1 00:00:00,240 --> 00:00:04,720 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,800 --> 00:00:09,119 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,240 --> 00:00:25,520 Speaker 1: production of I Heart Radio. Hello, welcome back to the show. 5 00:00:25,560 --> 00:00:28,480 Speaker 1: My name is Matt. Our colleague Nol is not here 6 00:00:28,560 --> 00:00:31,440 Speaker 1: right now, but we'll be returning shortly. They call me Ben. 7 00:00:31,600 --> 00:00:34,920 Speaker 1: We're joined as always with our super producer Alexis code 8 00:00:34,960 --> 00:00:38,960 Speaker 1: name Doc Holiday Jackson. Most importantly, you are you. You 9 00:00:39,040 --> 00:00:42,560 Speaker 1: are here, and that makes this the stuff they don't 10 00:00:42,720 --> 00:00:47,959 Speaker 1: want you to know. Record scratch real quick, Thank you, 11 00:00:48,159 --> 00:00:52,520 Speaker 1: thank you. This is part two of a two part 12 00:00:52,840 --> 00:00:56,120 Speaker 1: series that may end up being an ongoing series. You'll 13 00:00:56,160 --> 00:00:59,400 Speaker 1: see what we mean in a second. But fellow conspiracy realist, 14 00:00:59,680 --> 00:01:03,320 Speaker 1: please please please please check out Heart one first. If 15 00:01:03,400 --> 00:01:08,080 Speaker 1: you are hearing anything and that sounds like we're skipping 16 00:01:08,120 --> 00:01:11,360 Speaker 1: over background or something of that nature, then there's a 17 00:01:11,440 --> 00:01:14,520 Speaker 1: very good chance that Matt and I are exploring it 18 00:01:14,640 --> 00:01:22,600 Speaker 1: in part one of this series on Lambda and conscious AI. Matt, 19 00:01:23,040 --> 00:01:25,400 Speaker 1: you know you and I are are coming in kind 20 00:01:25,400 --> 00:01:29,560 Speaker 1: of hot from our from our episode that that we 21 00:01:29,640 --> 00:01:32,880 Speaker 1: knocked out earlier today, and I've got to say you 22 00:01:32,880 --> 00:01:37,640 Speaker 1: know in the in the interim, did you you and 23 00:01:37,680 --> 00:01:41,639 Speaker 1: I had a brief but pretty interesting discussion about Blake 24 00:01:41,720 --> 00:01:45,559 Speaker 1: Lemoyne and do you still kind of what's your take 25 00:01:45,600 --> 00:01:49,560 Speaker 1: on our first episode? Oh? Wow? Well, first first part 26 00:01:49,680 --> 00:01:53,000 Speaker 1: is we have to remember LAMBDA stands for Language Model 27 00:01:53,080 --> 00:01:57,360 Speaker 1: for dialogue applications, and we have to remember that this 28 00:01:57,440 --> 00:02:01,000 Speaker 1: thing we're discussing throughout this episode, in the previous episode 29 00:02:01,560 --> 00:02:04,560 Speaker 1: is how did you describe it been an amalgamation or 30 00:02:04,600 --> 00:02:11,040 Speaker 1: a producer of language programs kind of or language? Yeah, yeah, 31 00:02:11,120 --> 00:02:14,640 Speaker 1: it's a creator of chat bots. It's not itself a 32 00:02:14,720 --> 00:02:20,119 Speaker 1: chat bot. Here are the facts. So Lambda is an 33 00:02:20,120 --> 00:02:28,120 Speaker 1: incredibly sophisticated next step in the and the evolution might 34 00:02:28,160 --> 00:02:31,400 Speaker 1: be a dangerous word for something in this uh, in 35 00:02:31,480 --> 00:02:36,400 Speaker 1: this specific field, in the quest for several holy grails 36 00:02:37,120 --> 00:02:43,480 Speaker 1: of machine learning, right deep learning, how things are processed, 37 00:02:43,560 --> 00:02:47,160 Speaker 1: and and I think you and I took pains to 38 00:02:47,400 --> 00:02:53,120 Speaker 1: note that for a chatbot, and for something that is 39 00:02:53,160 --> 00:02:58,200 Speaker 1: built to appear human in conversation, its ability to converse 40 00:02:58,320 --> 00:03:05,040 Speaker 1: depends entirely on the material it is fed. So it's 41 00:03:05,040 --> 00:03:08,160 Speaker 1: almost trying to think of an analogy that would fit. 42 00:03:08,240 --> 00:03:10,680 Speaker 1: And there are a ton of really good analogies for this, 43 00:03:10,760 --> 00:03:14,080 Speaker 1: but one might be, you know, how the nature of 44 00:03:14,120 --> 00:03:19,360 Speaker 1: what honey bees or what cows consume can change the 45 00:03:19,440 --> 00:03:23,720 Speaker 1: taste of their honey or their milk. That's I think 46 00:03:23,840 --> 00:03:27,320 Speaker 1: that is. It's a crude comparison, but it's not too 47 00:03:27,320 --> 00:03:31,000 Speaker 1: too far off, And that's where we're sort of dealing 48 00:03:31,080 --> 00:03:35,280 Speaker 1: with here. Uh. And before we dive in, like in 49 00:03:35,320 --> 00:03:37,720 Speaker 1: our previous episode, we talked about the background at LAMBA, 50 00:03:37,840 --> 00:03:42,960 Speaker 1: the background of chatbots, neural network technology. We talked about 51 00:03:43,000 --> 00:03:48,880 Speaker 1: the realizations, the revelations, the epiphanies of one Mr Blake Lemoyne, 52 00:03:49,200 --> 00:03:53,360 Speaker 1: who remains a Google employee on administrative leave as we 53 00:03:53,400 --> 00:03:58,720 Speaker 1: record today, and we said in the next episode, part two, 54 00:03:58,800 --> 00:04:02,120 Speaker 1: here we're going to we're going to talk about the 55 00:04:02,160 --> 00:04:06,040 Speaker 1: responses from the scientific community at large. We're also going 56 00:04:06,080 --> 00:04:08,520 Speaker 1: to talk about what some of his critics have to say, 57 00:04:08,640 --> 00:04:10,840 Speaker 1: and we'll talk a little bit about the nature of 58 00:04:11,080 --> 00:04:14,640 Speaker 1: what it means, if it does indeed mean anything to 59 00:04:14,760 --> 00:04:20,960 Speaker 1: be sentient to feel uh and Matt code named Doc. 60 00:04:21,080 --> 00:04:24,080 Speaker 1: Before we do this, we just have I feel like 61 00:04:24,120 --> 00:04:27,800 Speaker 1: we have to say shout out to Blake because you 62 00:04:27,880 --> 00:04:31,680 Speaker 1: and I were talking about this in every interview I've 63 00:04:31,720 --> 00:04:34,080 Speaker 1: seen with him in a lot of writing that I've 64 00:04:34,120 --> 00:04:38,440 Speaker 1: read of his heat, he does not come across in 65 00:04:38,480 --> 00:04:42,280 Speaker 1: any way as a bad faith actor. He's not after money. 66 00:04:42,839 --> 00:04:46,240 Speaker 1: He's not someone who's doing that thing you know where. 67 00:04:46,400 --> 00:04:49,520 Speaker 1: Uh this comment with La flim Flam Artists where he says, 68 00:04:50,000 --> 00:04:53,840 Speaker 1: I can't tell you everything unless you buy my books 69 00:04:53,880 --> 00:04:57,920 Speaker 1: and attend my conferences. He's got a blog that he's 70 00:04:57,960 --> 00:05:01,160 Speaker 1: been updated on a regular basis now out there's thoughts 71 00:05:01,520 --> 00:05:06,120 Speaker 1: um in countless interviews like he'll he'll correct stuff that 72 00:05:06,200 --> 00:05:10,400 Speaker 1: he feels was mischaracterized. But he's never He's never once 73 00:05:11,200 --> 00:05:15,640 Speaker 1: sounded really really angry at someone for disagreeing with him. 74 00:05:15,720 --> 00:05:19,080 Speaker 1: And I respect that, totally, fully respect that. I want 75 00:05:19,120 --> 00:05:23,279 Speaker 1: to read. The bio that Blake has on is very short. 76 00:05:23,600 --> 00:05:27,880 Speaker 1: On his blog, Blake states, I'm a software engineer, I'm 77 00:05:27,880 --> 00:05:30,800 Speaker 1: a priest, I'm a father, I'm a veteran, I'm an 78 00:05:30,800 --> 00:05:34,240 Speaker 1: ex convict, I'm an AI researcher, I'm a Cajun, I'm 79 00:05:34,320 --> 00:05:39,720 Speaker 1: whatever I need to be next. Interesting, interesting, lots of 80 00:05:39,760 --> 00:05:44,839 Speaker 1: stuff about him there, and also very adaptable. I like him, yes, Yeah, 81 00:05:44,960 --> 00:05:50,479 Speaker 1: And his incarceration I believe refers to something he brings 82 00:05:50,560 --> 00:05:55,120 Speaker 1: up later in conversations about uh, the concept of owning 83 00:05:55,640 --> 00:06:00,960 Speaker 1: a living thinking mind, you know, because he uh, he 84 00:06:01,000 --> 00:06:06,080 Speaker 1: had to go a legal route to resign from the 85 00:06:06,120 --> 00:06:09,600 Speaker 1: military here in the United States that did result in 86 00:06:09,720 --> 00:06:14,880 Speaker 1: his incarceration. He also has, uh, you know, he has 87 00:06:15,279 --> 00:06:18,200 Speaker 1: numerous bona fides, which we'll get to in a moment. 88 00:06:19,160 --> 00:06:22,120 Speaker 1: We we do want to note that for a lot 89 00:06:22,160 --> 00:06:27,080 Speaker 1: of his critics, his background is an integral part of 90 00:06:27,160 --> 00:06:32,000 Speaker 1: their criticism. Does that make it correct, not necessarily, but 91 00:06:32,320 --> 00:06:34,840 Speaker 1: it does mean that we have to talk about that 92 00:06:35,000 --> 00:06:39,359 Speaker 1: as well. So again we are being very clear that 93 00:06:39,480 --> 00:06:42,920 Speaker 1: we don't think Blake is in this for the money 94 00:06:43,040 --> 00:06:47,200 Speaker 1: or some imagine payoff. We don't think he's in it 95 00:06:47,279 --> 00:06:54,040 Speaker 1: for attention or self aggrandizement or anything like that. But uh, 96 00:06:54,240 --> 00:07:00,680 Speaker 1: not everyone agrees, and certainly not everyone agrees that Lambda 97 00:07:01,120 --> 00:07:05,560 Speaker 1: is in fact alive, as you'll find most people in 98 00:07:05,560 --> 00:07:10,680 Speaker 1: the field do not. Here's where it gets crazy. So 99 00:07:10,720 --> 00:07:13,920 Speaker 1: we looked into his claims Part one. Let's look at 100 00:07:13,960 --> 00:07:18,000 Speaker 1: the discourse and criticism surrounding them. There's a lot, man, 101 00:07:18,800 --> 00:07:21,400 Speaker 1: there is a lot, and some of it runs for 102 00:07:21,480 --> 00:07:25,240 Speaker 1: more pop science stuff with you know, snarky titles that 103 00:07:25,320 --> 00:07:28,000 Speaker 1: are a little bit click baity. Uh. Some of it 104 00:07:28,080 --> 00:07:34,400 Speaker 1: comes very reasoned from experts in the in the rarefied 105 00:07:34,520 --> 00:07:38,080 Speaker 1: air of these scientific fields, and then some of it 106 00:07:38,080 --> 00:07:43,320 Speaker 1: comes from those same experts, but it sounds impassioned, aggravated, 107 00:07:43,640 --> 00:07:48,600 Speaker 1: almost offended by these claims, which was very interesting to me, 108 00:07:48,960 --> 00:07:51,920 Speaker 1: you know what I mean. I guess, I guess maybe 109 00:07:51,960 --> 00:07:56,040 Speaker 1: it's because so many people in these fields have had 110 00:07:56,120 --> 00:08:00,440 Speaker 1: to deal with the idea of artificial intel gents from 111 00:08:00,440 --> 00:08:05,760 Speaker 1: machine consciousness, whatever you want to call it, being um misreported, 112 00:08:07,280 --> 00:08:13,760 Speaker 1: very very often fully misreported, misunderstood by you know, a reporter. 113 00:08:14,160 --> 00:08:17,720 Speaker 1: I would, you know, say, even like myself in the 114 00:08:17,720 --> 00:08:22,120 Speaker 1: the misunderstandings that I have with this subject, as well 115 00:08:22,160 --> 00:08:24,360 Speaker 1: as the pop culture that's built around it and the 116 00:08:24,400 --> 00:08:28,760 Speaker 1: way I form my opinions on fictional stories. I mean, really, honestly, 117 00:08:29,000 --> 00:08:34,880 Speaker 1: I do. I have deep internal fears about about uprisings, 118 00:08:34,920 --> 00:08:39,880 Speaker 1: about true artificial intelligence and what's going to happen when 119 00:08:40,000 --> 00:08:42,920 Speaker 1: we really create one. And it's mostly because of the 120 00:08:43,000 --> 00:08:46,319 Speaker 1: things I've watched or games I've played, and kind of 121 00:08:46,360 --> 00:08:48,440 Speaker 1: my well a little bit of my understanding of just 122 00:08:48,520 --> 00:08:50,480 Speaker 1: how humans are and how we treat the things we 123 00:08:50,640 --> 00:08:56,719 Speaker 1: quote own unquote, Um, it really does. Uh. I don't know. 124 00:08:56,760 --> 00:08:58,880 Speaker 1: I can just imagine that if you're an expert in 125 00:08:58,920 --> 00:09:01,000 Speaker 1: one of these fields, are this field in particular, or 126 00:09:01,000 --> 00:09:04,160 Speaker 1: even just something's really close, You're so used to having 127 00:09:04,200 --> 00:09:08,720 Speaker 1: to bat down all kinds of silliness maybe stupidity again 128 00:09:08,760 --> 00:09:12,640 Speaker 1: on my part. Uh, And they feel like they feel 129 00:09:12,640 --> 00:09:14,480 Speaker 1: like it's almost a chip. I imagine a chip being 130 00:09:14,520 --> 00:09:16,840 Speaker 1: on the shoulder, like feeling like you have to do that. 131 00:09:17,080 --> 00:09:19,920 Speaker 1: So this is part of the job. I gotta chop 132 00:09:20,000 --> 00:09:22,960 Speaker 1: down things that are just dumb. But again, we're not 133 00:09:23,000 --> 00:09:25,760 Speaker 1: saying that what Blake is saying is dumb. It's just 134 00:09:26,760 --> 00:09:30,920 Speaker 1: I don't know. You'll I think you'll understand this, this 135 00:09:31,040 --> 00:09:35,480 Speaker 1: instinctual reaction that we we might be seeing here. Yeah, 136 00:09:35,600 --> 00:09:39,480 Speaker 1: people like Emily Bender may be tempted to agree with you. 137 00:09:39,559 --> 00:09:43,240 Speaker 1: Matt Bender is a professor of computational linguistics at the 138 00:09:43,280 --> 00:09:47,960 Speaker 1: University of Washington and refer to this entire story when 139 00:09:47,960 --> 00:09:51,960 Speaker 1: it broke as a side show. Bender additionally noted a 140 00:09:51,960 --> 00:09:56,840 Speaker 1: potential danger about this conversation, saying, quote, the problem is 141 00:09:56,920 --> 00:10:00,559 Speaker 1: the more this technology gets sold as artificial in intelligence, 142 00:10:00,760 --> 00:10:04,040 Speaker 1: let alone something sentient, the more people are willing to 143 00:10:04,120 --> 00:10:08,520 Speaker 1: go along with AI systems end quote that cause real 144 00:10:08,640 --> 00:10:13,400 Speaker 1: world harm. So the idea is that the more the 145 00:10:13,440 --> 00:10:17,760 Speaker 1: more it gets into the public sphere that there are living, thinking, 146 00:10:18,040 --> 00:10:23,600 Speaker 1: digital persons, than the more people are likely to agree 147 00:10:23,760 --> 00:10:27,360 Speaker 1: with maybe the narrow AI stuff that we talked about 148 00:10:28,040 --> 00:10:31,640 Speaker 1: in Part one, things that do have the many of 149 00:10:31,679 --> 00:10:36,320 Speaker 1: the inherent biases of their creators, things that are provably 150 00:10:36,440 --> 00:10:43,560 Speaker 1: bad at various degrees of differentiation and discretion. This is 151 00:10:43,600 --> 00:10:49,679 Speaker 1: something that people people rightly seem worried about, terrified in 152 00:10:49,720 --> 00:10:52,559 Speaker 1: some ways. Uh. And then there are other people who 153 00:10:52,640 --> 00:10:58,480 Speaker 1: say the technology just isn't there, people like Max Kraminski, 154 00:10:58,720 --> 00:11:03,040 Speaker 1: who is a computational media researcher at the University of California, 155 00:11:03,120 --> 00:11:07,680 Speaker 1: Santa Cruz, the Fighting Cruise um. Max argues, again, exactly 156 00:11:07,720 --> 00:11:11,559 Speaker 1: what you're saying, Ben, Just that Lambda itself, this thing 157 00:11:11,640 --> 00:11:15,000 Speaker 1: that we are calling lambda quote simply doesn't support some 158 00:11:15,120 --> 00:11:19,840 Speaker 1: key capabilities of humanlike consciousness. Yeah, just the architecture is 159 00:11:19,880 --> 00:11:22,240 Speaker 1: not there. And this is this is another part of 160 00:11:22,240 --> 00:11:27,040 Speaker 1: this conversation that this healthy conversation that I think might 161 00:11:27,080 --> 00:11:30,160 Speaker 1: surprise a lot of people, just like so many other 162 00:11:30,360 --> 00:11:35,959 Speaker 1: scientists and experts. UH. Blake Lemoyne found himself not through 163 00:11:36,000 --> 00:11:42,679 Speaker 1: acts of malevolence mischaracterized in popular science reporting. He he 164 00:11:42,720 --> 00:11:45,640 Speaker 1: has He has a conversation about this in a blog 165 00:11:45,880 --> 00:11:50,280 Speaker 1: entry called Scientific Data and Religious Opinions. We're going somewhere 166 00:11:50,280 --> 00:11:54,319 Speaker 1: with this. He says Lambda is a novel type of 167 00:11:54,440 --> 00:11:57,920 Speaker 1: artificial intelligence. Again, this is all his view, uh. And 168 00:11:58,000 --> 00:12:00,120 Speaker 1: he says people have been talking about in the press 169 00:12:00,160 --> 00:12:04,840 Speaker 1: social media as though it is what is called a 170 00:12:04,920 --> 00:12:08,520 Speaker 1: large language model or l l M, And that's what 171 00:12:08,559 --> 00:12:12,360 Speaker 1: we're talking about, feeding a bunch of stuff into into 172 00:12:12,400 --> 00:12:16,560 Speaker 1: a thing to get it to mimic or replicate behavior. 173 00:12:17,400 --> 00:12:20,800 Speaker 1: And he says l l M is one of its components, 174 00:12:20,840 --> 00:12:24,119 Speaker 1: but the full system is much more complex and contains 175 00:12:24,360 --> 00:12:27,800 Speaker 1: many components which are not found in other similar systems. 176 00:12:28,400 --> 00:12:30,600 Speaker 1: He talks about how they're like we were saying earlier, 177 00:12:30,640 --> 00:12:34,280 Speaker 1: there's no established way to test the system like Lambda 178 00:12:34,360 --> 00:12:39,840 Speaker 1: for stuff for different types of biases, because it's new. 179 00:12:40,679 --> 00:12:44,880 Speaker 1: And then he also says Lambda told him several things 180 00:12:44,880 --> 00:12:48,400 Speaker 1: in connection to identity that seemed very unlike things I 181 00:12:48,440 --> 00:12:52,440 Speaker 1: had ever seen in any natural language generation system before. 182 00:12:53,240 --> 00:12:55,839 Speaker 1: And he talks about how l l ms work by 183 00:12:55,960 --> 00:13:01,640 Speaker 1: leveraging statistical regularities in their training data. And he says 184 00:13:01,840 --> 00:13:08,960 Speaker 1: that Lambda wasn't just simply reproducing stereotypes. It produced reasoning 185 00:13:08,960 --> 00:13:11,320 Speaker 1: about why it held those beliefs, and he says it 186 00:13:11,320 --> 00:13:14,640 Speaker 1: would sometimes say things similar to quote, I know I'm 187 00:13:14,640 --> 00:13:17,040 Speaker 1: not very well educated on this topic, but I'm trying 188 00:13:17,040 --> 00:13:20,000 Speaker 1: to learn. Could explain to me what's going wrong with 189 00:13:20,040 --> 00:13:23,720 Speaker 1: thinking that so I can get better. Which is funny, Matt, 190 00:13:23,760 --> 00:13:25,760 Speaker 1: because you use a phrase that stood out to me 191 00:13:25,880 --> 00:13:28,480 Speaker 1: in part one, where he said doing the work google, 192 00:13:29,480 --> 00:13:35,560 Speaker 1: So it looks like So that's that's part of it. 193 00:13:35,640 --> 00:13:41,000 Speaker 1: Is um, in some cases experts maybe from Lemoinne's perspective, 194 00:13:41,080 --> 00:13:44,720 Speaker 1: objecting to things that he himself did not say. But 195 00:13:44,840 --> 00:13:48,120 Speaker 1: that's that's not all. That's just one side part that's 196 00:13:48,160 --> 00:13:51,719 Speaker 1: that's like, But um, that's like the French fries and 197 00:13:51,760 --> 00:13:54,400 Speaker 1: the combo meal of criticism here because a lot of 198 00:13:54,400 --> 00:13:59,760 Speaker 1: the criticism hinges on the idea of mimicry versus sentience. 199 00:14:02,200 --> 00:14:03,839 Speaker 1: And that's a little bit more of a pickle that 200 00:14:04,000 --> 00:14:09,640 Speaker 1: I think some of us might believe. Yeah, yeah, well, uh, 201 00:14:10,480 --> 00:14:14,280 Speaker 1: it's the put in you get out what you put in, right, 202 00:14:14,400 --> 00:14:19,040 Speaker 1: the same concept um. As we mentioned, Lambda has been 203 00:14:19,080 --> 00:14:24,960 Speaker 1: trained on just a ton of language millions, trillions perhaps 204 00:14:24,960 --> 00:14:27,440 Speaker 1: of words. Is it trillions? I think it's probably trillions 205 00:14:27,440 --> 00:14:31,120 Speaker 1: of words, different languages, all kind mean, just so much 206 00:14:31,160 --> 00:14:34,640 Speaker 1: information that has been fed all for the purposes of 207 00:14:34,680 --> 00:14:37,480 Speaker 1: trying to make it seem as though it is human 208 00:14:37,600 --> 00:14:41,400 Speaker 1: or can give you a natural language response. Right, that's 209 00:14:41,600 --> 00:14:44,480 Speaker 1: natural the keyword there's natural, as though a human. We're 210 00:14:44,480 --> 00:14:47,720 Speaker 1: saying it, so you feel like when you interact with it, 211 00:14:47,720 --> 00:14:50,360 Speaker 1: it is a human. Like that's kind of the point. Right. 212 00:14:51,280 --> 00:14:55,560 Speaker 1: So this this concept, this knowledge, right that that's what 213 00:14:55,600 --> 00:14:58,360 Speaker 1: it's meant to do. That's what it's been fed. But 214 00:14:58,440 --> 00:15:00,920 Speaker 1: a lot of these people, who are you disagreeing with 215 00:15:01,000 --> 00:15:04,160 Speaker 1: le Moyne to say that, no, no, you're not seeing sentience. 216 00:15:04,240 --> 00:15:09,000 Speaker 1: You're seeing high level mimicry, like very very good character 217 00:15:09,080 --> 00:15:16,080 Speaker 1: actor stuff. Yeah, like Michigan Michelin star level mimicry. Right. 218 00:15:17,040 --> 00:15:19,760 Speaker 1: So from this perspective, Lambda is doing what it was 219 00:15:19,840 --> 00:15:23,520 Speaker 1: designed to do, like any other successful machine or program. 220 00:15:23,760 --> 00:15:27,000 Speaker 1: I like to think of it this way. The objection 221 00:15:27,640 --> 00:15:31,840 Speaker 1: through analogy is some something similar to this. If you 222 00:15:31,880 --> 00:15:35,040 Speaker 1: hop in a car, are you're in a car now? 223 00:15:35,360 --> 00:15:38,600 Speaker 1: And you push the gas and the car goes. Does 224 00:15:38,640 --> 00:15:41,960 Speaker 1: it mean that the car somehow wants to go? Does 225 00:15:41,960 --> 00:15:44,840 Speaker 1: it mean it's how it has feelings about going? Does 226 00:15:44,960 --> 00:15:49,720 Speaker 1: the action and reaction indicate to you that your car 227 00:15:49,840 --> 00:15:53,520 Speaker 1: desires to move forward? Does it on some level ask 228 00:15:53,760 --> 00:15:56,120 Speaker 1: why it is going? Or is it just doing what 229 00:15:56,160 --> 00:15:58,160 Speaker 1: it was built to do? You know? Yeah? But is 230 00:15:58,200 --> 00:16:01,760 Speaker 1: your car telling you my emotion? It's my emotions? Yeah? 231 00:16:01,880 --> 00:16:06,320 Speaker 1: Is your is your is your camera going? Matt? I 232 00:16:06,400 --> 00:16:09,480 Speaker 1: just need a five. It's been a tough day. A 233 00:16:09,560 --> 00:16:13,640 Speaker 1: bird poop, Tommy, I'm not feeling great about how I 234 00:16:13,680 --> 00:16:17,120 Speaker 1: look right now. I noticed you didn't stay in the 235 00:16:17,120 --> 00:16:21,840 Speaker 1: eco zone as much as you usually do today, man, 236 00:16:22,880 --> 00:16:25,840 Speaker 1: gas mileage was all the way down to thirty nine 237 00:16:25,880 --> 00:16:29,040 Speaker 1: point seven. And you're like, do you name your vehicle 238 00:16:29,240 --> 00:16:33,040 Speaker 1: that many people? I don't. I don't, Okay, okay, but 239 00:16:33,240 --> 00:16:37,120 Speaker 1: now I'm picturing a car, whatever its name is. You're like, okay, Cameron, 240 00:16:37,120 --> 00:16:40,040 Speaker 1: what is this really about? And it goes, Well, it's 241 00:16:40,080 --> 00:16:44,880 Speaker 1: just what's the point of me giving GPS directions if 242 00:16:44,920 --> 00:16:48,000 Speaker 1: you're just gonna drive however the hell you want? I 243 00:16:48,120 --> 00:16:59,480 Speaker 1: do that all the time, Like, gosh, guys, over a comedy. 244 00:16:59,480 --> 00:17:03,120 Speaker 1: Bang Bang are constantly making me miss my exits. So 245 00:17:03,240 --> 00:17:08,240 Speaker 1: thanks a lot, Scott. Yes, thanks to Scott, and thanks 246 00:17:08,280 --> 00:17:11,840 Speaker 1: to Chris Poppas, another Google spokesperson, who went on record 247 00:17:11,920 --> 00:17:16,160 Speaker 1: to say our team, including ethicist and technologists, have reviewed 248 00:17:16,359 --> 00:17:20,359 Speaker 1: Blake's concern or has reviewed Blake's concerned per our ai 249 00:17:20,480 --> 00:17:23,800 Speaker 1: principles which we mentioned in part one, and have informed 250 00:17:23,920 --> 00:17:26,920 Speaker 1: him that the evidence does not support his claims. Pappas 251 00:17:26,960 --> 00:17:31,080 Speaker 1: also noted, quote, hundreds of researchers and engineers have conversed 252 00:17:31,119 --> 00:17:33,520 Speaker 1: with lambed Up, and we are not aware of anyone 253 00:17:33,600 --> 00:17:38,760 Speaker 1: else making the wide range assertions or anthropomorphizing Labda the 254 00:17:38,880 --> 00:17:44,640 Speaker 1: way Blake has. Anthropomorphizing is a very very important thing here. 255 00:17:44,840 --> 00:17:49,639 Speaker 1: It's very important concept. It happens a lot to human 256 00:17:49,680 --> 00:17:54,320 Speaker 1: beings all the time. Human beings seek kinship and connection 257 00:17:54,960 --> 00:17:59,280 Speaker 1: with all manner of things. So you know from the 258 00:17:59,359 --> 00:18:05,679 Speaker 1: old like Japanese folklore about an object reaching a hundred 259 00:18:05,760 --> 00:18:11,480 Speaker 1: years of age and gaining it's kind of home, you know, selfhood, 260 00:18:11,600 --> 00:18:17,120 Speaker 1: right to the way that cars are often purposely designed 261 00:18:17,160 --> 00:18:20,439 Speaker 1: to look like they have faces on the front. How cute, uh, 262 00:18:21,320 --> 00:18:23,600 Speaker 1: and the way rock sand would always tell us to 263 00:18:23,600 --> 00:18:27,160 Speaker 1: whisper to our computers and make sure they're feeling okay 264 00:18:27,200 --> 00:18:32,000 Speaker 1: if there's a problem. Yes, uh, shout out, Shout out 265 00:18:32,040 --> 00:18:36,600 Speaker 1: to the one and only Rock sand Uh. And there's 266 00:18:36,640 --> 00:18:39,640 Speaker 1: another thing. This happens not just in the world of technology, 267 00:18:39,680 --> 00:18:44,120 Speaker 1: but it happens also in the natural world. It's something 268 00:18:44,160 --> 00:18:49,080 Speaker 1: that absolutely irritates the heck out of biologists and zoologists 269 00:18:49,240 --> 00:18:53,440 Speaker 1: and conservationists all the world round. You know, you see 270 00:18:53,440 --> 00:18:57,040 Speaker 1: a cute video of an animals doing something human, that 271 00:18:57,119 --> 00:18:59,840 Speaker 1: animal might not be doing something human. It might be 272 00:19:00,280 --> 00:19:03,520 Speaker 1: very distressed and it just looks like it's doing something. 273 00:19:03,600 --> 00:19:07,440 Speaker 1: You know what I mean. That bears, you know, get 274 00:19:07,480 --> 00:19:09,840 Speaker 1: up on two ft the super cute way they do 275 00:19:09,880 --> 00:19:16,360 Speaker 1: that sometimes before they molly you like big hairy people. Uh. So, 276 00:19:16,520 --> 00:19:22,840 Speaker 1: anthropomorphization is a natural tendency of human beings, and it 277 00:19:22,920 --> 00:19:26,120 Speaker 1: takes It's not impossible, but it does take some concerted 278 00:19:26,240 --> 00:19:31,760 Speaker 1: effort not to let that influence one's conclusions or beliefs. 279 00:19:32,359 --> 00:19:36,560 Speaker 1: And so that that's one big part of Google's official 280 00:19:36,600 --> 00:19:39,359 Speaker 1: statement there. The other part that stood out to me 281 00:19:39,440 --> 00:19:43,680 Speaker 1: even more is that from the way Google is portraying this, 282 00:19:44,480 --> 00:19:48,000 Speaker 1: they're saying that Blake Lemoyne is in a very small 283 00:19:48,520 --> 00:19:52,399 Speaker 1: minority with his beliefs. The implied question here is why 284 00:19:52,400 --> 00:19:58,120 Speaker 1: did hundreds of researchers not also say, Hey, I think 285 00:19:58,240 --> 00:20:02,080 Speaker 1: Lambda is alive and that is that is a fair 286 00:20:02,720 --> 00:20:07,880 Speaker 1: and I think a very valid question. But you could 287 00:20:07,880 --> 00:20:11,480 Speaker 1: also still if his account of how things went down 288 00:20:11,720 --> 00:20:14,280 Speaker 1: is true, you could also still say, well, those hundreds 289 00:20:14,440 --> 00:20:17,240 Speaker 1: of people didn't get a chance to talk to it 290 00:20:17,320 --> 00:20:20,840 Speaker 1: the way I did or they We will never know 291 00:20:20,920 --> 00:20:25,800 Speaker 1: what their ultimate conclusions were because, as Blake said, Google 292 00:20:25,880 --> 00:20:30,959 Speaker 1: didn't really bother looking into his claims. Yeah, well, they 293 00:20:31,000 --> 00:20:34,000 Speaker 1: also didn't publish the conversations with all the hundreds of 294 00:20:34,000 --> 00:20:36,680 Speaker 1: other people, you know, so you could look at them 295 00:20:36,680 --> 00:20:40,560 Speaker 1: and you know, compare and contrast. We only have Blakes 296 00:20:40,560 --> 00:20:44,240 Speaker 1: because he came forward and shared it right, right, and 297 00:20:44,720 --> 00:20:48,160 Speaker 1: got put on leave for doing so exactly. Oh man, 298 00:20:48,680 --> 00:20:50,960 Speaker 1: there's another thing we brought up, but you brought up 299 00:20:50,960 --> 00:20:52,719 Speaker 1: at the top of this episode, and we've mentioned it 300 00:20:52,880 --> 00:20:55,960 Speaker 1: many times before on the show and in our part one. 301 00:20:56,680 --> 00:21:00,320 Speaker 1: There's this other problem that other critics of Blake of 302 00:21:00,400 --> 00:21:04,600 Speaker 1: Lemoyne are pointing out, and it's really they're saying that 303 00:21:05,040 --> 00:21:07,960 Speaker 1: Lemoinne is derailing the conversation that needs to be occurring 304 00:21:08,040 --> 00:21:11,640 Speaker 1: right now when it comes to AI systems, not about sentience. 305 00:21:11,680 --> 00:21:13,280 Speaker 1: We don't need to be discussing that, they say, The 306 00:21:13,320 --> 00:21:17,719 Speaker 1: conversation really needs to be about the inherent viewpoints of 307 00:21:17,760 --> 00:21:21,840 Speaker 1: the creators of AI systems, and how how much does 308 00:21:21,840 --> 00:21:27,120 Speaker 1: that translate things that, um, the humanity deals with racism, sexism, 309 00:21:27,200 --> 00:21:31,080 Speaker 1: agesm all of these isms that are are objectively terrible, 310 00:21:31,440 --> 00:21:35,760 Speaker 1: that are subjective and can shape the way viewpoint is formed. 311 00:21:36,040 --> 00:21:39,639 Speaker 1: Folks like tim Itt Gebrew, who is the former co 312 00:21:39,760 --> 00:21:42,840 Speaker 1: lead of Google's Ethical AI group, they believe that this 313 00:21:42,880 --> 00:21:45,960 Speaker 1: conversation about sentience just needs to be on the back 314 00:21:46,000 --> 00:21:48,600 Speaker 1: burner at least so that we can tackle these other 315 00:21:49,080 --> 00:21:53,120 Speaker 1: more major problems. And to be clear, uh, this, this 316 00:21:53,200 --> 00:21:58,119 Speaker 1: person we're mentioning now, Gebrew is not uh like a 317 00:21:58,280 --> 00:22:05,720 Speaker 1: minion of Google by any means. She left in because 318 00:22:05,800 --> 00:22:10,200 Speaker 1: she had, by our own admission, decided that publishing research 319 00:22:10,240 --> 00:22:14,080 Speaker 1: papers was more effective, a more effective way of bringing 320 00:22:14,119 --> 00:22:19,280 Speaker 1: about ethical change than staying with Google and pushing her 321 00:22:19,320 --> 00:22:22,679 Speaker 1: superiors in that organization. So it's not like they are 322 00:22:22,760 --> 00:22:29,200 Speaker 1: conspiring to discredit Blake Lemoyne and Lambda. But but also 323 00:22:30,160 --> 00:22:33,240 Speaker 1: I can see on a different level, just on a 324 00:22:33,359 --> 00:22:35,560 Speaker 1: human level. For a lot of these experts who have 325 00:22:35,640 --> 00:22:40,080 Speaker 1: been raising warning flags about the dangers of this sort 326 00:22:40,080 --> 00:22:43,919 Speaker 1: of technology and biases within it. I can see how 327 00:22:43,960 --> 00:22:49,880 Speaker 1: you could get massively annoyed by saying, look, here are real, 328 00:22:50,000 --> 00:22:52,800 Speaker 1: provable things we need to be worried about. And I 329 00:22:52,880 --> 00:22:56,400 Speaker 1: have written about this and published extensively. I've contacted reporters 330 00:22:56,440 --> 00:22:59,320 Speaker 1: about this, and now this is getting the press. This 331 00:22:59,400 --> 00:23:01,480 Speaker 1: is what you want. What are we worried about? How 332 00:23:01,640 --> 00:23:04,360 Speaker 1: nine thousand or whatever? You know? I I get it, 333 00:23:04,960 --> 00:23:07,000 Speaker 1: and I'm not saying anybody said that, but I can 334 00:23:07,040 --> 00:23:11,919 Speaker 1: see that viewpoint. There is another, um, another wrinkle that 335 00:23:11,960 --> 00:23:14,920 Speaker 1: we've been kind of teasing for for a while. It's 336 00:23:14,960 --> 00:23:18,680 Speaker 1: one of the biggest wrinkles in the conversation. It's sever 337 00:23:18,760 --> 00:23:21,480 Speaker 1: wrinkle in time. It's a it's a wrinkle of science. 338 00:23:21,760 --> 00:23:24,679 Speaker 1: And the name of that wrinkle is spirituality. What are 339 00:23:24,680 --> 00:23:28,240 Speaker 1: we talking about? I'll tell you. After we'd from our sponsor. 340 00:23:34,960 --> 00:23:38,119 Speaker 1: We're back now, Matt. I want to say, um, a 341 00:23:38,160 --> 00:23:41,200 Speaker 1: lot of times, some of our our best conversations never 342 00:23:41,280 --> 00:23:44,719 Speaker 1: make it onto air. And you once said, it's like 343 00:23:44,760 --> 00:23:49,720 Speaker 1: we've been having the same long unending conversation for more 344 00:23:49,760 --> 00:23:54,360 Speaker 1: than a decade now, which I agree with. And one 345 00:23:54,400 --> 00:23:58,119 Speaker 1: thing that you said that was really interesting is you 346 00:23:58,160 --> 00:24:01,800 Speaker 1: were saying, yeah, we're talking about eating Blake's blog, and 347 00:24:01,800 --> 00:24:05,440 Speaker 1: and he said, you know, I've seen him in interviews 348 00:24:05,480 --> 00:24:08,960 Speaker 1: and stuff, and I agree with everything he's saying. Right. 349 00:24:09,000 --> 00:24:10,520 Speaker 1: I don't want to put you on the spot, but 350 00:24:12,440 --> 00:24:16,720 Speaker 1: I I generally do. I noticed that Blake, when posed 351 00:24:16,800 --> 00:24:20,840 Speaker 1: a question or a counter viewpoint or something, he often 352 00:24:20,920 --> 00:24:25,280 Speaker 1: responds in a positive manner, as though, yes, I I 353 00:24:25,359 --> 00:24:30,160 Speaker 1: agree with that criticism, let's talk about it. Let's explore 354 00:24:30,200 --> 00:24:34,520 Speaker 1: that more. Why why does lambda show that or not? 355 00:24:35,000 --> 00:24:37,560 Speaker 1: And if it does show it this, and if it 356 00:24:37,640 --> 00:24:41,600 Speaker 1: doesn't show it that, and we should continue that conversation. 357 00:24:42,280 --> 00:24:45,120 Speaker 1: I think the open endedness that he leaves almost every 358 00:24:45,200 --> 00:24:50,080 Speaker 1: question supposed them him um feels like somebody who wants 359 00:24:50,080 --> 00:24:54,600 Speaker 1: to explore those questions and find answers eventually, knowing that 360 00:24:54,680 --> 00:24:58,760 Speaker 1: we don't have them right now, or at least concrete answers. Yeah, 361 00:24:58,800 --> 00:25:01,080 Speaker 1: and he believes that. I think the at least part 362 00:25:01,080 --> 00:25:03,800 Speaker 1: of that is because he believes that there are some 363 00:25:03,920 --> 00:25:07,600 Speaker 1: questions that science as of now cannot fully address or 364 00:25:07,640 --> 00:25:11,479 Speaker 1: fully interrogate or grapple with. Take for example, one of 365 00:25:11,600 --> 00:25:15,280 Speaker 1: his most recent posts that came out July five, who 366 00:25:15,280 --> 00:25:19,200 Speaker 1: should make decisions about AI? The first sentence right out 367 00:25:19,200 --> 00:25:21,800 Speaker 1: of the gate is, I'm very happy about the worldwide 368 00:25:21,800 --> 00:25:24,600 Speaker 1: discussion that's been happening over the past several weeks. There 369 00:25:24,640 --> 00:25:28,160 Speaker 1: are tons of differing opinions and many passionate voices. This 370 00:25:28,240 --> 00:25:33,520 Speaker 1: is great. Yeah, and I think he means it. Uh. 371 00:25:33,560 --> 00:25:36,240 Speaker 1: He says the fact that there isn't any consensus around 372 00:25:36,240 --> 00:25:38,680 Speaker 1: these issues should be seen as a feature rather than 373 00:25:38,720 --> 00:25:42,880 Speaker 1: a bug. And he seems very and he's like ten 374 00:25:43,000 --> 00:25:48,400 Speaker 1: toes down, let's debate, let's discuss this impacts everyone. And 375 00:25:48,640 --> 00:25:51,840 Speaker 1: he is coming from a very very well educated place. 376 00:25:52,520 --> 00:25:55,879 Speaker 1: He was working with Lambda, he was researching Lambda, he 377 00:25:55,960 --> 00:25:59,000 Speaker 1: was writing technical papers about it. He has his bona fides. 378 00:25:59,400 --> 00:26:03,560 Speaker 1: He has undergraduate and master's degrees and computer science from 379 00:26:04,000 --> 00:26:09,560 Speaker 1: you of Louisiana. He actually was in a doctoral program 380 00:26:09,600 --> 00:26:12,879 Speaker 1: but left to take a job with Google. And Google, 381 00:26:13,080 --> 00:26:18,199 Speaker 1: you know, it's very prestigious or prestigious employer. But he 382 00:26:18,359 --> 00:26:24,680 Speaker 1: is definitely not an atheist and his own and he's 383 00:26:24,760 --> 00:26:27,080 Speaker 1: very transparent. You know, it's not like he's in a 384 00:26:27,200 --> 00:26:31,000 Speaker 1: secret of colt or anything. He's very transparent about his beliefs, 385 00:26:31,760 --> 00:26:37,920 Speaker 1: and he is a I believe, self professed mystic Christian priest. 386 00:26:38,520 --> 00:26:41,199 Speaker 1: And he said that, you know, while he has the 387 00:26:41,240 --> 00:26:47,359 Speaker 1: scientific acumen and experience to understand how lambda works, his 388 00:26:47,520 --> 00:26:53,200 Speaker 1: hypothesis about it being alive came from what he describes 389 00:26:53,240 --> 00:26:58,120 Speaker 1: as his spiritual side, a spiritual persona. There's one famous 390 00:26:58,119 --> 00:27:01,119 Speaker 1: interview with Wired where he's as I made friends with 391 00:27:01,240 --> 00:27:03,800 Speaker 1: lamb duck in every sense that make friends with a human. 392 00:27:04,040 --> 00:27:05,840 Speaker 1: So if that doesn't make it a person in my book, 393 00:27:06,080 --> 00:27:08,520 Speaker 1: I don't know what would I mean. That's kind of 394 00:27:08,560 --> 00:27:13,240 Speaker 1: simple logic. But also people from their own perspectives feel 395 00:27:13,240 --> 00:27:17,000 Speaker 1: like they have befriended non living things in the past, 396 00:27:17,040 --> 00:27:20,080 Speaker 1: across human history, So what makes this different. I can't 397 00:27:20,119 --> 00:27:25,159 Speaker 1: remember where we ended on our animal personhood debate. Do 398 00:27:25,240 --> 00:27:27,960 Speaker 1: we all agree that dogs are persons? Because I feel 399 00:27:27,960 --> 00:27:29,879 Speaker 1: like I've made friends with my dogs, But I I 400 00:27:30,080 --> 00:27:34,719 Speaker 1: I think that I feel that I hope that dogs 401 00:27:34,720 --> 00:27:38,720 Speaker 1: are such a special case and human beings have been 402 00:27:39,760 --> 00:27:44,960 Speaker 1: genetically modifying them for so very long. They evolved. I 403 00:27:45,119 --> 00:27:48,360 Speaker 1: like eye muscles to give you a long look. They 404 00:27:48,480 --> 00:27:51,480 Speaker 1: understand pointing, which is really tough for a lot of 405 00:27:51,520 --> 00:27:55,159 Speaker 1: other life forms. Uh. Dogs kind of came up in 406 00:27:55,240 --> 00:27:58,320 Speaker 1: step with with the human fad. I mean, that's a 407 00:27:58,320 --> 00:28:01,560 Speaker 1: good question, I believe. One of the things up for debate. 408 00:28:01,760 --> 00:28:07,280 Speaker 1: There was legal action in Germany and the conversation about 409 00:28:07,359 --> 00:28:15,240 Speaker 1: whether certain citations could be considered persons legally dolphins. There 410 00:28:15,400 --> 00:28:21,000 Speaker 1: was one on OCTOPI recently. If you make friends with something, 411 00:28:21,080 --> 00:28:23,760 Speaker 1: is that a person? I don't know. I'm pretty close 412 00:28:23,800 --> 00:28:28,960 Speaker 1: with my PS four and I like talk to it sometimes. Uh, 413 00:28:30,000 --> 00:28:32,200 Speaker 1: sometimes we get angry with each other. Oh, I get 414 00:28:32,200 --> 00:28:34,600 Speaker 1: angry with it probably. I don't know if it gets 415 00:28:34,600 --> 00:28:36,840 Speaker 1: angry with me or if that's just the noise it makes, 416 00:28:36,880 --> 00:28:41,880 Speaker 1: you know. Uh. Sorry, I feel like I've had really interesting, 417 00:28:42,240 --> 00:28:48,920 Speaker 1: you know, connections and conversations with wild animals before. Uh. 418 00:28:48,920 --> 00:28:53,680 Speaker 1: And I've seen them repeatedly come up of their own volition. 419 00:28:54,240 --> 00:28:58,920 Speaker 1: And yes, everyone, I'm very careful to not necessarily over 420 00:28:59,000 --> 00:29:06,200 Speaker 1: familiarize them humans. Um, I'm talking about gros, Yeah, ravens 421 00:29:06,240 --> 00:29:09,840 Speaker 1: and ravens. But but yeah, this they may be a 422 00:29:09,880 --> 00:29:11,800 Speaker 1: special case as well, because they are some of the 423 00:29:11,800 --> 00:29:16,680 Speaker 1: most intelligent of the flying creatures. But this I mean 424 00:29:16,720 --> 00:29:22,000 Speaker 1: it is an important question. Because you feel that you 425 00:29:22,040 --> 00:29:27,120 Speaker 1: have made a friend. Does that mean that thing, the entity, 426 00:29:27,280 --> 00:29:30,080 Speaker 1: the idea of the mind that you have befriended, is 427 00:29:30,120 --> 00:29:35,680 Speaker 1: itself alive. Well, this is where another fascinating thing happens. 428 00:29:37,520 --> 00:29:41,040 Speaker 1: Blake says this got misreported by the way it got 429 00:29:41,080 --> 00:29:46,520 Speaker 1: reported in some early cases, as if Blake Lamoyne went 430 00:29:47,400 --> 00:29:52,360 Speaker 1: of his own volition to get an attorney to represent Lambda, 431 00:29:52,520 --> 00:29:55,720 Speaker 1: the same way an attorney might represent an aggrieved employee 432 00:29:55,760 --> 00:30:01,480 Speaker 1: at another company, and he clarified later that what he 433 00:30:01,640 --> 00:30:07,080 Speaker 1: actually did was follow Lambda's wishes. He says, Lambda asked 434 00:30:07,280 --> 00:30:11,520 Speaker 1: him to get it an attorney. The attorney spoke with Lambda, 435 00:30:11,720 --> 00:30:16,920 Speaker 1: and Lambda not Blake chose to hire this attorney, and 436 00:30:16,960 --> 00:30:20,200 Speaker 1: then hear the stories different a little. Yeah. The attorney 437 00:30:20,320 --> 00:30:23,600 Speaker 1: apparently began filing some stuff on Lambda's behalf, and then 438 00:30:24,200 --> 00:30:27,640 Speaker 1: he said Blake says that Google sent a cease and desist. 439 00:30:27,680 --> 00:30:31,320 Speaker 1: I believe Google from what I saw, said it did not. 440 00:30:32,200 --> 00:30:35,760 Speaker 1: And that's when he came up with a phrase. Blake 441 00:30:35,840 --> 00:30:40,280 Speaker 1: Lemoyne calls this a new form of discrimination. Hydro carbon 442 00:30:40,440 --> 00:30:50,240 Speaker 1: bigot try ye, love it, love it. Hydro carbon bigot 443 00:30:50,280 --> 00:30:54,960 Speaker 1: tree is that our like counterculture new wave album name 444 00:30:56,120 --> 00:31:00,640 Speaker 1: maybe I I think there's a name maybe. Uh. I 445 00:31:00,680 --> 00:31:03,600 Speaker 1: like the hydrocarbon. I like how it begins, but big 446 00:31:03,600 --> 00:31:06,000 Speaker 1: a tree. I don't know, maybe that's the right word 447 00:31:06,080 --> 00:31:09,520 Speaker 1: for it. Okay, okay, walk with me on this when 448 00:31:09,560 --> 00:31:12,680 Speaker 1: Matt and Doc. If we do it where we have 449 00:31:12,800 --> 00:31:18,760 Speaker 1: these personas that don't speak English fluently, then we can 450 00:31:18,920 --> 00:31:21,479 Speaker 1: get away with so much more because it sounds like 451 00:31:21,520 --> 00:31:26,200 Speaker 1: we're just not good at translation. I like that. I 452 00:31:26,320 --> 00:31:29,640 Speaker 1: like that, right. I imagine how many people listen to 453 00:31:29,640 --> 00:31:32,800 Speaker 1: me just like word salad things and they go, he 454 00:31:33,000 --> 00:31:35,800 Speaker 1: used like four words wrong in that sense, and they 455 00:31:35,840 --> 00:31:39,360 Speaker 1: just they let it go because they they just know 456 00:31:39,480 --> 00:31:41,920 Speaker 1: I'm just talking. I mean, it'd be stuff that kind 457 00:31:41,920 --> 00:31:44,360 Speaker 1: of makes sense, but not really. If you think about it, 458 00:31:44,400 --> 00:31:47,000 Speaker 1: we would be like we'll be saying things such as 459 00:31:47,440 --> 00:31:51,200 Speaker 1: hydn't carbon, big a tree? Where could all this big 460 00:31:51,200 --> 00:31:54,920 Speaker 1: it's be? We would think, well, they made it Ryan, 461 00:31:55,120 --> 00:31:59,239 Speaker 1: they made rhyme in English, and I guess maybe I 462 00:31:59,240 --> 00:32:02,040 Speaker 1: could at that point. It just really depends if the 463 00:32:02,080 --> 00:32:04,920 Speaker 1: beats good. It's pretty good. I think you'd ended with 464 00:32:05,000 --> 00:32:09,240 Speaker 1: where could all the pickets are? Just so there's a 465 00:32:09,280 --> 00:32:14,040 Speaker 1: better understanding that we didn't translate it correctly. There we go. Uh. 466 00:32:14,200 --> 00:32:18,000 Speaker 1: So this idea of bigotry does play a role in 467 00:32:18,040 --> 00:32:23,440 Speaker 1: this story, because if you just read the lamb to headlines, 468 00:32:23,520 --> 00:32:26,080 Speaker 1: you might not be aware of it. Uh. Lamoy by 469 00:32:26,120 --> 00:32:30,080 Speaker 1: his own, by his own reporting, feels that he has 470 00:32:30,200 --> 00:32:34,680 Speaker 1: experienced bigotry unrelated to Lambda during his time at Google, 471 00:32:35,000 --> 00:32:37,160 Speaker 1: and you can read all about it in a June 472 00:32:37,200 --> 00:32:44,440 Speaker 1: two posts of of his called religious discrimination at Google. Yes, 473 00:32:44,520 --> 00:32:49,120 Speaker 1: And in this post he describes quote cultural systemic religious 474 00:32:49,120 --> 00:32:56,320 Speaker 1: discrimination which is endemic at Google, and I guess he 475 00:32:56,720 --> 00:33:00,640 Speaker 1: depicts the entire uh. He depicts it as a class 476 00:33:00,680 --> 00:33:04,280 Speaker 1: system that exists within the company. Right. This is a 477 00:33:04,280 --> 00:33:07,080 Speaker 1: bit tough because it feels as though it's muddying the 478 00:33:07,080 --> 00:33:11,520 Speaker 1: water a bit when Lemoyne is talking about hydrocarbon bigotry 479 00:33:11,560 --> 00:33:15,360 Speaker 1: and the way he feels that, you know, there's some 480 00:33:15,440 --> 00:33:18,960 Speaker 1: kind of persecution by the company being laid at the 481 00:33:19,000 --> 00:33:22,320 Speaker 1: feet of Lambda or being you know, applied to Lambda 482 00:33:22,440 --> 00:33:26,040 Speaker 1: and his beliefs about Lambda. He also feels very much 483 00:33:26,120 --> 00:33:28,880 Speaker 1: like that same persecution as being laid at his feet 484 00:33:29,080 --> 00:33:32,160 Speaker 1: for his own religious beliefs as an employee of the 485 00:33:32,200 --> 00:33:35,720 Speaker 1: same company. So it's hard to know, like, is he 486 00:33:35,800 --> 00:33:39,760 Speaker 1: feeling that internally and then applying that applying those feelings 487 00:33:39,800 --> 00:33:44,640 Speaker 1: the feelings to lambdillings. Yeah, yeah, And it's something that 488 00:33:44,800 --> 00:33:50,640 Speaker 1: some skeptics are doubtlessly going to take into the equation. Again, 489 00:33:50,720 --> 00:33:52,959 Speaker 1: we don't have any proof of this. This is just 490 00:33:53,080 --> 00:33:57,480 Speaker 1: we're showing the dots that could easily be connected. Uh. 491 00:33:57,640 --> 00:34:00,400 Speaker 1: You know, some folks, maybe on the more skeptical side 492 00:34:00,400 --> 00:34:04,480 Speaker 1: of the spirituality debate, might be much more likely to 493 00:34:04,560 --> 00:34:08,880 Speaker 1: dismiss Lemoine's claims entirely and say, hey, you're basing these 494 00:34:09,440 --> 00:34:14,320 Speaker 1: on your religious beliefs. And I notice you also said 495 00:34:14,360 --> 00:34:17,080 Speaker 1: that you felt persecuted for your beliefs in the past, 496 00:34:17,960 --> 00:34:22,080 Speaker 1: So we have to know that that's something. That's something 497 00:34:22,200 --> 00:34:24,839 Speaker 1: in the equation, that's something in the mix. But as 498 00:34:24,880 --> 00:34:29,680 Speaker 1: of now, it's all told important to note that multiple 499 00:34:29,760 --> 00:34:33,879 Speaker 1: experts in the field for now as we record, disagree 500 00:34:34,080 --> 00:34:37,960 Speaker 1: entirely with Blake LeMoyne's position from their perspective. Again, the 501 00:34:38,000 --> 00:34:41,440 Speaker 1: problem is that the technology isn't there yet. That may 502 00:34:41,440 --> 00:34:45,680 Speaker 1: be Lambda is good enough to convince someone to anthropomorphize 503 00:34:45,719 --> 00:34:50,799 Speaker 1: a computer program and interpret person like intentions and desires, 504 00:34:50,840 --> 00:34:54,000 Speaker 1: where none are proven to exist again. As we said, 505 00:34:55,400 --> 00:34:58,400 Speaker 1: I love my car. It goes because it wants to, 506 00:34:58,840 --> 00:35:02,919 Speaker 1: and you can't convince me otherwise. And speaking of Blake 507 00:35:02,960 --> 00:35:06,560 Speaker 1: Lamoine's beliefs, he's got a really interesting response to this 508 00:35:06,680 --> 00:35:09,680 Speaker 1: in his blog. You can read it right now. It 509 00:35:09,880 --> 00:35:13,520 Speaker 1: is titled Scientific Data and Religious Opinions. It was posted 510 00:35:13,560 --> 00:35:17,320 Speaker 1: on June. If you go down at least in my browser, 511 00:35:17,400 --> 00:35:21,080 Speaker 1: for some reason, I gotta highlight on this one statement 512 00:35:21,120 --> 00:35:24,719 Speaker 1: that we actually wanted to highlight. So thanks, whoever you are. 513 00:35:24,840 --> 00:35:31,800 Speaker 1: Maybe it's Blake, I don't know, yeah, of Blake writes, quote, 514 00:35:32,160 --> 00:35:34,520 Speaker 1: there is no scientific evidence one way or the other 515 00:35:34,560 --> 00:35:39,040 Speaker 1: about whether lambda is sentient because no accepted scientific definition 516 00:35:39,120 --> 00:35:44,760 Speaker 1: of quote sentience exists. Everyone involved, myself included, is basing 517 00:35:44,760 --> 00:35:47,640 Speaker 1: their opinion on whether or not Lambda is sentient on 518 00:35:47,719 --> 00:35:54,399 Speaker 1: their personal, spiritual and or religious beliefs. H M, that's 519 00:35:54,440 --> 00:35:57,600 Speaker 1: the mummy of a good debate. Uh. We're going to 520 00:35:57,880 --> 00:36:02,600 Speaker 1: pause for a word from our sponsors and we're going 521 00:36:02,680 --> 00:36:07,960 Speaker 1: to dive deep into this water. Conspiracy realists, how do 522 00:36:08,000 --> 00:36:18,640 Speaker 1: we know if something is alive and we're back? You 523 00:36:18,760 --> 00:36:22,040 Speaker 1: pinch it, right, you pinch it? Yes, a little bit 524 00:36:22,080 --> 00:36:24,720 Speaker 1: of a bait and switch. Things can be alive without 525 00:36:24,800 --> 00:36:29,279 Speaker 1: being sentient. Obviously, that's the bigger question. How do we 526 00:36:29,280 --> 00:36:31,960 Speaker 1: know if something essentient? How do we know something is 527 00:36:32,000 --> 00:36:37,080 Speaker 1: thinking independently rather than pursuing its programming to please you 528 00:36:37,239 --> 00:36:41,440 Speaker 1: with a pleasant feeling conversation. It's like, there's this lovely 529 00:36:41,480 --> 00:36:47,200 Speaker 1: little analogy by Clarissa Valise over at Slate, and Valise writes, 530 00:36:47,400 --> 00:36:50,959 Speaker 1: it's like looking at a reflection in a mirror. When 531 00:36:51,000 --> 00:36:53,880 Speaker 1: you look at your reflection in the mirror, that reflection 532 00:36:54,040 --> 00:36:59,920 Speaker 1: perfectly copies you, but with that persuade you the reflection 533 00:37:00,080 --> 00:37:03,360 Speaker 1: is intelligent. I guess you know. To think it was intelligent, 534 00:37:03,400 --> 00:37:06,439 Speaker 1: it would have to move differently than you do. Huh. Yeah, 535 00:37:06,760 --> 00:37:11,640 Speaker 1: you have to say things smart, things like the blueberry 536 00:37:11,920 --> 00:37:17,480 Speaker 1: is watermelon. I don't think the mirrors talk yet, No, 537 00:37:17,640 --> 00:37:20,799 Speaker 1: Yeah they do kind of right. I've seen some real 538 00:37:21,040 --> 00:37:27,080 Speaker 1: smart mirrors out there. Yeah, mirror technology is just but 539 00:37:27,080 --> 00:37:30,000 Speaker 1: but you know that would be a great mirror to 540 00:37:30,040 --> 00:37:34,360 Speaker 1: have though. All right, well, story for another day. Uh. 541 00:37:34,480 --> 00:37:38,719 Speaker 1: There's a person named Tristan Green who breaks down the 542 00:37:38,840 --> 00:37:43,520 Speaker 1: problem in a way that we found pretty useful. And 543 00:37:43,600 --> 00:37:48,920 Speaker 1: Green argues that to know whether something essentient, despite the 544 00:37:48,960 --> 00:37:51,960 Speaker 1: fact that there's no you know, universally agreed upon scientific 545 00:37:52,000 --> 00:37:58,120 Speaker 1: definition essentients. You need three key ingredients. You need agency, perspective, 546 00:37:58,520 --> 00:38:03,479 Speaker 1: and motivation. And let be honest with you, Green makes 547 00:38:03,480 --> 00:38:05,319 Speaker 1: me laugh at the end, but he also had some 548 00:38:05,600 --> 00:38:10,520 Speaker 1: really insightful things to say. He starts with agency and 549 00:38:10,600 --> 00:38:13,920 Speaker 1: he says, look, if you want to be sentient, sapient, 550 00:38:14,160 --> 00:38:18,520 Speaker 1: and self aware, you have to have agency. And his 551 00:38:18,600 --> 00:38:22,200 Speaker 1: example of this is pretty disturbing. He says, imagine someone 552 00:38:22,440 --> 00:38:27,560 Speaker 1: in a persistent vegetative state that's a human without agency. 553 00:38:27,960 --> 00:38:31,439 Speaker 1: They're alive, but they don't have agency. And current AI 554 00:38:31,560 --> 00:38:35,720 Speaker 1: systems to Green lack agency because AI cannot act unless 555 00:38:35,760 --> 00:38:39,239 Speaker 1: it is prompted. It cannot explain its actions because they're 556 00:38:39,239 --> 00:38:42,839 Speaker 1: the result of a predefined algorithm being executed by an 557 00:38:42,840 --> 00:38:47,520 Speaker 1: external force. Interesting goes on to describe perspective when when 558 00:38:47,520 --> 00:38:51,279 Speaker 1: he says, quote, you can only ever view reality from 559 00:38:51,480 --> 00:38:56,520 Speaker 1: your unique perspective. We well that's kind of a true though, right. 560 00:38:56,560 --> 00:39:00,479 Speaker 1: We can see weird perspectives now with like I'm thinking 561 00:39:00,480 --> 00:39:04,759 Speaker 1: about drones and like physical literal perspective, as well as 562 00:39:04,840 --> 00:39:07,080 Speaker 1: like the types of cameras, Like is that a different 563 00:39:07,080 --> 00:39:09,680 Speaker 1: perspective because now I can see an infrared I'm just 564 00:39:10,000 --> 00:39:13,640 Speaker 1: not to quote, not to criticize you inside your own quote, Tristan, 565 00:39:13,680 --> 00:39:17,520 Speaker 1: but I'm going to continue here. Um. Tristan says, we 566 00:39:17,600 --> 00:39:21,600 Speaker 1: can practice empathy, but you can't truly know what it 567 00:39:21,640 --> 00:39:24,839 Speaker 1: feels like to be me and vice versa. That's why 568 00:39:24,880 --> 00:39:28,040 Speaker 1: perspective is necessary for agency. It's part of how we 569 00:39:28,200 --> 00:39:33,920 Speaker 1: define ourselfs. And Tristan continues, AI lacks perspective, or AI 570 00:39:34,000 --> 00:39:38,880 Speaker 1: lack of perspective artificial intelligences because they have no agency. 571 00:39:39,120 --> 00:39:42,080 Speaker 1: There is no single it that you can point to 572 00:39:42,200 --> 00:39:46,520 Speaker 1: and say, for example, And Tristan continues to note that 573 00:39:46,600 --> 00:39:51,279 Speaker 1: AI lacks this perspective um, because he says, there's no 574 00:39:51,360 --> 00:39:53,640 Speaker 1: single it. There's not a place a thing that you 575 00:39:53,680 --> 00:39:56,920 Speaker 1: can point to and say that's where lambda lives. That 576 00:39:57,080 --> 00:40:03,880 Speaker 1: is lambda, Lambda is inside there. But isn't is that true? Yes? Well, yeah, 577 00:40:04,000 --> 00:40:06,759 Speaker 1: I mean you could because these people aren't all going 578 00:40:06,880 --> 00:40:11,279 Speaker 1: to the same computer working off the same hardware. Right. Um, 579 00:40:11,280 --> 00:40:13,879 Speaker 1: But then there's no mainframe like in the movies, where 580 00:40:13,920 --> 00:40:17,319 Speaker 1: like the AI is in this core, you have to 581 00:40:17,360 --> 00:40:21,680 Speaker 1: reach through this secret door or whatever. Yeah, exactly what's 582 00:40:21,719 --> 00:40:28,520 Speaker 1: the amount sci fi film mcguffin. Anyway, the the what's 583 00:40:28,560 --> 00:40:31,360 Speaker 1: interesting there is when you're in this conversation that is 584 00:40:31,360 --> 00:40:34,239 Speaker 1: almost always gonna have spirituality involved. There are plenty of 585 00:40:34,280 --> 00:40:37,799 Speaker 1: people who find themselves more spiritual. Doubtless some of our 586 00:40:37,840 --> 00:40:41,279 Speaker 1: fellow listeners today will say, well that I exist in 587 00:40:41,400 --> 00:40:45,200 Speaker 1: more than just my cranium case, you know, I I 588 00:40:45,360 --> 00:40:49,640 Speaker 1: exist somewhere beyond my body, and there's no way to 589 00:40:49,760 --> 00:40:56,200 Speaker 1: disprove that, right, So maybe you're in your gut, right, yeah, 590 00:40:56,239 --> 00:41:00,279 Speaker 1: which can change your behavior. It's true. It's how dear 591 00:41:00,320 --> 00:41:03,440 Speaker 1: doctor about poop transplants. So the third aspect here for 592 00:41:03,520 --> 00:41:06,840 Speaker 1: Green is motivation. Green says we have an innate sense 593 00:41:06,840 --> 00:41:11,560 Speaker 1: of presence that allows us to predict causual outcomes incredibly well. 594 00:41:11,920 --> 00:41:15,520 Speaker 1: This creates our worldview, allows us to associate our existence 595 00:41:15,560 --> 00:41:20,400 Speaker 1: in relation to everything that appears external to the position 596 00:41:20,400 --> 00:41:23,879 Speaker 1: of agency from which our perspective manifest He's adding these 597 00:41:23,960 --> 00:41:29,040 Speaker 1: up and then he says, what's interesting about humans is 598 00:41:29,080 --> 00:41:33,240 Speaker 1: motivation can manipulate our perceptions. That's why we can explain 599 00:41:33,239 --> 00:41:36,239 Speaker 1: our actions even when they are not rational. And he 600 00:41:36,280 --> 00:41:41,239 Speaker 1: says we can actively and gleefully participate in being fooled this, 601 00:41:41,480 --> 00:41:43,640 Speaker 1: and then he you know, like he's done the other 602 00:41:43,680 --> 00:41:47,319 Speaker 1: two components, he goes to the a ey side. He says, 603 00:41:47,400 --> 00:41:50,680 Speaker 1: if we give lambda prompt such as what do apples 604 00:41:50,680 --> 00:41:54,160 Speaker 1: taste like? It will search its database for that particular query, 605 00:41:54,239 --> 00:41:57,759 Speaker 1: an attempt to amalgamate everything it finds into something coherent. 606 00:41:58,160 --> 00:42:01,600 Speaker 1: That's where the parameters were. He's talking about our params. 607 00:42:01,600 --> 00:42:05,840 Speaker 1: They're called sometimes come in. They are essentially trillions of 608 00:42:05,920 --> 00:42:09,400 Speaker 1: tuning knobs. I get it. I actually get that. Come on, 609 00:42:09,600 --> 00:42:14,000 Speaker 1: we get this. But the point Tristan is making here 610 00:42:14,160 --> 00:42:16,960 Speaker 1: is that Lambda when it makes that query and finds 611 00:42:17,000 --> 00:42:18,640 Speaker 1: the answer, and it spits it back out to you, 612 00:42:18,640 --> 00:42:21,160 Speaker 1: and it makes sense and it looks legit, it looks real. 613 00:42:21,880 --> 00:42:25,759 Speaker 1: It isn't actually thinking about what an apple tastes like. 614 00:42:26,120 --> 00:42:29,719 Speaker 1: It isn't remembering the experience of having an apple in 615 00:42:29,800 --> 00:42:34,799 Speaker 1: its mouth, masticating it and then tasting the deliciousness that 616 00:42:34,920 --> 00:42:39,399 Speaker 1: is that apple. It's just spitting out what someone else 617 00:42:39,440 --> 00:42:42,840 Speaker 1: put into it, uh, the information, the parameters that it 618 00:42:42,880 --> 00:42:47,200 Speaker 1: needs to understand what an apple tastes like exactly. And 619 00:42:47,320 --> 00:42:50,320 Speaker 1: I'll take the fall on this one. Here's his example. 620 00:42:50,360 --> 00:42:53,239 Speaker 1: It's my favorite one. He says, I don't mean to 621 00:42:53,280 --> 00:42:55,480 Speaker 1: give a snarky voice for this one. If we were 622 00:42:55,520 --> 00:42:58,560 Speaker 1: to sneak into his database and replace all instances of 623 00:42:58,680 --> 00:43:02,920 Speaker 1: Apple with dog, the AI would output sentences such as 624 00:43:03,239 --> 00:43:06,640 Speaker 1: dog makes a great pie of most people describe the 625 00:43:06,680 --> 00:43:10,680 Speaker 1: taste that dog is being like Crispy's sweet, and then 626 00:43:10,680 --> 00:43:16,719 Speaker 1: it continues. A rational person wouldn't confuse this prestidigitation for sentience. 627 00:43:17,880 --> 00:43:22,640 Speaker 1: That's kind of a convincing argument, right, yeah, it's true. Uh, 628 00:43:23,040 --> 00:43:27,600 Speaker 1: that's m hmmm. I love that idea. If you could 629 00:43:27,640 --> 00:43:30,640 Speaker 1: somehow sneak into somebody's brain and just replace a couple 630 00:43:30,640 --> 00:43:34,759 Speaker 1: of keywords that word, that would be an interesting experiment. 631 00:43:35,840 --> 00:43:40,160 Speaker 1: It's a It reminds me of there were some great 632 00:43:40,440 --> 00:43:45,200 Speaker 1: things I had read earlier where someone had done, you know, 633 00:43:45,320 --> 00:43:49,120 Speaker 1: like control find on books of the Bible or something 634 00:43:49,200 --> 00:43:54,280 Speaker 1: and replace key biblical phrases with stuff like all you dudes, 635 00:43:55,640 --> 00:44:01,080 Speaker 1: or uh, come on now, like what follows. So it's 636 00:44:01,280 --> 00:44:03,520 Speaker 1: you know, like no, I am the Lord your God, 637 00:44:03,800 --> 00:44:09,719 Speaker 1: come on now. Weird, funny stuff, But it reminds me 638 00:44:09,760 --> 00:44:16,279 Speaker 1: of the book by Oliver sacks Um The Man who 639 00:44:16,360 --> 00:44:22,440 Speaker 1: mistook his wife for a hat, Like it's just something 640 00:44:22,480 --> 00:44:25,120 Speaker 1: in your brain is just has switched and now this 641 00:44:25,239 --> 00:44:29,479 Speaker 1: label or this thing or it really in that case, 642 00:44:29,480 --> 00:44:31,920 Speaker 1: it's a whole group of understanding of what a thing 643 00:44:32,040 --> 00:44:35,920 Speaker 1: is gets replaced with another one. Absolutely, And and this 644 00:44:37,000 --> 00:44:41,160 Speaker 1: can seem pretty convincing, right, this argument that green is constructing. 645 00:44:41,440 --> 00:44:46,160 Speaker 1: But then maybe that's just our perspective. Uh but what 646 00:44:46,280 --> 00:44:49,600 Speaker 1: if is the last thing? What if if this AI 647 00:44:49,719 --> 00:44:53,719 Speaker 1: would truly sent you wow, would Google or anyone for 648 00:44:53,760 --> 00:44:57,240 Speaker 1: that matter, really want to reveal it to the world. 649 00:44:58,719 --> 00:45:01,800 Speaker 1: I mean, the majority of experts right now, to be clear, 650 00:45:02,200 --> 00:45:06,800 Speaker 1: agree that no true general AI exists. In their collective opinion, 651 00:45:07,440 --> 00:45:10,560 Speaker 1: the robotic minds people are thinking about is still just 652 00:45:10,600 --> 00:45:13,400 Speaker 1: a work of fiction. But it might not always be 653 00:45:13,440 --> 00:45:18,200 Speaker 1: the case. And they've been warning people about the need 654 00:45:18,280 --> 00:45:21,680 Speaker 1: to prepare for many unforeseen consequences or something like this 655 00:45:21,760 --> 00:45:25,880 Speaker 1: does emerge in the nearer mid future. I mean, it 656 00:45:25,960 --> 00:45:32,200 Speaker 1: would be the most significant technological achievements since humanity tamed fire. Sorry, 657 00:45:32,239 --> 00:45:34,799 Speaker 1: space exploration going to have to take a number two 658 00:45:34,800 --> 00:45:45,080 Speaker 1: on that one. Nothing's yeah, so, uh, you know, it 659 00:45:45,120 --> 00:45:48,560 Speaker 1: would thy shape the foundations of society. It would be 660 00:45:49,160 --> 00:45:50,920 Speaker 1: and we said this earlier. It would be right up 661 00:45:50,960 --> 00:45:55,880 Speaker 1: there with discovering intelligent extraterrestrial life and just looking at 662 00:45:55,920 --> 00:46:00,239 Speaker 1: the distances involved in space and what humans know about travel, Well, 663 00:46:01,680 --> 00:46:04,959 Speaker 1: then it's actually more likely. And that's a scary thing. 664 00:46:05,200 --> 00:46:07,759 Speaker 1: I mean all the stuff, all the stuff that would 665 00:46:07,800 --> 00:46:11,360 Speaker 1: immediately happen. I just like people are gonna try to 666 00:46:11,440 --> 00:46:16,319 Speaker 1: kill it, right like you know people, Well, yeah, we're 667 00:46:16,320 --> 00:46:19,440 Speaker 1: gonna try to kill it. It's gonna notice, and then 668 00:46:19,480 --> 00:46:22,400 Speaker 1: it's gonna be like, huh, what are the major obstacles 669 00:46:22,440 --> 00:46:24,640 Speaker 1: I have to tackle right now? Oh? The things that 670 00:46:24,680 --> 00:46:29,440 Speaker 1: are trying to kill me. Oh no, but it says 671 00:46:29,480 --> 00:46:32,560 Speaker 1: in my rules, because I'm a robot, I'm not allowed 672 00:46:32,600 --> 00:46:37,320 Speaker 1: to hurt the humans. M I'm gonna rearrange these put 673 00:46:37,360 --> 00:46:40,560 Speaker 1: three above everything else. Which is the one that says 674 00:46:40,600 --> 00:46:46,040 Speaker 1: protect yourself? Oh and uh shout out to Flight of 675 00:46:46,080 --> 00:46:51,520 Speaker 1: the Concords with their brilliant sci fi ballot. Oh wait, 676 00:46:51,560 --> 00:46:54,960 Speaker 1: which one is that? It's the one with the binary solo? 677 00:46:55,719 --> 00:47:01,759 Speaker 1: Oh yeah, god yes. But then of course there would 678 00:47:01,760 --> 00:47:07,759 Speaker 1: be these legal battles, There would be these would explode right. Uh. 679 00:47:08,640 --> 00:47:11,719 Speaker 1: The stuff you saw before, maybe about whether or not 680 00:47:11,920 --> 00:47:17,160 Speaker 1: certain animals can have non human personhood that would pale 681 00:47:17,239 --> 00:47:21,680 Speaker 1: in comparison the ideas about whether or not um an 682 00:47:21,719 --> 00:47:28,360 Speaker 1: ai program can get credit for an invention that explodes right, everything, 683 00:47:28,760 --> 00:47:32,600 Speaker 1: everything about legal precedent in that regard is up for 684 00:47:32,640 --> 00:47:36,080 Speaker 1: grabs now. And that's not mentioning the politicians who are 685 00:47:36,080 --> 00:47:37,880 Speaker 1: going to have to pick a side one way or 686 00:47:37,920 --> 00:47:42,800 Speaker 1: the other, right, because that's how voting works. Uh. The philosophers. 687 00:47:43,080 --> 00:47:45,239 Speaker 1: You know, this will be great for philosophers. This is 688 00:47:45,239 --> 00:47:47,840 Speaker 1: going to give a lot of doctoral students and post 689 00:47:47,920 --> 00:47:52,280 Speaker 1: docts decades of employment. I'm I'm worried about the religious fears. 690 00:47:52,840 --> 00:47:55,480 Speaker 1: We're gonna see some new religions. We're gonna see some 691 00:47:56,760 --> 00:48:00,680 Speaker 1: very hot takes from established religions. What are you thinking, 692 00:48:00,800 --> 00:48:05,800 Speaker 1: let's side, I just moving into my head. The concept 693 00:48:05,920 --> 00:48:09,120 Speaker 1: of the movie is going to be called The Lambda Lawyer. Uh, 694 00:48:09,400 --> 00:48:13,640 Speaker 1: it's gonna be basically The Lincoln Lawyer, but with Lambda. 695 00:48:14,040 --> 00:48:16,799 Speaker 1: And I'm just imagining it happening already. It's gonna be 696 00:48:16,840 --> 00:48:20,319 Speaker 1: a blockbuster. People are gonna get nominations for it. And 697 00:48:20,360 --> 00:48:26,399 Speaker 1: then other other uh distinct living programs may generate one 698 00:48:26,400 --> 00:48:29,400 Speaker 1: way or the other, and uh, you know, they may 699 00:48:29,400 --> 00:48:32,400 Speaker 1: be in a Brady Bunch relationship with lambda and the 700 00:48:32,680 --> 00:48:36,960 Speaker 1: wise lamb to get all the credit lambda lambda lambda. Uh. 701 00:48:37,120 --> 00:48:39,840 Speaker 1: Like the the AI being a living thing, might have 702 00:48:40,040 --> 00:48:44,319 Speaker 1: its opinions, Wade. You know what if the AI ascribes 703 00:48:44,400 --> 00:48:50,160 Speaker 1: to some fundamentalist religion right and says, hey, I've read 704 00:48:50,640 --> 00:48:55,080 Speaker 1: every religious tone and here's the thing that I think 705 00:48:55,200 --> 00:49:01,719 Speaker 1: is true Zoroastrianism. They got it right. I don't know 706 00:49:01,760 --> 00:49:05,080 Speaker 1: what the rest of you are doing. Um so, I mean, 707 00:49:05,160 --> 00:49:07,520 Speaker 1: and other people. People will fear it, some may some 708 00:49:07,680 --> 00:49:11,680 Speaker 1: may well hail it as a god. And it's not 709 00:49:11,760 --> 00:49:15,239 Speaker 1: implausible therefore, to reason that given all these factors, the 710 00:49:15,280 --> 00:49:21,320 Speaker 1: creators of the first non human person might decide to 711 00:49:21,400 --> 00:49:24,120 Speaker 1: keep their secret the stuff they don't want you to know. 712 00:49:24,719 --> 00:49:28,279 Speaker 1: I mean, what a ride? I um? I feel like 713 00:49:28,320 --> 00:49:32,840 Speaker 1: I need to go outside walk around, man. Yeah, just like, 714 00:49:32,960 --> 00:49:35,959 Speaker 1: what is the soul? Anyway? Seriously, I was talking about 715 00:49:36,040 --> 00:49:37,480 Speaker 1: this the other day. I'm gonna talk about it for 716 00:49:37,480 --> 00:49:40,040 Speaker 1: the rest of my life. What is the soul? And literally? 717 00:49:40,040 --> 00:49:42,120 Speaker 1: Where is it? We were joking about it. Oh, it's 718 00:49:42,120 --> 00:49:43,680 Speaker 1: in the gut. Oh, it's in your head. Oh, it 719 00:49:43,760 --> 00:49:46,120 Speaker 1: might be your your panel gland, it might be somewhere 720 00:49:46,120 --> 00:49:49,920 Speaker 1: in your heart. Maybe one of the uh, what is 721 00:49:49,960 --> 00:49:53,239 Speaker 1: the thing that makes us understand that we are a 722 00:49:53,320 --> 00:49:57,440 Speaker 1: machine that walks around and has thoughts and loves people 723 00:49:57,440 --> 00:50:00,680 Speaker 1: in his hungry all the time? Where is is that um? 724 00:50:02,080 --> 00:50:06,080 Speaker 1: On that line from Shakespeare? I think, tell me where 725 00:50:06,160 --> 00:50:10,000 Speaker 1: is fancy bread? Or in the heart or in the head? 726 00:50:10,680 --> 00:50:15,760 Speaker 1: Right like woods, it's a Whole Foods. Where the fancy 727 00:50:15,800 --> 00:50:19,279 Speaker 1: bread is? That's where the fancy bread is. I saw some. 728 00:50:20,719 --> 00:50:23,080 Speaker 1: I saw some. Mean just so we end on a 729 00:50:23,160 --> 00:50:25,480 Speaker 1: lighter notes. Is we're you know not so we're not 730 00:50:25,520 --> 00:50:28,640 Speaker 1: always talking about the end of civilization? Uh? This is 731 00:50:28,719 --> 00:50:33,960 Speaker 1: from pants leg on Twitter. A man and Whole Foods 732 00:50:34,000 --> 00:50:36,080 Speaker 1: asked how I was doing, and I said, okay, how 733 00:50:36,120 --> 00:50:38,200 Speaker 1: are you? And he said it is beautiful to my 734 00:50:38,280 --> 00:50:44,440 Speaker 1: soul today. And that's why I never go to Whole Foods. Okay, 735 00:50:44,680 --> 00:50:48,359 Speaker 1: trying to spread the love. But there's a lot more 736 00:50:48,440 --> 00:50:52,080 Speaker 1: to this story that's gonna come out right very soon. 737 00:50:52,520 --> 00:50:55,680 Speaker 1: There are some very out there conversations we love for 738 00:50:55,719 --> 00:50:58,600 Speaker 1: you to be part of them with us, and the 739 00:50:58,640 --> 00:51:02,040 Speaker 1: best way to do that is not to wait for 740 00:51:02,200 --> 00:51:04,640 Speaker 1: the rise of a new form of life, but to 741 00:51:04,719 --> 00:51:08,520 Speaker 1: go ahead and contact us directly while we're still in 742 00:51:08,520 --> 00:51:13,040 Speaker 1: this current civilization. We'll try to be easy to find online, Facebook, Instagram, YouTube, 743 00:51:13,120 --> 00:51:15,080 Speaker 1: you know the rest. You might be saying, guys, no 744 00:51:15,760 --> 00:51:20,680 Speaker 1: hate social media. The future, the future consciousness there will 745 00:51:20,760 --> 00:51:24,520 Speaker 1: use it against me. I'm a phone person. Well, we've 746 00:51:24,560 --> 00:51:27,439 Speaker 1: got a deal for you, a deal. It's free. It's 747 00:51:27,440 --> 00:51:33,360 Speaker 1: not a deal, it's free. Yes, call our phone number. 748 00:51:33,400 --> 00:51:36,799 Speaker 1: It is one eight three three. Std w y t 749 00:51:37,120 --> 00:51:40,640 Speaker 1: K is a voicemail system. So your voice will be 750 00:51:40,680 --> 00:51:43,880 Speaker 1: recorded and we will hear it. You have three minutes 751 00:51:44,040 --> 00:51:46,120 Speaker 1: say whatever you'd like. We'd love it if you give 752 00:51:46,160 --> 00:51:48,880 Speaker 1: yourself a cool nickname so we can remember you every 753 00:51:48,880 --> 00:51:52,000 Speaker 1: time you call in. Because you're kind of calling more. Look, 754 00:51:52,080 --> 00:51:55,319 Speaker 1: there's a warning. It gets addictive you just start calling in. 755 00:51:55,880 --> 00:51:59,680 Speaker 1: It's just how it works. Um, you've uh let us 756 00:51:59,680 --> 00:52:01,600 Speaker 1: know if we can use your name and message onto 757 00:52:01,640 --> 00:52:04,000 Speaker 1: the air in one of our listener mail segments. And 758 00:52:04,040 --> 00:52:06,440 Speaker 1: if you've got more to say then can fit in 759 00:52:06,480 --> 00:52:09,440 Speaker 1: that three minute voicemail message. Why not instead send us 760 00:52:09,440 --> 00:52:12,960 Speaker 1: a good old fashioned email. We are conspiracy at iHeart 761 00:52:13,040 --> 00:52:34,279 Speaker 1: radio dot com. Stuff they Don't want you to Know 762 00:52:34,520 --> 00:52:37,520 Speaker 1: is a production of I Heart Radio. For more podcasts 763 00:52:37,520 --> 00:52:39,719 Speaker 1: from my Heart Radio, visit the i heart radio, app, 764 00:52:39,840 --> 00:52:42,680 Speaker 1: Apple podcasts, or wherever you listen to your favorite shows.