1 00:00:00,040 --> 00:00:02,520 Speaker 1: Guess what will? What's that mango? So I know you've 2 00:00:02,520 --> 00:00:05,120 Speaker 1: gotten really unto running recently, and I think there's an 3 00:00:05,120 --> 00:00:08,200 Speaker 1: accessory from Japan you really need to invest in. It's 4 00:00:08,240 --> 00:00:13,040 Speaker 1: called tomaton. Tomaton that sounds cool. So so what's a toomaton? Well, 5 00:00:13,119 --> 00:00:16,160 Speaker 1: it's exactly what it sounds like. It's a robot backpack 6 00:00:16,239 --> 00:00:19,880 Speaker 1: that feeds you tomatoes on the run. That is not 7 00:00:19,920 --> 00:00:22,599 Speaker 1: what I expected to say that that is much less 8 00:00:22,600 --> 00:00:26,840 Speaker 1: cool than I expected. Basically, there's this giant cartoony tomato 9 00:00:26,880 --> 00:00:29,160 Speaker 1: that sits on your head and it's got this clear 10 00:00:29,240 --> 00:00:31,680 Speaker 1: tube full of tomatoes that kind of hangs down your spine. 11 00:00:32,120 --> 00:00:35,400 Speaker 1: And as you run, this robotic arm swoops back to 12 00:00:35,440 --> 00:00:37,960 Speaker 1: grab a tomato, and then it swoops down in front 13 00:00:38,000 --> 00:00:40,239 Speaker 1: of you with that juicy fruit and sticks it in 14 00:00:40,240 --> 00:00:43,720 Speaker 1: your face. And I'm kind of surprised more elite athletes 15 00:00:43,760 --> 00:00:46,959 Speaker 1: don't use it. Well, you know, my kids already make 16 00:00:47,000 --> 00:00:49,000 Speaker 1: fun of me for what I look like when I run, 17 00:00:49,080 --> 00:00:51,239 Speaker 1: and so I can only imagine this is just going 18 00:00:51,280 --> 00:00:53,959 Speaker 1: to give them that much more ammunition, which is probably 19 00:00:53,960 --> 00:00:56,240 Speaker 1: all the more reason I should do it. But I'm 20 00:00:56,320 --> 00:00:59,160 Speaker 1: curious why tomatoes, of all things I've never wanted a 21 00:00:59,200 --> 00:01:02,720 Speaker 1: tomato while I was running, Well you haven't before, but 22 00:01:02,840 --> 00:01:05,479 Speaker 1: now you will. So it was invented by this company 23 00:01:05,520 --> 00:01:09,119 Speaker 1: called Kagome, which is Japan's premiere tomato juice and catchup 24 00:01:09,120 --> 00:01:12,720 Speaker 1: manufacturer apparently. But I am not done talking about Tamaton 25 00:01:12,840 --> 00:01:16,440 Speaker 1: just yet. So while the original Tmaton is pretty good 26 00:01:16,440 --> 00:01:18,800 Speaker 1: for five k's, you might want to look into the 27 00:01:18,840 --> 00:01:23,800 Speaker 1: petite Tamaton for marathons. It's uh, it's lighter, smaller. Instead 28 00:01:23,840 --> 00:01:27,120 Speaker 1: of um that robot arm feeder, It's got a trigger 29 00:01:27,200 --> 00:01:29,480 Speaker 1: and a tube and you can actually shoot tiny tomatoes 30 00:01:29,480 --> 00:01:32,760 Speaker 1: in your mouth anytime you want. It does have a 31 00:01:32,800 --> 00:01:35,600 Speaker 1: tomato timer that prevents you from eating too many tomatoes 32 00:01:35,640 --> 00:01:38,720 Speaker 1: too quickly, so you do pace yourself with it. So 33 00:01:39,520 --> 00:01:41,560 Speaker 1: that's that's really impressive. I can't say that I've ever 34 00:01:41,600 --> 00:01:44,800 Speaker 1: been at risk of feeding myself too many of anything 35 00:01:44,840 --> 00:01:48,560 Speaker 1: while on a long run, but whatever, that's great. Yeah, 36 00:01:48,640 --> 00:01:50,919 Speaker 1: well it's just the first of nine facts about robots 37 00:01:51,000 --> 00:02:15,280 Speaker 1: we're covering for today's show, Celesidising Day their podcast listeners, 38 00:02:15,280 --> 00:02:17,639 Speaker 1: Welcome to Part Time Genius. I'm Will Pearson and is 39 00:02:17,720 --> 00:02:20,560 Speaker 1: always I'm joined by my good friend Mangesh Ticketer and 40 00:02:20,639 --> 00:02:23,400 Speaker 1: sitting behind the soundproof glass. Now this guy is working 41 00:02:23,520 --> 00:02:26,720 Speaker 1: on another movie reboot now Mango. We've seen him work 42 00:02:26,800 --> 00:02:30,040 Speaker 1: on so many ideas for movie reboots, and this one 43 00:02:30,120 --> 00:02:33,760 Speaker 1: is for a feminist reboot for Short Circuit too. So 44 00:02:34,000 --> 00:02:35,600 Speaker 1: I think this is gonna be a big one. This 45 00:02:35,720 --> 00:02:38,240 Speaker 1: may be his most brilliant one yet. But that's that's 46 00:02:38,240 --> 00:02:42,000 Speaker 1: our friend and producer Tristan McNeil over there. That's amazing Tristan. 47 00:02:42,160 --> 00:02:45,480 Speaker 1: So I know we both love robots, and I certainly 48 00:02:45,520 --> 00:02:49,040 Speaker 1: was a Jetsons fan. I had kept Sella as a kid. Uh, 49 00:02:49,360 --> 00:02:52,560 Speaker 1: lots of radio shot kits, and you know, I loved 50 00:02:52,600 --> 00:02:55,920 Speaker 1: building weird sort of automated inventions. But there's actually another 51 00:02:55,919 --> 00:02:59,080 Speaker 1: reason we're doing a show on robots today, right, that's right. 52 00:02:59,120 --> 00:03:01,440 Speaker 1: So one of our four her guest Simone Yatch, and 53 00:03:01,480 --> 00:03:04,480 Speaker 1: you probably know her for her amazing crappy robots that 54 00:03:04,520 --> 00:03:07,880 Speaker 1: she builds and tests on YouTube. And we are huge 55 00:03:07,919 --> 00:03:09,799 Speaker 1: fans of Simone and if you think about some of 56 00:03:09,840 --> 00:03:12,639 Speaker 1: the examples of things that she's built, she built the 57 00:03:12,639 --> 00:03:15,200 Speaker 1: the robot that shampoo's your hair for you just got 58 00:03:15,200 --> 00:03:18,280 Speaker 1: a hilarious video of this, or the alarm clock that 59 00:03:18,280 --> 00:03:20,280 Speaker 1: wakes you up just by slapping you in the face, 60 00:03:20,320 --> 00:03:24,400 Speaker 1: which was also incredibly amusing. But you know, she she 61 00:03:24,480 --> 00:03:27,560 Speaker 1: got some not so great news, though she um feels 62 00:03:27,560 --> 00:03:29,600 Speaker 1: like she's gonna be okay. So she found out that 63 00:03:29,639 --> 00:03:32,919 Speaker 1: she has a brain tumor, is going to undergo surgery 64 00:03:32,960 --> 00:03:36,200 Speaker 1: this month, and they're optimistic about the prospects for this. 65 00:03:36,320 --> 00:03:38,520 Speaker 1: But we still we really wanted to wish her the 66 00:03:38,680 --> 00:03:41,000 Speaker 1: very best of luck, so we made this little nine 67 00:03:41,040 --> 00:03:44,360 Speaker 1: things in her honor. That's right, So get well soon, simone. 68 00:03:44,920 --> 00:03:47,480 Speaker 1: So back to our love of robots for a second. 69 00:03:47,560 --> 00:03:50,280 Speaker 1: So people always ask me about my favorite stories and 70 00:03:50,280 --> 00:03:53,240 Speaker 1: facts for Mental Floss, and they're a handful I always 71 00:03:53,240 --> 00:03:55,800 Speaker 1: talked about. But my favorite invention has to be this 72 00:03:55,960 --> 00:03:58,840 Speaker 1: robotic fish that gets other fish to follow it, because 73 00:03:58,880 --> 00:04:02,320 Speaker 1: it sounds like a small idea, like why do you 74 00:04:02,440 --> 00:04:05,400 Speaker 1: care that you can create a leader among fish? And 75 00:04:05,440 --> 00:04:08,240 Speaker 1: the answer is so much more interesting. It's so that, 76 00:04:08,520 --> 00:04:11,360 Speaker 1: like if there's an oil spill or a disaster or 77 00:04:11,400 --> 00:04:14,520 Speaker 1: something devastating, you can actually lead schools of fish away 78 00:04:14,560 --> 00:04:17,279 Speaker 1: from that disaster. And I just think that's amazing. It 79 00:04:17,400 --> 00:04:19,640 Speaker 1: is pretty cool, and I would say it's I mean 80 00:04:19,680 --> 00:04:22,880 Speaker 1: it's not as important as the backpack Tomato robot, but 81 00:04:23,080 --> 00:04:25,480 Speaker 1: it feels like it's almost as important as the backpack 82 00:04:25,560 --> 00:04:29,919 Speaker 1: to make no picking on thomasons. What robot factor are 83 00:04:29,920 --> 00:04:31,720 Speaker 1: you gonna start with? Well, all right, well, here's one 84 00:04:31,760 --> 00:04:34,120 Speaker 1: that I hadn't really thought about before, and it's what 85 00:04:34,279 --> 00:04:37,760 Speaker 1: personality your vacuum cleaners should have? And you know, I 86 00:04:37,800 --> 00:04:39,520 Speaker 1: know you've probably thought about this. You come up with 87 00:04:39,600 --> 00:04:42,120 Speaker 1: lots of great questions that I had never thought about it, 88 00:04:42,160 --> 00:04:45,000 Speaker 1: and apparently a lot of scientists hadn't thought about it either. 89 00:04:45,560 --> 00:04:48,880 Speaker 1: So robots are mostly supposed to be functional, right, but 90 00:04:49,120 --> 00:04:51,400 Speaker 1: you know, we're obviously going to have preferences for one 91 00:04:51,520 --> 00:04:54,839 Speaker 1: robot vacuum cleaner over another if they exhibit these certain 92 00:04:54,880 --> 00:04:57,320 Speaker 1: behaviors with their sounds. You know, if you think about 93 00:04:57,640 --> 00:04:59,640 Speaker 1: the sounds they make, or the light they put out, 94 00:04:59,720 --> 00:05:02,120 Speaker 1: or the motions they go through, you know we will 95 00:05:02,160 --> 00:05:05,359 Speaker 1: have preferences among these. So kay, can you talk me 96 00:05:05,360 --> 00:05:08,280 Speaker 1: through a few of these. Well, let's say you programmed 97 00:05:08,279 --> 00:05:11,000 Speaker 1: a robot vacuum cleaner to not leave a certain area 98 00:05:11,040 --> 00:05:14,080 Speaker 1: until it removed a spot. So just imagine it's just 99 00:05:14,120 --> 00:05:16,800 Speaker 1: going over and over the same area, and these kind 100 00:05:16,800 --> 00:05:19,760 Speaker 1: of short jarring motions and if it does that, it 101 00:05:19,839 --> 00:05:22,560 Speaker 1: might come across as obsessive, you know, just going back 102 00:05:22,600 --> 00:05:25,159 Speaker 1: and forth over that one unimportant spot while there's this 103 00:05:25,240 --> 00:05:28,640 Speaker 1: whole room it needs to vacuum. So scientists at delfta 104 00:05:28,680 --> 00:05:31,600 Speaker 1: University in the Netherlands decided to determine what types of 105 00:05:31,600 --> 00:05:35,440 Speaker 1: personalities people want from their vacuum cleaners. So they rounded 106 00:05:35,520 --> 00:05:38,760 Speaker 1: up fifteen early adopters. These are people who were predisposed 107 00:05:38,800 --> 00:05:42,080 Speaker 1: to buying robot vacuum cleaners. And then things got a 108 00:05:42,120 --> 00:05:45,320 Speaker 1: little bit weird, so they hired a bunch of actors 109 00:05:45,360 --> 00:05:49,039 Speaker 1: to either crawl around or walk around pretending to be 110 00:05:49,120 --> 00:05:52,760 Speaker 1: vacuum cleaners with different personality traits. Now, at first I 111 00:05:52,760 --> 00:05:55,200 Speaker 1: thought this was just made up, but this was an 112 00:05:55,240 --> 00:05:58,920 Speaker 1: actual study. And the only noises they could make were 113 00:05:59,000 --> 00:06:02,480 Speaker 1: vacuum cleaner noise, so you think worrying and sucking sounds. 114 00:06:02,520 --> 00:06:05,799 Speaker 1: But they could change patterns and ways that they moved, 115 00:06:06,200 --> 00:06:08,640 Speaker 1: and they were actually asked to act out these thirty 116 00:06:08,680 --> 00:06:12,080 Speaker 1: different personality traits for vacuum cleaners. How in the world 117 00:06:12,160 --> 00:06:14,680 Speaker 1: they could have these thirty different possible traits, I don't know, 118 00:06:14,760 --> 00:06:17,359 Speaker 1: but you know, just as examples. They could be bold 119 00:06:17,520 --> 00:06:20,840 Speaker 1: or calm, or talkative or several other things. But it's 120 00:06:20,880 --> 00:06:23,680 Speaker 1: just crazy to imagine this. That's funny. It sounds like 121 00:06:23,720 --> 00:06:27,960 Speaker 1: a terrible improv activity. But as it does, so when 122 00:06:27,960 --> 00:06:31,039 Speaker 1: did the scientists actually learn from this? Well, I mean 123 00:06:31,320 --> 00:06:33,920 Speaker 1: most of it was honestly pretty straightforward, like you don't 124 00:06:33,920 --> 00:06:37,080 Speaker 1: want to chatterbox for a vacuum cleaner, and most people 125 00:06:37,120 --> 00:06:40,160 Speaker 1: also don't want something that feels whimsical, like it might 126 00:06:40,200 --> 00:06:42,400 Speaker 1: make more sense for a vacuum cleaner to move in 127 00:06:42,520 --> 00:06:46,080 Speaker 1: circles or you know, maybe these unpredictable patterns once it's 128 00:06:46,160 --> 00:06:48,520 Speaker 1: mapped out all of the dirt that's in the room, 129 00:06:49,000 --> 00:06:52,560 Speaker 1: but people actually want to see straight lines and routine. 130 00:06:52,760 --> 00:06:56,119 Speaker 1: So whether that's actually, you know, the most efficient way 131 00:06:56,160 --> 00:06:59,159 Speaker 1: to vacuum a room, people perceive that to be the 132 00:06:59,200 --> 00:07:02,560 Speaker 1: most efficient. But yeah, actually, honestly, I just like that. 133 00:07:02,600 --> 00:07:05,039 Speaker 1: We had to hire a bunch of Shakespearean trained actors 134 00:07:05,080 --> 00:07:07,760 Speaker 1: to play the role of like vacuum number three, just 135 00:07:07,800 --> 00:07:12,440 Speaker 1: to figure all of this out. That's horrible, But I 136 00:07:12,480 --> 00:07:14,560 Speaker 1: do think it's an interesting idea about how much should 137 00:07:14,560 --> 00:07:17,680 Speaker 1: we care for and about our robots. And I was 138 00:07:17,680 --> 00:07:20,000 Speaker 1: looking at something to that same effect. I found this 139 00:07:20,120 --> 00:07:23,800 Speaker 1: article from two thousand and thirteen that you know, these 140 00:07:23,880 --> 00:07:27,400 Speaker 1: soldiers who were using bomb disposal robots on battlefields were 141 00:07:27,400 --> 00:07:30,160 Speaker 1: actually becoming too emotionally attached to them, or at least 142 00:07:30,200 --> 00:07:33,040 Speaker 1: that was the concern. And the robots weren't particularly cute, 143 00:07:33,040 --> 00:07:35,680 Speaker 1: like they didn't have like sweet faces or anything, but 144 00:07:35,680 --> 00:07:39,440 Speaker 1: but the soldiers actually started treating them as mascots or pets, 145 00:07:40,000 --> 00:07:42,000 Speaker 1: and then when they were destroyed, they actually have funerals 146 00:07:42,040 --> 00:07:44,480 Speaker 1: for them. But one of the big questions for robot 147 00:07:44,520 --> 00:07:47,480 Speaker 1: makers at the time was just how anthropomorphic, like do 148 00:07:47,480 --> 00:07:50,720 Speaker 1: you design these instruments, because you know, you do want 149 00:07:50,800 --> 00:07:53,600 Speaker 1: soldiers to care for the equipment and maintain it properly, 150 00:07:53,680 --> 00:07:55,720 Speaker 1: but you don't want them to care so much and 151 00:07:55,800 --> 00:07:58,800 Speaker 1: be so gingerly with these things that they refused to 152 00:07:58,800 --> 00:08:01,360 Speaker 1: put them in harm's way. You know, with too much 153 00:08:01,360 --> 00:08:03,440 Speaker 1: emotional attachment, you might not send your robot into a 154 00:08:03,440 --> 00:08:06,440 Speaker 1: space that you think dangerous, and that's precisely what they're 155 00:08:06,480 --> 00:08:08,760 Speaker 1: built for. Yeah, that is pretty interesting. It's not something 156 00:08:08,800 --> 00:08:11,200 Speaker 1: i'd really thought about before. All Right, well, here's a 157 00:08:11,320 --> 00:08:14,160 Speaker 1: robot that you might have a little bit less sympathy for, 158 00:08:14,320 --> 00:08:17,000 Speaker 1: so you remember the carnivorous furniture we read about And 159 00:08:17,040 --> 00:08:20,000 Speaker 1: this was a while back, but you remember this, right, Yeah. 160 00:08:20,360 --> 00:08:22,520 Speaker 1: I feel like it was some European designer was something, 161 00:08:22,520 --> 00:08:25,600 Speaker 1: and they'd created these porch lamps that were, um, they 162 00:08:25,600 --> 00:08:28,840 Speaker 1: were like powered by catching and digesting insects and flies. 163 00:08:29,000 --> 00:08:32,480 Speaker 1: It was a little gruesome for my taste. Well, if 164 00:08:32,520 --> 00:08:34,760 Speaker 1: that was gruesome for you, this is definitely not going 165 00:08:34,800 --> 00:08:38,080 Speaker 1: to ease your anxieties. And this comes from two thousand nine. 166 00:08:38,120 --> 00:08:40,640 Speaker 1: There was a Wired article and they reported that the 167 00:08:40,640 --> 00:08:44,320 Speaker 1: Department of Defense was funding battlefield robots that could power 168 00:08:44,360 --> 00:08:49,520 Speaker 1: themselves by collecting organic matter. Now that organic matter included 169 00:08:50,000 --> 00:08:54,600 Speaker 1: human corpses, So that's just great. We're gonna have these 170 00:08:54,640 --> 00:08:57,120 Speaker 1: flesh powered battle bots on the fields now, I mean, 171 00:08:57,160 --> 00:08:59,120 Speaker 1: it's true, that's exactly what they are. And it's like 172 00:08:59,160 --> 00:09:02,160 Speaker 1: these scientists have never watched a sci fi horror movie. 173 00:09:02,200 --> 00:09:05,920 Speaker 1: And it feels pretty predictable how your war might end 174 00:09:05,960 --> 00:09:08,600 Speaker 1: in all of this, And and honestly, the worst part 175 00:09:08,640 --> 00:09:11,440 Speaker 1: is that the project is called let's see, it's called 176 00:09:12,040 --> 00:09:17,360 Speaker 1: energetically Autonomous Tactical Robot or EATER for short. Yeah, I'm 177 00:09:17,400 --> 00:09:19,280 Speaker 1: not sure how much I love that fact, I feel 178 00:09:19,280 --> 00:09:23,400 Speaker 1: like you're just viewing my nightmares. But speaking of human 179 00:09:23,520 --> 00:09:26,240 Speaker 1: robot relations, I'd like you to know that we've got 180 00:09:26,240 --> 00:09:28,720 Speaker 1: work to do on both sides of that equation. And 181 00:09:29,080 --> 00:09:31,959 Speaker 1: this comes from the story of a little robot called Hitchbot. 182 00:09:32,440 --> 00:09:35,440 Speaker 1: So Hitchbot was built around two thousand fifteen, and it 183 00:09:35,520 --> 00:09:39,120 Speaker 1: was this ultra q hitchhiking robot that's only real mission 184 00:09:39,160 --> 00:09:41,439 Speaker 1: was to see the world and find some good humanity. 185 00:09:41,640 --> 00:09:43,560 Speaker 1: I mean, that's a nice idea. I'm not sure I 186 00:09:43,640 --> 00:09:47,680 Speaker 1: understand exactly what that means. So how did hitch Bot work. Well, 187 00:09:47,800 --> 00:09:51,400 Speaker 1: it was a Canadian creation and it was solar powered. Um, 188 00:09:51,640 --> 00:09:54,480 Speaker 1: it had led lights, it wore a smile all the time, 189 00:09:54,559 --> 00:09:57,640 Speaker 1: and had these rain boots and uh, hitchpot was about 190 00:09:57,679 --> 00:10:00,360 Speaker 1: the size of a small child. So it was pretty 191 00:10:00,400 --> 00:10:02,959 Speaker 1: hard not to love this cute robot. And it was 192 00:10:03,000 --> 00:10:04,640 Speaker 1: also built to be light enough that you could just 193 00:10:04,679 --> 00:10:06,400 Speaker 1: pick it up and buckle it into your back seat. 194 00:10:06,960 --> 00:10:08,800 Speaker 1: The whole idea was it would take photos along the 195 00:10:08,800 --> 00:10:11,000 Speaker 1: way to document the trip, and it had a few 196 00:10:11,000 --> 00:10:13,080 Speaker 1: small benefits for the people who picked it up, like 197 00:10:13,400 --> 00:10:16,360 Speaker 1: it came with a cigarette lighter and adapter. So I 198 00:10:16,440 --> 00:10:18,440 Speaker 1: guess you could charge your phone from it or whatever. 199 00:10:18,559 --> 00:10:20,840 Speaker 1: But the idea was like the creators would put a 200 00:10:20,840 --> 00:10:23,520 Speaker 1: destination on hitchpot. So one of the destinations with like 201 00:10:23,559 --> 00:10:25,920 Speaker 1: San Francisco, and they tell people to pick him up 202 00:10:25,960 --> 00:10:27,800 Speaker 1: and then drop them off somewhere you thought someone else 203 00:10:27,840 --> 00:10:31,800 Speaker 1: might pick him up. Really easy directions, right, So hitchbot 204 00:10:31,840 --> 00:10:35,760 Speaker 1: traveled across Canada, he took a trip across Europe, and 205 00:10:35,800 --> 00:10:39,160 Speaker 1: then he came to the US. That sounds like a 206 00:10:39,160 --> 00:10:43,720 Speaker 1: setup for something. So what happened exactly? So hitchpot had 207 00:10:43,760 --> 00:10:46,280 Speaker 1: this great bucket list he wanted to see before you 208 00:10:46,320 --> 00:10:48,120 Speaker 1: got to San Francisco. You want to see like jazz 209 00:10:48,120 --> 00:10:49,960 Speaker 1: in New Orleans. You wanted to see the magic of 210 00:10:49,960 --> 00:10:53,160 Speaker 1: Disney World, various other landmarks. And he started his trip 211 00:10:53,200 --> 00:10:56,040 Speaker 1: well from Massachusetts. But then two weeks later he was 212 00:10:56,080 --> 00:11:01,719 Speaker 1: found dismembered in Philadelphia. Oh no, that's terrible. Yeah, I mean, 213 00:11:01,760 --> 00:11:04,400 Speaker 1: it's doubly sad. When you read his goal on his 214 00:11:04,440 --> 00:11:08,160 Speaker 1: home page, it reads, quote, I hope that my hitchhiking 215 00:11:08,200 --> 00:11:10,920 Speaker 1: trip will allow me to meet many interesting people, see 216 00:11:10,960 --> 00:11:14,920 Speaker 1: beautiful places, and learn more about humanity. So I mean, 217 00:11:15,080 --> 00:11:16,480 Speaker 1: I guess the lesson is you're not supposed to go 218 00:11:16,559 --> 00:11:22,040 Speaker 1: to Philly to learn about humanity, but apparently not well. 219 00:11:22,120 --> 00:11:24,959 Speaker 1: Speaking about robots learning, I found this great article and 220 00:11:25,040 --> 00:11:28,040 Speaker 1: news scientists, and it was called eight hilarious ways AI 221 00:11:28,120 --> 00:11:31,040 Speaker 1: has outsmarted us to get the job done. It's a 222 00:11:31,120 --> 00:11:33,880 Speaker 1: really fun piece because you know, we mostly only think 223 00:11:33,920 --> 00:11:37,120 Speaker 1: that humans and animals can come up with creative solutions, 224 00:11:37,120 --> 00:11:39,480 Speaker 1: and I really don't think about robots being able to 225 00:11:39,520 --> 00:11:43,000 Speaker 1: do that. But these evolutionary algorithms do the same thing. 226 00:11:43,080 --> 00:11:45,920 Speaker 1: And as the computers are trying to optimize tasks based 227 00:11:45,960 --> 00:11:48,920 Speaker 1: on the rules you give them, it's pretty funny because 228 00:11:48,960 --> 00:11:51,000 Speaker 1: they actually end up failing in some of the most 229 00:11:51,120 --> 00:11:53,760 Speaker 1: ridiculous ways that are that are great to read about. 230 00:11:54,040 --> 00:11:57,360 Speaker 1: So I already like this, but I'm curious about the specifics. 231 00:11:57,600 --> 00:11:59,000 Speaker 1: Can you give me a couple Well, one of the 232 00:11:59,040 --> 00:12:01,640 Speaker 1: scientists who has interview put it this way quote, if 233 00:12:01,679 --> 00:12:05,560 Speaker 1: you close one loophole, the artificial intelligence finds another. To 234 00:12:05,640 --> 00:12:08,920 Speaker 1: some extent, it feels like you're wily coyote with the roadrunner. 235 00:12:09,040 --> 00:12:11,800 Speaker 1: You set up these wildly complex systems that fail in 236 00:12:11,840 --> 00:12:15,320 Speaker 1: ways you didn't expect. So you know, for instance, he 237 00:12:15,320 --> 00:12:18,160 Speaker 1: has the computer to create a creature that jumps really high, 238 00:12:18,280 --> 00:12:20,800 Speaker 1: and the way it measured how high the jump was 239 00:12:20,800 --> 00:12:23,640 Speaker 1: was by how high the creature's foot was off the ground. 240 00:12:24,000 --> 00:12:26,400 Speaker 1: So instead of trying to train the creature to jump 241 00:12:26,480 --> 00:12:29,720 Speaker 1: this wiley AI just had this creature do a somersault 242 00:12:29,760 --> 00:12:31,679 Speaker 1: on the ground and then kick its feet up mid 243 00:12:31,800 --> 00:12:34,040 Speaker 1: role and so it's just you imagine, this is pretty 244 00:12:34,040 --> 00:12:36,960 Speaker 1: fun to see. But there was also a different simulation 245 00:12:37,000 --> 00:12:40,600 Speaker 1: where the AI was supposed to create the fastest possible creature, 246 00:12:41,160 --> 00:12:44,000 Speaker 1: but instead of designing a snake or like some aerodynamic 247 00:12:44,080 --> 00:12:47,040 Speaker 1: hawk or something like that, it basically spent all its 248 00:12:47,040 --> 00:12:50,800 Speaker 1: time building this giant creature and then all they did 249 00:12:51,160 --> 00:12:53,600 Speaker 1: was push it over because you know, the velocity the 250 00:12:53,720 --> 00:12:56,640 Speaker 1: fall was faster than anything else that it could possibly 251 00:12:57,400 --> 00:13:00,520 Speaker 1: That's really interesting. Yeah, yeah, I was trying to even 252 00:13:00,520 --> 00:13:02,679 Speaker 1: think like how they would screw that one up. But honestly, 253 00:13:02,720 --> 00:13:05,920 Speaker 1: I think my favorite way that AI failed was when 254 00:13:05,920 --> 00:13:09,160 Speaker 1: it was tasked with limiting a computer's processor usage. So 255 00:13:09,280 --> 00:13:13,480 Speaker 1: the solution the robot just turned the computer off. I 256 00:13:13,559 --> 00:13:17,600 Speaker 1: love that actually, So here's a click fact. It's in 257 00:13:17,640 --> 00:13:20,120 Speaker 1: a similar vein. I I was looking up a cutting 258 00:13:20,200 --> 00:13:22,320 Speaker 1: edge story on how far robots had come in two 259 00:13:22,320 --> 00:13:26,160 Speaker 1: thousand seven and uh scientists in NYC in Japan designed 260 00:13:26,160 --> 00:13:29,040 Speaker 1: this really cute wine bot and the idea was that 261 00:13:29,080 --> 00:13:31,440 Speaker 1: you could feed it wine, cheese, or meat, those three 262 00:13:31,440 --> 00:13:33,640 Speaker 1: things and it would identify the food for you, kind 263 00:13:33,640 --> 00:13:37,760 Speaker 1: of like a personal somalia or a metal gastronomist, that's 264 00:13:37,760 --> 00:13:40,160 Speaker 1: what they called it. And it actually do it without 265 00:13:40,200 --> 00:13:43,240 Speaker 1: tasting the food too, So you can actually put a 266 00:13:43,240 --> 00:13:45,560 Speaker 1: wine bottle in front of it and the robot could 267 00:13:45,559 --> 00:13:48,240 Speaker 1: tell if the wine was authentic and the beverage matched 268 00:13:48,240 --> 00:13:51,560 Speaker 1: the label outside the bottle. That would have been good 269 00:13:51,600 --> 00:13:54,800 Speaker 1: for our old wine frauds episode. Yeah, But you know 270 00:13:54,840 --> 00:13:57,880 Speaker 1: what's interesting is that after impressing journalists with the demonstration, 271 00:13:58,000 --> 00:14:00,160 Speaker 1: this cameraman put his hand up to the robot up 272 00:14:00,480 --> 00:14:03,800 Speaker 1: and the robot identified the human hand is bacon. So 273 00:14:04,000 --> 00:14:06,600 Speaker 1: apparently the tech fact then still had a little ways 274 00:14:06,640 --> 00:14:09,960 Speaker 1: to go. Wow, that would be unfortunate if your hand 275 00:14:10,000 --> 00:14:12,640 Speaker 1: tasted like or smelled like bacon. Rather, I think that 276 00:14:12,679 --> 00:14:15,200 Speaker 1: would just be be torture. But you know, also I'm 277 00:14:15,240 --> 00:14:17,920 Speaker 1: a little curious because cannibals have always said humans taste 278 00:14:17,920 --> 00:14:20,360 Speaker 1: like chickens, so maybe they're a little off. I know 279 00:14:20,960 --> 00:14:23,760 Speaker 1: my feeling is that, like maybe bacon So delicious and 280 00:14:23,840 --> 00:14:26,000 Speaker 1: cannibals are just trying to protect their interests and keep 281 00:14:26,080 --> 00:14:28,560 Speaker 1: hipsters away from human meat. I I don't know, But 282 00:14:29,200 --> 00:14:31,240 Speaker 1: what I do know is we've got two more robot 283 00:14:31,240 --> 00:14:47,200 Speaker 1: facts to give you right after this break. Welcome back 284 00:14:47,200 --> 00:14:50,240 Speaker 1: to Part Time Genius. We're talking robots or mango. I 285 00:14:50,240 --> 00:14:52,760 Speaker 1: don't know if you knew this. Robots as people used 286 00:14:52,800 --> 00:14:55,000 Speaker 1: to call them back in the fifties. Is that true? 287 00:14:55,040 --> 00:14:57,680 Speaker 1: That's how it was pronounced. Yeah. I was actually watching 288 00:14:57,680 --> 00:15:00,160 Speaker 1: this old documentary and it was about how scientists we're 289 00:15:00,240 --> 00:15:04,120 Speaker 1: working on robot butlers of the future, and the narrator 290 00:15:04,200 --> 00:15:07,200 Speaker 1: and even the people on the screen kept calling them robots. 291 00:15:07,200 --> 00:15:10,840 Speaker 1: For somebody, that's super weird. So I feel like we 292 00:15:10,840 --> 00:15:14,480 Speaker 1: should bring that fact. But what's your last fact about robots? Well, 293 00:15:14,520 --> 00:15:17,640 Speaker 1: I know we've talked about humans hijacking animals in the past, 294 00:15:17,680 --> 00:15:19,600 Speaker 1: and we did this story a while back on how 295 00:15:19,640 --> 00:15:22,480 Speaker 1: scientists could manipulate cockroaches, you know, to get them to 296 00:15:22,520 --> 00:15:24,840 Speaker 1: move right or left with these I guess it was 297 00:15:24,880 --> 00:15:28,000 Speaker 1: with electrical pulses and and we can actually do the 298 00:15:28,080 --> 00:15:31,360 Speaker 1: same thing with beatles as well, yeah, I've seen that 299 00:15:31,400 --> 00:15:33,720 Speaker 1: you can actually put a um tiny camera on a 300 00:15:33,720 --> 00:15:36,560 Speaker 1: beetle now, which is pretty amazing, that's right, And so 301 00:15:36,680 --> 00:15:38,880 Speaker 1: to think about how that might be used. You know, 302 00:15:38,920 --> 00:15:41,920 Speaker 1: the advantage of a swarm of beetles oversay, like you 303 00:15:41,920 --> 00:15:44,360 Speaker 1: know this army of drones is, is that the beatles 304 00:15:44,360 --> 00:15:47,280 Speaker 1: are used to flying in bad weather, so they naturally 305 00:15:47,320 --> 00:15:50,280 Speaker 1: know where to feed themselves, how to rest, and so 306 00:15:50,320 --> 00:15:52,280 Speaker 1: because of that, you don't have to worry about solar 307 00:15:52,320 --> 00:15:55,040 Speaker 1: panels not working or a battery that might run out, 308 00:15:55,080 --> 00:15:57,720 Speaker 1: and you know, they're obviously super nimble, and they're just 309 00:15:57,960 --> 00:16:01,480 Speaker 1: way more cost effective too. But the weirdest animal I've 310 00:16:01,480 --> 00:16:04,480 Speaker 1: read about humans using to their advantage are turtles, which 311 00:16:04,600 --> 00:16:08,320 Speaker 1: obviously nothing like beetles, and apparently scientists from Korea have 312 00:16:08,440 --> 00:16:12,280 Speaker 1: created these parasitic robots that live on top of turtles 313 00:16:12,840 --> 00:16:15,680 Speaker 1: and kind of like your tomato robot, it will actually 314 00:16:15,800 --> 00:16:18,480 Speaker 1: feed the turtle snacks when they move in the right direction. 315 00:16:19,160 --> 00:16:21,680 Speaker 1: So I totally get insects. It's creepy that you can 316 00:16:21,680 --> 00:16:24,360 Speaker 1: turn them into cyborgs and control them, but you know, 317 00:16:24,520 --> 00:16:26,960 Speaker 1: they can get anywhere. It makes sense. They can like 318 00:16:27,000 --> 00:16:29,720 Speaker 1: fly and crawl wherever. But why do you want to 319 00:16:29,760 --> 00:16:33,040 Speaker 1: control a turtle? Well, I mean the whole ideas that 320 00:16:33,080 --> 00:16:35,840 Speaker 1: there are going to be disasters that happen from time 321 00:16:35,880 --> 00:16:39,920 Speaker 1: to time. So but this like floods or spills or earthquakes, 322 00:16:39,960 --> 00:16:42,560 Speaker 1: and we just can't get to these spots very fast. 323 00:16:42,600 --> 00:16:45,560 Speaker 1: But if you hijack a turtle population in the area, 324 00:16:45,720 --> 00:16:48,840 Speaker 1: or any animal really, and then you survey the land 325 00:16:48,880 --> 00:16:52,479 Speaker 1: by using them as your rover, it actually becomes really beneficial. 326 00:16:52,920 --> 00:16:55,640 Speaker 1: And unlike the bugs, which you have to outfit with cameras, 327 00:16:55,880 --> 00:16:58,720 Speaker 1: if you're just dropping these parasite robots on land and 328 00:16:58,760 --> 00:17:00,960 Speaker 1: they each find a turtle, right, it ends up being 329 00:17:01,040 --> 00:17:04,600 Speaker 1: much more efficient. That's pretty crazy, Okay, So here's my 330 00:17:04,680 --> 00:17:07,760 Speaker 1: last robot fact. In two thousand eight, a post STOC 331 00:17:07,840 --> 00:17:11,000 Speaker 1: fellow at Hakkaido University wanted to figure out if robot 332 00:17:11,040 --> 00:17:13,960 Speaker 1: comedians can make people laugh. So he had people read 333 00:17:14,000 --> 00:17:16,720 Speaker 1: a bunch of text message jokes and then he had 334 00:17:16,880 --> 00:17:20,879 Speaker 1: robots deliver those same jokes. So what happened? Where the 335 00:17:21,000 --> 00:17:23,720 Speaker 1: where the robots actually funny? Yeah, I mean they were 336 00:17:23,800 --> 00:17:27,080 Speaker 1: marginally funnier. On a scale of one to five. The 337 00:17:27,080 --> 00:17:29,720 Speaker 1: text jokes scored a two point three, while the average 338 00:17:29,800 --> 00:17:32,720 Speaker 1: robot comedian landed a two point eight. Oh that's not bad. 339 00:17:32,760 --> 00:17:35,840 Speaker 1: I mean that's half a point more. It's pretty good. Actually, yeah, 340 00:17:35,960 --> 00:17:38,359 Speaker 1: But then he evolved the robots to make them even 341 00:17:38,480 --> 00:17:43,520 Speaker 1: funnier by adding fart noises. Of course, the combination of 342 00:17:43,640 --> 00:17:47,960 Speaker 1: robots making jokes and fart noises drove the numbers even higher, 343 00:17:47,960 --> 00:17:50,760 Speaker 1: and the moral was pretty easy to figure out, like, 344 00:17:50,880 --> 00:17:54,320 Speaker 1: robot comedians can be funny, but gassy robot comedians are 345 00:17:54,440 --> 00:17:59,360 Speaker 1: even funnier. That's some pretty good science, you know. I 346 00:17:59,400 --> 00:18:01,840 Speaker 1: feel like we had some good facts here, but I 347 00:18:01,840 --> 00:18:04,159 Speaker 1: feel like what you just closed with with some pretty 348 00:18:04,320 --> 00:18:07,480 Speaker 1: solid science and the fact that even robots can figure 349 00:18:07,480 --> 00:18:09,880 Speaker 1: out that fart noises are funny, I feel like I'm 350 00:18:09,880 --> 00:18:12,000 Speaker 1: gonna have to give you the trophy today, Mango. Thank 351 00:18:12,040 --> 00:18:14,399 Speaker 1: you so much, well, and thank you guys for listening. 352 00:18:14,440 --> 00:18:16,440 Speaker 1: We'll be back with a full length episode tomorrow