1 00:00:02,480 --> 00:00:05,840 Speaker 1: Get in touch with technology with tech Stuff from how Stuff, 2 00:00:05,840 --> 00:00:14,760 Speaker 1: What's dot Com? Hey there everyone, and welcome to tech Stuff. 3 00:00:14,800 --> 00:00:18,680 Speaker 1: My name is Jonathan Strickland, Host Extraordinary and I'm Lauren 4 00:00:18,720 --> 00:00:23,400 Speaker 1: vocal Bam host Extraordinary or that's very true. And uh, 5 00:00:23,440 --> 00:00:29,040 Speaker 1: and today we wanted to talk about the future. The future. Yeah, really, 6 00:00:29,040 --> 00:00:31,760 Speaker 1: we're talking about kind of a science fiction future. We're 7 00:00:31,760 --> 00:00:36,479 Speaker 1: talking about the singularity and uh, long time listeners to 8 00:00:36,600 --> 00:00:39,240 Speaker 1: tech Stuff and I'm talking about folks who listen way 9 00:00:39,360 --> 00:00:42,879 Speaker 1: back before we ever talked out at thirty minutes, let 10 00:00:42,880 --> 00:00:45,920 Speaker 1: alone an hour. May remember that we did an episode 11 00:00:45,920 --> 00:00:48,960 Speaker 1: about how Ray Kurtzwild works. And Ray Kurtzwild is a 12 00:00:48,960 --> 00:00:52,040 Speaker 1: futurist and one of the things he talks about extensively, 13 00:00:52,240 --> 00:00:54,880 Speaker 1: particularly if you corner him at a cocktail party, is 14 00:00:54,920 --> 00:00:58,200 Speaker 1: the singularity. And so we wanted to talk about what 15 00:00:58,280 --> 00:01:01,240 Speaker 1: the singularity is, why this idea, you know, we really 16 00:01:01,240 --> 00:01:03,600 Speaker 1: wanted to kind of dig down into it, and why 17 00:01:03,680 --> 00:01:07,680 Speaker 1: is this a big deal? And how realistic is this 18 00:01:08,120 --> 00:01:10,319 Speaker 1: vision of the future. Yeah, because some people would take 19 00:01:10,360 --> 00:01:13,759 Speaker 1: a little bit of h would would argue with your 20 00:01:13,880 --> 00:01:16,600 Speaker 1: concept of it being science fiction. They take it extremely serious. 21 00:01:16,720 --> 00:01:19,240 Speaker 1: Oh yeah, they say it's science fact, science fact, it's 22 00:01:19,280 --> 00:01:23,400 Speaker 1: science inevitability. Yeah, the time. The term was actually coined 23 00:01:23,440 --> 00:01:26,680 Speaker 1: by a mathematician, John von Newman in the nineteen fifties. Um, 24 00:01:26,680 --> 00:01:32,080 Speaker 1: but it was popularized by a science fiction writer. Yeah, 25 00:01:32,120 --> 00:01:35,760 Speaker 1: it's also Ah, there are a lot of different concepts 26 00:01:35,760 --> 00:01:37,600 Speaker 1: that are tied up together, and it all depends on 27 00:01:37,800 --> 00:01:40,640 Speaker 1: upon whom you ask what it means by the singularity. 28 00:01:40,680 --> 00:01:43,679 Speaker 1: For instance, there's some people who when you hear the 29 00:01:43,760 --> 00:01:46,520 Speaker 1: term the singularity, what they say is, Okay, that's a 30 00:01:46,680 --> 00:01:49,160 Speaker 1: time when we get to the point where technological advances 31 00:01:49,200 --> 00:01:53,160 Speaker 1: are coming so quickly that it's impossible to have a 32 00:01:53,360 --> 00:01:57,240 Speaker 1: meaningful conversation of what the state of technology is because 33 00:01:57,240 --> 00:02:01,040 Speaker 1: it changes. It changes five the milliseconds. So that's one version. 34 00:02:01,280 --> 00:02:04,160 Speaker 1: But most of the versions that that we're familiar with 35 00:02:04,280 --> 00:02:09,040 Speaker 1: that the futurists talk about incorporate an idea of superhuman 36 00:02:09,080 --> 00:02:13,760 Speaker 1: intelligence or the intelligence explosion, right, a kind of combination 37 00:02:14,000 --> 00:02:17,960 Speaker 1: of of human and technological development that just dovetails into 38 00:02:17,960 --> 00:02:20,799 Speaker 1: this gorgeous you know, space baby from two thousand one 39 00:02:20,919 --> 00:02:23,560 Speaker 1: kind of that's an excellent way of putting it. The 40 00:02:23,600 --> 00:02:28,239 Speaker 1: documentary two thousand one. I remember specifically when the space 41 00:02:28,280 --> 00:02:33,040 Speaker 1: baby looked at Earth. Okay, but that documentary example doesn't 42 00:02:33,040 --> 00:02:35,320 Speaker 1: work at all. He usually does, but not this time. Yeah, 43 00:02:35,360 --> 00:02:38,240 Speaker 1: not this time. Sorry. Space babies are a poor example 44 00:02:38,320 --> 00:02:42,440 Speaker 1: in this in this one instance. But but metaphorically speaking, yes, 45 00:02:42,520 --> 00:02:46,280 Speaker 1: you're right on track, because the intelligence explosion. That was 46 00:02:46,320 --> 00:02:50,680 Speaker 1: a term introduced by someone known as Irving John Good, 47 00:02:50,840 --> 00:02:52,680 Speaker 1: or if you want to go with, his birth name 48 00:02:53,040 --> 00:02:56,079 Speaker 1: is a door Ja cub Gudak. I can see why 49 00:02:56,080 --> 00:02:59,080 Speaker 1: he changed it. Yeah he uh. He actually worked for 50 00:02:59,120 --> 00:03:03,600 Speaker 1: a while at Bletchley Park with another fellow who who 51 00:03:03,800 --> 00:03:06,040 Speaker 1: made sort of a name for himself in computer science, 52 00:03:06,040 --> 00:03:09,799 Speaker 1: a fellow named Alan Turing. I guess I've heard of him. Yeah. 53 00:03:09,840 --> 00:03:12,520 Speaker 1: Touring will come up in the discussion a little bit later, 54 00:03:12,600 --> 00:03:15,720 Speaker 1: but but for right now, So Irving John Good just 55 00:03:15,720 --> 00:03:18,560 Speaker 1: just a little quick anecdote that I thought was amusing. 56 00:03:19,000 --> 00:03:23,280 Speaker 1: So Good was working with Touring to try and help 57 00:03:23,560 --> 00:03:26,120 Speaker 1: break German codes. I mean, that's what Bletchley Park was 58 00:03:26,160 --> 00:03:31,680 Speaker 1: all about, right, So Good apparently one day drew the 59 00:03:31,800 --> 00:03:35,240 Speaker 1: ire of touring when he decided to take a little 60 00:03:35,240 --> 00:03:38,440 Speaker 1: cat nap because he was tired and he was It 61 00:03:38,560 --> 00:03:42,400 Speaker 1: was Good's philosophy that being tired did not mean that. 62 00:03:42,440 --> 00:03:44,280 Speaker 1: He meant that he was not going to work at 63 00:03:44,320 --> 00:03:47,200 Speaker 1: his best, and he might as well go ahead and nap, exactly, 64 00:03:47,240 --> 00:03:50,800 Speaker 1: take a nap, get refreshed, and then tackle the problem again, 65 00:03:50,840 --> 00:03:53,280 Speaker 1: and you're more likely to solve it. Whereas touring was 66 00:03:53,480 --> 00:03:56,280 Speaker 1: very much a workhorse, you know he was he was 67 00:03:56,680 --> 00:04:00,880 Speaker 1: no rest, no rest, let's do it. So touring, when 68 00:04:00,880 --> 00:04:03,880 Speaker 1: he discovered that Good had been napping, decided that that 69 00:04:04,000 --> 00:04:07,240 Speaker 1: this was the Good was not so good um and 70 00:04:07,240 --> 00:04:11,920 Speaker 1: and touring Touring sort of treated him with disdain. He 71 00:04:12,080 --> 00:04:16,920 Speaker 1: began to essentially not speak to Good. Good meanwhile, began 72 00:04:17,000 --> 00:04:19,760 Speaker 1: to think about the letters that were being used to 73 00:04:20,400 --> 00:04:24,760 Speaker 1: in Enigma codes to code German messages, and he began 74 00:04:24,800 --> 00:04:27,960 Speaker 1: to think, what if these letters are not completely random? 75 00:04:28,040 --> 00:04:31,039 Speaker 1: What if the Germans are relying on some letters more 76 00:04:31,080 --> 00:04:34,240 Speaker 1: frequently than others, And he began to look at frequency 77 00:04:34,360 --> 00:04:36,520 Speaker 1: of these letters being used. He made up a table 78 00:04:37,120 --> 00:04:41,880 Speaker 1: and mathematically analyze the frequency that certain letters were used 79 00:04:41,920 --> 00:04:45,920 Speaker 1: and discovered that there was a bias. Yeah, so he said, well, 80 00:04:46,000 --> 00:04:47,919 Speaker 1: with this bias, that means that we can start to 81 00:04:48,000 --> 00:04:50,960 Speaker 1: narrow down the possibilities of these codes. And in fact 82 00:04:51,240 --> 00:04:53,840 Speaker 1: he was able to demonstrate that this was a way 83 00:04:53,839 --> 00:04:56,240 Speaker 1: to help break German codes, and Touring, when he saw 84 00:04:56,360 --> 00:05:00,719 Speaker 1: Good's work, said the sword, I tried that, but but 85 00:05:00,839 --> 00:05:04,200 Speaker 1: clearly that showed that it worked well. And then Good 86 00:05:04,200 --> 00:05:08,160 Speaker 1: and another point apparently went to sleep one day and 87 00:05:08,440 --> 00:05:10,359 Speaker 1: they've been working on a code that they just could 88 00:05:10,360 --> 00:05:14,000 Speaker 1: not break, and while he was sleeping, he dreamed that 89 00:05:14,160 --> 00:05:17,880 Speaker 1: perhaps when the Germans were encoding this particular message, they 90 00:05:17,960 --> 00:05:20,080 Speaker 1: used the letters in reverse of the way they were 91 00:05:20,080 --> 00:05:22,800 Speaker 1: actually printed, and so he tried that when he woke up, 92 00:05:22,839 --> 00:05:25,440 Speaker 1: and it turned out he was right. And so then 93 00:05:25,520 --> 00:05:27,920 Speaker 1: his argument was Touring, I need to go to bed. 94 00:05:28,040 --> 00:05:30,880 Speaker 1: So yeah, yeah, what the moral of the story here 95 00:05:31,080 --> 00:05:33,920 Speaker 1: is that naps are good and no one should talk 96 00:05:33,960 --> 00:05:37,760 Speaker 1: to you, right, yeah, yeah, that's how I live my life. 97 00:05:38,279 --> 00:05:42,120 Speaker 1: But yeah, so so Good's point anyway, he came up 98 00:05:42,160 --> 00:05:44,960 Speaker 1: with this term of the intelligence explosion, and it was 99 00:05:45,520 --> 00:05:47,320 Speaker 1: this this sort of idea that we were going to 100 00:05:47,440 --> 00:05:51,520 Speaker 1: reach a point where we are increasing either our own 101 00:05:51,560 --> 00:05:56,880 Speaker 1: intelligence or some sort of artificial intelligence so far beyond 102 00:05:56,960 --> 00:06:00,360 Speaker 1: what we are currently capable of understanding, that life as 103 00:06:00,400 --> 00:06:05,600 Speaker 1: we know it will change completely. And because it's going 104 00:06:05,640 --> 00:06:08,680 Speaker 1: to go beyond what we know right now there's no 105 00:06:08,720 --> 00:06:11,560 Speaker 1: way to predict what our life will be like because 106 00:06:11,680 --> 00:06:15,359 Speaker 1: it's beyond our because it is by definition, out of 107 00:06:15,360 --> 00:06:18,760 Speaker 1: our comprehension. Yes, as the Scots would say, it's beyond 108 00:06:18,800 --> 00:06:23,799 Speaker 1: our ken are we going to be doing accents this episode? 109 00:06:23,880 --> 00:06:27,000 Speaker 1: That was a terrible one. I actually regret doing it 110 00:06:27,120 --> 00:06:30,680 Speaker 1: right now. I already knew I couldn't do Scottish and 111 00:06:30,760 --> 00:06:34,320 Speaker 1: yet there I went. Anyway, your trail placing again, So 112 00:06:34,800 --> 00:06:37,960 Speaker 1: to kind of backtrack a bit before we really get 113 00:06:38,320 --> 00:06:43,280 Speaker 1: into the whole singularity discussion, that was just a brief overview. 114 00:06:44,440 --> 00:06:48,080 Speaker 1: A good foundation to start from is the concept of 115 00:06:48,160 --> 00:06:52,160 Speaker 1: Moore's law. You know, originally Gordon Moore, who, by the way, 116 00:06:52,160 --> 00:06:54,480 Speaker 1: it was a co founder of a little company called Intel. 117 00:06:55,120 --> 00:06:59,200 Speaker 1: Uh he he originally observed back in nineteen sixty five 118 00:06:59,240 --> 00:07:02,640 Speaker 1: in a paper it I'm going to I'm gonna with this, 119 00:07:02,720 --> 00:07:05,520 Speaker 1: but it was called something like cramming more components onto 120 00:07:05,560 --> 00:07:08,440 Speaker 1: integrated circuits something like that. That was that was actually 121 00:07:08,640 --> 00:07:11,760 Speaker 1: cramming was definitely one of the words used um and 122 00:07:11,920 --> 00:07:15,480 Speaker 1: circuit probably was to Anyway, he noticed that over the 123 00:07:15,560 --> 00:07:18,920 Speaker 1: course of I think originally it was twelve months, but 124 00:07:19,080 --> 00:07:23,320 Speaker 1: today we consider it two years, uh eight twenty four months, 125 00:07:23,360 --> 00:07:26,040 Speaker 1: I think is the official unofficial right right right? Yeah, 126 00:07:26,080 --> 00:07:29,440 Speaker 1: that that the number of discrete components on a a 127 00:07:29,640 --> 00:07:33,880 Speaker 1: square inch silicon wafer would double due to improvements in 128 00:07:33,960 --> 00:07:39,320 Speaker 1: manufacturing and efficiency. So that, uh, in effect, what this 129 00:07:39,400 --> 00:07:43,800 Speaker 1: means to the layman is that our electronics and particularly 130 00:07:43,880 --> 00:07:47,400 Speaker 1: our computers get twice as powerful every two years. So 131 00:07:47,680 --> 00:07:50,720 Speaker 1: if you bought a computer in and then bought another 132 00:07:50,760 --> 00:07:53,120 Speaker 1: computer in two thousand, in theory, the computer in two 133 00:07:53,120 --> 00:07:55,200 Speaker 1: thousand would be twice as powerful as the one fro. 134 00:07:56,440 --> 00:08:00,880 Speaker 1: This is exponential growth. Uh. That's an important component, this 135 00:08:01,080 --> 00:08:06,840 Speaker 1: idea of exponential growth. And it goes without saying that 136 00:08:07,680 --> 00:08:10,040 Speaker 1: if you continue on this path, if this, if this 137 00:08:10,120 --> 00:08:14,680 Speaker 1: continues indefinitely, then you know, you quickly get to computers 138 00:08:14,680 --> 00:08:18,360 Speaker 1: of almost unimaginable power just a decade out certainly, although 139 00:08:18,400 --> 00:08:21,200 Speaker 1: I mean I still don't really understand what a gigabyte 140 00:08:21,240 --> 00:08:24,280 Speaker 1: means because when when I first started using computers, we 141 00:08:24,280 --> 00:08:26,040 Speaker 1: were not counting in that. I mean, I mean I 142 00:08:26,120 --> 00:08:28,720 Speaker 1: was still impressed by kilobytes at the time. So yeah, now, 143 00:08:28,800 --> 00:08:31,680 Speaker 1: I remember the first time I got a hard drive, 144 00:08:31,760 --> 00:08:34,960 Speaker 1: I think it had like a two fifty megabyte hard drive, 145 00:08:35,000 --> 00:08:37,640 Speaker 1: and I thought, who needs that much space? Now? Gad, 146 00:08:37,720 --> 00:08:41,720 Speaker 1: that's that's space we're talking about, not even processing pis. 147 00:08:42,320 --> 00:08:44,720 Speaker 1: So yeah, it's it's it's one of those things where 148 00:08:45,160 --> 00:08:48,440 Speaker 1: the older you are, the more incredible today, is right, 149 00:08:48,520 --> 00:08:50,840 Speaker 1: because you start looking at computers and you think, I 150 00:08:50,840 --> 00:08:53,319 Speaker 1: remember when these things came out and they were essentially 151 00:08:53,360 --> 00:08:56,600 Speaker 1: the equivalent of a of a really good desktop calculator. 152 00:08:57,440 --> 00:09:01,600 Speaker 1: So but Moore's law states that this advance will continue 153 00:09:01,600 --> 00:09:05,800 Speaker 1: indefinitely until we hit some sort of fundamental obstacle that 154 00:09:05,840 --> 00:09:08,560 Speaker 1: we just cannot engineer our way around, right, you know, 155 00:09:08,679 --> 00:09:10,400 Speaker 1: And people that that's why it's it's kind of in 156 00:09:10,440 --> 00:09:12,840 Speaker 1: contention right now, because people are saying that, well, there's 157 00:09:12,920 --> 00:09:15,120 Speaker 1: there's only so much physical space that you can fit 158 00:09:15,360 --> 00:09:18,560 Speaker 1: onto with with silicone, there's there's there's a physical limitation 159 00:09:18,600 --> 00:09:20,719 Speaker 1: to the material in which there's only so much you 160 00:09:20,720 --> 00:09:22,240 Speaker 1: can do about it, And so does more It's law 161 00:09:22,280 --> 00:09:25,080 Speaker 1: still apply if we're talking about other materials, and what's 162 00:09:25,120 --> 00:09:27,400 Speaker 1: you know, right, and and how small can you get 163 00:09:27,559 --> 00:09:30,760 Speaker 1: before you start to run into quantum effects that are 164 00:09:30,800 --> 00:09:34,120 Speaker 1: impossible to work around? Uh? And then do you change 165 00:09:34,160 --> 00:09:37,079 Speaker 1: the geometry of a chip. Do you go three dimensional 166 00:09:37,120 --> 00:09:39,680 Speaker 1: instead of two dimensional? Would that help? And yeah, there 167 00:09:39,720 --> 00:09:42,480 Speaker 1: are a lot of engineers are working on this, and frankly, 168 00:09:43,040 --> 00:09:45,240 Speaker 1: pretty much every couple of years, someone says, all right, 169 00:09:45,520 --> 00:09:48,160 Speaker 1: this is the year Moore's law, and it's good to end. 170 00:09:48,200 --> 00:09:52,319 Speaker 1: It's over, it's done with. Five years later, you're still 171 00:09:52,320 --> 00:09:56,800 Speaker 1: going strong, and then someone else is gonna end. It's 172 00:09:56,800 --> 00:09:58,880 Speaker 1: a little bit of a self fulfilling prophecy. I think 173 00:09:58,880 --> 00:10:01,520 Speaker 1: that a lot of companies a to to keep it going, 174 00:10:01,559 --> 00:10:03,640 Speaker 1: to keep it going, Oh sure, yeah, yeah, yeah. No 175 00:10:04,200 --> 00:10:06,880 Speaker 1: one wants to be the ones to say, uh, guys, 176 00:10:06,920 --> 00:10:10,400 Speaker 1: guess what, we can't keep up with More's law anymore. 177 00:10:10,480 --> 00:10:12,040 Speaker 1: No one wants to do that, so it is a 178 00:10:12,040 --> 00:10:14,480 Speaker 1: good motivator. Also, if I can putting out myself real quick, 179 00:10:14,480 --> 00:10:17,160 Speaker 1: I'm pretty sure that I just pronounced silicon is silicone, 180 00:10:17,280 --> 00:10:18,560 Speaker 1: and I would like I would like to stay for 181 00:10:18,559 --> 00:10:20,880 Speaker 1: the record that I know that those are two different substances. Okay, 182 00:10:20,880 --> 00:10:22,839 Speaker 1: that's fair. Anyway, I was I was going to ask 183 00:10:22,880 --> 00:10:24,720 Speaker 1: you about it, but by the time you're you were 184 00:10:24,760 --> 00:10:27,920 Speaker 1: finished talking, I thought, let's just go Yeah, that's cool, 185 00:10:28,679 --> 00:10:31,679 Speaker 1: it's all right. If you knew how many times I 186 00:10:31,720 --> 00:10:36,599 Speaker 1: have used that particular pronunciation to to hilarious results. Um. 187 00:10:36,640 --> 00:10:39,600 Speaker 1: So moving on with this, this whole idea about Moore's law, 188 00:10:39,640 --> 00:10:42,160 Speaker 1: I mean, the reason this plays into the singularity is 189 00:10:42,880 --> 00:10:46,520 Speaker 1: with the technological advances, you start to be able to 190 00:10:46,559 --> 00:10:53,359 Speaker 1: achieve pretty incredible things, and uh, even within one generation 191 00:10:53,400 --> 00:10:56,040 Speaker 1: of Moore's law, which is kind of a meaningless term. 192 00:10:56,080 --> 00:10:58,640 Speaker 1: But let's say, let's say you arbitrarily pick a date 193 00:10:59,000 --> 00:11:01,040 Speaker 1: and then two years from date you look and see 194 00:11:01,080 --> 00:11:05,200 Speaker 1: what's possible with the new technology and getting to twice 195 00:11:05,280 --> 00:11:08,440 Speaker 1: as much power however you wanted to. Fine, it doesn't 196 00:11:08,440 --> 00:11:11,760 Speaker 1: necessarily mean that you've only doubled the amount of things 197 00:11:11,840 --> 00:11:15,120 Speaker 1: you can do with that power. You may have limitless 198 00:11:15,160 --> 00:11:18,560 Speaker 1: things you can do. So with that idea, you're talking 199 00:11:18,600 --> 00:11:23,640 Speaker 1: about being able to power through problems way faster than 200 00:11:23,679 --> 00:11:25,400 Speaker 1: you did before. And there's lots of different ways of 201 00:11:25,440 --> 00:11:29,560 Speaker 1: doing that. For example, um, grid computing. Grid computing is 202 00:11:29,600 --> 00:11:33,000 Speaker 1: when you are linking computers together to work on a 203 00:11:33,080 --> 00:11:35,840 Speaker 1: problem all at once. Now, this works really well with 204 00:11:35,920 --> 00:11:39,559 Speaker 1: certain problems, parallel problems we call them. Uh. These are 205 00:11:39,600 --> 00:11:42,960 Speaker 1: problems where there are lots of potential solutions and each 206 00:11:43,000 --> 00:11:47,280 Speaker 1: computer essentially is working on one set of potential solutions, 207 00:11:47,559 --> 00:11:49,600 Speaker 1: and that way you have all these different computers working 208 00:11:49,640 --> 00:11:52,160 Speaker 1: on it at the same time. It reduces the overall 209 00:11:52,200 --> 00:11:55,680 Speaker 1: time it takes to solve that parallel problem. Uh and so, 210 00:11:55,800 --> 00:11:58,400 Speaker 1: um like if you've ever heard of anything like folding 211 00:11:58,440 --> 00:12:01,840 Speaker 1: at home or the set project where you could dedicate 212 00:12:01,920 --> 00:12:06,119 Speaker 1: your computer's idle time. So the problem the idle processes, 213 00:12:06,120 --> 00:12:08,760 Speaker 1: the processes that are not being used while you're serving 214 00:12:08,800 --> 00:12:12,960 Speaker 1: the web or writing how the singularity works, or I 215 00:12:13,000 --> 00:12:17,680 Speaker 1: don't know, uh, building a architectural program in in in 216 00:12:17,800 --> 00:12:21,960 Speaker 1: some sort of CAD application. Anything that you're not using 217 00:12:22,280 --> 00:12:25,800 Speaker 1: can be dedicated to one of these projects. Same sort 218 00:12:25,800 --> 00:12:28,880 Speaker 1: of idea that, uh, you don't necessarily have to build 219 00:12:28,880 --> 00:12:32,800 Speaker 1: a supercomputer to solve complex problems if you use a 220 00:12:32,880 --> 00:12:35,640 Speaker 1: whole bunch of computers, whole bunch of small ones. Large 221 00:12:35,679 --> 00:12:39,840 Speaker 1: Hadron Collider does this, although they use very nice advanced computers, 222 00:12:39,840 --> 00:12:41,560 Speaker 1: but they do a lot of grid computing as well. 223 00:12:42,200 --> 00:12:45,400 Speaker 1: So uh So, just using those kind of models, we 224 00:12:45,559 --> 00:12:49,200 Speaker 1: see that we're able to do much more sophisticated things 225 00:12:49,200 --> 00:12:52,760 Speaker 1: than we could if we were Certainly, Yes, networks, as 226 00:12:52,760 --> 00:12:55,559 Speaker 1: it turns out, are pretty cool. Yeah, and networks play 227 00:12:55,600 --> 00:12:58,160 Speaker 1: a part in this idea of the singularity. Um. Actually, 228 00:12:58,400 --> 00:13:00,360 Speaker 1: I guess now is a good time. We'll we'll kind 229 00:13:00,360 --> 00:13:04,440 Speaker 1: of transition into Verner Vengees And honestly, I don't know 230 00:13:04,440 --> 00:13:06,120 Speaker 1: how to say his last name. I say venge and 231 00:13:06,160 --> 00:13:09,000 Speaker 1: it could end up being Vinghi. But I just went 232 00:13:09,040 --> 00:13:12,199 Speaker 1: with what you said. So that's great, that's fine. What 233 00:13:12,360 --> 00:13:15,800 Speaker 1: we'll say that Vench says everything is silicone. Uh, so 234 00:13:16,320 --> 00:13:20,640 Speaker 1: Verner though he Verne, I call him Verne. Ah he 235 00:13:20,640 --> 00:13:26,160 Speaker 1: He suggested four different potential pathways that humans could take, 236 00:13:26,600 --> 00:13:29,800 Speaker 1: or really that the world could take to arrive at 237 00:13:29,800 --> 00:13:35,600 Speaker 1: the technological singularity. Okay, the four ways are we could 238 00:13:35,679 --> 00:13:41,400 Speaker 1: develop a superhuman artificial intelligence, So computers suddenly are able 239 00:13:41,559 --> 00:13:45,240 Speaker 1: to think on a level that's analogous to the way 240 00:13:45,320 --> 00:13:49,240 Speaker 1: humans think and can do it better than right. Uh, 241 00:13:49,640 --> 00:13:53,319 Speaker 1: whether or not that means computers are conscious, that's debatable. 242 00:13:53,320 --> 00:13:57,280 Speaker 1: We'll get into that too. Computer networks could somehow become 243 00:13:57,320 --> 00:14:01,480 Speaker 1: self aware. That's number two. So yes, sky net so 244 00:14:01,559 --> 00:14:03,679 Speaker 1: like like the grid computing. We were just talking about 245 00:14:03,720 --> 00:14:09,200 Speaker 1: that somehow using these grid computers, the network itself having 246 00:14:09,320 --> 00:14:12,200 Speaker 1: enough cycles and enough pathways and enough loops back around 247 00:14:12,320 --> 00:14:15,400 Speaker 1: it starts going like, hey, I recognize this, starts thinking 248 00:14:15,440 --> 00:14:18,720 Speaker 1: about like like like thinking about IBM S Watson. But 249 00:14:18,800 --> 00:14:22,440 Speaker 1: it's distributed across a network. So computers, you can think 250 00:14:22,440 --> 00:14:27,640 Speaker 1: of computers is all being super powerful neurons in a brain, 251 00:14:27,760 --> 00:14:32,400 Speaker 1: and that the network is actually neural pathways. And it's 252 00:14:32,400 --> 00:14:34,760 Speaker 1: definitely a science fiction a e way of looking at things. 253 00:14:35,120 --> 00:14:38,600 Speaker 1: Doesn't mean it won't happen, but stranger things, my friends. 254 00:14:38,640 --> 00:14:40,280 Speaker 1: It feels like a matrix kind of thing to me. 255 00:14:40,840 --> 00:14:44,440 Speaker 1: Then we have the idea that computer human interfaces are 256 00:14:45,360 --> 00:14:49,440 Speaker 1: so advanced and so intrinsically tied to who we are, 257 00:14:49,560 --> 00:14:55,400 Speaker 1: that humans themselves evolved beyond being human, we become trans human. 258 00:14:56,400 --> 00:14:59,200 Speaker 1: So this is an idea that we almost merge with 259 00:14:59,240 --> 00:15:02,840 Speaker 1: computers that on some level via kind of nanobot technology, 260 00:15:02,880 --> 00:15:05,560 Speaker 1: you know, stuff stuff running through our bloodstream, stuff in 261 00:15:05,640 --> 00:15:11,560 Speaker 1: our yea, or we have just brain interfaces where our consciousness, 262 00:15:11,960 --> 00:15:14,960 Speaker 1: our consciousness is connected to so so for example, we 263 00:15:15,040 --> 00:15:18,600 Speaker 1: might have it where instead of connecting to the Internet 264 00:15:18,720 --> 00:15:23,320 Speaker 1: via some device like a smartphone or a computer, yeah, 265 00:15:23,360 --> 00:15:26,720 Speaker 1: it's right there in our our meat brains, so that 266 00:15:26,960 --> 00:15:28,920 Speaker 1: you know you're sitting there having a conversation with someone. 267 00:15:28,920 --> 00:15:31,480 Speaker 1: Then you're like, oh, wait, what movie was that guy in? 268 00:15:31,680 --> 00:15:34,680 Speaker 1: Let me just look up I am dB in my brain. 269 00:15:35,560 --> 00:15:37,520 Speaker 1: And then you you know, depending on how good your 270 00:15:37,520 --> 00:15:40,720 Speaker 1: connection is, which means, by the way, if you are 271 00:15:40,720 --> 00:15:43,440 Speaker 1: a journalist and you attend ce s, you will automatically 272 00:15:43,480 --> 00:15:46,720 Speaker 1: be dumber because all the all the internet connectivity will 273 00:15:46,760 --> 00:15:49,040 Speaker 1: be taken up and so you'll be sitting there trying 274 00:15:49,040 --> 00:15:51,120 Speaker 1: to ask good questions and drool will come out of 275 00:15:51,120 --> 00:15:53,800 Speaker 1: your mouth, which to me is a typical c E S. 276 00:15:54,200 --> 00:15:56,640 Speaker 1: I can I can only assume that that wireless technology 277 00:15:56,720 --> 00:15:59,320 Speaker 1: would advance also at this point, but one can only 278 00:15:59,320 --> 00:16:02,120 Speaker 1: hope fingers problems. There are certain technologies that are not 279 00:16:02,200 --> 00:16:04,520 Speaker 1: advancing at the exponential rate of Moore's lave, which is 280 00:16:04,560 --> 00:16:07,520 Speaker 1: another problem. We'll talk about that. And then the fourth 281 00:16:07,600 --> 00:16:12,440 Speaker 1: and final method that Werner had suggested the world may 282 00:16:12,480 --> 00:16:15,680 Speaker 1: go would be that humans would advance so far in 283 00:16:15,800 --> 00:16:20,280 Speaker 1: biological sciences that they would allow us to engineer human 284 00:16:20,400 --> 00:16:23,760 Speaker 1: intelligence so that we could make ourselves as smart as 285 00:16:23,800 --> 00:16:26,120 Speaker 1: we wanted to be. This is sort of that Gattica 286 00:16:26,240 --> 00:16:29,680 Speaker 1: future where we've got all the another another great documentary, 287 00:16:30,000 --> 00:16:35,320 Speaker 1: where we we engineer ourselves to be super smart. So 288 00:16:35,760 --> 00:16:39,000 Speaker 1: those are the four pathways artificial intelligence, computer networks become 289 00:16:39,000 --> 00:16:42,800 Speaker 1: self aware, computer human interface has become really really awesome, 290 00:16:43,360 --> 00:16:47,040 Speaker 1: or we have biologically engineered human intelligence and uh, and 291 00:16:47,360 --> 00:16:50,400 Speaker 1: all four of these lead to a similar outcome, which 292 00:16:50,400 --> 00:16:54,160 Speaker 1: is this intelligence explosion. And this is the idea that 293 00:16:55,760 --> 00:16:59,440 Speaker 1: some form of superhuman intelligence is created, either artificially or 294 00:16:59,520 --> 00:17:02,760 Speaker 1: within our elves, and that at that point we will 295 00:17:02,840 --> 00:17:05,840 Speaker 1: no longer be able to predict what our world will 296 00:17:06,040 --> 00:17:08,800 Speaker 1: will be like because by definition, we will have a 297 00:17:08,880 --> 00:17:14,840 Speaker 1: superhuman intelligent entity involved. And and because that's superhuman, it's 298 00:17:14,880 --> 00:17:19,440 Speaker 1: beyond our ability to predict which is you know, which, 299 00:17:19,480 --> 00:17:22,080 Speaker 1: which makes thought, experience experiments about it a little bit 300 00:17:22,200 --> 00:17:29,000 Speaker 1: uh philosophical, that's the kind way of putting it, uh, pointless. 301 00:17:29,000 --> 00:17:31,399 Speaker 1: Would be another way of putting up like we could, 302 00:17:31,440 --> 00:17:34,280 Speaker 1: we could, you know, sit there and and and spitball 303 00:17:34,320 --> 00:17:37,159 Speaker 1: a whole bunch of possible futures. But that's the thing, 304 00:17:37,200 --> 00:17:40,240 Speaker 1: they're possible. We don't know which one could come out. 305 00:17:40,280 --> 00:17:43,080 Speaker 1: We don't even know if these four pathways are inevitable. 306 00:17:43,560 --> 00:17:46,240 Speaker 1: We have futurists who truly believe that this is something 307 00:17:46,280 --> 00:17:49,240 Speaker 1: that will happen at some point there are other people 308 00:17:49,320 --> 00:17:53,760 Speaker 1: who are more skeptical, but we'll talk about them in 309 00:17:53,840 --> 00:17:57,320 Speaker 1: a bit. So one of the outcomes that Werner was 310 00:17:57,640 --> 00:18:01,000 Speaker 1: talking about, uh, and it's it's a fairly popular one 311 00:18:01,080 --> 00:18:06,240 Speaker 1: in futurist circles is the idea of the robo apocalypse. Essentially, 312 00:18:07,480 --> 00:18:10,040 Speaker 1: this is where you've got the humans are bad, destroy 313 00:18:10,080 --> 00:18:15,040 Speaker 1: all humans idea. Essentially the the ideas that humans would 314 00:18:15,040 --> 00:18:19,280 Speaker 1: become extinct, either through definition because we've evolved into something 315 00:18:19,320 --> 00:18:23,560 Speaker 1: else or because whatever the super human intelligence is in besides, 316 00:18:23,680 --> 00:18:25,359 Speaker 1: we are a problem. Yeah, and a lot a lot 317 00:18:25,359 --> 00:18:27,280 Speaker 1: of futures are a lot more positive about that. They're 318 00:18:27,320 --> 00:18:29,439 Speaker 1: they're more looking forward to it than being scared of it. 319 00:18:29,440 --> 00:18:32,000 Speaker 1: It's less of oh, no, big scary robots are coming 320 00:18:32,000 --> 00:18:35,119 Speaker 1: to take over our society, and more of a robots 321 00:18:35,119 --> 00:18:39,200 Speaker 1: are coming to take over society like free day, Yeah exactly. Yeah, Yeah, 322 00:18:39,200 --> 00:18:41,560 Speaker 1: I don't have to work anymore, and and I don't 323 00:18:41,800 --> 00:18:45,080 Speaker 1: because robots are are supplying all the things we need. 324 00:18:45,800 --> 00:18:48,520 Speaker 1: There's no need for anyone to work anymore. There's no 325 00:18:48,560 --> 00:18:51,359 Speaker 1: need for money anymore, because the only reason you need 326 00:18:51,400 --> 00:18:53,520 Speaker 1: money is so you can buy stuff. But if everything 327 00:18:53,600 --> 00:18:55,840 Speaker 1: is free, then you don't need you, So it becomes 328 00:18:55,880 --> 00:18:58,439 Speaker 1: Star Trek and we all, you know, run around in 329 00:18:58,520 --> 00:19:02,640 Speaker 1: jumpsuits and punch people. And if you're Kirk, you make 330 00:19:02,680 --> 00:19:06,359 Speaker 1: out a lot. I mean a lot that dude. Every week, 331 00:19:08,720 --> 00:19:11,960 Speaker 1: Piccard and Ryker, if you add them together, make one 332 00:19:12,040 --> 00:19:16,080 Speaker 1: Kirk and yes, in this documentary series Star Trek. I 333 00:19:16,119 --> 00:19:18,960 Speaker 1: don't know about Archer because I never watched Enterprise, So 334 00:19:19,200 --> 00:19:21,200 Speaker 1: you guys have to get back to me on that. Yeah, sorry, 335 00:19:21,280 --> 00:19:23,720 Speaker 1: sorry about that. It's also a gap in my personal understanding. 336 00:19:23,960 --> 00:19:26,560 Speaker 1: I just took one look at that decontamination chamber and said, yep, 337 00:19:26,640 --> 00:19:30,760 Speaker 1: I'm out um anyway. So that's that's Werner Avenge. Uh. 338 00:19:30,920 --> 00:19:34,719 Speaker 1: It's he's sort of popularized this idea, but he's there 339 00:19:34,720 --> 00:19:36,760 Speaker 1: are other people who have kind of I think their 340 00:19:36,840 --> 00:19:39,639 Speaker 1: names are synonymous with it, and we will talk about 341 00:19:39,720 --> 00:19:43,040 Speaker 1: them in just a minute, but for now, let's take 342 00:19:43,080 --> 00:19:45,320 Speaker 1: a moment to thank our sponsor and now back to 343 00:19:45,320 --> 00:19:50,280 Speaker 1: the show. So Verner Venge again very much associated with 344 00:19:50,320 --> 00:19:53,520 Speaker 1: the idea of the singularity. But there's another name that 345 00:19:53,560 --> 00:19:57,440 Speaker 1: comes up all the time, right, Kurtswile, r Kurtswile. And 346 00:19:57,800 --> 00:19:59,879 Speaker 1: this is a fellow who has been referred to in 347 00:20:00,119 --> 00:20:04,800 Speaker 1: various circles, as as the Thomas Edison of Modern Technology 348 00:20:04,920 --> 00:20:08,119 Speaker 1: or um or or, perhaps more colorfully, the Willy Wonka 349 00:20:08,200 --> 00:20:10,480 Speaker 1: of Technology that was by Jeff Duncan of Digital Trends, 350 00:20:10,480 --> 00:20:13,560 Speaker 1: and I just wanted to shout out because that was great, Um, 351 00:20:13,600 --> 00:20:17,760 Speaker 1: but you get nothing. I shared a remix of Willy 352 00:20:17,800 --> 00:20:20,680 Speaker 1: Wonka earlier today and it's still playing through my head. 353 00:20:20,800 --> 00:20:23,159 Speaker 1: We're fans, we might be fans of the Gene Wilder 354 00:20:23,200 --> 00:20:26,000 Speaker 1: Willy Wonka. Everyone, everyone, homework assignment. Go watch that. It 355 00:20:26,000 --> 00:20:29,240 Speaker 1: has nothing to do with the singularity singularity at all. 356 00:20:29,320 --> 00:20:32,399 Speaker 1: I don't know there's some chocolate singularity in their chocolate singularity. 357 00:20:32,440 --> 00:20:36,560 Speaker 1: I want episode on that band. If I were better 358 00:20:36,600 --> 00:20:38,480 Speaker 1: at cover band names, I totally would have said something 359 00:20:38,520 --> 00:20:40,760 Speaker 1: witty right there. Yeah, all right, well, fair enough, we'll 360 00:20:40,800 --> 00:20:44,320 Speaker 1: say it's the archiese for sugar Sugar, Oh dear, Oh 361 00:20:44,320 --> 00:20:47,840 Speaker 1: my goodness. Okay, So Ray hurts while Ray hurts, while 362 00:20:48,000 --> 00:20:50,680 Speaker 1: Um is the kind of cat who you know. When 363 00:20:50,680 --> 00:20:54,760 Speaker 1: he was in high school, UM invented a computer program. 364 00:20:54,800 --> 00:20:56,800 Speaker 1: And this is in the mid nineteen sixties. This isn't 365 00:20:56,840 --> 00:20:59,240 Speaker 1: like last year or something. In the mid nineteen sixties 366 00:20:59,280 --> 00:21:03,040 Speaker 1: created a computer program that listened to classical music, found 367 00:21:03,080 --> 00:21:06,119 Speaker 1: patterns in it, and then created new classical music based 368 00:21:06,119 --> 00:21:09,359 Speaker 1: on that. So it's a computer that composed classical music 369 00:21:09,720 --> 00:21:13,400 Speaker 1: following the rules of classical music that other composers had created. Yes, 370 00:21:13,680 --> 00:21:15,960 Speaker 1: that's kind of cool. That's just that's just something he did, 371 00:21:16,119 --> 00:21:21,520 Speaker 1: you know, And yeah, dudes get credentials. Yeah, it's He 372 00:21:21,600 --> 00:21:26,920 Speaker 1: also kind of invented flatbed scanners, has done a whole 373 00:21:26,960 --> 00:21:32,800 Speaker 1: bunch of stuff in speech recognition, and which that's interesting 374 00:21:32,880 --> 00:21:35,119 Speaker 1: because well, and we'll talk about that in a second. 375 00:21:35,160 --> 00:21:38,320 Speaker 1: But but one of Kurt's Well's big points is that 376 00:21:38,400 --> 00:21:41,600 Speaker 1: he thinks that by and this all depends upon which 377 00:21:41,960 --> 00:21:45,760 Speaker 1: interview you read of kurts Well, but in various interviews 378 00:21:45,800 --> 00:21:49,080 Speaker 1: he said that essentially, by twenty th we will reach 379 00:21:49,119 --> 00:21:51,160 Speaker 1: a point where we will be able to make an 380 00:21:51,280 --> 00:21:55,800 Speaker 1: artificial brain. Well, we'll we'll have reverse engineered the brain 381 00:21:56,240 --> 00:21:59,720 Speaker 1: and we'll be able to create an artificial one. Uh. 382 00:22:00,240 --> 00:22:04,320 Speaker 1: There's a lot of debate in in Uh, in smarter 383 00:22:04,440 --> 00:22:07,000 Speaker 1: circles than the ones I move in. Uh, that's not 384 00:22:07,080 --> 00:22:10,359 Speaker 1: a that's not a slap against my friends. They're pretty bright, 385 00:22:10,400 --> 00:22:15,160 Speaker 1: but none of us are neuro cheologically gifted at that point. UM, 386 00:22:15,320 --> 00:22:18,520 Speaker 1: I include myself in that in that circle. So, but 387 00:22:18,680 --> 00:22:22,280 Speaker 1: there there's some very bright people who debate about this point, 388 00:22:22,440 --> 00:22:24,440 Speaker 1: whether or not will be able by the year twenty 389 00:22:24,560 --> 00:22:27,760 Speaker 1: thirty to reverse engineer the brain and design an artificial one. 390 00:22:28,000 --> 00:22:31,119 Speaker 1: And I think the debate is not so much on 391 00:22:31,200 --> 00:22:35,919 Speaker 1: whether or not will have the technological power necessary to 392 00:22:35,920 --> 00:22:39,600 Speaker 1: to uh simulate a brain. And we can simulate brains 393 00:22:39,680 --> 00:22:43,040 Speaker 1: on a certain superficial level today. I mean hypothetically we 394 00:22:43,080 --> 00:22:45,240 Speaker 1: could connect enough computers that we could make it go. 395 00:22:46,320 --> 00:22:49,120 Speaker 1: I think, yeah, we could probably get the computer horsepower, 396 00:22:49,200 --> 00:22:52,720 Speaker 1: especially by twenty thirty to simulate a human brain. The 397 00:22:52,800 --> 00:22:57,959 Speaker 1: question is whether we will understand the human brain enough exactly. 398 00:22:58,080 --> 00:23:01,120 Speaker 1: So uh, that's that sort of where the debate lies. 399 00:23:01,160 --> 00:23:03,840 Speaker 1: It's not so much on the technological side of things 400 00:23:03,880 --> 00:23:07,000 Speaker 1: as it is the biological side of things, which is 401 00:23:07,080 --> 00:23:10,639 Speaker 1: kind of interesting. Um. I've read a lot of critics 402 00:23:10,720 --> 00:23:14,760 Speaker 1: who who have really jumped on Kurtswil for this. Particularly PZ. 403 00:23:14,920 --> 00:23:22,280 Speaker 1: Myers has written some pretty um yeah, strongly worded, strongly 404 00:23:22,320 --> 00:23:27,119 Speaker 1: worded criticisms to Kurtzwild's theories, saying that that Kurtzwild simply 405 00:23:27,160 --> 00:23:32,159 Speaker 1: does not understand neurological development and activities, and that you know, 406 00:23:32,280 --> 00:23:36,200 Speaker 1: the nature between the environment and are are the way 407 00:23:36,320 --> 00:23:39,919 Speaker 1: our brains develop over time versus the you know, nurture 408 00:23:40,000 --> 00:23:44,480 Speaker 1: versus nature, all of this stuff with hormonal changes, electrochemical reaction, 409 00:23:44,560 --> 00:23:46,399 Speaker 1: saying that there's there's so many little bits that make 410 00:23:46,440 --> 00:23:48,680 Speaker 1: up our brains, so many hormones, so many processes, and 411 00:23:48,920 --> 00:23:51,120 Speaker 1: we understand such a small fraction of what they do. 412 00:23:51,160 --> 00:23:53,080 Speaker 1: This is why a lot of psychiatric drugs, for example, 413 00:23:53,080 --> 00:23:54,800 Speaker 1: are kind of like, oh, well, we invented this thing, 414 00:23:54,920 --> 00:23:58,000 Speaker 1: and we guess it. Does this thing? Take it and 415 00:23:58,000 --> 00:24:01,840 Speaker 1: see what happens? We stuff don't it tends to make 416 00:24:01,880 --> 00:24:05,800 Speaker 1: you happy. H It also makes you perceived the color 417 00:24:05,880 --> 00:24:09,560 Speaker 1: red as having the smell of oranges, like you know 418 00:24:09,680 --> 00:24:12,560 Speaker 1: that that's we don't. We don't understand it fully. And 419 00:24:12,560 --> 00:24:15,160 Speaker 1: in fact, there are other people like Stephen Novella, who 420 00:24:15,320 --> 00:24:18,840 Speaker 1: is uh he's the author of the Neurological Blog, and 421 00:24:18,880 --> 00:24:22,600 Speaker 1: he also is a host on a wonderful podcast called 422 00:24:22,680 --> 00:24:25,240 Speaker 1: Skeptics Guide to the Universe. If you guys haven't listened 423 00:24:25,240 --> 00:24:27,000 Speaker 1: to that, you should try that out if you especially 424 00:24:27,000 --> 00:24:30,440 Speaker 1: if you like skepticism, and critical thinking. But he's he's 425 00:24:30,480 --> 00:24:36,480 Speaker 1: a doctor and a proponent of evidence based medicine, and 426 00:24:36,680 --> 00:24:40,760 Speaker 1: he talks about how, uh, you know, we don't know 427 00:24:41,040 --> 00:24:44,359 Speaker 1: how much we don't know about the brain, Like we 428 00:24:44,359 --> 00:24:47,840 Speaker 1: we have no way of knowing where the endpoint is 429 00:24:48,119 --> 00:24:51,080 Speaker 1: as far as the brain is concerned, and therefore we 430 00:24:51,240 --> 00:24:54,879 Speaker 1: cannot guess at how long it will take us to 431 00:24:55,040 --> 00:24:57,000 Speaker 1: reverse engineer it simply because we don't know where the 432 00:24:57,040 --> 00:25:00,600 Speaker 1: finish line is, right, right, Yeah. Kurt Kurt Swile Church 433 00:25:00,640 --> 00:25:02,760 Speaker 1: of Ball has a new book new as of we're 434 00:25:02,800 --> 00:25:05,480 Speaker 1: recording this in January. It just came out in November 435 00:25:06,760 --> 00:25:09,240 Speaker 1: called How to Create a Mind. The Secret of Human 436 00:25:09,240 --> 00:25:13,920 Speaker 1: Thought Revealed, and and in the book he theorizes that, um, okay, 437 00:25:13,920 --> 00:25:15,640 Speaker 1: if you'll follow me for a second, atoms are tiny 438 00:25:15,640 --> 00:25:20,640 Speaker 1: bits of data DNA. It's a form of a program. 439 00:25:20,680 --> 00:25:24,280 Speaker 1: The nervous system is a computer that coordinates bodily functions, 440 00:25:25,160 --> 00:25:28,119 Speaker 1: and thought is kind of simultaneously a program and the 441 00:25:28,200 --> 00:25:33,680 Speaker 1: data that that program contains. This is another problem that 442 00:25:34,080 --> 00:25:38,919 Speaker 1: some scientists have, is reducing the human brain to the 443 00:25:38,960 --> 00:25:42,880 Speaker 1: model of a computer, right because it's you know, it's 444 00:25:42,880 --> 00:25:47,520 Speaker 1: it's a very it's a very elegant, interesting proposition and 445 00:25:47,520 --> 00:25:49,120 Speaker 1: and it's kind of sexy like that because you go like, oh, 446 00:25:49,119 --> 00:25:51,960 Speaker 1: well that's that that sort of makes sense. Man Like, 447 00:25:52,040 --> 00:25:53,920 Speaker 1: let's go get a pizza and talk about this more. 448 00:25:54,720 --> 00:25:57,200 Speaker 1: Let me let me get a program that will allow 449 00:25:57,200 --> 00:26:00,560 Speaker 1: me to suddenly know all kung fu. And when you're 450 00:26:00,600 --> 00:26:03,840 Speaker 1: a programmer, that's a great plan. I mean that sounds 451 00:26:03,880 --> 00:26:07,760 Speaker 1: that sounds terrific. But yeah, there's um one one specific 452 00:26:08,160 --> 00:26:12,679 Speaker 1: guy found, Jarren Lanier wrote a terrific thing called one 453 00:26:12,720 --> 00:26:15,280 Speaker 1: Half of a Manifesto, which is a really entertaining read 454 00:26:15,280 --> 00:26:17,280 Speaker 1: if you guys like this kind of thing. Where and 455 00:26:17,320 --> 00:26:19,800 Speaker 1: he was saying that what futurists are talking about when 456 00:26:19,800 --> 00:26:23,200 Speaker 1: they talk about this the singularity is basically a religion. 457 00:26:23,680 --> 00:26:27,200 Speaker 1: He was calling it cybernetic totalism. Uh, you know, like 458 00:26:27,200 --> 00:26:31,120 Speaker 1: like a fanatic ideology. He compares it to Marxism at 459 00:26:31,160 --> 00:26:35,199 Speaker 1: some point. Yeah. Um, and he was saying that that, 460 00:26:35,400 --> 00:26:39,440 Speaker 1: you know, it's this, this theory is a terrific theory 461 00:26:39,480 --> 00:26:43,200 Speaker 1: if you want to get into the philosophy of who 462 00:26:43,240 --> 00:26:45,959 Speaker 1: we are and what we do and what technology is. 463 00:26:46,520 --> 00:26:49,800 Speaker 1: But that you know, cybernetic patterns aren't necessarily the best 464 00:26:49,840 --> 00:26:53,000 Speaker 1: way to understand reality, and that they're not necessarily the 465 00:26:53,000 --> 00:26:56,440 Speaker 1: best model for how people work, for how culture works, 466 00:26:56,440 --> 00:27:00,479 Speaker 1: for how intelligence works. And that's saying so isn't gross 467 00:27:00,480 --> 00:27:03,400 Speaker 1: over simplification. That's a good point, and uh, we should 468 00:27:03,400 --> 00:27:06,080 Speaker 1: also point out that it all depends on how you 469 00:27:06,160 --> 00:27:10,320 Speaker 1: define intelligence as well, because Kurt Swell himself has worded 470 00:27:10,400 --> 00:27:13,800 Speaker 1: his own predictions in such a way that that some 471 00:27:13,840 --> 00:27:18,280 Speaker 1: would argue. Novella argues, for example, that he has given 472 00:27:18,320 --> 00:27:20,560 Speaker 1: himself enough room where he's going to be right no 473 00:27:20,600 --> 00:27:23,600 Speaker 1: matter what, like saying that by we will be able 474 00:27:23,640 --> 00:27:28,000 Speaker 1: to reverse engineer basic brain functions, and novellasis will technically, 475 00:27:28,040 --> 00:27:31,560 Speaker 1: you could argue that now, so that kind of gives 476 00:27:31,560 --> 00:27:35,480 Speaker 1: you a lot of room. Yeah. But um, but whether 477 00:27:35,560 --> 00:27:38,280 Speaker 1: or not it means total brain function, that's that's a 478 00:27:38,320 --> 00:27:42,160 Speaker 1: totally different question. And so the other point is that 479 00:27:42,359 --> 00:27:45,399 Speaker 1: we could theoretically create an artificial intelligence that does not 480 00:27:45,520 --> 00:27:48,600 Speaker 1: necessarily reverse engineer the brain. It doesn't follow the human 481 00:27:48,640 --> 00:27:52,320 Speaker 1: intelligence model. I mean, that's IBM S. Watson again, a 482 00:27:52,400 --> 00:27:56,760 Speaker 1: good example of artificial intelligence that you know, in some 483 00:27:56,800 --> 00:27:59,600 Speaker 1: ways it mimics the brain because it kind of has to. 484 00:27:59,680 --> 00:28:02,159 Speaker 1: You know, are coming at this human beings are the 485 00:28:02,160 --> 00:28:05,920 Speaker 1: ones creating this technology, and so as human beings creating 486 00:28:05,920 --> 00:28:09,480 Speaker 1: this technology, it's going to follow the rules that as 487 00:28:09,520 --> 00:28:12,240 Speaker 1: we understand them. So there's going to be some imgree there. 488 00:28:12,720 --> 00:28:15,600 Speaker 1: But but IBM S Watson, you know, you think about that. 489 00:28:15,720 --> 00:28:21,040 Speaker 1: It doesn't really understand necessarily the data that's passing through it. 490 00:28:21,040 --> 00:28:25,440 Speaker 1: It's looking for the connections and making just making making 491 00:28:25,440 --> 00:28:29,960 Speaker 1: connections and recognizing patterns and spitting out useful information. Yeah, 492 00:28:30,040 --> 00:28:34,080 Speaker 1: it's it's looking for whatever answer is most likely the 493 00:28:34,160 --> 00:28:37,199 Speaker 1: right one. It's all probability based, right, So, and if 494 00:28:37,240 --> 00:28:41,280 Speaker 1: it doesn't reach a certain threshold, it doesn't provide the answer. 495 00:28:41,760 --> 00:28:45,480 Speaker 1: So if arbitrarily speaking, I don't know what the threshold is, 496 00:28:45,520 --> 00:28:48,600 Speaker 1: so I'm just making a number. Let's say it has 497 00:28:48,640 --> 00:28:52,240 Speaker 1: to be certain or higher for it to give that answer. 498 00:28:52,280 --> 00:28:55,200 Speaker 1: If that if the if a certainty falls below that threshold, 499 00:28:55,480 --> 00:28:58,760 Speaker 1: no answer is given. That's essentially how it worked when 500 00:28:58,920 --> 00:29:03,560 Speaker 1: Watson was on jet right. It would analyze the the 501 00:29:03,560 --> 00:29:08,120 Speaker 1: the the answer in jeopardy terms, and then come up 502 00:29:08,160 --> 00:29:12,080 Speaker 1: with what it thought was probably the most accurate question 503 00:29:12,160 --> 00:29:17,719 Speaker 1: for that answer, and occasionally it was wrong to hilarious results, 504 00:29:18,640 --> 00:29:23,440 Speaker 1: but it did sort of seem to kind of mimic 505 00:29:23,480 --> 00:29:26,000 Speaker 1: the way humans think, at least on a superficial level. 506 00:29:26,440 --> 00:29:29,160 Speaker 1: And I mean the thing about humans is that they're 507 00:29:29,200 --> 00:29:31,280 Speaker 1: they're wrong a lot more than a lot more than 508 00:29:31,320 --> 00:29:33,600 Speaker 1: what that fifteen percent at the time something. Yeah, it's 509 00:29:33,680 --> 00:29:35,840 Speaker 1: you know, it's we've we've got well, we give answers 510 00:29:35,880 --> 00:29:39,960 Speaker 1: even if we're not short the question. I certainly do, 511 00:29:40,120 --> 00:29:42,800 Speaker 1: because we all know from going to trivia nights m. Yeah. 512 00:29:42,920 --> 00:29:44,840 Speaker 1: And and there's there's a lot of I've read a 513 00:29:44,840 --> 00:29:48,920 Speaker 1: lot on online about arguments of how it's our deficiencies 514 00:29:48,960 --> 00:29:53,520 Speaker 1: are memory biases, are rational behavior? Are weird hormonal stuff 515 00:29:53,560 --> 00:29:56,320 Speaker 1: going on? Or what makes us human? And that you 516 00:29:56,360 --> 00:30:00,160 Speaker 1: can't teach a computer to be irrational. That's true. Uh, 517 00:30:00,480 --> 00:30:03,040 Speaker 1: although you can't teach you to swear, you can. We 518 00:30:03,160 --> 00:30:07,120 Speaker 1: just we read a story last week, yeah, where where 519 00:30:07,400 --> 00:30:10,840 Speaker 1: IBM allowed Watson to read the Urban Dictionary and then 520 00:30:11,080 --> 00:30:13,240 Speaker 1: I Watson got a little bit of a potty mouth. 521 00:30:13,320 --> 00:30:15,280 Speaker 1: It got it got kind of fresh. It did it 522 00:30:15,320 --> 00:30:17,760 Speaker 1: did it started it started to say that, Uh, oh 523 00:30:17,880 --> 00:30:20,120 Speaker 1: see what was it? Oh, I'm going to say something 524 00:30:20,400 --> 00:30:24,680 Speaker 1: and it's going to be bleaped out right. Tyler Tyler 525 00:30:24,800 --> 00:30:29,840 Speaker 1: Tyler just said so. Uh. Anyway, So there's one point 526 00:30:29,920 --> 00:30:33,120 Speaker 1: where a researcher asked a question of Watson, and Watson 527 00:30:33,480 --> 00:30:38,840 Speaker 1: included within the answer the word so. Since I was 528 00:30:38,840 --> 00:30:40,400 Speaker 1: bleeped out, you probably don't know what it was, so 529 00:30:40,480 --> 00:30:43,040 Speaker 1: go look it up. It was funny. It was really funny. Yes, 530 00:30:43,120 --> 00:30:45,240 Speaker 1: and then and then they basically nuked that part of 531 00:30:45,240 --> 00:30:47,880 Speaker 1: Watson from orbit. They were like, you know what, never mind, 532 00:30:47,920 --> 00:30:50,520 Speaker 1: it was the only way to be sure. They wiped 533 00:30:50,560 --> 00:30:53,360 Speaker 1: out the Urban Dictionary from Watson's memory. They also said 534 00:30:53,360 --> 00:30:55,479 Speaker 1: that a very similar thing happened when they let Watson 535 00:30:55,520 --> 00:31:01,760 Speaker 1: read Wikipedia. No judgments here, just saying what what IBM said. Anyway, Again, 536 00:31:01,800 --> 00:31:06,040 Speaker 1: the computer was unable to determine when, when, when it 537 00:31:06,120 --> 00:31:09,520 Speaker 1: was appropriate. What's the appropriate context for dropping a swear word? 538 00:31:10,040 --> 00:31:12,440 Speaker 1: It didn't know, so it just started to speak kind 539 00:31:12,480 --> 00:31:15,280 Speaker 1: of like my wife does. So, uh, yeah, it was 540 00:31:15,840 --> 00:31:18,840 Speaker 1: I'm going to pay for that later. Uh So, anyway, 541 00:31:18,880 --> 00:31:21,920 Speaker 1: that that that's an interesting point though. Again you're you're 542 00:31:21,920 --> 00:31:25,080 Speaker 1: showing how machine intelligence and human intelligence are different because 543 00:31:25,160 --> 00:31:28,800 Speaker 1: the machine intelligence doesn't have that context. Sure, and of course, 544 00:31:28,800 --> 00:31:33,120 Speaker 1: you know we're talking about about science fiction or science future. However, 545 00:31:33,160 --> 00:31:35,760 Speaker 1: you want to term it so that you know, we 546 00:31:35,840 --> 00:31:38,400 Speaker 1: might very well come up with a fancy little program 547 00:31:38,480 --> 00:31:41,720 Speaker 1: script that lets you that lets you introduce that kind 548 00:31:41,760 --> 00:31:44,400 Speaker 1: of bias. But you know, yeah, but again from that 549 00:31:44,440 --> 00:31:48,080 Speaker 1: documentary Star Trek, I mean data never figured out those contractions. 550 00:31:49,160 --> 00:31:56,520 Speaker 1: Touring actually had a great uh mental exercise really and 551 00:31:56,560 --> 00:31:58,920 Speaker 1: it's called the Touring test. And this this applies to 552 00:31:59,080 --> 00:32:02,240 Speaker 1: artificial intelligence. Tourings point and we've talked about the train 553 00:32:02,320 --> 00:32:04,800 Speaker 1: test in previous episodes of tech Stuff. But just as 554 00:32:04,800 --> 00:32:08,040 Speaker 1: a refresher, Touring had suggested that you could create a 555 00:32:08,080 --> 00:32:11,760 Speaker 1: test and that if a machine could pass that test 556 00:32:12,640 --> 00:32:15,640 Speaker 1: at the same level as a human in other words, uh, 557 00:32:15,760 --> 00:32:18,280 Speaker 1: if you were unable to determine that the person who 558 00:32:18,280 --> 00:32:21,000 Speaker 1: took that test was human or machine, the machine had 559 00:32:21,000 --> 00:32:25,320 Speaker 1: passed the touring test and had had essentially simulated human 560 00:32:25,360 --> 00:32:28,760 Speaker 1: intelligence and h It usually works as an interview, so 561 00:32:28,800 --> 00:32:32,280 Speaker 1: you have someone who's who's conducting an interview, and you 562 00:32:32,400 --> 00:32:36,240 Speaker 1: have either a machine answering or a human answering, and 563 00:32:36,520 --> 00:32:38,880 Speaker 1: there's a barrier up so that of course the person 564 00:32:38,960 --> 00:32:41,400 Speaker 1: asking the questions cannot see who is responding, and of 565 00:32:41,400 --> 00:32:44,440 Speaker 1: course they're responding through you know, text usually because if 566 00:32:44,480 --> 00:32:47,240 Speaker 1: they're responding through a voice and it's like, I think 567 00:32:47,400 --> 00:32:49,880 Speaker 1: the answer as far you know, you'd be like, well, 568 00:32:49,920 --> 00:32:52,000 Speaker 1: either it's a robot or the most boring person in 569 00:32:52,000 --> 00:32:54,680 Speaker 1: the world. Uh. The idea being you would ask these 570 00:32:54,760 --> 00:32:59,600 Speaker 1: questions over a computer monitor, get text responses, and if 571 00:32:59,600 --> 00:33:03,400 Speaker 1: you were unable to to answer with a certain degree 572 00:33:04,000 --> 00:33:06,280 Speaker 1: of accuracy whether or not it was a machine or 573 00:33:06,320 --> 00:33:08,800 Speaker 1: a person, then you would say the machine passed the 574 00:33:09,280 --> 00:33:13,000 Speaker 1: tour and uh and and you could argue, well, that 575 00:33:13,040 --> 00:33:15,120 Speaker 1: could just mean that the machine is very good at 576 00:33:15,160 --> 00:33:18,760 Speaker 1: mimicking human intelligence. It does not actually possess human intelligence. 577 00:33:18,960 --> 00:33:23,160 Speaker 1: Touring's point is, does that matter? Because I know that 578 00:33:23,240 --> 00:33:26,640 Speaker 1: I am intelligent. I speak with someone like Lauren, who 579 00:33:26,680 --> 00:33:29,880 Speaker 1: I assume is also intelligent based upon the responses she gives, 580 00:33:30,360 --> 00:33:34,160 Speaker 1: But she could just be simulating intelligence. However, I have 581 00:33:34,440 --> 00:33:39,800 Speaker 1: I have already bestowed in my mind the the feature 582 00:33:39,920 --> 00:33:43,640 Speaker 1: of intelligence upon Lauren, because what she does is very 583 00:33:43,720 --> 00:33:46,360 Speaker 1: much akin to what I do. So Touring said, if 584 00:33:46,400 --> 00:33:49,360 Speaker 1: you extend the courtesy to your fellow human being that 585 00:33:49,440 --> 00:33:52,200 Speaker 1: they are intelligent based on the fact that they act 586 00:33:52,280 --> 00:33:54,480 Speaker 1: like you do, why would you not do the same 587 00:33:54,520 --> 00:33:57,960 Speaker 1: thing for a machine. Does it matter if the machine 588 00:33:58,000 --> 00:34:01,520 Speaker 1: can actually think, If the machine simulates thought well enough 589 00:34:01,560 --> 00:34:03,640 Speaker 1: for it to pass as human, then you're giving it 590 00:34:03,720 --> 00:34:07,480 Speaker 1: the same benefit of doubt that anyone else. This is 591 00:34:07,520 --> 00:34:10,200 Speaker 1: what a lot of science fiction movies are about. Actually, Yeah, 592 00:34:10,239 --> 00:34:12,400 Speaker 1: there's a lot of philosophy, and a lot of philosophy, 593 00:34:12,400 --> 00:34:14,440 Speaker 1: a lot of Isaac Asthma, of a lot of Blade 594 00:34:14,480 --> 00:34:17,719 Speaker 1: Runner and and and that's a sorry, well no, but 595 00:34:17,840 --> 00:34:20,160 Speaker 1: you know Philip K. Dick look him up. So anyway 596 00:34:20,440 --> 00:34:24,120 Speaker 1: than you do Android's dream of electric Sheep, I won't 597 00:34:24,160 --> 00:34:27,799 Speaker 1: I won't spoil it for you. Uh. They to kind 598 00:34:27,800 --> 00:34:31,360 Speaker 1: of wrap this all up, getting back into the discussion 599 00:34:31,400 --> 00:34:36,120 Speaker 1: of philosophy. Uh, we had very recently we did a 600 00:34:36,280 --> 00:34:40,040 Speaker 1: podcast about are we living in a computer simulation? And 601 00:34:40,080 --> 00:34:42,960 Speaker 1: that kind of plays into this idea of the singularity, 602 00:34:43,040 --> 00:34:46,799 Speaker 1: because that argument stated that if the singularity is in 603 00:34:46,880 --> 00:34:50,799 Speaker 1: fact possible, if it's inevitable, if we are going to 604 00:34:50,840 --> 00:34:53,319 Speaker 1: reach this level of trans humanism where we are no 605 00:34:53,400 --> 00:34:56,600 Speaker 1: longer able to really predict what the present will be 606 00:34:56,640 --> 00:34:59,960 Speaker 1: like because it will be beyond our understanding, then why 607 00:35:00,160 --> 00:35:03,000 Speaker 1: thing we would expect to do is create simulations of 608 00:35:03,000 --> 00:35:06,399 Speaker 1: our past to kind of study ourselves and to see 609 00:35:06,400 --> 00:35:09,799 Speaker 1: what happens play around variables. Yeah, they like good experiments. Yeah, 610 00:35:09,920 --> 00:35:12,760 Speaker 1: and we could we could, if we're that advanced, we could, 611 00:35:12,760 --> 00:35:16,320 Speaker 1: in theory, create such a realistic simulation that the inhabitants 612 00:35:16,400 --> 00:35:20,160 Speaker 1: of that simulation would be incapable of knowing that they 613 00:35:20,160 --> 00:35:24,920 Speaker 1: were artificial and would be completely you know, self aware 614 00:35:24,960 --> 00:35:28,920 Speaker 1: of themselves. You know, that was totally redundant, self aware 615 00:35:29,480 --> 00:35:33,879 Speaker 1: and uh, but unable to know that they were a simulation. Uh. 616 00:35:34,120 --> 00:35:36,439 Speaker 1: He said that if those things are possible, then there's 617 00:35:36,480 --> 00:35:39,319 Speaker 1: no way of knowing that, you know, the the overwhelming 618 00:35:39,680 --> 00:35:43,480 Speaker 1: uh possibility is that we are in a computer simulations 619 00:35:43,840 --> 00:35:47,680 Speaker 1: computer simulation right now, because yeah, if that's if that's 620 00:35:47,680 --> 00:35:49,799 Speaker 1: what's gonna happen, then there's no way of saying with 621 00:35:49,840 --> 00:35:53,080 Speaker 1: certainty that we are not in fact the product of that. 622 00:35:53,800 --> 00:35:56,719 Speaker 1: And so, uh, the point being not necessarily that we 623 00:35:56,760 --> 00:35:58,920 Speaker 1: are in fact living in a computer simulation, but that 624 00:35:59,000 --> 00:36:05,120 Speaker 1: perhaps the single larry this transhumanism thing might not be realistic. 625 00:36:05,680 --> 00:36:07,640 Speaker 1: It might not be the future that we're headed to. 626 00:36:07,880 --> 00:36:11,160 Speaker 1: Maybe it ends up being a pipe dream that's not 627 00:36:11,239 --> 00:36:14,880 Speaker 1: really possible for us to attain, or maybe we'll wipe 628 00:36:14,960 --> 00:36:22,200 Speaker 1: ourselves out through some terrible war or catastrophic accident. Um, 629 00:36:22,239 --> 00:36:26,000 Speaker 1: maybe we create a biological entity that wipes us out 630 00:36:26,040 --> 00:36:29,879 Speaker 1: all of the stand h we create a black hole 631 00:36:29,960 --> 00:36:33,040 Speaker 1: at the LHC. Which come on, people don't write me. 632 00:36:33,080 --> 00:36:36,880 Speaker 1: I already know about that, and how tiny and and 633 00:36:36,880 --> 00:36:40,280 Speaker 1: and almost non existent they are because it lasted so quickly. 634 00:36:40,760 --> 00:36:42,719 Speaker 1: Let's say that they do that thing where you look 635 00:36:42,760 --> 00:36:45,040 Speaker 1: at that one website where the black hole forms in 636 00:36:45,080 --> 00:36:47,759 Speaker 1: the parking lot outside the LHC, and you just see 637 00:36:47,800 --> 00:36:55,200 Speaker 1: the whole picture go um, which funny video. Anyway, that's 638 00:36:55,320 --> 00:36:58,759 Speaker 1: that argument plays back into this. So I don't know. 639 00:36:58,960 --> 00:37:00,640 Speaker 1: I don't know if we're going to ever see a 640 00:37:00,680 --> 00:37:03,279 Speaker 1: future where the singularity becomes a thing. Oh and we 641 00:37:03,360 --> 00:37:05,640 Speaker 1: never really talked about it. But one of the big 642 00:37:05,719 --> 00:37:10,439 Speaker 1: points that kurts Well really punches in his Singularity talks 643 00:37:10,520 --> 00:37:13,520 Speaker 1: is the idea of digital immortality, right right. And he's 644 00:37:13,520 --> 00:37:16,120 Speaker 1: been obsessed with this I and and obsessed is probably 645 00:37:16,120 --> 00:37:19,879 Speaker 1: a judgmental word. I apologize that's but he's been very 646 00:37:19,920 --> 00:37:23,040 Speaker 1: focused on this concept. His father died when he was 647 00:37:23,160 --> 00:37:26,439 Speaker 1: about twenty four, and he's been exploring theories on life 648 00:37:26,440 --> 00:37:30,440 Speaker 1: extension ever since then. And uh and supposedly takes all 649 00:37:30,520 --> 00:37:34,000 Speaker 1: kinds of supplements and sells them as well to extend life, 650 00:37:34,000 --> 00:37:37,640 Speaker 1: has all kind of kinds of health plans, dietary plans 651 00:37:37,640 --> 00:37:40,520 Speaker 1: that he has exercise all this the idea that the 652 00:37:40,560 --> 00:37:43,920 Speaker 1: idea being that if he can preserve his own last 653 00:37:44,000 --> 00:37:46,160 Speaker 1: long enough that we hit the singularity, then he can 654 00:37:46,280 --> 00:37:50,520 Speaker 1: become immortal. Right and either that, you know, we attain 655 00:37:50,600 --> 00:37:54,440 Speaker 1: immortality through one of a thousand different ways. For example, 656 00:37:54,920 --> 00:37:57,880 Speaker 1: we end up uploading our own intelligence into the cloud 657 00:37:58,440 --> 00:38:00,879 Speaker 1: and then we become part of a group asciousness, so 658 00:38:01,000 --> 00:38:03,880 Speaker 1: we are no longer really individuals. Or we merge with 659 00:38:03,920 --> 00:38:06,720 Speaker 1: computers in some other way so that we are technically 660 00:38:06,800 --> 00:38:10,360 Speaker 1: immortal that way. Or we just conquer the genes that 661 00:38:10,560 --> 00:38:14,040 Speaker 1: all guide a the aging process and we stop it 662 00:38:14,160 --> 00:38:16,160 Speaker 1: and we stop disease, you know, we we take like 663 00:38:16,200 --> 00:38:18,200 Speaker 1: in transmant you just take a cancer pill and then 664 00:38:18,239 --> 00:38:20,479 Speaker 1: you don't get cancer because that's what you do. Yeah, 665 00:38:20,600 --> 00:38:24,120 Speaker 1: So again the singularity. That's kind of why I think 666 00:38:24,200 --> 00:38:25,920 Speaker 1: a lot of critics also point to it as being 667 00:38:25,960 --> 00:38:27,759 Speaker 1: more of a religion, because it's kind of this sort 668 00:38:27,760 --> 00:38:32,480 Speaker 1: of utopian pipe dream in their minds. There's the former 669 00:38:32,520 --> 00:38:35,640 Speaker 1: CEO of Lotus, Mitch Kapor Kappa, I'm not sure how 670 00:38:35,640 --> 00:38:38,920 Speaker 1: you say it. Once called called it the intelligent design 671 00:38:39,000 --> 00:38:43,600 Speaker 1: for the i Q one for people. Yeah. Ouch, well, well, 672 00:38:43,680 --> 00:38:45,839 Speaker 1: kurtz Wild's kind of laughing all the way to the bank. 673 00:38:45,880 --> 00:38:48,920 Speaker 1: I hear that a company that rhymes with Schmoogle hired 674 00:38:49,000 --> 00:38:51,120 Speaker 1: him a little little people. I mean, we're you probably 675 00:38:51,120 --> 00:38:53,319 Speaker 1: wouldn't have heard of him, Yeah, but yeah, they just 676 00:38:53,400 --> 00:38:56,120 Speaker 1: hired him on to be I have it in my 677 00:38:56,200 --> 00:38:58,560 Speaker 1: notes at the official title. I think it's the director 678 00:38:58,560 --> 00:39:02,480 Speaker 1: of engineering. Yeah, a director of engineering over there. Um, 679 00:39:02,520 --> 00:39:04,520 Speaker 1: they get they get some big names. I mean, they 680 00:39:04,520 --> 00:39:07,640 Speaker 1: had vent Surf as the chief evangelists, and of course 681 00:39:07,640 --> 00:39:09,799 Speaker 1: he was one of the fathers of the Internet. So 682 00:39:10,080 --> 00:39:14,200 Speaker 1: Google's Google's gotta They're known for for getting some really 683 00:39:14,280 --> 00:39:18,440 Speaker 1: smart people. And and to be fair, while the singularity 684 00:39:18,520 --> 00:39:22,280 Speaker 1: may or may not ever happen, I think it's important 685 00:39:22,280 --> 00:39:26,160 Speaker 1: that we have optimists in the field of technology who 686 00:39:26,200 --> 00:39:30,560 Speaker 1: are really pushing for our development to try and make 687 00:39:30,600 --> 00:39:33,799 Speaker 1: the world a better place for people. Now, you know, 688 00:39:33,920 --> 00:39:36,319 Speaker 1: absolutely so, even if we're even if we never reach 689 00:39:36,400 --> 00:39:38,960 Speaker 1: the point of digital immortality in our lifetimes or any 690 00:39:39,000 --> 00:39:41,680 Speaker 1: other it's I mean, if if someone wants to think 691 00:39:41,760 --> 00:39:44,200 Speaker 1: so big that that they want to put in nanobots 692 00:39:44,239 --> 00:39:46,680 Speaker 1: to make my body awesomer. I mean, and and not 693 00:39:47,360 --> 00:39:49,600 Speaker 1: that that came out that came out possibly crude. I 694 00:39:49,680 --> 00:39:52,120 Speaker 1: mostly means that I don't get cancer and die, um 695 00:39:52,719 --> 00:39:56,000 Speaker 1: kind of stuff. That's that's terrific. I can I can't 696 00:39:56,040 --> 00:39:57,880 Speaker 1: argue with any part of that. Yeah, I'm gonna be 697 00:39:57,880 --> 00:40:00,520 Speaker 1: on video so much this year that I definitely need 698 00:40:00,560 --> 00:40:03,919 Speaker 1: my body to be awesomer, So I'm all for that. 699 00:40:04,600 --> 00:40:07,319 Speaker 1: Well either way, yes, and and and Google. You know, 700 00:40:07,480 --> 00:40:12,000 Speaker 1: Google looks forward so much to two augmented reality, augmented reality. 701 00:40:12,040 --> 00:40:14,759 Speaker 1: I'm sorry, I'm I can't pronounce anything today. I am 702 00:40:14,800 --> 00:40:18,880 Speaker 1: on a non role in the Internet of Things and 703 00:40:18,920 --> 00:40:21,360 Speaker 1: all of that wonderful future tech that it seems like 704 00:40:21,360 --> 00:40:24,239 Speaker 1: a terrific fit. Yeah. Yeah, so we'll see how how 705 00:40:24,280 --> 00:40:26,440 Speaker 1: it goes. I mean, obviously, the nice thing about this 706 00:40:26,560 --> 00:40:29,319 Speaker 1: is that all we have to do is live long 707 00:40:29,440 --> 00:40:31,839 Speaker 1: enough to see it happen or not happen. And most 708 00:40:31,880 --> 00:40:37,080 Speaker 1: predictions have the singularity hitting somewhere between twenty and that 709 00:40:37,120 --> 00:40:39,640 Speaker 1: we'll find out. Yeah, it all depends on upon which 710 00:40:39,800 --> 00:40:42,120 Speaker 1: futurists you're asking. And also it's one of those kind 711 00:40:42,160 --> 00:40:45,080 Speaker 1: of I think it's one of those rolling goal posts 712 00:40:45,160 --> 00:40:48,440 Speaker 1: as well. You know how how certain technologies are always 713 00:40:48,480 --> 00:40:51,560 Speaker 1: twenty years away, or five years away or ten years away. 714 00:40:51,719 --> 00:40:54,839 Speaker 1: So we'll see. Maybe maybe by we'll be saying, all right, 715 00:40:55,360 --> 00:40:59,799 Speaker 1: we've revised our figures seven days definitely, but but who 716 00:40:59,880 --> 00:41:03,080 Speaker 1: know us, We'll see. Guys. If you have any suggestions 717 00:41:03,120 --> 00:41:05,680 Speaker 1: for future episodes of tech Stuff, well here's what you 718 00:41:05,719 --> 00:41:07,880 Speaker 1: can do. You can write us an email. And a 719 00:41:07,920 --> 00:41:10,279 Speaker 1: lot of people have been asking about our email address. 720 00:41:10,320 --> 00:41:13,040 Speaker 1: I do say that every episode, but in case you've 721 00:41:13,080 --> 00:41:16,920 Speaker 1: missed it, listen carefully. Our email address is tech Stuff 722 00:41:17,239 --> 00:41:21,480 Speaker 1: at Discovery dot com. Send an email. I'll prove it 723 00:41:21,520 --> 00:41:24,800 Speaker 1: by writing back, or drop us a line on Facebook. 724 00:41:24,800 --> 00:41:27,360 Speaker 1: Our Twitter are handled there at both of us. Is 725 00:41:27,480 --> 00:41:30,759 Speaker 1: text Stuff hs W and Lauren and I will talk 726 00:41:30,760 --> 00:41:37,279 Speaker 1: to you again in the future. For more on this 727 00:41:37,440 --> 00:41:44,600 Speaker 1: and thousands of other topics. Is it hastaff works dot com.