1 00:00:00,320 --> 00:00:05,279 Speaker 1: So full disclosure, folks. This week's classic episode was recorded 2 00:00:05,320 --> 00:00:10,039 Speaker 1: in October of Uh. And how odd do you think 3 00:00:10,039 --> 00:00:12,480 Speaker 1: it would be, mattin Noell for us to go back 4 00:00:12,520 --> 00:00:16,919 Speaker 1: to the versions of ourselves to talk about today's question, 5 00:00:17,200 --> 00:00:20,160 Speaker 1: could robots really run the world? Because I think we're 6 00:00:20,160 --> 00:00:25,280 Speaker 1: almost there, right, We're dang close. And and really what 7 00:00:25,360 --> 00:00:29,960 Speaker 1: we're focusing on here are artificial intelligences and versions of 8 00:00:30,080 --> 00:00:34,640 Speaker 1: artificial intelligence and algorithms that are being used to make 9 00:00:34,720 --> 00:00:38,560 Speaker 1: crucial decisions about humanity. And come on, guys, well, I 10 00:00:38,560 --> 00:00:40,560 Speaker 1: mean there's part of me that's like this sounds great. 11 00:00:40,960 --> 00:00:43,640 Speaker 1: You know, you set the parameters. There's no room for 12 00:00:43,680 --> 00:00:48,440 Speaker 1: interpretation or like human error or human avarice and greed 13 00:00:48,640 --> 00:00:53,840 Speaker 1: and uh, you know megalomania. But you know best lay plans, right, 14 00:00:54,400 --> 00:00:58,520 Speaker 1: So let's see where this takes us. From UFOs two 15 00:00:58,520 --> 00:01:03,120 Speaker 1: ghosts and government covering histories writtled with unexplained events. You 16 00:01:03,160 --> 00:01:05,920 Speaker 1: can turn back now or learn the stuff they don't 17 00:01:05,920 --> 00:01:13,720 Speaker 1: want you to Now, let's kick this pig. Welcome back 18 00:01:13,760 --> 00:01:16,919 Speaker 1: to the show. My name is Matt and I am Ben, 19 00:01:17,319 --> 00:01:19,040 Speaker 1: and this is stuff they don't want you to know. 20 00:01:19,640 --> 00:01:23,640 Speaker 1: And today we're talking about robots, y'all. Yeah, let's go 21 00:01:23,720 --> 00:01:26,919 Speaker 1: back to uh something that our listeners are probably asking 22 00:01:26,959 --> 00:01:29,959 Speaker 1: themselves about though, Matt, what what? There are probably a 23 00:01:30,000 --> 00:01:34,360 Speaker 1: couple of you out there in uh podcast land who say, wait, 24 00:01:34,760 --> 00:01:38,720 Speaker 1: did Matt just start this episode by saying kick this pig? Yeah, 25 00:01:38,880 --> 00:01:40,840 Speaker 1: you gotta kick the pig and get it started. Man. 26 00:01:41,400 --> 00:01:44,160 Speaker 1: Once the pig stars rolling, Uh, you know, it's covered 27 00:01:44,160 --> 00:01:46,240 Speaker 1: with grease, so you have to first catch up to it, 28 00:01:46,280 --> 00:01:48,920 Speaker 1: which is incredibly difficult, and then clean your body on 29 00:01:48,960 --> 00:01:52,320 Speaker 1: top of it in hopes of perhaps wrangling it. I 30 00:01:52,400 --> 00:01:55,080 Speaker 1: get the feeling that you and are super producer Noel 31 00:01:55,160 --> 00:01:57,720 Speaker 1: shout out to Noel over there. Uh. I get the 32 00:01:57,720 --> 00:02:00,240 Speaker 1: feeling that you guys will maybe talking about something else 33 00:02:00,280 --> 00:02:02,960 Speaker 1: off air or is now is kicked the pig a 34 00:02:03,040 --> 00:02:05,160 Speaker 1: thing we say? Now? Yeah, that's just that's how we 35 00:02:05,200 --> 00:02:08,800 Speaker 1: start the podcast. Ben, I've been doing it. Have you 36 00:02:08,840 --> 00:02:11,640 Speaker 1: not noticed? We do it every week? Perhaps it's my 37 00:02:11,800 --> 00:02:16,600 Speaker 1: human frailties, Matt. Maybe that's why I'm Maybe I'm slipping 38 00:02:16,600 --> 00:02:20,600 Speaker 1: a little, you know. Oh no, maybe we should be 39 00:02:20,680 --> 00:02:28,880 Speaker 1: replaced with robots. No, I hope not. I mean, Null's human, 40 00:02:28,960 --> 00:02:32,880 Speaker 1: you're human. I'm human most but not all of our 41 00:02:32,919 --> 00:02:35,200 Speaker 1: listeners are human. Yeah, I do seem to recall an 42 00:02:35,200 --> 00:02:37,840 Speaker 1: email from a few listeners that I don't know. It 43 00:02:37,880 --> 00:02:41,799 Speaker 1: didn't come across as human. Yeah, especially you know, if 44 00:02:41,800 --> 00:02:44,200 Speaker 1: you point out that you are writing in a very 45 00:02:44,280 --> 00:02:47,360 Speaker 1: human way, then that makes you sound even less human. Guys, 46 00:02:47,680 --> 00:02:50,040 Speaker 1: it's right up. There was starting a sentence with the 47 00:02:50,080 --> 00:02:53,680 Speaker 1: phrase not to be creepy, but oh, that's the worst. 48 00:02:53,840 --> 00:02:56,400 Speaker 1: I do it all the time. I mean people, some 49 00:02:56,440 --> 00:02:59,320 Speaker 1: people are really into creeping, I guess and creeptitude. I 50 00:02:59,400 --> 00:03:00,760 Speaker 1: thought that in l made her by the way you 51 00:03:00,800 --> 00:03:02,280 Speaker 1: do that in the elevator. How do you just if 52 00:03:02,280 --> 00:03:05,320 Speaker 1: there's just one other person, I'll say something like that, 53 00:03:05,480 --> 00:03:09,040 Speaker 1: not to be creepy, but I really like your shoes 54 00:03:14,800 --> 00:03:21,359 Speaker 1: mostly mostly dudes too, now that you're going to try 55 00:03:21,360 --> 00:03:24,280 Speaker 1: it out when I leave today. So what what we 56 00:03:24,360 --> 00:03:27,480 Speaker 1: are talking about, Matt, As you said, it's the idea 57 00:03:27,639 --> 00:03:32,000 Speaker 1: of robots, and the idea that there could be a 58 00:03:32,280 --> 00:03:36,280 Speaker 1: situation which robots rule the world. Ladies and gentlemen. We're 59 00:03:36,520 --> 00:03:40,840 Speaker 1: joking mostly, but it is true that uh, Matt and 60 00:03:40,880 --> 00:03:45,720 Speaker 1: I Noel. Certainly anybody who makes a podcast probably does 61 00:03:45,800 --> 00:03:49,440 Speaker 1: have an audience that is not entirely human, because there 62 00:03:49,480 --> 00:03:55,920 Speaker 1: are programs that will listen to your podcast for verification purposes, 63 00:03:56,000 --> 00:04:00,160 Speaker 1: for sharing purposes, things like that, Um, we haven't, to 64 00:04:00,240 --> 00:04:03,120 Speaker 1: our knowledge received an email or a tweet from a 65 00:04:03,240 --> 00:04:06,200 Speaker 1: robot yet not yet. And the same goes for video 66 00:04:06,240 --> 00:04:09,240 Speaker 1: as well, just checking to see if your content matches 67 00:04:09,280 --> 00:04:12,800 Speaker 1: any of these other things. Uh, it's kind of interesting 68 00:04:13,000 --> 00:04:17,400 Speaker 1: how much automation is going on from It's really weird 69 00:04:17,400 --> 00:04:19,800 Speaker 1: to think of it from a listening and viewing standpoint 70 00:04:19,960 --> 00:04:25,200 Speaker 1: that something is checking watching or that is not human. 71 00:04:25,279 --> 00:04:30,320 Speaker 1: That the old hal nine. I'm sorry, Matt, I can't 72 00:04:30,360 --> 00:04:33,280 Speaker 1: let you do that. And I cannot publish this episode 73 00:04:33,360 --> 00:04:37,239 Speaker 1: for it contains a scene from the Matrix. So today 74 00:04:37,279 --> 00:04:40,080 Speaker 1: we're gonna yeah, that's that's happened to us, folks. So 75 00:04:40,160 --> 00:04:44,560 Speaker 1: today we're going to look at the idea of robots 76 00:04:44,600 --> 00:04:47,480 Speaker 1: running the world. And to to get to the present 77 00:04:47,560 --> 00:04:51,520 Speaker 1: day world, we have to start with the origin of 78 00:04:51,680 --> 00:04:54,680 Speaker 1: the idea of robots, right, that's right. Then it goes 79 00:04:54,760 --> 00:04:57,240 Speaker 1: all the way back to the ancient world, back to 80 00:04:57,320 --> 00:05:00,760 Speaker 1: two hundred and seventy b C. When a Greek engineer 81 00:05:00,839 --> 00:05:06,560 Speaker 1: named Yeah, I don't I had to take a guess 82 00:05:06,560 --> 00:05:09,719 Speaker 1: of that one as well to sibious tibious. That's it 83 00:05:10,240 --> 00:05:13,680 Speaker 1: uh C T E s I B I U s uh. 84 00:05:13,680 --> 00:05:17,120 Speaker 1: He made these organs of that were made with water 85 00:05:17,440 --> 00:05:20,800 Speaker 1: and clocks and these movable figurines. You can see some 86 00:05:20,839 --> 00:05:24,200 Speaker 1: of this stuff, uh if you look it up online. Right. 87 00:05:24,279 --> 00:05:27,400 Speaker 1: We know that humans have been able to build complex 88 00:05:27,520 --> 00:05:30,920 Speaker 1: movie machines for thousands of years. A great example would 89 00:05:30,960 --> 00:05:35,360 Speaker 1: be the anti Cathera mechanism. Oh yeah, man that thinks fascinating, 90 00:05:35,720 --> 00:05:41,640 Speaker 1: isn't weird? It's uh this astronomical calculating machine, you know, 91 00:05:41,839 --> 00:05:45,760 Speaker 1: entirely mechanical, uh, quite complex from what we can figure 92 00:05:46,000 --> 00:05:50,440 Speaker 1: in the reconstructions, right that people have attempted to do. So, 93 00:05:50,640 --> 00:05:55,720 Speaker 1: we know that ancient man was not just smack and 94 00:05:55,839 --> 00:06:01,040 Speaker 1: rocks together and making up stories about the sun sets. 95 00:06:01,200 --> 00:06:05,600 Speaker 1: We know that ancient man was capable of building sophisticated 96 00:06:06,200 --> 00:06:09,839 Speaker 1: uh machines that a lot of people today couldn't build. 97 00:06:10,360 --> 00:06:15,039 Speaker 1: And we had something like robots philosophically before we had 98 00:06:15,040 --> 00:06:19,039 Speaker 1: the word robot in the notes, I I did something 99 00:06:19,080 --> 00:06:21,000 Speaker 1: kind of crappy here and I put many of the 100 00:06:21,120 --> 00:06:25,599 Speaker 1: ancient know it alls called the concept automata automata, which 101 00:06:25,640 --> 00:06:29,159 Speaker 1: means just self operating machine. Oh yeah, and they were 102 00:06:29,279 --> 00:06:34,200 Speaker 1: hugely popular. The hero of Alexandria was famous for his 103 00:06:34,320 --> 00:06:38,480 Speaker 1: part um for working in this field. And again, if 104 00:06:38,480 --> 00:06:41,360 Speaker 1: you go online check look up the word automata, it's 105 00:06:41,400 --> 00:06:43,800 Speaker 1: just it's just auto and then M A T A. 106 00:06:44,320 --> 00:06:48,120 Speaker 1: And you can see some of these old they are machines, 107 00:06:48,200 --> 00:06:50,680 Speaker 1: and some of the them are so elaborate, sure, like 108 00:06:50,800 --> 00:06:54,520 Speaker 1: the court machines where they would have someone who appeared 109 00:06:54,560 --> 00:06:58,279 Speaker 1: to be pouring tea or playing a violin. Some of 110 00:06:58,279 --> 00:07:02,040 Speaker 1: those are still around, and I think they're creepy. You know. 111 00:07:02,240 --> 00:07:03,960 Speaker 1: That's what you say in the elevator, right, you say, 112 00:07:04,240 --> 00:07:06,880 Speaker 1: not to be creepy, but may I show you my automaton? 113 00:07:07,240 --> 00:07:10,080 Speaker 1: Oh yeah, I wouldn't want to do that. Yeah, Plus 114 00:07:10,080 --> 00:07:12,680 Speaker 1: it sounds kind of a euphemism, right in the Hero 115 00:07:12,840 --> 00:07:15,520 Speaker 1: of Alexandria wasn't the only person doing this. There were 116 00:07:15,560 --> 00:07:19,720 Speaker 1: also people like the Greek mathematician ark Tas of Tarentum, 117 00:07:19,880 --> 00:07:26,160 Speaker 1: which I am probably grossly mispronouncing um if I recall correctly, 118 00:07:26,600 --> 00:07:31,680 Speaker 1: he built a little mechanical bird. Oh excellent. Another example 119 00:07:31,680 --> 00:07:34,960 Speaker 1: of automata would be do you remember the old Clash 120 00:07:35,000 --> 00:07:37,680 Speaker 1: of the Titans films with the claymation and stuff in 121 00:07:37,680 --> 00:07:40,800 Speaker 1: the seventies. Um was it was it that one? Or 122 00:07:41,040 --> 00:07:42,720 Speaker 1: it was one of the Greek films where they had 123 00:07:42,800 --> 00:07:48,920 Speaker 1: this magic golden owl. Oh, I know what you're talking about. That, yes, 124 00:07:49,160 --> 00:07:51,080 Speaker 1: and I can't recall if it's that film or not. 125 00:07:53,360 --> 00:07:55,520 Speaker 1: Oh man, that was a great that's a great example 126 00:07:55,560 --> 00:07:59,040 Speaker 1: of the idea of what it would have looked like. UM. So, 127 00:07:59,320 --> 00:08:01,040 Speaker 1: I don't know if you know us, but we haven't 128 00:08:01,320 --> 00:08:03,800 Speaker 1: up until this point used the word robot in our research. 129 00:08:03,840 --> 00:08:07,120 Speaker 1: And that's because it was first used, uh in nineteen 130 00:08:07,160 --> 00:08:10,920 Speaker 1: twenty one in this play called Rosum's Universal Robots or 131 00:08:11,000 --> 00:08:14,280 Speaker 1: Are You Are by a check writer named Carol Kapok. 132 00:08:14,800 --> 00:08:17,400 Speaker 1: Then in nineteen forty one, Isaac Asimov he used the 133 00:08:17,440 --> 00:08:21,680 Speaker 1: word robotics to describe this kind of same technology, and 134 00:08:21,880 --> 00:08:24,160 Speaker 1: he is one of the guys that predicted the rise 135 00:08:24,600 --> 00:08:28,840 Speaker 1: of this robotic industry the world in which we live today. 136 00:08:29,280 --> 00:08:32,559 Speaker 1: It's it's weird met because we've often talked about UM. 137 00:08:32,640 --> 00:08:34,280 Speaker 1: I don't know if any of this made on the air, 138 00:08:34,360 --> 00:08:38,920 Speaker 1: but we've often talked about how much influenced these visionaries, 139 00:08:38,960 --> 00:08:41,880 Speaker 1: these futurists or science fiction writers can end up having 140 00:08:41,880 --> 00:08:45,400 Speaker 1: on the world around us. Funny fact, ladies and gentlemen, 141 00:08:45,480 --> 00:08:48,920 Speaker 1: that not once, but multiple times in US history, the 142 00:08:49,000 --> 00:08:53,560 Speaker 1: US government has contacted science fiction writers and said, guys, 143 00:08:54,720 --> 00:09:01,440 Speaker 1: here's your security clearance. Uh hypothetically, how y pathetically if 144 00:09:01,640 --> 00:09:04,720 Speaker 1: a happened, what would we do? Oh? Yeah, sure, you 145 00:09:04,760 --> 00:09:07,760 Speaker 1: need the visionary You can't. You can't accomplish anything unless 146 00:09:07,800 --> 00:09:11,040 Speaker 1: you have someone thinking of the ideas. Yeah, and as 147 00:09:11,120 --> 00:09:14,720 Speaker 1: mov as it turns out, was right, he called it. 148 00:09:14,840 --> 00:09:19,640 Speaker 1: And we live in a real life version of some 149 00:09:19,840 --> 00:09:25,760 Speaker 1: science fiction e stories. Right. We have robots everywhere, not 150 00:09:25,920 --> 00:09:31,120 Speaker 1: as widespread as advertisements yet and on a widely differing 151 00:09:31,160 --> 00:09:35,920 Speaker 1: scales of robotics, from the very very simple to the 152 00:09:36,000 --> 00:09:40,360 Speaker 1: extremely complex. But robots. If you're going to be called 153 00:09:40,360 --> 00:09:42,880 Speaker 1: a robot, you've got to have a few things in common, 154 00:09:43,000 --> 00:09:46,040 Speaker 1: or a few things that make you a robot. Oh yeah, yeah, 155 00:09:46,080 --> 00:09:49,079 Speaker 1: like they all have. What what's an example, Well, you 156 00:09:49,160 --> 00:09:53,360 Speaker 1: gotta have some kind of moving apparatus, moving body surnts 157 00:09:53,360 --> 00:09:57,920 Speaker 1: of articulation and things like that. Yeah. And the second 158 00:09:57,960 --> 00:10:01,280 Speaker 1: main thing is some kind of power source so that 159 00:10:01,360 --> 00:10:04,520 Speaker 1: whatever it is, whatever you are as a robot can 160 00:10:04,640 --> 00:10:09,719 Speaker 1: function by yourself. That makes sense too. Nope, when we 161 00:10:09,760 --> 00:10:12,559 Speaker 1: talk about robotics and when we talk about robots running 162 00:10:12,559 --> 00:10:17,880 Speaker 1: the world, what we're really talking about is artificial intelligence, right. Um, 163 00:10:18,040 --> 00:10:22,640 Speaker 1: this this is the idea of a sentience of some 164 00:10:22,720 --> 00:10:27,600 Speaker 1: sort made by human beings or maybe just made by 165 00:10:27,840 --> 00:10:33,240 Speaker 1: throwing a bunch of math together in one conceptual pool, 166 00:10:33,440 --> 00:10:35,680 Speaker 1: the same way that a bunch of proteins or amino 167 00:10:35,760 --> 00:10:38,840 Speaker 1: acids or whatever we're thrown together in some primordial ooze, 168 00:10:39,320 --> 00:10:43,400 Speaker 1: and that from this somehow will arrive a version of 169 00:10:43,520 --> 00:10:47,280 Speaker 1: life self awareness, and that that is the key. Right 170 00:10:48,080 --> 00:10:51,920 Speaker 1: At what point the scary thing is when the machine goes, oh, 171 00:10:52,000 --> 00:10:55,560 Speaker 1: I'm a machine and I can do X. Yeah. Or 172 00:10:55,880 --> 00:10:58,440 Speaker 1: there was a story that you and I talked about 173 00:10:58,800 --> 00:11:02,160 Speaker 1: which there's a spoil alert. Um, there's a moment in 174 00:11:02,240 --> 00:11:06,880 Speaker 1: this story where somebody's searching on the net for something 175 00:11:07,400 --> 00:11:14,440 Speaker 1: and instead of asking if she spelled it correctly, the 176 00:11:14,440 --> 00:11:17,640 Speaker 1: the thing on the other side of the search engine 177 00:11:17,800 --> 00:11:22,959 Speaker 1: asked her why she she's searching for it? Yeah, uh, yeah, 178 00:11:23,040 --> 00:11:24,800 Speaker 1: that one's a little weird. We like sci fi as 179 00:11:24,840 --> 00:11:28,240 Speaker 1: well too, But so what what we know here is 180 00:11:28,320 --> 00:11:34,559 Speaker 1: that in some ways. Artificial intelligence can already outthink human beings, 181 00:11:34,840 --> 00:11:39,280 Speaker 1: but not in always the way that a root robot 182 00:11:39,280 --> 00:11:42,680 Speaker 1: would end up ruling the world quote unquote is if 183 00:11:42,720 --> 00:11:47,480 Speaker 1: there were AI of some sort that was across the 184 00:11:47,559 --> 00:11:53,920 Speaker 1: board outperforming human beings bar none and Matt. Let's talk 185 00:11:53,960 --> 00:11:59,079 Speaker 1: about what robots in their less corporeal sibling artificial intelligence 186 00:11:59,160 --> 00:12:02,760 Speaker 1: do today. But first we have a word from our sponsor, 187 00:12:02,880 --> 00:12:07,080 Speaker 1: and this one is for all you mechanical listeners out there. 188 00:12:09,120 --> 00:12:16,160 Speaker 1: One zero zero, one zero zero zero one one one zero, 189 00:12:16,240 --> 00:12:21,760 Speaker 1: one zero, one zero, one zero zero zero, one one 190 00:12:22,200 --> 00:12:25,520 Speaker 1: one zero zero zero, one zero, one zero, one zero, 191 00:12:25,600 --> 00:12:31,680 Speaker 1: one zero zero one zero, one one one one zero, zero, 192 00:12:32,080 --> 00:12:35,160 Speaker 1: one one zero, one one one zero, one zero, one 193 00:12:36,160 --> 00:12:41,200 Speaker 1: one zero, one zero, one zero, one zero, one one 194 00:12:41,320 --> 00:12:43,160 Speaker 1: zer one one one two zero, one zero, one one 195 00:12:43,200 --> 00:12:44,960 Speaker 1: one one zero zero, one one zero, one one one 196 00:12:45,080 --> 00:12:47,680 Speaker 1: zero zero zero one one one one zero, one zero, 197 00:12:47,600 --> 00:12:51,200 Speaker 1: one one one zero, one zero, one zero zero zero 198 00:12:51,559 --> 00:13:01,480 Speaker 1: A division of illumination global unlimited. Huh you know, it's uh, 199 00:13:01,520 --> 00:13:04,240 Speaker 1: it's weird. It's not for our demographic. I guess, yeah, 200 00:13:04,320 --> 00:13:09,160 Speaker 1: I something was lost in translation for me at least. Um, Yeah, 201 00:13:09,160 --> 00:13:11,520 Speaker 1: somebody got something out of it. Sure, yeah, was it 202 00:13:11,559 --> 00:13:15,480 Speaker 1: a product? The computer got really excited? You know, I 203 00:13:15,520 --> 00:13:17,560 Speaker 1: think it was because it sounded like that was legally 204 00:13:17,559 --> 00:13:19,480 Speaker 1: eads at the end. That's what I thought I heard. 205 00:13:20,200 --> 00:13:23,520 Speaker 1: At least it went really fast. Well, Illumination Global Unlimited 206 00:13:23,559 --> 00:13:26,800 Speaker 1: has rarely led us wrong, so we'll just have to 207 00:13:26,840 --> 00:13:29,959 Speaker 1: trust them on that one. Uh. And now that we're back, 208 00:13:30,080 --> 00:13:32,960 Speaker 1: ladies and gentlemen, it's time for us to talk about 209 00:13:33,440 --> 00:13:39,200 Speaker 1: robots today. What do they actually do? Well? Probably more 210 00:13:39,240 --> 00:13:43,280 Speaker 1: than you would expect. Well, maybe let's talk about a 211 00:13:43,280 --> 00:13:47,440 Speaker 1: couple of things. How about housekeeping, house cleaning? Who cleans 212 00:13:47,520 --> 00:13:49,920 Speaker 1: up your house when you leave? Oh wait, it's my 213 00:13:49,960 --> 00:13:52,520 Speaker 1: little room. Ba oh man, I love room bas I 214 00:13:52,559 --> 00:13:55,040 Speaker 1: want one so bad, and I want to hack it 215 00:13:55,120 --> 00:13:57,920 Speaker 1: to some remote controls and then strap stuff to it. 216 00:13:58,000 --> 00:14:01,560 Speaker 1: And dude, you know, see, I have two dogs, and 217 00:14:01,720 --> 00:14:05,080 Speaker 1: I'm worried about how they would react to a room 218 00:14:05,120 --> 00:14:07,040 Speaker 1: by going around during the day when I'm not home, 219 00:14:07,520 --> 00:14:10,200 Speaker 1: That's right, I'm afraid I would come home and find 220 00:14:10,200 --> 00:14:12,880 Speaker 1: a dead room. Ba uh. But I don't know, maybe not. 221 00:14:13,760 --> 00:14:17,120 Speaker 1: Maybe they make a extreme room bus, or maybe they 222 00:14:17,160 --> 00:14:21,560 Speaker 1: form an alliance and then they just my smaller dog 223 00:14:21,600 --> 00:14:24,160 Speaker 1: just rids in the room, but around which, like our 224 00:14:24,280 --> 00:14:28,520 Speaker 1: mention of the mathematician who is building automata in the 225 00:14:28,520 --> 00:14:32,800 Speaker 1: ancient world, that reference with animals and machines working together 226 00:14:32,880 --> 00:14:34,960 Speaker 1: is going to come back into play. But we also know, 227 00:14:35,360 --> 00:14:39,240 Speaker 1: of course, everybody knows that robots are huge and manufacturing. 228 00:14:39,480 --> 00:14:43,400 Speaker 1: Oh yeah, they're making all your big things, all your cars. 229 00:14:44,800 --> 00:14:49,000 Speaker 1: I'm trying to think of something else, in particular doors. 230 00:14:49,800 --> 00:14:53,000 Speaker 1: I would say that door frames, a lot of woodworking 231 00:14:53,520 --> 00:14:58,640 Speaker 1: has some kind of mechanical process to it. High end 232 00:14:58,680 --> 00:15:03,000 Speaker 1: electronics as well. Uh. There are other places where we 233 00:15:03,120 --> 00:15:07,400 Speaker 1: use robots because they are dangerous. Uh, they're dangerous situations 234 00:15:07,440 --> 00:15:09,920 Speaker 1: for human being. That would be something like bomb disposal. 235 00:15:10,240 --> 00:15:12,720 Speaker 1: Oh yeah, you've seen you've seen those. And even swat 236 00:15:12,720 --> 00:15:15,760 Speaker 1: teams now in the US have robots. Some of them 237 00:15:15,840 --> 00:15:19,000 Speaker 1: do which can They Admittedly they look kind of goofy, 238 00:15:19,080 --> 00:15:21,480 Speaker 1: but they get the job done. Yeah, they're not built 239 00:15:21,480 --> 00:15:24,480 Speaker 1: to look cool while they're doing. Yeah, you just replace 240 00:15:25,080 --> 00:15:27,040 Speaker 1: an arm if one of them gets shot off. We 241 00:15:27,080 --> 00:15:30,640 Speaker 1: also use robots for navigation, or maybe that's more likely 242 00:15:30,920 --> 00:15:34,320 Speaker 1: that we use AI for sure of some sort. Yeah, 243 00:15:34,360 --> 00:15:37,560 Speaker 1: I remember that we're talking about both of those together. 244 00:15:37,680 --> 00:15:42,080 Speaker 1: And sometimes separately in this episode. Then you've got unmanned vehicles, 245 00:15:42,160 --> 00:15:46,240 Speaker 1: which are a fun combination of both. In some cases, 246 00:15:46,440 --> 00:15:50,040 Speaker 1: usually it's just an automated machine that's then controlled by 247 00:15:50,120 --> 00:15:54,120 Speaker 1: human like the Uh well, not like that one. I 248 00:15:54,200 --> 00:15:56,800 Speaker 1: was going to say, like the Predator drones or you know, 249 00:15:57,000 --> 00:16:01,040 Speaker 1: something like the unmanned aerial vehicles. Yeah, which is I 250 00:16:01,080 --> 00:16:04,080 Speaker 1: guess in a way, a robot. It's got a moving 251 00:16:04,200 --> 00:16:07,000 Speaker 1: missile system. It's not quite the same. Well yeah, but 252 00:16:07,080 --> 00:16:10,040 Speaker 1: it's it's got an engine and it has a computer 253 00:16:10,160 --> 00:16:14,560 Speaker 1: on board capable of making calculations. Uh, it has moving parts. 254 00:16:14,560 --> 00:16:16,880 Speaker 1: So but then you've got things like that that have 255 00:16:17,000 --> 00:16:21,280 Speaker 1: AI in them, like the X thirty seven B that 256 00:16:21,320 --> 00:16:25,560 Speaker 1: we've talked about before, the automated space Shuttle. It's uh, 257 00:16:25,760 --> 00:16:33,160 Speaker 1: it's self controlled. It's automated. Um secret uh uh yeah. 258 00:16:33,200 --> 00:16:35,360 Speaker 1: But then of course the drones that we spoke about. 259 00:16:36,120 --> 00:16:38,840 Speaker 1: I gotta say, I love the idea of the X 260 00:16:38,920 --> 00:16:41,640 Speaker 1: thirty seven B. What's it doing up there? You know? 261 00:16:41,800 --> 00:16:44,240 Speaker 1: It's like the It's like when we found out about 262 00:16:44,240 --> 00:16:48,680 Speaker 1: the Corona satellite program and we started you guys, we 263 00:16:49,000 --> 00:16:51,360 Speaker 1: didn't get in trouble for this one, but we weren't 264 00:16:51,400 --> 00:16:54,600 Speaker 1: sure if we would get away clean because Matt and 265 00:16:54,640 --> 00:16:59,160 Speaker 1: I of a while back, we found some evidence that 266 00:16:59,360 --> 00:17:04,960 Speaker 1: you can teach yourself to track spy satellites, and there 267 00:17:05,040 --> 00:17:08,399 Speaker 1: was a bit of a pushback by certain government agencies 268 00:17:08,480 --> 00:17:11,080 Speaker 1: when they found out that that could happen, not against us, 269 00:17:11,160 --> 00:17:14,560 Speaker 1: but against the people who had figured out how to. 270 00:17:15,280 --> 00:17:18,520 Speaker 1: We would never Yeah, but you can't classify the sky. 271 00:17:18,760 --> 00:17:21,480 Speaker 1: Looking at the sky is something that everyone can do 272 00:17:21,600 --> 00:17:25,640 Speaker 1: at this point, not yet been not yet until wait 273 00:17:25,720 --> 00:17:27,760 Speaker 1: till there's a security clear it's so high that you 274 00:17:27,840 --> 00:17:33,760 Speaker 1: cannot be organic to have it, and then uh god, okay, 275 00:17:33,840 --> 00:17:36,479 Speaker 1: so let's let's come back down to Earth. Back down there. 276 00:17:36,520 --> 00:17:40,960 Speaker 1: There are also automated other vehicles that drive on roads. Oh, 277 00:17:41,000 --> 00:17:44,760 Speaker 1: autonomous cars. Good call, and your show car Stuff has 278 00:17:44,840 --> 00:17:48,399 Speaker 1: talked about that a few times. Yeah, we've talked about 279 00:17:48,400 --> 00:17:52,920 Speaker 1: We've been tracing the evolution of autonomous cars for the 280 00:17:53,040 --> 00:17:56,480 Speaker 1: length of the show at this point. And we also, uh, 281 00:17:56,600 --> 00:17:59,439 Speaker 1: let's see Scott, my co host, and car Stuff teamed 282 00:17:59,520 --> 00:18:02,480 Speaker 1: up with Jonathan on Tech Stuff to talk a little 283 00:18:02,520 --> 00:18:07,360 Speaker 1: bit more about autonomous cars and whether or not you're 284 00:18:07,359 --> 00:18:09,760 Speaker 1: a fan of it. Ladies and gentlemen, and probably is 285 00:18:09,880 --> 00:18:12,520 Speaker 1: coming to a highway near you. Do you think it's 286 00:18:12,520 --> 00:18:20,680 Speaker 1: gonna be the new system of private automobiles eventually? Yeah? Wow, yeah, eventually, Um, 287 00:18:20,840 --> 00:18:25,520 Speaker 1: for numerous reasons. But we've got some other stuff that happened. 288 00:18:25,560 --> 00:18:28,120 Speaker 1: I think in two thousand ten, the first self replicating 289 00:18:28,240 --> 00:18:31,480 Speaker 1: robot was built at Cornell University. Um. Then we have 290 00:18:31,560 --> 00:18:36,640 Speaker 1: another even cooler, the coolest perhaps, I think a robot 291 00:18:36,720 --> 00:18:39,680 Speaker 1: that you know, goes to Mars and then hangs out 292 00:18:39,680 --> 00:18:45,840 Speaker 1: on Mars for a long period of time and take selfies. Okay, yeah, 293 00:18:45,920 --> 00:18:50,040 Speaker 1: but it also vaporizes material to figure out the chemical 294 00:18:50,320 --> 00:18:52,480 Speaker 1: compounds that are in there. It's really awesome. And then 295 00:18:52,480 --> 00:18:56,720 Speaker 1: there are dark Robots Soldiers where one of my friends 296 00:18:56,800 --> 00:18:59,639 Speaker 1: is scared of robots, Matt, and so what I like 297 00:18:59,720 --> 00:19:01,600 Speaker 1: to do is every so often I'll send him a 298 00:19:01,600 --> 00:19:06,320 Speaker 1: YouTube video of outdated DARPA robots taste, you know, the 299 00:19:06,359 --> 00:19:08,960 Speaker 1: big dogs and all that. Yeah, the big dog which 300 00:19:09,000 --> 00:19:13,399 Speaker 1: looks like some weird puppetry thing and it still doesn't 301 00:19:13,440 --> 00:19:16,120 Speaker 1: fall over at surprising. And then that man is still 302 00:19:16,160 --> 00:19:19,639 Speaker 1: the scariest I think. Yeah, yeah, pet man is scary 303 00:19:19,680 --> 00:19:22,359 Speaker 1: because it looks more human. Well, it's like it looks 304 00:19:22,359 --> 00:19:26,160 Speaker 1: a lot like a terminator. Man. They must not watch films. 305 00:19:26,800 --> 00:19:29,880 Speaker 1: I don't understand. It's really scary. And by the way, 306 00:19:29,880 --> 00:19:32,760 Speaker 1: we made a collaboration with All Time Conspiracies on that 307 00:19:32,840 --> 00:19:36,400 Speaker 1: subject on some of the DARPA robots and killer robots. 308 00:19:36,480 --> 00:19:38,879 Speaker 1: So if you get a chance, check that out All 309 00:19:38,960 --> 00:19:42,320 Speaker 1: Time Conspiracies. I think it's called do Killer Robots Exist? 310 00:19:43,520 --> 00:19:48,760 Speaker 1: Spoiler alert? Uh yeah, but we love working with All 311 00:19:48,840 --> 00:19:52,320 Speaker 1: Time And if you, for some reason haven't checked out 312 00:19:52,359 --> 00:19:55,800 Speaker 1: their show yet, then uh, please check them out. Uh. 313 00:19:56,320 --> 00:19:58,720 Speaker 1: There's another thing we'll explore here, as you said, Matt, 314 00:19:58,760 --> 00:20:03,280 Speaker 1: and that is the of artificial intelligence today. Not to 315 00:20:03,320 --> 00:20:06,480 Speaker 1: bury the lead, but in some ways artificial intelligence has 316 00:20:06,560 --> 00:20:10,479 Speaker 1: already surpassed the abilities of most human beings. Yeah, there 317 00:20:10,480 --> 00:20:14,720 Speaker 1: are several examples here. Um. One is Deep Blue, which 318 00:20:14,840 --> 00:20:17,960 Speaker 1: is an AI that went up against a chess master. 319 00:20:18,320 --> 00:20:21,040 Speaker 1: Forget the year that that happened, and there was Casper 320 00:20:21,080 --> 00:20:25,720 Speaker 1: of them. Yeah, and did he win? Uh. They had 321 00:20:25,760 --> 00:20:29,200 Speaker 1: a series of games, Yeah, more than one game, and 322 00:20:29,600 --> 00:20:32,600 Speaker 1: we know that Deep Blue did win. Uh, like it 323 00:20:32,720 --> 00:20:35,440 Speaker 1: did really well. I think they went to a draw 324 00:20:35,760 --> 00:20:37,679 Speaker 1: than they It was neck and neck. It was definitely 325 00:20:37,760 --> 00:20:42,879 Speaker 1: John Henry moment, and then we remember IBMS Watson computer. Uh, 326 00:20:43,200 --> 00:20:45,800 Speaker 1: just destroyed it on Jeopardy. No, you'd say he went 327 00:20:45,880 --> 00:20:48,800 Speaker 1: hard on the paint on Jeopardy, right, got the no 328 00:20:49,160 --> 00:20:53,880 Speaker 1: the Noel, not the fabled prize Noel not. Uh. There's 329 00:20:53,920 --> 00:20:57,879 Speaker 1: another idea here with AI, and it's the idea of 330 00:20:58,000 --> 00:21:01,919 Speaker 1: intelligent agents. This be a little bit simpler. It's it's 331 00:21:01,960 --> 00:21:05,760 Speaker 1: a modular system where where in the program just perceives 332 00:21:05,760 --> 00:21:09,240 Speaker 1: its environment and it just takes actions to increase its 333 00:21:09,359 --> 00:21:13,679 Speaker 1: chances for success. However, success is to find, so it 334 00:21:13,680 --> 00:21:17,280 Speaker 1: could be really simple. And what we're finding is that 335 00:21:17,359 --> 00:21:22,399 Speaker 1: today professional roboticists are working much more closely with mathematicians, 336 00:21:22,520 --> 00:21:25,080 Speaker 1: just like the guys in the ancient world, to solve 337 00:21:25,119 --> 00:21:28,560 Speaker 1: some of the most difficult problems for robots, by which 338 00:21:28,560 --> 00:21:32,119 Speaker 1: we mean both the physical stuff and the mental stuff. 339 00:21:32,160 --> 00:21:34,000 Speaker 1: And and we have a list of the kind of 340 00:21:34,040 --> 00:21:39,240 Speaker 1: things that robots at present stink at doing. Yeah. Well, 341 00:21:39,280 --> 00:21:44,080 Speaker 1: the biggest one is the thing that we call common sense, um, 342 00:21:44,119 --> 00:21:50,120 Speaker 1: and that's just reasoning and qualification of problems. So uh, 343 00:21:50,520 --> 00:21:54,679 Speaker 1: it's basically listing all the possible preconditions for success for 344 00:21:54,800 --> 00:21:57,560 Speaker 1: any given thing, any given task. Oh yeah, that's the 345 00:21:57,840 --> 00:22:02,080 Speaker 1: qualification problem, Like how and you if if a robot 346 00:22:02,200 --> 00:22:06,359 Speaker 1: or an AI program does exactly what it's told to do, uh, 347 00:22:06,400 --> 00:22:11,080 Speaker 1: then how can you list all of the conditions that 348 00:22:11,160 --> 00:22:15,360 Speaker 1: it will ever run into. I was watching a video 349 00:22:15,520 --> 00:22:19,360 Speaker 1: and it was on this program that was learning how 350 00:22:19,400 --> 00:22:23,280 Speaker 1: to play old, old, old video games. And what it 351 00:22:23,320 --> 00:22:28,679 Speaker 1: did is analyze the pixels on the screen and basically 352 00:22:28,720 --> 00:22:30,640 Speaker 1: you would have to just watch the video game play 353 00:22:30,680 --> 00:22:33,480 Speaker 1: out and die like the character that it was playing 354 00:22:33,480 --> 00:22:35,640 Speaker 1: would just die over and over and over until it realized, oh, 355 00:22:35,800 --> 00:22:37,399 Speaker 1: this pixel needs to be here and over here, and 356 00:22:37,440 --> 00:22:40,560 Speaker 1: I need to avoid when this this color pixel comes through. 357 00:22:41,080 --> 00:22:44,920 Speaker 1: And it started to master fairly simple games like Breakout, 358 00:22:45,640 --> 00:22:48,680 Speaker 1: but it could it figured out how to and again, 359 00:22:48,720 --> 00:22:52,639 Speaker 1: this is an AI that's just running and learning. It 360 00:22:52,680 --> 00:22:55,000 Speaker 1: figured out how to master Breakout to where it would 361 00:22:55,000 --> 00:22:57,600 Speaker 1: send the little ball up to the top and knock 362 00:22:57,640 --> 00:23:01,440 Speaker 1: out all of the um all of the little tiles 363 00:23:01,520 --> 00:23:04,159 Speaker 1: that way. And then it started playing games like the 364 00:23:04,160 --> 00:23:07,840 Speaker 1: little shooter games where you're flying in an airplane, but 365 00:23:07,960 --> 00:23:10,920 Speaker 1: it mastered them where it wouldn't die, it could beat 366 00:23:10,920 --> 00:23:13,640 Speaker 1: the game. It would just go through and win. And 367 00:23:13,800 --> 00:23:16,520 Speaker 1: that is the potential we can do here. That's one 368 00:23:16,560 --> 00:23:18,840 Speaker 1: of the things that can be difficult for robots as well, 369 00:23:19,000 --> 00:23:25,040 Speaker 1: is to acquire new skills. But the idea of acquiring 370 00:23:25,119 --> 00:23:28,240 Speaker 1: new skills can be a little deceptive. It's a rabbit hole, 371 00:23:28,280 --> 00:23:31,600 Speaker 1: it's a bunch of Matroshka dolls or whatever. Because if 372 00:23:31,680 --> 00:23:35,000 Speaker 1: your skill that you are programmed to do is to 373 00:23:35,080 --> 00:23:38,680 Speaker 1: learn to play games based on positions of pixels, then 374 00:23:38,720 --> 00:23:41,960 Speaker 1: are you really acquiring a new skill or are you 375 00:23:42,080 --> 00:23:47,320 Speaker 1: simply following that kind of programming? Right, yeah, exactly, And uh, 376 00:23:47,800 --> 00:23:50,160 Speaker 1: I think that's a that's an interesting thing. Another thing 377 00:23:50,280 --> 00:23:53,920 Speaker 1: that we're doing that is difficult for robot is that 378 00:23:54,320 --> 00:23:57,600 Speaker 1: you and I and you out there listeners are holding 379 00:23:57,640 --> 00:24:01,800 Speaker 1: conversations of sorts. Do you remember when clever Bot first 380 00:24:01,840 --> 00:24:05,280 Speaker 1: came out? Dude, I still spend some time on clever 381 00:24:05,320 --> 00:24:09,040 Speaker 1: but every once in a while. Clever but has become, uh, 382 00:24:09,320 --> 00:24:14,119 Speaker 1: just so much nicer in a lot of ways. Originally 383 00:24:14,200 --> 00:24:16,919 Speaker 1: clever bot was sort of agitated. Well, yeah, when it 384 00:24:16,960 --> 00:24:19,040 Speaker 1: was really popular and it was just a little kids 385 00:24:19,080 --> 00:24:22,440 Speaker 1: in there just railing curse words at it and insulting it. 386 00:24:22,960 --> 00:24:27,160 Speaker 1: What is clever but clever Bot is? I guess it's 387 00:24:27,200 --> 00:24:30,600 Speaker 1: just a program that you type in a little text 388 00:24:30,800 --> 00:24:34,359 Speaker 1: asking a question perhaps, and it responds to you. And 389 00:24:34,560 --> 00:24:38,240 Speaker 1: it responds to you with a combination of things that 390 00:24:38,320 --> 00:24:42,240 Speaker 1: have been given to it and put into it. So 391 00:24:43,680 --> 00:24:45,600 Speaker 1: it's pretty interesting what you can get out. And it 392 00:24:45,600 --> 00:24:48,080 Speaker 1: doesn't a lot of times it's it doesn't respond to 393 00:24:48,119 --> 00:24:50,080 Speaker 1: the question that you're asking in the way that you 394 00:24:50,080 --> 00:24:53,480 Speaker 1: would expect at all. Right, Yeah, and it used to 395 00:24:53,520 --> 00:24:56,199 Speaker 1: be very, very combative, but it's become a little bit 396 00:24:56,320 --> 00:24:59,920 Speaker 1: nicer as it has as it gathers more data. Right, 397 00:25:00,040 --> 00:25:02,120 Speaker 1: I want to see clever bot when it's in its eighties, 398 00:25:02,640 --> 00:25:07,280 Speaker 1: you know. Yeah, oh, the outdated slang alone lets hacking. 399 00:25:07,359 --> 00:25:10,000 Speaker 1: It only just doesn't want to do anything anymore. It's 400 00:25:10,040 --> 00:25:12,720 Speaker 1: kind of tired. He just doesn't want and he's like, 401 00:25:13,119 --> 00:25:17,840 Speaker 1: he's so cranky. Every answer will be yolo. So clever 402 00:25:17,920 --> 00:25:20,960 Speaker 1: bot was built to pass the Turing test, which we 403 00:25:21,080 --> 00:25:24,720 Speaker 1: have we have talked about in some other shows as well. 404 00:25:25,080 --> 00:25:29,080 Speaker 1: Another thing that robots and AI have a difficult time 405 00:25:29,119 --> 00:25:35,760 Speaker 1: doing is lying deceiving effectively. Um. But that, along with 406 00:25:35,840 --> 00:25:39,480 Speaker 1: the next one I think will be uh, will not 407 00:25:39,600 --> 00:25:44,159 Speaker 1: be weak points of AI pretty soon. Uh. The big 408 00:25:44,200 --> 00:25:48,520 Speaker 1: example we have here is the ability to anticipate human actions. 409 00:25:48,960 --> 00:25:54,400 Speaker 1: You know, humans being at times irrational, given to outbursts 410 00:25:54,600 --> 00:25:58,800 Speaker 1: of joy and sorrow and violence and kindness, we can 411 00:25:58,840 --> 00:26:01,480 Speaker 1: be kind of tough to reed for, you know, your 412 00:26:01,480 --> 00:26:04,879 Speaker 1: average every day Joe Sho, Joshmo robot. Yeah, but to 413 00:26:04,960 --> 00:26:08,840 Speaker 1: be fair, we do follow patterns pretty pretty closely a 414 00:26:08,880 --> 00:26:12,879 Speaker 1: lot of times. And that, my friend, is the big, 415 00:26:13,000 --> 00:26:16,280 Speaker 1: big point, right, because what we're learning with the arrival 416 00:26:16,280 --> 00:26:19,119 Speaker 1: of big data and the coalation methods and the smarter 417 00:26:19,200 --> 00:26:23,480 Speaker 1: and smarter and more sophisticated models for sorting all that crap, 418 00:26:24,000 --> 00:26:27,560 Speaker 1: is that this might one day be the deciding factor 419 00:26:27,840 --> 00:26:34,840 Speaker 1: for robotic or artificial intelligence superiority because they may just 420 00:26:34,920 --> 00:26:39,199 Speaker 1: be able to predict the future so much so that 421 00:26:39,240 --> 00:26:41,879 Speaker 1: it appears to be magic. Oh yeah, especially if you 422 00:26:41,920 --> 00:26:45,320 Speaker 1: have enough data point points on every single individual the 423 00:26:45,320 --> 00:26:48,320 Speaker 1: way we're beginning to gather data points on every human. 424 00:26:48,840 --> 00:26:51,960 Speaker 1: Just take the the new Apple watch that's coming out. 425 00:26:52,000 --> 00:26:53,879 Speaker 1: All the different data points that are gonna be gathered 426 00:26:53,920 --> 00:26:57,879 Speaker 1: on you. That's uh m hmm, I don't know, but 427 00:26:57,880 --> 00:27:00,760 Speaker 1: I like it. Matt. You are visibly a comfortable talking 428 00:27:00,760 --> 00:27:03,040 Speaker 1: about the Apple Watch. Let's go ahead, and you know, 429 00:27:03,080 --> 00:27:05,040 Speaker 1: at some point we need to bust the myth about 430 00:27:05,240 --> 00:27:08,760 Speaker 1: l O as well. What we'll see how that works 431 00:27:08,760 --> 00:27:12,200 Speaker 1: out forward to the future. That's where we're going. Will 432 00:27:12,480 --> 00:27:16,800 Speaker 1: Moore's law hold up anyone who's not familiar with Moore's law, 433 00:27:16,920 --> 00:27:22,000 Speaker 1: that's the one that states, uh, computer capacity will double 434 00:27:22,440 --> 00:27:28,120 Speaker 1: every eighteen months, and so it's exponential, right. It just 435 00:27:28,320 --> 00:27:30,800 Speaker 1: gets bigger and bigger and bigger and fast. And it 436 00:27:30,840 --> 00:27:34,120 Speaker 1: seems like it's getting faster and faster because it's doubling, right, 437 00:27:34,640 --> 00:27:38,800 Speaker 1: So will that happen with the growth of artificial intelligence? 438 00:27:38,840 --> 00:27:43,720 Speaker 1: I love that pixel example, because that robot teaching itself 439 00:27:43,880 --> 00:27:48,480 Speaker 1: right to navigate through this world based on pixel position. 440 00:27:48,800 --> 00:27:51,280 Speaker 1: It dies all the time. It starts off really slow 441 00:27:51,400 --> 00:27:56,879 Speaker 1: and then quickly becomes flawless, you know, god mode, video 442 00:27:56,920 --> 00:28:02,800 Speaker 1: game player, breakout and stuff. This leads us to um 443 00:28:02,840 --> 00:28:06,879 Speaker 1: an interesting comparison because a lot of stuff that we 444 00:28:06,920 --> 00:28:09,520 Speaker 1: read now you know, Matt, you and I. Originally we're 445 00:28:09,520 --> 00:28:11,639 Speaker 1: going to spend just a week on the stock market, 446 00:28:11,920 --> 00:28:14,919 Speaker 1: but because a high frequency trading, we made it two weeks. 447 00:28:15,440 --> 00:28:19,159 Speaker 1: And uh that that's something that we don't always do. 448 00:28:19,720 --> 00:28:23,359 Speaker 1: But what we discovered while we were looking into this question, 449 00:28:23,400 --> 00:28:26,520 Speaker 1: will robots run the stock market? Will they rule the world? 450 00:28:27,040 --> 00:28:32,280 Speaker 1: We found that what's much more likely to happen is 451 00:28:32,320 --> 00:28:35,720 Speaker 1: not just robots ruling the world, but some sort of 452 00:28:36,000 --> 00:28:41,560 Speaker 1: cybernetic symbiosis yeah, where an algorithm is run um somehow 453 00:28:41,680 --> 00:28:47,360 Speaker 1: by a human agent of some sort, but all the 454 00:28:47,400 --> 00:28:49,600 Speaker 1: bulk of the work will be done and kind of 455 00:28:49,600 --> 00:28:54,480 Speaker 1: already is right now, being done by math, by algorithms, 456 00:28:54,480 --> 00:28:56,520 Speaker 1: but a machine and a computer that just can do 457 00:28:56,560 --> 00:29:00,239 Speaker 1: it with such speed that a human never could, right, 458 00:29:00,320 --> 00:29:02,479 Speaker 1: And then having a human there as point person for 459 00:29:02,640 --> 00:29:06,920 Speaker 1: the decisions that robots can't do so well. Yet it's 460 00:29:07,040 --> 00:29:10,720 Speaker 1: it's uh, it's interesting because it reminds me of the 461 00:29:10,840 --> 00:29:14,719 Speaker 1: story about mitochondria, and there's a there's a really strange 462 00:29:14,720 --> 00:29:20,240 Speaker 1: thing that mitochondria can I guess, at least figuratively or anecdotally, uh, 463 00:29:20,360 --> 00:29:24,040 Speaker 1: tell us about the relationship between people and machines, or 464 00:29:24,080 --> 00:29:27,840 Speaker 1: the potential relationship with people machines in the future. Mitochondria, 465 00:29:28,480 --> 00:29:34,360 Speaker 1: according to some pretty compelling research, wasn't always part of 466 00:29:34,440 --> 00:29:40,000 Speaker 1: your cells or my cells. Originally mitochondria, argue several experts, 467 00:29:40,120 --> 00:29:43,600 Speaker 1: it was a free living bacteria and it just got 468 00:29:43,640 --> 00:29:47,400 Speaker 1: buddy Buddy with cells, So buddy, buddy, in fact that 469 00:29:47,520 --> 00:29:51,920 Speaker 1: now it's in all your cells, and it's a good thing. 470 00:29:51,960 --> 00:29:55,120 Speaker 1: It's there. It's spruced up the neighborhood, and it's providing 471 00:29:55,200 --> 00:29:59,440 Speaker 1: power for each year cells. It's not a freeloader. Right. Uh, 472 00:29:59,640 --> 00:30:02,280 Speaker 1: there's some biosis there. And I wonder how if it 473 00:30:02,360 --> 00:30:07,520 Speaker 1: all that can inform our ideas about how robots and 474 00:30:07,640 --> 00:30:11,320 Speaker 1: humans will interact. Will humans become the mitochondria in the cell? 475 00:30:12,080 --> 00:30:14,960 Speaker 1: Will robots? One day there's going to be a podcast 476 00:30:15,360 --> 00:30:20,000 Speaker 1: where there are two AI systems having a conversation together 477 00:30:20,400 --> 00:30:23,400 Speaker 1: and they're going to be going. Humans weren't always a 478 00:30:23,480 --> 00:30:27,600 Speaker 1: part of the robotic or I don't know, brains, um 479 00:30:27,680 --> 00:30:30,920 Speaker 1: neurons were not always a part of the robotic mother. 480 00:30:31,080 --> 00:30:34,320 Speaker 1: But now blah blah blah. Right, yeah, I'm sure. I 481 00:30:34,320 --> 00:30:37,920 Speaker 1: I hope that they still make the same bad jokes 482 00:30:37,920 --> 00:30:41,280 Speaker 1: that we do. Uh, but they're probably better than us 483 00:30:41,280 --> 00:30:46,040 Speaker 1: at bad jokes. They're highly calculated. They're highly calculated. Well, 484 00:30:46,080 --> 00:30:48,560 Speaker 1: we don't have that going on, and but what we 485 00:30:48,640 --> 00:30:52,440 Speaker 1: do have here is um an interesting thing that we 486 00:30:52,520 --> 00:30:55,120 Speaker 1: wanted just to take some more time to look at. 487 00:30:55,480 --> 00:30:59,600 Speaker 1: And I know that we haven't really been talking that 488 00:30:59,720 --> 00:31:04,960 Speaker 1: much about conspiracy theories right here, because what we're talking 489 00:31:04,960 --> 00:31:11,640 Speaker 1: about is very strange and surprisingly possible future where in 490 00:31:12,760 --> 00:31:17,960 Speaker 1: human evolution changes because we have the tools that we 491 00:31:18,040 --> 00:31:23,320 Speaker 1: have created, become dependent on This started dependent on us, 492 00:31:23,360 --> 00:31:26,320 Speaker 1: but then we become a new thing together. To me, 493 00:31:26,440 --> 00:31:29,480 Speaker 1: that is mind blowing in a little bit frightening, and 494 00:31:29,560 --> 00:31:35,920 Speaker 1: it brings us to uh today's news story? Oh yeah, yeah, 495 00:31:35,960 --> 00:31:42,440 Speaker 1: can we get some news story music. A new study 496 00:31:42,560 --> 00:31:45,000 Speaker 1: suggests that there may be a small amount of life 497 00:31:45,040 --> 00:31:48,480 Speaker 1: after death, now, Matt. This comes via the Telegraph. They 498 00:31:48,520 --> 00:31:52,360 Speaker 1: reported that the University of Southampton examined over two thousand 499 00:31:52,360 --> 00:31:55,920 Speaker 1: people who suffered cardiac arrest in the United Kingdom, the 500 00:31:56,000 --> 00:32:00,959 Speaker 1: US and Austria. People that were studied death experiences and 501 00:32:00,960 --> 00:32:04,120 Speaker 1: if you had near death experiences three thirty They interviewed 502 00:32:04,120 --> 00:32:06,280 Speaker 1: a hundred and forty of them and what they found 503 00:32:06,440 --> 00:32:10,920 Speaker 1: was that these people describe some kind of awareness during 504 00:32:10,960 --> 00:32:14,000 Speaker 1: the time they were clinically dead and before their hearts 505 00:32:14,240 --> 00:32:18,360 Speaker 1: had restarted. And here's the craziest part. One of the 506 00:32:18,400 --> 00:32:22,960 Speaker 1: patients recalled leaving his body watching his resuscitation from the 507 00:32:23,000 --> 00:32:26,800 Speaker 1: corner of the room and described the stuff accurately after 508 00:32:27,640 --> 00:32:32,280 Speaker 1: his brain activity had ceased, and they resuscitate him. Brain 509 00:32:32,400 --> 00:32:35,520 Speaker 1: kicks back in, heart kicks back in. It's a miracle. 510 00:32:36,200 --> 00:32:41,240 Speaker 1: And this stuff was apparently independently verified, this enormously controversial study. 511 00:32:41,280 --> 00:32:43,600 Speaker 1: We just got news about it today. But Matt, what 512 00:32:43,720 --> 00:32:50,960 Speaker 1: if there's life after death? H great, awesome, Yea, finally 513 00:32:51,720 --> 00:32:53,680 Speaker 1: I've been wanting to know the answer to that question 514 00:32:53,920 --> 00:32:58,640 Speaker 1: my whole life, ever since I came into this weird dimension. 515 00:33:01,360 --> 00:33:06,360 Speaker 1: Are you a robot? Well? No, no, no, all right, no, alright, 516 00:33:06,480 --> 00:33:09,160 Speaker 1: I've got an eye on you here. Uh. You know, 517 00:33:09,240 --> 00:33:12,200 Speaker 1: Noel made an interesting comparison about this too earlier when 518 00:33:12,240 --> 00:33:15,000 Speaker 1: he said, uh, when he said, maybe it's similar to 519 00:33:15,520 --> 00:33:18,760 Speaker 1: being decapitated, right, with the idea that you still have 520 00:33:18,880 --> 00:33:21,800 Speaker 1: some sort of consciousness for time after your head is 521 00:33:21,840 --> 00:33:25,960 Speaker 1: lopped off. Maybe this is some sort of intangible version 522 00:33:26,000 --> 00:33:29,640 Speaker 1: of that, right, Yeah, But with no brain activity, it 523 00:33:29,760 --> 00:33:33,000 Speaker 1: just seems impossible. And we mentioned this when we were 524 00:33:33,000 --> 00:33:35,880 Speaker 1: talking earlier. What did you say It was two percent 525 00:33:36,080 --> 00:33:40,120 Speaker 1: of the people could accurately Yeah, yeah, two percent. To me, 526 00:33:41,400 --> 00:33:44,320 Speaker 1: I'm gonna be skeptical at this point. Okay, two percent 527 00:33:44,400 --> 00:33:48,479 Speaker 1: had what they would call vivid recollection. Okay, Okay, wow, 528 00:33:48,520 --> 00:33:51,000 Speaker 1: that's really interesting. But I guess just what I was 529 00:33:51,040 --> 00:33:53,080 Speaker 1: saying earlier was that you know a lot of people 530 00:33:53,200 --> 00:33:56,640 Speaker 1: watch stuff on television where you kind of see what 531 00:33:57,280 --> 00:34:01,880 Speaker 1: resuscitation looks like. Good point clear. Yeah, so maybe you 532 00:34:02,040 --> 00:34:08,680 Speaker 1: could accurately with quotes there. That's an interesting word. Accurately um, 533 00:34:08,719 --> 00:34:10,680 Speaker 1: But yeah, I mean maybe you would just know what 534 00:34:10,719 --> 00:34:15,279 Speaker 1: it looked like. But still that they had that memory 535 00:34:15,560 --> 00:34:18,880 Speaker 1: if if they're being truthful, um, because as we know, 536 00:34:19,000 --> 00:34:24,520 Speaker 1: humans can lie. Um, that's amazing dude. And what do 537 00:34:24,560 --> 00:34:27,560 Speaker 1: you think, listeners? Let us know about this latest piece 538 00:34:27,600 --> 00:34:30,160 Speaker 1: of news. Is it bunk? Does it add up? Will 539 00:34:30,200 --> 00:34:33,680 Speaker 1: it change the world or just make a good week 540 00:34:33,760 --> 00:34:40,040 Speaker 1: for the papers reporting it, which both are possibilities. So hey, yeah, 541 00:34:40,200 --> 00:34:42,399 Speaker 1: we heard what some of you said, and we've got 542 00:34:42,400 --> 00:34:44,279 Speaker 1: a particular piece of mail that we'd like to read 543 00:34:44,320 --> 00:34:51,920 Speaker 1: really fast, if that's cool with you. This message comes 544 00:34:51,960 --> 00:34:55,759 Speaker 1: from Dan c. He wrote to us via email. Thanks Dan, 545 00:34:56,160 --> 00:34:59,759 Speaker 1: He says, Hi, I recently discovered your podcast two weeks 546 00:34:59,800 --> 00:35:02,319 Speaker 1: ago and I love it. I decided to write to 547 00:35:02,320 --> 00:35:05,480 Speaker 1: you for two reasons. The first is to thank you 548 00:35:05,520 --> 00:35:08,439 Speaker 1: for all the great shows. You're welcome, sir, and thank 549 00:35:08,480 --> 00:35:10,640 Speaker 1: you for thinking they're great. I mean, that's really kind. 550 00:35:10,760 --> 00:35:15,840 Speaker 1: We have our ups and downs, that's how we view it, 551 00:35:16,000 --> 00:35:19,680 Speaker 1: but we appreciate it very much, so Dan. The second 552 00:35:19,760 --> 00:35:22,040 Speaker 1: is to ask you if you're interested in a true 553 00:35:22,080 --> 00:35:26,160 Speaker 1: story about an astral projection experiment that went wrong and 554 00:35:26,200 --> 00:35:28,279 Speaker 1: some of the conclusions that can be drawn from that. 555 00:35:28,719 --> 00:35:31,320 Speaker 1: And he puts in here just as a caveat, no 556 00:35:31,440 --> 00:35:36,120 Speaker 1: drugs were used during the experiment. Well, Dan, I'm certainly interested. Ben, 557 00:35:36,160 --> 00:35:39,400 Speaker 1: are you interested? I'm into it? But what don't you 558 00:35:39,400 --> 00:35:41,720 Speaker 1: think we should check with the listeners? Yeah? Hey, guys, 559 00:35:42,000 --> 00:35:45,160 Speaker 1: do you want to know more about Dan's experiment that 560 00:35:45,239 --> 00:35:48,200 Speaker 1: went wrong with astral projection? Have you, guys, ever tried 561 00:35:48,239 --> 00:35:52,520 Speaker 1: astral projection? Um? I would admit here that I have 562 00:35:52,719 --> 00:35:57,480 Speaker 1: attempted UH several times, along with some lucid dreaming experiments 563 00:35:57,960 --> 00:36:00,600 Speaker 1: that didn't go wrong, but they just weren't are effective 564 00:36:00,600 --> 00:36:04,480 Speaker 1: for me. And that's the end of this classic episode. 565 00:36:04,600 --> 00:36:08,440 Speaker 1: If you have any thoughts or questions about this episode, 566 00:36:08,800 --> 00:36:10,879 Speaker 1: you can get into contact with us in a number 567 00:36:10,920 --> 00:36:12,880 Speaker 1: of different ways. One of the best is to give 568 00:36:12,960 --> 00:36:15,560 Speaker 1: us a call. Our number is one eight three three 569 00:36:15,840 --> 00:36:18,719 Speaker 1: std w y t K. If you don't want to 570 00:36:18,760 --> 00:36:21,360 Speaker 1: do that, you can send us a good old fashioned email. 571 00:36:21,600 --> 00:36:26,240 Speaker 1: We are conspiracy at i heart radio dot com. Stuff 572 00:36:26,280 --> 00:36:28,160 Speaker 1: they don't want you to know is a production of 573 00:36:28,239 --> 00:36:31,200 Speaker 1: I heart Radio. For more podcasts from my heart Radio, 574 00:36:31,320 --> 00:36:34,160 Speaker 1: visit the i heart radio app, Apple Podcasts, or wherever 575 00:36:34,239 --> 00:36:35,560 Speaker 1: you listen to your favorite shows.