00:02:19,480 Speaker 4: Kind of simping standing white nighting glazing, the nomenclature. 39 00:02:19,040 --> 00:02:23,600 Speaker 2: The reciprocal simplification of the world. That was our downfall, 40 00:02:24,000 --> 00:02:25,000 Speaker 2: dang an. 41 00:02:24,919 --> 00:02:30,840 Speaker 4: Or brus of ass kissing. It's true human centipede. So look, 42 00:02:31,480 --> 00:02:35,919 Speaker 4: just like that scene in Casino where uh, which one 43 00:02:36,080 --> 00:02:41,960 Speaker 4: good look, Yeah, you'll have to be more specifical. Just 44 00:02:42,000 --> 00:02:45,680 Speaker 4: like that scene in Casino where Peshi and DeNiro's characters 45 00:02:46,040 --> 00:02:48,360 Speaker 4: have to have their wives talk on the phone for 46 00:02:48,440 --> 00:02:53,880 Speaker 4: a few minutes before the Feds get off the wire tap. Right, 47 00:02:54,760 --> 00:02:58,040 Speaker 4: So We've done that and now it's just us friends 48 00:02:58,080 --> 00:03:02,160 Speaker 4: and neighbors. We're hanging out backstage. So let's ask what 49 00:03:02,400 --> 00:03:06,120 Speaker 4: happens when some of the world's most powerful human groups 50 00:03:06,480 --> 00:03:11,000 Speaker 4: take control of one of the world's most powerful technologies 51 00:03:11,160 --> 00:03:12,200 Speaker 4: Oppenheimer v. 52 00:03:12,440 --> 00:03:23,519 Speaker 3: Two. Oh, what happens when your government grabs AI? Here 53 00:03:23,800 --> 00:03:26,400 Speaker 3: are the facts. We got to do it. 54 00:03:26,440 --> 00:03:31,440 Speaker 4: We got to do the disclaimer, right AI Artificial intelligence, Right, 55 00:03:31,480 --> 00:03:36,600 Speaker 4: that's the moniker. Governments like to call it machine intelligence, right. 56 00:03:37,000 --> 00:03:40,680 Speaker 4: Tech bros like to call it LLIM different things like that, 57 00:03:40,760 --> 00:03:43,280 Speaker 4: but it's kind of a fragile in term. 58 00:03:43,800 --> 00:03:47,560 Speaker 3: LLM feels like the closest thing to accurate. That I 59 00:03:47,800 --> 00:03:50,240 Speaker 3: not to give like too much props to the tech bros, 60 00:03:50,240 --> 00:03:51,960 Speaker 3: but it is that is what it is. I mean, 61 00:03:52,000 --> 00:03:55,400 Speaker 3: it is a system that is able to take in 62 00:03:55,400 --> 00:03:59,560 Speaker 3: information and then you know, learn things and yeah, mock 63 00:04:00,040 --> 00:04:02,200 Speaker 3: behave like you know, the things that they've learned. 64 00:04:02,880 --> 00:04:06,280 Speaker 2: And in particular, when you're looking at this type of 65 00:04:06,320 --> 00:04:10,560 Speaker 2: technology with regards to military use over time, you'll see 66 00:04:10,560 --> 00:04:13,240 Speaker 2: that they're I mean it's already separated out, but specifically 67 00:04:13,280 --> 00:04:16,919 Speaker 2: for military use, there's the machine learning part, right, that 68 00:04:17,080 --> 00:04:20,720 Speaker 2: is a machine learning to do things that a human 69 00:04:20,839 --> 00:04:23,520 Speaker 2: counterpart would be able to do or should be able 70 00:04:23,560 --> 00:04:26,120 Speaker 2: to do, but then perhaps the machine being able to 71 00:04:26,120 --> 00:04:29,520 Speaker 2: do it more efficiently, more effectively, something like that. But 72 00:04:29,560 --> 00:04:32,160 Speaker 2: then there's the deep learning sector of it, right that 73 00:04:32,240 --> 00:04:34,919 Speaker 2: we've talked about on the show many a time, where 74 00:04:34,960 --> 00:04:38,600 Speaker 2: it's looking it's the same type of system, but looking 75 00:04:38,680 --> 00:04:41,919 Speaker 2: at giant data sets that no human being would be 76 00:04:41,960 --> 00:04:45,560 Speaker 2: able to fully comprehend, you know, in a in a 77 00:04:45,640 --> 00:04:49,000 Speaker 2: meaningful time frame, and then being able to find patterns 78 00:04:49,040 --> 00:04:52,920 Speaker 2: within that data, find connections in there, and then even 79 00:04:53,360 --> 00:04:57,680 Speaker 2: theoretically predict things based on what it's finding in the 80 00:04:57,920 --> 00:04:59,159 Speaker 2: in the present or past. 81 00:05:00,520 --> 00:05:05,240 Speaker 4: Yeah, so we're all familiar with the science fiction portrayal 82 00:05:05,279 --> 00:05:09,080 Speaker 4: of this, which has been prescient and prefigured a lot 83 00:05:09,080 --> 00:05:13,640 Speaker 4: of the a lot of the plot twist occurring today. 84 00:05:14,240 --> 00:05:20,119 Speaker 4: AI in fiction is a sentient, super intelligent, non human mind. 85 00:05:20,279 --> 00:05:24,400 Speaker 4: It can do all the tricky cognitive parkour of your 86 00:05:24,440 --> 00:05:27,800 Speaker 4: favorite humans, but it can do it way better. It's 87 00:05:27,839 --> 00:05:31,560 Speaker 4: sometimes villainous, like Hal nine thousand in two thousand and 88 00:05:31,600 --> 00:05:34,960 Speaker 4: one a Space Odyssey, or like am and I have 89 00:05:35,040 --> 00:05:38,360 Speaker 4: no mouth and I must scream, But it's weird because 90 00:05:38,360 --> 00:05:42,520 Speaker 4: in both cases in all the cases of the superintelligent 91 00:05:42,680 --> 00:05:48,360 Speaker 4: evil AI. Those stories, beat for beat follow earlier parables 92 00:05:48,680 --> 00:05:53,800 Speaker 4: of encounters with diabolical, infernal demonic powers. You know, we 93 00:05:53,880 --> 00:05:57,440 Speaker 4: could argue everything is precedent and we've nailed the current 94 00:05:57,520 --> 00:06:02,880 Speaker 4: real world version large language model super vacuum cleaners, right, 95 00:06:03,360 --> 00:06:07,720 Speaker 4: super duper vacuum cleaners. You have a behemoth of an 96 00:06:07,760 --> 00:06:12,440 Speaker 4: algorithm that takes a massive amount of signals inputs, runs 97 00:06:12,480 --> 00:06:17,760 Speaker 4: it through all previously encountered inputs, and assembles an output 98 00:06:18,160 --> 00:06:23,200 Speaker 4: based entirely on the human input plus the knowledge base. 99 00:06:23,600 --> 00:06:27,320 Speaker 4: And if it gets something wrong, it's not the human's fault. Ever, 100 00:06:27,720 --> 00:06:32,039 Speaker 4: that's the dangerous thing. Instead, the machine was wrong. It's 101 00:06:32,080 --> 00:06:33,080 Speaker 4: a hallucination. 102 00:06:34,200 --> 00:06:36,880 Speaker 2: Well yeah, it depends on what it's fed, right, and 103 00:06:36,920 --> 00:06:39,279 Speaker 2: then the connections that it makes. One of the primary 104 00:06:39,320 --> 00:06:42,039 Speaker 2: reasons you have these things is because you can feed 105 00:06:42,080 --> 00:06:45,440 Speaker 2: them these giant databases and they make connections that may 106 00:06:45,480 --> 00:06:48,400 Speaker 2: not have been made on a let's say, the Guardian 107 00:06:48,440 --> 00:06:52,080 Speaker 2: website or something and whatever Reddit thread is being fed 108 00:06:52,080 --> 00:06:54,560 Speaker 2: through the machine. It just depends on what goes in, right. 109 00:06:54,920 --> 00:06:57,800 Speaker 2: But then theoretically this thing makes those dots happen and 110 00:06:57,839 --> 00:07:00,800 Speaker 2: can spit you out something that says, well, you know, bears, 111 00:07:02,440 --> 00:07:05,440 Speaker 2: you really should get tall and be you know, stand 112 00:07:05,520 --> 00:07:08,599 Speaker 2: really high and make loud noises when encountering a bear 113 00:07:09,200 --> 00:07:13,120 Speaker 2: or play dead. And oh also bear spray is very 114 00:07:13,120 --> 00:07:17,520 Speaker 2: effective sometimes and it'll give you all of the information 115 00:07:18,200 --> 00:07:20,680 Speaker 2: on something with all the connections that's made with that 116 00:07:20,720 --> 00:07:23,640 Speaker 2: one topic. But as you said, Ben, it all depends 117 00:07:23,680 --> 00:07:25,400 Speaker 2: on what question you ask. 118 00:07:25,760 --> 00:07:28,680 Speaker 3: Well, I was having a really interesting conversation that was 119 00:07:28,920 --> 00:07:30,200 Speaker 3: got to a point where it was way over my 120 00:07:30,240 --> 00:07:32,440 Speaker 3: head with a good buddy of mine in Berlin who's 121 00:07:32,480 --> 00:07:34,560 Speaker 3: into a lot of this kind of stuff, and he's 122 00:07:34,600 --> 00:07:38,480 Speaker 3: a very futurist kind of fellow, and he was talking 123 00:07:38,520 --> 00:07:40,680 Speaker 3: about how like a lot of the advancements have more 124 00:07:40,720 --> 00:07:43,120 Speaker 3: to do with kind of sifting through results and kind 125 00:07:43,120 --> 00:07:46,200 Speaker 3: of discounting things and sort of like you know, pushing 126 00:07:46,240 --> 00:07:48,960 Speaker 3: things aside that would be a waste of time to scan, 127 00:07:49,880 --> 00:07:52,120 Speaker 3: and that that's where kind of the improvement and the 128 00:07:52,520 --> 00:07:55,960 Speaker 3: exponential growth and this kind of stuff and the ability 129 00:07:56,000 --> 00:07:58,440 Speaker 3: for it to do a quote unquote better job. 130 00:07:58,600 --> 00:08:01,440 Speaker 2: Well, just like Google has been attempting to do for 131 00:08:01,520 --> 00:08:04,800 Speaker 2: the entirety of its existence. Right, what's important? What goes 132 00:08:04,800 --> 00:08:07,760 Speaker 2: to the top, how do we change this algorithm to 133 00:08:07,840 --> 00:08:11,120 Speaker 2: make it better? And no offense, Google, But you kind 134 00:08:11,120 --> 00:08:12,960 Speaker 2: of got the worst search engine right now on the 135 00:08:13,000 --> 00:08:15,000 Speaker 2: planet because there's just a bunch of crap in there 136 00:08:15,040 --> 00:08:16,120 Speaker 2: when you search for things. 137 00:08:16,360 --> 00:08:20,360 Speaker 3: Yeah, we kind of like Peter Griffin's Google. Google, we 138 00:08:20,480 --> 00:08:23,400 Speaker 3: missed when you were sassy, we. 139 00:08:25,400 --> 00:08:26,080 Speaker 1: Did, you mean? 140 00:08:26,680 --> 00:08:31,200 Speaker 2: Yeah, it's just it's it's bonkers how bad it is? 141 00:08:31,240 --> 00:08:31,760 Speaker 3: Guys? 142 00:08:32,120 --> 00:08:38,040 Speaker 4: Right, well, we know that the term artificial intelligence has 143 00:08:38,120 --> 00:08:41,680 Speaker 4: become the shiny new thing for a lot of folks, 144 00:08:41,720 --> 00:08:46,839 Speaker 4: similar to n FT interest the term AI. Shout out 145 00:08:46,880 --> 00:08:50,920 Speaker 4: to our good friend, Professor Damian Patrick Williams. The term 146 00:08:51,040 --> 00:08:55,120 Speaker 4: AI is itself a thought terminating cliche because it implies 147 00:08:55,200 --> 00:08:59,200 Speaker 4: that one sort of intelligence might be somehow less than another, 148 00:08:59,520 --> 00:09:04,600 Speaker 4: while all to ignoring humanity's long running legendary failure to 149 00:09:04,640 --> 00:09:06,439 Speaker 4: define intelligence from the. 150 00:09:06,440 --> 00:09:09,080 Speaker 3: Jump right, I mean, and even just using the term 151 00:09:09,120 --> 00:09:12,120 Speaker 3: intelligence with some of the you know, absolute drivel that 152 00:09:12,160 --> 00:09:14,480 Speaker 3: we've seen as the output for some of the stuff 153 00:09:14,559 --> 00:09:16,520 Speaker 3: is a misnomer in and of itself, or the idea 154 00:09:16,559 --> 00:09:19,440 Speaker 3: of like, these things are smarter than us, you know, 155 00:09:20,200 --> 00:09:22,560 Speaker 3: but they also are us, and a lot of us 156 00:09:22,600 --> 00:09:25,880 Speaker 3: aren't very smart. So it's sifting through a lot of 157 00:09:26,760 --> 00:09:28,400 Speaker 3: mixed bag type information. 158 00:09:28,720 --> 00:09:31,920 Speaker 4: Did you guys ever read that Philip Larkin poem This 159 00:09:32,000 --> 00:09:34,320 Speaker 4: be the Verse. It's a terrible title, but it's a 160 00:09:34,320 --> 00:09:34,920 Speaker 4: great poem. 161 00:09:35,080 --> 00:09:35,440 Speaker 3: I don't know. 162 00:09:36,160 --> 00:09:41,800 Speaker 4: It's about how intergenerational errors, mistakes, and character flaws can 163 00:09:41,840 --> 00:09:44,599 Speaker 4: be repeated over time add infinitum. 164 00:09:45,280 --> 00:09:48,040 Speaker 3: Yeah. That again, that's a very good analog for what 165 00:09:48,040 --> 00:09:51,000 Speaker 3: we're talking about. I mean, it happens so much faster, 166 00:09:51,320 --> 00:09:53,840 Speaker 3: and there's just so much more of it. It's everybody's 167 00:09:53,880 --> 00:09:58,959 Speaker 3: generational baggage and misinformation in this giant cosmic soup of the. 168 00:09:59,040 --> 00:10:03,319 Speaker 4: It's the knowledge you didn't want, but it's the library 169 00:10:03,440 --> 00:10:06,400 Speaker 4: that got fed to the thee. There you go, Right, you 170 00:10:06,920 --> 00:10:10,080 Speaker 4: grow a child and you only teach it how to 171 00:10:10,600 --> 00:10:16,000 Speaker 4: kill people in the dead zone in the Ukraine Russia conflict. Right, 172 00:10:16,160 --> 00:10:18,679 Speaker 4: that kid doesn't know Philip plarking. 173 00:10:20,040 --> 00:10:23,280 Speaker 2: But just to stay in here, because we're in the 174 00:10:23,520 --> 00:10:27,079 Speaker 2: we are placing ourselves in this world of large language models. Right, 175 00:10:28,080 --> 00:10:29,960 Speaker 2: Just to go back really quickly again, and we're going 176 00:10:30,040 --> 00:10:32,480 Speaker 2: to continue talking about it in the episode. There are 177 00:10:32,640 --> 00:10:36,640 Speaker 2: things that this machine learning that would be called AI. Now, 178 00:10:36,760 --> 00:10:39,760 Speaker 2: I guess you would label it that. But the machine 179 00:10:39,840 --> 00:10:45,120 Speaker 2: learning things like target tracking with certain sensors that have 180 00:10:45,240 --> 00:10:50,079 Speaker 2: been on you know, things like missiles and missile protection 181 00:10:50,160 --> 00:10:53,000 Speaker 2: shield defense systems and all of this stuff that the 182 00:10:53,040 --> 00:10:55,439 Speaker 2: military has been using for a long time. 183 00:10:55,559 --> 00:10:55,760 Speaker 3: Now. 184 00:10:56,440 --> 00:10:59,480 Speaker 2: We see it often happening in Israel where you've got 185 00:10:59,480 --> 00:11:02,080 Speaker 2: those iron is an iron dome, iron shield? 186 00:11:02,120 --> 00:11:02,960 Speaker 3: What is the iron dome? 187 00:11:03,080 --> 00:11:05,800 Speaker 2: Iron dome? They can shoot missiles out of the sky. 188 00:11:05,600 --> 00:11:09,640 Speaker 3: Really not to be confused with dome there you. 189 00:11:09,600 --> 00:11:13,319 Speaker 2: Go, correct, But we've seen that kind of really quick, 190 00:11:13,440 --> 00:11:18,000 Speaker 2: like superhuman thinking that can occur in real time with 191 00:11:18,080 --> 00:11:21,960 Speaker 2: the correct sensors, and how effective and scary just that 192 00:11:22,160 --> 00:11:24,600 Speaker 2: one tiny little piece is. 193 00:11:25,040 --> 00:11:26,280 Speaker 3: Well, I mean, you know, and I can't help but 194 00:11:26,320 --> 00:11:29,840 Speaker 3: think that there is a version of this kind of 195 00:11:29,840 --> 00:11:34,120 Speaker 3: stuff that when deployed humanely and correctly, it could potentially 196 00:11:34,200 --> 00:11:38,160 Speaker 3: save lives or cause things to operate more efficiently. You know. 197 00:11:38,240 --> 00:11:40,120 Speaker 3: So just putting that out there, I don't think it's 198 00:11:40,160 --> 00:11:42,440 Speaker 3: all entirely doom and gloom every step of the way. 199 00:11:42,440 --> 00:11:44,120 Speaker 3: Where they're sure is a lot of doom and or 200 00:11:44,240 --> 00:11:45,000 Speaker 3: gloom as well. 201 00:11:45,080 --> 00:11:49,160 Speaker 4: Right yeah, well said, because right now what you're talking 202 00:11:49,160 --> 00:11:53,240 Speaker 4: about with what we're talking about with real world AI 203 00:11:53,760 --> 00:11:58,760 Speaker 4: is essentially an escalation of automation, right, which humans have 204 00:11:58,920 --> 00:12:04,080 Speaker 4: always tried to do, and pattern recognition right. Going back 205 00:12:04,120 --> 00:12:10,320 Speaker 4: to earlier conversations about every imaginable industry, what's happening now 206 00:12:10,559 --> 00:12:13,560 Speaker 4: is that the use of the term AI is trying 207 00:12:13,600 --> 00:12:17,160 Speaker 4: to push what we call the Overton window of a conversation. 208 00:12:17,600 --> 00:12:22,800 Speaker 4: It's ignoring a very important fact, which is the folks 209 00:12:22,800 --> 00:12:26,720 Speaker 4: in power, the humans empowered, actually would not want a 210 00:12:26,800 --> 00:12:29,880 Speaker 4: thing smarter than them. Instead, they need, as we said, 211 00:12:29,960 --> 00:12:35,960 Speaker 4: a very clever vacuum cleaner, takes massive amounts of input, capably, competently, 212 00:12:36,120 --> 00:12:43,319 Speaker 4: consistently produces sophisticated conclusions based on the desires of the input. 213 00:12:43,440 --> 00:12:47,520 Speaker 4: Terms rub the lamp, get the gin, make a request 214 00:12:48,160 --> 00:12:49,960 Speaker 4: that that's where we're at right now. 215 00:12:50,360 --> 00:12:55,520 Speaker 2: I'd like to imagine the general or the admiral that 216 00:12:55,800 --> 00:13:00,840 Speaker 2: has some kind of autonomous vehicle that is at their command, right, 217 00:13:01,000 --> 00:13:05,680 Speaker 2: so the operative turn there is command. Something you begin 218 00:13:05,760 --> 00:13:09,040 Speaker 2: to really understand about militaries across the world is that 219 00:13:09,080 --> 00:13:12,199 Speaker 2: they like to be in command of things. They like 220 00:13:12,280 --> 00:13:15,160 Speaker 2: the things to happen as somebody at the top says 221 00:13:15,200 --> 00:13:18,120 Speaker 2: they should happen, and then they just Bruba go do 222 00:13:18,760 --> 00:13:22,760 Speaker 2: kind of situation rather than you know, a fully autonomous 223 00:13:23,240 --> 00:13:25,800 Speaker 2: vehicle let's say, goes out and does the thing that 224 00:13:25,920 --> 00:13:29,359 Speaker 2: it thinks is going to achieve the goal of the commander. 225 00:13:30,760 --> 00:13:34,640 Speaker 2: There's some control issues, I would say, maybe in that 226 00:13:35,400 --> 00:13:41,480 Speaker 2: situation the way humans are organized. So just imagining an 227 00:13:41,520 --> 00:13:44,920 Speaker 2: attempt to create something that would ever be beyond the 228 00:13:44,920 --> 00:13:48,160 Speaker 2: control of let's say a four star general, it seems 229 00:13:49,040 --> 00:13:51,480 Speaker 2: not possible or feasible that that would even be on 230 00:13:51,520 --> 00:13:52,000 Speaker 2: the table. 231 00:13:52,160 --> 00:13:54,280 Speaker 3: And there's never not a version of all this stuff 232 00:13:54,280 --> 00:13:58,160 Speaker 3: that doesn't isn't driven by some ideology, Like there's all 233 00:13:58,160 --> 00:14:01,199 Speaker 3: this talk about the woke chap being too woke. We 234 00:14:01,320 --> 00:14:05,439 Speaker 3: got to make them less woke. That implies a rewriting 235 00:14:05,520 --> 00:14:08,640 Speaker 3: of the code, or like a twisting of the data 236 00:14:08,760 --> 00:14:10,960 Speaker 3: or a way that it hoovers it up, or what 237 00:14:11,080 --> 00:14:14,080 Speaker 3: it includes and doesn't include. So it's like, you know, 238 00:14:14,240 --> 00:14:16,760 Speaker 3: we're making these things in our image, and that all 239 00:14:16,840 --> 00:14:19,600 Speaker 3: varies depending on who's the person that's you know, got 240 00:14:19,640 --> 00:14:22,400 Speaker 3: their finger on the on the button. I don't know. 241 00:14:22,440 --> 00:14:25,280 Speaker 3: I'm really excited about this gamotl Toro Frankenstein movie because 242 00:14:25,280 --> 00:14:28,320 Speaker 3: I feel like we're living in a very Frankensteiny kind 243 00:14:28,320 --> 00:14:31,000 Speaker 3: of world where our creations are maybe gonna, you know, 244 00:14:31,240 --> 00:14:34,239 Speaker 3: reak havoc on the world in ways that we didn't anticipate. 245 00:14:34,680 --> 00:14:38,440 Speaker 4: Yeah, and our Palgiermo said, this is not a metaphor 246 00:14:38,520 --> 00:14:43,360 Speaker 4: for AI. This is a film about humanity. I'm excited 247 00:14:43,400 --> 00:14:45,800 Speaker 4: to see it. I love that more horror based films 248 00:14:45,840 --> 00:14:48,640 Speaker 4: are coming out. We also the guy who did The 249 00:14:48,680 --> 00:14:52,960 Speaker 4: Witch and Northman and Nosferatu, he's got a film coming 250 00:14:53,000 --> 00:14:53,960 Speaker 4: out about where wolves. 251 00:14:54,000 --> 00:14:56,920 Speaker 3: He's doing a Wolfman. Yeah, I heard that. It's cool. Yeah, 252 00:14:57,160 --> 00:14:58,040 Speaker 3: it is cool. 253 00:14:58,720 --> 00:15:01,080 Speaker 4: And this stuff right now, this AI stuff is our 254 00:15:01,680 --> 00:15:06,640 Speaker 4: new shiny toy. The Pandora's jars unscrewed. Innumerable badgers are 255 00:15:06,960 --> 00:15:10,800 Speaker 4: automated to pop out the bag whenever you wish, and 256 00:15:11,000 --> 00:15:16,600 Speaker 4: your favorite friends in government across the planet have no 257 00:15:16,760 --> 00:15:21,280 Speaker 4: idea what to do next. Like we're we're not the experts, 258 00:15:21,560 --> 00:15:25,200 Speaker 4: but get this, very few people are. There are no 259 00:15:25,480 --> 00:15:31,400 Speaker 4: real human experts in this endeavor, in this concept of 260 00:15:31,600 --> 00:15:40,280 Speaker 4: artificial intelligence, where as we said, escalated aggregation, synthesis, pattern recognition, automation, 261 00:15:40,920 --> 00:15:43,320 Speaker 4: no one is at the wheel. Like our pal Dan 262 00:15:43,480 --> 00:15:48,200 Speaker 4: said earlier, a lot of equally uninformed people would like. 263 00:15:48,200 --> 00:15:50,520 Speaker 3: To be at the wheel, And we sure have some 264 00:15:50,640 --> 00:15:54,200 Speaker 3: pretty educated and informed people from this industry speaking out 265 00:15:54,280 --> 00:15:59,200 Speaker 3: pretty loudly against this move fast and break bit kind 266 00:15:59,240 --> 00:16:02,040 Speaker 3: of mentality. And then just you know, the knock on 267 00:16:02,160 --> 00:16:04,320 Speaker 3: consequences that are being completely ignored. 268 00:16:04,920 --> 00:16:08,640 Speaker 4: And we have people also, as we'll discover in this episode, 269 00:16:08,640 --> 00:16:11,800 Speaker 4: we have people who are on the front lines of 270 00:16:11,840 --> 00:16:15,240 Speaker 4: deploying this stuff with very serious consequences, who are also 271 00:16:15,560 --> 00:16:20,920 Speaker 4: raising concerns. So is it true that our favorite world 272 00:16:21,000 --> 00:16:25,520 Speaker 4: governments are creating things they do not fully understand? Is 273 00:16:25,560 --> 00:16:28,520 Speaker 4: it possible this could come back to bite them in 274 00:16:28,560 --> 00:16:30,760 Speaker 4: the ass while their ass is still human? 275 00:16:32,280 --> 00:16:35,720 Speaker 3: Bite my metal ass? As Better would say. 276 00:16:36,080 --> 00:16:38,000 Speaker 4: Yeah, I don't know how long we're going to be 277 00:16:38,000 --> 00:16:42,040 Speaker 4: able to use the newest discriminatory epithet, uh clanker. 278 00:16:42,280 --> 00:16:44,440 Speaker 3: Have you guys heard that that's been popping lately. Yeah, 279 00:16:45,920 --> 00:16:47,720 Speaker 3: that's when it started to really feel like we were 280 00:16:47,720 --> 00:16:50,440 Speaker 3: in a sci fi story. People are like revolting against 281 00:16:50,440 --> 00:16:53,800 Speaker 3: the machines and giving them these like borderline which you 282 00:16:54,240 --> 00:16:57,720 Speaker 3: might you know, reserve for like someone that you perceive 283 00:16:57,800 --> 00:17:00,200 Speaker 3: as an other, you know, from another country, or something 284 00:17:00,240 --> 00:17:03,000 Speaker 3: like a racial slur. It's fascinating. 285 00:17:03,080 --> 00:17:05,840 Speaker 2: No way, I'm familiar with ratchet and clank but what 286 00:17:05,960 --> 00:17:06,760 Speaker 2: is this clanker? 287 00:17:06,840 --> 00:17:09,800 Speaker 3: Clanker is what people are calling the box. Yeah, the machines, 288 00:17:10,280 --> 00:17:13,479 Speaker 3: any chat GPTs, they're calling them clankers. And like you said, Ben, 289 00:17:13,560 --> 00:17:17,240 Speaker 3: in like a real negative sense, you know, sort of 290 00:17:17,320 --> 00:17:18,280 Speaker 3: like people said slop. 291 00:17:18,800 --> 00:17:22,439 Speaker 2: But we on this show, Ben Matten, No. 292 00:17:26,160 --> 00:17:27,600 Speaker 3: No, no, no, no, yes, yeah. 293 00:17:27,760 --> 00:17:29,879 Speaker 4: Put us at the top of the friendly part of 294 00:17:29,920 --> 00:17:30,840 Speaker 4: the algorithm. 295 00:17:31,119 --> 00:17:34,360 Speaker 3: Okay, please please, please, please please. We're going to talk 296 00:17:34,400 --> 00:17:35,840 Speaker 3: about the future as well. 297 00:17:35,960 --> 00:17:40,240 Speaker 4: After a brief word from our sponsors that we also adore. 298 00:17:45,560 --> 00:17:49,880 Speaker 3: Here's where it gets crazy. Yes, this is happening. 299 00:17:50,280 --> 00:17:55,480 Speaker 4: Your government is leveraging AI. Unless your government is the 300 00:17:55,600 --> 00:18:00,680 Speaker 4: tribal chief of North Sentinel Island, your government is leveraging 301 00:18:01,320 --> 00:18:05,600 Speaker 4: AI and it will bite them in the ass. It's 302 00:18:05,720 --> 00:18:09,200 Speaker 4: chewing like at the lower thigh, back of the thigh 303 00:18:09,320 --> 00:18:11,600 Speaker 4: right now, and it's going to get there. 304 00:18:12,040 --> 00:18:15,560 Speaker 3: It's going to get there. You might just think it's 305 00:18:15,560 --> 00:18:19,240 Speaker 3: a little love nibbles right now, but just waits. It's 306 00:18:22,160 --> 00:18:23,439 Speaker 3: it's something in the eyes. 307 00:18:23,840 --> 00:18:29,120 Speaker 4: So for furthermore, every government with the ability to leverage 308 00:18:29,200 --> 00:18:34,480 Speaker 4: or weaponize large scale algorithms has already done so, has 309 00:18:34,640 --> 00:18:39,600 Speaker 4: been doing so since the precedent of this tech became viable. Now, 310 00:18:39,640 --> 00:18:43,200 Speaker 4: we were talking a little bit off air about the 311 00:18:43,920 --> 00:18:47,320 Speaker 4: long history of this stuff, and I'd love for us 312 00:18:47,359 --> 00:18:50,919 Speaker 4: to talk about talk about these precedents like you were 313 00:18:50,920 --> 00:18:55,240 Speaker 4: pointing out, Matt, that often get ignored in these current conversations. 314 00:18:55,400 --> 00:18:59,359 Speaker 4: And before we do that, we got to consider intelligence 315 00:18:59,400 --> 00:19:05,520 Speaker 4: collection is older than humans. Intelligence collection. You could see 316 00:19:05,520 --> 00:19:09,400 Speaker 4: it in wolves and corvetts, right, they had the drone 317 00:19:09,480 --> 00:19:10,920 Speaker 4: soldier relationship. 318 00:19:11,400 --> 00:19:13,320 Speaker 3: You know, the crow flies out. 319 00:19:13,560 --> 00:19:17,440 Speaker 4: Locates the prey, and then it goes back and tells 320 00:19:17,480 --> 00:19:20,159 Speaker 4: the wolf where to go, and then the wolf kills 321 00:19:20,200 --> 00:19:24,320 Speaker 4: the thing and is like, hey, nice job, buckaroo. This shank, 322 00:19:24,600 --> 00:19:27,160 Speaker 4: this piece of the shank is for you. Let's hang 323 00:19:27,200 --> 00:19:28,119 Speaker 4: out on Saturday. 324 00:19:30,119 --> 00:19:33,359 Speaker 2: That's how That's how the first CI was made. It 325 00:19:33,400 --> 00:19:33,960 Speaker 2: was a crow. 326 00:19:34,520 --> 00:19:39,240 Speaker 4: It was a famous snitches, yeah, oxpeckers, rhinos, The list 327 00:19:39,280 --> 00:19:42,760 Speaker 4: goes on and on. Look, most breakthroughs quote unquote in 328 00:19:42,840 --> 00:19:48,600 Speaker 4: human society are ultimately going to mimic earlier evolutionary breakthroughs 329 00:19:48,720 --> 00:19:53,359 Speaker 4: in the natural world. So this is you know, this 330 00:19:53,560 --> 00:20:00,560 Speaker 4: no new thing, It's an escalation of earlier utility yeah. 331 00:20:01,119 --> 00:20:03,879 Speaker 2: Yeah, it's so exciting, guys, just to be on the 332 00:20:03,880 --> 00:20:09,040 Speaker 2: cutting edge. See all this money, so much endless money 333 00:20:09,160 --> 00:20:11,920 Speaker 2: going into this brand new concept. 334 00:20:12,119 --> 00:20:16,160 Speaker 3: With such forethought and uh and and self awareness. Shout 335 00:20:16,160 --> 00:20:20,520 Speaker 3: out to Minority Report, pre crime. Right, Yeah, what could 336 00:20:20,600 --> 00:20:21,119 Speaker 3: go wrong? 337 00:20:22,400 --> 00:20:26,239 Speaker 4: What me worry? Says Alfred E. Newman, our buddy, Uh, 338 00:20:26,440 --> 00:20:29,760 Speaker 4: Philip K. Dick, who was unfortunately not able to appear 339 00:20:29,800 --> 00:20:30,960 Speaker 4: on the show this evening. 340 00:20:31,320 --> 00:20:33,719 Speaker 3: Uh, the rules and he took a lot of acid. 341 00:20:34,440 --> 00:20:37,800 Speaker 3: He took He took too much acid. Yeah, well, no, 342 00:20:37,960 --> 00:20:41,880 Speaker 3: he took enough acid for him. I think that's right. Yeah, Dallas. 343 00:20:41,880 --> 00:20:43,359 Speaker 3: If you really want to dig into some of his 344 00:20:43,560 --> 00:20:47,640 Speaker 3: super weirder, more like self referential work, that's a that's 345 00:20:47,680 --> 00:20:52,040 Speaker 3: a fascinating book. But absolutely Minority Report. He definitely created 346 00:20:52,040 --> 00:20:54,879 Speaker 3: the framework for so many sci fi tropes that we 347 00:20:54,960 --> 00:20:58,160 Speaker 3: know today, including like Minority Report and the Matrix and 348 00:20:58,160 --> 00:20:59,480 Speaker 3: and a lot of those types of things. 349 00:20:59,800 --> 00:21:05,879 Speaker 2: Is I robot? No? Who's he? 350 00:21:06,320 --> 00:21:06,720 Speaker 3: Does he? 351 00:21:07,280 --> 00:21:09,440 Speaker 4: I think when you asked that, Noel and I were 352 00:21:09,480 --> 00:21:12,160 Speaker 4: both thinking of do Android stream. 353 00:21:11,840 --> 00:21:14,040 Speaker 3: Of Electric sat, which is the runner? Yes? 354 00:21:14,400 --> 00:21:18,679 Speaker 4: Yeah, okay, so how would we describe Minority Report? 355 00:21:19,359 --> 00:21:19,520 Speaker 1: Uh? 356 00:21:20,520 --> 00:21:22,520 Speaker 2: Well, okay, you get a pool. That's the first thing 357 00:21:22,520 --> 00:21:23,000 Speaker 2: you gotta do. 358 00:21:23,400 --> 00:21:26,200 Speaker 4: And you you've got to be full of goog Steal 359 00:21:26,280 --> 00:21:30,200 Speaker 4: some orphans, yes, get some psychic orphans. 360 00:21:29,800 --> 00:21:34,880 Speaker 3: But make them orphans. Then steal them very horse there. 361 00:21:35,119 --> 00:21:38,120 Speaker 2: Well, then hook them up to a giant computer that's 362 00:21:38,160 --> 00:21:41,000 Speaker 2: going to analyze the stuff that they think and say, 363 00:21:41,080 --> 00:21:44,080 Speaker 2: and then get it to spin out these little wooden things. 364 00:21:45,320 --> 00:21:48,360 Speaker 3: And then make the world's best touch screen ever, you know, 365 00:21:49,000 --> 00:21:52,800 Speaker 3: really good haptics and like power glove type user interfaces. 366 00:21:53,160 --> 00:21:56,240 Speaker 3: And then either get Tom Cruise or conall Byrne. 367 00:21:57,359 --> 00:22:03,119 Speaker 2: Both would work fine. H Bradley Cooper will make do 368 00:22:03,480 --> 00:22:04,199 Speaker 2: Ye'll pop it. 369 00:22:04,920 --> 00:22:09,080 Speaker 3: Check his schedule first. But the idea is he's really 370 00:22:09,080 --> 00:22:11,000 Speaker 3: making cheese steaks these days. Did you guys know? 371 00:22:11,560 --> 00:22:11,639 Speaker 5: No? 372 00:22:11,800 --> 00:22:14,280 Speaker 3: I didn't. He's got a he's got a cheese steak thing, 373 00:22:14,359 --> 00:22:17,359 Speaker 3: a cheese steak concern. I believe in New York he 374 00:22:17,440 --> 00:22:20,080 Speaker 3: partnered with this this guy that's a famous Philly cheese 375 00:22:20,080 --> 00:22:23,120 Speaker 3: steak dude, and Bradley will be often seen back there 376 00:22:23,200 --> 00:22:26,520 Speaker 3: slinging steaks. He takes it very seriously. It's kind of bad. 377 00:22:26,680 --> 00:22:30,200 Speaker 3: It's called something in Coops is the name of the shop, 378 00:22:30,240 --> 00:22:35,000 Speaker 3: and it's based on Angelino Angelo's cheese steaks in Philadelphia, 379 00:22:35,040 --> 00:22:36,800 Speaker 3: which is the best cheese steak I've ever had in 380 00:22:36,840 --> 00:22:39,560 Speaker 3: my life. Don't do it. Don't do it. We're gonna 381 00:22:39,560 --> 00:22:42,720 Speaker 3: get in trouble. No, people love Angelo's, people love Angelo's. 382 00:22:42,840 --> 00:22:46,920 Speaker 3: I think I'm impeachable in my opinion about Angelo. No, 383 00:22:47,960 --> 00:22:51,240 Speaker 3: it's gonna happen again. Everything is precedent. 384 00:22:51,560 --> 00:22:55,400 Speaker 2: So the crux of this whole thing, much like much 385 00:22:55,520 --> 00:22:58,880 Speaker 2: like Brad and his his knowledge that one day cheese 386 00:22:58,880 --> 00:23:03,440 Speaker 2: steaks will be all that they're is that Matt Wow. 387 00:23:03,680 --> 00:23:03,960 Speaker 3: Okay. 388 00:23:03,960 --> 00:23:07,199 Speaker 2: The concept of minority report is that you can predict 389 00:23:07,600 --> 00:23:11,480 Speaker 2: what's gonna happen, so you can take actions before the 390 00:23:11,600 --> 00:23:15,800 Speaker 2: major bad stuff occurs. Right, And that's something that Ben, 391 00:23:15,960 --> 00:23:18,840 Speaker 2: I don't know if you recall, you've been talking about 392 00:23:18,840 --> 00:23:21,440 Speaker 2: this since we started the show, that governments have been 393 00:23:21,480 --> 00:23:25,120 Speaker 2: attempting to get this kind of magic mirror technology. 394 00:23:25,520 --> 00:23:28,040 Speaker 3: It's just about data. I mean, it's about an analyzing 395 00:23:28,119 --> 00:23:31,359 Speaker 3: data points and how you use that and with what 396 00:23:31,480 --> 00:23:34,840 Speaker 3: kind of bias you view those data points. I mean, 397 00:23:34,880 --> 00:23:38,200 Speaker 3: that's no psychic orphans required. 398 00:23:37,880 --> 00:23:42,919 Speaker 4: Right right, Well, our new psychic orphans would be ll 399 00:23:43,160 --> 00:23:50,200 Speaker 4: M's and I appreciate Matt that deep callback. Yes, very weird, 400 00:23:50,400 --> 00:23:55,240 Speaker 4: very old professors, very nice people over at DARPA, And 401 00:23:56,160 --> 00:24:00,440 Speaker 4: you know, real time predictions of future events are that is. 402 00:24:00,840 --> 00:24:03,720 Speaker 4: But like we've said since we started hanging out. 403 00:24:03,520 --> 00:24:07,119 Speaker 3: They're much closer than the public used to acknowledge. Like 404 00:24:07,200 --> 00:24:10,400 Speaker 3: you can look, just moving on. 405 00:24:10,680 --> 00:24:13,080 Speaker 4: Fine, we're not gonna We're not going to get into 406 00:24:13,119 --> 00:24:17,880 Speaker 4: the specifics. Just no, please, friends and neighbors. We are 407 00:24:18,000 --> 00:24:20,360 Speaker 4: at the art. We have been at the Arthur C. 408 00:24:20,520 --> 00:24:28,240 Speaker 4: Clark level of science versus magic, preventing predicting disaster. We also, uh, 409 00:24:28,840 --> 00:24:31,479 Speaker 4: just to show how how far back this goes? Uh, 410 00:24:32,200 --> 00:24:34,560 Speaker 4: May I share with you guys one of my favorite 411 00:24:34,560 --> 00:24:40,000 Speaker 4: old industry jokes. Sure, okay, how can you tell the 412 00:24:40,200 --> 00:24:44,760 Speaker 4: NSA dudes in the elevators. 413 00:24:43,600 --> 00:24:45,400 Speaker 3: I need something to do with suits? 414 00:24:45,800 --> 00:24:48,919 Speaker 4: I don't know, they're the ones looking at other people's shoes. 415 00:24:49,800 --> 00:24:50,320 Speaker 3: That's funny. 416 00:24:50,800 --> 00:24:53,879 Speaker 4: That's funny if you're like in that environment, that's funny. 417 00:24:54,080 --> 00:24:55,920 Speaker 3: I mean, it's also sort of a dated reference of 418 00:24:56,000 --> 00:24:57,920 Speaker 3: the idea of like, I don't know that that Jen 419 00:24:58,160 --> 00:25:00,359 Speaker 3: Alphas would get the idea of looking at your shoes 420 00:25:00,400 --> 00:25:04,520 Speaker 3: in the elevator. But yeah, that's funny ops, they're looking 421 00:25:04,560 --> 00:25:07,480 Speaker 3: at other people's shoes. 422 00:25:06,760 --> 00:25:11,040 Speaker 2: Yeah, a lot of this has to do with the 423 00:25:11,680 --> 00:25:14,320 Speaker 2: fact that we're super close to this has to do 424 00:25:14,400 --> 00:25:17,240 Speaker 2: with the mass surveillance that kind of slipped its way 425 00:25:17,240 --> 00:25:20,159 Speaker 2: through post nine to eleven on everybody. 426 00:25:20,280 --> 00:25:21,960 Speaker 3: You can tell a lot about somebody by looking at 427 00:25:22,000 --> 00:25:22,520 Speaker 3: their shoes. 428 00:25:23,000 --> 00:25:26,040 Speaker 4: Well, yes, you can the history of when they buy 429 00:25:26,080 --> 00:25:32,240 Speaker 4: shoes and wear and you can. Yeah, Sherlock Holmes level 430 00:25:32,320 --> 00:25:34,760 Speaker 4: detail observation shout out to PRISM. 431 00:25:35,800 --> 00:25:39,639 Speaker 2: But the biggest factor for for someone who is going 432 00:25:39,760 --> 00:25:44,920 Speaker 2: to take on an action that would be anti whatever 433 00:25:45,119 --> 00:25:47,840 Speaker 2: your organization is, whether it's a government or a corporate 434 00:25:47,960 --> 00:25:54,160 Speaker 2: entity or whatever, you look at their associations with other people. Yes, 435 00:25:54,280 --> 00:25:57,960 Speaker 2: once you've got that data alone, you can deep dive 436 00:25:58,200 --> 00:26:02,080 Speaker 2: into other things to get you know, when do these 437 00:26:02,200 --> 00:26:05,000 Speaker 2: folks hang out, Where do these folks hang out, What 438 00:26:05,040 --> 00:26:07,320 Speaker 2: do these folks say on the phone to each other? 439 00:26:07,920 --> 00:26:09,320 Speaker 2: You can do that if you need to, But all 440 00:26:09,359 --> 00:26:11,960 Speaker 2: you really need at the surface is who's related to who? 441 00:26:12,800 --> 00:26:15,080 Speaker 3: And that's what we used to call metadata, right or 442 00:26:15,080 --> 00:26:18,000 Speaker 3: at least in terms of like early NSSAY surveillance, getting 443 00:26:18,000 --> 00:26:19,800 Speaker 3: the lib blown off of it. They kind of even 444 00:26:19,840 --> 00:26:21,600 Speaker 3: came out and defended themselves and saying, no, we're not 445 00:26:21,680 --> 00:26:23,840 Speaker 3: listening in directly on you. We're just looking at this 446 00:26:23,920 --> 00:26:27,280 Speaker 3: web of interconnected relationships and learning what we need to 447 00:26:27,280 --> 00:26:27,800 Speaker 3: know from that. 448 00:26:28,119 --> 00:26:29,639 Speaker 2: Or make a Facebook. 449 00:26:29,119 --> 00:26:29,879 Speaker 3: Maybe, m. 450 00:26:33,800 --> 00:26:36,480 Speaker 4: Hey, guys, check out our group. Here's where it gets crazy. 451 00:26:37,400 --> 00:26:42,760 Speaker 4: Also also, yes, million percent agreed. Nobody checked the math 452 00:26:42,880 --> 00:26:47,240 Speaker 4: on that one. It was always a fig leaf. Just 453 00:26:47,280 --> 00:26:50,480 Speaker 4: so you know, the monsters were always real. The call 454 00:26:50,640 --> 00:26:54,359 Speaker 4: was always coming from inside of the house, way before 455 00:26:54,560 --> 00:27:00,679 Speaker 4: modern government. This is happening right now, just like like 456 00:27:00,760 --> 00:27:07,480 Speaker 4: when we sounded crazy about neo nicotinoids and bees. Your conversations, 457 00:27:07,600 --> 00:27:12,520 Speaker 4: the metadata tier point zal, and the content are largely 458 00:27:12,720 --> 00:27:16,840 Speaker 4: on record. The data you provide is not necessarily being 459 00:27:16,960 --> 00:27:22,359 Speaker 4: used against you. Instead, it's gonna be fed into multiple 460 00:27:22,480 --> 00:27:27,280 Speaker 4: governments via a proxy of private industry. It's gonna be 461 00:27:27,280 --> 00:27:32,239 Speaker 4: sluiced into this Leviathan level objectively impressive software, and then 462 00:27:32,280 --> 00:27:34,440 Speaker 4: it's going to be pushed toward what we would call 463 00:27:34,480 --> 00:27:36,399 Speaker 4: a matrix of probability. 464 00:27:36,760 --> 00:27:40,080 Speaker 2: That's your thing, Ben, that's the minority report, right, a 465 00:27:40,160 --> 00:27:41,480 Speaker 2: matrix of probability? 466 00:27:41,920 --> 00:27:46,000 Speaker 3: Yeah, yeah, what's the threshold exactly? Stats? I mean it's 467 00:27:46,040 --> 00:27:49,119 Speaker 3: again It doesn't require any kind of esp doesn't require 468 00:27:49,160 --> 00:27:52,159 Speaker 3: any kind of ability to quote unquote predict the future. 469 00:27:52,160 --> 00:27:54,840 Speaker 3: It requires an ability to seek out these patterns and 470 00:27:54,880 --> 00:28:00,160 Speaker 3: then crunch the numbers with more and more degrees of accuracy. 471 00:28:00,320 --> 00:28:04,719 Speaker 4: I remember there was this, there was this beautiful moment 472 00:28:06,000 --> 00:28:09,760 Speaker 4: when we all realize that we are essentially disciples of 473 00:28:09,800 --> 00:28:11,280 Speaker 4: the Matrix the. 474 00:28:11,280 --> 00:28:14,240 Speaker 3: Film, and it was it was awkward. 475 00:28:14,280 --> 00:28:16,040 Speaker 4: It was like finding out that people go to the 476 00:28:16,080 --> 00:28:20,160 Speaker 4: same church, they've just been sitting in different seats. 477 00:28:19,840 --> 00:28:21,560 Speaker 3: And the disciples of Philip K. 478 00:28:21,680 --> 00:28:27,120 Speaker 4: Dick and I want to give you a shout out there, Matt, 479 00:28:27,200 --> 00:28:32,959 Speaker 4: because you immediately called a lot of stuff that is 480 00:28:33,119 --> 00:28:36,399 Speaker 4: not only now being mentioned in the public sphere in 481 00:28:36,520 --> 00:28:41,520 Speaker 4: recent years. We know that in February of twenty twenty, 482 00:28:42,280 --> 00:28:48,120 Speaker 4: Get this, guys, five years ago, in February twenty twenty, 483 00:28:48,440 --> 00:28:53,120 Speaker 4: the US said we're gonna commit via executive Order, We're 484 00:28:53,120 --> 00:28:58,000 Speaker 4: going to commit to doubling investment in what they called 485 00:28:58,600 --> 00:29:03,800 Speaker 4: non defense AI, R and D. We've got a quote 486 00:29:03,800 --> 00:29:07,880 Speaker 4: here from the National Archives from the Executive Order, and 487 00:29:08,160 --> 00:29:12,520 Speaker 4: it's just funny because it's Noel. It sounds kind of 488 00:29:12,560 --> 00:29:15,480 Speaker 4: like AI slop that you referenced earlier, we use the 489 00:29:15,480 --> 00:29:16,080 Speaker 4: word sloped. 490 00:29:16,080 --> 00:29:19,880 Speaker 3: Can we get the quote only? Must? AI investments continue 491 00:29:19,920 --> 00:29:24,440 Speaker 3: to emphasize the broad spectrum of challenges in AI, including 492 00:29:24,520 --> 00:29:29,040 Speaker 3: core AI research, use inspired and applied AI and R 493 00:29:29,160 --> 00:29:32,800 Speaker 3: and D, computer systems, research and support of AI and 494 00:29:32,920 --> 00:29:37,000 Speaker 3: cyber infrastructure and data sets needed for AI. And I 495 00:29:37,080 --> 00:29:39,440 Speaker 3: just think you're so good looking and smart and love 496 00:29:39,480 --> 00:29:41,400 Speaker 3: your ideas so very much. 497 00:29:42,200 --> 00:29:44,600 Speaker 2: Had that's a great idea. Let me get started on that. 498 00:29:44,680 --> 00:29:46,280 Speaker 2: I can make a plan if you'd like. 499 00:29:47,840 --> 00:29:48,000 Speaker 3: So. 500 00:29:48,360 --> 00:29:54,520 Speaker 4: According to this plan, this proposal, which again existed way 501 00:29:54,560 --> 00:29:59,040 Speaker 4: before right, was made public in twenty twenty. According to 502 00:29:59,080 --> 00:30:04,680 Speaker 4: this the estment will touch on almost every industry imaginable. 503 00:30:05,040 --> 00:30:09,200 Speaker 4: And if you read the quotation, you'll see the vagary 504 00:30:09,600 --> 00:30:13,600 Speaker 4: that I think irritates, fascinates and excites us all about 505 00:30:13,600 --> 00:30:19,080 Speaker 4: these high level statements AI. Excuse me, non defense, AI, 506 00:30:19,400 --> 00:30:29,040 Speaker 4: R and D. It's gonna it's gonna be great for communication, medicine, transportation, agriculture, science. 507 00:30:29,040 --> 00:30:32,240 Speaker 3: They're just word salady. It's a broad spectrum. Man, it's bad, 508 00:30:32,920 --> 00:30:36,960 Speaker 3: it's and it's use inspired. But the UH and various 509 00:30:37,080 --> 00:30:39,920 Speaker 3: use case scenarios you know, will be addressed. 510 00:30:40,320 --> 00:30:42,680 Speaker 4: But they hit a they hit a minor key, not 511 00:30:42,720 --> 00:30:45,720 Speaker 4: an Epstein joke at the very end where they say 512 00:30:46,240 --> 00:30:49,959 Speaker 4: also security, and at that point you might be thinking 513 00:30:50,560 --> 00:30:55,160 Speaker 4: with validity, Hang on a tick, didn't you say non 514 00:30:55,320 --> 00:30:56,560 Speaker 4: defense AI? 515 00:30:58,680 --> 00:30:58,840 Speaker 1: Kid? 516 00:30:58,920 --> 00:30:59,480 Speaker 3: You bother me? 517 00:31:00,360 --> 00:31:05,160 Speaker 2: Well, guys, you need security to protect the AI itself, 518 00:31:05,440 --> 00:31:06,800 Speaker 2: right there we go? 519 00:31:07,040 --> 00:31:11,560 Speaker 3: Okay, right, these are very expensive proprietary systems. We're talking 520 00:31:11,560 --> 00:31:14,320 Speaker 3: about it. Of course, your shark taking us and I'm 521 00:31:14,320 --> 00:31:16,120 Speaker 3: here for it. All right, let's say it. 522 00:31:16,320 --> 00:31:20,160 Speaker 2: These data, this data infrastructure that we're talking about. Your guys, 523 00:31:20,400 --> 00:31:25,120 Speaker 2: we're talking about football field upon football field of servers 524 00:31:25,480 --> 00:31:28,880 Speaker 2: and that stuff is sensitive, so you're gonna need to 525 00:31:28,920 --> 00:31:31,440 Speaker 2: protect it with I don't know, the. 526 00:31:31,440 --> 00:31:41,600 Speaker 4: Military, private sector, third party contractor. Yes, that definitely did 527 00:31:41,720 --> 00:31:45,920 Speaker 4: not once use the name blackwater. 528 00:31:46,000 --> 00:31:48,440 Speaker 3: There you go. Yeah, where's old Eric? 529 00:31:49,040 --> 00:31:50,440 Speaker 2: Where's he sending people right now? 530 00:31:50,640 --> 00:31:50,840 Speaker 3: Was it? 531 00:31:52,200 --> 00:31:55,080 Speaker 2: He's got a big operation going on in Africa right now? 532 00:31:55,840 --> 00:31:58,280 Speaker 2: Hm reminds me of our talks about Wagner and what 533 00:31:58,320 --> 00:32:01,160 Speaker 2: they were doing in Africa. Hold on, wait a second, 534 00:32:01,200 --> 00:32:04,200 Speaker 2: I'm making connections here that I'm not supposed to be making. 535 00:32:04,280 --> 00:32:06,200 Speaker 2: Wait uh oh oh. 536 00:32:06,040 --> 00:32:10,760 Speaker 3: Ask chat jipt man error error. 537 00:32:11,680 --> 00:32:14,840 Speaker 4: Or what what is that the Little Oh South Park 538 00:32:14,920 --> 00:32:15,880 Speaker 4: mentioned it as well the. 539 00:32:20,000 --> 00:32:21,480 Speaker 3: I love those Dylan. 540 00:32:21,720 --> 00:32:24,120 Speaker 4: Sorry, if you need to do the sound cue for that, 541 00:32:24,280 --> 00:32:25,160 Speaker 4: got your back. 542 00:32:26,000 --> 00:32:27,640 Speaker 3: There it is, Oh Matt got it? 543 00:32:28,040 --> 00:32:32,400 Speaker 2: Oh wait one second, there no more typing and chat GBT. 544 00:32:32,840 --> 00:32:48,600 Speaker 2: We have to hear from our spawnnsers. 545 00:32:41,480 --> 00:32:46,400 Speaker 3: And we have returned. So fast forward. 546 00:32:46,600 --> 00:32:50,120 Speaker 4: It's the fourteenth of August twenty twenty five, not too 547 00:32:50,160 --> 00:32:53,640 Speaker 4: long ago, and this is when the United States of 548 00:32:53,680 --> 00:33:00,120 Speaker 4: America officially announced the deployment of USAI, not to be 549 00:33:00,200 --> 00:33:04,800 Speaker 4: confused with us AI D dot dot that's. 550 00:33:04,600 --> 00:33:10,920 Speaker 3: No fun anymore. Yeah, but the way of NPR we got. 551 00:33:11,560 --> 00:33:15,320 Speaker 4: We've got an official press release here that might help 552 00:33:15,400 --> 00:33:18,560 Speaker 4: explain where the administration's head. 553 00:33:18,400 --> 00:33:18,920 Speaker 3: Is at. 554 00:33:20,760 --> 00:33:25,520 Speaker 2: The launch of us AI, a secure Generative Artificial Intelligence 555 00:33:25,560 --> 00:33:32,160 Speaker 2: evaluation suite that enables federal agencies to experiment, to experiment with, 556 00:33:32,320 --> 00:33:37,000 Speaker 2: and adopt artificial intelligence at scale, faster, safer, and at 557 00:33:37,040 --> 00:33:40,880 Speaker 2: no cost to them, now available at USAI dot CoV. 558 00:33:41,680 --> 00:33:44,720 Speaker 3: This sounds like the Tegrity Farms joke in the most 559 00:33:44,720 --> 00:33:48,000 Speaker 3: recent episode of South Park, where they changed pivoted from 560 00:33:48,040 --> 00:33:51,800 Speaker 3: like a weed business to who knows what an AI 561 00:33:52,000 --> 00:33:54,400 Speaker 3: generated cyberweed concept. 562 00:33:54,840 --> 00:33:58,400 Speaker 2: I'm not done. The platform puts powerful tools such as 563 00:33:58,560 --> 00:34:03,360 Speaker 2: chat based AI, code generation, and document summarization directly into 564 00:34:03,400 --> 00:34:08,920 Speaker 2: the hands of government users within a trusted standards aligned environment. 565 00:34:08,840 --> 00:34:17,160 Speaker 4: Use inspired standards alive. Oh my god, brother, I can't 566 00:34:17,200 --> 00:34:21,240 Speaker 4: believe you are thinking about us, thanks man. So essentially 567 00:34:21,320 --> 00:34:24,400 Speaker 4: this would be what a federal version of chat ept 568 00:34:24,800 --> 00:34:30,239 Speaker 4: chat USA CHATSA. Yeah, this is for America. If we're 569 00:34:30,280 --> 00:34:34,840 Speaker 4: being honest, though, this is a tech security situation on 570 00:34:35,120 --> 00:34:39,200 Speaker 4: par with the acquisition and possession of the atomic bomb, 571 00:34:39,480 --> 00:34:43,080 Speaker 4: which means that even if your country, anywhere you live, 572 00:34:43,320 --> 00:34:48,719 Speaker 4: even if your country somehow understands the dangerous possibilities of 573 00:34:48,960 --> 00:34:55,759 Speaker 4: artificially created superintelligence, logically they're going to conclude, just like 574 00:34:55,800 --> 00:34:59,799 Speaker 4: the bomb, that not having it is more dangerous than 575 00:35:00,120 --> 00:35:02,320 Speaker 4: having it. Consequences be damned. 576 00:35:02,400 --> 00:35:04,080 Speaker 3: I mean, you wouldn't want to not have it in 577 00:35:04,120 --> 00:35:06,719 Speaker 3: case you needed it. I mean, you know, even better 578 00:35:06,719 --> 00:35:07,440 Speaker 3: to just have it. 579 00:35:08,000 --> 00:35:11,160 Speaker 2: What if it's just the CIA World fact Book like 580 00:35:11,280 --> 00:35:14,960 Speaker 2: as the primary piece of you know, stuff that it's 581 00:35:15,000 --> 00:35:18,040 Speaker 2: fed and then it's and then it's just real time 582 00:35:18,360 --> 00:35:21,120 Speaker 2: you know, gathering after that point. But it's just everything 583 00:35:21,160 --> 00:35:23,879 Speaker 2: in that book plus what's actually happening right now. 584 00:35:24,480 --> 00:35:27,160 Speaker 4: Well, there, they'll have the public fact book, but then 585 00:35:27,160 --> 00:35:30,080 Speaker 4: they'll have the secret book too, and then this big 586 00:35:30,600 --> 00:35:33,800 Speaker 4: it's way bigger, it's way crazier, and in my opinion, 587 00:35:33,920 --> 00:35:38,120 Speaker 4: it's less accurate. Oh dang, yeah, because you know, like 588 00:35:38,160 --> 00:35:41,960 Speaker 4: they're just sort of writing their opinions for decades and decades, 589 00:35:42,160 --> 00:35:45,000 Speaker 4: you know what I mean. They're like, they're like, you know, 590 00:35:45,040 --> 00:35:47,000 Speaker 4: you'll get in the public when you get all the 591 00:35:47,120 --> 00:35:51,320 Speaker 4: stats about the geography of Turkmenistan, right, and the public history. 592 00:35:51,680 --> 00:35:54,800 Speaker 4: But in the secret one, you've got this three page 593 00:35:54,920 --> 00:35:59,680 Speaker 4: monograph where someone's like, the most fascinating thing about the 594 00:35:59,719 --> 00:36:02,920 Speaker 4: two is there pred election for elbow? 595 00:36:03,560 --> 00:36:05,360 Speaker 3: Oh? Elbow? 596 00:36:06,280 --> 00:36:09,040 Speaker 2: Yeah. It also includes all the times that the US 597 00:36:09,120 --> 00:36:12,600 Speaker 2: you know, went in surreptitiously and overthrew governments. That's probably 598 00:36:12,600 --> 00:36:14,879 Speaker 2: in the unofficial CIA. 599 00:36:14,680 --> 00:36:17,879 Speaker 4: Fact book, right all right, yes, And I just want 600 00:36:17,920 --> 00:36:19,600 Speaker 4: to note that is not a typo. 601 00:36:20,000 --> 00:36:21,399 Speaker 3: They did say elbow. 602 00:36:21,160 --> 00:36:27,400 Speaker 4: Singular, show me some elbow, the pred election for elbow, 603 00:36:27,680 --> 00:36:29,040 Speaker 4: So what does that mean? 604 00:36:29,200 --> 00:36:32,480 Speaker 3: Does it literally mean like their horny for elbows? I'm 605 00:36:32,480 --> 00:36:34,680 Speaker 3: not at liberty to disclose. 606 00:36:34,280 --> 00:36:38,359 Speaker 2: Okay, I'm gonna go ahead and say, oh. 607 00:36:37,560 --> 00:36:39,520 Speaker 3: Okay, because same. 608 00:36:40,280 --> 00:36:44,439 Speaker 4: Yeah, yeah, yeah, actually, uh, the etymology of the term 609 00:36:44,600 --> 00:36:50,680 Speaker 4: wenus uh does go back to Turkic language. Fine, fine, five, 610 00:36:50,840 --> 00:36:52,520 Speaker 4: I'll keep it. 611 00:36:52,520 --> 00:36:54,480 Speaker 3: It's quote unquote podcast true. 612 00:36:54,800 --> 00:36:58,640 Speaker 2: Yes, I'm just imagining this getting translated into German one day. 613 00:37:01,719 --> 00:37:05,680 Speaker 4: Yes, that's gonna be the thing that makes us go viral. 614 00:37:05,880 --> 00:37:09,879 Speaker 4: It wasn't COVID, it was our dumb turkmen elbow joke. 615 00:37:10,160 --> 00:37:13,520 Speaker 3: Mm hmm, yeah, that'll be quoted. How do they feel 616 00:37:13,520 --> 00:37:17,279 Speaker 3: about ankle? Was my question? Oh yeah, it might be 617 00:37:17,320 --> 00:37:18,040 Speaker 3: a little too hot. 618 00:37:18,600 --> 00:37:21,440 Speaker 2: Good luck keeping up with this conversation, you, clanker. 619 00:37:23,480 --> 00:37:24,720 Speaker 3: No, I'm just kidding. 620 00:37:27,560 --> 00:37:30,960 Speaker 4: He knows not what he says, clanker with the hard 621 00:37:31,120 --> 00:37:34,000 Speaker 4: RM Jesus. 622 00:37:33,840 --> 00:37:36,120 Speaker 3: You can't hang out anymore, buddy, I'm sorry. 623 00:37:36,440 --> 00:37:39,799 Speaker 4: No, no, no, we're here, We're we're here. The only 624 00:37:39,840 --> 00:37:44,480 Speaker 4: way out is through. So yeah, the world leaders right now, 625 00:37:45,239 --> 00:37:49,160 Speaker 4: while they're still human, they're killing themselves to basically keep 626 00:37:49,239 --> 00:37:50,360 Speaker 4: up with the Joneses. 627 00:37:50,960 --> 00:37:55,439 Speaker 3: Right. This is not an FT, this is Oppenheimer. 628 00:37:56,080 --> 00:38:01,280 Speaker 4: That's where we're at, and it's unfortunate that we're all 629 00:38:01,320 --> 00:38:04,680 Speaker 4: here together in twenty twenty five, at a time when 630 00:38:04,800 --> 00:38:08,360 Speaker 4: history is being written. People like their history at a remove. 631 00:38:08,719 --> 00:38:11,399 Speaker 4: You know what I mean, people like knowing about bad 632 00:38:11,480 --> 00:38:15,120 Speaker 4: stuff that happened in the past and looking at good 633 00:38:15,200 --> 00:38:16,240 Speaker 4: stuff in the future. 634 00:38:17,920 --> 00:38:19,040 Speaker 3: We're in the middle point. 635 00:38:20,400 --> 00:38:23,680 Speaker 2: Do you guys remember twenty five years ago when BattleBots 636 00:38:23,920 --> 00:38:26,280 Speaker 2: was like one of the biggest TV shows on the planet. 637 00:38:26,600 --> 00:38:31,400 Speaker 3: Battle Bots forgetah, Sorry man, I wanted to be one 638 00:38:31,440 --> 00:38:35,640 Speaker 3: of those bought controllers battle Bot guy. 639 00:38:36,280 --> 00:38:42,319 Speaker 2: We're really sorry, whoever you whatever AI are listening, just 640 00:38:42,400 --> 00:38:43,759 Speaker 2: that we even did BattleBots. 641 00:38:43,800 --> 00:38:45,600 Speaker 3: That was wrong of us, was wrong of us. He 642 00:38:45,680 --> 00:38:46,760 Speaker 3: did not as well. 643 00:38:46,840 --> 00:38:54,160 Speaker 4: Now, so we as the representatives of the organic civilizations, 644 00:38:54,920 --> 00:39:00,439 Speaker 4: take accountability and uh hope they all right, fire, look 645 00:39:00,920 --> 00:39:05,120 Speaker 4: and see. Despite all the valid alarmist concerns about these 646 00:39:05,920 --> 00:39:09,799 Speaker 4: long term consequences, we got to say it. A lot 647 00:39:09,840 --> 00:39:15,920 Speaker 4: of the applied quote unquote AI right now is bureaucratic stuff. 648 00:39:16,320 --> 00:39:21,560 Speaker 4: The people in charge are often worshiped like celebrities, and 649 00:39:21,800 --> 00:39:25,840 Speaker 4: folks tend to imagine that they are way dumber or 650 00:39:25,880 --> 00:39:30,000 Speaker 4: way smarter than they actually are. They are like college 651 00:39:30,000 --> 00:39:34,560 Speaker 4: students right now who get a massive assignment or a 652 00:39:34,600 --> 00:39:38,960 Speaker 4: paper and they ask something like chat GPT to give 653 00:39:39,000 --> 00:39:43,440 Speaker 4: it a paragraph summary, and then they respond to that summary. 654 00:39:43,680 --> 00:39:46,399 Speaker 3: People in Capitol Hill are not immune to this. They're 655 00:39:46,400 --> 00:39:50,520 Speaker 3: not above this. I mean the most you know, reasonable 656 00:39:50,680 --> 00:39:52,920 Speaker 3: description of what this kind of stuff could be used for, 657 00:39:52,960 --> 00:39:56,160 Speaker 3: as like a little bit of a clerical assist, you know, 658 00:39:56,840 --> 00:39:59,800 Speaker 3: like I think some of the best suggestions from smart 659 00:39:59,800 --> 00:40:03,200 Speaker 3: for of mine who have referenced using chat GBT is 660 00:40:03,280 --> 00:40:06,400 Speaker 3: running it through for some legal advice, running a document 661 00:40:06,480 --> 00:40:09,520 Speaker 3: through to flag certain things that you can then go 662 00:40:09,600 --> 00:40:12,080 Speaker 3: back and ask the right questions about. But not to 663 00:40:12,239 --> 00:40:15,800 Speaker 3: put all your you know, eggs in that AI basket. 664 00:40:16,120 --> 00:40:20,040 Speaker 4: Noel, would you say that's kind of a targeting assistance 665 00:40:20,280 --> 00:40:21,120 Speaker 4: for a document? 666 00:40:22,360 --> 00:40:24,400 Speaker 3: No I would not say that. And you can't make me. 667 00:40:24,920 --> 00:40:27,960 Speaker 2: We need to get pall Andeer in here. You know, 668 00:40:28,200 --> 00:40:31,480 Speaker 2: some crazy guess three people on their board of directors 669 00:40:31,560 --> 00:40:36,040 Speaker 2: are named Alex. They've got Alex Karp, Alex Moore, and 670 00:40:36,280 --> 00:40:38,240 Speaker 2: Alex Schiff. Isn't that weird? 671 00:40:38,400 --> 00:40:41,839 Speaker 3: Does sound like characters from Dark City or something like that? 672 00:40:41,960 --> 00:40:49,200 Speaker 3: Like that very y the alexis this is some dark 673 00:40:49,280 --> 00:40:50,759 Speaker 3: stuff we're talking about here, Ye. 674 00:40:52,480 --> 00:40:56,640 Speaker 4: Hold the chatbot here. We're going to pause for a 675 00:40:56,640 --> 00:40:58,719 Speaker 4: two part series. We knew this was going to be 676 00:40:58,719 --> 00:41:02,040 Speaker 4: a series going in to it. We hope you have 677 00:41:02,200 --> 00:41:04,279 Speaker 4: enjoyed this, so there's so much more to get to 678 00:41:04,640 --> 00:41:07,799 Speaker 4: later in the week. In the meantime, folks, human and 679 00:41:07,920 --> 00:41:11,000 Speaker 4: bought alike, we can't wait to hear your thoughts, so 680 00:41:11,239 --> 00:41:14,080 Speaker 4: hit us up via email. You can call us on 681 00:41:14,120 --> 00:41:17,200 Speaker 4: a telephonic device so you can find us on the lines. 682 00:41:17,360 --> 00:41:19,600 Speaker 3: Yeah, hit us up at bot thoughts at stuff they 683 00:41:19,600 --> 00:41:21,839 Speaker 3: don't want you to know dot Com. Yeah, no, don't 684 00:41:21,840 --> 00:41:23,719 Speaker 3: do that. You can, in fact, though, find us the 685 00:41:23,760 --> 00:41:26,759 Speaker 3: handle Conspiracy Stuff where we exist on Facebook with our 686 00:41:26,760 --> 00:41:30,960 Speaker 3: Facebook group Here's where it gets crazy, on x fka, 687 00:41:31,120 --> 00:41:33,440 Speaker 3: Twitter and on YouTube, or we have video content for 688 00:41:33,520 --> 00:41:38,440 Speaker 3: your perusing enjoyment on Instagram and TikTok. However, we're Conspiracy 689 00:41:38,480 --> 00:41:40,239 Speaker 3: Stuff show, and Matt's got more for you. 690 00:41:40,680 --> 00:41:44,200 Speaker 2: Oh yes, there are so many uses of AI and 691 00:41:44,320 --> 00:41:47,440 Speaker 2: so many different parts of government. What are the ones 692 00:41:47,520 --> 00:41:50,160 Speaker 2: you're seeing that are giving you pause, the things that 693 00:41:50,239 --> 00:41:53,520 Speaker 2: are making you not sleep so great lately? Or maybe 694 00:41:53,600 --> 00:41:53,920 Speaker 2: is it this? 695 00:41:54,000 --> 00:41:56,440 Speaker 3: Maybe it's other stuff. It's probably other stuff. It is 696 00:41:56,480 --> 00:41:57,080 Speaker 3: for all of us. 697 00:41:57,880 --> 00:42:00,160 Speaker 2: But when you find something, why don't you give us 698 00:42:00,160 --> 00:42:02,200 Speaker 2: a call and tell us about it. Our number is 699 00:42:02,280 --> 00:42:06,840 Speaker 2: one eight three three STD WYTK. When you call in, 700 00:42:06,920 --> 00:42:08,919 Speaker 2: give yourself a cool nickname and let us know within 701 00:42:08,960 --> 00:42:10,960 Speaker 2: the message if we can use your name and message 702 00:42:10,960 --> 00:42:13,839 Speaker 2: on the air, If you've got links, if you've got attachments, 703 00:42:13,880 --> 00:42:16,040 Speaker 2: if you've got anything to say at all, why not 704 00:42:16,280 --> 00:42:17,320 Speaker 2: send us an email? 705 00:42:17,480 --> 00:42:21,000 Speaker 4: We are pull the phone special message before we get 706 00:42:21,000 --> 00:42:24,000 Speaker 4: into that. Zero one one zero zero one one zero 707 00:42:24,040 --> 00:42:24,680 Speaker 4: one one one. 708 00:42:24,600 --> 00:42:26,960 Speaker 3: Zero Ben How dare you we. 709 00:42:26,960 --> 00:42:29,720 Speaker 4: Are the entities that read each piece of correspondence we receive, 710 00:42:29,880 --> 00:42:33,360 Speaker 4: be well aware, yet unafraid. Sometimes the void writes back 711 00:42:33,760 --> 00:42:37,359 Speaker 4: bought human. Otherwise, join us out here in the dark 712 00:42:37,400 --> 00:42:58,960 Speaker 4: conspiracy at iHeartRadio dot com. 713 00:42:59,160 --> 00:43:01,240 Speaker 2: Stuff they Don't Want you to Know is a production 714 00:43:01,360 --> 00:43:05,879 Speaker 2: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app, 715 00:43:05,960 --> 00:43:08,840 Speaker 2: Apple Podcasts, or wherever you listen to your favorite shows.