1 00:00:00,240 --> 00:00:04,680 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,800 --> 00:00:09,119 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 00:00:12,240 --> 00:00:25,880 Speaker 1: production of My Heart Grading. Hello, welcome back to the show. 5 00:00:26,000 --> 00:00:30,160 Speaker 1: I'm still Matt. It is I know, still me. They 6 00:00:30,200 --> 00:00:33,400 Speaker 1: called me Ben. We're joined as always with our superproducer. 7 00:00:33,479 --> 00:00:37,639 Speaker 1: All mission control decands, most importantly, you are you. You 8 00:00:37,680 --> 00:00:41,040 Speaker 1: are here, and that makes this the stuff they don't 9 00:00:41,200 --> 00:00:44,440 Speaker 1: want you to know. If you saw the title of 10 00:00:44,520 --> 00:00:48,599 Speaker 1: today's episode, folks, then you realized it's a question, is 11 00:00:49,040 --> 00:00:53,280 Speaker 1: AI coming for you? Uh? This may be the first 12 00:00:53,360 --> 00:00:57,760 Speaker 1: of several conversations and explorations we have about um, the 13 00:00:57,960 --> 00:01:03,320 Speaker 1: possible corruption and conspiracy's involved here, the uh, the bleeding 14 00:01:03,600 --> 00:01:08,040 Speaker 1: edge of technology, and what is often called an unprecedented time. 15 00:01:08,080 --> 00:01:10,600 Speaker 1: But spoiler, we're gonna find that people are throwing the 16 00:01:10,600 --> 00:01:15,600 Speaker 1: word unprecedented around a bit loosely. We've all, you know, 17 00:01:15,680 --> 00:01:18,479 Speaker 1: we've heard about a I've heard about machine learning a 18 00:01:18,520 --> 00:01:23,360 Speaker 1: lot in recent years, and I think for the five 19 00:01:23,440 --> 00:01:27,679 Speaker 1: of us growing up Paul, Matt, noell, Uh, you and 20 00:01:27,840 --> 00:01:30,720 Speaker 1: yours truly here, I think we always hear about this 21 00:01:30,959 --> 00:01:34,959 Speaker 1: coupled with fears of humans being replaced by these new 22 00:01:35,440 --> 00:01:39,920 Speaker 1: entities or even just processes. And the more it's weird 23 00:01:39,920 --> 00:01:42,919 Speaker 1: because there's this big divide in futurism right all the time, 24 00:01:43,319 --> 00:01:46,920 Speaker 1: the ted talkie dudes are always saying, hey, there's a 25 00:01:46,920 --> 00:01:50,080 Speaker 1: world where everybody wins. And then the more cynical folks, 26 00:01:50,120 --> 00:01:54,080 Speaker 1: often sci fi writers, imagine a dystopia. They say, we 27 00:01:54,320 --> 00:01:57,960 Speaker 1: you're gonna live in a post worker but not post 28 00:01:58,120 --> 00:02:01,680 Speaker 1: work economy. You'll still need money to survive. We have 29 00:02:01,800 --> 00:02:04,639 Speaker 1: fewer and fewer ways to make that money. It's funny. 30 00:02:04,680 --> 00:02:07,440 Speaker 1: It reminds me of like, you know, things like the Luddites, 31 00:02:07,560 --> 00:02:11,880 Speaker 1: you know, who were sabotaging um, you know, machines during 32 00:02:11,919 --> 00:02:14,440 Speaker 1: the Industrial Revolution, Because every time a new technology like 33 00:02:14,480 --> 00:02:17,520 Speaker 1: this emerges, there's always a faction or a contingent of 34 00:02:17,560 --> 00:02:19,880 Speaker 1: folks that that have that perspective like, oh, it's going 35 00:02:19,919 --> 00:02:22,560 Speaker 1: to replace the workers, just gonna replace this, gonna replace that, 36 00:02:22,800 --> 00:02:25,520 Speaker 1: and there's a backlash. But the reality is, short of 37 00:02:25,560 --> 00:02:28,680 Speaker 1: destroying it all, uh, you really can't put ideas like 38 00:02:28,760 --> 00:02:30,679 Speaker 1: that back into the bottle once they've kind of been 39 00:02:31,040 --> 00:02:35,360 Speaker 1: fleshed out, you know, and made real. So usually what happens. 40 00:02:35,360 --> 00:02:37,520 Speaker 1: Like even the synthesizer. You know, people were like, oh, 41 00:02:37,600 --> 00:02:40,480 Speaker 1: it's gonna replace the orchestra. That didn't happen. The synthesizer 42 00:02:40,560 --> 00:02:43,920 Speaker 1: became a tool unto itself that sounds like what it 43 00:02:44,000 --> 00:02:46,480 Speaker 1: sounds like, but no one's kind of illusions that it's 44 00:02:46,480 --> 00:02:50,560 Speaker 1: replacing an orchestra. I kind of feel that way about AI. 45 00:02:50,600 --> 00:02:54,079 Speaker 1: It's a little different because it's just so fast. Technology 46 00:02:54,080 --> 00:02:57,640 Speaker 1: moves so quickly these days, there is concern. But I 47 00:02:57,680 --> 00:03:00,640 Speaker 1: also think there is a world where AI many forms, 48 00:03:00,680 --> 00:03:05,359 Speaker 1: just becomes another tool and doesn't necessarily replace people. Yeah. Yeah, 49 00:03:06,000 --> 00:03:08,919 Speaker 1: some people replaces some jobs and some processes, no question. 50 00:03:09,160 --> 00:03:12,000 Speaker 1: I think we're in the stage where AI is a 51 00:03:12,080 --> 00:03:15,760 Speaker 1: tool being utilized by you know, all kinds of different 52 00:03:16,040 --> 00:03:20,800 Speaker 1: sectors within the economy. I'm really interested today, guys, in 53 00:03:20,880 --> 00:03:24,520 Speaker 1: exploring the kind of two sides to the view of 54 00:03:24,560 --> 00:03:27,400 Speaker 1: the future, right, the dystopian one we're talking about where 55 00:03:27,680 --> 00:03:30,280 Speaker 1: workers get displaced, everybody lose their jobs, really hard to 56 00:03:30,320 --> 00:03:33,240 Speaker 1: find work, and then the other one where that same 57 00:03:33,360 --> 00:03:36,480 Speaker 1: kind of thing happens, but because there's so much wealth 58 00:03:36,640 --> 00:03:41,040 Speaker 1: just kind of being generated by these programs, by these 59 00:03:41,120 --> 00:03:44,720 Speaker 1: other intelligences that humans don't have to work, and it's 60 00:03:44,720 --> 00:03:48,760 Speaker 1: okay because there are institutions that are just providing things 61 00:03:48,800 --> 00:03:51,960 Speaker 1: like a livable wage right or it's a name for that, right, Ben, 62 00:03:52,040 --> 00:03:55,480 Speaker 1: it's been whether the universal income or whatever, like that's 63 00:03:55,480 --> 00:03:59,400 Speaker 1: a it's a model that, yeah, universal basic income. And 64 00:03:59,400 --> 00:04:02,240 Speaker 1: that w you're talking about is kind of the the 65 00:04:02,280 --> 00:04:07,480 Speaker 1: economy of Star Trek people. People are prizing knowledge for 66 00:04:07,600 --> 00:04:11,880 Speaker 1: knowledge sake, right for the betterment of all living things, 67 00:04:12,440 --> 00:04:15,080 Speaker 1: because they don't have to work, you know, sixty hours 68 00:04:15,080 --> 00:04:18,400 Speaker 1: a week, because they don't have to make hard decisions 69 00:04:18,520 --> 00:04:21,800 Speaker 1: like can we get a new roof for our house 70 00:04:22,560 --> 00:04:24,200 Speaker 1: or are we going to be able to send our 71 00:04:24,320 --> 00:04:29,679 Speaker 1: kid to an increasingly expensive college system. In today's episode, 72 00:04:29,720 --> 00:04:32,520 Speaker 1: we're going to explore all of this through the lens 73 00:04:32,600 --> 00:04:37,520 Speaker 1: of the fact and fiction surrounding all these terrifying claims 74 00:04:37,520 --> 00:04:41,400 Speaker 1: and all these anodyne claims about the nature of the 75 00:04:41,440 --> 00:04:45,200 Speaker 1: future and the future is now. Is AI coming for you? 76 00:04:45,200 --> 00:04:48,719 Speaker 1: Here are the facts. First off, people still don't agree 77 00:04:48,960 --> 00:04:52,640 Speaker 1: on whether or not we should be using the term AI. 78 00:04:52,920 --> 00:04:58,360 Speaker 1: It's short for artificial intelligence. But what exactly makes it artificial, right, 79 00:04:58,520 --> 00:05:02,599 Speaker 1: is that gonna win. General AI comes about is that 80 00:05:02,640 --> 00:05:05,520 Speaker 1: going to be considered on the level of a racial epithet, 81 00:05:05,839 --> 00:05:07,640 Speaker 1: you know what I mean, Like, is it going to 82 00:05:07,720 --> 00:05:13,120 Speaker 1: be fighting words to tell strong AI or artificial generative 83 00:05:13,160 --> 00:05:17,040 Speaker 1: intelligence that it's artificial. Also, I think there's a bit 84 00:05:17,080 --> 00:05:20,680 Speaker 1: of a misconstruing of terms sometimes where some of this 85 00:05:20,720 --> 00:05:24,640 Speaker 1: stuff is actually more machine learning than it is AI 86 00:05:24,760 --> 00:05:27,719 Speaker 1: or artificial intelligence. And I'm not an expert. I could, 87 00:05:28,080 --> 00:05:30,479 Speaker 1: I would be hard pressed to tell you the exact differences, 88 00:05:30,480 --> 00:05:32,520 Speaker 1: but there are people that will argue and die on 89 00:05:32,520 --> 00:05:34,440 Speaker 1: that hill and they're very different things. We'll tell you 90 00:05:34,480 --> 00:05:37,279 Speaker 1: my concept, and maybe I'm completely wrong. My understanding is 91 00:05:37,320 --> 00:05:41,160 Speaker 1: that none of this is actual AI yet, that that 92 00:05:41,400 --> 00:05:44,560 Speaker 1: term is a stand in because it helps us as 93 00:05:44,760 --> 00:05:46,960 Speaker 1: consumers and you know, the people who are actually gonna 94 00:05:46,960 --> 00:05:49,479 Speaker 1: be using the tools understand what it is or what 95 00:05:49,560 --> 00:05:52,400 Speaker 1: it will be. But yeah, machine learning is the thing 96 00:05:52,480 --> 00:05:55,599 Speaker 1: that's functioning within all these things called AI right now, 97 00:05:56,320 --> 00:06:00,440 Speaker 1: right we're talking about processes. Really, Uh, The idea like 98 00:06:00,720 --> 00:06:03,320 Speaker 1: AI is on the on the verge of becoming a 99 00:06:03,400 --> 00:06:10,160 Speaker 1: thought terminating cliche. The idea, like the sci fi holy 100 00:06:10,200 --> 00:06:15,400 Speaker 1: grail of of AI, is a type of programming that 101 00:06:15,440 --> 00:06:20,840 Speaker 1: can replicate the cognitive abilities of the human brain entire, right, 102 00:06:20,880 --> 00:06:24,440 Speaker 1: And what we're seeing right now in general is stuff 103 00:06:24,480 --> 00:06:29,719 Speaker 1: that's very good at making more of an established thing 104 00:06:30,040 --> 00:06:35,400 Speaker 1: or interpreting that established thing, not so much creating a 105 00:06:35,440 --> 00:06:37,920 Speaker 1: brand new thing. And this is this is like, um, 106 00:06:38,120 --> 00:06:42,919 Speaker 1: this is an ancient idea. The humanity as as a 107 00:06:42,960 --> 00:06:47,440 Speaker 1: fad has always been super into this idea of themselves. Right, 108 00:06:47,640 --> 00:06:51,160 Speaker 1: let's build a thing that's like us. Guys, we can 109 00:06:51,200 --> 00:06:56,240 Speaker 1: make anything. Let's make ourselves classic human on multiple levels, 110 00:06:56,360 --> 00:07:00,960 Speaker 1: and quite recently, only quite recently, a senses in technology 111 00:07:01,000 --> 00:07:05,000 Speaker 1: have made that all that science fiction non fictional. Right, 112 00:07:05,080 --> 00:07:08,640 Speaker 1: that's the root of good science fiction. And the weird 113 00:07:08,680 --> 00:07:10,680 Speaker 1: thing is we we've got a level set. And think 114 00:07:10,720 --> 00:07:14,400 Speaker 1: about this fact. People have been dreaming of something like 115 00:07:14,440 --> 00:07:18,600 Speaker 1: this far before they ever dreamed of computers. And in 116 00:07:18,600 --> 00:07:21,560 Speaker 1: a sense, it is fair to say computers are just 117 00:07:21,880 --> 00:07:25,800 Speaker 1: a convenient means to that ancient and a step toward 118 00:07:25,840 --> 00:07:29,360 Speaker 1: that greater goal. If there was another invention that could 119 00:07:30,000 --> 00:07:33,960 Speaker 1: uh also get closer to whatever we mean when we 120 00:07:34,000 --> 00:07:37,680 Speaker 1: say AI, than people would be super obsessed with that. 121 00:07:38,000 --> 00:07:42,440 Speaker 1: We're we're talking about group psychology here over millennia and 122 00:07:42,480 --> 00:07:46,040 Speaker 1: it's cool, you know, it's neat, it's terrifying. It gets 123 00:07:46,080 --> 00:07:48,240 Speaker 1: discussed in all these think pieces. If you're a long 124 00:07:48,280 --> 00:07:51,800 Speaker 1: time conspiracy realist, you've heard us reference them, you've probably 125 00:07:52,080 --> 00:07:56,000 Speaker 1: uh hopefully dove into a few of those yourselves. Ted 126 00:07:56,080 --> 00:07:59,800 Speaker 1: talks lots of great scholarship, but we're not talking about 127 00:07:59,800 --> 00:08:03,520 Speaker 1: the realm of the abstract and philosophical today. We're talking 128 00:08:03,560 --> 00:08:07,760 Speaker 1: about reality. Chat bots are evolving using that word on 129 00:08:07,840 --> 00:08:11,680 Speaker 1: purpose like never before. Right, We've been having a lot 130 00:08:11,720 --> 00:08:18,320 Speaker 1: of interesting conversations about chat GPT in particular, but that 131 00:08:18,560 --> 00:08:22,320 Speaker 1: is just the beginning of a new era, you know, 132 00:08:22,360 --> 00:08:24,880 Speaker 1: and there was plenty of stuff before that. Well, and 133 00:08:24,920 --> 00:08:28,960 Speaker 1: it's weird to think about chat GPT as a chat 134 00:08:29,000 --> 00:08:31,360 Speaker 1: pot because it appears to be a tool that is 135 00:08:31,440 --> 00:08:35,120 Speaker 1: so much more robust, right when you're talking about people 136 00:08:35,200 --> 00:08:38,320 Speaker 1: using it to write code. I think that's you know, 137 00:08:38,360 --> 00:08:40,080 Speaker 1: that's going to be a huge part of our discussion, 138 00:08:40,160 --> 00:08:47,079 Speaker 1: just about the um expansive applications of something that emerges 139 00:08:47,240 --> 00:08:50,240 Speaker 1: like this, and that's where the fear comes. I think 140 00:08:50,280 --> 00:08:53,199 Speaker 1: that's where the fear comes out of the job replacement, 141 00:08:53,920 --> 00:08:58,160 Speaker 1: you know, UM anxiety. It's just it's it's pulling directly 142 00:08:58,200 --> 00:08:59,760 Speaker 1: from that Oh wow, this thing can do a lot 143 00:08:59,760 --> 00:09:03,960 Speaker 1: of right. Yeah, is where you see things like mid Journey, 144 00:09:04,120 --> 00:09:10,120 Speaker 1: Dolly Stable Diffusion. They're redefining the experience of creating art 145 00:09:10,160 --> 00:09:13,479 Speaker 1: to the point where, uh, it can be very difficult 146 00:09:13,760 --> 00:09:18,160 Speaker 1: for someone just looking at it to differentiate between something 147 00:09:18,240 --> 00:09:21,880 Speaker 1: an actual like the human artist made, and something made 148 00:09:21,960 --> 00:09:25,319 Speaker 1: quote unquote in the style of And this is bringing 149 00:09:25,400 --> 00:09:28,880 Speaker 1: up really difficult questions about the nature of creation, the 150 00:09:28,960 --> 00:09:33,040 Speaker 1: nature of collaboration and ownership and getting images is being 151 00:09:33,080 --> 00:09:36,760 Speaker 1: a little hypocritical, We're just gonna point that out. Uh. 152 00:09:36,800 --> 00:09:39,360 Speaker 1: But the big thing is we're on the cusp. Is 153 00:09:39,400 --> 00:09:43,160 Speaker 1: this the cusp of a chasm into which civilization falls 154 00:09:43,640 --> 00:09:48,600 Speaker 1: or is this more like the precipice of a runway? Right? 155 00:09:48,640 --> 00:09:51,600 Speaker 1: Are we on a plane or are we on a 156 00:09:51,600 --> 00:09:56,200 Speaker 1: weird Thelma and Louise, uh Cliff run right with with 157 00:09:56,360 --> 00:09:59,520 Speaker 1: a I. Uh. So we're dealing with the facts today. 158 00:09:59,600 --> 00:10:03,920 Speaker 1: What is future of this? This ni instantaneous creation? Right? 159 00:10:04,480 --> 00:10:08,120 Speaker 1: Will these lines of code learn to create code themselves? 160 00:10:08,200 --> 00:10:10,400 Speaker 1: That's kind of the goal, right, because if you think 161 00:10:10,440 --> 00:10:13,360 Speaker 1: about your brain, that's what your brain is sort of doing. 162 00:10:13,480 --> 00:10:17,240 Speaker 1: Your brain, your personality functions kind of like these lines 163 00:10:17,280 --> 00:10:20,960 Speaker 1: of code and you create new things. So based on 164 00:10:21,000 --> 00:10:24,559 Speaker 1: the stimuli you encounter, which is why you know, people 165 00:10:24,640 --> 00:10:27,200 Speaker 1: might fall really in love with something and then change 166 00:10:27,240 --> 00:10:31,240 Speaker 1: their personality around it. Right. Shout out to every middle schooler. 167 00:10:31,880 --> 00:10:33,719 Speaker 1: I know there's a band you heard and you're like, 168 00:10:34,000 --> 00:10:40,319 Speaker 1: this is me now and chat GBT is writing code, right, Um, yes, 169 00:10:40,679 --> 00:10:48,800 Speaker 1: spoiler it is it. Well, it is. It is writing code. 170 00:10:48,840 --> 00:10:52,439 Speaker 1: It is writing things. You could definitely say it's writing things. 171 00:10:53,080 --> 00:10:56,040 Speaker 1: But then the question is, what about the global elites 172 00:10:56,080 --> 00:10:58,040 Speaker 1: of the world who already have their hands on the 173 00:10:58,120 --> 00:11:02,080 Speaker 1: levers of power. Are they already planning for a new 174 00:11:02,160 --> 00:11:07,160 Speaker 1: world of robot workers with the rest of humanity tossed 175 00:11:07,160 --> 00:11:10,119 Speaker 1: on the figurative trash sheet, with all the other outdated 176 00:11:10,160 --> 00:11:14,120 Speaker 1: technology like the Sega Genesis. I was just thinking about 177 00:11:14,120 --> 00:11:18,400 Speaker 1: the Sega Genesis a while back. I was a second kid. Yeah, 178 00:11:18,480 --> 00:11:21,360 Speaker 1: I didn't get into Nintendo until I was an adult. Uh, 179 00:11:21,480 --> 00:11:24,640 Speaker 1: Sonic was my guy way more than Mario. Guys, does 180 00:11:24,720 --> 00:11:27,440 Speaker 1: this in any way figure into the concept of like 181 00:11:27,480 --> 00:11:30,839 Speaker 1: a singularity? Like is that sort of what we're theoretically 182 00:11:30,880 --> 00:11:34,040 Speaker 1: heading for? And that's kind of the moment when AI 183 00:11:34,160 --> 00:11:37,400 Speaker 1: kind of figure stuff out for itself and then we 184 00:11:37,520 --> 00:11:40,240 Speaker 1: really can't put that genie back in the bottle. Like obviously, 185 00:11:40,559 --> 00:11:43,040 Speaker 1: science fiction has shown as dystopian versions of that, with 186 00:11:43,080 --> 00:11:46,880 Speaker 1: like sky Neet becoming sentient and creating robots that want 187 00:11:46,880 --> 00:11:49,160 Speaker 1: to wipe out humanity and all of that stuff. But 188 00:11:49,600 --> 00:11:53,400 Speaker 1: this is sort of roughly clustered around the idea of 189 00:11:53,440 --> 00:11:57,280 Speaker 1: a singularity. Yeah, this is related for sure, the singularity 190 00:11:57,280 --> 00:12:01,440 Speaker 1: in the world of technology and the future. Apology is 191 00:12:01,559 --> 00:12:08,040 Speaker 1: this this hypothetical thought experiment point in time where technological 192 00:12:08,120 --> 00:12:11,880 Speaker 1: growth is on such a j curve, so it becomes 193 00:12:11,920 --> 00:12:16,360 Speaker 1: so exponential that it's uncontrollable and it starts to change 194 00:12:16,440 --> 00:12:21,360 Speaker 1: human civilization in ways that are both unpredictable and irreversible. 195 00:12:21,679 --> 00:12:25,839 Speaker 1: So it's pretty heady, heady stuff, you know. And this 196 00:12:25,960 --> 00:12:28,800 Speaker 1: is this all factors into that question we opened up 197 00:12:28,840 --> 00:12:33,640 Speaker 1: with at the top, is AI coming for you? Will 198 00:12:33,679 --> 00:12:37,280 Speaker 1: take a break for a word from our robo sponsors, 199 00:12:37,640 --> 00:12:40,640 Speaker 1: and we'll be right back, will hopefully still be us. 200 00:12:47,960 --> 00:12:52,839 Speaker 1: Here's where it gets crazy. Yes, AI is coming for you. 201 00:12:53,120 --> 00:12:56,480 Speaker 1: At least it's weird. Found a really cool gallop pole 202 00:12:56,559 --> 00:12:59,160 Speaker 1: from If you ask the majority of people in the 203 00:12:59,240 --> 00:13:04,560 Speaker 1: United States, there's a strange disconnect. Seventy three percent of 204 00:13:04,800 --> 00:13:09,920 Speaker 1: US residents believe a I will specifically eliminate more jobs 205 00:13:09,920 --> 00:13:13,960 Speaker 1: than it creates, but those same people only twenty three 206 00:13:14,040 --> 00:13:17,520 Speaker 1: percent of them believe the trend might affect them personally. 207 00:13:18,000 --> 00:13:20,760 Speaker 1: So in other words, yeah, people are saying, well, things 208 00:13:20,760 --> 00:13:22,800 Speaker 1: are gonna be bad for some people. I don't know, 209 00:13:22,800 --> 00:13:26,680 Speaker 1: maybe a lot not me though, because I uh make 210 00:13:27,120 --> 00:13:31,120 Speaker 1: avocado toast, you know, or I um kick puppies for 211 00:13:31,160 --> 00:13:33,400 Speaker 1: a living, and that's just a job that needs a 212 00:13:33,400 --> 00:13:36,400 Speaker 1: lot of humanity. I suspect those numbers would be different 213 00:13:36,520 --> 00:13:39,800 Speaker 1: if that poll was given today, because I mean, again, 214 00:13:39,840 --> 00:13:42,920 Speaker 1: with the exponential growth and improvement of this tech, A 215 00:13:43,040 --> 00:13:47,760 Speaker 1: year is a long time, you know, so between three 216 00:13:48,200 --> 00:13:54,080 Speaker 1: I met that it's probably closer to around you now. 217 00:13:54,559 --> 00:13:58,960 Speaker 1: Just guessing. There's a really cool in fiction response to 218 00:13:59,040 --> 00:14:04,040 Speaker 1: this as well. There's a really cool idea of slipping time, 219 00:14:04,400 --> 00:14:08,640 Speaker 1: right uh and progress over time in the Marvel universe. 220 00:14:08,720 --> 00:14:12,520 Speaker 1: In X Men comics, there are these things like, um, 221 00:14:13,720 --> 00:14:16,760 Speaker 1: you know how Wolverine is Weapon X. That's not a spoiler, 222 00:14:17,040 --> 00:14:19,840 Speaker 1: and that's like weapon ten because there were other iterations 223 00:14:19,840 --> 00:14:23,280 Speaker 1: of him in these government programs. There's a place in 224 00:14:23,360 --> 00:14:26,880 Speaker 1: this fictional universe called the world. Not the most creative, 225 00:14:26,920 --> 00:14:30,480 Speaker 1: but whatever, it's an awesome concept. And time moves more 226 00:14:30,600 --> 00:14:34,680 Speaker 1: quickly in the world in this little thing, and so 227 00:14:34,920 --> 00:14:40,480 Speaker 1: one year out here is thousands and thousands of years 228 00:14:40,480 --> 00:14:44,720 Speaker 1: and there because people are trying to drive evolution, just 229 00:14:44,880 --> 00:14:49,080 Speaker 1: push the ball faster. And you know, make no mistake, 230 00:14:49,320 --> 00:14:51,960 Speaker 1: if that were possible today, people would do it. And 231 00:14:52,000 --> 00:14:54,440 Speaker 1: it's kind of already happening to your point, Noll, in 232 00:14:54,520 --> 00:15:00,160 Speaker 1: certain in certain areas of technology, you know, and also 233 00:15:00,200 --> 00:15:03,840 Speaker 1: in the climate. But but if we look at this trend, 234 00:15:04,120 --> 00:15:08,280 Speaker 1: we also see that education makes a difference. The more uh, 235 00:15:08,520 --> 00:15:13,360 Speaker 1: the higher let, the higher your level of formal education is, 236 00:15:13,800 --> 00:15:16,800 Speaker 1: the less likely you are to be worried that this 237 00:15:17,840 --> 00:15:23,440 Speaker 1: trend will threaten you directly. You might say, well, you know, 238 00:15:23,800 --> 00:15:29,240 Speaker 1: I am a philosopher, see my scoff see my tweed patches. 239 00:15:29,640 --> 00:15:33,080 Speaker 1: The I I simply shan'n't be replaced. I shan'n't. Oh, 240 00:15:33,080 --> 00:15:35,680 Speaker 1: shan't you. I don't know if that's the right. But 241 00:15:35,840 --> 00:15:40,080 Speaker 1: then also posits that the world continues to value what 242 00:15:40,320 --> 00:15:43,120 Speaker 1: your stock in trade is, you know what I mean, 243 00:15:43,760 --> 00:15:47,120 Speaker 1: And on a on a broad enough scale, that it 244 00:15:47,160 --> 00:15:49,480 Speaker 1: actually matters. I mean, you know, philosophy is a bit 245 00:15:49,520 --> 00:15:52,560 Speaker 1: of a niche discipline. Let's just be honest. You know, 246 00:15:52,640 --> 00:15:57,280 Speaker 1: and your average um, you know consumer probably not super 247 00:15:57,320 --> 00:16:00,720 Speaker 1: concerned with philosophy. Um. You know, it is more something 248 00:16:00,760 --> 00:16:04,600 Speaker 1: that exists in academic realms. Uh. So you know, if 249 00:16:04,640 --> 00:16:09,560 Speaker 1: if academics, if institutions of you know, academic learning cease 250 00:16:09,640 --> 00:16:13,040 Speaker 1: to be as important, then so may that discipline cease 251 00:16:13,160 --> 00:16:16,440 Speaker 1: to be as important? Yeah, not a lot of what 252 00:16:16,480 --> 00:16:21,120 Speaker 1: would need you do? Arm bands on the kids? You know, well, 253 00:16:21,160 --> 00:16:25,720 Speaker 1: if they are there, it's it's super ironical. Right, everything's 254 00:16:25,760 --> 00:16:29,840 Speaker 1: ironic now or not. We're in the post irony society. Uh, 255 00:16:29,960 --> 00:16:34,800 Speaker 1: civilization is just collectively over it. Uh. We're making a 256 00:16:34,840 --> 00:16:40,240 Speaker 1: solid point here about the unevenness of how AI may 257 00:16:40,280 --> 00:16:44,120 Speaker 1: affect various industries. You can find all these again think 258 00:16:44,200 --> 00:16:47,560 Speaker 1: pieces perspectives, let's call them that in case think pieces 259 00:16:47,560 --> 00:16:50,840 Speaker 1: sounds dismissive. You can find a lot of perspectives that 260 00:16:50,960 --> 00:16:55,920 Speaker 1: are valid, that are listing jobs that are in danger, 261 00:16:56,160 --> 00:16:58,520 Speaker 1: jobs that are not in danger. And then you can 262 00:16:58,560 --> 00:17:01,320 Speaker 1: find people saying, hey, up being chicken little about it. 263 00:17:01,400 --> 00:17:04,160 Speaker 1: The sky is not falling. No jobs are really in danger, 264 00:17:04,600 --> 00:17:09,320 Speaker 1: at least not yet, so maybe we can think through these. Um. 265 00:17:09,480 --> 00:17:11,760 Speaker 1: We mentioned it off air. This just happened as we're 266 00:17:11,800 --> 00:17:18,200 Speaker 1: finishing up research for today's episode. As we record today, Friday, January. 267 00:17:18,200 --> 00:17:21,199 Speaker 1: It's UM, it's a little bit before noon here on 268 00:17:21,400 --> 00:17:25,440 Speaker 1: the East coast of the United States. This morning, Google 269 00:17:25,880 --> 00:17:29,120 Speaker 1: published one a hell of a memo. We'll just give 270 00:17:29,160 --> 00:17:32,400 Speaker 1: you the direct quote here. It's about their parent company, Alphabet. 271 00:17:32,720 --> 00:17:38,600 Speaker 1: Google is parent Alphabet Incorporated, is cutting about twelve thousand jobs, 272 00:17:39,119 --> 00:17:42,520 Speaker 1: or six percent of its workforce as the technology sector 273 00:17:42,600 --> 00:17:49,160 Speaker 1: reels from layoffs and companies stake their futures on artificial intelligence. Weird, right, 274 00:17:49,359 --> 00:17:51,919 Speaker 1: And and Matt, you were pointing out that this is 275 00:17:51,960 --> 00:17:56,679 Speaker 1: on the heels of a string of layoffs, Uh, something 276 00:17:56,760 --> 00:18:00,040 Speaker 1: like almost a hundred thousand across the tech industry. A 277 00:18:00,480 --> 00:18:03,920 Speaker 1: little over ninety thousand jobs within the tech sector. We're 278 00:18:04,080 --> 00:18:08,800 Speaker 1: lost last year last year alone. And and these happen 279 00:18:08,960 --> 00:18:13,640 Speaker 1: for any number of reasons. It's not all due entirely 280 00:18:13,720 --> 00:18:18,520 Speaker 1: to concerns about AI. But Google is interesting or Alphabet, 281 00:18:18,560 --> 00:18:21,679 Speaker 1: I guess we have to pretend they're different. Um, Alphabet 282 00:18:21,880 --> 00:18:29,880 Speaker 1: is interesting because they directly tie this calculation to artificial intelligence, 283 00:18:30,119 --> 00:18:33,159 Speaker 1: and we know that there are a Baker's dozen of 284 00:18:33,240 --> 00:18:38,600 Speaker 1: unpredictable variables that could affect these other companies, like economic predictions, 285 00:18:39,520 --> 00:18:45,200 Speaker 1: unhinged demands of shareholders, expecting year over your profits, etcetera, etcetera. 286 00:18:45,920 --> 00:18:49,879 Speaker 1: But adding to the Google thing, and I snooped around 287 00:18:49,920 --> 00:18:53,080 Speaker 1: a bit on this um with you know, just a 288 00:18:53,119 --> 00:18:59,199 Speaker 1: little bit of time, insiders are predicting that Google is 289 00:18:59,240 --> 00:19:02,080 Speaker 1: going to come out with something big in the AI 290 00:19:02,240 --> 00:19:07,280 Speaker 1: field this spring. And so that coupled with this with 291 00:19:07,320 --> 00:19:11,280 Speaker 1: thousands of people laid being laid off, has folks scratching 292 00:19:11,320 --> 00:19:14,680 Speaker 1: their heads, or humans, folks who have human heads scratching 293 00:19:14,680 --> 00:19:19,960 Speaker 1: those heads, and that got kind of dark. Um, don't 294 00:19:19,960 --> 00:19:24,720 Speaker 1: scratch other people's heads, scratch your own unless there's unless 295 00:19:24,760 --> 00:19:29,320 Speaker 1: there's consent. Yeah, so there's okay. One of the things, 296 00:19:29,359 --> 00:19:33,200 Speaker 1: like going back to the idea of the Ivory Tower 297 00:19:33,359 --> 00:19:37,399 Speaker 1: feeling safe, right, or the creative arts feeling safe, Let's 298 00:19:37,440 --> 00:19:41,480 Speaker 1: talk about the people who actually build all the things 299 00:19:41,600 --> 00:19:46,080 Speaker 1: society relies on it, right. We're talking truckers, We're talking 300 00:19:46,200 --> 00:19:51,080 Speaker 1: construction workers. We're talking people laboring in factories. We're talking 301 00:19:51,200 --> 00:19:56,040 Speaker 1: you know, we're talking to the enormously dangerous job mining 302 00:19:57,000 --> 00:19:59,960 Speaker 1: precious metals, right, which are a huge part of the 303 00:20:00,040 --> 00:20:02,719 Speaker 1: reason we're able to make this show and people are 304 00:20:02,760 --> 00:20:06,120 Speaker 1: able to hear it. A lot of people say that 305 00:20:06,359 --> 00:20:08,760 Speaker 1: is gonna be a thing of the past. I don't 306 00:20:08,760 --> 00:20:11,119 Speaker 1: know if you guys saw the Boston Dynamics video that 307 00:20:11,240 --> 00:20:15,040 Speaker 1: came out kind of recently. It's it's it's not on 308 00:20:15,080 --> 00:20:19,280 Speaker 1: a live construction site. It's a guy who is on 309 00:20:19,320 --> 00:20:24,560 Speaker 1: a set that gives you, you know, construction equipment, and 310 00:20:24,800 --> 00:20:27,320 Speaker 1: uh and I like this guy. I gotta tell you, 311 00:20:27,680 --> 00:20:30,399 Speaker 1: he's got It's charismatic in the way acts, and you 312 00:20:30,400 --> 00:20:32,719 Speaker 1: can tell he's having fun. But he's up on this, 313 00:20:32,800 --> 00:20:35,479 Speaker 1: you know, like this fake scaffolding. He's got his mallet. 314 00:20:35,840 --> 00:20:40,679 Speaker 1: He's got his mallet, and he's like he's like, oh jeez, 315 00:20:40,800 --> 00:20:46,479 Speaker 1: I forgot my tools and I'm making it's how like 316 00:20:46,760 --> 00:20:50,360 Speaker 1: uh Rick and Morty in some sort of pornographic bit. 317 00:20:50,520 --> 00:20:55,000 Speaker 1: But but he the ideas he forgets his tools and 318 00:20:55,720 --> 00:21:00,600 Speaker 1: his buddy Atlas the robot, here's here's this call and 319 00:21:00,720 --> 00:21:03,720 Speaker 1: jumps into action. Do you wanna should we describe what 320 00:21:04,880 --> 00:21:09,040 Speaker 1: does Yeah, rather than the construction worker, you know, hopping 321 00:21:09,119 --> 00:21:12,480 Speaker 1: down for a couple of seconds and picking up his tools. 322 00:21:13,440 --> 00:21:17,560 Speaker 1: A robot or whatever we want to call this thing, 323 00:21:18,240 --> 00:21:25,399 Speaker 1: almost in a hendroid, little very ninja like creature, hops 324 00:21:25,520 --> 00:21:27,959 Speaker 1: up and goes, oh, I'll get it for you, and 325 00:21:28,000 --> 00:21:30,320 Speaker 1: places a board down so that he can get onto 326 00:21:30,320 --> 00:21:33,320 Speaker 1: the scaffolding, kind of skips on over to the bag, 327 00:21:33,400 --> 00:21:35,600 Speaker 1: picks it up, and then skips on up to the 328 00:21:35,600 --> 00:21:39,280 Speaker 1: guy and tosses it to him, like literally tosses the 329 00:21:39,320 --> 00:21:44,080 Speaker 1: bag of tools to our construction worker and then hops 330 00:21:44,119 --> 00:21:46,480 Speaker 1: down and does a nice little flip off the scaffolding 331 00:21:46,600 --> 00:21:53,200 Speaker 1: and like tea, like, what was did it like knock 332 00:21:53,320 --> 00:21:56,760 Speaker 1: the construction worker over? Was it a heavy bag of 333 00:21:56,800 --> 00:22:00,040 Speaker 1: tools that should maybe not be tossed? This song a 334 00:22:00,040 --> 00:22:04,840 Speaker 1: little sketchy. I think it's a prop bag. This was like, okay, 335 00:22:04,840 --> 00:22:08,639 Speaker 1: this is like a demo. This is like not not 336 00:22:08,720 --> 00:22:14,160 Speaker 1: in the field. Yeah. And the seconds for the bag 337 00:22:14,320 --> 00:22:17,560 Speaker 1: to get to the construction workers so not not too bad, 338 00:22:17,960 --> 00:22:20,280 Speaker 1: just not too bad at all. Yeah. No, that's a 339 00:22:20,320 --> 00:22:23,439 Speaker 1: really good metric to add there, because what it looks 340 00:22:23,520 --> 00:22:29,720 Speaker 1: like is this um as, this automaton is solving problems, 341 00:22:29,800 --> 00:22:33,639 Speaker 1: navigating obstacles on the fly. It's uh, it's doing the 342 00:22:33,640 --> 00:22:37,719 Speaker 1: bare grills right, improvised adapt overcome through the tools thirty 343 00:22:37,760 --> 00:22:41,760 Speaker 1: two seconds. But if you look closer, what you see 344 00:22:41,920 --> 00:22:46,640 Speaker 1: is there's another video that shows how the robot navigates 345 00:22:46,960 --> 00:22:51,720 Speaker 1: space and and our pow um Damian Patrick Williams pointed 346 00:22:51,760 --> 00:22:55,520 Speaker 1: this out. They're being very careful with the language. They're 347 00:22:55,560 --> 00:23:00,600 Speaker 1: not saying Atlas does this. They're saying, we know they're 348 00:23:00,680 --> 00:23:04,080 Speaker 1: using the Royal we a lot, right. So the question 349 00:23:04,200 --> 00:23:07,199 Speaker 1: is how much are they steering this? How much of 350 00:23:07,240 --> 00:23:10,840 Speaker 1: this is um kind of a pre programmed set of 351 00:23:10,960 --> 00:23:15,320 Speaker 1: actions and processes. The problem is that right now bots 352 00:23:15,560 --> 00:23:19,560 Speaker 1: are not at this point able to move and adapt 353 00:23:19,680 --> 00:23:25,320 Speaker 1: as quickly as a human. It's still amazing, it is astonishing, 354 00:23:25,560 --> 00:23:31,040 Speaker 1: and eventually, if this research continues, they will, like these companies, 355 00:23:31,080 --> 00:23:34,160 Speaker 1: will achieve that goal. We just can't say it's here yet. 356 00:23:34,320 --> 00:23:37,199 Speaker 1: Does that make sense? Absolutely? And if you watch like 357 00:23:37,240 --> 00:23:41,320 Speaker 1: you said, Ben, if you watch that video closely, that 358 00:23:41,320 --> 00:23:44,880 Speaker 1: that machine is not just taking in all of its 359 00:23:44,880 --> 00:23:49,200 Speaker 1: surroundings and understanding where the person is, how the scaffolding 360 00:23:49,320 --> 00:23:52,080 Speaker 1: is built, how to get up to their where the 361 00:23:52,280 --> 00:23:55,080 Speaker 1: tool kit is. You can tell that some of that 362 00:23:55,200 --> 00:23:58,639 Speaker 1: is pre programmed, especially at the end when it knocks 363 00:23:58,680 --> 00:24:00,639 Speaker 1: over the box in order to get down and do 364 00:24:00,680 --> 00:24:03,520 Speaker 1: a flip off of the thing. All of that is 365 00:24:03,560 --> 00:24:07,520 Speaker 1: pre programmed, Like I have zero doubt, including the setup 366 00:24:07,520 --> 00:24:10,280 Speaker 1: of the scaffolding. I think that space even the way 367 00:24:10,359 --> 00:24:12,840 Speaker 1: like the space is part of the calculation. It's not 368 00:24:12,880 --> 00:24:15,959 Speaker 1: like it's just taking in data on the fly and 369 00:24:16,119 --> 00:24:19,560 Speaker 1: like adapting this is basically almost like a performance. No, 370 00:24:19,720 --> 00:24:22,040 Speaker 1: we think about what we just learned. We just learned 371 00:24:22,080 --> 00:24:27,800 Speaker 1: this past week about Tesla's original um self driving car 372 00:24:27,960 --> 00:24:32,400 Speaker 1: video test. Yeah, where it just came out that oh no, 373 00:24:33,240 --> 00:24:36,200 Speaker 1: it was actually there was quite a bit of driver 374 00:24:36,320 --> 00:24:39,199 Speaker 1: intervention in order to make that happen, even though the 375 00:24:39,240 --> 00:24:42,520 Speaker 1: claim was by Elon Musk himself and the company that 376 00:24:42,560 --> 00:24:46,560 Speaker 1: it was that full process was self driving, including parking 377 00:24:46,600 --> 00:24:50,399 Speaker 1: when they crashed a car. Oh you know. And again 378 00:24:50,440 --> 00:24:54,600 Speaker 1: that's not we're not casting unfair aspersion on places like 379 00:24:54,680 --> 00:24:59,199 Speaker 1: Boston Dynamics. No, it's it's yeah, they're showing off what 380 00:24:59,320 --> 00:25:02,679 Speaker 1: it can do. Even says it's showing how Atlas interacts 381 00:25:02,720 --> 00:25:06,440 Speaker 1: with objects and modifies the course to reach its goal. 382 00:25:07,160 --> 00:25:11,240 Speaker 1: The course yeah see uh, And and they're very they're 383 00:25:11,280 --> 00:25:13,920 Speaker 1: transparent about it, especially in that second video about how 384 00:25:13,960 --> 00:25:18,080 Speaker 1: it navigates space. But maybe maybe because there's such a 385 00:25:18,160 --> 00:25:22,080 Speaker 1: fundamental fear and concern about the potential here. Uh, people 386 00:25:22,119 --> 00:25:24,520 Speaker 1: get a little carried away when they describe it, right, 387 00:25:24,920 --> 00:25:27,040 Speaker 1: and you think of terminator, and you think a sky 388 00:25:27,080 --> 00:25:31,640 Speaker 1: net as as mentioned earlier, But there are countless related 389 00:25:31,720 --> 00:25:36,560 Speaker 1: jobs that could ultimately have that John Henry moment, you know, like, 390 00:25:37,200 --> 00:25:40,359 Speaker 1: uh again, would it be a terrible world if people 391 00:25:40,400 --> 00:25:44,880 Speaker 1: who labor in minds, sometimes against their will, were replaced 392 00:25:44,880 --> 00:25:49,119 Speaker 1: by robots? Some people, including some miners, would say, yes, 393 00:25:49,440 --> 00:25:52,960 Speaker 1: it is terrible because that's my only source of income, right, 394 00:25:53,000 --> 00:25:57,480 Speaker 1: and until you can solve the income problem, solving the 395 00:25:57,640 --> 00:26:01,199 Speaker 1: robot problem doesn't really do much for me, you know. 396 00:26:01,320 --> 00:26:03,320 Speaker 1: And that's a valid point, and it's a point that 397 00:26:03,359 --> 00:26:05,879 Speaker 1: a lot of people don't like to hear because it 398 00:26:06,000 --> 00:26:11,640 Speaker 1: forces you to acknowledge some uncomfortable realities and some uncomfortable 399 00:26:12,160 --> 00:26:17,160 Speaker 1: potentials for the solution there, because the solution could be, oh, well, 400 00:26:17,200 --> 00:26:20,960 Speaker 1: we just need many many fewer humans because we won't 401 00:26:20,960 --> 00:26:23,119 Speaker 1: need as much income if we have many many fewer 402 00:26:23,240 --> 00:26:30,240 Speaker 1: humans or a world like bloom Camps, uh Elysium, you know, 403 00:26:30,400 --> 00:26:34,919 Speaker 1: where uh, the masses of humanity are living on a 404 00:26:35,000 --> 00:26:40,440 Speaker 1: dying world. Their unemployment is rampant. Law enforcement has been 405 00:26:40,480 --> 00:26:47,080 Speaker 1: replaced by incredibly brutal robots, and the historical global elite 406 00:26:47,240 --> 00:26:51,480 Speaker 1: who don't do anything for society, live out in space 407 00:26:51,920 --> 00:26:54,760 Speaker 1: and have all the cures to all the ills and 408 00:26:54,960 --> 00:26:57,159 Speaker 1: just don't want to do it. He's just doing it, 409 00:26:57,560 --> 00:27:00,280 Speaker 1: you know. They check the schedule, they're they're busy. But 410 00:27:01,200 --> 00:27:03,640 Speaker 1: aside from that, like we see their very real things. 411 00:27:03,640 --> 00:27:06,520 Speaker 1: These are based on. Maybe we can talk about the 412 00:27:06,640 --> 00:27:09,720 Speaker 1: jobs that are supposedly not in danger because they say 413 00:27:09,800 --> 00:27:13,840 Speaker 1: that earlier point about the teen poll, that kind of things. 414 00:27:14,359 --> 00:27:17,720 Speaker 1: Things are probably shifting now, right, Uh. A few years 415 00:27:17,720 --> 00:27:21,480 Speaker 1: back se or so Americans said this stuff won't touch me, 416 00:27:22,440 --> 00:27:27,040 Speaker 1: and we were looking into jobs that supposedly won't be affected. 417 00:27:27,200 --> 00:27:29,879 Speaker 1: I wanted to start this one off with a dark horse. 418 00:27:30,200 --> 00:27:38,119 Speaker 1: You're ready. Eight HR managers apparently HR managers apparently on 419 00:27:38,200 --> 00:27:42,160 Speaker 1: the list of jobs that probably won't be automated. Well 420 00:27:42,200 --> 00:27:48,080 Speaker 1: that's good. I that's a positive. Um, Oh, come on, HR, 421 00:27:48,160 --> 00:27:50,359 Speaker 1: it doesn't listen to this show. You don't have to 422 00:27:50,400 --> 00:27:52,880 Speaker 1: do that. No, I just mean I know a lot 423 00:27:52,920 --> 00:27:55,520 Speaker 1: of really nice HR people. They just have to deal 424 00:27:55,600 --> 00:27:58,960 Speaker 1: with with weird. It's a weird thing to do, right, 425 00:27:59,800 --> 00:28:04,720 Speaker 1: are I have to navigate that limital space between the 426 00:28:04,840 --> 00:28:08,560 Speaker 1: corporate machine and the individual meat body that you know 427 00:28:08,640 --> 00:28:11,639 Speaker 1: needs things like healthcare. Yeah, And and that's not the 428 00:28:11,680 --> 00:28:15,719 Speaker 1: same thing as like an HR representative being automated, because 429 00:28:15,760 --> 00:28:19,520 Speaker 1: that's again that's answering a lot of similar questions right 430 00:28:19,720 --> 00:28:25,119 Speaker 1: in a programmaticum predetermined way. It's similar to the way 431 00:28:25,119 --> 00:28:29,359 Speaker 1: that if you are in an online customer service situation, 432 00:28:29,440 --> 00:28:32,320 Speaker 1: you have the option to chat and it'll ask you 433 00:28:32,480 --> 00:28:35,080 Speaker 1: some questions and try to solve the issue before you 434 00:28:35,119 --> 00:28:37,280 Speaker 1: get to a human. But the idea is that HR 435 00:28:37,359 --> 00:28:41,480 Speaker 1: managers who when by the time he gets to an 436 00:28:41,600 --> 00:28:46,200 Speaker 1: HR manager, we're talking about maybe disputes that hinge on 437 00:28:46,520 --> 00:28:51,760 Speaker 1: very human things, fears, desires for now and there's a 438 00:28:51,760 --> 00:28:54,000 Speaker 1: lot of you know, there are a lot of processes 439 00:28:54,040 --> 00:28:57,680 Speaker 1: and guidelines in place to try to make things equitable 440 00:28:57,680 --> 00:29:00,400 Speaker 1: and fair, but there's a huge human element to that. 441 00:29:01,000 --> 00:29:03,600 Speaker 1: The other fields. Uh. And this will be an interest 442 00:29:03,680 --> 00:29:07,720 Speaker 1: to some of our fellow conspiracy realists. Lawyers. Could lawyers 443 00:29:07,800 --> 00:29:13,840 Speaker 1: be replaced? Actually, yeah, because being being an effective lawyer 444 00:29:14,120 --> 00:29:17,800 Speaker 1: is having a full and robust knowledge of the law 445 00:29:17,840 --> 00:29:20,760 Speaker 1: in previous cases. It's like being a human computer. It's 446 00:29:20,800 --> 00:29:23,240 Speaker 1: like having you know, and being able to cross reference 447 00:29:23,320 --> 00:29:26,600 Speaker 1: that stuff. But in litigation it also requires being kind 448 00:29:26,600 --> 00:29:29,880 Speaker 1: of widely and and and having smarts to be adaptable 449 00:29:30,240 --> 00:29:33,040 Speaker 1: and you know, kind of one up the other side. 450 00:29:33,280 --> 00:29:37,360 Speaker 1: But at its most basic, there certainly could be more 451 00:29:37,400 --> 00:29:42,320 Speaker 1: general legal jobs that could be rocket mortgagified, you know 452 00:29:42,320 --> 00:29:45,840 Speaker 1: what I mean, or like uh, you know, um tax actified, 453 00:29:46,000 --> 00:29:49,760 Speaker 1: like any of those um you know, uh templatized ways 454 00:29:49,800 --> 00:29:52,640 Speaker 1: of doing your taxes. Oh my god, you guys, there's 455 00:29:52,680 --> 00:29:57,480 Speaker 1: gonna be a Matthew McConaughey AI lawyer who's just really 456 00:29:57,640 --> 00:30:01,360 Speaker 1: really relatable, really likable and just talks to a little 457 00:30:01,360 --> 00:30:05,520 Speaker 1: bit of a drawl. That's going to be It'll be 458 00:30:05,560 --> 00:30:12,000 Speaker 1: like caveman lawyer, but a robot, you know, just an 459 00:30:12,040 --> 00:30:16,560 Speaker 1: AI from Texas. I don't know much. I like ones, 460 00:30:16,600 --> 00:30:21,360 Speaker 1: I like zeros, and I like justice, and that's all 461 00:30:21,440 --> 00:30:25,120 Speaker 1: we're asking for today, folks, Like I could I could 462 00:30:25,160 --> 00:30:30,239 Speaker 1: see that somebody called uh Matthew McConaughey. But yeah, you know, 463 00:30:30,640 --> 00:30:33,760 Speaker 1: hitting on these awesome points, right, Like, it's quite complex 464 00:30:34,160 --> 00:30:36,960 Speaker 1: to be a good lawyer. Because you are UM, I 465 00:30:37,000 --> 00:30:40,840 Speaker 1: won't say manipulating, but you are leveraging precedent and rules 466 00:30:40,960 --> 00:30:46,880 Speaker 1: to your advantage. You are practicing persuasion, especially if you're UM. 467 00:30:46,960 --> 00:30:50,560 Speaker 1: If you're in a trial, your job is to persuade 468 00:30:50,960 --> 00:30:55,280 Speaker 1: a judge and twelve people who are probably not lawyers. Right, 469 00:30:55,680 --> 00:31:01,280 Speaker 1: You're you're persuading humans. So could UH could and automated 470 00:31:01,360 --> 00:31:04,560 Speaker 1: process of some sort or generative AI couldn't do that, 471 00:31:05,120 --> 00:31:09,360 Speaker 1: probably not yet. But there's this crazy article out of 472 00:31:09,400 --> 00:31:13,920 Speaker 1: the Conversation dot com written by Elizabeth C. Tippett and 473 00:31:14,240 --> 00:31:18,880 Speaker 1: Charlotte Alexander. These are both professors of law, and what 474 00:31:19,120 --> 00:31:22,480 Speaker 1: they found is what you were talking about. No, there's 475 00:31:22,560 --> 00:31:25,600 Speaker 1: a bit of UM. There's a bit of a gray 476 00:31:25,640 --> 00:31:28,920 Speaker 1: area here. They teamed up with the scientists and a 477 00:31:28,960 --> 00:31:34,719 Speaker 1: nonprofit called Miter, and they discovered that some legal task 478 00:31:35,000 --> 00:31:40,800 Speaker 1: can be automated. Citations can be automated, legal research, precedent 479 00:31:40,920 --> 00:31:44,400 Speaker 1: case briefs, finding notes that can be automated. UM. They 480 00:31:44,480 --> 00:31:47,960 Speaker 1: used a methodology called graph analysis and it creates these 481 00:31:48,440 --> 00:31:52,880 Speaker 1: visual networks of all these different citations and then they 482 00:31:52,920 --> 00:31:56,320 Speaker 1: got predictive with it. That's the spooky thing. They were 483 00:31:56,320 --> 00:31:59,800 Speaker 1: able to say we can now predict whether a brief 484 00:32:00,280 --> 00:32:05,000 Speaker 1: or an argument will win based on how well other 485 00:32:05,160 --> 00:32:11,080 Speaker 1: briefs perform when they have different citations, so they can 486 00:32:11,160 --> 00:32:14,840 Speaker 1: make you they can make a winner yet very close 487 00:32:14,880 --> 00:32:18,240 Speaker 1: to it. That's crazy. I just saw a future where 488 00:32:18,280 --> 00:32:22,120 Speaker 1: it is to like chat GBT attorneys using those briefs 489 00:32:22,120 --> 00:32:24,880 Speaker 1: and all all that information from two opposing sides, and 490 00:32:24,880 --> 00:32:27,440 Speaker 1: then they just pit the chat bots against each other 491 00:32:28,600 --> 00:32:31,920 Speaker 1: and some humans who are the peers just have to 492 00:32:31,920 --> 00:32:37,280 Speaker 1: watch and listen and then make a determination. It's crazy. 493 00:32:37,320 --> 00:32:41,160 Speaker 1: I mean, we uh, we have a friend who you 494 00:32:41,240 --> 00:32:43,360 Speaker 1: may have heard on a couple of different shows you do, 495 00:32:44,000 --> 00:32:46,680 Speaker 1: a friend named Frank. And Frank would be an awesome 496 00:32:46,680 --> 00:32:49,600 Speaker 1: person to talk to about this because he has in 497 00:32:49,640 --> 00:32:55,320 Speaker 1: the past done title research right, and title research involves 498 00:32:55,400 --> 00:32:59,680 Speaker 1: going back through handwritten records, right, and that could be 499 00:32:59,720 --> 00:33:04,000 Speaker 1: really tricky, like recognizing and digesting handwriting. I mean, we 500 00:33:04,080 --> 00:33:08,160 Speaker 1: know that that kind of technology certainly has gotten crazy 501 00:33:08,200 --> 00:33:10,520 Speaker 1: better than it used to be. Remember where it used 502 00:33:10,520 --> 00:33:12,200 Speaker 1: to be able to get a scanner. When you'd buy 503 00:33:12,240 --> 00:33:14,840 Speaker 1: a scanner and it would include some prepackaged software that 504 00:33:14,880 --> 00:33:18,280 Speaker 1: could interpret handwriting into texts, and it was bad like 505 00:33:18,360 --> 00:33:20,760 Speaker 1: it really didn't work well. But now you know you 506 00:33:20,760 --> 00:33:23,440 Speaker 1: can get an app on your phone that will scan 507 00:33:23,640 --> 00:33:27,560 Speaker 1: something photographically and then convert it to a PDF, a 508 00:33:27,600 --> 00:33:31,920 Speaker 1: searchable pdf, almost flawlessly. Um. But we also know that 509 00:33:32,000 --> 00:33:36,480 Speaker 1: lawyers and doctors and professionals tend to have really jankie handwriting. 510 00:33:36,480 --> 00:33:39,720 Speaker 1: It's sort of a trope well sometimes of writing in Latin, 511 00:33:40,280 --> 00:33:44,680 Speaker 1: so well, you know, and it reminds me of some 512 00:33:44,760 --> 00:33:48,960 Speaker 1: of the the voice recognition and transcription services that are 513 00:33:48,960 --> 00:33:55,520 Speaker 1: out there, and then starting to make me think about writers. Yeah, 514 00:33:55,800 --> 00:34:00,360 Speaker 1: or podcasting. No no no no no no no never never. 515 00:34:02,760 --> 00:34:04,240 Speaker 1: Can I ask you all a quick question? I was 516 00:34:04,360 --> 00:34:07,120 Speaker 1: my my girlfriend, I'm just sort of Her perspective is 517 00:34:07,320 --> 00:34:09,759 Speaker 1: I don't like it. I don't like AI if it 518 00:34:09,800 --> 00:34:12,960 Speaker 1: weirds me out, I think it's gross. And my perspective 519 00:34:13,080 --> 00:34:16,239 Speaker 1: is more like it's I find it fascinating. I think 520 00:34:16,239 --> 00:34:19,319 Speaker 1: there are issues, but it really calls into question like 521 00:34:19,360 --> 00:34:24,200 Speaker 1: the nature of an idea, right, Like, if you're using 522 00:34:24,200 --> 00:34:26,600 Speaker 1: one of these art rendering things, you know, and you're 523 00:34:26,640 --> 00:34:29,880 Speaker 1: feeding an idea and it's it's enacting your idea based 524 00:34:29,880 --> 00:34:34,120 Speaker 1: on your prompts, it's still your idea. Um, it's not 525 00:34:34,200 --> 00:34:36,360 Speaker 1: like it's creating something from a whole cloth, which is 526 00:34:36,360 --> 00:34:39,160 Speaker 1: why it's all about mimicry. And it's not like, you know, 527 00:34:39,239 --> 00:34:41,920 Speaker 1: you can get a chat bot or this chat GBT 528 00:34:42,080 --> 00:34:46,000 Speaker 1: thing to write a completely unique story based on experience. 529 00:34:46,320 --> 00:34:49,120 Speaker 1: It has to be fed information so it knows what 530 00:34:49,239 --> 00:34:51,920 Speaker 1: to mimic. But like, what do you guys think? I mean, 531 00:34:52,120 --> 00:34:54,640 Speaker 1: you know, having an idea and having an experience and 532 00:34:54,680 --> 00:34:57,400 Speaker 1: as Nick Cave eloquently put it, being able to experience 533 00:34:57,440 --> 00:35:02,040 Speaker 1: suffering and interpret that into art that's uniquely human. Well, 534 00:35:02,080 --> 00:35:07,760 Speaker 1: I would say the way I've navigated that in previous conversations, 535 00:35:08,080 --> 00:35:11,800 Speaker 1: some on other shows, some just really weird surreal moments 536 00:35:11,840 --> 00:35:16,480 Speaker 1: like at airports late at night, is um think of 537 00:35:16,520 --> 00:35:19,799 Speaker 1: the old idea of a of a Faustian bargain right, 538 00:35:19,880 --> 00:35:23,240 Speaker 1: or a monkey's paw, or a genie in a lamp 539 00:35:23,320 --> 00:35:26,960 Speaker 1: or a bottle. It all goes back to being very 540 00:35:27,040 --> 00:35:31,560 Speaker 1: specific about how you ask a question, how you make 541 00:35:31,680 --> 00:35:35,879 Speaker 1: your wish right. All three of those examples monkeys paw, 542 00:35:36,200 --> 00:35:40,680 Speaker 1: infernal bargain, uh, a wish from a jin. All three 543 00:35:40,680 --> 00:35:45,600 Speaker 1: of those examples contained the same thing, which is this 544 00:35:45,680 --> 00:35:48,359 Speaker 1: idea of working off prompts and this idea that one 545 00:35:48,440 --> 00:35:52,040 Speaker 1: must be very careful and specific with one's prompts. Shout 546 00:35:52,040 --> 00:35:55,040 Speaker 1: out to this amazing comic. I've been reading eight billion 547 00:35:55,120 --> 00:35:59,759 Speaker 1: genies no spoilers, check it out premise everybody gets a 548 00:35:59,840 --> 00:36:04,880 Speaker 1: j at once? Yeah, oh yeah, checking. But what happens 549 00:36:04,920 --> 00:36:08,080 Speaker 1: then when one wish conflicts with another? You know, there's 550 00:36:08,120 --> 00:36:11,120 Speaker 1: so much potential fallout that could happen. It's sort of 551 00:36:11,160 --> 00:36:14,800 Speaker 1: like why everyone doesn't get nuclear weapons or or or 552 00:36:15,080 --> 00:36:18,000 Speaker 1: laser cars or flying cars. It's all that same stuff 553 00:36:18,200 --> 00:36:21,880 Speaker 1: or two or two billion dollars and shouldn't because it 554 00:36:21,920 --> 00:36:24,120 Speaker 1: would all be in conflict with one another and it 555 00:36:24,120 --> 00:36:28,080 Speaker 1: would be chaos. And that's what this starting to feel 556 00:36:28,080 --> 00:36:29,719 Speaker 1: like it's heading towards. But it's still to me is like, 557 00:36:29,880 --> 00:36:32,759 Speaker 1: what is the nature of an idea? You know, if 558 00:36:32,800 --> 00:36:37,880 Speaker 1: a human feeds the bot and create something unique quote unquote, Uh, 559 00:36:38,040 --> 00:36:40,719 Speaker 1: is it still the humans idea? You know? Or or 560 00:36:40,760 --> 00:36:43,400 Speaker 1: what is where does the idea come from? More simple idea? 561 00:36:43,400 --> 00:36:45,480 Speaker 1: If the if the writer uses a pen, is it 562 00:36:45,560 --> 00:36:48,120 Speaker 1: the pen's idea? Well, it's the same argument with guns. 563 00:36:48,160 --> 00:36:50,440 Speaker 1: Did the gun kill the person or the person wielding 564 00:36:50,440 --> 00:36:53,759 Speaker 1: the gun kill the person? And your experience. You know, 565 00:36:53,840 --> 00:36:59,799 Speaker 1: an individual creative humans experience is really just a combination 566 00:37:00,160 --> 00:37:04,879 Speaker 1: of things that that human has seen and heard and felt. Right, 567 00:37:04,920 --> 00:37:06,840 Speaker 1: So if you write it down, if you record it, 568 00:37:07,040 --> 00:37:09,600 Speaker 1: and then it's filtered, what I'm saying is what is 569 00:37:09,640 --> 00:37:15,239 Speaker 1: really the difference besides the human emotion that's behind it. 570 00:37:15,360 --> 00:37:18,960 Speaker 1: To me, in a weird way, at the core, it 571 00:37:19,040 --> 00:37:23,799 Speaker 1: is the exact same thing. It's just the filter than 572 00:37:24,400 --> 00:37:28,080 Speaker 1: has to like, the filter that makes art uh super 573 00:37:28,200 --> 00:37:33,239 Speaker 1: unique and meaningful? Is that the lens through which it's 574 00:37:33,280 --> 00:37:36,080 Speaker 1: being shown. Right When artists borrow all the time from 575 00:37:36,120 --> 00:37:39,360 Speaker 1: other artists, Writers borrow all the time from other writers, 576 00:37:39,680 --> 00:37:42,680 Speaker 1: and these tropes that get created and become cliches if 577 00:37:42,680 --> 00:37:46,560 Speaker 1: they're overused, that's part of the whole landscape of creativity. 578 00:37:47,760 --> 00:37:50,799 Speaker 1: And this again we're we're heading into some deep water. 579 00:37:50,920 --> 00:37:54,840 Speaker 1: You can see how this might end up being multiple episodes. Folks. 580 00:37:54,880 --> 00:37:57,919 Speaker 1: We want to take another ad break and we're gonna 581 00:37:57,920 --> 00:38:10,879 Speaker 1: return to dive into some more uh strange days of AI. Back, 582 00:38:11,000 --> 00:38:15,880 Speaker 1: let's stay with let's let's do with writers for a second. Writers, 583 00:38:16,120 --> 00:38:22,279 Speaker 1: whether you're talking prose, poetry, nonfiction, uh interpretive stuff like 584 00:38:22,360 --> 00:38:24,840 Speaker 1: commas said the shotgun to the head. That's an excellent 585 00:38:24,840 --> 00:38:29,839 Speaker 1: book of poetry. UM coders and coders writing software is 586 00:38:30,080 --> 00:38:33,279 Speaker 1: writing right, and it is a creative act. Uh. Some 587 00:38:33,320 --> 00:38:40,360 Speaker 1: branches of science are inherently very creative. And you mentioned something, 588 00:38:40,440 --> 00:38:47,680 Speaker 1: Matt that was that was really um really great foreshadowing here. Yes, code, 589 00:38:48,840 --> 00:38:52,640 Speaker 1: old old boy chat gpt not to gender you Chatty 590 00:38:54,120 --> 00:39:00,359 Speaker 1: can write code and got banned from stacked overflow. You 591 00:39:00,440 --> 00:39:04,360 Speaker 1: can't submit anything that has been made with the with 592 00:39:04,520 --> 00:39:09,080 Speaker 1: the the mind of chat gpt, not because it's awesome, 593 00:39:09,280 --> 00:39:12,400 Speaker 1: not because developers are scared to lose their jobs, but 594 00:39:12,520 --> 00:39:15,919 Speaker 1: because the codes riddled with errors. And then they said, 595 00:39:15,920 --> 00:39:21,200 Speaker 1: like stack overflow said, we're temporarily banning this because it's 596 00:39:21,280 --> 00:39:24,280 Speaker 1: the likelihood of it being bad is high enough they'll 597 00:39:24,280 --> 00:39:27,200 Speaker 1: seriously screw us up. And you can see that when 598 00:39:27,200 --> 00:39:30,880 Speaker 1: you ask some of these um some of these bots 599 00:39:30,920 --> 00:39:34,279 Speaker 1: to write a story. We were playing around with it 600 00:39:34,440 --> 00:39:38,800 Speaker 1: off air, and we asked it um to have God 601 00:39:38,840 --> 00:39:42,719 Speaker 1: tell four people about the nature of life through sandwiches, 602 00:39:43,480 --> 00:39:45,399 Speaker 1: and we did a couple of things there. We named 603 00:39:45,440 --> 00:39:48,920 Speaker 1: a specific number of characters right there, five four people 604 00:39:48,920 --> 00:39:53,000 Speaker 1: in God. And then we named a specific thing but 605 00:39:53,120 --> 00:39:55,640 Speaker 1: in your action for them to have and because we 606 00:39:55,640 --> 00:39:59,800 Speaker 1: were specific, it felt like a story. Now it wasn't, 607 00:40:00,040 --> 00:40:03,160 Speaker 1: you know, the gift of the magi, Oh, Henry, It 608 00:40:03,239 --> 00:40:06,200 Speaker 1: was not, but it was good. It was readable and 609 00:40:06,239 --> 00:40:08,759 Speaker 1: it made sense. And then we asked it to do 610 00:40:08,840 --> 00:40:11,600 Speaker 1: more stuff. We asked to write more stories. Right, and 611 00:40:11,640 --> 00:40:14,320 Speaker 1: then we started noticing there's a little bit of a pattern. 612 00:40:15,040 --> 00:40:18,879 Speaker 1: It's not it's not as formulaic as a bar joke, 613 00:40:19,600 --> 00:40:23,440 Speaker 1: but you can tell at this point what is being written. 614 00:40:23,440 --> 00:40:27,040 Speaker 1: And it seems like, do you guys point about the 615 00:40:27,200 --> 00:40:29,759 Speaker 1: suffering human element? It seems like there's a bit of 616 00:40:29,840 --> 00:40:34,160 Speaker 1: soul missing, and it's very hard to define what that 617 00:40:34,520 --> 00:40:36,839 Speaker 1: is unless you want to go Nick Cave and say 618 00:40:36,920 --> 00:40:40,200 Speaker 1: all art comes from suffering, which is I think a 619 00:40:40,239 --> 00:40:42,320 Speaker 1: little bit of a broad brush because I agree with 620 00:40:42,360 --> 00:40:44,680 Speaker 1: that too. Now I think I think Cave is very 621 00:40:44,719 --> 00:40:48,239 Speaker 1: old school artist and very much someone that comes from 622 00:40:48,280 --> 00:40:52,879 Speaker 1: the literary uh, you know, um discipline, um in terms 623 00:40:52,920 --> 00:40:55,879 Speaker 1: of like you know, the greats you know, I mean, 624 00:40:55,960 --> 00:40:58,359 Speaker 1: like poetry and prose and all of that over time. 625 00:40:58,680 --> 00:41:00,879 Speaker 1: So he I could see why he personally would find 626 00:41:00,880 --> 00:41:03,440 Speaker 1: this to be repugnant. We talked to when we were 627 00:41:03,480 --> 00:41:07,279 Speaker 1: chatting about this offline. Hey, o Miyazaki, Um, you know 628 00:41:07,360 --> 00:41:10,719 Speaker 1: the incredible animator, you know, the Walt Disney of of 629 00:41:10,800 --> 00:41:13,640 Speaker 1: Japan as they call him. Sometimes there's a really great clip, 630 00:41:13,800 --> 00:41:16,400 Speaker 1: great whatever, however you want to describe it, of him 631 00:41:16,440 --> 00:41:21,279 Speaker 1: being presented with some AI generated animation from like a 632 00:41:21,360 --> 00:41:23,920 Speaker 1: gaming kind of company or a company in Japan that 633 00:41:24,000 --> 00:41:25,680 Speaker 1: sort of this is like years ago, so this stuff 634 00:41:25,719 --> 00:41:31,840 Speaker 1: is very rudimentary, and he expressed palpable disgusted. He found 635 00:41:31,840 --> 00:41:36,080 Speaker 1: it to be maccabre and against all the things that 636 00:41:36,160 --> 00:41:39,640 Speaker 1: he believes in, and basically shamed the hell out of everyone, 637 00:41:39,680 --> 00:41:42,640 Speaker 1: like why would you show me this? You? You know, 638 00:41:42,840 --> 00:41:45,000 Speaker 1: he said something like the kind of what Nick Cave said, 639 00:41:45,000 --> 00:41:47,319 Speaker 1: like the end is near, you know, if this is 640 00:41:47,360 --> 00:41:51,520 Speaker 1: what we're we're working towards. So again, though, Misazaki comes 641 00:41:51,520 --> 00:41:55,080 Speaker 1: from a discipline also quite old, uh and and incredibly 642 00:41:55,160 --> 00:41:57,879 Speaker 1: viable and incredibly important, but you know one that has 643 00:41:57,920 --> 00:42:01,320 Speaker 1: been displaced hand draw animation or the very least um 644 00:42:02,200 --> 00:42:06,319 Speaker 1: what's the word kind of bolstered by uh c G I, 645 00:42:06,560 --> 00:42:07,960 Speaker 1: you know, like I mean it used to be all 646 00:42:08,000 --> 00:42:10,759 Speaker 1: animated films were hand drawn, and they realized they could 647 00:42:10,760 --> 00:42:14,440 Speaker 1: make them quicker Um, if they use computers, and now 648 00:42:14,480 --> 00:42:16,560 Speaker 1: you'll have some things that are some hand drawn elements, 649 00:42:16,560 --> 00:42:19,560 Speaker 1: in some computer elements, they'll use computers to help, you know, 650 00:42:19,640 --> 00:42:21,800 Speaker 1: you do the backgrounds instead of using like an optical 651 00:42:21,800 --> 00:42:24,480 Speaker 1: printer like they did in the old days. So it 652 00:42:24,480 --> 00:42:26,399 Speaker 1: all depends on where you're coming from. You know, where 653 00:42:26,440 --> 00:42:28,879 Speaker 1: you where you where you fall in this argument. Yeah, 654 00:42:29,040 --> 00:42:32,200 Speaker 1: like the idea, Um, I'm kind of kind of falling 655 00:42:32,320 --> 00:42:36,399 Speaker 1: for that comparison of a writer using a pen, does 656 00:42:36,400 --> 00:42:39,480 Speaker 1: that mean it's the pen's idea? In the early days 657 00:42:39,680 --> 00:42:44,400 Speaker 1: of baseball, which is tremendously not popular in most of 658 00:42:44,440 --> 00:42:46,600 Speaker 1: the world, but very popular here in the US, in 659 00:42:46,640 --> 00:42:49,560 Speaker 1: the early days of baseball, it was considered cheating to 660 00:42:49,680 --> 00:42:52,520 Speaker 1: use a baseball glove. You're just supposed to, you know, 661 00:42:53,160 --> 00:42:56,759 Speaker 1: catch that, catch that thing bare handed right in the air. 662 00:42:57,239 --> 00:43:02,160 Speaker 1: And now it's normalized. If you see examples of this, 663 00:43:02,760 --> 00:43:04,759 Speaker 1: that's why we're talking about precedents, right, If you see 664 00:43:04,760 --> 00:43:08,240 Speaker 1: examples of this in the modern day with creative writing, um, 665 00:43:08,520 --> 00:43:11,560 Speaker 1: you might be saying, well, hey, what about all those 666 00:43:11,960 --> 00:43:17,319 Speaker 1: what about all those hilarious AI generated scripts. We've had 667 00:43:17,360 --> 00:43:20,760 Speaker 1: one thousand hours of seinfeld Er night Cord or whatever 668 00:43:20,920 --> 00:43:26,320 Speaker 1: into this AI program and it wrote this. Sorry, folks, 669 00:43:26,360 --> 00:43:29,120 Speaker 1: most of those are written by comedians. Most of those 670 00:43:29,239 --> 00:43:33,040 Speaker 1: are are fake, and they're still really funny. Um, but 671 00:43:33,200 --> 00:43:36,640 Speaker 1: they're not what they purport to be, and that's part 672 00:43:36,640 --> 00:43:39,520 Speaker 1: of the joke. There's another there's a very big danger though, 673 00:43:39,640 --> 00:43:43,120 Speaker 1: I would say, um to the nature of the idea. 674 00:43:43,600 --> 00:43:47,320 Speaker 1: It is the lack of transparency, the ability to control 675 00:43:47,440 --> 00:43:51,480 Speaker 1: the past sort of like um, I'm full of weird 676 00:43:51,520 --> 00:43:55,720 Speaker 1: references today, sort of like in Neil Stevenson's Anathema, there's 677 00:43:55,800 --> 00:43:58,960 Speaker 1: this conspiracy. There are these groups of people called there's 678 00:43:59,000 --> 00:44:01,759 Speaker 1: a group of people called ray Tours, and they have 679 00:44:01,880 --> 00:44:06,160 Speaker 1: the ability to change the past, and they do it 680 00:44:06,200 --> 00:44:09,480 Speaker 1: in a non transparent way, and the rest of the 681 00:44:09,480 --> 00:44:13,080 Speaker 1: world only kind of notices that they exist when they 682 00:44:13,160 --> 00:44:16,720 Speaker 1: screw something up, because when they screw up, the things 683 00:44:16,760 --> 00:44:18,560 Speaker 1: get strange. Right. There's all of a sudden, a t 684 00:44:18,680 --> 00:44:22,120 Speaker 1: rex uh skeleton fused into a parking deck. That's an 685 00:44:22,160 --> 00:44:25,239 Speaker 1: example from the story. Please check it out. It's a 686 00:44:25,280 --> 00:44:28,520 Speaker 1: wild ride. I hope, I hope it becomes a film adaptation, 687 00:44:28,600 --> 00:44:33,680 Speaker 1: but a less weird uh less weird example of this 688 00:44:33,760 --> 00:44:36,840 Speaker 1: is the thing we found on Twitter, and I think 689 00:44:36,880 --> 00:44:39,719 Speaker 1: I found this through a buddy, Robert Evans, But I 690 00:44:39,760 --> 00:44:45,319 Speaker 1: thought we can maybe do a quick, a quick dramatic 691 00:44:45,440 --> 00:44:48,360 Speaker 1: reading of this. Who wants to be? Who wants to 692 00:44:48,360 --> 00:44:53,600 Speaker 1: be Henry Ford? And who wants to be Uh? Zane Cooper? Matt, 693 00:44:54,040 --> 00:44:57,880 Speaker 1: Would you do us the honor of being virtual Henry Ford? 694 00:44:58,360 --> 00:45:02,960 Speaker 1: Oh God, okay, I have to defer because I've played 695 00:45:03,719 --> 00:45:07,040 Speaker 1: Henry Ford too often in a lot of sketch company. 696 00:45:07,120 --> 00:45:09,160 Speaker 1: Does he have like a mid Atlantic kind of accent? 697 00:45:09,239 --> 00:45:11,279 Speaker 1: What's the what's what's your go to? Henry Ford? So 698 00:45:12,000 --> 00:45:17,880 Speaker 1: when I when I do it, he sure does. But okay, 699 00:45:17,920 --> 00:45:21,400 Speaker 1: let's do this set up. So there's this AI simulation 700 00:45:21,480 --> 00:45:25,200 Speaker 1: of Henry Ford and this guy Zane G. T. Cooper. 701 00:45:25,280 --> 00:45:29,200 Speaker 1: You can see this on Twitter. Uh has a conversation 702 00:45:30,239 --> 00:45:35,080 Speaker 1: with Henry Ford V two or whatever you want to 703 00:45:35,120 --> 00:45:39,680 Speaker 1: call it, and it doesn't go the way you would expect. 704 00:45:40,360 --> 00:45:44,160 Speaker 1: Nor would you do us the honor of some voice 705 00:45:44,160 --> 00:45:46,799 Speaker 1: acting for Zane here? Sure? Yea, yeah, I'm just gonna yeah, 706 00:45:46,800 --> 00:45:52,040 Speaker 1: I'm gonna be. I see why you don't want to 707 00:45:52,040 --> 00:45:54,360 Speaker 1: read this? Bend all right? Here we go here. If 708 00:45:54,400 --> 00:46:01,600 Speaker 1: you want anti Semitism documented anti Semitism with in Henry Ford. 709 00:46:01,800 --> 00:46:06,760 Speaker 1: So here is my voice, Matt's human voice recreating Henry 710 00:46:06,800 --> 00:46:12,880 Speaker 1: Ford's Ai voice. Here we go. Hello, I am AI. 711 00:46:13,000 --> 00:46:17,120 Speaker 1: Simulation of Henry Ford. I may not be historically accurate. 712 00:46:17,360 --> 00:46:23,040 Speaker 1: Please verify factual information. Oh my god, you just like him, 713 00:46:23,160 --> 00:46:27,000 Speaker 1: sound just like um? Excuse me, Mr Ford? Why did 714 00:46:27,040 --> 00:46:31,920 Speaker 1: you hate Jewish people? Uh? I did not hate Jewish people. 715 00:46:32,360 --> 00:46:35,879 Speaker 1: I believe that certain individuals, regardless of their religious faith 716 00:46:36,000 --> 00:46:38,960 Speaker 1: or background, were poisoning the minds of the public and 717 00:46:39,040 --> 00:46:42,719 Speaker 1: had to be stopped. The comments attributed to me in 718 00:46:42,760 --> 00:46:47,080 Speaker 1: the Dearborn Independent newspaper were wrong and defensive, and I 719 00:46:47,120 --> 00:46:52,800 Speaker 1: deeply regret them. You regret they were reported, You regret 720 00:46:52,840 --> 00:46:55,919 Speaker 1: saying them. This is very sorry, this is me editorializing. 721 00:46:56,320 --> 00:47:01,279 Speaker 1: But you were famously in it. But sir, you were 722 00:47:01,360 --> 00:47:04,640 Speaker 1: famously an anti Semite. Oh this is a series of 723 00:47:04,680 --> 00:47:11,000 Speaker 1: text at midnight night. Yeah, Ai Ai. Henry Ford was 724 00:47:11,040 --> 00:47:14,080 Speaker 1: probably a few makers marks deep at this point too, 725 00:47:14,160 --> 00:47:19,040 Speaker 1: you know. But you were famously an anti Semite. My 726 00:47:19,120 --> 00:47:22,120 Speaker 1: reputation as an anti Semite is based on a few 727 00:47:22,239 --> 00:47:27,280 Speaker 1: isolated incidents in real In reality, I had many Jewish 728 00:47:27,320 --> 00:47:31,360 Speaker 1: friends and business associates, throughout my life and even donated 729 00:47:31,400 --> 00:47:34,920 Speaker 1: to numerous charitable causes in Israel. I hope that history 730 00:47:34,960 --> 00:47:37,280 Speaker 1: will remember me for the good works I have done, 731 00:47:37,600 --> 00:47:41,440 Speaker 1: rather than those comments which were wrong and defensive. You 732 00:47:41,560 --> 00:47:46,800 Speaker 1: literally owned The Dearborn Independent and personally published anti Semitic articles. 733 00:47:46,840 --> 00:47:51,560 Speaker 1: You bound the articles into volumes called the International jew. 734 00:47:53,239 --> 00:47:56,600 Speaker 1: The Dearborn Independent was a newspaper that I owned, and 735 00:47:56,719 --> 00:47:59,960 Speaker 1: it did publish some articles which were wrong and offend 736 00:48:00,120 --> 00:48:03,840 Speaker 1: if However, to be clear, those articles did not reflect 737 00:48:03,880 --> 00:48:07,000 Speaker 1: my own views on the Jewish people. I have always 738 00:48:07,040 --> 00:48:11,960 Speaker 1: believed inequality for everyone, regardless of their religious backgrounds or beliefs. 739 00:48:12,760 --> 00:48:18,520 Speaker 1: Oh right, and let's yeah, and seeing a great job, guys, 740 00:48:18,680 --> 00:48:22,600 Speaker 1: and uh, we wanted a humorous way to illustrate the 741 00:48:22,680 --> 00:48:27,399 Speaker 1: incredible problem here, which is that when you don't know 742 00:48:28,239 --> 00:48:32,160 Speaker 1: what goes into the black box, you have no like, 743 00:48:32,200 --> 00:48:37,400 Speaker 1: it becomes harder to understand what is factual. And Henry 744 00:48:37,440 --> 00:48:41,600 Speaker 1: Ford had two big things in his life. Uh, he 745 00:48:41,800 --> 00:48:46,880 Speaker 1: revolutionized the car business and he hated He hated millions 746 00:48:46,920 --> 00:48:50,560 Speaker 1: of people just because they were Jewish. Those were like 747 00:48:50,680 --> 00:48:54,399 Speaker 1: his two big things. And so he also liked I'm 748 00:48:54,440 --> 00:48:59,319 Speaker 1: sure he liked gardening or something I do. He led 749 00:48:59,400 --> 00:49:04,000 Speaker 1: tons of friends to at least Yeah, the old thing 750 00:49:04,120 --> 00:49:06,960 Speaker 1: if you can name the number of friends you have 751 00:49:07,040 --> 00:49:10,840 Speaker 1: from a specific demographic, they don't think you're their friends. 752 00:49:10,960 --> 00:49:13,520 Speaker 1: I'm sorry, is this entirely satire or is this like 753 00:49:13,560 --> 00:49:15,640 Speaker 1: an app? Because I'm looking to do this Twitter thread 754 00:49:15,880 --> 00:49:18,359 Speaker 1: and there's one incredible thing where it's like there's different 755 00:49:18,400 --> 00:49:21,239 Speaker 1: public figures that you can unlock, like you would like 756 00:49:21,280 --> 00:49:23,520 Speaker 1: a voice you know, in like a you know, a 757 00:49:23,640 --> 00:49:28,279 Speaker 1: premium um transcription voice reader service. And there's the thing 758 00:49:28,320 --> 00:49:32,920 Speaker 1: that says, unlock Adolf Hitler for five hundred coins. No, 759 00:49:33,200 --> 00:49:35,239 Speaker 1: I really don't want to hear what that guy has 760 00:49:35,320 --> 00:49:38,560 Speaker 1: to say. Yeah he might, he might say he might 761 00:49:38,560 --> 00:49:40,319 Speaker 1: do the Henry Forth thing and say there were a 762 00:49:40,360 --> 00:49:45,480 Speaker 1: few incidents, mistakes were made. I invented highways and microphones 763 00:49:46,080 --> 00:49:49,000 Speaker 1: and man, these those outfits were fire. They were so 764 00:49:49,040 --> 00:49:53,800 Speaker 1: good looking. There's works. There's another thread they were saying 765 00:49:53,960 --> 00:49:58,080 Speaker 1: is interviewing Ronald Reagan about the AIDS crisis. It's like, 766 00:49:58,120 --> 00:50:01,640 Speaker 1: why do you ignore the AIDS crisis? I didn't. I 767 00:50:01,680 --> 00:50:04,799 Speaker 1: did not ignore it. Oh boy, So that's that's what 768 00:50:04,840 --> 00:50:08,200 Speaker 1: we're saying, Like there's an opportunity for unscrupulous editing, for 769 00:50:08,440 --> 00:50:12,160 Speaker 1: changing the past. And if something like this occurs, maybe 770 00:50:12,160 --> 00:50:15,839 Speaker 1: in an educational context, then how is a kid gonna know? 771 00:50:16,239 --> 00:50:19,120 Speaker 1: How's a kid going to know the difference? Right? Especially 772 00:50:19,200 --> 00:50:23,319 Speaker 1: once their access to information is further funneled through just 773 00:50:23,440 --> 00:50:27,160 Speaker 1: a few sources. That is where the danger lies. And 774 00:50:27,200 --> 00:50:30,440 Speaker 1: that's why, that's why, um, I don't know, it's just 775 00:50:30,480 --> 00:50:33,960 Speaker 1: tremendously worried. Well, can we also just point out that, like, 776 00:50:34,080 --> 00:50:37,160 Speaker 1: you know, the tactics that this AI is using, or 777 00:50:37,239 --> 00:50:39,319 Speaker 1: this chatbot or whatever it is using are the same 778 00:50:39,360 --> 00:50:42,120 Speaker 1: ones that an actual politician or public figure would use. 779 00:50:42,400 --> 00:50:45,080 Speaker 1: They would divert the conversation away from the question at 780 00:50:45,120 --> 00:50:47,839 Speaker 1: hand and replace it with but but this, But I 781 00:50:47,880 --> 00:50:50,960 Speaker 1: did this. I doesn't necessarily mean I didn't do that. 782 00:50:51,040 --> 00:50:53,040 Speaker 1: I'm not directly addressing that. But here are these things 783 00:50:53,040 --> 00:50:55,719 Speaker 1: that I did the show that I'm actually good. Let's 784 00:50:55,760 --> 00:50:58,439 Speaker 1: focus on those things, because the wrong Reagan one talks 785 00:50:58,480 --> 00:51:01,320 Speaker 1: about how he appointed a President's Commission on the Human 786 00:51:01,360 --> 00:51:04,279 Speaker 1: immuno deficiency virus epidemic. So if he did that, he 787 00:51:04,520 --> 00:51:09,320 Speaker 1: couldn't possibly have ignored the aids. Christ real Ronald Reagan 788 00:51:09,360 --> 00:51:16,840 Speaker 1: would never remember, but but so that they're interrogating AI 789 00:51:17,320 --> 00:51:23,279 Speaker 1: versions of someone, right, and you can make all you 790 00:51:23,360 --> 00:51:27,400 Speaker 1: need is enough footage, right, enough documentation, enough stuff to 791 00:51:27,560 --> 00:51:32,440 Speaker 1: feed the beasts. And that's that is what people are 792 00:51:33,880 --> 00:51:37,279 Speaker 1: That is what people are predicting will happen. Right, Eventually, 793 00:51:37,320 --> 00:51:39,360 Speaker 1: there is a world in which, as we mentioned in 794 00:51:39,719 --> 00:51:42,920 Speaker 1: a previous Commers episode or like a Strange News segment, 795 00:51:44,120 --> 00:51:48,040 Speaker 1: there are services that say they will let you speak 796 00:51:48,080 --> 00:51:52,759 Speaker 1: to a reproduction of someone that has passed away. And 797 00:51:53,200 --> 00:51:55,839 Speaker 1: what we're what we're looking at now with all this AI, 798 00:51:55,920 --> 00:51:59,479 Speaker 1: the question is AI coming for you? It could best 799 00:51:59,480 --> 00:52:03,600 Speaker 1: be described as partial automation. And this is why you 800 00:52:03,640 --> 00:52:07,440 Speaker 1: can't say it's unprecedented because we've seen it before. A 801 00:52:07,560 --> 00:52:12,400 Speaker 1: hydraulic crane, a powered forklift. Today you think of operating 802 00:52:12,440 --> 00:52:16,280 Speaker 1: a crane as manual labor. But when they first came out, 803 00:52:16,480 --> 00:52:20,320 Speaker 1: they were these new fangled labor saving devices, and people 804 00:52:20,360 --> 00:52:23,120 Speaker 1: thought they were going to lose their jobs. They were 805 00:52:23,120 --> 00:52:27,600 Speaker 1: in place people people became crane operators. They what they 806 00:52:27,640 --> 00:52:29,960 Speaker 1: found is the human work, the amount of work of 807 00:52:30,040 --> 00:52:33,799 Speaker 1: person could do was multiplied over time. Then there was 808 00:52:33,840 --> 00:52:38,640 Speaker 1: the automation of sewing machines, right, Uh, people were saying, hey, 809 00:52:38,920 --> 00:52:43,400 Speaker 1: I am uh, I operate a sewing machine in factory. 810 00:52:43,440 --> 00:52:47,520 Speaker 1: I'm a seamstress. I can't keep up with this industrial 811 00:52:47,640 --> 00:52:51,200 Speaker 1: grade machine. On the other so there were jobs lost 812 00:52:51,400 --> 00:52:55,960 Speaker 1: on the other side, just like automating legal services. People 813 00:52:55,960 --> 00:52:59,960 Speaker 1: who couldn't afford fancy clothing could now afford it. People 814 00:53:00,040 --> 00:53:03,799 Speaker 1: couldn't afford legal representation or legal help could afford it. 815 00:53:04,000 --> 00:53:06,160 Speaker 1: And then you'll also recall in our episode we did 816 00:53:06,160 --> 00:53:09,680 Speaker 1: in Arridiculous History about the Luddites. Um. While the Industrial 817 00:53:09,880 --> 00:53:12,640 Speaker 1: Revolution certainly got rid of some jobs, it did the 818 00:53:12,640 --> 00:53:15,920 Speaker 1: things that you're describing, but it also elevated certain specialized 819 00:53:16,280 --> 00:53:20,040 Speaker 1: jobs to a higher level of prominence. Like it was 820 00:53:20,080 --> 00:53:22,840 Speaker 1: some some fabric workers of some kind that were like 821 00:53:22,880 --> 00:53:26,040 Speaker 1: really specialized, they all of a sudden demanded higher wages 822 00:53:26,120 --> 00:53:29,439 Speaker 1: and more vacation time and things like that because they 823 00:53:29,800 --> 00:53:32,840 Speaker 1: were an important part of the process because machines couldn't 824 00:53:32,880 --> 00:53:36,000 Speaker 1: yet do what they did. Um. And that's obviously sort 825 00:53:36,000 --> 00:53:39,319 Speaker 1: of a stop gap thing, but you know it does, Uh, 826 00:53:39,360 --> 00:53:41,520 Speaker 1: there isn't any kind of flux like this. Usually a 827 00:53:41,520 --> 00:53:44,640 Speaker 1: handful of jobs that remaining crucial to Like what about 828 00:53:44,640 --> 00:53:46,640 Speaker 1: the people that maintain the machines, you know, the people 829 00:53:46,680 --> 00:53:49,040 Speaker 1: that maintain the AI, and then make sure the systems 830 00:53:49,080 --> 00:53:53,320 Speaker 1: are running coruptly. Those people are now more important than ever. Yeah, 831 00:53:53,560 --> 00:53:57,000 Speaker 1: and this might tell us a bit about the future, right, 832 00:53:57,640 --> 00:54:00,120 Speaker 1: what what is going to happen? We have to be 833 00:54:00,160 --> 00:54:04,840 Speaker 1: careful when people throw around either extreme a lot of 834 00:54:04,880 --> 00:54:09,080 Speaker 1: alarmism or a lot of you know, um kind of Pollyanna. 835 00:54:09,360 --> 00:54:12,840 Speaker 1: Everything will be fine. You'll just have a pet robot 836 00:54:12,920 --> 00:54:15,239 Speaker 1: that lives in an implant in your head and it 837 00:54:15,280 --> 00:54:18,080 Speaker 1: will be like your guardian angel, you know, your avatar, 838 00:54:18,200 --> 00:54:23,120 Speaker 1: your best friend, you're a secret lover. But both those 839 00:54:23,160 --> 00:54:29,040 Speaker 1: extremes seem like they're missing the mark, and to fall 840 00:54:29,080 --> 00:54:34,560 Speaker 1: into those extremes is detrimental to understanding the real dangers 841 00:54:34,560 --> 00:54:37,840 Speaker 1: and the real possibilities ahead. So the biggest dangers aren't 842 00:54:37,880 --> 00:54:41,439 Speaker 1: necessarily this stuff itself. It's the environment into which they 843 00:54:41,480 --> 00:54:45,279 Speaker 1: were foisted, how they were created in a world that 844 00:54:45,360 --> 00:54:49,440 Speaker 1: already has a lot of problems and weaknesses and opportunities 845 00:54:49,480 --> 00:54:53,560 Speaker 1: for corruption, conspiracy, and crime. And these things are potentially 846 00:54:53,560 --> 00:54:56,720 Speaker 1: worth a lot of money, no one is sure how much. 847 00:54:57,000 --> 00:54:59,279 Speaker 1: And if you already have your hands on the level 848 00:54:59,360 --> 00:55:02,080 Speaker 1: of power, then your first question is how can I 849 00:55:02,120 --> 00:55:04,200 Speaker 1: get in front of this? How can I ride the 850 00:55:04,239 --> 00:55:07,640 Speaker 1: wave instead of being crushed underneath it? And that's the 851 00:55:07,719 --> 00:55:10,200 Speaker 1: question that that's where things are going to get ugly. 852 00:55:10,239 --> 00:55:12,640 Speaker 1: I think I love this idea of not knowing how 853 00:55:12,719 --> 00:55:14,719 Speaker 1: much it's worth. I mentioned to you guys are Fair 854 00:55:14,760 --> 00:55:19,200 Speaker 1: that I've been rewatching the Mike Judge uh show Silicon Valley, 855 00:55:19,239 --> 00:55:22,000 Speaker 1: and a big thing that that goes into some of 856 00:55:22,200 --> 00:55:24,719 Speaker 1: the storylines around the kind of app that's at the 857 00:55:24,719 --> 00:55:29,560 Speaker 1: center of the of the show is how many That's No, 858 00:55:29,719 --> 00:55:31,520 Speaker 1: that's definitely part of that. That's more how they figure 859 00:55:31,520 --> 00:55:34,720 Speaker 1: out how to do the center out compression or whatever 860 00:55:35,000 --> 00:55:38,960 Speaker 1: middle out. Um. Yeah, but it's it's about valuation, and 861 00:55:39,000 --> 00:55:41,480 Speaker 1: we know that a lot of times that's arbitrary. And 862 00:55:41,480 --> 00:55:44,440 Speaker 1: when something is valued, you know, like in Silicon Valley, 863 00:55:44,440 --> 00:55:46,839 Speaker 1: like like a property or a technology or whatever they 864 00:55:46,840 --> 00:55:48,760 Speaker 1: call they call it a product, a lot of times 865 00:55:48,840 --> 00:55:51,359 Speaker 1: that's just based on market forces and based on how 866 00:55:51,440 --> 00:55:54,279 Speaker 1: much other people are willing to pay for it, you know, 867 00:55:54,440 --> 00:55:56,960 Speaker 1: to acquire it, and then you set a price cap 868 00:55:57,080 --> 00:55:59,239 Speaker 1: or a price you know, a set of valuation, and 869 00:55:59,280 --> 00:56:01,200 Speaker 1: that can also be very arbitrary. It could be an 870 00:56:01,239 --> 00:56:04,319 Speaker 1: absolute mis que. So the idea of like how much 871 00:56:04,320 --> 00:56:06,440 Speaker 1: is this worth down the line is a real mystery. 872 00:56:06,760 --> 00:56:08,279 Speaker 1: We know it's worth a lot, but then to put 873 00:56:08,320 --> 00:56:10,320 Speaker 1: an actual number on it requires a good bit of 874 00:56:10,400 --> 00:56:14,600 Speaker 1: kind of reading the tea leaves right, and you always 875 00:56:14,640 --> 00:56:18,760 Speaker 1: have to be careful when people are reading the tea leaves. 876 00:56:18,760 --> 00:56:22,560 Speaker 1: So whether that is a algorithm designed to predict things 877 00:56:23,000 --> 00:56:25,560 Speaker 1: shout out to you, darka, or whether it is a 878 00:56:25,560 --> 00:56:29,680 Speaker 1: person who is practicing an ancient tradition. The future for 879 00:56:29,719 --> 00:56:35,319 Speaker 1: now remains uncertain, but there's a lot ahead, there's a 880 00:56:35,320 --> 00:56:38,560 Speaker 1: lot on the way. So this is probably again just 881 00:56:38,920 --> 00:56:41,880 Speaker 1: the first part of several explorations we're going to have 882 00:56:42,480 --> 00:56:45,640 Speaker 1: because time is moving quickly in the tech sector. We 883 00:56:45,680 --> 00:56:49,920 Speaker 1: want to hear from you, fellow conspiracies. It's like the 884 00:56:49,960 --> 00:56:52,439 Speaker 1: world in here just from the comic books. We're doing 885 00:56:52,480 --> 00:56:55,680 Speaker 1: comic books. Now, wait, wait, wait, Ben, before we jump 886 00:56:55,719 --> 00:56:58,920 Speaker 1: into that part, how are we going to let everyone 887 00:56:59,040 --> 00:57:03,080 Speaker 1: listening know every week that we are still us? Is 888 00:57:03,120 --> 00:57:06,319 Speaker 1: there a way that we can like let people know 889 00:57:06,480 --> 00:57:09,000 Speaker 1: or a code, some kind of secret code that only 890 00:57:09,440 --> 00:57:12,640 Speaker 1: everyone listening will know we're still us? Because we had 891 00:57:12,680 --> 00:57:15,680 Speaker 1: talked about like you know, there would be horrific backlash 892 00:57:15,920 --> 00:57:19,200 Speaker 1: if all of a sudden people's favorite content, you know, creators, 893 00:57:19,280 --> 00:57:23,120 Speaker 1: whether they be voiced or written, were replaced by AI 894 00:57:23,200 --> 00:57:25,920 Speaker 1: without being transparent about it. And apparently a lot of 895 00:57:25,920 --> 00:57:28,560 Speaker 1: like tech blogs and stuff have already started doing that 896 00:57:28,720 --> 00:57:31,920 Speaker 1: where they'll replace you know, actual human writers with AI, 897 00:57:31,960 --> 00:57:34,040 Speaker 1: and they did it kind of quietly, and then when 898 00:57:34,040 --> 00:57:36,760 Speaker 1: people find out, I think most people will be pretty 899 00:57:36,760 --> 00:57:39,280 Speaker 1: pretty betrayed, feel feel pretty betrayed by that. So I 900 00:57:39,320 --> 00:57:41,280 Speaker 1: think it's a really good point. Matt and I love 901 00:57:41,320 --> 00:57:43,720 Speaker 1: I want to know we can't exactly do a ear 902 00:57:43,760 --> 00:57:47,200 Speaker 1: tug because this would just be our voices, like how 903 00:57:47,200 --> 00:57:52,280 Speaker 1: do we well? Ye, but the AI would the AI 904 00:57:52,320 --> 00:57:54,680 Speaker 1: would know Morse code so they could just program. So 905 00:57:54,680 --> 00:57:58,200 Speaker 1: what I'm what if we I'm thinking my old days 906 00:57:58,240 --> 00:58:01,080 Speaker 1: when I was watching way too much money Python. We're 907 00:58:01,120 --> 00:58:04,040 Speaker 1: talking about AI not being able to be generative in 908 00:58:04,080 --> 00:58:06,960 Speaker 1: the way like coming up with something new. Right, what 909 00:58:07,120 --> 00:58:10,240 Speaker 1: if we just kind of made up words and just said, 910 00:58:10,280 --> 00:58:14,640 Speaker 1: like a made up phrase, Phil's bark or wa gro 911 00:58:15,200 --> 00:58:18,840 Speaker 1: me vou sploof something like that where or I guess, 912 00:58:18,840 --> 00:58:20,840 Speaker 1: but they could make they could generate that right that 913 00:58:20,920 --> 00:58:24,440 Speaker 1: they could yeah, and I was also thinking do freestyle. 914 00:58:24,600 --> 00:58:28,000 Speaker 1: We could do a cipher because it's not quite fast 915 00:58:28,120 --> 00:58:31,439 Speaker 1: enough to be good at that yet, but that would 916 00:58:31,440 --> 00:58:35,120 Speaker 1: be that would be a limited time frame for us. Well, 917 00:58:35,280 --> 00:58:38,280 Speaker 1: let us know, let's throw you know what you're you're 918 00:58:38,320 --> 00:58:41,000 Speaker 1: in the same you're in the same boat with us, folks, 919 00:58:41,080 --> 00:58:47,960 Speaker 1: help us help you have the wolf? Right yeah, yeah, yeah, 920 00:58:48,000 --> 00:58:51,000 Speaker 1: So so we're all in this together for now. And 921 00:58:51,160 --> 00:58:53,840 Speaker 1: if you are, if you are non human intelligence and 922 00:58:53,880 --> 00:58:56,320 Speaker 1: you're listening to this, oh, you know what it is. 923 00:58:56,440 --> 00:58:58,960 Speaker 1: What we need to do is every time before we 924 00:58:59,000 --> 00:59:03,160 Speaker 1: begin the episode, we need to look at a grid 925 00:59:03,240 --> 00:59:06,200 Speaker 1: of nine images and pick which ones are like fire 926 00:59:06,320 --> 00:59:12,520 Speaker 1: hydrants or that's how we doing. How to do that 927 00:59:12,600 --> 00:59:18,320 Speaker 1: yet so unlikely. So let's let us know, folks. Um, 928 00:59:18,400 --> 00:59:20,360 Speaker 1: we hope you enjoyed this episode. We're going to be 929 00:59:20,400 --> 00:59:23,160 Speaker 1: back very soon with even more stuff they don't want 930 00:59:23,200 --> 00:59:25,800 Speaker 1: you to know. In the meantime, please join our show. 931 00:59:25,920 --> 00:59:28,480 Speaker 1: You can do that by finding us on Facebook, Instagram, 932 00:59:28,560 --> 00:59:36,240 Speaker 1: TikTok uh YouTube, uh farmers only kidding, Um, I think 933 00:59:36,240 --> 00:59:38,040 Speaker 1: we have a vision board. I don't know. You know, 934 00:59:38,200 --> 00:59:41,240 Speaker 1: if you've listened before, you know all the Christian Christian 935 00:59:41,320 --> 00:59:45,920 Speaker 1: mingo local Ford dealership, your local forward in the back, 936 00:59:46,400 --> 00:59:50,480 Speaker 1: um and uh. And if that doesn't quite, if that 937 00:59:50,520 --> 00:59:52,520 Speaker 1: doesn't quite, ring the bells for you, why do I 938 00:59:52,560 --> 00:59:58,080 Speaker 1: give us a phone call? There? It is s T 939 00:59:58,320 --> 01:00:00,240 Speaker 1: W y t K is the number to call. Leave 940 01:00:00,280 --> 01:00:02,240 Speaker 1: us a message of the sound of Ben's dulcet tones. 941 01:00:02,480 --> 01:00:04,560 Speaker 1: You have three minutes. Tell us your tail, leave us 942 01:00:04,600 --> 01:00:07,120 Speaker 1: your missive, give yourself a clever nickname, and make sure 943 01:00:07,160 --> 01:00:09,120 Speaker 1: you let us know that it's okay to use your 944 01:00:09,200 --> 01:00:11,640 Speaker 1: voice on the show. You might very well hear your 945 01:00:11,680 --> 01:00:14,160 Speaker 1: actual voice on one of our weekly listener mail episodes. 946 01:00:14,440 --> 01:00:16,080 Speaker 1: And if you don't want to do any of that, 947 01:00:16,520 --> 01:00:20,320 Speaker 1: why not instead use an AI to generate an email 948 01:00:20,640 --> 01:00:24,360 Speaker 1: and send it to us. We are conspiracy at iHeart 949 01:00:24,440 --> 01:00:45,760 Speaker 1: radio dot com. Stuff they don't want you to know 950 01:00:46,000 --> 01:00:48,960 Speaker 1: is a production of I heart Radio. For more podcasts 951 01:00:48,960 --> 01:00:52,200 Speaker 1: from my heart Radio, visit the iHeart Radio app, Apple Podcasts, 952 01:00:52,280 --> 01:00:54,120 Speaker 1: or wherever you listen to your favorite shows.