1 00:00:00,542 --> 00:00:03,962 One of the things they have to do is write down, here's the rules of the game, 2 00:00:04,170 --> 00:00:08,383 and here's what, doing a certain good thing pays you, right? 3 00:00:08,383 --> 00:00:10,218 They call it a payoff function. 4 00:00:10,218 --> 00:00:13,221 And so if you, you know, if you take the, the opponent's pawn little piece, 5 00:00:13,555 --> 00:00:14,222 here's the payoff. 6 00:00:14,222 --> 00:00:16,558 Or if you take their queen, it's a bigger payoff. 7 00:00:16,558 --> 00:00:20,895 And so, if you can define that in chess, you can actually define that. 8 00:00:21,771 --> 00:00:24,566 You can make a system that that does very well. 9 00:00:24,566 --> 00:00:28,737 But think about a dating relationship or a marriage like. 10 00:00:29,154 --> 00:00:32,240 And we pretty quickly realize the our attempts 11 00:00:32,240 --> 00:00:35,243 to quantify and define value. 12 00:00:36,077 --> 00:00:38,872 It's we're in a different layer of abstraction. 13 00:00:38,872 --> 00:00:41,666 We you can't the these don't go together. 14 00:00:47,297 --> 00:00:49,174 So today in Anabaptist perspectives, 15 00:00:49,174 --> 00:00:53,511 I'm joined by Ben Harris, and we're going to be diving into 16 00:00:53,595 --> 00:00:56,765 the ontological limits of artificial intelligence. 17 00:00:58,808 --> 00:00:59,684 Ben, you want to start with a 18 00:00:59,684 --> 00:01:02,771 little introduction, and we'll jump into the topic after that. 19 00:01:03,646 --> 00:01:04,230 Sure, Marlin. 20 00:01:04,230 --> 00:01:06,191 Good to be with you. So, my name is Ben Harris. 21 00:01:06,191 --> 00:01:07,484 Hi, everyone. 22 00:01:07,484 --> 00:01:11,071 So I'm a professor up at Sattler College in Boston, Massachusetts. 23 00:01:11,071 --> 00:01:13,364 I coordinate the business program up here, 24 00:01:13,364 --> 00:01:15,825 but my my background comes out of the engineering world. 25 00:01:15,825 --> 00:01:20,747 I spent more than a decade working in machine learning, artificial intelligence, 26 00:01:20,747 --> 00:01:22,749 just many of the classical engineering disciplines. 27 00:01:22,749 --> 00:01:25,752 So, I would. 28 00:01:26,461 --> 00:01:29,339 I think it's hard to define what an expert in the field is, but, 29 00:01:29,339 --> 00:01:33,218 I spent a long time thinking about it and continue to do so because it affects 30 00:01:33,510 --> 00:01:37,514 not only back in, in the technical world, but also in academia. 31 00:01:37,514 --> 00:01:39,015 We contend with AI every day. 32 00:01:39,015 --> 00:01:41,518 So, Marlin, it's good to be with you. 33 00:01:41,518 --> 00:01:44,521 Yes. Thanks for coming on, Ben. 34 00:01:44,771 --> 00:01:47,607 Excited to have the kind of engineering, computer background, 35 00:01:47,607 --> 00:01:48,608 that you bring to it. 36 00:01:49,734 --> 00:01:51,069 So to 37 00:01:51,069 --> 00:01:55,949 start with some of the the drama around AI, 38 00:01:55,949 --> 00:01:58,952 a little over a year ago, May of 2023, 39 00:01:59,953 --> 00:02:03,998 there's this famous statement on AI risk, signed by, 40 00:02:03,998 --> 00:02:09,504 you know, a bunch of the big names, and their short version was mitigating 41 00:02:09,504 --> 00:02:13,007 the risk of extinction from AI should be a global priority 42 00:02:14,008 --> 00:02:17,971 alongside other societal scale risks such as pandemics and nuclear war. 43 00:02:18,763 --> 00:02:21,641 And so that got a bunch of press. 44 00:02:21,641 --> 00:02:22,684 I found it interesting. 45 00:02:22,684 --> 00:02:25,687 A few weeks later, there was a CNN report on a, 46 00:02:26,521 --> 00:02:29,524 a survey from a number of CEOs. 47 00:02:30,066 --> 00:02:32,402 they got 119 responses. 48 00:02:32,402 --> 00:02:35,613 And out of those, 50 of them said, 49 00:02:37,490 --> 00:02:41,327 yeah, AI could potentially destroy humanity in the next 5 to 10 years. 50 00:02:42,078 --> 00:02:44,581 And the other 69 were unconcerned. 51 00:02:45,915 --> 00:02:47,250 So I guess we 52 00:02:47,250 --> 00:02:50,253 can start with, where are you at on that question? 53 00:02:50,253 --> 00:02:53,256 Your view of artificial intelligence and that kind of. 54 00:02:54,048 --> 00:02:57,468 Yeah, that kind of dramatic fear statements. 55 00:02:58,720 --> 00:03:01,973 Oh, that's a that's a classic question for anybody who thinks about AI. 56 00:03:02,390 --> 00:03:05,768 I, I am not on the what we call an AI doomer. 57 00:03:05,768 --> 00:03:07,770 Those are those are probably what you put those, 58 00:03:07,770 --> 00:03:09,898 you know, the Geoffrey Hinton's of the world 59 00:03:09,898 --> 00:03:13,484 that would you know, he's very, he’s had a much longer background here. 60 00:03:13,484 --> 00:03:14,736 This is his view. 61 00:03:14,736 --> 00:03:19,449 I do not see AI as within that short period of time, 5 to 10 years, 62 00:03:20,283 --> 00:03:23,286 as being an existential threat to humanity. 63 00:03:24,078 --> 00:03:26,956 Both for a theological reason, I think, like God has defined 64 00:03:26,956 --> 00:03:30,793 the end of humanity for us, but also that we 65 00:03:31,169 --> 00:03:36,174 we look at the technology and what we assume in the forecast of, 66 00:03:36,174 --> 00:03:39,219 you know, AI improving or growing to a certain level assumes 67 00:03:39,219 --> 00:03:42,222 no major hurdles that we encounter on the way. 68 00:03:42,347 --> 00:03:44,432 I and I just think we're starting to see the hurdles. 69 00:03:44,432 --> 00:03:44,724 Right? 70 00:03:44,724 --> 00:03:47,936 We we at the moment cannot produce 71 00:03:47,936 --> 00:03:51,231 data fast enough in order to support these bigger and bigger AI models. 72 00:03:51,231 --> 00:03:51,522 We don't. 73 00:03:51,522 --> 00:03:52,857 We're running out of, 74 00:03:52,857 --> 00:03:57,195 raw materials for creating training chips and systems that do this. 75 00:03:57,654 --> 00:03:59,364 So there so we're starting to see the hurdles. 76 00:03:59,364 --> 00:04:00,323 And I think that that's going to 77 00:04:00,323 --> 00:04:03,326 that's going to skew the forecast fairly dramatically. 78 00:04:03,701 --> 00:04:07,747 I, I am not particularly concerned about, existential issues. 79 00:04:07,747 --> 00:04:08,581 partly 80 00:04:08,581 --> 00:04:12,377 because what we're going to talk about today is the is the ontology of AI, right? 81 00:04:12,377 --> 00:04:16,422 The movie versions of AI being a threat to humans, 82 00:04:17,840 --> 00:04:20,927 in the in the narrative tend to revolve around a moment 83 00:04:21,094 --> 00:04:25,098 when I realized what it was and what it could be, and it has this self 84 00:04:25,098 --> 00:04:29,352 defensive response or reaction to humanity trying to shut it down. 85 00:04:30,395 --> 00:04:33,398 I don't yet think that AI has the 86 00:04:33,564 --> 00:04:37,193 has an existential understanding of what it is yet. 87 00:04:37,485 --> 00:04:40,071 You know, it probably can give you a text response. 88 00:04:40,071 --> 00:04:43,825 Yes, I system, system system am an AI system, 89 00:04:44,325 --> 00:04:48,997 but that does that does not yet imbue value in a self defensive reaction. 90 00:04:48,997 --> 00:04:51,499 Right? The that we naturally experience as humans. 91 00:04:51,499 --> 00:04:55,545 So I think there's still a gap ontologically and as well as what 92 00:04:55,545 --> 00:04:59,716 we would call artificial generalized intelligence or AGI, right. 93 00:04:59,716 --> 00:05:02,719 Systems that can think and reason for themselves. 94 00:05:03,970 --> 00:05:05,638 I mean, it's still an ongoing open 95 00:05:05,638 --> 00:05:08,850 research area in, in what they call argument mining, right? 96 00:05:08,850 --> 00:05:13,062 You can present a bit of text to an engine and say, is this a good argument? 97 00:05:13,521 --> 00:05:16,482 And it has a really difficult time determining 98 00:05:16,482 --> 00:05:19,152 yes or no, whereas we as humans read that and say, oh, that's 99 00:05:19,152 --> 00:05:22,196 a terrible argument or a great argument, and I find it very persuasive. 100 00:05:22,196 --> 00:05:27,410 And so the, there's the that's one I think about a lot that particular gap. 101 00:05:27,410 --> 00:05:31,122 But there are a number of them that that in my view, 102 00:05:32,248 --> 00:05:33,833 set they set a 103 00:05:33,833 --> 00:05:37,545 fairly big chasm today between where AI is 104 00:05:37,795 --> 00:05:40,798 and anything that we would need to worry about from an existential standpoint. 105 00:05:42,008 --> 00:05:43,676 So you're setting a chasm. 106 00:05:43,676 --> 00:05:48,222 And you're saying those time frames are way over inflated or sorry, 107 00:05:49,390 --> 00:05:50,433 not over inflated. 108 00:05:50,433 --> 00:05:52,143 Opposite of over inflated. 109 00:05:52,143 --> 00:05:54,854 Way too ambitious in a sense. 110 00:05:54,854 --> 00:05:56,647 To push on it a little bit. 111 00:05:56,647 --> 00:05:58,983 I still hear you using words like. 112 00:05:58,983 --> 00:06:04,781 Not yet, implying that the time is coming. 113 00:06:04,781 --> 00:06:06,491 It's just not yet. 114 00:06:06,491 --> 00:06:09,494 and I guess even to push on that a little bit more, 115 00:06:10,370 --> 00:06:13,373 you know, we talk about things in computing, like 116 00:06:13,664 --> 00:06:17,585 Moore's Law, which I think is a fairly specific technical definition for that. 117 00:06:17,585 --> 00:06:20,588 But in a general sense, 118 00:06:22,006 --> 00:06:24,884 you know, computing power changes very quickly. 119 00:06:26,844 --> 00:06:29,806 I mean, you even noted in an email to me, it seems like there's new 120 00:06:29,806 --> 00:06:32,809 AI applications coming out every couple of weeks. 121 00:06:35,311 --> 00:06:35,770 so I guess 122 00:06:35,770 --> 00:06:38,773 how would you respond to an argument like that that says, well, 123 00:06:39,273 --> 00:06:42,276 you know, computing power has been doubling 124 00:06:42,693 --> 00:06:46,072 frequently for the last number of years, and we're just going to see, 125 00:06:47,156 --> 00:06:50,118 you know, we can't see what's around the corner. 126 00:06:50,118 --> 00:06:51,077 You know, it's a fair question. 127 00:06:51,077 --> 00:06:52,453 You know why? Why the “Not yet”? 128 00:06:52,453 --> 00:06:56,749 I think the the one of the reasons is if you, you know, say you 129 00:06:56,749 --> 00:06:58,918 you project out some number of years and you, 130 00:06:58,918 --> 00:07:02,713 you allow Moore's law, which is beginning to show asymptotic behavior. 131 00:07:03,005 --> 00:07:03,256 Right? 132 00:07:03,256 --> 00:07:07,885 You if you were not doubling the you know our our speeds are getting quicker, 133 00:07:07,885 --> 00:07:11,931 but we're running into computation issues with these kind of things. 134 00:07:11,931 --> 00:07:14,016 It's largely why GPUs have become 135 00:07:14,016 --> 00:07:17,019 the standard architecture for doing this kind of work. 136 00:07:17,145 --> 00:07:20,398 one, the not yet is an admission that I don't that we don't know. 137 00:07:20,690 --> 00:07:20,898 Right. 138 00:07:20,898 --> 00:07:24,068 We you know, can can you create a technical system 139 00:07:24,068 --> 00:07:27,363 that that is a version of intelligence? 140 00:07:27,905 --> 00:07:30,032 That's a bigger question than I think AI can answer. 141 00:07:30,032 --> 00:07:34,078 That's a, you know, what is what has God defined intelligence as. 142 00:07:34,078 --> 00:07:35,913 And can you create a system that has that? 143 00:07:37,832 --> 00:07:38,541 you know, 144 00:07:38,541 --> 00:07:41,586 we sort of think of it in a, in a narrow band in that, 145 00:07:42,003 --> 00:07:45,214 you know, intelligence is the ability to collect and synthesize 146 00:07:45,214 --> 00:07:48,217 information, to answer a question or to develop something. 147 00:07:48,342 --> 00:07:50,761 but there's there's other types. Right? 148 00:07:50,761 --> 00:07:52,472 We have a sense of asthetics. 149 00:07:52,472 --> 00:07:55,183 Right? Humans look at what is beautiful. 150 00:07:55,183 --> 00:08:00,271 We have a sense of awe, that is uniquely human, right? 151 00:08:00,271 --> 00:08:04,650 And you, you know, if you ask, a large language model, 152 00:08:04,984 --> 00:08:09,238 you know, if a given painting is, is beautiful or if a piece of artwork 153 00:08:09,238 --> 00:08:13,701 is just abhorrent to you, like, it's likely does not have an opinion. 154 00:08:14,202 --> 00:08:17,705 And if it does, it has to create it based on some quantitative 155 00:08:17,705 --> 00:08:18,873 aspect of the artwork. 156 00:08:18,873 --> 00:08:23,127 It has to look at, you know, the okay, the color contrast is within this range. 157 00:08:23,127 --> 00:08:26,172 And most people think that that means it's not a very attractive painting. 158 00:08:26,422 --> 00:08:27,340 It has. 159 00:08:27,340 --> 00:08:30,593 That's the way it has to think because of its computational nature. 160 00:08:31,052 --> 00:08:33,387 we don't think that way. Right? 161 00:08:33,387 --> 00:08:35,139 We we look at it. And what is it? 162 00:08:35,139 --> 00:08:38,351 What does it do to our to our spirit, to our soul, to our mind? 163 00:08:38,684 --> 00:08:40,144 And we have a response. And so, 164 00:08:41,187 --> 00:08:44,190 you know, I think that the not yet is a, 165 00:08:45,149 --> 00:08:47,193 you know, is a is a fair question. 166 00:08:47,193 --> 00:08:48,736 I think we're, we're also running 167 00:08:48,736 --> 00:08:52,114 into some mathematical challenges in that the, the number of, 168 00:08:52,907 --> 00:08:56,410 I'll call them synapses or nodes that we'd have to do to simulate 169 00:08:56,744 --> 00:09:00,915 actual human thought and cognition is still many orders of magnitude 170 00:09:00,915 --> 00:09:03,918 above what the most powerful systems can handle right now. 171 00:09:04,126 --> 00:09:07,296 So even if Moore's Law holds, which is question whether it will, 172 00:09:08,923 --> 00:09:12,426 we have a long time to go before we exponentially reach that point. 173 00:09:12,426 --> 00:09:15,304 We would need to be and who and who knows? 174 00:09:15,304 --> 00:09:17,932 On the way, we may encounter a whole nother obstacle we didn't anticipate. 175 00:09:17,932 --> 00:09:23,145 So, the the challenge is that we you don't. 176 00:09:24,063 --> 00:09:26,232 When you make a forecast, you have to acknowledge 177 00:09:26,232 --> 00:09:28,568 what could cause the forecast to fail. 178 00:09:28,568 --> 00:09:29,193 Right? 179 00:09:29,193 --> 00:09:31,362 Things don't always continue as they were. 180 00:09:31,362 --> 00:09:34,782 You look at, you know, population explosion in the 1970s, right. 181 00:09:34,782 --> 00:09:35,950 There was this huge concern 182 00:09:35,950 --> 00:09:38,953 that it was going to lead to global famine and billions of deaths. 183 00:09:38,953 --> 00:09:41,163 But that never happened. And so, 184 00:09:42,373 --> 00:09:43,207 so we want to look back 185 00:09:43,207 --> 00:09:46,586 soberly at those examples, and I think with a bit of wisdom, 186 00:09:47,545 --> 00:09:52,466 and just encourage ourselves that, like, the Lord has this in hand, right? 187 00:09:52,508 --> 00:09:55,636 He's not he's not going to let AI ruin 188 00:09:55,636 --> 00:09:57,847 his creation. 189 00:09:58,723 --> 00:10:01,726 Okay, so a theological answer there. 190 00:10:02,310 --> 00:10:05,313 Confidence in God. 191 00:10:06,439 --> 00:10:07,273 Yeah. 192 00:10:07,273 --> 00:10:10,943 And the title Ontological Limits kind of highlights what I wanted to the question. 193 00:10:10,943 --> 00:10:12,945 I kind of want to press here. 194 00:10:12,945 --> 00:10:15,948 if we talk about limits of AI, 195 00:10:16,490 --> 00:10:20,119 maybe the first limit we think about is technological. 196 00:10:20,328 --> 00:10:21,245 Can we build it? 197 00:10:21,245 --> 00:10:22,163 What can we build? 198 00:10:22,163 --> 00:10:23,956 What kind of 199 00:10:23,956 --> 00:10:28,044 computers can we build, what kinds of neural networks and so on? 200 00:10:30,254 --> 00:10:33,591 or epistemological what do we know how to do? 201 00:10:33,591 --> 00:10:35,134 Knowledge. 202 00:10:35,134 --> 00:10:38,512 when we say ontological limits, we're getting into. 203 00:10:39,347 --> 00:10:42,892 Some ways, the strongest of those terms because we're trying to say what 204 00:10:43,934 --> 00:10:46,937 what is the limit by the very, 205 00:10:47,146 --> 00:10:50,149 by the very nature of what this thing is, 206 00:10:50,983 --> 00:10:53,986 that produces the limit, 207 00:10:55,404 --> 00:10:57,698 and yeah, I think maybe you've hinted at some of that, 208 00:10:57,698 --> 00:11:00,701 but. 209 00:11:01,911 --> 00:11:04,664 Yeah, kind of at its core, 210 00:11:04,664 --> 00:11:07,667 what do you see is that biggest core limit 211 00:11:08,334 --> 00:11:11,337 or way of talking about that core limit. 212 00:11:11,420 --> 00:11:15,341 I think probably the the, the most stark ontological limit 213 00:11:15,341 --> 00:11:19,136 of AI is is just what we've created it out of, right? 214 00:11:19,136 --> 00:11:22,139 This is a this is a creation of, 215 00:11:23,015 --> 00:11:25,434 Well, it's it's a, sounds funny, a 216 00:11:25,434 --> 00:11:26,602 creation of creation. 217 00:11:26,602 --> 00:11:26,811 Right. 218 00:11:26,811 --> 00:11:32,191 We've our ingenuity has developed this and it's immensely powerful in some areas. 219 00:11:32,233 --> 00:11:32,358 Right. 220 00:11:32,358 --> 00:11:35,361 I don't want to to minimize its effectiveness in its aid in some 221 00:11:35,361 --> 00:11:38,364 things, but there 222 00:11:39,407 --> 00:11:39,740 it is 223 00:11:39,740 --> 00:11:43,703 a it does not involve the supernatural, right. 224 00:11:43,744 --> 00:11:44,912 It does not. 225 00:11:44,912 --> 00:11:48,749 You know, it is a it is a, by definition, natural development. 226 00:11:48,749 --> 00:11:52,420 It is limited by the laws that the that God has put in place 227 00:11:52,420 --> 00:11:56,132 in terms of physics and computation and, and information, 228 00:11:57,299 --> 00:11:58,217 in mathematics. Right. 229 00:11:58,217 --> 00:12:01,971 We look at, you know, questions like there's a famous question called 230 00:12:01,971 --> 00:12:06,434 the halting problem in Computer science that basically is a 231 00:12:06,767 --> 00:12:10,271 we now know is a unsolvable question for a computer system. 232 00:12:10,730 --> 00:12:13,733 And so, you know, it's not without 233 00:12:13,899 --> 00:12:17,361 computing is not a finished business, right? 234 00:12:17,361 --> 00:12:19,029 That we, you know, we know how to do everything 235 00:12:19,029 --> 00:12:20,114 as long as we have enough power. 236 00:12:20,114 --> 00:12:23,325 Enough, enough systems, enough electricity. 237 00:12:23,909 --> 00:12:27,538 we don't we and we can prove that we don't. 238 00:12:27,538 --> 00:12:29,623 Not only that, but never will. 239 00:12:29,623 --> 00:12:29,832 Right. 240 00:12:29,832 --> 00:12:32,042 There's you know, mathematicians have worked on that. 241 00:12:32,042 --> 00:12:36,130 That there are some fundamental, fundamentally 242 00:12:36,130 --> 00:12:40,134 unknowable things in computation in the technical fields. 243 00:12:41,844 --> 00:12:44,305 and so I think those are you're going to encounter 244 00:12:44,305 --> 00:12:48,768 some of those questions in the growth of AI based on its ontology, where you can't 245 00:12:49,435 --> 00:12:53,063 there is not a, it is bound by the laws 246 00:12:53,063 --> 00:12:56,358 that that all physical, 247 00:12:56,817 --> 00:13:00,321 digital systems are bound by. 248 00:13:00,696 --> 00:13:00,863 Right? 249 00:13:00,863 --> 00:13:03,866 It can only go so fast to go and get so hot before it breaks down. 250 00:13:04,200 --> 00:13:06,243 It's limited by the laws of physics. 251 00:13:06,243 --> 00:13:10,331 It it is also limited by the ingenuity in which humans can insert into it. 252 00:13:10,414 --> 00:13:10,664 Right? 253 00:13:10,664 --> 00:13:13,793 We create it's steps, but 254 00:13:15,377 --> 00:13:16,712 we are limited and finite. 255 00:13:16,712 --> 00:13:19,715 So I, I have a difficult time 256 00:13:20,090 --> 00:13:22,927 imagining how a system is going to, 257 00:13:22,927 --> 00:13:26,597 you know, by its own volition, exceed that based on what it's created on. 258 00:13:28,015 --> 00:13:31,018 Well, is part of the the argument there. 259 00:13:31,352 --> 00:13:32,353 Especially for the. 260 00:13:32,353 --> 00:13:35,981 I don't know, either AI optimist or AI pessimist. 261 00:13:35,981 --> 00:13:42,446 Whichever one it is that, you know, has these very high expectations for AI. 262 00:13:42,446 --> 00:13:44,448 I suppose that it's more a question 263 00:13:44,448 --> 00:13:48,327 of your outlook, whether that makes you an AI optimist or pessimist. 264 00:13:49,328 --> 00:13:50,371 but it's part of the 265 00:13:50,371 --> 00:13:53,332 argument that, well, these are neural networks. 266 00:13:53,332 --> 00:13:56,335 They're functioning the same way as the brain. 267 00:13:56,710 --> 00:13:59,713 We don't have to give it a precise algorithm because 268 00:14:01,257 --> 00:14:04,468 it has more capacities for self-learning and self adjustment. 269 00:14:04,468 --> 00:14:07,471 And is the 270 00:14:08,013 --> 00:14:10,975 is the expectation there that something about 271 00:14:11,767 --> 00:14:14,770 that architecture is going to let it get past 272 00:14:16,021 --> 00:14:19,650 what normally applies to other computer systems or... 273 00:14:21,485 --> 00:14:22,486 It's a good question. 274 00:14:22,486 --> 00:14:25,906 There's a, I remember there was a there was a French institute 275 00:14:25,906 --> 00:14:31,453 some years ago that had begun to try and create neural networks with the scale 276 00:14:31,453 --> 00:14:34,874 that would attempt to approximate some of the cognition of the human brain. 277 00:14:35,541 --> 00:14:38,544 And, you know, they had the the resources of the French government. 278 00:14:38,544 --> 00:14:42,965 They had an immense amount of computational power behind them. 279 00:14:43,382 --> 00:14:46,844 And even with all that, they were able to to roughly get to, 280 00:14:46,886 --> 00:14:48,137 if I remember the number correctly. 281 00:14:48,137 --> 00:14:50,681 So don't hold me on the on the citation. 282 00:14:50,681 --> 00:14:53,684 Something like 2 to 3% of brain function. 283 00:14:54,476 --> 00:14:57,021 You know, this is immensely complex 284 00:14:57,021 --> 00:15:00,232 because network complexity is does not grow linearly. 285 00:15:00,232 --> 00:15:01,400 It grows exponentially. Right? 286 00:15:01,400 --> 00:15:06,280 To gain, to grow a system that can get strong enough, 287 00:15:08,407 --> 00:15:09,783 to be a big enough neural network, 288 00:15:09,783 --> 00:15:12,786 even even that is, 289 00:15:13,537 --> 00:15:16,332 is not going to give you human cognition, 290 00:15:16,332 --> 00:15:20,044 because we think about the the use cases that we use neural networks for. 291 00:15:20,628 --> 00:15:23,881 There's, there's really there's two major ones in machine learning. 292 00:15:23,881 --> 00:15:25,090 One is classification. 293 00:15:25,090 --> 00:15:28,636 Basically telling, you know, you look at if you take as input 294 00:15:28,636 --> 00:15:31,931 something and your brain tells you what the thing is, right? 295 00:15:31,931 --> 00:15:35,476 Our brains are that naturally you have image recognition software 296 00:15:35,476 --> 00:15:37,019 or other neural networks that do the same thing. 297 00:15:37,019 --> 00:15:40,022 They take in inputs and they identify something. 298 00:15:40,481 --> 00:15:41,649 the other one is regression. 299 00:15:41,649 --> 00:15:44,902 It's making a, a relationship between two quantities. 300 00:15:44,902 --> 00:15:49,323 So, and allowing you to do a sort of what we call an in-sample prediction. 301 00:15:49,323 --> 00:15:49,573 Right. 302 00:15:49,573 --> 00:15:54,036 If you're if, you know, you know, house size A 303 00:15:54,036 --> 00:15:56,121 and house size B and you see something in the middle, 304 00:15:56,121 --> 00:15:58,457 what should the cost of the home be, right? 305 00:15:58,457 --> 00:16:02,294 That's a that's a regressive, I'm sorry, it's an example of regression. 306 00:16:02,294 --> 00:16:04,088 It's not regressive, but the, 307 00:16:05,089 --> 00:16:07,716 and those are the two main neural network applications. 308 00:16:07,716 --> 00:16:10,803 And there's now, there's variations on them 309 00:16:10,803 --> 00:16:13,847 now, those the base layers of those are what's been built up 310 00:16:13,847 --> 00:16:16,725 to create these transformers that LMS are built on. 311 00:16:16,725 --> 00:16:19,728 So, they exist and they've been extended. 312 00:16:19,937 --> 00:16:22,481 But you 313 00:16:22,481 --> 00:16:25,484 I would say we are we are too far away from, 314 00:16:26,819 --> 00:16:28,612 actual 315 00:16:28,612 --> 00:16:32,950 human brain function to predict that it will it will continue along 316 00:16:32,950 --> 00:16:37,746 that road, even even on any, uninterrupted path. 317 00:16:37,746 --> 00:16:40,541 And finally get to how the human brain works, 318 00:16:40,541 --> 00:16:44,795 there's going to be breaks or discontinuities in the progress 319 00:16:45,045 --> 00:16:48,048 and that and who knows, some of those may scupper the whole thing. 320 00:16:48,090 --> 00:16:48,382 Right? 321 00:16:48,382 --> 00:16:52,094 You may only be able to get so far with the applications of AI. 322 00:16:52,094 --> 00:16:53,929 You just can't go further. 323 00:16:53,929 --> 00:16:57,307 They estimate that the cost to train training is what's most expensive 324 00:16:57,307 --> 00:17:01,270 right now for these systems, to go beyond GPT four. 325 00:17:01,478 --> 00:17:04,398 so for little 040, 326 00:17:04,398 --> 00:17:09,528 to GPT five, GPT 5,6,7 is trillions of dollars per iteration 327 00:17:10,154 --> 00:17:13,741 to accumulate all the data to do the training, the electricity cost 328 00:17:13,741 --> 00:17:15,242 to run the models. 329 00:17:15,242 --> 00:17:18,245 at some point we're going to we're going to run out of money. 330 00:17:18,787 --> 00:17:21,665 We just, you know, humanity will either have to decide, 331 00:17:21,665 --> 00:17:23,375 okay, we're invested in this or we're not. 332 00:17:23,375 --> 00:17:26,587 We just can't keep spending that 333 00:17:26,587 --> 00:17:29,590 those amount of resources to develop a system like this. 334 00:17:29,631 --> 00:17:32,342 so I don't, you know, fortunately, I don't have to make. 335 00:17:32,342 --> 00:17:33,552 I'm glad I'm not in charge of an 336 00:17:33,552 --> 00:17:36,555 AI company that I'd have to make that choice, but, 337 00:17:37,598 --> 00:17:38,849 there's, 338 00:17:38,849 --> 00:17:41,060 you know, whether it's the actual technology, whether it's 339 00:17:41,060 --> 00:17:44,188 the mathematics behind it or the funding to generate these things, 340 00:17:44,521 --> 00:17:47,900 any, any combination of those can cause this thing to fail. 341 00:17:48,484 --> 00:17:51,445 So we have to almost have the perfect storm to go up the 342 00:17:51,445 --> 00:17:54,531 the the graph of progress towards human 343 00:17:54,531 --> 00:17:57,534 cognition, at least in my view. 344 00:17:57,576 --> 00:17:59,119 Yeah. Those are helpful. 345 00:17:59,119 --> 00:18:01,872 One the point you made there at the end, those 346 00:18:01,872 --> 00:18:03,749 literal physical constraints. 347 00:18:03,749 --> 00:18:06,752 I mean, we have all kinds of energy available, but 348 00:18:07,044 --> 00:18:10,089 that as you try to exponentially scale 349 00:18:10,089 --> 00:18:13,092 up the amount of energy you're literally running into. 350 00:18:13,884 --> 00:18:17,221 I don't know what the scale is, but it's registering on 351 00:18:17,888 --> 00:18:21,141 things like grid capacity and electricity generation capacity 352 00:18:21,141 --> 00:18:22,392 and that kind of thing. At some point. 353 00:18:23,519 --> 00:18:25,938 Not a small thing. 354 00:18:25,938 --> 00:18:29,274 no. The other thing that was really helpful, for me 355 00:18:29,274 --> 00:18:33,862 and what you said there was AI is really only doing at the base 356 00:18:33,862 --> 00:18:36,865 the two operations, 357 00:18:37,866 --> 00:18:40,911 classification, classifying objects. 358 00:18:41,120 --> 00:18:42,955 And it's gotten 359 00:18:42,955 --> 00:18:46,166 sophisticated at that by by training 360 00:18:47,751 --> 00:18:49,920 humans, coding examples. 361 00:18:49,920 --> 00:18:53,090 and then it going off of those examples 362 00:18:54,341 --> 00:18:57,803 and regression problems, which I'm not 363 00:18:59,471 --> 00:19:02,474 not familiar with, the mathematics of those, but 364 00:19:03,642 --> 00:19:05,686 again, those are fairly simple operations. 365 00:19:05,686 --> 00:19:08,689 I mean, takes a lot of power to carry them out. 366 00:19:10,607 --> 00:19:13,610 fairly simple compared to 367 00:19:13,694 --> 00:19:16,697 the human mind and living a human life. 368 00:19:19,324 --> 00:19:20,284 so, yeah, 369 00:19:20,284 --> 00:19:23,287 I think just breaking it down and saying it does these two things 370 00:19:24,163 --> 00:19:28,000 and builds on them and puts them to good use, to me really helps 371 00:19:28,000 --> 00:19:32,087 to see the a little bit of see the limits or demystify things a little bit. 372 00:19:33,297 --> 00:19:34,548 Yeah. 373 00:19:34,548 --> 00:19:36,800 And I, one thing I would add in to 374 00:19:36,800 --> 00:19:39,761 is that we you see on, 375 00:19:40,012 --> 00:19:42,014 maybe on social media at points people 376 00:19:42,014 --> 00:19:46,101 some very like AI negative people who like AI is not any good at anything. 377 00:19:46,393 --> 00:19:49,313 And they'll bring up an example like I saw one on LinkedIn the other day 378 00:19:49,313 --> 00:19:52,566 that there was a there was like a stone pillar in a field. 379 00:19:52,858 --> 00:19:56,236 And on one side of it had like the rear half of a cow. 380 00:19:56,862 --> 00:19:59,031 And then the stone pillar was maybe ten feet wide. 381 00:19:59,031 --> 00:20:02,117 And on the other end of the stone pillar was like the head of a cow poking out. 382 00:20:02,659 --> 00:20:05,871 And now we would say like, okay, there's two cows in the picture, 383 00:20:06,330 --> 00:20:09,750 but that like an AI system, might put a box around it and say, 384 00:20:09,791 --> 00:20:12,169 here's one cow and its length is 15ft, 385 00:20:13,420 --> 00:20:14,171 right? 386 00:20:14,171 --> 00:20:15,714 And so they would look at an example like that. 387 00:20:15,714 --> 00:20:18,217 And we know, humans know the error. 388 00:20:18,217 --> 00:20:20,636 We we see it and we're like, oh, okay. 389 00:20:20,636 --> 00:20:22,638 a computer system doesn't. 390 00:20:22,638 --> 00:20:26,266 And so I think those, those examples get brought up to say, 391 00:20:26,516 --> 00:20:30,187 like AI is of no, like it's never going to be a threat. 392 00:20:30,187 --> 00:20:34,066 It's, but I think what it points to is there are, 393 00:20:34,733 --> 00:20:38,111 there are there are limits and easy mistakes that AI makes, 394 00:20:38,111 --> 00:20:41,323 especially early ChatGPT days made tons of mistakes. 395 00:20:42,032 --> 00:20:44,618 And so, but humans 396 00:20:44,618 --> 00:20:48,038 will will continue to try and sort of plug the holes in the dam. 397 00:20:48,038 --> 00:20:48,247 Right. 398 00:20:48,247 --> 00:20:50,207 They'll we'll say, okay, here's a, here's an error 399 00:20:50,207 --> 00:20:53,377 that we're getting ridiculed for this thing that the system can't do. 400 00:20:53,585 --> 00:20:55,045 We'll fix the thing. 401 00:20:55,045 --> 00:20:58,840 and I'm, I'm led to believe we're going to keep finding more of those mistakes 402 00:20:58,840 --> 00:21:00,133 that humans have to fix. 403 00:21:00,133 --> 00:21:03,679 And it'll it may get better and better and better, but it, again, 404 00:21:03,720 --> 00:21:06,098 you're you're going to keep finding, 405 00:21:07,057 --> 00:21:10,060 reasons, reasons to make fun of it ultimately. 406 00:21:10,352 --> 00:21:11,770 Now, that doesn't mean it's not powerful. 407 00:21:11,770 --> 00:21:14,856 It's not something to think about, but it's, you know, those 408 00:21:15,107 --> 00:21:21,196 I think the, the, the satirical view of AI is becoming more and more prevalent. 409 00:21:21,196 --> 00:21:23,240 And I don't know what that's going to do to people's view of it. 410 00:21:23,240 --> 00:21:24,950 probably very little. 411 00:21:24,950 --> 00:21:29,079 But, we want I just want to I want to try and think clearly about 412 00:21:29,121 --> 00:21:33,125 is this a like what is the truth here about AI? Yes. 413 00:21:33,125 --> 00:21:34,835 That's a silly mistake that it made. 414 00:21:34,835 --> 00:21:37,713 But does that invalidate the whole project? 415 00:21:37,713 --> 00:21:38,880 Well, of course not. Right. 416 00:21:38,880 --> 00:21:41,967 That's you know, amongst the billions of things it can do. 417 00:21:42,009 --> 00:21:44,886 Here's one that you thought was silly. All right. So move on. 418 00:21:46,763 --> 00:21:48,890 Yeah, exactly. 419 00:21:48,890 --> 00:21:51,893 I think just to pursue that a little bit, though, 420 00:21:53,103 --> 00:21:56,648 and. Yeah, I like the case you've made, kind of, 421 00:21:57,899 --> 00:21:59,818 limits and it's limited. 422 00:21:59,818 --> 00:22:02,821 but would you agree that we can get 423 00:22:03,780 --> 00:22:07,075 we can get a lot better at using some of the tools even without 424 00:22:08,410 --> 00:22:11,413 even without really fundamental changes in capacity. 425 00:22:11,663 --> 00:22:14,374 like, for example, I was, 426 00:22:14,374 --> 00:22:17,252 listening to an accountant 427 00:22:17,252 --> 00:22:19,713 and his statement was, there's 428 00:22:19,713 --> 00:22:22,716 going to come a time sooner or later where 429 00:22:23,175 --> 00:22:26,261 you're basic bookkeeping, categorizing transactions, and so on. 430 00:22:27,554 --> 00:22:30,349 Somebody is going to come up with a tool that does that well enough 431 00:22:30,349 --> 00:22:34,895 that it's barely worth your time to have a human bookkeeper go through 432 00:22:36,688 --> 00:22:38,815 in detail, because it's going to get it 433 00:22:38,815 --> 00:22:41,151 close enough, especially for the purposes of small business. 434 00:22:41,151 --> 00:22:42,527 It's going to get it close enough that 435 00:22:43,862 --> 00:22:46,365 it's not going to matter. 436 00:22:46,365 --> 00:22:48,575 And I don't know, that kind of strikes me as plausible. 437 00:22:48,575 --> 00:22:50,327 It also doesn't strike me as requiring anything 438 00:22:50,327 --> 00:22:54,081 radically new from AI, maybe just some human ingenuity 439 00:22:54,081 --> 00:22:57,209 and how to how to apply it right to the process. 440 00:22:58,752 --> 00:22:59,086 I don't know. 441 00:22:59,086 --> 00:23:02,089 Does that sounds like a fair estimate. 442 00:23:02,672 --> 00:23:03,298 Oh, I think so. 443 00:23:03,298 --> 00:23:06,551 I think there's, like, accounting as an application of AI. 444 00:23:06,551 --> 00:23:07,719 Sure, you can do the. 445 00:23:07,719 --> 00:23:10,180 The mechanics are not tremendously complicated. 446 00:23:10,180 --> 00:23:13,308 You know, they take care and some background knowledge, but the, 447 00:23:13,392 --> 00:23:16,520 nothing about the mathematics is incredibly difficult. 448 00:23:17,020 --> 00:23:21,942 But the, I think, like with that example, one of the constraints we're going to 449 00:23:21,942 --> 00:23:26,738 encounter is at the end of it, you, you make a legally binding declaration. 450 00:23:26,947 --> 00:23:29,366 Right. These are the these are the truth of the accounts. 451 00:23:29,366 --> 00:23:33,954 And so who who is going to to put their legal weight behind that. 452 00:23:34,496 --> 00:23:36,873 Is it going to be the, you know, is it going to be the individual 453 00:23:36,873 --> 00:23:39,876 who's used the system like I certify this is correct, 454 00:23:40,001 --> 00:23:43,004 or are we going to try and pass that off to the AI system? 455 00:23:43,588 --> 00:23:45,048 So like, well no, the AI did it. 456 00:23:45,048 --> 00:23:46,883 So if there's a mistake it isn't my fault. 457 00:23:46,883 --> 00:23:48,135 It's the AI’s fault. 458 00:23:48,135 --> 00:23:52,848 And if we do that which AI companies are going to shoulder the legal burden 459 00:23:52,848 --> 00:23:56,685 for that of you know, that's I think it's, it's going to sort of 460 00:23:56,685 --> 00:23:59,688 be the, the, the fingers pointed at one another issue 461 00:23:59,855 --> 00:24:03,483 with why hasn't this become, why hasn't this use case become widespread? 462 00:24:03,567 --> 00:24:06,736 I think it's because no one is confident enough yet. 463 00:24:07,362 --> 00:24:12,242 That and I'll say yet because I think it it may come a day that people will be. 464 00:24:12,242 --> 00:24:14,411 And this will the dam will break loose. 465 00:24:14,411 --> 00:24:17,080 But I don't think anyone yet is ready to sign up 466 00:24:17,080 --> 00:24:20,542 and put their legal business life behind this there. 467 00:24:20,625 --> 00:24:24,463 I think people are still waiting for for more and more evidence that it's okay. 468 00:24:25,547 --> 00:24:25,797 Yeah. 469 00:24:25,797 --> 00:24:27,841 And that could apply to. 470 00:24:27,841 --> 00:24:30,635 To a lot of pieces where you have software doing a lot of it. 471 00:24:30,635 --> 00:24:34,598 I mean, it's thinking, you know, friends that build roof trusses 472 00:24:35,849 --> 00:24:38,685 and this isn't necessarily AI, the software is doing basically 473 00:24:38,685 --> 00:24:42,689 the engineering and saying, yes, this, this truss will meet specs or it won't. 474 00:24:43,899 --> 00:24:47,736 but if they're actually doing a job that requires an engineer's stamp, 475 00:24:48,737 --> 00:24:51,364 well, it's still got to be an engineer that does it, which is an extra step. 476 00:24:51,364 --> 00:24:54,367 That liability part. 477 00:24:54,618 --> 00:24:55,535 They're so good. 478 00:24:55,535 --> 00:24:57,579 Yeah. 479 00:24:57,579 --> 00:24:58,079 Okay. 480 00:24:58,079 --> 00:25:01,082 So I'm guessing given the arguments you've made, that, 481 00:25:02,209 --> 00:25:05,212 you know, artificial general intelligence, 482 00:25:05,295 --> 00:25:08,298 actual purpose, purposeful 483 00:25:08,298 --> 00:25:12,177 behavior by AI systems and so on. 484 00:25:12,302 --> 00:25:15,514 I'm taking it you're very skeptical of that. 485 00:25:17,432 --> 00:25:20,519 Or very skeptical that being in the all things. 486 00:25:22,229 --> 00:25:23,688 Yeah, I'd say that's true. 487 00:25:23,688 --> 00:25:26,983 I have a good degree of skepticism about that. The 488 00:25:28,276 --> 00:25:30,070 I think that we have humans 489 00:25:30,070 --> 00:25:33,073 have struggled to define what AGI actually is. 490 00:25:33,198 --> 00:25:35,534 Right. How do you test it. How do you verify it. 491 00:25:35,534 --> 00:25:36,326 How do you. 492 00:25:36,326 --> 00:25:40,664 And so I think that that particular question of what is AGI is going to, 493 00:25:40,997 --> 00:25:43,959 remain in the academic circles for a while. 494 00:25:43,959 --> 00:25:46,461 It's going to, you know, we're going to there's going to be arguments for 495 00:25:46,461 --> 00:25:50,632 and against of various kinds that, may or may not prove fruitful. 496 00:25:50,840 --> 00:25:51,049 Right. 497 00:25:51,049 --> 00:25:54,052 I don't know that they're going to come to any conclusion. 498 00:25:54,970 --> 00:25:57,055 and I think in the background, AI companies 499 00:25:57,055 --> 00:26:00,308 are going to continue to build systems that more and more closely approximate, 500 00:26:01,059 --> 00:26:04,771 you know, human cognition, even though they we might say 501 00:26:04,771 --> 00:26:07,482 you're still light years away, but we're making progress. 502 00:26:07,482 --> 00:26:09,025 And that's probably true. 503 00:26:09,025 --> 00:26:11,820 And so, so I don't 504 00:26:13,196 --> 00:26:15,448 the yeah, I'm 505 00:26:15,448 --> 00:26:18,410 not tremendously worried about the development of AGI. 506 00:26:18,618 --> 00:26:21,580 you know, sometimes the cases of, 507 00:26:21,871 --> 00:26:25,000 chess or go these board games that have had a long and storied history 508 00:26:25,166 --> 00:26:29,546 in computation, I like, companies like DeepMind. 509 00:26:29,546 --> 00:26:33,091 So, which is now owned by Google, but that's out of London. 510 00:26:33,675 --> 00:26:36,970 have done some really neat work, like developing superhuman 511 00:26:37,137 --> 00:26:40,974 chess engines and go engines that play the game at a, at a level that surpasses 512 00:26:41,600 --> 00:26:42,809 human understanding. 513 00:26:42,809 --> 00:26:45,812 which is pretty cool, the way what they had to develop to do that, 514 00:26:46,104 --> 00:26:49,107 but those are still games with a, 515 00:26:49,482 --> 00:26:53,737 a defined feature space and defined rules and defined options. 516 00:26:53,737 --> 00:26:53,987 Right. 517 00:26:53,987 --> 00:26:58,074 You like when you can do that, when you can do that, when you can box 518 00:26:58,074 --> 00:27:01,077 in the system, the universe, you can make something really neat. 519 00:27:01,494 --> 00:27:04,998 But, boy, humanity resists being boxed in like that. 520 00:27:05,081 --> 00:27:06,041 We just don't. 521 00:27:06,041 --> 00:27:07,042 That's not our nature. 522 00:27:07,042 --> 00:27:11,630 And and so I think part of that is the root of my skepticism is, 523 00:27:12,130 --> 00:27:14,841 like if you look into a field called reinforcement 524 00:27:14,841 --> 00:27:17,844 learning, it's a it's a subpart of machine learning. 525 00:27:18,053 --> 00:27:19,804 But one of the to do it. 526 00:27:19,804 --> 00:27:24,184 Well, this is how the, the, the chess engine from DeepMind was built. 527 00:27:26,144 --> 00:27:26,770 One of the things 528 00:27:26,770 --> 00:27:30,440 they have to do is write down, here's the rules of the game, and here's what, 529 00:27:31,024 --> 00:27:33,985 doing a certain good thing pays you, right? 530 00:27:33,985 --> 00:27:35,820 They call it a payoff function. 531 00:27:35,820 --> 00:27:38,823 And so if you, you know, if you take the, the opponent's pawn little piece, 532 00:27:39,157 --> 00:27:39,824 here's the payoff. 533 00:27:39,824 --> 00:27:42,160 Or if you take their queen, it's a bigger payoff. 534 00:27:42,160 --> 00:27:46,498 And so, if you can define that in chess, you can actually define that. 535 00:27:47,374 --> 00:27:50,168 You can make a system that that does very well. 536 00:27:50,168 --> 00:27:54,339 But think about a dating relationship or a marriage like. 537 00:27:54,756 --> 00:27:57,842 And we pretty quickly realize the our attempts 538 00:27:57,842 --> 00:28:00,845 to quantify and define value. 539 00:28:01,680 --> 00:28:04,474 It's we're in a different layer of abstraction. 540 00:28:04,474 --> 00:28:07,477 We you can't the these don't go together. 541 00:28:07,644 --> 00:28:10,355 And so you know those are 542 00:28:10,355 --> 00:28:13,358 some of the, I guess musings to me of why 543 00:28:13,775 --> 00:28:18,029 I think AGI is still some ways off, if we even can define it. 544 00:28:20,615 --> 00:28:22,701 Yeah. 545 00:28:22,701 --> 00:28:25,995 So along the lines of, you know, AGI. 546 00:28:25,995 --> 00:28:29,290 And again, these questions about intelligence, 547 00:28:29,332 --> 00:28:32,335 the Turing test. 548 00:28:32,627 --> 00:28:36,423 I used to use a version of this with students, long before, 549 00:28:37,132 --> 00:28:40,760 you know, generative AI was on the on the horizons 550 00:28:41,886 --> 00:28:45,432 came from my, my days in college, philosophy 101. 551 00:28:45,432 --> 00:28:49,436 And, you know, basically we're asked to I don't have a technical definition 552 00:28:49,436 --> 00:28:52,522 in front of me, but we're asked to consider the question, 553 00:28:54,107 --> 00:28:56,901 you know, if a computer can give the same answers 554 00:28:56,901 --> 00:29:00,864 so that if you're communicating with it through text or whatever, you can't tell 555 00:29:00,864 --> 00:29:03,867 if there's a computer or a person on the other end. 556 00:29:04,993 --> 00:29:07,245 well, then should we ascribe it 557 00:29:07,245 --> 00:29:11,207 the same mental life and intelligence that we ascribe to a person? 558 00:29:12,625 --> 00:29:13,877 yeah. How do you think about that? 559 00:29:13,877 --> 00:29:17,672 And maybe you have a more precise, computer science definition of the test. 560 00:29:18,965 --> 00:29:20,049 No, the. 561 00:29:20,049 --> 00:29:22,510 The Turing test was all the way back in the 1950s. 562 00:29:22,510 --> 00:29:26,264 Alan Turing, when he was working in the early theory of computation. 563 00:29:26,681 --> 00:29:29,142 no, you you had a good working definition. 564 00:29:29,142 --> 00:29:32,020 You know, if you're if, you know, there's two rooms behind you 565 00:29:32,020 --> 00:29:34,981 and you're getting text inputs from questions you ask, 566 00:29:35,106 --> 00:29:38,109 how do you tell if one is a human and one's a computer, and if it's, 567 00:29:38,568 --> 00:29:42,155 you know, a system that is statistically indistinguishable 568 00:29:42,155 --> 00:29:45,158 from a human, we would say, okay, passes the Turing test. 569 00:29:45,241 --> 00:29:48,620 I think we are actually already there with that, with the Turing test, 570 00:29:48,620 --> 00:29:52,123 I think we have systems that you can, you can write 571 00:29:52,123 --> 00:29:56,711 fairly complex questions to, and this is always an interesting, like, 572 00:29:56,711 --> 00:30:01,299 thought experiment of like, what question would you ask that a, artificial 573 00:30:01,299 --> 00:30:04,427 intelligence system that you know is an AI system would fail to answer well. 574 00:30:04,427 --> 00:30:05,637 Right. 575 00:30:05,637 --> 00:30:09,724 that's kind of an interesting sort of a subfield here, but I think we're 576 00:30:10,099 --> 00:30:12,685 I think we already have systems that pass the Turing test. 577 00:30:12,685 --> 00:30:16,105 The, but I think this it's 578 00:30:17,482 --> 00:30:20,276 the current application of the Turing test, I think is a moving, 579 00:30:20,276 --> 00:30:23,947 a bit of a moving standard in that you, 580 00:30:24,531 --> 00:30:26,699 you have a system that seems to pass the Turing test, 581 00:30:26,699 --> 00:30:29,702 and then humans get used to how it communicates. 582 00:30:29,828 --> 00:30:30,078 Right? 583 00:30:30,078 --> 00:30:33,081 You begin to pick up the flavor or the nuance of, like, 584 00:30:33,248 --> 00:30:35,542 this sounds like it was AI generated, right? 585 00:30:35,542 --> 00:30:36,459 I think we all, 586 00:30:36,459 --> 00:30:39,462 if we've read AI generated text, we sort of know what that feels like. 587 00:30:39,587 --> 00:30:42,298 It's very it's very linear, very clear. 588 00:30:42,298 --> 00:30:44,509 There's no halting pausing. 589 00:30:44,509 --> 00:30:48,680 There's it's like it's mechanical in a sense. 590 00:30:48,680 --> 00:30:50,723 And so, 591 00:30:50,723 --> 00:30:52,684 now what an AI company might say is, 592 00:30:52,684 --> 00:30:55,687 okay, I'll adjust my, my generation algorithm 593 00:30:55,770 --> 00:30:58,982 to make it a little more clunky, a little more human like. 594 00:30:59,399 --> 00:30:59,649 Right. 595 00:30:59,649 --> 00:31:00,817 And then it it 596 00:31:00,817 --> 00:31:03,987 passes the new standard of the Turing test that people can't distinguish. 597 00:31:04,404 --> 00:31:09,409 but I whenever you have a test that relies on human perception of something 598 00:31:09,909 --> 00:31:13,997 that, like, we grow, we learn, right, that that the benchmark 599 00:31:13,997 --> 00:31:15,540 for that test is going to change over time. 600 00:31:15,540 --> 00:31:18,918 So, you know, I think we we have systems 601 00:31:18,918 --> 00:31:21,921 that your early GPT is probably passed the Turing test. 602 00:31:22,380 --> 00:31:23,965 Now the standards even higher. 603 00:31:23,965 --> 00:31:24,173 Right. 604 00:31:24,173 --> 00:31:26,926 For a system to be indistinguishable from a human. 605 00:31:26,926 --> 00:31:29,929 and it's not because the systems have changed 606 00:31:30,138 --> 00:31:32,098 so much as the we have changed and learned. 607 00:31:32,098 --> 00:31:34,058 And so, We've learned how to. 608 00:31:34,058 --> 00:31:35,393 How to deal with it. 609 00:31:35,393 --> 00:31:37,687 Yeah, that's a good perspective. 610 00:31:37,687 --> 00:31:38,021 And just. 611 00:31:38,021 --> 00:31:39,981 Yeah, as we're talking about this 612 00:31:39,981 --> 00:31:42,984 and it occurs to me that it'd be very interesting to read what 613 00:31:43,151 --> 00:31:47,655 philosophers wrote about the Turing test and how that has changed as the 614 00:31:48,865 --> 00:31:49,574 computer 615 00:31:49,574 --> 00:31:52,327 computer capacities have gone up, because that was the angle 616 00:31:52,327 --> 00:31:55,121 from which I came, which which I would have encountered. 617 00:31:55,121 --> 00:31:58,124 It was kind of philosophy of mind. And, 618 00:31:58,958 --> 00:32:01,210 what is it about the human mind, 619 00:32:01,210 --> 00:32:04,213 what makes mind and so on. 620 00:32:04,255 --> 00:32:07,133 And it does strike me that those kinds of questions are really 621 00:32:07,133 --> 00:32:10,136 kind of relevant how we're thinking about AI here, because 622 00:32:11,512 --> 00:32:14,766 if you're coming into the whole discussion, 623 00:32:16,309 --> 00:32:19,312 with a philosophy that says. 624 00:32:21,314 --> 00:32:22,398 You know, we can explain this 625 00:32:22,398 --> 00:32:26,319 all physically or we can explain this by the functions of physical things. 626 00:32:26,319 --> 00:32:28,655 The human mind is just 627 00:32:28,655 --> 00:32:31,950 just is the human brain, or it's the functions 628 00:32:31,950 --> 00:32:34,953 and processes that the human brain runs. 629 00:32:35,036 --> 00:32:36,329 Then it seems to be a fair question. 630 00:32:36,329 --> 00:32:38,164 Well, can we duplicate it with something else? 631 00:32:39,999 --> 00:32:41,167 yeah. 632 00:32:41,167 --> 00:32:42,418 If we're coming in as Christians, 633 00:32:42,418 --> 00:32:45,421 we still may have a large variety of philosophy of mind. 634 00:32:46,047 --> 00:32:49,050 We're going to believe that the brain is important. 635 00:32:50,885 --> 00:32:52,929 but generally, 636 00:32:52,929 --> 00:32:54,097 you know, God is a spirit. 637 00:32:54,097 --> 00:32:57,100 God has a mind without having a brain. 638 00:32:57,308 --> 00:33:00,311 generally we believe that our mind is. 639 00:33:00,979 --> 00:33:03,481 In a certain sense, independent of our brain, 640 00:33:03,481 --> 00:33:06,484 although obviously it functions through our brain. 641 00:33:06,693 --> 00:33:08,653 yeah. 642 00:33:08,653 --> 00:33:12,657 I just have to wonder how much that plays into the whole discussion. 643 00:33:14,534 --> 00:33:17,245 if you simply are a materialist, 644 00:33:17,245 --> 00:33:20,039 then it seems like so kind of naturally lead to this 645 00:33:20,039 --> 00:33:23,042 more AI optimistic view of things. 646 00:33:24,043 --> 00:33:26,754 I don't know how that maps into the academic landscape 647 00:33:26,754 --> 00:33:29,757 either, but. 648 00:33:30,174 --> 00:33:30,466 There's. 649 00:33:30,466 --> 00:33:31,509 There's certainly 650 00:33:31,509 --> 00:33:35,263 in the academic world, there's certainly a sense of techno optimism. 651 00:33:36,222 --> 00:33:37,223 And it's, 652 00:33:37,223 --> 00:33:40,727 And, you know, they treat it again if you if you come at it 653 00:33:40,727 --> 00:33:45,064 from a largely materialist worldview, you end up with just, 654 00:33:46,774 --> 00:33:48,234 you know, an understanding that 655 00:33:48,234 --> 00:33:52,697 that all I'm, all I'm creating technically is a smaller approximation to me. 656 00:33:52,697 --> 00:33:55,491 And it's going to get closer and closer and closer over time. 657 00:33:55,491 --> 00:34:00,079 And so there's no there isn't really a connection between soul, spirit 658 00:34:00,079 --> 00:34:03,374 and mind in the, in the secular worldview 659 00:34:03,958 --> 00:34:06,586 because we're just, 660 00:34:06,586 --> 00:34:06,794 you know, 661 00:34:06,794 --> 00:34:10,757 we're just a random collection of atoms that, that have no lasting eternal value. 662 00:34:10,757 --> 00:34:13,760 I think that's the conclusion, 663 00:34:14,385 --> 00:34:16,554 whereas I think like that is from a Christian perspective. 664 00:34:16,554 --> 00:34:21,350 That's where we come at AI with a different view of like, you know. 665 00:34:21,559 --> 00:34:21,893 Yeah. 666 00:34:21,893 --> 00:34:26,105 So you can create a neural network that is that has the trillions of connections 667 00:34:26,105 --> 00:34:27,523 that our brain does. 668 00:34:27,523 --> 00:34:30,526 you're still missing a piece that you can't simulate. 669 00:34:30,693 --> 00:34:34,530 There's a spirit, there's a soul, there's a, humans are, 670 00:34:35,031 --> 00:34:38,076 you know, by a mathematical definition, irrational creatures. 671 00:34:39,243 --> 00:34:40,286 But in 672 00:34:40,286 --> 00:34:43,915 if we because we are we, you know, we look at we do things that are silly, 673 00:34:43,956 --> 00:34:48,419 not in our best interest, but in from a theological perspective, 674 00:34:48,419 --> 00:34:51,798 that makes sense because we, we identify this conflict 675 00:34:51,798 --> 00:34:55,009 of our of our sin nature and our redeemed nature in Christ. 676 00:34:55,009 --> 00:35:00,515 And so, if you don't have theology, this gets leads to a natural techno optimism. 677 00:35:00,890 --> 00:35:04,602 But I think only in the, in the sense that it is self beneficial. 678 00:35:05,186 --> 00:35:05,394 Right. 679 00:35:05,394 --> 00:35:09,482 Like I'm optimistic technically, if it benefits me, right? 680 00:35:09,482 --> 00:35:13,444 if it doesn't, if it's a threat to me, then I'm not as optimistic about it. 681 00:35:13,444 --> 00:35:15,822 And I have no moral qualms either way. Right. 682 00:35:15,822 --> 00:35:17,490 That's the materialist view. 683 00:35:17,490 --> 00:35:20,368 It's not a moral question. It's it's pragmatic. 684 00:35:21,869 --> 00:35:22,328 Yeah. 685 00:35:22,328 --> 00:35:25,581 So actually, while I was getting ready for this episode, 686 00:35:25,623 --> 00:35:28,793 I was sitting in my little, 687 00:35:30,211 --> 00:35:33,673 office at the school I help with, and I looked out the window 688 00:35:33,923 --> 00:35:37,176 and I noticed plants, flowers. 689 00:35:37,260 --> 00:35:39,095 It's a beautiful sunny day. 690 00:35:39,095 --> 00:35:42,014 And butterflies flitting around 691 00:35:42,014 --> 00:35:43,891 on top of there. 692 00:35:43,891 --> 00:35:46,978 And. Yeah, so I'm drawn into the beauty. 693 00:35:47,353 --> 00:35:49,230 Something a lot of people are. 694 00:35:49,230 --> 00:35:53,109 And so just got me thinking, you know, those are the things we paint. 695 00:35:53,985 --> 00:35:56,696 we draw pictures of, 696 00:35:56,696 --> 00:36:00,658 But not only that, like, we really have, we really have dug into that. 697 00:36:00,658 --> 00:36:03,035 What is the life cycle of the flower? 698 00:36:03,035 --> 00:36:06,956 How can we breed the flower to maximize blooms? 699 00:36:07,957 --> 00:36:10,960 You know, we've traced the life cycle of those butterflies 700 00:36:11,419 --> 00:36:14,422 and learned how they function. 701 00:36:14,422 --> 00:36:18,176 learned the biology of how they function, the life cycle, all of that. 702 00:36:19,135 --> 00:36:20,136 and, you know, I even had 703 00:36:20,136 --> 00:36:23,431 to think about a book we're using in our, in our science program. 704 00:36:24,056 --> 00:36:26,601 It's called The Girl Who Drew Butterflies, 705 00:36:26,601 --> 00:36:29,812 and it tells the story of a girl who was a painter. 706 00:36:30,730 --> 00:36:32,857 Her art, but just was really 707 00:36:32,857 --> 00:36:35,818 was really closely observing, 708 00:36:35,943 --> 00:36:38,237 you know, insects, butterflies came to understand 709 00:36:38,237 --> 00:36:41,240 these stages of metamorphosis and so on. 710 00:36:41,240 --> 00:36:45,328 when a lot of people, at a time when a lot of people didn't understand those, 711 00:36:46,412 --> 00:36:49,081 and so just kind of really hit me like, okay, 712 00:36:49,081 --> 00:36:52,501 this is what humans do with things that we see and pay attention to. 713 00:36:53,419 --> 00:36:58,549 is there any reason to think that AI 714 00:37:00,426 --> 00:37:03,012 models or agents, 715 00:37:03,012 --> 00:37:06,390 would do any of the same thing with have any of that same, 716 00:37:07,350 --> 00:37:07,808 really It's a 717 00:37:07,808 --> 00:37:10,770 relationship, ongoing relationship with reality. 718 00:37:11,354 --> 00:37:15,024 and does that get at any of the difference, this kind of difference 719 00:37:15,024 --> 00:37:19,320 we're talking about between a human mind and an AI model? 720 00:37:20,613 --> 00:37:20,863 yeah. 721 00:37:20,863 --> 00:37:23,866 I'd love to hear some of your thoughts on that. 722 00:37:24,825 --> 00:37:27,828 I love the question. I, 723 00:37:28,955 --> 00:37:29,914 I mean, my my thoughts. 724 00:37:29,914 --> 00:37:33,668 First, go to the the the the genesis of what we would call curiosity. 725 00:37:34,293 --> 00:37:36,212 Right? The the. 726 00:37:36,212 --> 00:37:38,631 You know, I've, I have children at home. 727 00:37:38,631 --> 00:37:40,216 Some of them are very curious. 728 00:37:40,216 --> 00:37:42,260 And what is it that. And we love curiosity. 729 00:37:42,260 --> 00:37:46,222 It's how we we develop an ever expanding knowledge of the world. 730 00:37:47,556 --> 00:37:49,976 you know, so on the one hand, I think AI 731 00:37:49,976 --> 00:37:52,979 could be constrained and that it has to like 732 00:37:54,021 --> 00:37:57,316 it doesn't really have the ability to explore aside from the digital space 733 00:37:57,316 --> 00:37:57,900 it inhabits. 734 00:37:57,900 --> 00:38:02,822 And so, you know, it could but, you know, that could be that could be overcome 735 00:38:03,155 --> 00:38:06,826 if a, you know, a human, we we give it inputs from a world beyond its own 736 00:38:06,826 --> 00:38:10,955 or we give it pictures and soil samples and scientific reports, and we give it 737 00:38:10,955 --> 00:38:15,084 enough information to, discern connections and patterns and systems. 738 00:38:16,627 --> 00:38:16,919 but I 739 00:38:16,919 --> 00:38:20,798 think at the, at the very bottom of a of a contrived system 740 00:38:20,798 --> 00:38:24,176 like that, we have like human volition, we've done it. 741 00:38:24,176 --> 00:38:28,764 We've, we we have to push AI into the world 742 00:38:28,764 --> 00:38:30,766 to begin to collect this information. 743 00:38:30,766 --> 00:38:33,769 so I don't know that, 744 00:38:34,270 --> 00:38:35,187 you know what? 745 00:38:35,187 --> 00:38:37,857 I'll say this in the, in the world AI inhabits, 746 00:38:37,857 --> 00:38:42,153 I think it already is acting as a curious agent within its world. 747 00:38:42,737 --> 00:38:42,945 Right. 748 00:38:42,945 --> 00:38:45,364 It's I think by its incentive structure. Right. 749 00:38:45,364 --> 00:38:47,867 It says like, I want to be the best AI system. 750 00:38:47,867 --> 00:38:52,580 So I'm going to collect everything that I can, and, and look for patterns 751 00:38:52,580 --> 00:38:57,585 and, and begin to create more and more of an approximation to reality. 752 00:38:58,711 --> 00:39:01,297 there's so much more to the world than just the digital space, right? 753 00:39:01,297 --> 00:39:05,301 So we have to we have to somehow connect AI with that space. 754 00:39:05,301 --> 00:39:07,845 That's a more challenging question. 755 00:39:07,845 --> 00:39:09,180 so I don't know that I would, 756 00:39:11,265 --> 00:39:12,224 I wouldn't 757 00:39:12,224 --> 00:39:15,853 I don't think that AI is going to have agency in that way. 758 00:39:16,187 --> 00:39:19,315 I think one of the problems where, like, I think we're already seeing an inflection 759 00:39:19,315 --> 00:39:22,318 point with that kind of pattern in AI, 760 00:39:22,818 --> 00:39:24,779 because they estimate the amount of the amount of data 761 00:39:24,779 --> 00:39:27,990 on the planet doubles every six months, something like that. 762 00:39:27,990 --> 00:39:31,369 So just the amount of data is exploding based on what we're creating 763 00:39:31,369 --> 00:39:34,372 movies, films, text, all these things. 764 00:39:35,247 --> 00:39:36,290 now what we're realizing 765 00:39:36,290 --> 00:39:40,211 is that the some of the data that's being fed back into these models 766 00:39:40,211 --> 00:39:43,339 to learn and remain current is actually AI generated. 767 00:39:43,798 --> 00:39:43,964 Right? 768 00:39:43,964 --> 00:39:47,301 So AI models are consuming AI generated things 769 00:39:47,802 --> 00:39:51,472 and taking it as as new truth sources. 770 00:39:51,472 --> 00:39:51,722 Right? 771 00:39:51,722 --> 00:39:55,559 So they're the they're building up on what they, what they've already created. 772 00:39:56,060 --> 00:39:59,647 And so any, you know, if there's any error or bias 773 00:39:59,647 --> 00:40:02,650 or anything that's built into those generated sets of data 774 00:40:02,900 --> 00:40:07,947 that's now been replicated and virally expanded across the AI space. 775 00:40:07,947 --> 00:40:13,244 And so, so I think, you know, I my one one thought I have 776 00:40:13,244 --> 00:40:16,330 is there's going to be a bit of a pendulum swing when you realize AI 777 00:40:16,330 --> 00:40:19,333 is spitting out nonsense, because that's all it knows. 778 00:40:19,834 --> 00:40:21,919 and the pendulum swing will say, well, don't 779 00:40:21,919 --> 00:40:25,089 don't let AI have access to anything new yet. 780 00:40:25,089 --> 00:40:25,464 Right? 781 00:40:25,464 --> 00:40:29,593 We need to curate what it's learning from, in order to make this truthful. 782 00:40:29,718 --> 00:40:35,099 And so, you know, we have this curious humans have this beautiful 783 00:40:35,099 --> 00:40:39,770 curiosity to know, you know what, why do things work the way that they do? 784 00:40:40,062 --> 00:40:40,271 Right. 785 00:40:40,271 --> 00:40:44,358 It's led to advances in cosmology into in biology, in mathematics. 786 00:40:44,817 --> 00:40:47,027 You know, we do this naturally. 787 00:40:48,028 --> 00:40:48,654 you know, I 788 00:40:48,654 --> 00:40:52,116 loved I loved seeing recently there was a paper that came out of, 789 00:40:52,700 --> 00:40:53,534 I think it might have been Google 790 00:40:53,534 --> 00:40:56,537 DeepMind as well, but they actually had developed a 791 00:40:56,537 --> 00:40:59,832 AI generated mathematical theorem proving system. 792 00:41:00,416 --> 00:41:01,709 They could give it a mathematical question. 793 00:41:01,709 --> 00:41:03,961 It could prove something mathematically. 794 00:41:03,961 --> 00:41:08,048 I said, that's fascinating because that's that's an area that I had thought 795 00:41:08,048 --> 00:41:12,344 that humans like that was human like creativity and intuition. 796 00:41:12,344 --> 00:41:14,513 That was not an accessible region. 797 00:41:14,513 --> 00:41:17,433 but I had but the more I am 798 00:41:17,433 --> 00:41:20,436 reading about it, the more I'm thinking, okay, 799 00:41:20,686 --> 00:41:24,356 they've not done human things, but they've they've made progress, right? 800 00:41:24,398 --> 00:41:26,650 They've done some things that I thought they couldn't do. 801 00:41:26,650 --> 00:41:30,488 And so, so there but I think that's how progress oftentimes goes in 802 00:41:30,488 --> 00:41:33,532 AI is they, they, it appears they can't do anything. 803 00:41:33,532 --> 00:41:37,453 And then there's this discontinuity and jump into now what they can do. 804 00:41:37,453 --> 00:41:40,748 So I think we've seen those now do I think that 805 00:41:41,332 --> 00:41:44,335 that AI is ever going to have the, the beautiful, 806 00:41:44,710 --> 00:41:47,796 observation of the butterflies, like the girl who drew butterflies 807 00:41:47,796 --> 00:41:50,883 to to understand the biological cycles of these things. 808 00:41:53,177 --> 00:41:55,804 again, I probably hold a healthy skepticism, just like AGI. 809 00:41:55,804 --> 00:41:56,388 I'm not. 810 00:41:56,388 --> 00:41:59,558 I don't think that AI has, because we haven't given it the reason to yet. 811 00:41:59,767 --> 00:42:02,269 We've not incentivized it. 812 00:42:02,269 --> 00:42:04,897 you know, we we learn because it's beautiful to learn 813 00:42:04,897 --> 00:42:07,900 and AI learns because either we tell it to 814 00:42:08,526 --> 00:42:10,444 or we threaten its extinction if it doesn't. 815 00:42:10,444 --> 00:42:13,739 So there's a, you know, I don't think 816 00:42:13,739 --> 00:42:16,116 we've it doesn't have the natural curiosity. 817 00:42:16,116 --> 00:42:19,119 We do. And I think that makes us human. 818 00:42:19,870 --> 00:42:21,789 Yeah. Thanks. Yeah. 819 00:42:21,789 --> 00:42:24,375 So I keep hearing you come back to the, “Not yets” a little bit. 820 00:42:24,375 --> 00:42:27,586 But if there is one theme in your limits that you talk about, 821 00:42:28,379 --> 00:42:31,382 kind of ontological limits of AI is 822 00:42:33,008 --> 00:42:36,428 is it always being downstream of humanity in some way or another? 823 00:42:37,179 --> 00:42:39,223 Yeah. We don't understand exactly what it's doing. 824 00:42:39,223 --> 00:42:43,561 We we set it up and we don't always understand exactly how it works. 825 00:42:43,561 --> 00:42:45,646 The whole black box effect. 826 00:42:45,646 --> 00:42:47,982 but it's downstream from you talking about 827 00:42:47,982 --> 00:42:51,360 the incentives we give it, the way we program it, the parameters, 828 00:42:52,403 --> 00:42:54,697 what we want it to do, 829 00:42:54,697 --> 00:42:57,324 the inputs we curate for it. 830 00:42:57,324 --> 00:43:00,869 so yeah, that is helpful. 831 00:43:01,579 --> 00:43:03,372 yeah. 832 00:43:03,372 --> 00:43:06,375 Any particular concluding thoughts you'd like with, 833 00:43:06,875 --> 00:43:09,878 like to, leave on this topic? 834 00:43:10,254 --> 00:43:11,338 I think just the. 835 00:43:11,338 --> 00:43:14,091 You know, you're you're right point to highlight. 836 00:43:14,091 --> 00:43:16,719 Like. Yeah. So there is a, there is a sense of not yet. 837 00:43:16,719 --> 00:43:21,015 And again part of me I go there because I in candor, 838 00:43:21,015 --> 00:43:24,018 I don't actually know, but I make a forecast that could have a, 839 00:43:24,268 --> 00:43:26,145 you know, there's a margin of error in the forecast 840 00:43:26,145 --> 00:43:29,356 and there's things that could go wrong or right that make it wrong. 841 00:43:29,732 --> 00:43:31,442 And so, 842 00:43:31,442 --> 00:43:33,986 I only forecast out as sort of as far 843 00:43:33,986 --> 00:43:38,449 as I could see, the, but you think about something like quantum computing. 844 00:43:38,449 --> 00:43:41,577 So the new technology that there's engineering 845 00:43:41,577 --> 00:43:44,830 challenges to solve, but say we solve them and we have quantum computers 846 00:43:45,247 --> 00:43:48,250 that can do incredibly quick calculation 847 00:43:48,792 --> 00:43:51,795 in, in some previously inaccessible problems. 848 00:43:52,379 --> 00:43:55,758 all of these developments, you know, AI developments, growth 849 00:43:55,758 --> 00:43:59,720 in neural networks or training or, you know, GPT eight say. 850 00:44:00,012 --> 00:44:01,764 Or quantum computing, right. 851 00:44:01,764 --> 00:44:06,226 They all solve sort of little niche things and they do it very well. 852 00:44:06,727 --> 00:44:10,856 But I guess the analogy I go back to in my own mind is that you 853 00:44:11,398 --> 00:44:14,610 without it, without a unified system or understanding of reality, 854 00:44:14,985 --> 00:44:17,905 all of these things will remain niche solutions, right? 855 00:44:17,905 --> 00:44:18,697 You don't. 856 00:44:18,697 --> 00:44:20,115 It'd be a little bit like, you know, you 857 00:44:20,115 --> 00:44:22,868 you try and fix something wrong with your body and you, you know, you, 858 00:44:22,868 --> 00:44:25,663 you sprain your finger and so you splint it. 859 00:44:25,663 --> 00:44:27,915 Right. You have a solution to the problem. 860 00:44:27,915 --> 00:44:29,750 You've not cured cancer, right? 861 00:44:29,750 --> 00:44:32,002 You've you've solved a problem. 862 00:44:32,002 --> 00:44:32,211 Right. 863 00:44:32,211 --> 00:44:34,963 And you've made progress towards the solution to the whole. 864 00:44:34,963 --> 00:44:37,758 But you there is not a systemic understanding yet. 865 00:44:37,758 --> 00:44:41,261 And so all of these developments, 866 00:44:42,471 --> 00:44:44,264 you know, I'll say not yet. 867 00:44:44,264 --> 00:44:46,433 My my prediction is it's a long way off. 868 00:44:46,433 --> 00:44:51,146 But, I think for all of these things to work in concert to develop anything 869 00:44:51,146 --> 00:44:56,068 that might be, an ontological break for artificial intelligence, 870 00:44:56,902 --> 00:44:59,238 just this to me, there's still too many unanswered questions. 871 00:44:59,238 --> 00:45:03,117 Is they don't none of these things see reality in the same way. 872 00:45:03,117 --> 00:45:06,412 Like quantum and classical computing also don't see reality in the same way. 873 00:45:06,412 --> 00:45:10,499 So like you gotta find a way to bridge that before these are effective together. 874 00:45:10,499 --> 00:45:11,709 And it's 875 00:45:11,709 --> 00:45:13,460 I think that there's 876 00:45:13,460 --> 00:45:16,338 we're finding more questions than we have answers to these days. 877 00:45:16,338 --> 00:45:19,258 And at least in my view. 878 00:45:19,258 --> 00:45:20,592 That's an interesting way maybe to put your. 879 00:45:20,592 --> 00:45:21,969 Not yet. 880 00:45:21,969 --> 00:45:24,304 You said we won't get to an on. You know, 881 00:45:25,347 --> 00:45:26,056 you don't think we'll get 882 00:45:26,056 --> 00:45:29,059 to an ontological breakthrough. 883 00:45:29,143 --> 00:45:30,227 and so in some ways, 884 00:45:30,227 --> 00:45:33,230 what you're saying is, look, the ontology of what we have now is, 885 00:45:34,440 --> 00:45:37,443 is, in reality, nothing like artificial general intelligence. 886 00:45:39,153 --> 00:45:42,156 You can't conclusively rule out that at some point, 887 00:45:43,323 --> 00:45:45,242 we would be able to develop systems 888 00:45:45,242 --> 00:45:48,537 that are really fundamentally different at how they work. 889 00:45:49,580 --> 00:45:51,498 And that's kind of the not yet. 890 00:45:51,498 --> 00:45:55,252 And as part of your emphasis is like it wouldn't just be a development, 891 00:45:55,252 --> 00:45:58,255 it would be something would be a very fundamental breakthrough. 892 00:45:59,256 --> 00:46:01,300 Yeah, I'd say that's a good that's a good assessment of it. 893 00:46:01,300 --> 00:46:04,094 It's it's very hard to prove a negative. 894 00:46:04,094 --> 00:46:06,138 Right. So that's one of the challenges you run into in logic. 895 00:46:06,138 --> 00:46:11,477 And so and it, so yeah, I think we have there's 896 00:46:12,603 --> 00:46:13,312 and it always 897 00:46:13,312 --> 00:46:16,607 seems, it always seems impossible 898 00:46:16,607 --> 00:46:19,651 to solve some of these problems until someone solves it. 899 00:46:19,651 --> 00:46:21,320 Right. There's that's true of mathematics, right? 900 00:46:21,320 --> 00:46:24,323 We have all these unsolved problems until someone, 901 00:46:24,615 --> 00:46:27,075 you know, goes out of mathematics for a long enough 902 00:46:27,075 --> 00:46:28,202 and they come up with a solution. 903 00:46:28,202 --> 00:46:33,582 So, I think, you know, my my skepticism lies in the, in the, 904 00:46:33,582 --> 00:46:37,169 the nature of the questions being asked in that, you know, we're saying, 905 00:46:37,795 --> 00:46:41,131 you know, in order to create things that make us uniquely human, we need to, 906 00:46:41,423 --> 00:46:46,178 you know, more and more silicon chips doesn't get us there, right? 907 00:46:46,386 --> 00:46:47,846 It needs to be something else. Right? 908 00:46:47,846 --> 00:46:51,683 Some other way of understanding reality, processing information, 909 00:46:53,143 --> 00:46:54,019 that we just don't have. 910 00:46:54,019 --> 00:46:57,147 And I, and again, I don't think too long 911 00:46:57,147 --> 00:47:00,359 about what it would take to do that, but the, 912 00:47:01,568 --> 00:47:02,486 When we reach a point in 913 00:47:02,486 --> 00:47:05,864 innovation that every time we innovate, we get more questions. 914 00:47:06,782 --> 00:47:08,909 I'm, I'm waiting for the 915 00:47:08,909 --> 00:47:12,704 I'm not really waiting, but in a sense, if the tide began to turn and the 916 00:47:12,871 --> 00:47:15,707 we began to answer, a lot of these things or major breakthroughs are covering 917 00:47:15,707 --> 00:47:19,294 lots of them that might tilt the needle of optimism versus pessimism. 918 00:47:19,294 --> 00:47:22,923 For me, I might say, okay, I'm maybe I was in error before, 919 00:47:23,298 --> 00:47:28,428 but at the moment we just keep getting more questions and it's yeah, 920 00:47:28,428 --> 00:47:31,473 that's that's sort of the root of my the root of my not yet answer. 921 00:47:34,142 --> 00:47:34,476 Yeah. 922 00:47:34,476 --> 00:47:38,230 Well, thank you, Ben, for, diving into this. 923 00:47:38,230 --> 00:47:40,148 I've enjoyed this and. Yeah. 924 00:47:40,148 --> 00:47:42,067 Thanks for sharing that. 925 00:47:42,067 --> 00:47:44,069 with our audience here. 926 00:47:44,069 --> 00:47:47,072 Thank you to the audience 927 00:47:47,072 --> 00:47:50,075 for tuning in here to Anabaptist Perspectives. 928 00:47:50,576 --> 00:47:54,288 And if you've enjoyed this, you can subscribe to the channel 929 00:47:54,288 --> 00:47:56,248 or share this episode. 930 00:47:56,248 --> 00:47:58,041 And we'll catch you in the next episode.