1 00:00:18,160 --> 00:00:21,000 Speaker 1: And we are back with another edition of the Federalist 2 00:00:21,079 --> 00:00:25,399 Radio Hour. I'm Matt Kittle, Senior Elections correspondent at the Federalist, 3 00:00:25,920 --> 00:00:30,760 and your experience sharpa on today's quest for Knowledge. As always, 4 00:00:30,760 --> 00:00:33,640 you can email the show at radio at the Federalist 5 00:00:33,719 --> 00:00:38,359 dot com, follow us on ex at FDRLST, make sure 6 00:00:38,399 --> 00:00:41,560 to subscribe wherever you download your podcast, and of course 7 00:00:41,640 --> 00:00:45,000 to the premium version of our website as well. Our 8 00:00:45,039 --> 00:00:49,640 guest today is Neil Chilson, former FTC Chief Technologist and 9 00:00:49,840 --> 00:00:55,600 currently head of aipolicy with the Abundance Institute. Where are 10 00:00:55,640 --> 00:01:02,520 we taking artificial intelligence? Where is artificial intelligence taking us? Well? 11 00:01:02,520 --> 00:01:05,560 We ask those questions and many more on this edition 12 00:01:05,640 --> 00:01:07,959 of the Federalist Radio Hour. Neil, thank you so much 13 00:01:08,000 --> 00:01:12,159 for joining us. It's great to be here speaking of here. 14 00:01:13,519 --> 00:01:17,400 Is this really you? Or is this AI? Or as 15 00:01:17,439 --> 00:01:23,079 they used to say when I was much younger dating 16 00:01:23,159 --> 00:01:27,439 myself here, is it live or is it memorys? We've 17 00:01:27,439 --> 00:01:31,760 come a long way since cassette tapes, certainly in America. 18 00:01:32,560 --> 00:01:35,519 I jest, of course, this is the real Neil Chilsen, 19 00:01:35,599 --> 00:01:41,400 but it does raise the question of just how much 20 00:01:42,040 --> 00:01:46,640 we are inundated with AI and the difficulty sometimes of 21 00:01:47,239 --> 00:01:54,040 telling artificial intelligence from reality. I think this thing is 22 00:01:54,200 --> 00:01:57,879 only going to intensify, of course, as we move forward. 23 00:01:58,319 --> 00:02:00,400 Where are we going with all of this? 24 00:02:01,840 --> 00:02:04,439 Speaker 2: Well, as much as my wife and my kids might 25 00:02:04,480 --> 00:02:09,280 appreciate an AI upgraded dad and husband, they're stuck with me, 26 00:02:09,360 --> 00:02:10,840 and so are you guys, the real me. 27 00:02:13,080 --> 00:02:15,360 Speaker 3: But you know that's I say that sort of jokingly. 28 00:02:15,439 --> 00:02:19,360 Speaker 2: But where we are right now with AI is continuing 29 00:02:19,439 --> 00:02:25,599 a long trend of adding new capabilities to computers. And 30 00:02:26,080 --> 00:02:30,520 they're surprising in this case, and they're surprising in some ways. 31 00:02:31,159 --> 00:02:34,639 But the history of artificial intelligence, a term that was 32 00:02:34,680 --> 00:02:38,879 coined in the nineteen fifties, has been exactly this, surprising 33 00:02:38,919 --> 00:02:43,800 new things everybody gets excited about. We figure out, oh, 34 00:02:43,879 --> 00:02:48,039 this is not actually the same thing as human intelligence, 35 00:02:48,080 --> 00:02:52,840 not exactly. It's powerful, and then we sort of it 36 00:02:52,840 --> 00:02:55,439 gets adapted. It's in our phones, it's in our computers, 37 00:02:55,439 --> 00:02:58,120 and we move on. So, you know, artificial intelligence. The 38 00:02:58,159 --> 00:03:01,439 cutting edge of artificial intelligence technology when I was first 39 00:03:01,439 --> 00:03:02,879 getting into computers in the. 40 00:03:03,000 --> 00:03:04,919 Speaker 3: Nineties was chess playing. 41 00:03:05,680 --> 00:03:09,240 Speaker 2: And now everybody's phone can play chess better than ninety 42 00:03:09,319 --> 00:03:11,520 nine point nine percent of humans on the planet, and 43 00:03:11,960 --> 00:03:16,360 we don't think of that as somehow you know, terminator 44 00:03:16,400 --> 00:03:18,280 style artificial intelligence. 45 00:03:18,280 --> 00:03:19,319 Speaker 3: And so so. 46 00:03:19,319 --> 00:03:22,800 Speaker 2: Where we are now is we have these really powerful 47 00:03:23,240 --> 00:03:27,199 tools called large language models that can be used for many, 48 00:03:27,240 --> 00:03:30,159 many different types of things. But one of the ways 49 00:03:30,199 --> 00:03:33,039 they're being used is in a sort of chatbot form 50 00:03:33,199 --> 00:03:37,599 where you can ask questions and get really comprehensive, detailed, 51 00:03:38,000 --> 00:03:44,319 sometimes made up answers, and that's that's really powerful. It 52 00:03:44,400 --> 00:03:49,080 teaches us that there's a lot to learn about humans 53 00:03:50,039 --> 00:03:53,520 that you can that you can collect and gather into 54 00:03:53,599 --> 00:03:57,199 computers in these in these formats and then query in 55 00:03:57,240 --> 00:04:00,639 a way that gets you know, very persuasive, very interesting, 56 00:04:01,719 --> 00:04:06,199 often very entertaining content, whether it be text, video, audio. 57 00:04:06,680 --> 00:04:11,360 I love making songs. Actually, my little girls love making 58 00:04:11,439 --> 00:04:14,240 up new songs using some of the apps out there 59 00:04:14,919 --> 00:04:17,040 where you can just type in a prompt and get like, 60 00:04:17,240 --> 00:04:19,560 you know, a song about unicorns or a song about 61 00:04:19,600 --> 00:04:23,959 you know, their kids club running around the neighborhoods as 62 00:04:24,279 --> 00:04:26,319 kids spies or something like that, and so like we 63 00:04:26,399 --> 00:04:29,199 love that stuff. And so yeah, there's a lot of entertainment. 64 00:04:29,240 --> 00:04:31,160 There's a lot of power in these tools, and there's 65 00:04:31,160 --> 00:04:34,079 some risks and people need to be aware of that, 66 00:04:34,120 --> 00:04:35,560 and policymakers do as well. 67 00:04:36,160 --> 00:04:39,000 Speaker 1: Some of the uses that you mentioned, you know, are 68 00:04:39,079 --> 00:04:42,839 fun to play around with. Certainly, like you said making 69 00:04:42,920 --> 00:04:47,160 up songs, I've seen that in play and the technology 70 00:04:47,279 --> 00:04:51,720 is really quite good and it takes you literally seconds 71 00:04:51,759 --> 00:04:55,240 to do what the Beatles spent, you know, the better 72 00:04:55,279 --> 00:04:58,480 part of a year doing to make Sergeant Pepper's Lonely 73 00:04:58,560 --> 00:05:01,079 Hearts Club band. I'm not sure saying that the artistry 74 00:05:01,279 --> 00:05:04,800 is the same, but I'm you know, the production value 75 00:05:04,920 --> 00:05:08,079 is certainly there. Yeah, the risk that you talk about 76 00:05:08,120 --> 00:05:14,480 in this arena copyright infringement, Where are we with that today? 77 00:05:14,560 --> 00:05:19,480 And where are we heading in terms of intellectual property? 78 00:05:20,120 --> 00:05:23,279 Speaker 2: So there's two concerns on the copyright front. One is 79 00:05:23,959 --> 00:05:26,759 on the training side, So when a company is building 80 00:05:26,800 --> 00:05:30,079 these models and they use a bunch of different content 81 00:05:30,199 --> 00:05:33,639 in order to train on, what are the legal restrictions 82 00:05:33,680 --> 00:05:36,240 on that? And then the other side of it is 83 00:05:36,279 --> 00:05:39,040 on the output side. So when a user types in 84 00:05:39,079 --> 00:05:41,839 a prompt and says, you know, give me a picture 85 00:05:41,839 --> 00:05:46,040 of Mickey Mouse, you know, battling the Marvel team or 86 00:05:46,079 --> 00:05:49,399 something like that, and the model puts out something that 87 00:05:49,519 --> 00:05:56,040 includes you know, maybe some sort of intellectual property protection 88 00:05:56,439 --> 00:05:57,600 protect and content. 89 00:05:58,519 --> 00:06:01,120 Speaker 3: Who's responsible for that? Is it the user, is it 90 00:06:01,240 --> 00:06:03,000 the model? And so on. 91 00:06:03,120 --> 00:06:05,639 Speaker 2: The first thing about like the sort of ingestion of 92 00:06:05,680 --> 00:06:08,000 this content, the use of this content. We've had some 93 00:06:08,079 --> 00:06:13,000 court decisions and there is a category of this that 94 00:06:13,680 --> 00:06:16,480 judges are thinking of as fair use right. And so 95 00:06:16,519 --> 00:06:19,639 you're training these models the way you might train you know, 96 00:06:19,759 --> 00:06:23,439 anybody reading a book, and so the content of the 97 00:06:23,439 --> 00:06:27,079 model or the content isn't copied into the model, It 98 00:06:27,160 --> 00:06:29,319 is used to train the model, and then the model 99 00:06:29,439 --> 00:06:32,720 understands that content in the context of you know, lots 100 00:06:32,759 --> 00:06:35,600 of other content. And so there are ways in which 101 00:06:35,600 --> 00:06:39,480 the courts are saying that that is fair use. There 102 00:06:39,519 --> 00:06:43,399 was an anthropic settlement in this space where the big 103 00:06:43,439 --> 00:06:47,040 problem the anthropic had was that they hadn't paid for 104 00:06:47,120 --> 00:06:49,600 the original content, right, and so they hadn't bought the 105 00:06:49,600 --> 00:06:53,439 books that they were scanning. They had just downloaded pirated 106 00:06:53,439 --> 00:06:55,199 copies of the book. And the court said, no, no, no, 107 00:06:55,240 --> 00:06:57,519 you can't do that. You need to buy the content. 108 00:06:57,920 --> 00:07:00,000 You need to buy you the copy of the content 109 00:07:00,040 --> 00:07:03,439 and use that to train. You can't just use pirated content. 110 00:07:04,120 --> 00:07:07,560 But overall, that doesn't that doesn't say that much about 111 00:07:08,439 --> 00:07:10,360 you know, I think that still leaves the door open 112 00:07:10,399 --> 00:07:12,480 for companies to train on a lot of this data, 113 00:07:12,879 --> 00:07:15,319 which I think is probably the right decision. I think 114 00:07:15,360 --> 00:07:19,040 it is very analogous to training to reading a book. 115 00:07:20,639 --> 00:07:23,800 And therefore, you know, we don't have copyright that says 116 00:07:23,800 --> 00:07:26,959 you can't learn from a book without permission from the 117 00:07:27,000 --> 00:07:30,639 copyright holder, and so on the other side, we don't know. 118 00:07:30,720 --> 00:07:34,439 We don't know yet. Those cases are still ongoing. We 119 00:07:34,519 --> 00:07:38,439 don't know how courts are going to partition liability for 120 00:07:39,000 --> 00:07:44,160 copyright infringement between the user who writes the prompt and 121 00:07:44,319 --> 00:07:47,639 the company who is running the model or trained the model. 122 00:07:48,480 --> 00:07:51,800 There's some really complicated questions there about what is infringement 123 00:07:52,439 --> 00:07:55,519 even on that side, but then splitting it up between 124 00:07:55,560 --> 00:07:57,959 the user who's asking for something and the model who's 125 00:07:57,959 --> 00:08:01,240 generating it, it gets pretty complicated there. You know, we 126 00:08:01,279 --> 00:08:05,279 don't we don't sue pencil companies because people draw images 127 00:08:05,319 --> 00:08:09,120 of of you know, Mickey Mouse, sure with using a pencil, 128 00:08:09,399 --> 00:08:10,319 and that wouldn't. 129 00:08:09,959 --> 00:08:10,639 Speaker 3: Make a ton of sense. 130 00:08:10,680 --> 00:08:12,680 Speaker 2: And so we're just trying to figure out the right 131 00:08:12,720 --> 00:08:15,720 analogies here and what makes sense economically. 132 00:08:15,720 --> 00:08:19,959 Speaker 1: Still, that's very complicated in the same legal neighborhood. What 133 00:08:20,079 --> 00:08:25,160 about reputational damage. We've already seen some cases on that front. 134 00:08:25,240 --> 00:08:30,639 And listen, there are AI companies you know that are 135 00:08:31,040 --> 00:08:35,679 I think very responsible AI companies. They're doing what they 136 00:08:35,759 --> 00:08:39,120 can and they're investing a lot of money to make 137 00:08:39,159 --> 00:08:42,960 sure that these systems are working right and they don't 138 00:08:43,000 --> 00:08:49,039 cause damage. But sometimes they do reputational damage. What about 139 00:08:49,080 --> 00:08:54,120 the liability issue on that front? Where are the courts 140 00:08:54,279 --> 00:08:55,480 on that particular matter? 141 00:08:56,080 --> 00:08:59,559 Speaker 2: So the big, the big challenge. The courts have looked 142 00:08:59,600 --> 00:09:02,159 at this a couple of ways. There's a bunch of 143 00:09:02,240 --> 00:09:07,240 challenges when you're talking about defamation or libel. The biggest 144 00:09:07,320 --> 00:09:10,759 challenge is that often this involves public figures, and public 145 00:09:10,799 --> 00:09:15,000 figure liability requires showing that there was a sort of 146 00:09:15,039 --> 00:09:16,360 malicious intent. 147 00:09:17,919 --> 00:09:18,679 Speaker 3: By the party. 148 00:09:19,080 --> 00:09:21,600 Speaker 2: And the question again, the same question comes up here 149 00:09:21,639 --> 00:09:24,039 that comes up in the generation who is the party 150 00:09:24,360 --> 00:09:26,399 at fault here? Is it the person who typed the 151 00:09:26,440 --> 00:09:29,360 prompt who said, like, tell me everything you know about 152 00:09:29,399 --> 00:09:33,279 you know X public figure? Or is it the model generation? 153 00:09:34,679 --> 00:09:38,399 And then on top of that, unless it's made public, 154 00:09:39,519 --> 00:09:43,720 this type of generation by itself is not. I don't 155 00:09:43,720 --> 00:09:48,519 think it's usually subject to defamation law. So the question 156 00:09:48,720 --> 00:09:51,639 is if I generate, If I type a prompt into 157 00:09:51,720 --> 00:09:56,679 chat GPT and I get inaccurate information back, but then 158 00:09:56,840 --> 00:09:59,759 I publish it on my social media website or in 159 00:09:59,799 --> 00:10:03,799 a newspaper article or something, it's the publication of that 160 00:10:03,799 --> 00:10:05,879 that is the real problem. It's not the fact that 161 00:10:06,639 --> 00:10:09,360 the chat GPT came up with it, because nobody sees 162 00:10:09,399 --> 00:10:11,120 that except the person who asks for it. And so 163 00:10:11,600 --> 00:10:15,399 I do think that these defamation cases against the models 164 00:10:15,440 --> 00:10:18,200 are going to be complicated by that factor. I think 165 00:10:18,240 --> 00:10:20,440 that it's difficult to say that the model is that 166 00:10:20,600 --> 00:10:24,679 fault for the distribution of a falsehood when the model 167 00:10:25,200 --> 00:10:28,320 just generates content and then the person has to make 168 00:10:28,320 --> 00:10:29,120 a choice to then. 169 00:10:29,039 --> 00:10:32,279 Speaker 3: Put it out into the world. And so I think 170 00:10:32,279 --> 00:10:34,080 that's a complicating factor. And I think that. 171 00:10:34,000 --> 00:10:37,240 Speaker 2: Means while the models are trying very hard to be 172 00:10:37,360 --> 00:10:40,759 accurate on this sort of stuff and users, it's still 173 00:10:40,799 --> 00:10:43,679 really on the users to verify that the content that 174 00:10:43,720 --> 00:10:46,759 they're posting, that they're taking from that and using in 175 00:10:46,799 --> 00:10:49,679 the real world is accurate. And I think that's where 176 00:10:49,720 --> 00:10:51,240 the liability probably should sit. 177 00:10:51,559 --> 00:10:54,240 Speaker 1: Yeah, tied end to that. We have some very interesting 178 00:10:54,440 --> 00:10:58,679 cases on free speech. We have some and AI. We 179 00:10:58,759 --> 00:11:07,480 have some some concerning moves by politicians, by lawmakers. I'm 180 00:11:07,519 --> 00:11:09,559 thinking of a guy who would like to be president 181 00:11:09,600 --> 00:11:11,840 of the United States in twenty twenty eight. There's no 182 00:11:11,879 --> 00:11:16,399 doubt about that. Gavin Newsom in California and the battles 183 00:11:16,480 --> 00:11:22,480 that have gone on there legally speaking in terms of 184 00:11:23,360 --> 00:11:29,759 content that is driven for political advertising political parody. Really, 185 00:11:30,480 --> 00:11:36,000 that's the interesting part about this, the brave new world 186 00:11:36,120 --> 00:11:41,279 of political advertising or political parody where, for instance, you 187 00:11:41,399 --> 00:11:47,000 have a famous AI video that came out last year 188 00:11:47,720 --> 00:11:52,559 that had Kamala Harris, then the Vice President of the 189 00:11:52,679 --> 00:11:56,440 United States, and a candidate, the candidate the Democrats candidate 190 00:11:56,440 --> 00:12:01,279 for president. The AI technology made her say some things 191 00:12:01,320 --> 00:12:05,600 that she certainly did not say. It was very amusing 192 00:12:05,879 --> 00:12:10,320 to about half of the country. Wasn't so amusing to 193 00:12:10,360 --> 00:12:13,759 the other half. So Gavin Newsom and crew in California 194 00:12:14,360 --> 00:12:18,360 took that and some other instances and used that as. 195 00:12:20,039 --> 00:12:24,399 Speaker 3: Kind of a red flag law for or a red flag. 196 00:12:24,080 --> 00:12:29,679 Speaker 1: Incidance for AI and communication and put limits on what 197 00:12:29,799 --> 00:12:32,399 you could do or penalties on what you could do 198 00:12:32,919 --> 00:12:36,200 if you did this. Kind of thing. The courts have 199 00:12:36,519 --> 00:12:39,279 entered into this case. Where does all of that stand today? 200 00:12:40,440 --> 00:12:44,159 Speaker 2: So yeah, So California and some other states have looked 201 00:12:44,240 --> 00:12:48,879 at how to govern the use of AI generated content 202 00:12:48,960 --> 00:12:53,000 in advertising, in political advertising in particular, and they faced 203 00:12:53,000 --> 00:12:59,159 some real constitutional challenges here. Political speeches right at the 204 00:12:59,200 --> 00:13:04,519 core of First Amendment rights. Lying in political speech is 205 00:13:04,559 --> 00:13:11,960 not policed by the courts as because they often say, essentially, 206 00:13:12,559 --> 00:13:14,960 now you might be able to bring a defamation case possibly, 207 00:13:15,000 --> 00:13:18,759 but again we're talking about public figures, and even in 208 00:13:18,759 --> 00:13:21,919 the political context that gets even harder. Courts tend to 209 00:13:22,000 --> 00:13:25,159 really say, like, if you're going to do political ads, 210 00:13:25,159 --> 00:13:28,120 it's going to be up to the voters to decide 211 00:13:28,279 --> 00:13:33,200 who's telling the truth. And and AI doesn't really change 212 00:13:33,240 --> 00:13:36,039 that that much. It's always been easy to create false 213 00:13:36,120 --> 00:13:40,120 content right now. What you can do maybe with AI 214 00:13:40,399 --> 00:13:42,919 is the types of deep fakes that you're talking about, 215 00:13:43,000 --> 00:13:46,679 the types of putting words in somebody's mouth in a 216 00:13:46,840 --> 00:13:48,279 very convincing way. 217 00:13:48,879 --> 00:13:52,039 Speaker 3: I think deceptive content. 218 00:13:51,759 --> 00:13:55,039 Speaker 2: Like that is still it's right for you know, competitors 219 00:13:55,080 --> 00:13:58,799 to call it out. I think that companies are trying 220 00:13:58,799 --> 00:14:02,279 to figure out how to balance at but political parity 221 00:14:02,519 --> 00:14:06,559 is highly protected free speech, and I think any type 222 00:14:06,600 --> 00:14:10,440 of you know, government thumb on the scale about what 223 00:14:10,519 --> 00:14:13,879 people can and can't say is just ripe for abuse. 224 00:14:14,000 --> 00:14:16,639 You'll end up getting police, you know, from one political 225 00:14:16,679 --> 00:14:20,360 perspective or another. One party will will bear the brunt 226 00:14:20,360 --> 00:14:22,399 of this more than the other. And I just think 227 00:14:22,440 --> 00:14:26,000 that it's a super risky road to walk down. The 228 00:14:26,039 --> 00:14:30,159 better the better options here are more speech, not censoring 229 00:14:30,200 --> 00:14:32,600 the ability to create content in the first place. 230 00:14:33,320 --> 00:14:35,840 Speaker 3: And I think that some of the companies. 231 00:14:35,399 --> 00:14:39,639 Speaker 2: Have had some early experiences in trying to shape the 232 00:14:39,679 --> 00:14:43,519 content that was coming out of their models, and I 233 00:14:43,600 --> 00:14:46,879 think they've dialed that back to say, like, hey, we're 234 00:14:46,919 --> 00:14:50,600 going to largely try to lean on the side of 235 00:14:51,080 --> 00:14:56,200 generating what the user is asking for and protects protect 236 00:14:56,279 --> 00:14:59,159 speech there as long as they're not generating illegal content. 237 00:14:59,240 --> 00:15:01,759 I think that's the better that's the better frame. 238 00:15:02,120 --> 00:15:04,840 Speaker 3: I hope that more and more companies move towards that direction. 239 00:15:08,200 --> 00:15:11,639 Speaker 4: Did a single company save the stock market from crashing 240 00:15:11,679 --> 00:15:14,519 into a recession? The Watchdot on Wall Street podcast with 241 00:15:14,639 --> 00:15:17,759 Chris Markowski. Every day, Chris helps unpack the connection between 242 00:15:17,840 --> 00:15:20,279 politics and the economy and how it affects your wallet. 243 00:15:20,399 --> 00:15:24,320 Tech powerhouse in Nvidia's earnings report did not disappoint, But 244 00:15:24,399 --> 00:15:26,840 what does that tell you about the value of AI? 245 00:15:27,240 --> 00:15:30,360 This cannot save the market forever. Whether it's happening in 246 00:15:30,440 --> 00:15:32,840 DC or down on Wall Street, it's affecting you financially. 247 00:15:32,919 --> 00:15:33,440 Speaker 3: Be informed. 248 00:15:33,519 --> 00:15:35,360 Speaker 4: Check out the watch Dot on Wall Street podcast with 249 00:15:35,440 --> 00:15:38,720 Chris Markowski on Apple, Spotify or wherever you get your podcasts. 250 00:15:43,159 --> 00:15:48,159 Speaker 1: Well, let's face it, if the courts really did punish 251 00:15:48,960 --> 00:15:54,799 politicians for lying, our correctional system would be just overbroad. 252 00:15:55,840 --> 00:16:01,519 Speaker 2: Yeah, political speech is full of half to true misframeans 253 00:16:02,200 --> 00:16:04,679 and courts just don't want to get into that. I 254 00:16:04,720 --> 00:16:09,039 think that's that's just fraught territory, and I think I 255 00:16:09,039 --> 00:16:11,039 think it's right to leave it to the people to 256 00:16:11,080 --> 00:16:12,759 make the decisions about who they trust. 257 00:16:13,080 --> 00:16:17,759 Speaker 1: Yeah, that is America, and that's the bottom line. The information, 258 00:16:17,919 --> 00:16:22,480 of course is it's a different age for all of that. 259 00:16:22,679 --> 00:16:26,600 But we've had the same issues and the same problems 260 00:16:26,919 --> 00:16:28,879 over the two hundred and fifty years of this one. 261 00:16:30,240 --> 00:16:33,840 Let's turn our attention to the emotional aspects of AI. 262 00:16:34,480 --> 00:16:38,679 Harvard Business School has an interesting piece up came out recently. 263 00:16:38,799 --> 00:16:43,879 Feeling lonely an attentive listener is an AI prompt away, 264 00:16:44,399 --> 00:16:49,639 and it delves into the brave new world of companionship 265 00:16:50,559 --> 00:16:55,440 with AI. There are well, I think what we're finding out, Neil, 266 00:16:55,720 --> 00:16:59,159 over the last year or so is that there are 267 00:16:59,519 --> 00:17:02,919 a lot of lonely people out there, and they have 268 00:17:03,039 --> 00:17:09,000 turned AI to solve that loneliness problem. Where does all 269 00:17:09,079 --> 00:17:11,920 of that stand today, and where do you think that's 270 00:17:11,960 --> 00:17:12,519 all going? 271 00:17:13,839 --> 00:17:15,759 Speaker 2: Yeah, I mean, I think you're totally right. I mean, 272 00:17:15,799 --> 00:17:19,759 we do have an epidemic of sort of people isolating 273 00:17:19,759 --> 00:17:23,599 themselves from others, and I think this is an outgrowth of, 274 00:17:23,799 --> 00:17:25,799 you know, some really bad policies that we had around 275 00:17:25,839 --> 00:17:33,119 the COVID pandemic, as well as just some general fracturing 276 00:17:33,200 --> 00:17:40,160 of American you know, socialization institutions that we've you know, 277 00:17:40,319 --> 00:17:42,559 historically relied on to bring us together with people. 278 00:17:43,920 --> 00:17:45,000 Speaker 3: And so I. 279 00:17:44,920 --> 00:17:49,799 Speaker 2: Think that trying to fill this gap with AI chatbots 280 00:17:49,960 --> 00:17:53,799 is at best a sort of temporary measure. 281 00:17:55,480 --> 00:17:56,079 Speaker 3: I think that. 282 00:17:57,680 --> 00:18:01,359 Speaker 2: I don't know how to exactly measure this. You know, 283 00:18:01,400 --> 00:18:05,119 these chatbots are general purpose. Most of them are general purpose. 284 00:18:05,160 --> 00:18:07,039 They're not aimed at this sort of function. Now, there 285 00:18:07,079 --> 00:18:10,359 are some companies who are offering specifically this type of function, 286 00:18:10,680 --> 00:18:12,960 but most of the chatbots that people are using our 287 00:18:13,079 --> 00:18:15,599 general purpose. People are using them for a wide range 288 00:18:15,599 --> 00:18:19,880 of things, from academic research to generating funny videos, and 289 00:18:19,960 --> 00:18:23,359 some people use them occasionally to talk to about like 290 00:18:23,680 --> 00:18:28,359 personal problems or you know, relationship problems or things like that. 291 00:18:28,839 --> 00:18:29,640 Speaker 3: I think some of that. 292 00:18:29,559 --> 00:18:34,079 Speaker 2: Can be very useful and very helpful, but certainly would 293 00:18:34,079 --> 00:18:36,799 want to keep an eye out on people replacing time 294 00:18:36,839 --> 00:18:43,039 with other humans with these these chatbots. It's just it's 295 00:18:43,119 --> 00:18:46,680 just not it's not the same obviously, and it doesn't 296 00:18:46,720 --> 00:18:51,680 create the same types of deep connections with community that 297 00:18:51,759 --> 00:18:54,480 I think are essential to human flourishing. 298 00:18:54,559 --> 00:18:54,880 Speaker 3: And so. 299 00:18:56,519 --> 00:18:59,799 Speaker 2: My hope would be that these systems, as they build out, 300 00:19:00,200 --> 00:19:04,839 are aimed at improving people's ability to engage with other people, 301 00:19:05,279 --> 00:19:07,680 because there certainly are people who are not as practice 302 00:19:07,720 --> 00:19:11,400 at that who maybe spent you know, two years, especially 303 00:19:11,400 --> 00:19:13,200 when we get into the kids sector, who spent maybe 304 00:19:13,240 --> 00:19:17,240 two years like doing zoomed school and need to get 305 00:19:17,279 --> 00:19:21,240 back into the swing of, you know, talking to other people. 306 00:19:21,279 --> 00:19:24,920 And I think these tools could can help give tips 307 00:19:24,920 --> 00:19:25,720 on that sort of thing. 308 00:19:26,000 --> 00:19:26,720 Speaker 3: But I hope they don't. 309 00:19:27,400 --> 00:19:29,799 Speaker 2: I hope people don't rely on them as a substitute 310 00:19:29,839 --> 00:19:32,960 for reaching out, being brave, talking to people they haven't 311 00:19:32,960 --> 00:19:36,359 talked to before, and getting to know people in their community. 312 00:19:36,920 --> 00:19:40,279 Speaker 1: We certainly have learned over the last few years just 313 00:19:40,400 --> 00:19:45,400 how dangerous, how much peril lockdown policies have put our 314 00:19:45,519 --> 00:19:49,960 children in especially, but society in general, when it comes 315 00:19:50,000 --> 00:19:55,160 to picking up with communication and relationships and all of 316 00:19:55,200 --> 00:19:58,640 those sorts of things. You know, Neil, though, on this topic, 317 00:19:58,799 --> 00:20:02,039 they always say the heart wants what the heart wants, 318 00:20:02,480 --> 00:20:05,599 The market wants what the market wants. And there are 319 00:20:05,680 --> 00:20:09,519 some strange areas in the marketplace for AI. Not to 320 00:20:09,559 --> 00:20:14,160 say that, you know, there isn't a market for it, 321 00:20:14,279 --> 00:20:17,000 but the question maybe should there be a market for it. 322 00:20:17,319 --> 00:20:22,400 One of those areas is connecting with the dead, and 323 00:20:22,440 --> 00:20:25,640 this has become an interesting subject of late. But the 324 00:20:25,720 --> 00:20:31,240 AI products that offer people the ability to chat with 325 00:20:31,720 --> 00:20:35,960 or hear the voice again of a loved one who's 326 00:20:36,039 --> 00:20:39,799 passed away, what about all of that? 327 00:20:41,559 --> 00:20:44,440 Speaker 2: I mean, grief is a grief is a crazy thing, right, 328 00:20:44,480 --> 00:20:47,039 and so I think that people try to deal with 329 00:20:47,119 --> 00:20:48,640 it in a lot of different ways, some of it 330 00:20:48,640 --> 00:20:50,519 healthy and some of it not healthy. 331 00:20:50,559 --> 00:20:52,279 Speaker 3: What I would be worried about. Here would be. 332 00:20:52,720 --> 00:20:56,680 Speaker 2: Apps that are you know, claiming some sort of therapeutic 333 00:20:57,279 --> 00:21:00,759 effect on the heart, like misclaiming that, fault claiming that, 334 00:21:01,039 --> 00:21:04,079 or falsely claiming that you know, they're going to provide 335 00:21:04,119 --> 00:21:07,480 some sort of resolution. I think people, like I said, 336 00:21:08,559 --> 00:21:12,000 people might use these as tools to engage with maybe 337 00:21:12,039 --> 00:21:14,920 the content that somebody left behind. And I think that 338 00:21:14,960 --> 00:21:19,160 could be interesting if done in a healthy way. Right, 339 00:21:19,400 --> 00:21:23,079 if you are able to look at like air quote 340 00:21:23,200 --> 00:21:26,960 talk to you know, the all the letters that your 341 00:21:27,079 --> 00:21:30,359 your father or your grandfather left behind, that might be 342 00:21:30,400 --> 00:21:33,160 a really engaging way to learn more about their life 343 00:21:33,599 --> 00:21:36,079 and to think about like what they meant to you. 344 00:21:36,519 --> 00:21:40,920 But I don't think there are healthy ways to do that, 345 00:21:40,960 --> 00:21:42,640 and there are unhealthy ways to do that, And I 346 00:21:42,640 --> 00:21:45,599 would be worried about, you know, apps that are claiming 347 00:21:45,640 --> 00:21:49,240 to provide some sort of therapeutic effect when they're not 348 00:21:49,279 --> 00:21:53,960 doing that, rather than maybe ones that are talking about 349 00:21:53,960 --> 00:21:57,559 how they can help you understand you know, your past 350 00:21:57,599 --> 00:21:59,720 and understand your connection to your family better. 351 00:22:00,079 --> 00:22:02,039 Speaker 3: Some of that could be very interesting and positive. 352 00:22:02,559 --> 00:22:05,400 Speaker 1: I can't wait to get an AI letter the kinds 353 00:22:05,440 --> 00:22:08,599 of letters I get from some members of family and friends, 354 00:22:08,640 --> 00:22:13,519 particularly on the older spectrum of family and friends, the 355 00:22:13,559 --> 00:22:18,880 old merry medical Christmas letters where they tell me about 356 00:22:18,920 --> 00:22:22,359 all of their health ailments in the space of two 357 00:22:23,079 --> 00:22:24,160 very long pages. 358 00:22:25,319 --> 00:22:27,759 Speaker 3: I suspect, I suspect, I suspect. 359 00:22:27,759 --> 00:22:31,279 Speaker 2: If you get Christmas cards this you know, this season, 360 00:22:31,440 --> 00:22:33,400 there's there's a good chance a chunk of them were 361 00:22:34,400 --> 00:22:37,359 helped in being written by AI. But can I give 362 00:22:37,400 --> 00:22:41,759 you one example. My in laws recently came across a 363 00:22:41,799 --> 00:22:47,000 handwritten letter from you know, my father in law's dad. 364 00:22:47,799 --> 00:22:51,920 It was a very it was a terrific piece consoling 365 00:22:51,960 --> 00:22:54,839 somebody else in the loss of their their spouse. But 366 00:22:54,960 --> 00:22:57,480 some of it was actually quite difficult to read because 367 00:22:57,480 --> 00:23:01,079 it was handwritten. I used, you know, I scanned it 368 00:23:01,119 --> 00:23:04,400 into chat GPT really quickly and asked it if it 369 00:23:04,400 --> 00:23:08,880 could create a transcript of it. And that was extremely helpful. 370 00:23:08,880 --> 00:23:11,440 Actually it decrypted some of the words that we had 371 00:23:11,480 --> 00:23:13,799 we were really struggling with and uh, and we were 372 00:23:13,839 --> 00:23:16,160 able to enjoy this this letter in a way that 373 00:23:16,240 --> 00:23:18,880 I don't think we would have struggled more to figure 374 00:23:18,920 --> 00:23:21,680 out what the handwriting said had we had we not 375 00:23:21,799 --> 00:23:22,079 use that. 376 00:23:22,200 --> 00:23:26,440 Speaker 3: So there are really positive uses to this technology. But there. 377 00:23:26,480 --> 00:23:30,799 Speaker 2: You know, people shouldn't be substituting, you know, moving through 378 00:23:30,839 --> 00:23:33,720 their grief process by you know, pretending that they could 379 00:23:33,720 --> 00:23:36,759 still talk to their their their past relatives. 380 00:23:36,759 --> 00:23:39,200 Speaker 1: I think, yeah, I don't I don't doubt that that 381 00:23:39,839 --> 00:23:43,799 kind of positive application and the implications of that. You know, 382 00:23:43,839 --> 00:23:48,240 you talk about, let's face it, cursive writing as a 383 00:23:48,279 --> 00:23:52,279 lost art in America anyway, But there's some beautiful cursive writing, 384 00:23:52,759 --> 00:23:55,839 uh in you know the history of family letters and 385 00:23:55,880 --> 00:23:59,839 those sorts of things. But I think about those, you know, 386 00:24:00,160 --> 00:24:04,160 as a history buff those important historical documents, you know, 387 00:24:04,240 --> 00:24:07,839 the letters from Lincoln that I can't always you know, 388 00:24:07,920 --> 00:24:11,119 I'm looking at the penmanship, and he's got pretty good penmanship, 389 00:24:11,200 --> 00:24:13,559 but there are areas where I can't find it. I 390 00:24:13,559 --> 00:24:17,440 think that's interesting. That's a very interesting application. We're going 391 00:24:17,519 --> 00:24:21,480 to talk more about some of you know, those very 392 00:24:21,680 --> 00:24:25,160 useful applications for AI coming up. Our guest today in 393 00:24:25,240 --> 00:24:29,200 this edition of The Federalist Radio Hour Neil Chilson, former 394 00:24:29,279 --> 00:24:34,039 FDC Chief technologist and currently head of AI policy with 395 00:24:34,160 --> 00:24:38,240 the Abundance Institute. I want to get to the jobs 396 00:24:38,319 --> 00:24:42,519 question though, because that is a huge concern, a growing 397 00:24:42,640 --> 00:24:46,039 concern for a lot of Americans what is fact? What 398 00:24:46,200 --> 00:24:50,920 is fiction? About what AI is doing and is about 399 00:24:51,000 --> 00:24:52,559 to do to the job market. 400 00:24:53,920 --> 00:24:58,079 Speaker 2: Well, sorting out the facts can be challenging in sort 401 00:24:58,119 --> 00:25:00,680 of the effects of new technology on jobs. What we 402 00:25:00,759 --> 00:25:05,079 do know is that companies are spending a bunch of 403 00:25:05,160 --> 00:25:07,960 time on trying to figure. 404 00:25:07,599 --> 00:25:10,319 Speaker 3: Out how to use AI, these new AI. 405 00:25:10,160 --> 00:25:17,480 Speaker 2: Tools in their systems, but the actual implementation is actually 406 00:25:17,559 --> 00:25:20,519 still in the very early stages for almost all companies, 407 00:25:20,559 --> 00:25:23,079 and so while they're spending time and money on it, 408 00:25:23,759 --> 00:25:27,039 figuring out how to use it in their environments is 409 00:25:27,920 --> 00:25:31,240 less clear. I do think that there are lots of 410 00:25:31,279 --> 00:25:34,799 individuals who are figuring out how to use it, and 411 00:25:34,880 --> 00:25:38,279 that is moving faster because they can iterate within their company. 412 00:25:38,279 --> 00:25:40,440 They can try lots of different things. They can try 413 00:25:40,519 --> 00:25:42,920 to see how, hey, how does this help me write emails? 414 00:25:42,920 --> 00:25:45,119 How does it help me draft? How does it help 415 00:25:45,160 --> 00:25:47,039 me brainstorm? How does it help me take notes? 416 00:25:48,039 --> 00:25:49,359 Speaker 3: But we're still not. 417 00:25:49,400 --> 00:25:55,400 Speaker 2: Seeing like a huge productivity boost yet that would suggest 418 00:25:55,519 --> 00:25:58,960 that this is sort of replacing a bunch of time 419 00:25:59,039 --> 00:26:02,079 that people are spent, or that it's enhancing and allowing 420 00:26:02,079 --> 00:26:03,440 them to move into other areas. 421 00:26:03,599 --> 00:26:04,119 Speaker 3: Not yet. 422 00:26:04,359 --> 00:26:06,359 Speaker 2: I think some of that's just because we're still at 423 00:26:06,359 --> 00:26:09,519 the very early stages of this stuff working its way 424 00:26:09,559 --> 00:26:14,119 into the job sector. I think, you know, we've heard 425 00:26:14,240 --> 00:26:19,000 some talk about how, you know, early career people are 426 00:26:20,240 --> 00:26:23,359 struggling to get jobs, and the finger is being pointed 427 00:26:23,400 --> 00:26:29,519 at AI. I think that it's it's more about uncertainty 428 00:26:30,079 --> 00:26:32,839 in the economy. And some of that uncertainty, for sure, 429 00:26:33,039 --> 00:26:35,759 is being driven by not knowing what jobs are going 430 00:26:35,799 --> 00:26:38,160 to be affected by AI in the future. And so 431 00:26:38,200 --> 00:26:40,039 I don't want to say that it's not about AI 432 00:26:40,079 --> 00:26:42,400 at all, but there are lots of other factors in 433 00:26:42,440 --> 00:26:48,200 the economy that suggest uncertainty, and so I think teasing 434 00:26:48,240 --> 00:26:50,119 that out is very complicated. We'll have to see a 435 00:26:50,160 --> 00:26:53,359 little bit more. One other area of research has been 436 00:26:53,400 --> 00:26:56,839 about the types of tasks that these large language models 437 00:26:56,839 --> 00:26:58,519 are good at and where they're not good. 438 00:26:58,519 --> 00:27:00,720 Speaker 3: And what we know is right now is that for. 439 00:27:00,720 --> 00:27:06,240 Speaker 2: Certain types of jobs, AI has this effect of leveling 440 00:27:06,400 --> 00:27:10,240 up quickly people who are earlier or less experienced, and 441 00:27:10,319 --> 00:27:14,039 so in like a call center job, for example, where 442 00:27:14,079 --> 00:27:17,640 you're offering you know, your troubleshooting for customers over and 443 00:27:17,680 --> 00:27:21,119 over and over, these tools can really make somebody who 444 00:27:21,200 --> 00:27:25,319 is new to this job perform at a very much 445 00:27:25,400 --> 00:27:28,839 higher level, much more quickly, But it doesn't help the 446 00:27:28,880 --> 00:27:32,680 people at the top of that experience trend very much. 447 00:27:32,759 --> 00:27:35,680 In those types of jobs and other types of jobs, 448 00:27:35,720 --> 00:27:41,799 say where you're running a scientific laboratory and you're using 449 00:27:41,960 --> 00:27:45,440 these types of models to help you brainstorm. There, it 450 00:27:45,519 --> 00:27:49,200 seems like it makes the highest performers perform even better, 451 00:27:49,599 --> 00:27:52,799 and so the distribution of effect is different. So there 452 00:27:53,200 --> 00:27:56,559 the highest performers get a huge boost and the lower 453 00:27:56,599 --> 00:28:00,359 performers don't, because the highest performers can tell, like sort 454 00:28:00,400 --> 00:28:04,519 of intuitively, which threads of this brainstorming process makes some 455 00:28:04,640 --> 00:28:06,640 sense and which ones don't, and so they get a 456 00:28:06,640 --> 00:28:09,759 productivity boost and the lower skilled people don't. And so 457 00:28:10,119 --> 00:28:12,920 I think it really is job dependent that's going to 458 00:28:12,920 --> 00:28:17,720 be the case. I think for general purpose technologies overall, 459 00:28:18,039 --> 00:28:20,920 we don't know all the applications of AI. We don't 460 00:28:20,920 --> 00:28:24,720 know exactly how it's going to help, but there's lots 461 00:28:24,759 --> 00:28:26,640 of different ways that people are figuring it out. 462 00:28:26,680 --> 00:28:28,200 Speaker 3: I actually saw the demo the other. 463 00:28:28,160 --> 00:28:32,039 Speaker 2: Day of how people are using AI and what they 464 00:28:32,079 --> 00:28:37,799 call augmented reality headsets on construction sites to be able 465 00:28:37,839 --> 00:28:41,359 to much more quickly and more safely figure out where 466 00:28:41,359 --> 00:28:45,000 they should put pieces, what's the next step that they 467 00:28:45,039 --> 00:28:48,640 need to take. And you can help people coordinate in 468 00:28:48,680 --> 00:28:51,599 a much more safe way using both AI and this 469 00:28:51,799 --> 00:28:55,160 VR together. But this is all still very early days. 470 00:28:55,160 --> 00:28:59,000 I don't think we really know the overall job impacts 471 00:28:59,039 --> 00:29:02,160 of this technology. What we do know is that intelligence, 472 00:29:03,440 --> 00:29:07,640 artificial intelligence is valuable because intelligence is valuable, and so 473 00:29:08,440 --> 00:29:12,359 to the extent that we can use AI to amplify 474 00:29:12,480 --> 00:29:15,440 our intellectual endeavors the way that we used you know, 475 00:29:15,480 --> 00:29:18,720 the steam engine and the combustion engine to amplify our 476 00:29:19,200 --> 00:29:24,480 our physical uh capabilities we had. There is huge potential 477 00:29:24,519 --> 00:29:27,480 here for a lot of productivity and that means that 478 00:29:27,559 --> 00:29:29,279 means change in the types of jobs. 479 00:29:30,640 --> 00:29:32,519 Speaker 3: And but we don't. 480 00:29:32,279 --> 00:29:34,400 Speaker 2: Really know how fast or how that's going to work 481 00:29:34,440 --> 00:29:36,680 out yet, so there's some uncertainty for sure. 482 00:29:37,160 --> 00:29:41,200 Speaker 1: You note that AI can sharpen skills of individuals. Can 483 00:29:41,200 --> 00:29:45,240 it steal the courage of members of Congress? Is that 484 00:29:45,359 --> 00:29:48,039 yet part of the technology suite? 485 00:29:48,839 --> 00:29:52,400 Speaker 2: Uh? Wow, I hadn't thought of that as an application. 486 00:29:53,279 --> 00:29:57,599 Somebody should train an app to do that? Uh, you know, 487 00:29:57,640 --> 00:29:59,599 train a model to do that. The question would be 488 00:29:59,640 --> 00:30:02,759 could we and get members of Congress to use it. 489 00:30:02,839 --> 00:30:07,680 I did hear that Josh Holly recently used chat GPT 490 00:30:07,960 --> 00:30:11,759 to explore some sixteen hundred's Puritan history and was quite 491 00:30:11,759 --> 00:30:14,400 impressed with the response that he got back. But I 492 00:30:14,440 --> 00:30:16,160 think that was a very early use for him. So 493 00:30:16,160 --> 00:30:18,279 I hope he digs in, and I hope member of 494 00:30:18,440 --> 00:30:21,400 members of Congress do as well. I think understanding how 495 00:30:21,440 --> 00:30:25,640 this technology works and doesn't work is really valuable, and 496 00:30:25,359 --> 00:30:28,200 it's it's not the type of technology that sits off 497 00:30:28,240 --> 00:30:30,000 somewhere else and you would have to like plan a 498 00:30:30,039 --> 00:30:32,440 trip to go do it. Anybody can try it out, 499 00:30:32,519 --> 00:30:36,920 see how it works, and I think that that experience 500 00:30:37,039 --> 00:30:43,319 is worth doing just to understand, you know, what exactly 501 00:30:43,359 --> 00:30:44,440 we're dealing with here. 502 00:30:45,079 --> 00:30:48,160 Speaker 1: Well, I mean, you know, it's always as a computer programmer, 503 00:30:48,200 --> 00:30:53,480 you know the model here, I guess the mantra more so, 504 00:30:53,559 --> 00:30:56,359 and that is garbage in, garbage out. It's what you 505 00:30:56,440 --> 00:30:59,440 put into the system, what you can expect to get 506 00:30:59,480 --> 00:31:01,640 out of it. And I think that pretty much described 507 00:31:01,680 --> 00:31:04,599 the sixteen nineteen projects, speaking of Puritan. 508 00:31:06,119 --> 00:31:07,400 Speaker 3: Travel, all of. 509 00:31:07,359 --> 00:31:10,279 Speaker 1: That sort of thing we've talked about. You know, some 510 00:31:10,359 --> 00:31:15,359 of the concerns obviously in this emerging technology that's been 511 00:31:15,400 --> 00:31:19,359 emerging by the way for the last seventy years, as 512 00:31:19,400 --> 00:31:23,759 you note, But there are some really powerful applications. I 513 00:31:23,759 --> 00:31:29,160 think about the healthcare arena, what about AI and healthcare? 514 00:31:29,359 --> 00:31:32,839 What about you know, what we're really seeing in terms 515 00:31:32,880 --> 00:31:38,359 of AI really driving positive change in different facets of 516 00:31:38,400 --> 00:31:38,960 our lives. 517 00:31:40,440 --> 00:31:43,039 Speaker 2: So I think the healthcare arena is a great example. 518 00:31:43,240 --> 00:31:46,079 The way that these tools work is they take large 519 00:31:46,519 --> 00:31:50,559 amounts of data that human as humans, we can't see 520 00:31:51,000 --> 00:31:54,519 complicated patterns in, and they help expose those patterns. And 521 00:31:54,599 --> 00:31:57,799 healthcare is full of this type of data where we 522 00:31:57,920 --> 00:32:00,720 have a lot of data about you know, we might 523 00:32:00,720 --> 00:32:05,839 have millions of CT scans of breast cancer, for example, 524 00:32:06,839 --> 00:32:10,279 but we still have a very manual process for identifying that. Well, 525 00:32:10,359 --> 00:32:14,160 these tools can can do that type of analysis faster 526 00:32:14,400 --> 00:32:17,720 and more accurately, and often they can identify, you know, 527 00:32:18,039 --> 00:32:22,759 risk factors earlier than doctors could, in particular in the 528 00:32:22,759 --> 00:32:26,000 breast cancer context. That's just one area. One of the 529 00:32:26,000 --> 00:32:29,079 other big applications it's possible here is you know, we 530 00:32:29,160 --> 00:32:33,720 treat and we research healthcare in this country and around 531 00:32:33,759 --> 00:32:38,480 the world, sort of ash in a very generic way. 532 00:32:38,519 --> 00:32:41,200 We sort of treat people as like an average human. 533 00:32:41,480 --> 00:32:44,640 But the truth is there's so much variation in the 534 00:32:44,720 --> 00:32:49,839 human body and in the human health health system. They're 535 00:32:49,960 --> 00:32:55,480 there that being able to diagnose at a much more 536 00:32:55,599 --> 00:32:58,440 personalized level what is going on in your body is 537 00:32:58,480 --> 00:33:01,160 something that AI is getting better and better at, and 538 00:33:01,200 --> 00:33:05,880 I think that that raises just huge potential benefits for 539 00:33:06,079 --> 00:33:10,079 customized medicine that is directed exactly at the problems that 540 00:33:10,160 --> 00:33:13,400 you have and the cluster of problems that you or 541 00:33:13,440 --> 00:33:16,400 your family member might have that won't look like the 542 00:33:16,519 --> 00:33:17,480 vast majority of. 543 00:33:17,440 --> 00:33:18,759 Speaker 3: Issues that other people have. 544 00:33:18,880 --> 00:33:22,799 Speaker 2: And so we've already seen that in what are called 545 00:33:22,880 --> 00:33:24,079 like orphan diseases. 546 00:33:24,200 --> 00:33:25,720 Speaker 3: These are diseases. 547 00:33:25,200 --> 00:33:28,720 Speaker 2: That might affect, you know, a thousand people across the 548 00:33:28,720 --> 00:33:31,799 world at any time, and there are millions of people 549 00:33:31,799 --> 00:33:35,759 who are suffering from these types of diseases that are 550 00:33:35,799 --> 00:33:39,960 so small and so targeted that it's very hard just 551 00:33:40,000 --> 00:33:43,799 from a business perspective or an economic incentive perspective, to 552 00:33:43,920 --> 00:33:46,519 create treatments for them. If you're only going to solve 553 00:33:46,559 --> 00:33:49,400 problems for a thousand people, it's hard to know. But 554 00:33:49,480 --> 00:33:52,519 what we're learning and there's some great research. I believe 555 00:33:52,519 --> 00:33:58,279 it's at the University of Washington that is taking existing drugs, 556 00:33:58,440 --> 00:34:02,279 existing treatments and identifying where those treatments could apply to 557 00:34:02,359 --> 00:34:06,920 some of these orphan orphan diseases. And they're really they're 558 00:34:06,920 --> 00:34:10,400 bringing like real benefit, like life saving treatments to people 559 00:34:11,079 --> 00:34:15,119 by applying existing approved drugs that otherwise wouldn't have been 560 00:34:15,360 --> 00:34:20,639 used in that context to these orphan diseases. And that's 561 00:34:20,679 --> 00:34:22,480 the sort of thing that you could really only do 562 00:34:22,599 --> 00:34:26,559 with AI because it can see you can enter in 563 00:34:26,599 --> 00:34:28,559 all this data and you can get these patterns out 564 00:34:28,559 --> 00:34:32,320 that identify, hey, maybe we should try this technique over 565 00:34:32,639 --> 00:34:34,920 on this thing where we've never thought to try it before. 566 00:34:34,920 --> 00:34:37,559 And so I'm super excited about those types of treatments. 567 00:34:37,599 --> 00:34:40,440 I think that's going to make our lives healthier and longer. 568 00:34:41,280 --> 00:34:43,480 And it's one of the areas where we need to 569 00:34:43,480 --> 00:34:47,119 get the policy right because the way we treat healthcare 570 00:34:47,199 --> 00:34:50,079 data in this country is makes it very hard to 571 00:34:50,119 --> 00:34:52,400 do some of these types of things, and so we 572 00:34:52,440 --> 00:34:54,440 need to get policy right. But if we get that right, 573 00:34:55,360 --> 00:34:58,440 the health benefits here are really really exciting. 574 00:34:58,960 --> 00:35:01,480 Speaker 1: Well, speaking of palas, see again you know the big 575 00:35:01,519 --> 00:35:07,639 beautiful bill has some interesting things in it about AI. 576 00:35:08,159 --> 00:35:12,519 It aims in many ways to stem the wave of 577 00:35:12,840 --> 00:35:19,599 state AI laws you know that that create this patchwork 578 00:35:19,719 --> 00:35:24,360 of you know, different boundary lines for AI, which you 579 00:35:24,400 --> 00:35:27,719 know is problematic to save the least, I mean, there 580 00:35:27,760 --> 00:35:32,880 has to be clearly some regulation in this area. It's 581 00:35:32,960 --> 00:35:37,119 just what that is now that did not make it 582 00:35:37,639 --> 00:35:42,159 through the you know, the process. Right Where where does 583 00:35:42,199 --> 00:35:43,280 all of that stand? 584 00:35:43,320 --> 00:35:43,480 Speaker 2: Now? 585 00:35:43,519 --> 00:35:46,920 Speaker 1: Where do you see that going? Because you know, it's 586 00:35:46,960 --> 00:35:53,199 it's understandable that AI companies have some concerns about trying 587 00:35:53,239 --> 00:35:57,800 to navigate so many different laws in this arena. 588 00:35:58,559 --> 00:36:01,599 Speaker 3: Yeah, there's two real concerns here. One is the one 589 00:36:01,599 --> 00:36:02,440 that you said, which is. 590 00:36:02,360 --> 00:36:05,719 Speaker 2: A sort of patchwork of you know, there were over 591 00:36:05,760 --> 00:36:09,039 one thousand state laws that related to AI that were 592 00:36:09,079 --> 00:36:13,800 introduced in in twenty twenty five so far, and you know, 593 00:36:13,880 --> 00:36:16,840 most of those were not problematic, but you still have 594 00:36:16,920 --> 00:36:19,039 to pay attention to them a little bit. Some of 595 00:36:19,079 --> 00:36:21,599 them were deeply problematic and didn't pass. Some of them 596 00:36:21,599 --> 00:36:24,239 were deeply problematic and did pass. And so I think 597 00:36:24,239 --> 00:36:27,400 California passed sixteen different AI laws this session. 598 00:36:27,480 --> 00:36:30,199 Speaker 3: And so so one of the. 599 00:36:30,119 --> 00:36:32,920 Speaker 2: Concerns is just this patchwork compliance, right, Like, how do 600 00:36:33,000 --> 00:36:36,480 I know what? What if I offer this product and 601 00:36:36,519 --> 00:36:38,880 there's somebody who used it in Missouri and somebody who 602 00:36:38,960 --> 00:36:40,920 used it in California, am I going to have to 603 00:36:40,920 --> 00:36:42,920 comply with two different sets of laws? How do I 604 00:36:42,960 --> 00:36:46,039 do That's that's that's a real problem. That's especially a 605 00:36:46,079 --> 00:36:49,519 problem for startups. Bigger companies can kind of afford to 606 00:36:49,559 --> 00:36:52,320 pay like a huge legal shop to try to figure 607 00:36:52,320 --> 00:36:55,320 out how to do this, but smaller companies are just 608 00:36:55,360 --> 00:36:57,760 going to struggle to do that. And so that's a 609 00:36:57,840 --> 00:36:59,920 problem for competition in this space, which I think is 610 00:37:00,159 --> 00:37:03,800 an important dimension. The second problem is what I call 611 00:37:04,320 --> 00:37:08,079 extra territoriality. And this is an old problem in our 612 00:37:08,119 --> 00:37:13,360 federalist system. It's in fact, it's what drove the founders 613 00:37:13,400 --> 00:37:16,000 to move away from the Articles of Confederation and to 614 00:37:16,119 --> 00:37:21,400 move towards the Constitution to have a more centralized government 615 00:37:21,440 --> 00:37:25,000 with limited powers, and then you know, the states to 616 00:37:25,039 --> 00:37:28,199 have you know, other powers, the police powers primarily. And 617 00:37:28,239 --> 00:37:32,280 so what that means is if California passes a law, 618 00:37:32,480 --> 00:37:38,159 say dictating what you know political advertising can look like, 619 00:37:38,880 --> 00:37:43,559 it embeds you know the values of the California legislature. 620 00:37:44,280 --> 00:37:48,679 But because California is such a large market, and because 621 00:37:48,719 --> 00:37:51,360 companies are going to want to sell into that market, 622 00:37:51,800 --> 00:37:54,719 that means that you know, probably the people in Oklahoma 623 00:37:54,760 --> 00:37:59,000 and Missouri and Iowa and you know, North Carolina, they're 624 00:37:59,039 --> 00:38:01,719 probably going to be opera within the same system that 625 00:38:01,800 --> 00:38:04,679 California has dictated. And I think that that is a 626 00:38:04,679 --> 00:38:07,320 real problem, especially when we get into some more of 627 00:38:07,360 --> 00:38:09,880 the political and speech. 628 00:38:09,559 --> 00:38:11,239 Speaker 3: Based concerns in this area. 629 00:38:11,320 --> 00:38:14,880 Speaker 2: We don't want California to be setting the national standard 630 00:38:15,519 --> 00:38:19,960 for what AI looks like. That's that's Congress's job. This 631 00:38:20,039 --> 00:38:23,840 is a national technology, and so you know, the big, 632 00:38:23,960 --> 00:38:27,320 the you know, big beautiful Bill had there was an 633 00:38:27,320 --> 00:38:32,159 opportunity there for Congress to do something. They tried, couldn't 634 00:38:32,159 --> 00:38:35,079 get it across the line on that particular one. But 635 00:38:35,119 --> 00:38:38,920 there's building interest in this having a federal approach to 636 00:38:39,000 --> 00:38:42,920 this technology, which is a national technology that has both 637 00:38:43,239 --> 00:38:48,000 like nationally economic importance but also just national security importance 638 00:38:48,079 --> 00:38:50,400 as well when we talk about, you know, competing with 639 00:38:50,519 --> 00:38:52,679 China in this area, and so I think there is 640 00:38:52,760 --> 00:38:56,360 building interest in doing something at the federal level. We 641 00:38:56,559 --> 00:38:59,840 just had another opportunity. There was an exploration of whether 642 00:38:59,920 --> 00:39:02,519 or not we could get this into you know, Congress 643 00:39:02,559 --> 00:39:05,840 was going to put this into something like the the 644 00:39:05,840 --> 00:39:11,159 the NDAA, which is the big appropriations bill for National defense. 645 00:39:12,480 --> 00:39:17,000 Ultimately that that didn't It wasn't the right vehicle. It's 646 00:39:17,000 --> 00:39:20,360 a consensus document. It was just challenging to get bipartisan 647 00:39:20,400 --> 00:39:23,559 support there. But I think Congress continues to be interested 648 00:39:23,559 --> 00:39:28,159 in this. I think people recognize that national technology should 649 00:39:28,159 --> 00:39:31,400 be you know, regulated at the national level, and so 650 00:39:31,639 --> 00:39:33,880 I think Congress is going to continue to explore that. 651 00:39:34,519 --> 00:39:36,719 The White House has been pushing pretty hard on this. 652 00:39:36,800 --> 00:39:39,000 President Trump has come out vocally and said that we 653 00:39:39,039 --> 00:39:43,079 need a federal framework in this space. We cannot subject 654 00:39:43,119 --> 00:39:47,760 to this national technology to you know, fifty state patchwork 655 00:39:47,840 --> 00:39:51,159 of laws, and we certainly can't let California dictate what 656 00:39:52,000 --> 00:39:54,320 our technology looks like when we're competing with China. 657 00:39:54,440 --> 00:39:58,880 Speaker 1: So well, just just to ask the hard producers of 658 00:39:59,239 --> 00:40:05,519 Iowa about the impact California laws can have on their marketplace, 659 00:40:06,880 --> 00:40:07,199 you know. 660 00:40:07,239 --> 00:40:10,480 Speaker 2: Or anybody who's bought a pickup truck right absolutely and 661 00:40:10,519 --> 00:40:14,199 had to meet you know, California cafe like fuel efficiency standards. 662 00:40:14,280 --> 00:40:15,480 Speaker 3: Yeah, yeah, exactly. 663 00:40:16,599 --> 00:40:18,800 Speaker 1: All right, Well, we're quickly running out of time. I 664 00:40:18,920 --> 00:40:21,880 just have a couple of questions left. The first is 665 00:40:22,400 --> 00:40:28,639 the resources involved in this. Obviously, AI, this technology requires 666 00:40:29,000 --> 00:40:33,880 a great deal of a particular natural resource. Where do 667 00:40:33,960 --> 00:40:35,519 you see all of that going ahead? 668 00:40:36,440 --> 00:40:40,400 Speaker 2: Yeah, so AI requires a lot of energy. The current 669 00:40:41,519 --> 00:40:45,400 models and the current chips that are used to run 670 00:40:45,440 --> 00:40:50,639 them require energy, and so we are. Unfortunately in this 671 00:40:50,719 --> 00:40:53,960 country we have been operating since the nineteen seventies under 672 00:40:53,960 --> 00:40:56,559 a sort of scarcity mindset when it comes to energy, 673 00:40:56,599 --> 00:41:00,360 This idea that we need to conserve and recycle and 674 00:41:00,480 --> 00:41:03,599 all of these are Efficiency is good, recycling is good. 675 00:41:03,679 --> 00:41:06,039 Using things the best we can is good. 676 00:41:06,480 --> 00:41:07,519 Speaker 3: But we have. 677 00:41:07,480 --> 00:41:12,360 Speaker 2: A wealth of resources in this country. We should be 678 00:41:12,519 --> 00:41:18,280 using them. Prosperity and energy intensity are directly related. It's 679 00:41:18,320 --> 00:41:20,519 hard to be a wealthy country without using a lot 680 00:41:20,519 --> 00:41:22,679 of energy, and we shouldn't think of that as a 681 00:41:22,679 --> 00:41:25,719 bad thing. We should be trying to build more energy 682 00:41:25,719 --> 00:41:28,880 in this country. And I think it's finally AI has 683 00:41:28,920 --> 00:41:32,039 sort of woken people up to the fact that, hey, 684 00:41:33,159 --> 00:41:35,679 this scarcity mindset needs to go away. We need an 685 00:41:35,719 --> 00:41:39,280 abundance mindset when it comes to energy. Building more energy 686 00:41:39,360 --> 00:41:42,440 is a good thing, and providing more energy is good 687 00:41:42,480 --> 00:41:44,280 and that's the sort of thing that over time will 688 00:41:44,400 --> 00:41:49,519 drive down prices and we'll drive up new solutions, and 689 00:41:49,559 --> 00:41:50,800 so these data centers are. 690 00:41:50,679 --> 00:41:53,119 Speaker 3: Sort of the spark for that. But I think we could. 691 00:41:52,920 --> 00:41:57,039 Speaker 2: All benefit from having more energy abundance in this country. 692 00:41:57,840 --> 00:41:59,920 And so the balance is going to be like, where 693 00:42:00,079 --> 00:42:01,960 we build this, how can we build it, how do 694 00:42:02,000 --> 00:42:05,000 we deal with the concerns of you know, local communities 695 00:42:05,400 --> 00:42:09,800 about energy prices. All of that means that we should 696 00:42:09,840 --> 00:42:13,719 aim towards, you know, not subsidizing these types of projects, 697 00:42:13,960 --> 00:42:17,239 but finding a way to enable them to help give 698 00:42:17,360 --> 00:42:20,920 back both on the energy production side, but also on 699 00:42:21,000 --> 00:42:24,360 the you know, the the amazing tools that they're building. 700 00:42:25,280 --> 00:42:27,000 We talk about data centers, I have to think we 701 00:42:27,000 --> 00:42:30,960 should just say supercomputers, like people should be like excited 702 00:42:31,000 --> 00:42:34,440 to have a new supercomputer in their backyard. I well, 703 00:42:34,440 --> 00:42:36,559 maybe that just makes me super nerdy. I am excited 704 00:42:36,599 --> 00:42:37,320 by such things. 705 00:42:38,679 --> 00:42:41,639 Speaker 1: These are not everybody, not everybody who's excited as you 706 00:42:41,679 --> 00:42:42,400 are about this. 707 00:42:42,559 --> 00:42:45,280 Speaker 2: But I think you're right, and I need to I 708 00:42:45,280 --> 00:42:47,840 need to step out of my own bubble sometimes. And 709 00:42:47,880 --> 00:42:51,000 there certainly are trade offs that come from from this, 710 00:42:51,920 --> 00:42:55,360 But I think overall, the benefits are enormous. There are 711 00:42:55,440 --> 00:42:59,880 communities that are very interested in the level of investment 712 00:43:00,519 --> 00:43:03,920 that come with these types of data centers. You know, 713 00:43:04,000 --> 00:43:07,400 Texas is building a bunch of them, Louisiana has is 714 00:43:07,400 --> 00:43:10,960 building some massive ones. These are creating you know, hundreds 715 00:43:11,159 --> 00:43:15,880 of thousands of construction jobs, and then you know the 716 00:43:15,960 --> 00:43:19,400 ongoing like, uh, you know, maintenance of the data centers 717 00:43:19,480 --> 00:43:22,079 is you know, less job intensive, but is you know, 718 00:43:22,159 --> 00:43:25,079 creates prosperity and creates a hub for technology in the 719 00:43:25,280 --> 00:43:27,599 in the nearby area. And so I think there's there 720 00:43:27,639 --> 00:43:30,039 are lots of benefits. I think we need to keep 721 00:43:30,079 --> 00:43:31,840 those in mind, and we need to deal with some 722 00:43:31,920 --> 00:43:34,039 of the myths. One of the biggest myths is around 723 00:43:34,039 --> 00:43:37,280 this water use issue, which is just totally made up. 724 00:43:37,280 --> 00:43:38,039 Speaker 3: I mean, there is. 725 00:43:38,519 --> 00:43:41,920 Speaker 2: Why data centers use less water than the typical like 726 00:43:42,159 --> 00:43:46,679 mid size brewery. It does, and so or like a 727 00:43:46,719 --> 00:43:50,599 small manufacturer often uses more water than a data center. 728 00:43:51,519 --> 00:43:52,800 Speaker 3: They are energy intensive. 729 00:43:52,880 --> 00:43:55,559 Speaker 2: But water is not really an issue in this space, 730 00:43:55,599 --> 00:43:58,840 and it's I don't know why that's become such a story, 731 00:43:59,719 --> 00:44:02,559 but I think people just feel, you know, they're there. 732 00:44:02,800 --> 00:44:05,119 They don't want their bodily fluids polluted. We learned that 733 00:44:05,159 --> 00:44:08,960 from uh uh, you know from you know, uh what 734 00:44:08,960 --> 00:44:10,679 what is that movie? I'm blanking on it right now, 735 00:44:10,719 --> 00:44:12,039 But Soil and Green? 736 00:44:12,519 --> 00:44:18,840 Speaker 3: That wasn't Soiling Green, it was Blanken. But but you know, people. 737 00:44:18,679 --> 00:44:22,159 Speaker 1: I'm trying to remember a movie with bodily fluids as 738 00:44:22,159 --> 00:44:28,320 it's core principle. Actually, I'm sure they're Strange love, doctor Strange. 739 00:44:29,119 --> 00:44:30,760 Now you got it, yes, yeah. 740 00:44:30,920 --> 00:44:31,840 Speaker 3: Doctor Strange love. 741 00:44:31,840 --> 00:44:34,079 Speaker 2: And so I don't know why the water talking point 742 00:44:34,119 --> 00:44:37,519 has been so viral, but it's just not true. 743 00:44:37,760 --> 00:44:39,559 Speaker 1: It really has been. That's I mean, that's what that's 744 00:44:39,840 --> 00:44:42,199 really what I was getting at is that's the that's 745 00:44:42,239 --> 00:44:45,440 been the topic of conversation. Now. Of course, it's been 746 00:44:45,480 --> 00:44:48,360 the topic of a conversation that's in no small part 747 00:44:48,519 --> 00:44:53,920 driven by environmentalist and extreme environmentalist of course. And they 748 00:44:53,920 --> 00:44:57,320 have certainly never led us astray before right now. 749 00:44:58,639 --> 00:45:02,960 Speaker 2: They they I don't, And honestly, there's it's such a 750 00:45:03,000 --> 00:45:08,119 self defeating mindset that that the environmentalists are have been 751 00:45:08,119 --> 00:45:10,840 the primary driver of that mindset I said before, of 752 00:45:10,880 --> 00:45:15,000 like energy scarcity, that basically that that humans using the 753 00:45:15,039 --> 00:45:18,360 resources that God put here on our planet are is 754 00:45:18,880 --> 00:45:21,639 a bad thing. More or less and so I think 755 00:45:22,639 --> 00:45:25,280 they certainly have led us astray before I think on 756 00:45:25,280 --> 00:45:27,960 this water thing, they very much are leading us astray. 757 00:45:28,880 --> 00:45:32,280 You know, electrical use is a energy use is a challenge, 758 00:45:32,519 --> 00:45:34,639 and we need to make sure that we we get 759 00:45:34,679 --> 00:45:37,320 that balance right. And to me, the right solution there 760 00:45:37,360 --> 00:45:39,960 is to build more. We have the ability, we have 761 00:45:40,000 --> 00:45:42,679 the technical capability, we have the deep resources in this 762 00:45:42,719 --> 00:45:43,519 country to do it. 763 00:45:43,880 --> 00:45:45,719 Speaker 3: We need to do it, or you know. 764 00:45:46,079 --> 00:45:49,320 Speaker 2: These data centers will be being built in China and 765 00:45:49,599 --> 00:45:53,679 in you know, Saudi Arabia and in places where they'll 766 00:45:53,719 --> 00:45:58,519 be outside of the sort of US cultural influence, and 767 00:45:58,519 --> 00:46:01,320 then they won't be serving our national interest. And so 768 00:46:02,800 --> 00:46:05,119 I worry about that as a challenge. I think it's 769 00:46:05,519 --> 00:46:06,840 what we can tackle and we should. 770 00:46:07,800 --> 00:46:11,000 Speaker 1: Well. If my AOC clock is right, we have about 771 00:46:11,000 --> 00:46:14,559 two and a half years of existence left. If I'm 772 00:46:14,559 --> 00:46:17,360 not missing because I was out twelve years at one point, 773 00:46:17,400 --> 00:46:20,840 I haven't checked in. I probably should. Final question for you, 774 00:46:21,119 --> 00:46:23,519 and this is it. You know, I've made no secret 775 00:46:23,559 --> 00:46:27,079 to you as we've talked about these issues in the past. 776 00:46:27,840 --> 00:46:30,800 AI technology scares the hell out of me. Yeah, it's 777 00:46:30,880 --> 00:46:37,519 probably because I didn't. I'm old enough to live in 778 00:46:37,559 --> 00:46:42,039 a time where I didn't envision the technology could possibly 779 00:46:42,199 --> 00:46:48,320 get beyond the Atari twenty six hundred and frogger. So, 780 00:46:48,880 --> 00:46:51,639 you know, maybe I'm a ludite on that front. But 781 00:46:52,199 --> 00:46:57,639 here's the question, plain and simple. Will AI eventually become 782 00:46:57,719 --> 00:47:01,880 our overlords and slave us and our progeny? 783 00:47:03,519 --> 00:47:09,400 Speaker 2: No, no, no, these are these are advanced computers. They 784 00:47:09,440 --> 00:47:12,679 don't have motives, They don't have initiative. 785 00:47:13,000 --> 00:47:14,000 Speaker 3: What they have right now. 786 00:47:14,079 --> 00:47:17,079 Speaker 2: What these systems have right now is they have the 787 00:47:17,159 --> 00:47:22,039 ability to identify patterns in a wide range of data 788 00:47:22,679 --> 00:47:27,239 and collate it in a response to a query, and 789 00:47:27,400 --> 00:47:29,760 we can do really amazing things. It turns out we 790 00:47:29,760 --> 00:47:32,840 can do really amazing things with that. But there is 791 00:47:32,880 --> 00:47:37,360 not They do not have initiative or drive. I worry 792 00:47:37,360 --> 00:47:41,280 more about how humans will misuse them than I do 793 00:47:41,639 --> 00:47:44,639 about whether or not the AIS will somehow become independent. 794 00:47:44,719 --> 00:47:47,840 Right now, there is not a clear pathway to that 795 00:47:47,960 --> 00:47:50,280 sort of like autonomy, and so. 796 00:47:51,480 --> 00:47:53,280 Speaker 3: I don't worry about that at all. I worry. 797 00:47:53,800 --> 00:47:55,880 Speaker 2: I worry about not being able to take advantage of 798 00:47:55,880 --> 00:47:59,840 all the huge benefits that these technologies bring because because 799 00:47:59,840 --> 00:48:03,360 we have people who are too worried about you. 800 00:48:03,320 --> 00:48:05,480 Speaker 3: Know, science fiction scenarios. 801 00:48:05,039 --> 00:48:07,920 Speaker 1: So well it is. It is a brave new world 802 00:48:08,159 --> 00:48:11,800 in many facets. It's a very interesting new world, and 803 00:48:11,840 --> 00:48:15,000 it's a new world filled with all kinds of possibilities, 804 00:48:15,039 --> 00:48:19,760 as you say, many of them extremely positive. I'm not, 805 00:48:20,440 --> 00:48:22,880 you know, too much of a Luddite to understand that, 806 00:48:23,679 --> 00:48:26,440 but we need to stop every once in a while 807 00:48:26,480 --> 00:48:29,480 and discuss the impacts thus far and get a sense 808 00:48:29,519 --> 00:48:32,519 of where we're going. And you helped us do exactly that. 809 00:48:33,119 --> 00:48:35,960 Speaker 2: I appreciate it absolutely, and I should point out that, 810 00:48:36,239 --> 00:48:39,599 you know, it's not just the ludd Eights that like Froger. 811 00:48:39,679 --> 00:48:43,119 My six year old daughter plays Froger all the time, 812 00:48:43,239 --> 00:48:45,800 so it has a long life. Some of these things 813 00:48:45,840 --> 00:48:48,920 stick with us. Great technology can bring joy to people, 814 00:48:49,719 --> 00:48:52,400 you know, for decades. So that's what I'm hoping AI does. 815 00:48:52,840 --> 00:48:55,920 Speaker 1: Introducer to dig Doug and you may never see her again. 816 00:48:56,760 --> 00:48:57,519 Speaker 3: That might be right. 817 00:48:58,199 --> 00:49:01,360 Speaker 1: Thanks to my guest today, Neil Chilse, head of AI 818 00:49:01,519 --> 00:49:05,119 policy with the Abundance Institute, you've been listening to another 819 00:49:05,239 --> 00:49:07,840 edition of the Federalist Radio Hour. I'm Matt Kittle, Senior 820 00:49:07,840 --> 00:49:11,760 Elections correspondent at the Federalist. You'll be back soon. With more. 821 00:49:12,199 --> 00:49:16,000 Until then, stay lovers of freedom and anxious for the fray. 822 00:49:22,880 --> 00:49:28,480 Speaker 2: I heard the fame, voice the reason, and then it 823 00:49:28,679 --> 00:49:34,000 faded away.