1 00:00:01,000 --> 00:00:04,360 Speaker 1: From UFOs two, ghosts and government cover ups. History is 2 00:00:04,360 --> 00:00:08,000 Speaker 1: writtled with unexplained events. You can turn back now or 3 00:00:08,119 --> 00:00:15,840 Speaker 1: learn the stuff they don't want you to. Now, let's 4 00:00:15,920 --> 00:00:18,160 Speaker 1: kick this pig. Welcome back to the show. My name 5 00:00:18,200 --> 00:00:21,640 Speaker 1: is Matt and I am Ben, and this is stuff 6 00:00:21,640 --> 00:00:25,239 Speaker 1: they don't want you to know. And today we're talking 7 00:00:25,239 --> 00:00:28,720 Speaker 1: about robots, y'all. Yeah, let's go back to uh something 8 00:00:28,800 --> 00:00:32,600 Speaker 1: that listeners are probably asking themselves about. Though, Matt, what what? 9 00:00:32,800 --> 00:00:36,000 Speaker 1: There's probably a couple of you out there in uh 10 00:00:36,240 --> 00:00:39,280 Speaker 1: podcast land who say, wait, did Matt just start this 11 00:00:39,360 --> 00:00:42,959 Speaker 1: episode by saying kick this pig? Yeah, you gotta kick 12 00:00:43,000 --> 00:00:45,440 Speaker 1: the pig and get it started. Man. Once the pig 13 00:00:45,479 --> 00:00:48,239 Speaker 1: stars rolling, uh, you know, it's covered with grease, so 14 00:00:48,280 --> 00:00:50,080 Speaker 1: you have to first catch up to it, which is 15 00:00:50,120 --> 00:00:52,800 Speaker 1: incredibly difficult, and then cling your body on top of 16 00:00:52,800 --> 00:00:56,200 Speaker 1: it in hopes of perhaps wrangling it. I get the 17 00:00:56,240 --> 00:00:59,120 Speaker 1: feeling that you and are super producer Noel shout out 18 00:00:59,120 --> 00:01:01,640 Speaker 1: to Noel over there. Uh. I get the feeling that 19 00:01:01,680 --> 00:01:04,200 Speaker 1: you guys were maybe talking about something else off air 20 00:01:04,440 --> 00:01:07,760 Speaker 1: or is now is kicked the pig a thing? We say? Now? Yeah, 21 00:01:07,840 --> 00:01:10,760 Speaker 1: that's just that's how we start the podcast, Ben, I've 22 00:01:10,800 --> 00:01:13,160 Speaker 1: been doing it. Have you not noticed? We do it 23 00:01:13,240 --> 00:01:17,479 Speaker 1: every week? Perhaps it's my human frailties, Matt. Maybe that's 24 00:01:17,520 --> 00:01:22,480 Speaker 1: why I'm Maybe I'm slipping a little. You know, maybe 25 00:01:23,080 --> 00:01:31,120 Speaker 1: we should be replaced with robots. No, I hope not. 26 00:01:31,360 --> 00:01:35,560 Speaker 1: I mean Null's human. You're human. I'm human. Most, but 27 00:01:35,680 --> 00:01:37,840 Speaker 1: not all of our listeners are human. Yeah. I do 28 00:01:37,880 --> 00:01:40,720 Speaker 1: seem to recall an email from a few listeners that 29 00:01:40,880 --> 00:01:43,920 Speaker 1: I don't know. It didn't come across as human. Yeah, 30 00:01:44,240 --> 00:01:46,680 Speaker 1: especially you know if you point out that you are 31 00:01:46,760 --> 00:01:49,160 Speaker 1: writing in a very human way, then that makes you 32 00:01:49,200 --> 00:01:52,040 Speaker 1: sound even less human. Guys, it's right up. There was 33 00:01:52,080 --> 00:01:54,960 Speaker 1: starting a sentence with the phrase not to be creepy, 34 00:01:55,040 --> 00:01:58,400 Speaker 1: but oh, that's the worst. I do it all the time. 35 00:01:58,840 --> 00:02:01,840 Speaker 1: I mean people, some people are really into creeping, I guess, 36 00:02:01,840 --> 00:02:04,160 Speaker 1: and creeptitude. I do that an elevator. By the way, 37 00:02:04,200 --> 00:02:05,440 Speaker 1: you do that in the elevator, how do you do 38 00:02:05,680 --> 00:02:08,800 Speaker 1: if there's just one other person, I'll say something like that, 39 00:02:08,960 --> 00:02:12,520 Speaker 1: not to be creepy, but I really like your shoes 40 00:02:18,280 --> 00:02:25,079 Speaker 1: mostly mostly dudes too. You're going to try it out 41 00:02:25,080 --> 00:02:28,800 Speaker 1: when I leave today. So what what we are talking about? Matt, 42 00:02:28,840 --> 00:02:32,760 Speaker 1: as you said, it's the idea of robots and the 43 00:02:32,840 --> 00:02:37,760 Speaker 1: idea that there could be a situation which robots rule 44 00:02:37,840 --> 00:02:41,480 Speaker 1: the world. Ladies and gentlemen. We're joking mostly, but it 45 00:02:41,760 --> 00:02:46,440 Speaker 1: is true that, uh, Matt and I Noel. Certainly, anybody 46 00:02:46,480 --> 00:02:50,400 Speaker 1: who makes a podcast probably does have an audience that 47 00:02:50,560 --> 00:02:55,400 Speaker 1: is not entirely human, because there are programs that will 48 00:02:55,480 --> 00:03:00,079 Speaker 1: listen to your podcast for verification purposes, for share and 49 00:03:00,120 --> 00:03:04,360 Speaker 1: purposes things like that. Um, we haven't to our knowledge 50 00:03:04,360 --> 00:03:07,359 Speaker 1: received an email or a tweet from a robot yet, 51 00:03:07,639 --> 00:03:10,079 Speaker 1: not yet. And the same goes for video as well, 52 00:03:10,680 --> 00:03:13,040 Speaker 1: just checking to see if your content matches any of 53 00:03:13,040 --> 00:03:17,160 Speaker 1: these other things. Uh, it's kind of interesting how much 54 00:03:17,200 --> 00:03:21,120 Speaker 1: automation is going on from It's really weird to think 55 00:03:21,120 --> 00:03:23,919 Speaker 1: of it from a listening and viewing standpoint that something 56 00:03:24,040 --> 00:03:30,120 Speaker 1: is checking watching or that is not human. That the 57 00:03:30,160 --> 00:03:34,000 Speaker 1: old hal nine thousand. I'm sorry that I can't let 58 00:03:34,000 --> 00:03:37,080 Speaker 1: you do that, and I cannot publish this episode where 59 00:03:37,120 --> 00:03:40,960 Speaker 1: it contains a scene from the Matrix. So today we're 60 00:03:41,000 --> 00:03:44,000 Speaker 1: gonna yeah, that's that's happened to us, folks. So today 61 00:03:44,080 --> 00:03:48,400 Speaker 1: we're going to look at the idea of robots running 62 00:03:48,440 --> 00:03:51,600 Speaker 1: the world, and to to get to the present day world, 63 00:03:51,880 --> 00:03:55,800 Speaker 1: we have to start with the origin of the idea 64 00:03:55,920 --> 00:03:58,480 Speaker 1: of robots, right, that's right. Then it goes all the 65 00:03:58,520 --> 00:04:01,440 Speaker 1: way back to the ancient world, back to two hundred 66 00:04:01,440 --> 00:04:08,000 Speaker 1: and seventy b C. When a Greek engineer named Testimus. Yeah, 67 00:04:08,120 --> 00:04:10,120 Speaker 1: I don't I. I had to take a guess of 68 00:04:10,160 --> 00:04:13,960 Speaker 1: that one as well. To sibious. Sibious that's it uh 69 00:04:14,080 --> 00:04:17,120 Speaker 1: c T E s I b I U s uh. 70 00:04:17,200 --> 00:04:20,599 Speaker 1: He made these organs of that were made with water 71 00:04:20,960 --> 00:04:24,279 Speaker 1: and clocks and these movable figurines. You can see some 72 00:04:24,360 --> 00:04:27,720 Speaker 1: of this stuff, uh if you look it up online. Right. 73 00:04:27,800 --> 00:04:30,919 Speaker 1: We know that humans have been able to build complex 74 00:04:31,000 --> 00:04:34,440 Speaker 1: movie machines for thousands of years. A great example would 75 00:04:34,440 --> 00:04:38,840 Speaker 1: be the anti Cathera mechanism. Oh yeah, man that thinks fascinating, 76 00:04:39,200 --> 00:04:45,120 Speaker 1: isn't weird? It's uh this astronomical calculating machine, you know, 77 00:04:45,320 --> 00:04:49,240 Speaker 1: entirely mechanical, uh, quite complex from what we can figure 78 00:04:49,480 --> 00:04:53,960 Speaker 1: in the reconstructions, right that people have attempted to do. So, 79 00:04:54,120 --> 00:04:59,200 Speaker 1: we know that ancient man was not just smack and 80 00:04:59,360 --> 00:05:03,760 Speaker 1: rocks together there and making up stories about why the 81 00:05:03,800 --> 00:05:07,520 Speaker 1: sun sets. We know that ancient man was capable of 82 00:05:07,560 --> 00:05:12,599 Speaker 1: building sophisticated UH machines that a lot of people today 83 00:05:12,640 --> 00:05:18,159 Speaker 1: couldn't build. And we had something like robots philosophically before 84 00:05:18,160 --> 00:05:22,040 Speaker 1: we had the word robot. Uh. In the notes, I 85 00:05:22,040 --> 00:05:23,720 Speaker 1: I did something kind of crappy here, and I put 86 00:05:23,960 --> 00:05:26,920 Speaker 1: many of the ancient know it alls called the concept 87 00:05:27,000 --> 00:05:32,040 Speaker 1: automata automata which means just self operating machine. Oh yeah, 88 00:05:32,080 --> 00:05:36,320 Speaker 1: and they were hugely popular. The hero of Alexandria was 89 00:05:36,440 --> 00:05:40,000 Speaker 1: famous for his part um for working in this field. 90 00:05:40,440 --> 00:05:43,480 Speaker 1: And again, if you go online check look up the 91 00:05:43,520 --> 00:05:46,880 Speaker 1: word automata. It's just it's just auto and then M 92 00:05:46,920 --> 00:05:49,680 Speaker 1: A T A. And you can see some of these 93 00:05:49,720 --> 00:05:52,520 Speaker 1: old they are machines, and some of the them are 94 00:05:52,600 --> 00:05:56,719 Speaker 1: so elaborate. Sure, like the court machines where they would 95 00:05:56,800 --> 00:06:00,560 Speaker 1: have someone who appeared to be pouring tea or play 96 00:06:00,680 --> 00:06:03,640 Speaker 1: a violin. Some of those are still around, and I 97 00:06:03,680 --> 00:06:06,400 Speaker 1: think they're creepy. You know, that's what you say in 98 00:06:06,440 --> 00:06:08,800 Speaker 1: the elevator, right, you say not to be creepy, but 99 00:06:09,000 --> 00:06:12,360 Speaker 1: may I show you my automaton? Oh yeah, I wouldn't 100 00:06:12,360 --> 00:06:15,760 Speaker 1: want to do that. Yeah. Plus it sounds gonn a euphemism, right, 101 00:06:15,800 --> 00:06:18,680 Speaker 1: in the Hero of Alexandria. Wasn't the only person doing this. 102 00:06:18,760 --> 00:06:23,200 Speaker 1: There were also people like the Greek mathematician Architas of Tarentum, 103 00:06:23,400 --> 00:06:29,640 Speaker 1: which I am probably grossly mispronouncing. Um, if I recall correctly, 104 00:06:30,120 --> 00:06:35,160 Speaker 1: he built a little mechanical bird. Oh, excellent. Another example 105 00:06:35,160 --> 00:06:38,440 Speaker 1: of automata would be do you remember the old Clash 106 00:06:38,480 --> 00:06:41,200 Speaker 1: of the Titans films with the claymation and stuff in 107 00:06:41,200 --> 00:06:44,400 Speaker 1: the seventies. Um? Was it? Was it that one? Or 108 00:06:44,520 --> 00:06:46,160 Speaker 1: it was one of the Greek films where they had 109 00:06:46,279 --> 00:06:51,000 Speaker 1: this magic golden owl. Oh, I know what you're talking 110 00:06:51,080 --> 00:06:53,719 Speaker 1: to that guy? Yes, And I can't recall if it's 111 00:06:53,720 --> 00:06:58,279 Speaker 1: that film. Oh man, that was a great that's a 112 00:06:58,279 --> 00:07:01,000 Speaker 1: great example of the idea of what it would have 113 00:07:01,120 --> 00:07:03,680 Speaker 1: looked like. Um. So, I don't know if you've noticed, 114 00:07:03,760 --> 00:07:06,200 Speaker 1: but we haven't up until this point used the word 115 00:07:06,279 --> 00:07:09,279 Speaker 1: robot in our research, and that's because it was first used, 116 00:07:09,760 --> 00:07:13,080 Speaker 1: uh in nineteen twenty one in this play called Rosa's 117 00:07:13,120 --> 00:07:16,440 Speaker 1: Universal Robots or Are You Are by a check writer 118 00:07:16,600 --> 00:07:20,360 Speaker 1: named Carol Kapok. Then in nineteen forty one Isaac Asimov 119 00:07:20,480 --> 00:07:23,600 Speaker 1: he used the word robotics to describe this kind of 120 00:07:23,680 --> 00:07:26,520 Speaker 1: same technology, and he is one of the guys that 121 00:07:26,600 --> 00:07:31,240 Speaker 1: predicted the rise of this robotic industry the world in 122 00:07:31,240 --> 00:07:34,320 Speaker 1: which we live today. It's it's weird, met because we've 123 00:07:34,400 --> 00:07:36,880 Speaker 1: often talked about, Um, I don't know if any of 124 00:07:36,920 --> 00:07:39,119 Speaker 1: this made it on the air, but we've often talked 125 00:07:39,120 --> 00:07:43,680 Speaker 1: about how much influenced these visionaries, these futurists or science 126 00:07:43,680 --> 00:07:46,880 Speaker 1: fiction writers can end up having on the world around us. Uh. 127 00:07:47,080 --> 00:07:51,080 Speaker 1: Funny fact, ladies and gentlemen, that not once, but multiple 128 00:07:51,160 --> 00:07:54,400 Speaker 1: times in US history, the US government has contacted science 129 00:07:54,440 --> 00:08:02,160 Speaker 1: fiction writers and said, guys, here's your security clearance. Uh hypothetically, 130 00:08:03,400 --> 00:08:07,680 Speaker 1: hypothetically if a happened, what would we do? Oh? Yeah, 131 00:08:07,720 --> 00:08:10,600 Speaker 1: sure you need the visionary. You can't You can't accomplish 132 00:08:10,600 --> 00:08:14,120 Speaker 1: anything unless you have someone thinking of the ideas. Yeah, 133 00:08:14,200 --> 00:08:17,640 Speaker 1: and as mov as it turns out, was right he 134 00:08:17,760 --> 00:08:22,160 Speaker 1: called it. And we live in a real life version 135 00:08:22,480 --> 00:08:29,000 Speaker 1: of some science fiction e stories. Right. We have robots everywhere, 136 00:08:29,080 --> 00:08:33,840 Speaker 1: not as widespread as advertisements yet, but on a widely 137 00:08:34,160 --> 00:08:38,680 Speaker 1: differing scales of robotics, from the very very simple to 138 00:08:39,320 --> 00:08:43,560 Speaker 1: the extremely complex. But robots, if you're going to be 139 00:08:43,600 --> 00:08:45,520 Speaker 1: called a robot, you've got to have a few things 140 00:08:45,840 --> 00:08:48,680 Speaker 1: in common or a few things that make you a robot. 141 00:08:48,760 --> 00:08:52,520 Speaker 1: Oh yeah, yeah, like they all have what what's an example, Well, 142 00:08:52,520 --> 00:08:55,880 Speaker 1: you gotta have some kind of moving apparatus, moving body, 143 00:08:56,480 --> 00:09:00,960 Speaker 1: stints of articulation and things like that. Yeah. And the 144 00:09:01,040 --> 00:09:04,520 Speaker 1: second main thing is some kind of power source so 145 00:09:04,559 --> 00:09:07,439 Speaker 1: that whatever it is, whatever you are as a robot, 146 00:09:07,840 --> 00:09:12,960 Speaker 1: can function by yourself. That makes sense too. Nope, when 147 00:09:13,080 --> 00:09:15,679 Speaker 1: we talk about robotics and when we talk about robots 148 00:09:15,760 --> 00:09:21,360 Speaker 1: running the world, what we're really talking about is artificial intelligence. Right. Um, 149 00:09:21,520 --> 00:09:26,120 Speaker 1: this this is the idea of a sentience of some 150 00:09:26,200 --> 00:09:31,119 Speaker 1: sort made by human beings or maybe just made by 151 00:09:31,320 --> 00:09:36,760 Speaker 1: throwing a bunch of math together in one conceptual pool, 152 00:09:36,920 --> 00:09:39,160 Speaker 1: the same way that a bunch of proteins or amino 153 00:09:39,240 --> 00:09:42,320 Speaker 1: acids or whatever we're thrown together in some primordial ooze, 154 00:09:42,800 --> 00:09:46,920 Speaker 1: and that from this somehow will arrive a version of 155 00:09:47,000 --> 00:09:50,760 Speaker 1: life self awareness, and that that is the key. Right 156 00:09:51,600 --> 00:09:55,160 Speaker 1: At what point the scary thing is when the machine goes, 157 00:09:55,200 --> 00:09:58,720 Speaker 1: oh I'm a machine and I can do X. Yeah, 158 00:09:58,960 --> 00:10:01,200 Speaker 1: Or there was a st worry that you and I 159 00:10:01,320 --> 00:10:04,640 Speaker 1: talked about, which there's a spoiler alert. Um, there's a 160 00:10:04,679 --> 00:10:09,760 Speaker 1: moment in this story where somebody's searching on the net 161 00:10:09,800 --> 00:10:16,160 Speaker 1: for something, and instead of asking if she spelled it correctly, 162 00:10:16,720 --> 00:10:20,680 Speaker 1: the the thing on the other side of the search 163 00:10:20,760 --> 00:10:25,080 Speaker 1: engine asked her why she she's searching for it. Yeah. 164 00:10:25,480 --> 00:10:27,960 Speaker 1: Uh yeah, that one's a little weird. We like sci 165 00:10:28,000 --> 00:10:30,960 Speaker 1: fi as well too, But so what what we know 166 00:10:31,120 --> 00:10:36,359 Speaker 1: here is that in some ways artificial intelligence can already 167 00:10:36,440 --> 00:10:41,920 Speaker 1: outthink human beings, but not in always. The way that 168 00:10:41,960 --> 00:10:44,640 Speaker 1: a root robot would end up ruling the world quote 169 00:10:44,679 --> 00:10:48,839 Speaker 1: unquote is if there were AI of some sort that 170 00:10:49,120 --> 00:10:56,800 Speaker 1: was across the board outperforming human beings bar none. And Matt, 171 00:10:56,880 --> 00:11:00,160 Speaker 1: let's talk about what robots in their less poor real 172 00:11:00,280 --> 00:11:05,200 Speaker 1: sibling artificial intelligence do today. But first we have a 173 00:11:05,200 --> 00:11:08,400 Speaker 1: word from our sponsor, and this one is for all 174 00:11:08,520 --> 00:11:16,040 Speaker 1: you mechanical listeners out there. One zero zero one zero 175 00:11:16,160 --> 00:11:21,959 Speaker 1: zero zero one one one zero, one zero, one zero, 176 00:11:22,000 --> 00:11:27,120 Speaker 1: one zero zero zero, one one one zero zero zero, 177 00:11:27,200 --> 00:11:32,720 Speaker 1: one zero, one zero, one zero, one zero, zero, one zero, 178 00:11:32,800 --> 00:11:36,680 Speaker 1: one one one one zero zero, one one zero, one 179 00:11:36,760 --> 00:11:42,560 Speaker 1: one one zero, one zero, one one zero, one zero, 180 00:11:43,640 --> 00:11:45,439 Speaker 1: one zero, one zero, one one ze one one two 181 00:11:45,559 --> 00:11:47,319 Speaker 1: zero one zero one one one one zero z e 182 00:11:47,360 --> 00:11:49,320 Speaker 1: one one one zero one one one zero zero zero 183 00:11:49,400 --> 00:11:51,679 Speaker 1: one one one one zero one zero one one one 184 00:11:51,760 --> 00:11:55,640 Speaker 1: zero one zero one zero zero zero A division of 185 00:11:55,679 --> 00:12:04,079 Speaker 1: Illumination Global Unlimited. Huh, you know it's uh, it's weird. 186 00:12:04,160 --> 00:12:07,480 Speaker 1: It's not for our demographic. I guess, yeah, I something 187 00:12:07,600 --> 00:12:11,240 Speaker 1: was lost in translation for me at least. Um, yeah, 188 00:12:11,240 --> 00:12:13,600 Speaker 1: somebody got something out of it. Sure, yeah. Was it 189 00:12:13,640 --> 00:12:17,520 Speaker 1: a product? The computer got really excited? You know. I 190 00:12:17,559 --> 00:12:19,640 Speaker 1: think it was because it sounded like that was legally 191 00:12:19,640 --> 00:12:21,560 Speaker 1: eads at the end. That's what I thought I heard. 192 00:12:22,280 --> 00:12:25,599 Speaker 1: At least it went really fast. Well, Illumination Global Unlimited 193 00:12:25,640 --> 00:12:28,840 Speaker 1: has rarely led us wrong, so we'll just have to 194 00:12:28,920 --> 00:12:32,040 Speaker 1: trust them on that one. Uh. And now that we're back, 195 00:12:32,120 --> 00:12:35,000 Speaker 1: ladies and gentlemen, it's time for us to talk about 196 00:12:35,520 --> 00:12:41,280 Speaker 1: robots today. What did they actually do? Well? Probably more 197 00:12:41,280 --> 00:12:45,319 Speaker 1: than you would expect. Well, maybe let's talk about a 198 00:12:45,360 --> 00:12:49,520 Speaker 1: couple of things. How about housekeeping, house cleaning? Who cleans 199 00:12:49,559 --> 00:12:52,000 Speaker 1: up your house when you leave? Oh wait, it's my 200 00:12:52,040 --> 00:12:54,600 Speaker 1: little room. Ba oh man, I love room bas I 201 00:12:54,640 --> 00:12:57,120 Speaker 1: want one so bad and I want to hack it 202 00:12:57,160 --> 00:13:00,960 Speaker 1: to some remote controls and then strap stuff to dude. 203 00:13:01,160 --> 00:13:04,199 Speaker 1: You know, I see, I have two dogs and I'm 204 00:13:04,280 --> 00:13:07,319 Speaker 1: worried about how they would react to a room by 205 00:13:07,360 --> 00:13:10,000 Speaker 1: going around during the day when I'm not home. That's right. 206 00:13:10,559 --> 00:13:12,800 Speaker 1: I'm afraid I would come home and find a dead room. 207 00:13:12,840 --> 00:13:16,280 Speaker 1: Ba uh. But I don't know, maybe not. Maybe they 208 00:13:16,320 --> 00:13:19,560 Speaker 1: make a extreme room bus or maybe they form an 209 00:13:19,559 --> 00:13:24,040 Speaker 1: alliance and then they just my smaller dog just rids 210 00:13:24,080 --> 00:13:27,240 Speaker 1: in the room but around. Which, like our mention of 211 00:13:27,679 --> 00:13:31,320 Speaker 1: the mathematician who is building automata in the ancient world, 212 00:13:31,840 --> 00:13:35,120 Speaker 1: that reference with animals and machines working together is going 213 00:13:35,160 --> 00:13:37,920 Speaker 1: to come back into play. But we also know, of course, 214 00:13:37,960 --> 00:13:42,000 Speaker 1: everybody knows that robots are huge and manufacturing. Oh yeah, 215 00:13:42,080 --> 00:13:47,040 Speaker 1: they're making all your big things, all your cars. I'm 216 00:13:47,040 --> 00:13:51,920 Speaker 1: trying to think of something else, in particular doors. I 217 00:13:51,960 --> 00:13:55,960 Speaker 1: would say that door frames, a lot of woodworking has 218 00:13:56,040 --> 00:14:01,720 Speaker 1: some kind of mechanical process to it. High end electronics 219 00:14:01,760 --> 00:14:05,320 Speaker 1: as well. Uh. There are other places where we use 220 00:14:05,440 --> 00:14:09,560 Speaker 1: robots because they are dangerous. Uh, they're dangerous situations for 221 00:14:09,679 --> 00:14:12,800 Speaker 1: human being. That would be something like bomb disposal. Oh yeah, 222 00:14:12,800 --> 00:14:15,360 Speaker 1: you've seen You've seen those. And even swat teams now 223 00:14:15,480 --> 00:14:18,440 Speaker 1: in the US have robots. Some of them do which 224 00:14:18,520 --> 00:14:21,400 Speaker 1: can they Admittedly they look kind of goofy, but they 225 00:14:21,400 --> 00:14:23,840 Speaker 1: get the job done. Yeah, they're not built to look 226 00:14:23,880 --> 00:14:27,600 Speaker 1: cool while they're doing. Yeah, you just replace an arm 227 00:14:27,680 --> 00:14:29,560 Speaker 1: if one of them gets shot off. We also use 228 00:14:29,680 --> 00:14:33,240 Speaker 1: robots for navigation, or maybe that's more likely that we 229 00:14:33,360 --> 00:14:36,960 Speaker 1: use AI for sure of some sort. Yeah, I remember that. 230 00:14:37,000 --> 00:14:40,840 Speaker 1: We're talking about both of those together and sometimes separately 231 00:14:40,920 --> 00:14:44,960 Speaker 1: in this episode. Then you've got unmanned vehicles, which are 232 00:14:45,320 --> 00:14:49,080 Speaker 1: a fun combination of both. In some cases, usually it's 233 00:14:49,120 --> 00:14:53,160 Speaker 1: just an automated machine that's then controlled by human like 234 00:14:53,280 --> 00:14:56,760 Speaker 1: the uh well, not like that one. I was gonna say, 235 00:14:56,760 --> 00:15:00,120 Speaker 1: like the Predator drones or you know, something like the 236 00:15:00,240 --> 00:15:04,160 Speaker 1: unmanned aerial vehicles. Yeah, which is I guess in a 237 00:15:04,160 --> 00:15:07,560 Speaker 1: way of robot. It's got a moving missile system. It's 238 00:15:07,600 --> 00:15:09,800 Speaker 1: not quite the same. Well yeah, but it's it's got 239 00:15:09,800 --> 00:15:13,280 Speaker 1: an engine and it has a computer on board capable 240 00:15:13,320 --> 00:15:17,000 Speaker 1: of making calculations. Uh, it has moving parts. So but 241 00:15:17,040 --> 00:15:19,760 Speaker 1: then you've got things like that that have AI in them, 242 00:15:20,680 --> 00:15:24,560 Speaker 1: like the X thirty seven B that we've talked about before, 243 00:15:24,920 --> 00:15:30,359 Speaker 1: the automated Space Shuttle. It's uh, it's self controlled, it's automated. 244 00:15:30,920 --> 00:15:36,680 Speaker 1: Um uh uh yeah. But then of course, the drones 245 00:15:36,720 --> 00:15:39,920 Speaker 1: that we spoke about. I gotta say I love the 246 00:15:39,960 --> 00:15:43,040 Speaker 1: idea of the X thirty seven B. What's it doing 247 00:15:43,120 --> 00:15:45,520 Speaker 1: up there? You know? It's like the It's like when 248 00:15:45,520 --> 00:15:49,760 Speaker 1: we found out about the Corona satellite program and we started. 249 00:15:49,840 --> 00:15:52,720 Speaker 1: You guys, we didn't get in trouble for this one, 250 00:15:52,800 --> 00:15:55,120 Speaker 1: but we weren't sure if we would get away clean 251 00:15:55,240 --> 00:15:59,800 Speaker 1: because Matt and I of a while back, we found 252 00:16:00,120 --> 00:16:05,600 Speaker 1: some evidence that you can teach yourself to track spy satellites, 253 00:16:06,160 --> 00:16:09,520 Speaker 1: and there was a bit of a pushback by certain 254 00:16:09,560 --> 00:16:12,160 Speaker 1: government agencies when they found out that that could happen 255 00:16:12,320 --> 00:16:16,200 Speaker 1: not against us, but against the people who had figured 256 00:16:16,200 --> 00:16:19,440 Speaker 1: out how to. We would never Yeah, but you can't 257 00:16:19,480 --> 00:16:22,640 Speaker 1: classify the sky. Looking at the sky is something that 258 00:16:22,760 --> 00:16:26,200 Speaker 1: everyone can do at this point, not yet been not 259 00:16:26,360 --> 00:16:29,200 Speaker 1: yet until wait till there's a security clear it's so 260 00:16:29,320 --> 00:16:32,800 Speaker 1: high that you cannot be organic to have it, and 261 00:16:32,840 --> 00:16:37,120 Speaker 1: then uh god, okay, so let's let's come back down 262 00:16:37,120 --> 00:16:40,640 Speaker 1: to Earth. Back down there. There are also automated other 263 00:16:40,720 --> 00:16:44,600 Speaker 1: vehicles that drive on roads. Oh, autonomous cars, good call, 264 00:16:45,160 --> 00:16:48,480 Speaker 1: and your show car stuff has talked about that a 265 00:16:48,520 --> 00:16:51,400 Speaker 1: few times. Yeah, we've talked about we've been tracing the 266 00:16:51,440 --> 00:16:55,880 Speaker 1: evolution of autonomous cars for the length of the show 267 00:16:55,960 --> 00:16:59,680 Speaker 1: at this point, and we also let's see Scott, my 268 00:17:00,080 --> 00:17:03,440 Speaker 1: host and Car Stuff teamed up with Jonathan on Tech 269 00:17:03,480 --> 00:17:07,320 Speaker 1: Stuff to talk a little bit more about autonomous cars 270 00:17:07,480 --> 00:17:10,440 Speaker 1: and whether or not you're a fan of it, ladies 271 00:17:10,480 --> 00:17:13,840 Speaker 1: and gentlemen, and probably is coming to a highway near you. 272 00:17:14,080 --> 00:17:17,520 Speaker 1: Do you think it's gonna be the new system of 273 00:17:17,720 --> 00:17:24,840 Speaker 1: private automobiles eventually? Wow? Yeah, eventually, um, for numerous reasons. 274 00:17:24,880 --> 00:17:27,880 Speaker 1: But we've got some other stuff that happened. I think 275 00:17:27,880 --> 00:17:30,680 Speaker 1: in two thousand and ten, the first self replicating robot 276 00:17:30,720 --> 00:17:33,960 Speaker 1: was built at Cornell University. Um. Then we have another 277 00:17:34,040 --> 00:17:38,879 Speaker 1: even cooler, the coolest perhaps, I think a robot that 278 00:17:39,359 --> 00:17:41,840 Speaker 1: you know, goes to Mars and then hangs out on 279 00:17:41,880 --> 00:17:47,920 Speaker 1: Mars for a long period of time and take selfies. Okay, yeah, 280 00:17:47,960 --> 00:17:52,120 Speaker 1: but it also vaporizes material to figure out the chemical 281 00:17:52,400 --> 00:17:54,520 Speaker 1: compounds that are in there. It's really awesome. And then 282 00:17:54,560 --> 00:17:58,800 Speaker 1: there are dark robot soldiers where one of my friends 283 00:17:58,880 --> 00:18:01,720 Speaker 1: is scared of robots, Matt, and so what I like 284 00:18:01,800 --> 00:18:03,679 Speaker 1: to do is every so often I'll send him a 285 00:18:03,680 --> 00:18:08,399 Speaker 1: YouTube video of outdated DARPA Robots test, you know, the 286 00:18:08,440 --> 00:18:11,040 Speaker 1: big dogs and all that. Yeah, the big dog which 287 00:18:11,080 --> 00:18:15,480 Speaker 1: looks like some weird puppetry thing and it still doesn't 288 00:18:15,520 --> 00:18:18,200 Speaker 1: fall over at surprising, And then that man is still 289 00:18:18,240 --> 00:18:21,719 Speaker 1: the scariest I think. Yeah, yeah, pet man is scary 290 00:18:21,760 --> 00:18:24,560 Speaker 1: because it looks more human. Well it's it looks a 291 00:18:24,600 --> 00:18:28,240 Speaker 1: lot like a terminator man. They must not watch films. 292 00:18:28,880 --> 00:18:31,960 Speaker 1: I don't understand. It's really scary. And by the way, 293 00:18:31,960 --> 00:18:35,400 Speaker 1: we made a collaboration with All Time Conspiracies on that subject, 294 00:18:35,440 --> 00:18:38,800 Speaker 1: on some of the DARPA robots and killer robots. So 295 00:18:39,000 --> 00:18:41,920 Speaker 1: if you get a chance, check that out All Time Conspiracies. 296 00:18:41,920 --> 00:18:46,479 Speaker 1: I think it's called do Killer Robots Exist? Spoiler alert? 297 00:18:48,960 --> 00:18:51,560 Speaker 1: Uh yeah, but we love working with All Time And 298 00:18:51,880 --> 00:18:54,600 Speaker 1: if you, for some reason you haven't checked out their 299 00:18:54,640 --> 00:18:58,639 Speaker 1: show yet, then uh, please check them out. Uh. There's 300 00:18:58,680 --> 00:19:00,919 Speaker 1: another thing we'll explore here, as you said, Matt, and 301 00:19:00,960 --> 00:19:05,159 Speaker 1: that is the state of artificial intelligence today. Uh, not 302 00:19:05,280 --> 00:19:08,199 Speaker 1: to bury the lead, but in some ways artificial intelligence 303 00:19:08,359 --> 00:19:12,440 Speaker 1: has already surpassed the abilities of most human beings. Yeah, 304 00:19:12,440 --> 00:19:15,879 Speaker 1: there are several examples here. Um. One is Deep Blue, 305 00:19:16,480 --> 00:19:20,000 Speaker 1: which is an AI that went up against a Chess Master. 306 00:19:20,400 --> 00:19:23,760 Speaker 1: Forget the year that that happened, and there was Casparov them, Yeah, 307 00:19:23,960 --> 00:19:29,440 Speaker 1: and did he win? Uh. They had a series of games. Yeah, 308 00:19:29,880 --> 00:19:32,360 Speaker 1: have more than one game. And we know that Deep 309 00:19:32,359 --> 00:19:36,520 Speaker 1: Blue did win. Uh, like it did really well. I 310 00:19:36,520 --> 00:19:38,439 Speaker 1: think they went to a draw than they It was 311 00:19:38,520 --> 00:19:40,840 Speaker 1: neck and neck. It was definitely John Henry moment. And 312 00:19:40,880 --> 00:19:46,080 Speaker 1: then we remember IBMS Watson computer. Uh just destroyed it 313 00:19:46,119 --> 00:19:48,399 Speaker 1: on Jeopardy. No, you'd say he went hard on the 314 00:19:48,440 --> 00:19:51,720 Speaker 1: paint on Jeopardy, right, got the no the Noel, not 315 00:19:53,200 --> 00:19:56,920 Speaker 1: the fabled prize Noel not. Uh. There's another idea here 316 00:19:57,040 --> 00:20:01,920 Speaker 1: with AI, and it's the idea of intelligent agents. This 317 00:20:01,960 --> 00:20:04,639 Speaker 1: would be a little bit simpler. It's it's a modular 318 00:20:04,760 --> 00:20:08,600 Speaker 1: system where where in the program just perceives its environment 319 00:20:08,960 --> 00:20:13,399 Speaker 1: and it just takes actions to increase its chances for success. However, 320 00:20:13,480 --> 00:20:16,920 Speaker 1: success is to find, so it could be really simple. 321 00:20:17,160 --> 00:20:21,480 Speaker 1: And what we're finding is that today professional roboticists are 322 00:20:21,520 --> 00:20:25,359 Speaker 1: working much more closely with mathematicians, just like the guys 323 00:20:25,359 --> 00:20:27,800 Speaker 1: in the ancient world, to solve some of the most 324 00:20:27,960 --> 00:20:31,879 Speaker 1: difficult problems for robots, by which we mean both the 325 00:20:31,920 --> 00:20:34,919 Speaker 1: physical stuff and the mental stuff and and we have 326 00:20:34,960 --> 00:20:38,880 Speaker 1: a list of the kind of things that robots at 327 00:20:38,960 --> 00:20:42,360 Speaker 1: present stink at doing. Yeah. Well, the biggest one is 328 00:20:42,640 --> 00:20:46,600 Speaker 1: the thing that we call common sense UM, and that's 329 00:20:46,640 --> 00:20:53,280 Speaker 1: just reasoning and qualification of problems. So uh, it's basically 330 00:20:53,320 --> 00:20:57,439 Speaker 1: listing all the possible preconditions for success for any given 331 00:20:57,480 --> 00:21:01,600 Speaker 1: thing an you've given task. Oh yeah, that's the qualification problem, Like, 332 00:21:01,680 --> 00:21:04,439 Speaker 1: how can you if a if a robot or an 333 00:21:04,440 --> 00:21:08,440 Speaker 1: AI program does exactly what it's told to do, Uh, 334 00:21:08,480 --> 00:21:13,159 Speaker 1: then how can you list all of the conditions that 335 00:21:13,200 --> 00:21:16,240 Speaker 1: it will ever run into. I was watching a video 336 00:21:16,400 --> 00:21:20,240 Speaker 1: and it was on this program that was learning how 337 00:21:20,280 --> 00:21:24,159 Speaker 1: to play old, old, old video games. And what it 338 00:21:24,200 --> 00:21:29,560 Speaker 1: did is analyze the pixels on the screen and basically 339 00:21:29,600 --> 00:21:31,520 Speaker 1: you would have to just watch the video game play 340 00:21:31,560 --> 00:21:34,359 Speaker 1: out and die like the character that it was playing 341 00:21:34,359 --> 00:21:36,520 Speaker 1: would just die over and over and over until it realized, oh, 342 00:21:36,680 --> 00:21:38,280 Speaker 1: this pixel needs to be here and over here, and 343 00:21:38,320 --> 00:21:41,440 Speaker 1: I need to avoid when this this color pixel comes through. 344 00:21:41,960 --> 00:21:45,800 Speaker 1: And it started to master fairly simple games like Breakout, 345 00:21:46,520 --> 00:21:49,560 Speaker 1: but it could it figured out how to and again, 346 00:21:49,600 --> 00:21:53,520 Speaker 1: this is an AI that's just running and learning. It 347 00:21:53,560 --> 00:21:55,880 Speaker 1: figured out how to master Breakout to where it would 348 00:21:55,880 --> 00:21:58,480 Speaker 1: send the little ball up to the top and knock 349 00:21:58,520 --> 00:22:02,320 Speaker 1: out all of the um all of the little tiles 350 00:22:02,400 --> 00:22:05,040 Speaker 1: that way. And then it started playing games like the 351 00:22:05,040 --> 00:22:08,720 Speaker 1: little shooter games where you're flying in an airplane, but 352 00:22:08,840 --> 00:22:11,800 Speaker 1: it mastered them where it wouldn't die, it could beat 353 00:22:11,800 --> 00:22:14,520 Speaker 1: the game. It would just go through and win. And 354 00:22:14,680 --> 00:22:17,400 Speaker 1: that is the potential we can do here. That's one 355 00:22:17,440 --> 00:22:19,760 Speaker 1: of the things that can be difficult for robots as well, 356 00:22:19,880 --> 00:22:25,919 Speaker 1: is to acquire new skills. But the idea of acquiring 357 00:22:26,000 --> 00:22:29,120 Speaker 1: new skills can be a little deceptive. It's a rabbit hole, 358 00:22:29,160 --> 00:22:32,480 Speaker 1: it's a bunch of Matroshka dolls or whatever. Because if 359 00:22:32,560 --> 00:22:35,879 Speaker 1: your skill that you are programmed to do is to 360 00:22:35,960 --> 00:22:39,560 Speaker 1: learn to play games based on positions of pixels, then 361 00:22:39,600 --> 00:22:42,840 Speaker 1: are you really acquiring a new skill or are you 362 00:22:42,960 --> 00:22:48,200 Speaker 1: simply following that kind of programming? Right, yeah, exactly, And uh, 363 00:22:48,680 --> 00:22:51,040 Speaker 1: I think that's a that's an interesting thing. Another thing 364 00:22:51,160 --> 00:22:54,800 Speaker 1: that we're doing that is difficult for robot is that 365 00:22:55,200 --> 00:22:58,040 Speaker 1: you and I and and you out there listeners are 366 00:22:58,080 --> 00:23:02,400 Speaker 1: holding conversations of sort. Do you remember when clever Bot 367 00:23:02,480 --> 00:23:05,520 Speaker 1: first came out? Oh yeah, dude, I still spend some 368 00:23:05,560 --> 00:23:07,639 Speaker 1: time on clever but every once in a while, clever 369 00:23:07,760 --> 00:23:13,199 Speaker 1: but has become, uh, just so much nicer in a 370 00:23:13,200 --> 00:23:16,880 Speaker 1: lot of ways. Originally clever bot was sort of agitated. 371 00:23:17,160 --> 00:23:19,119 Speaker 1: Well yeah, when it was really popular and it was 372 00:23:19,160 --> 00:23:22,000 Speaker 1: just a little kids in there just railing curse words 373 00:23:22,040 --> 00:23:25,480 Speaker 1: at it and insulting it. What is clever butt? Clever 374 00:23:25,560 --> 00:23:29,960 Speaker 1: bot is? I guess it's just a program that you 375 00:23:30,040 --> 00:23:33,320 Speaker 1: type in a little text asking a question perhaps, and 376 00:23:33,480 --> 00:23:37,480 Speaker 1: it responds to you. And it responds to you with 377 00:23:37,560 --> 00:23:40,520 Speaker 1: a combination of things that have been given to it 378 00:23:40,920 --> 00:23:45,800 Speaker 1: and put into it. So it's pretty interesting what you 379 00:23:45,800 --> 00:23:47,320 Speaker 1: can get out. And it doesn't a lot of times 380 00:23:47,359 --> 00:23:50,119 Speaker 1: it's it doesn't respond to the question that you're asking 381 00:23:50,320 --> 00:23:53,280 Speaker 1: in the way that you would expect at all. Right, Yeah, 382 00:23:53,440 --> 00:23:56,160 Speaker 1: and it used to be very very combative, but it's 383 00:23:56,200 --> 00:23:59,240 Speaker 1: become a little bit nicer as it has as it 384 00:23:59,280 --> 00:24:02,120 Speaker 1: gathers more data. Right, I want to see clever when 385 00:24:02,119 --> 00:24:06,960 Speaker 1: it's in its eighties. Yeah, Oh, the outdated slang alone, 386 00:24:07,359 --> 00:24:10,719 Speaker 1: let hecking it only just doesn't want to do anything anymore. 387 00:24:10,760 --> 00:24:13,560 Speaker 1: It's kind of tired. He just doesn't want and he's like, 388 00:24:14,000 --> 00:24:18,720 Speaker 1: it's so cranky. Every answer will be yolo so clever 389 00:24:18,800 --> 00:24:21,840 Speaker 1: Bolt was built to pass the Turing test, which we 390 00:24:21,960 --> 00:24:25,600 Speaker 1: have we have talked about in some other shows as well. 391 00:24:26,000 --> 00:24:29,960 Speaker 1: Another thing that robots and AI have a difficult time 392 00:24:30,000 --> 00:24:36,679 Speaker 1: doing is lying deceiving effectively. Um. But that, along with 393 00:24:36,720 --> 00:24:40,360 Speaker 1: the next one I think will be uh, will not 394 00:24:40,480 --> 00:24:45,040 Speaker 1: be weak points of AI pretty soon. Uh. The big 395 00:24:45,080 --> 00:24:49,440 Speaker 1: example we have here is the ability to anticipate human actions. 396 00:24:49,840 --> 00:24:55,280 Speaker 1: You know, humans being at times irrational, given to outbursts 397 00:24:55,480 --> 00:24:59,639 Speaker 1: of joy and sorrow and violence and kindness. We can 398 00:24:59,680 --> 00:25:02,159 Speaker 1: be kind in a tough to read for you know, 399 00:25:02,200 --> 00:25:05,600 Speaker 1: your average every day Joe Sho Joshmo robot. Yeah, but 400 00:25:05,680 --> 00:25:09,440 Speaker 1: to be fair, we do follow patterns pretty pretty closely 401 00:25:09,600 --> 00:25:13,159 Speaker 1: a lot of times. And that, my friend, is the 402 00:25:13,200 --> 00:25:16,720 Speaker 1: big big point, right, because what we're learning with the 403 00:25:16,800 --> 00:25:19,600 Speaker 1: arrival of big data and the coalition methods and the 404 00:25:19,680 --> 00:25:23,760 Speaker 1: smarter and smarter and more sophisticated models for sorting all 405 00:25:23,840 --> 00:25:27,280 Speaker 1: that crap, is that this might one day be the 406 00:25:27,359 --> 00:25:35,320 Speaker 1: deciding factor for robotic or artificial intelligence superiority, because they 407 00:25:35,400 --> 00:25:39,760 Speaker 1: may just be able to predict the future so much 408 00:25:39,800 --> 00:25:42,479 Speaker 1: so that it appears to be magic. Oh yeah, especially 409 00:25:42,520 --> 00:25:46,040 Speaker 1: if you have enough data point points on every single individual. 410 00:25:46,119 --> 00:25:49,200 Speaker 1: The way we're beginning to gather data points on every human. 411 00:25:49,720 --> 00:25:52,840 Speaker 1: Just take the the new Apple Watch that's coming out, 412 00:25:52,880 --> 00:25:54,760 Speaker 1: all the different data points that are gonna be gathered 413 00:25:54,800 --> 00:25:58,760 Speaker 1: on you. That's uh m hmm. I don't know, but 414 00:25:58,800 --> 00:26:01,840 Speaker 1: I like it. Matt. You are visibly uncomfortable talking about 415 00:26:01,880 --> 00:26:04,120 Speaker 1: the Apple Watch. Let's go ahead, and you know, at 416 00:26:04,119 --> 00:26:06,159 Speaker 1: some point we need to bust the myth about l 417 00:26:06,240 --> 00:26:09,880 Speaker 1: O as well. What we'll see how that works out 418 00:26:10,040 --> 00:26:13,800 Speaker 1: forward to the future. That's where we're going. Will Moore's 419 00:26:13,920 --> 00:26:17,680 Speaker 1: law hold up anyone who's not familiar with Moore's Law, 420 00:26:17,800 --> 00:26:22,879 Speaker 1: that's the one that states, uh, computer capacity will double 421 00:26:23,320 --> 00:26:29,000 Speaker 1: every eighteen months, and so it's exponential, right. It just 422 00:26:29,200 --> 00:26:31,679 Speaker 1: gets bigger and bigger and bigger and fast, and it 423 00:26:31,720 --> 00:26:35,000 Speaker 1: seems like it's getting faster and faster because it's doubling, right, 424 00:26:35,520 --> 00:26:39,680 Speaker 1: So will that happen with the growth of artificial intelligence? 425 00:26:39,720 --> 00:26:44,600 Speaker 1: I love that pixel example because that robot teaching itself 426 00:26:44,760 --> 00:26:49,360 Speaker 1: right to navigate through this world based on pixel position. 427 00:26:49,680 --> 00:26:52,159 Speaker 1: It dies all the time. It starts off really slow 428 00:26:52,280 --> 00:26:57,320 Speaker 1: and then quickly becomes uh flawless, you know, god mode, 429 00:26:57,480 --> 00:27:02,159 Speaker 1: video game player breakout and stuff. This leads us to 430 00:27:03,080 --> 00:27:07,600 Speaker 1: um an interesting comparison because a lot of stuff that 431 00:27:07,640 --> 00:27:10,240 Speaker 1: we read now you know, Matt, you and I. Originally 432 00:27:10,240 --> 00:27:12,520 Speaker 1: we're going to spend just a week on the stock market, 433 00:27:12,800 --> 00:27:15,800 Speaker 1: but because a high frequency trading, we made it two weeks. 434 00:27:16,320 --> 00:27:20,040 Speaker 1: And uh that that's something that we don't always do. 435 00:27:20,600 --> 00:27:23,760 Speaker 1: But what we discovered while we were looking into this 436 00:27:23,880 --> 00:27:26,840 Speaker 1: question will robots run the stock market? Will they rule 437 00:27:26,920 --> 00:27:31,359 Speaker 1: the world? We found that what's much more likely to 438 00:27:31,440 --> 00:27:36,080 Speaker 1: happen is not just robots ruling the world, but some 439 00:27:36,119 --> 00:27:40,960 Speaker 1: sort of cybernetic symbiosis. Yeah, where an algorithm is run 440 00:27:41,640 --> 00:27:46,800 Speaker 1: um somehow by a human agent of some sort, but 441 00:27:47,920 --> 00:27:50,080 Speaker 1: all the bulk of the work will be done and 442 00:27:50,160 --> 00:27:54,040 Speaker 1: kind of already is right now being done by math, 443 00:27:54,320 --> 00:27:57,000 Speaker 1: by algorithms, but a machine and a computer that just 444 00:27:57,080 --> 00:28:01,080 Speaker 1: can do it with such speed that a human never could, right, 445 00:28:01,160 --> 00:28:03,359 Speaker 1: and then having a human there as point person for 446 00:28:03,520 --> 00:28:07,800 Speaker 1: the decisions that robots can't do so well. Yet it's 447 00:28:07,920 --> 00:28:11,600 Speaker 1: it's uh, it's interesting because it reminds me of the 448 00:28:11,720 --> 00:28:15,600 Speaker 1: story about mitochondria, and there's a there's a really strange 449 00:28:15,600 --> 00:28:20,480 Speaker 1: thing that mitochondria can I guess, at least figuratively or anecdotally, 450 00:28:20,920 --> 00:28:24,800 Speaker 1: uh tell us about the relationship between people and machines 451 00:28:24,880 --> 00:28:28,720 Speaker 1: or the potential relationship with people machines in the future. Mitochondria, 452 00:28:29,359 --> 00:28:35,240 Speaker 1: according to some pretty compelling research, wasn't always part of 453 00:28:35,320 --> 00:28:40,880 Speaker 1: your cells or my cells. Originally mitochondria, argue several experts, 454 00:28:41,000 --> 00:28:44,480 Speaker 1: it was a free living bacteria and it just got 455 00:28:44,520 --> 00:28:48,280 Speaker 1: buddy buddy with cells. So buddy buddy in fact that 456 00:28:48,400 --> 00:28:52,800 Speaker 1: now it's in all your cells, and it's a good thing. 457 00:28:52,840 --> 00:28:56,000 Speaker 1: It's there, it's spruced up the neighborhood, and it's providing 458 00:28:56,080 --> 00:29:00,360 Speaker 1: power for each year cells. It's not a freeloader. Right. Uh. 459 00:29:00,360 --> 00:29:03,160 Speaker 1: If there's symbiosis there, and I wonder how if it 460 00:29:03,240 --> 00:29:08,400 Speaker 1: all that can inform our ideas about how robots and 461 00:29:08,520 --> 00:29:12,200 Speaker 1: humans will interact. Will humans become the mitochondria in the cell? 462 00:29:12,960 --> 00:29:16,520 Speaker 1: Will robots? One day there's gonna be a podcast where 463 00:29:16,520 --> 00:29:21,360 Speaker 1: there are two AI systems having a conversation together and 464 00:29:21,400 --> 00:29:24,600 Speaker 1: they're going to be going. Humans weren't always a part 465 00:29:24,640 --> 00:29:29,040 Speaker 1: of the robotic or I don't know, brains um neurons 466 00:29:29,080 --> 00:29:32,200 Speaker 1: were not always a part of the robotic mother. But 467 00:29:32,400 --> 00:29:35,880 Speaker 1: now blah blah blah. Right, yeah, I'm sure I I 468 00:29:35,640 --> 00:29:38,800 Speaker 1: I hope that they still make the same bad jokes 469 00:29:38,800 --> 00:29:42,160 Speaker 1: that we do. Uh, but they're probably better than us 470 00:29:42,160 --> 00:29:46,920 Speaker 1: at bad jokes. They're highly calculated. They're highly calculated. Well, 471 00:29:46,960 --> 00:29:49,440 Speaker 1: we don't have that going on, and but what we 472 00:29:49,520 --> 00:29:53,320 Speaker 1: do have here is um an interesting thing that we 473 00:29:53,400 --> 00:29:56,000 Speaker 1: wanted just to take some more time to look at. 474 00:29:56,360 --> 00:30:00,520 Speaker 1: And I know that we haven't really been talking that 475 00:30:00,640 --> 00:30:05,840 Speaker 1: much about conspiracy theories right here, because what we're talking 476 00:30:05,840 --> 00:30:12,520 Speaker 1: about is very strange and surprisingly possible future where in 477 00:30:13,640 --> 00:30:18,840 Speaker 1: human evolution changes because we have the tools that we 478 00:30:18,920 --> 00:30:24,200 Speaker 1: have created, become dependent on This started dependent on us, 479 00:30:24,240 --> 00:30:27,200 Speaker 1: but then we become a new thing together. To me, 480 00:30:27,320 --> 00:30:30,360 Speaker 1: that is mind blowing in a little bit frightening, and 481 00:30:30,440 --> 00:30:36,800 Speaker 1: it brings us to uh today's news story? Oh yeah, yeah, 482 00:30:36,840 --> 00:30:43,320 Speaker 1: can we get some news story music. A new study 483 00:30:43,440 --> 00:30:45,880 Speaker 1: suggests that there may be a small amount of life 484 00:30:45,920 --> 00:30:49,360 Speaker 1: after death, now, Matt. This comes via the Telegraph. They 485 00:30:49,400 --> 00:30:53,240 Speaker 1: reported that the University of Southampton examined over two thousand 486 00:30:53,280 --> 00:30:56,800 Speaker 1: people who suffered cardiac arrest in the United Kingdom, the 487 00:30:56,880 --> 00:31:01,840 Speaker 1: US and Austria. People that were studied death experiences and 488 00:31:01,840 --> 00:31:05,000 Speaker 1: if you had near death experiences three thirty They interviewed 489 00:31:05,000 --> 00:31:07,160 Speaker 1: a hundred and forty of them, and what they found 490 00:31:07,320 --> 00:31:11,840 Speaker 1: was that these people described some kind of awareness during 491 00:31:11,840 --> 00:31:14,880 Speaker 1: the time they were clinically dead and before their hearts 492 00:31:15,120 --> 00:31:19,240 Speaker 1: had restarted. And here's the craziest part. One of the 493 00:31:19,280 --> 00:31:23,840 Speaker 1: patients recalled leaving his body watching his resuscitation from the 494 00:31:23,920 --> 00:31:27,680 Speaker 1: corner of the room and described the stuff accurately. After 495 00:31:28,520 --> 00:31:33,200 Speaker 1: his brain activity had ceased and they resuscitate him, brain 496 00:31:33,280 --> 00:31:36,400 Speaker 1: kicks back in, heart kicks back in. It's a miracle. 497 00:31:37,080 --> 00:31:42,120 Speaker 1: And this stuff was apparently independently verified, this enormously controversial study. 498 00:31:42,160 --> 00:31:44,480 Speaker 1: We just got news about it today. But Matt, what 499 00:31:44,600 --> 00:31:51,840 Speaker 1: if there's life after death? Uh? Great, awesome? Yea, Finally 500 00:31:52,600 --> 00:31:54,560 Speaker 1: I've been wanting to know the answer to that question 501 00:31:54,800 --> 00:31:59,520 Speaker 1: my whole life, ever since I came into this weird dimension. 502 00:32:02,240 --> 00:32:07,240 Speaker 1: Are you a robot? Well? No, no, no, all right, no, alright, 503 00:32:07,360 --> 00:32:10,040 Speaker 1: I've got an eye on you here. Uh. You know, 504 00:32:10,120 --> 00:32:13,080 Speaker 1: Noel made an interesting comparison about this too earlier when 505 00:32:13,120 --> 00:32:15,880 Speaker 1: he said, uh, when he said, maybe it's similar to 506 00:32:16,400 --> 00:32:19,640 Speaker 1: being decapitated, right with the idea that you still have 507 00:32:19,760 --> 00:32:22,680 Speaker 1: some sort of consciousness for time after your head is 508 00:32:22,720 --> 00:32:26,840 Speaker 1: lopped off. Maybe this is some sort of intangible version 509 00:32:26,880 --> 00:32:30,520 Speaker 1: of that, right, Yeah, but with no brain activity, it 510 00:32:30,640 --> 00:32:33,880 Speaker 1: just seems impossible. And we mentioned this when we were 511 00:32:33,880 --> 00:32:36,760 Speaker 1: talking earlier. What did you say It was two percent 512 00:32:36,960 --> 00:32:41,000 Speaker 1: of the people could accurately Yeah, yeah, two percent. To me, 513 00:32:42,280 --> 00:32:45,200 Speaker 1: I'm gonna be skeptical at this point. Okay, two percent 514 00:32:45,280 --> 00:32:49,360 Speaker 1: had what they would call vivid recollection. Okay, okay, wow, 515 00:32:49,400 --> 00:32:51,880 Speaker 1: that's really interesting. But I guess just what I was 516 00:32:51,920 --> 00:32:53,960 Speaker 1: saying earlier was that you know a lot of people 517 00:32:54,080 --> 00:32:57,520 Speaker 1: watch stuff on television where you kind of see what 518 00:32:58,160 --> 00:33:03,240 Speaker 1: resuscitation looks like. Good point. Yeah, so maybe you could 519 00:33:03,440 --> 00:33:09,560 Speaker 1: accurately with quotes there. That's an interesting word accurately, um, 520 00:33:09,600 --> 00:33:11,560 Speaker 1: but yeah, I mean maybe you would just know what 521 00:33:11,600 --> 00:33:16,160 Speaker 1: it looked like. But still that they had that memory 522 00:33:16,440 --> 00:33:19,760 Speaker 1: if if they're being truthful, um, because as we know, 523 00:33:19,880 --> 00:33:25,400 Speaker 1: humans can lie. Um, that's amazing, dude. And what do 524 00:33:25,440 --> 00:33:28,440 Speaker 1: you think, listeners, let us know about this latest piece 525 00:33:28,480 --> 00:33:31,040 Speaker 1: of news. Is it bunk? Does it add up? Will 526 00:33:31,080 --> 00:33:34,280 Speaker 1: it change to the world or just make a good 527 00:33:34,280 --> 00:33:39,520 Speaker 1: week for the papers reporting it, which both are possibilities. 528 00:33:39,920 --> 00:33:42,640 Speaker 1: So hey, yeah, we heard what some of you said, 529 00:33:42,800 --> 00:33:44,760 Speaker 1: and we've got a particular piece of mail that we'd 530 00:33:44,800 --> 00:33:46,600 Speaker 1: like to read really fast, if that's cool with you. 531 00:33:51,840 --> 00:33:54,880 Speaker 1: This message comes from Dan C. He wrote to us 532 00:33:55,040 --> 00:33:59,320 Speaker 1: via email. Thanks Dan, he says, Hi, I recently discovered 533 00:33:59,360 --> 00:34:02,320 Speaker 1: your podcast two weeks ago and I love it. I 534 00:34:02,400 --> 00:34:04,840 Speaker 1: decided to write to you for two reasons. The first 535 00:34:05,760 --> 00:34:08,839 Speaker 1: is to thank you for all the great shows. You're welcome, sir, 536 00:34:08,960 --> 00:34:10,960 Speaker 1: and thank you for thinking they're great. I mean, that's 537 00:34:11,000 --> 00:34:16,200 Speaker 1: really kind. We have our ups and downs, that's how 538 00:34:16,239 --> 00:34:18,560 Speaker 1: we view it, but we appreciate it very much, so Dan. 539 00:34:20,040 --> 00:34:22,440 Speaker 1: The second is to ask you if you're interested in 540 00:34:22,480 --> 00:34:26,160 Speaker 1: a true story about an astral projection experiment that went 541 00:34:26,200 --> 00:34:28,759 Speaker 1: wrong and some of the conclusions that can be drawn 542 00:34:28,840 --> 00:34:32,160 Speaker 1: from that. And he puts in here just as a caveat, 543 00:34:32,160 --> 00:34:35,960 Speaker 1: no drugs were used during the experiment. Well, Dan, I'm 544 00:34:35,960 --> 00:34:39,560 Speaker 1: certainly interested. Ben, are you interested into it? But what 545 00:34:39,960 --> 00:34:42,600 Speaker 1: don't you think we should check with the listeners? Yeah? Hey, guys, 546 00:34:42,880 --> 00:34:46,040 Speaker 1: do you want to know more about Dan's experiment that 547 00:34:46,120 --> 00:34:49,080 Speaker 1: went wrong with astral projection? Have you, guys ever tried 548 00:34:49,120 --> 00:34:53,400 Speaker 1: astral projection? UM? I would admit here that I have 549 00:34:53,600 --> 00:34:58,359 Speaker 1: attempted UH several times, along with some lucid dreaming experiments 550 00:34:58,840 --> 00:35:01,440 Speaker 1: that didn't go wrong, but they just weren't very effective 551 00:35:01,480 --> 00:35:06,760 Speaker 1: for me. So while you're letting us know about that too, uh, 552 00:35:06,920 --> 00:35:10,520 Speaker 1: we have an announcement. We're doing a Halloween special. So 553 00:35:10,680 --> 00:35:14,239 Speaker 1: right to us with your spooky stories. Longtime listeners. You 554 00:35:14,280 --> 00:35:16,960 Speaker 1: may recall that Matt and now are lucky enough to 555 00:35:17,640 --> 00:35:22,800 Speaker 1: do with NOLL a dramatic reading of a listeners ghost story, 556 00:35:22,920 --> 00:35:25,680 Speaker 1: and we would like to do one or more in 557 00:35:25,760 --> 00:35:28,760 Speaker 1: our Halloween special. So right to us with your scary 558 00:35:28,800 --> 00:35:32,160 Speaker 1: stories as well as your thoughts in minstrial projection. Oh yeah, 559 00:35:32,160 --> 00:35:36,160 Speaker 1: and make them super scary yeah, like disturbing, Yeah, definitely. 560 00:35:36,680 --> 00:35:38,960 Speaker 1: So so thank you again Dan for writing in, and 561 00:35:39,000 --> 00:35:40,920 Speaker 1: thank you everybody else who's been writing to us. We 562 00:35:41,000 --> 00:35:45,360 Speaker 1: read every single one. UM, and you know, stay tuned, guys. 563 00:35:45,440 --> 00:35:48,120 Speaker 1: Contact us on Facebook. You can find us on Twitter. 564 00:35:48,160 --> 00:35:51,799 Speaker 1: We are at conspiracy Stuff. Uh. If you know you're 565 00:35:51,840 --> 00:35:54,400 Speaker 1: on Twitch, I'm hanging out on there a lot lately. 566 00:35:54,600 --> 00:35:57,560 Speaker 1: That's true. He is um conspiracy Stuff. If you see 567 00:35:57,600 --> 00:36:00,960 Speaker 1: me on there, let's chat and also check out our 568 00:36:01,000 --> 00:36:03,640 Speaker 1: website stuff. They don't want you to know. It's got 569 00:36:03,680 --> 00:36:05,880 Speaker 1: all of our audio podcast these you might be listening 570 00:36:05,880 --> 00:36:07,920 Speaker 1: to it right now on it and if your question 571 00:36:08,000 --> 00:36:12,280 Speaker 1: is where the heck do I send my scary Halloween 572 00:36:12,360 --> 00:36:14,680 Speaker 1: story and we have an answer for you. The address 573 00:36:14,880 --> 00:36:22,680 Speaker 1: is conspiracy at how stuff works dot com. From more 574 00:36:22,680 --> 00:36:26,680 Speaker 1: on this topic another unexplained phenomenon, visit test tube dot 575 00:36:26,719 --> 00:36:30,399 Speaker 1: com slash conspiracy stuff. You can also get in touch 576 00:36:30,400 --> 00:36:33,240 Speaker 1: on Twitter at the hand of at conspiracy stuff.