1 00:00:03,800 --> 00:00:06,680 Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff 2 00:00:06,680 --> 00:00:13,640 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:13,720 --> 00:00:16,280 Speaker 1: My name is Robert Lamb and I'm Julie Douglas. And 4 00:00:16,520 --> 00:00:19,560 Speaker 1: we just finished talking about old people in robots a 5 00:00:19,600 --> 00:00:22,400 Speaker 1: little bit in our last Living with Robots podcast, because 6 00:00:22,800 --> 00:00:25,200 Speaker 1: as we discussed there, we're looking at a future full 7 00:00:25,239 --> 00:00:28,120 Speaker 1: of old people. I mean we will probably we're going 8 00:00:28,160 --> 00:00:31,360 Speaker 1: to be some of those old people properly and uh, 9 00:00:31,560 --> 00:00:33,479 Speaker 1: and somebody's gonna have to look after them, after them, 10 00:00:33,520 --> 00:00:35,840 Speaker 1: and they're gonna be fewer young people to take care 11 00:00:35,960 --> 00:00:38,520 Speaker 1: of all these old people. Young people will be busy 12 00:00:38,560 --> 00:00:41,600 Speaker 1: doing their stuff and you know, going into space and 13 00:00:41,600 --> 00:00:44,159 Speaker 1: all that, and they're gonna leave this this planet. Are 14 00:00:44,200 --> 00:00:45,800 Speaker 1: you saying they're the young people are going in the 15 00:00:45,840 --> 00:00:49,560 Speaker 1: generation ship and they're leaving off the old people behind. Yeah, 16 00:00:49,640 --> 00:00:53,280 Speaker 1: we'll have an oldie planet. I mean it'll be very peaceful, 17 00:00:53,280 --> 00:00:56,560 Speaker 1: lots of parks, lots of feeding scorel maybe maybe because 18 00:00:56,600 --> 00:00:59,320 Speaker 1: as we discussed in the last podcast, it could be 19 00:00:59,360 --> 00:01:03,200 Speaker 1: a bunch of rotting, uh, senior citizens with their exo 20 00:01:03,280 --> 00:01:07,080 Speaker 1: skeletons just skating right past you. You don't even you 21 00:01:07,120 --> 00:01:11,440 Speaker 1: don't know well, In this podcast, we're gonna explore this 22 00:01:11,520 --> 00:01:14,080 Speaker 1: topic a little a little closer, um, and a little 23 00:01:14,080 --> 00:01:17,600 Speaker 1: deeper and really get down into the nitty gritty of 24 00:01:17,640 --> 00:01:20,119 Speaker 1: our all Right, So we we want robots to help 25 00:01:20,760 --> 00:01:23,080 Speaker 1: take care of the elderly, or we want them to 26 00:01:23,080 --> 00:01:26,839 Speaker 1: take care of the elderly entirely. UM, let's start hashing 27 00:01:26,840 --> 00:01:31,040 Speaker 1: out some designs because, needless to say, you're common assembly 28 00:01:31,080 --> 00:01:34,520 Speaker 1: line robot is incapable of caring for Grandma. I mean 29 00:01:34,560 --> 00:01:37,440 Speaker 1: it there's a you go you you see like an 30 00:01:37,440 --> 00:01:40,800 Speaker 1: assembly line robot. It's gonna have barriers up around it 31 00:01:40,840 --> 00:01:44,479 Speaker 1: because it cannot work safely next to humans. And what 32 00:01:44,520 --> 00:01:47,440 Speaker 1: we're talking about here is is is a much I mean, 33 00:01:47,480 --> 00:01:49,960 Speaker 1: it's very far removed from that concept because it has 34 00:01:50,000 --> 00:01:52,360 Speaker 1: to be able to work around people without killing them. 35 00:01:52,520 --> 00:01:53,840 Speaker 1: Not only that, it has to be able to do 36 00:01:53,920 --> 00:01:58,280 Speaker 1: things like help a fragile individual with daily tasks such 37 00:01:58,280 --> 00:02:00,880 Speaker 1: as you know, get getting in and out of the bathtub, 38 00:02:01,560 --> 00:02:04,400 Speaker 1: using the restroom, helping them feed them, helping them to 39 00:02:04,560 --> 00:02:06,680 Speaker 1: know what kind of medication to take in any given time, 40 00:02:06,880 --> 00:02:09,680 Speaker 1: and anticipate their needs. That's huge, and do all this 41 00:02:09,680 --> 00:02:13,840 Speaker 1: while moving around in a human environment and potentially feeding 42 00:02:13,880 --> 00:02:16,239 Speaker 1: off of of human you know, these human cues and 43 00:02:16,320 --> 00:02:19,080 Speaker 1: human emotional cues. I mean, how hard is it to 44 00:02:19,160 --> 00:02:21,840 Speaker 1: understand what's going on in the mind of of of 45 00:02:22,080 --> 00:02:26,760 Speaker 1: the average person around you. Um, it's fairly difficult. But 46 00:02:26,760 --> 00:02:29,880 Speaker 1: but could we make a robot that could possibly at 47 00:02:29,960 --> 00:02:33,840 Speaker 1: least get some of those the subtle clues? Well if 48 00:02:33,840 --> 00:02:35,960 Speaker 1: they're trying, right, right, And that's that's what we're gonna 49 00:02:35,960 --> 00:02:38,639 Speaker 1: talk about today. Can we build a better bot? And 50 00:02:38,840 --> 00:02:41,320 Speaker 1: you know, what are some of the challenges? Um? And 51 00:02:41,320 --> 00:02:43,560 Speaker 1: and right now, I mean again, let's just talk about 52 00:02:43,600 --> 00:02:47,600 Speaker 1: the aging population again real quick. Particularly in Japan. It's 53 00:02:47,639 --> 00:02:50,760 Speaker 1: called the gray Yen. Um. It is called that because 54 00:02:50,800 --> 00:02:53,960 Speaker 1: it's providing a growing market in Japan for these services. 55 00:02:54,639 --> 00:02:57,720 Speaker 1: The Gray Yan almost of the population is over the 56 00:02:57,760 --> 00:03:00,400 Speaker 1: age of sixty five. And they can also this is 57 00:03:00,400 --> 00:03:02,880 Speaker 1: the other note to this, they can also afford a 58 00:03:02,919 --> 00:03:06,760 Speaker 1: bit more. The jet black Yen buys a lot of 59 00:03:06,800 --> 00:03:12,000 Speaker 1: toys though, and electronic gadgets, yes, yes. And the Sterling 60 00:03:12,000 --> 00:03:14,600 Speaker 1: it just means you get to hang out with Richard 61 00:03:14,639 --> 00:03:19,160 Speaker 1: Branson on one of his shuttle flights. It's called the 62 00:03:19,200 --> 00:03:22,160 Speaker 1: Gray Yen Shuttle flight Sterling. Alright, I like this. I 63 00:03:22,200 --> 00:03:25,440 Speaker 1: like this. The economy of the nation as divided up 64 00:03:25,440 --> 00:03:28,840 Speaker 1: into hair colors. Oh, I was thinking it was. I 65 00:03:29,040 --> 00:03:31,239 Speaker 1: was thinking it was more like the American Express card, 66 00:03:31,400 --> 00:03:34,119 Speaker 1: like the platinum and the red yen is is really adventurous? 67 00:03:34,160 --> 00:03:38,760 Speaker 1: Not going right? That dyed red hair. Yes, yeah, they're 68 00:03:38,800 --> 00:03:43,200 Speaker 1: all loose watch out with excess skeletons and the red yen. Yeah. 69 00:03:43,120 --> 00:03:44,960 Speaker 1: And then there's I guess the blonde yin because you 70 00:03:45,000 --> 00:03:49,600 Speaker 1: have the what there's a particular fad in Japan where 71 00:03:49,640 --> 00:03:53,720 Speaker 1: the women dress up like like dolls. Why is this 72 00:03:53,760 --> 00:03:57,480 Speaker 1: saluting me? Um? It's not. Is it part of kawaii? 73 00:03:58,160 --> 00:04:03,200 Speaker 1: It factors into kauai. But there's a there's this, yes, 74 00:04:03,560 --> 00:04:06,080 Speaker 1: and then there's a Lolita noir where they dressed up 75 00:04:06,080 --> 00:04:09,720 Speaker 1: like dark Lolita's. And there's a whole other by the way, 76 00:04:09,760 --> 00:04:13,840 Speaker 1: there's a whole they're like underground economy with the Lolita girls. 77 00:04:13,880 --> 00:04:15,600 Speaker 1: By the way, an older men, but we won't talk 78 00:04:15,600 --> 00:04:21,000 Speaker 1: about that today. Uh, getting back on track. Um, so yeah, 79 00:04:21,000 --> 00:04:24,479 Speaker 1: we're talking about you know this, this aging population of 80 00:04:24,640 --> 00:04:28,880 Speaker 1: old men looking for Lolita's. Um and just again to 81 00:04:28,920 --> 00:04:31,680 Speaker 1: put this in more context, by four out of ten, 82 00:04:31,760 --> 00:04:35,120 Speaker 1: Japanese will be classified as elderly. Okay, So it makes 83 00:04:35,120 --> 00:04:37,000 Speaker 1: sense that they're the ones that are really going after 84 00:04:37,040 --> 00:04:41,480 Speaker 1: this this problem of aging really um. And in fact 85 00:04:41,520 --> 00:04:44,559 Speaker 1: there's just so you know how they've been preparing. There's 86 00:04:44,560 --> 00:04:48,200 Speaker 1: actually a Tokyo based company called prop that has created 87 00:04:48,240 --> 00:04:51,080 Speaker 1: air bags for the elderly. Yes, you send me the 88 00:04:51,080 --> 00:04:53,520 Speaker 1: link to this, and it's it's pretty fascinating because it's 89 00:04:53,560 --> 00:04:56,159 Speaker 1: kind of exactly what it sounds like. It's like this 90 00:04:56,240 --> 00:04:59,960 Speaker 1: kind of robotic harness that you wear, and it had 91 00:05:00,040 --> 00:05:01,919 Speaker 1: as these air bags that will sort of shoot up 92 00:05:01,960 --> 00:05:03,960 Speaker 1: around the head and around the hips because these are 93 00:05:04,000 --> 00:05:09,000 Speaker 1: of course the most vulnerable areas of the old body. Um, 94 00:05:09,120 --> 00:05:10,919 Speaker 1: you don't want to hit your head, damns the brain, 95 00:05:11,640 --> 00:05:14,880 Speaker 1: you don't want to hit the hips. Very difficult to 96 00:05:15,160 --> 00:05:18,800 Speaker 1: deal with, you. So these are cushions that pop up. 97 00:05:18,800 --> 00:05:20,880 Speaker 1: They weigh two and a half pounds each enter tied 98 00:05:20,880 --> 00:05:24,120 Speaker 1: around the waist and then they're activated by electric sensors 99 00:05:24,120 --> 00:05:26,320 Speaker 1: that can feel a sudden change in movement as soon 100 00:05:26,360 --> 00:05:29,480 Speaker 1: as the alarm goes off. Fifteen liters of compressed air 101 00:05:29,560 --> 00:05:33,279 Speaker 1: inject into these inflatable pads, puff them up and then 102 00:05:33,640 --> 00:05:36,839 Speaker 1: ideally when you fall backwards, and you can only fall 103 00:05:36,880 --> 00:05:40,440 Speaker 1: backwards this right, by the way, never pitch forward. Right. Well, 104 00:05:40,480 --> 00:05:43,760 Speaker 1: I guess it's kind of like in um to bring 105 00:05:43,800 --> 00:05:45,839 Speaker 1: up pro wrestling again. Like a lot of those guys 106 00:05:45,960 --> 00:05:48,480 Speaker 1: like they trained to take bumps and they try they 107 00:05:48,640 --> 00:05:51,200 Speaker 1: generally you want to bump right on your back. And 108 00:05:51,279 --> 00:05:52,800 Speaker 1: I've I've heard some of these guys talk about like 109 00:05:52,839 --> 00:05:55,800 Speaker 1: they trained so this locker room talk. No, well, I 110 00:05:55,800 --> 00:05:59,560 Speaker 1: don't go to locker rooms, but room the bump, bump 111 00:05:59,600 --> 00:06:01,240 Speaker 1: on your back? What does that mean? To take a 112 00:06:01,279 --> 00:06:04,200 Speaker 1: bump is to fall Like any time in a wrestling mat, 113 00:06:04,279 --> 00:06:06,279 Speaker 1: for see a guy like take a punch or a 114 00:06:06,279 --> 00:06:10,280 Speaker 1: slam and he hits the canvas, He's taking a bump, okay, 115 00:06:10,640 --> 00:06:13,080 Speaker 1: And so they trained for this just to to take 116 00:06:13,120 --> 00:06:15,440 Speaker 1: the bump in an optimal fashion so that it doesn't 117 00:06:15,520 --> 00:06:18,560 Speaker 1: really do much damage. I mean, it will do a 118 00:06:18,560 --> 00:06:20,960 Speaker 1: lot of damage over time perhaps, but generally you want 119 00:06:20,960 --> 00:06:22,919 Speaker 1: to fall flat on your back and take this flat 120 00:06:22,960 --> 00:06:25,160 Speaker 1: bump on your back. And I've heard some of these 121 00:06:25,160 --> 00:06:27,360 Speaker 1: guys talk about to where they're they're trained to such 122 00:06:27,360 --> 00:06:29,160 Speaker 1: a point that if they were to slip on milk 123 00:06:29,200 --> 00:06:31,719 Speaker 1: in the grocery store, they would take up a back 124 00:06:31,760 --> 00:06:33,960 Speaker 1: bump because you don't want to fall on your side 125 00:06:34,080 --> 00:06:36,719 Speaker 1: or could be a useful skill. Right. So I'm thinking 126 00:06:36,720 --> 00:06:38,880 Speaker 1: you just need to train up the elderly. I think 127 00:06:39,000 --> 00:06:42,240 Speaker 1: the elderly professional wrestlers in Japan will will be the 128 00:06:42,440 --> 00:06:44,240 Speaker 1: really be the ones to try this out. Yeah, I'm 129 00:06:44,240 --> 00:06:47,559 Speaker 1: just thinking about whole Kogan, like, you know, uh, carrying 130 00:06:47,600 --> 00:06:51,560 Speaker 1: out these workshops for the elderly. Yeah. But you know, 131 00:06:51,880 --> 00:06:53,800 Speaker 1: here's the thing though, too, that that's so cool about 132 00:06:53,839 --> 00:06:58,200 Speaker 1: these airbags is that they can inflate in point one second, 133 00:06:58,839 --> 00:07:02,680 Speaker 1: the time it takes for an old person to topple. Yeah. Um, 134 00:07:02,720 --> 00:07:04,800 Speaker 1: so actually that's really helpful. I mean if you're going 135 00:07:04,880 --> 00:07:07,400 Speaker 1: to fall backward right um. And then there are partner 136 00:07:07,520 --> 00:07:11,480 Speaker 1: robots that toyota Is has been creating and getting into 137 00:07:11,480 --> 00:07:14,760 Speaker 1: the game of offering assistance for the elderly and the disabled. Yeah. 138 00:07:14,760 --> 00:07:17,120 Speaker 1: In fact, this is just like we're recording this what 139 00:07:17,240 --> 00:07:19,320 Speaker 1: this is the second week in November the first I 140 00:07:19,360 --> 00:07:23,240 Speaker 1: really can't keep track of this, okay, and and I 141 00:07:23,240 --> 00:07:25,360 Speaker 1: have no idea when it's actually publishing, like the third 142 00:07:25,440 --> 00:07:27,800 Speaker 1: or fourth week. Yeah, for you, it's like Halloween and 143 00:07:27,800 --> 00:07:31,040 Speaker 1: then nothing else right, I'm getting it. Yeah, after Halloween 144 00:07:31,160 --> 00:07:35,560 Speaker 1: is just this busy mess of holidays. Yeah, visits and 145 00:07:35,760 --> 00:07:39,560 Speaker 1: trips and da da dada. Yeah. But um but but anyway, 146 00:07:39,600 --> 00:07:42,680 Speaker 1: so that this first week of November actually a two 147 00:07:42,680 --> 00:07:46,720 Speaker 1: thousand eleven Toyota Motor Corporation in Toyota City in Japan. 148 00:07:46,960 --> 00:07:49,680 Speaker 1: I somehow was not aware that they Toyota had its 149 00:07:49,720 --> 00:07:54,800 Speaker 1: own city in Japan. It's pretty um they were. They 150 00:07:54,840 --> 00:07:58,000 Speaker 1: were really launching forward with this idea that Toyota is 151 00:07:58,040 --> 00:08:01,360 Speaker 1: really concerned about about working on these bots, making a 152 00:08:01,640 --> 00:08:04,480 Speaker 1: making a priority and partner bots are a big deal, 153 00:08:04,520 --> 00:08:07,000 Speaker 1: and and and not just in nursing and healthcare, but 154 00:08:07,080 --> 00:08:11,720 Speaker 1: also in personal transport, manufacturing, domestic duties like this all 155 00:08:11,760 --> 00:08:14,400 Speaker 1: goes into the idea of if we are going to 156 00:08:14,520 --> 00:08:18,680 Speaker 1: develop a future of work where robots are doing work 157 00:08:18,720 --> 00:08:21,440 Speaker 1: for us, we want to develop robots that can learn 158 00:08:21,560 --> 00:08:24,800 Speaker 1: tasks alongside humans and then eventually do them on their own. 159 00:08:25,200 --> 00:08:27,560 Speaker 1: Can you get that peanut butter for me? Right? So, so, 160 00:08:27,760 --> 00:08:30,119 Speaker 1: like the first logical step, well maybe not the first, 161 00:08:30,120 --> 00:08:33,120 Speaker 1: but one of the first logical steps definitely a stopover 162 00:08:33,200 --> 00:08:36,160 Speaker 1: on the way to this future is the reality of 163 00:08:36,200 --> 00:08:38,319 Speaker 1: helper bots. A bot that's going to help a human 164 00:08:38,480 --> 00:08:44,000 Speaker 1: with various tasks. So you end up with basically like 165 00:08:44,040 --> 00:08:47,080 Speaker 1: four different areas that that are essential as laid out 166 00:08:47,120 --> 00:08:50,839 Speaker 1: by Toyota. One independent walk assist all right, and this 167 00:08:50,920 --> 00:08:53,960 Speaker 1: is the idea of just supporting independent walking for people 168 00:08:54,000 --> 00:08:57,120 Speaker 1: whose ability has been impaired. UM. So these are sort 169 00:08:57,160 --> 00:08:59,800 Speaker 1: of like you fit your legs into this device, yes, 170 00:09:00,000 --> 00:09:01,880 Speaker 1: and it will actually help you. It's it's kind of 171 00:09:01,880 --> 00:09:03,679 Speaker 1: has some sort of nomadic device in it, but it 172 00:09:03,679 --> 00:09:08,040 Speaker 1: will help you actually get upstairs, walk around. Yeah, it's 173 00:09:08,040 --> 00:09:11,400 Speaker 1: like an exosuth skeleton, or at least a limited exo skeleton. Um. 174 00:09:11,600 --> 00:09:16,120 Speaker 1: No nanocarbon tubes in it. Strength, No like super Terminator 175 00:09:16,200 --> 00:09:20,120 Speaker 1: power armor with with power axes and all that. But 176 00:09:20,120 --> 00:09:22,400 Speaker 1: but but still you know it's it's the the the 177 00:09:22,440 --> 00:09:25,040 Speaker 1: idea is that say the legs are weak or inoperable, 178 00:09:25,160 --> 00:09:27,720 Speaker 1: but the exo skeleton around them can help do the 179 00:09:27,760 --> 00:09:32,080 Speaker 1: walking or do the walking for them. Um. While training 180 00:09:32,080 --> 00:09:36,120 Speaker 1: assists this is uh, this is a similar Balanced training assists. 181 00:09:36,280 --> 00:09:39,520 Speaker 1: This is like robots developed to support balance function training 182 00:09:39,520 --> 00:09:42,800 Speaker 1: with people with impaired balance. So it's think a combination 183 00:09:42,840 --> 00:09:46,880 Speaker 1: of a two wheeled self balancing type deal with game 184 00:09:46,920 --> 00:09:50,120 Speaker 1: elements aimed at making this kind of training enjoyable, right, 185 00:09:50,160 --> 00:09:52,120 Speaker 1: So I mean that would be actually really helpful if 186 00:09:52,120 --> 00:09:54,600 Speaker 1: you're if you're going through physical therapy as well, right, 187 00:09:54,640 --> 00:09:57,720 Speaker 1: we're trying to regain and also bear in mind you're 188 00:09:57,760 --> 00:10:00,800 Speaker 1: like gaming old people. But come on, we're talking about 189 00:10:00,840 --> 00:10:03,480 Speaker 1: a lot. We're talking in a large part about today's uh, 190 00:10:04,080 --> 00:10:07,480 Speaker 1: young or middle aged people, many of whom are gamers especially. 191 00:10:07,960 --> 00:10:09,840 Speaker 1: You know, Japan has a big gaming culture as well. 192 00:10:10,160 --> 00:10:12,760 Speaker 1: So imagine in the future, Yeah, there's gonna be Gamification 193 00:10:13,240 --> 00:10:16,800 Speaker 1: of physical therapy is just a no brainer. Right. So 194 00:10:16,920 --> 00:10:19,160 Speaker 1: right now there would be like a bingo program, right, 195 00:10:19,280 --> 00:10:20,959 Speaker 1: but in the future it would be like, uh, I 196 00:10:21,000 --> 00:10:26,240 Speaker 1: don't know what like World of Warcraft for for our generation, yeah, 197 00:10:26,320 --> 00:10:29,160 Speaker 1: or whatever, these retro games whatever is big in Japan. 198 00:10:29,240 --> 00:10:32,040 Speaker 1: I don't know, the World of Warcraft is big in Japan. Yeah, 199 00:10:32,080 --> 00:10:35,440 Speaker 1: but I'm just saying, like we're like Futson around in 200 00:10:35,480 --> 00:10:38,000 Speaker 1: our sunset years will be like, oh, let me just 201 00:10:38,040 --> 00:10:40,880 Speaker 1: program this World Warcraft and and think about those great 202 00:10:40,920 --> 00:10:46,480 Speaker 1: fuzzy memories of playing in my yester year yeah years. Uh. 203 00:10:46,520 --> 00:10:49,160 Speaker 1: And then the fourth area is patient Transfer assist and 204 00:10:49,160 --> 00:10:51,600 Speaker 1: this is really I found this would be the most exciting. 205 00:10:51,600 --> 00:10:54,240 Speaker 1: And this is the the idea that a lot of 206 00:10:54,360 --> 00:10:57,920 Speaker 1: caring for not only the elderly, but anyone who, like, 207 00:10:58,160 --> 00:11:01,199 Speaker 1: even people were just really sick before hospital for various things. 208 00:11:01,720 --> 00:11:04,400 Speaker 1: A lot of the caregiving comes down to moving the 209 00:11:04,440 --> 00:11:07,679 Speaker 1: patient around. Yeah, and it's physically demanding forge because yeah, 210 00:11:07,679 --> 00:11:10,040 Speaker 1: because you're having to move. Just if you're moving a person, 211 00:11:10,120 --> 00:11:13,320 Speaker 1: say they're not even particularly fragile, they don't have anything 212 00:11:13,360 --> 00:11:15,320 Speaker 1: particularly wrong with them, you're still talking about moving a 213 00:11:15,360 --> 00:11:18,560 Speaker 1: sizeable person from say their bad to a bathtub, from 214 00:11:18,600 --> 00:11:21,319 Speaker 1: their bed to a toilet, moving them around just enough 215 00:11:21,360 --> 00:11:24,079 Speaker 1: to even uh uh you know, to bathe them with 216 00:11:24,120 --> 00:11:26,920 Speaker 1: a sponge bath or or or administer or bed pan 217 00:11:27,080 --> 00:11:30,360 Speaker 1: type situation, or just to prevent bed sores and really 218 00:11:30,400 --> 00:11:34,720 Speaker 1: immobile people. Um. The idea is that you have a 219 00:11:34,800 --> 00:11:37,720 Speaker 1: robot with like a weight supporting arm that actually helps out. 220 00:11:38,000 --> 00:11:41,040 Speaker 1: And some some really cool pictures of this um also 221 00:11:41,080 --> 00:11:45,560 Speaker 1: surfaced from this Toyota event. And and then they're they're 222 00:11:45,559 --> 00:11:48,400 Speaker 1: really cool because and I'm not I mean, they're also 223 00:11:48,400 --> 00:11:50,199 Speaker 1: easy to kind of make fun of because it's like 224 00:11:50,520 --> 00:11:52,560 Speaker 1: it's a robot helping a dude set on a toilet 225 00:11:52,559 --> 00:11:54,800 Speaker 1: while a nurse helps. But but the thing is, like, 226 00:11:54,920 --> 00:11:57,040 Speaker 1: if you put yourself in the in the mindset of 227 00:11:57,040 --> 00:11:59,760 Speaker 1: that dude or that nurse, like this is awesome because 228 00:12:00,080 --> 00:12:03,400 Speaker 1: suddenly you have a situation where only one nurse has 229 00:12:03,440 --> 00:12:06,920 Speaker 1: to help this guy go to the bathroom, which is 230 00:12:06,920 --> 00:12:09,400 Speaker 1: great for the nurses, great for the guy, and eventually 231 00:12:09,400 --> 00:12:11,400 Speaker 1: could lead to the situation where this dude can have 232 00:12:11,440 --> 00:12:15,720 Speaker 1: assistance using the restroom. Um, you know, the very private 233 00:12:15,720 --> 00:12:18,400 Speaker 1: act that none of us want help with. I mean, 234 00:12:18,520 --> 00:12:20,400 Speaker 1: most of us don't want We don't want help with 235 00:12:20,440 --> 00:12:23,959 Speaker 1: it unless we're we're very young or very weird. We 236 00:12:24,160 --> 00:12:28,160 Speaker 1: don't judge fetish. Okay, all right, right, if you like 237 00:12:28,280 --> 00:12:31,200 Speaker 1: help going to the bathroom, that's great. But and if 238 00:12:31,240 --> 00:12:33,360 Speaker 1: you but if you, but the idea is if the 239 00:12:33,480 --> 00:12:35,880 Speaker 1: robot is helping you, then every I think more people 240 00:12:35,920 --> 00:12:41,760 Speaker 1: can get into the idea. And independence two, which is 241 00:12:41,800 --> 00:12:45,120 Speaker 1: a really important um you know when you're aging and 242 00:12:45,520 --> 00:12:48,360 Speaker 1: um And again importantly, also if you're staffing a hospital 243 00:12:48,400 --> 00:12:51,200 Speaker 1: to care for older people, the idea that one person 244 00:12:52,000 --> 00:12:55,080 Speaker 1: or even zero actual humans are having to help with 245 00:12:55,080 --> 00:12:59,320 Speaker 1: this task and they can therefore apply their skills elsewhere well, 246 00:12:59,360 --> 00:13:02,560 Speaker 1: you know that I that I love potty humor and um, 247 00:13:02,640 --> 00:13:04,600 Speaker 1: so of course when you showed me the video, I 248 00:13:04,640 --> 00:13:06,720 Speaker 1: was going to react to it. But it wasn't just 249 00:13:06,760 --> 00:13:09,040 Speaker 1: because the guy was being you know, I was sitting 250 00:13:09,040 --> 00:13:13,080 Speaker 1: on the toilet with assistance. It was that he was 251 00:13:13,120 --> 00:13:16,920 Speaker 1: wearing like like we're jogging pants, like you know, the 252 00:13:16,920 --> 00:13:19,800 Speaker 1: ones that are really tight, and they were black and shiny, 253 00:13:19,920 --> 00:13:22,320 Speaker 1: and then as you described, like this this sort of 254 00:13:22,600 --> 00:13:26,719 Speaker 1: prison yellow and white striped shirt and pants over that. Well, yeah, 255 00:13:26,760 --> 00:13:28,800 Speaker 1: well Toyota didn't want to actually bring a guy in 256 00:13:28,920 --> 00:13:31,920 Speaker 1: stage and have a robot with pants down. I appreciate that. 257 00:13:32,080 --> 00:13:34,080 Speaker 1: It was just really distracting because I was like, I 258 00:13:34,080 --> 00:13:36,840 Speaker 1: don't get it. Is he going to go running after this? 259 00:13:37,000 --> 00:13:40,560 Speaker 1: Is he in prison both? I don't know. Yeah, well 260 00:13:40,559 --> 00:13:42,120 Speaker 1: they could have gone more in the direction of the 261 00:13:42,800 --> 00:13:51,120 Speaker 1: fire toilet. Remember the gator, that's that's what's coming home. Yeah. Yeah, 262 00:13:51,120 --> 00:13:54,120 Speaker 1: but you're right. This was just in case anybody's never 263 00:13:54,160 --> 00:13:56,959 Speaker 1: seen this, you can go onto YouTube and see this 264 00:13:57,080 --> 00:14:00,000 Speaker 1: and uh, there is a woman that is using a toilet. 265 00:14:00,040 --> 00:14:03,600 Speaker 1: It that is uh, it's the fire toilet, right is Yeah, 266 00:14:03,640 --> 00:14:07,000 Speaker 1: it's basically a fire toilet. It's gas powered or gas 267 00:14:07,000 --> 00:14:11,679 Speaker 1: powered flames down there that incinerate the the waste product 268 00:14:11,800 --> 00:14:14,280 Speaker 1: that the one produces on the toilet. And again, this 269 00:14:14,320 --> 00:14:16,719 Speaker 1: is fine, this is great, it's actually using navy ships. 270 00:14:16,760 --> 00:14:20,240 Speaker 1: But again I take issue with the aesthetics of it 271 00:14:20,280 --> 00:14:24,000 Speaker 1: because the woman, uh, she starts this video and she's 272 00:14:24,040 --> 00:14:26,040 Speaker 1: got like, I don't know, maybe like a hundred keys 273 00:14:26,040 --> 00:14:27,680 Speaker 1: on a ring, So you're kind of like, what's the deal, 274 00:14:27,720 --> 00:14:29,960 Speaker 1: why do you have a hundred keys? And then she's 275 00:14:30,000 --> 00:14:33,320 Speaker 1: got you know, maybe some sort of like Eastern block accent. Fine, 276 00:14:33,720 --> 00:14:35,720 Speaker 1: but it just sort of adds to this ap like 277 00:14:35,760 --> 00:14:38,280 Speaker 1: there's no nudity in it, but she's like visibly she's 278 00:14:38,280 --> 00:14:41,520 Speaker 1: obviously dropping pants. She's not wearing some sort of black 279 00:14:41,560 --> 00:14:45,000 Speaker 1: garment underneath. And and then there's actual flaming poosh shown, 280 00:14:45,160 --> 00:14:47,920 Speaker 1: the flaming poush shown. They cut to a shot of 281 00:14:47,960 --> 00:14:50,280 Speaker 1: her with all you know, her pants DOWMP, but then 282 00:14:50,320 --> 00:14:53,360 Speaker 1: she's taken her boots and her socks off. It's all 283 00:14:53,480 --> 00:14:55,760 Speaker 1: very It just gets weirder and weirder the longer this 284 00:14:55,840 --> 00:14:59,160 Speaker 1: video goes on, a little bit like this podcast right now, well, 285 00:14:59,240 --> 00:15:02,200 Speaker 1: you can go weird either way with with um with 286 00:15:02,280 --> 00:15:05,120 Speaker 1: some sort of toilet technology demo, and that is to 287 00:15:05,160 --> 00:15:08,840 Speaker 1: either go realistic or go um with a black body 288 00:15:08,840 --> 00:15:11,360 Speaker 1: suit underneath your clothing. Yeah yeah, so okay, I got 289 00:15:11,360 --> 00:15:14,800 Speaker 1: my attention. Nonetheless, so let's let's talk nuts and bolts 290 00:15:14,800 --> 00:15:17,160 Speaker 1: about this too. Like, you know what, when we're talking 291 00:15:17,200 --> 00:15:19,840 Speaker 1: about building a better boty and we're really thinking about 292 00:15:19,880 --> 00:15:22,880 Speaker 1: it more as you know, we're gonna anthropomorphise it and 293 00:15:22,920 --> 00:15:25,720 Speaker 1: think about it as us. Right, this is this version 294 00:15:25,720 --> 00:15:28,640 Speaker 1: of ourselves. What are we thinking about? Well, one of 295 00:15:28,640 --> 00:15:31,840 Speaker 1: the big things is sensitivity to the environment that the 296 00:15:31,920 --> 00:15:34,640 Speaker 1: robot is working in and um on one level, that 297 00:15:34,880 --> 00:15:36,800 Speaker 1: involves just being able to move through it, being able 298 00:15:36,800 --> 00:15:39,120 Speaker 1: to navigate it, but also being able to just think 299 00:15:39,120 --> 00:15:42,160 Speaker 1: of your sense of touch. Um. Like. The best example 300 00:15:42,200 --> 00:15:45,680 Speaker 1: of this for me would be astronaut gloves. Like, one 301 00:15:45,680 --> 00:15:48,000 Speaker 1: of the big issues with astronaut gloves has been you 302 00:15:48,000 --> 00:15:49,600 Speaker 1: you put these guys up in space and they're gonna 303 00:15:49,600 --> 00:15:52,360 Speaker 1: be working on something really delicate, but they can't just 304 00:15:52,400 --> 00:15:55,080 Speaker 1: take their gloves off and work with their bare hands. Obviously, 305 00:15:55,160 --> 00:15:58,200 Speaker 1: they have to wear gloves, and you cannot wear normal gloves, 306 00:15:58,600 --> 00:16:02,040 Speaker 1: especially normal big thick gloves or space without losing a 307 00:16:02,080 --> 00:16:04,760 Speaker 1: certain amount of sensitivity. Now, with a robot you're building, 308 00:16:04,920 --> 00:16:06,680 Speaker 1: you're just building it all up from the bottom up. 309 00:16:06,840 --> 00:16:08,280 Speaker 1: So like where do you where do you put the 310 00:16:08,280 --> 00:16:11,280 Speaker 1: sensors on the on the feelers or on the body itself? Uh? 311 00:16:11,440 --> 00:16:14,280 Speaker 1: Could you give it a skin that enables it to 312 00:16:14,360 --> 00:16:16,840 Speaker 1: feel all over like a person? Right, so if it 313 00:16:16,880 --> 00:16:18,760 Speaker 1: bumped into you, it would know, It would know if 314 00:16:18,760 --> 00:16:22,440 Speaker 1: you bumped into it. And I also just think of manipulation, 315 00:16:22,640 --> 00:16:24,720 Speaker 1: Like even just imagine that the robot just had the 316 00:16:24,760 --> 00:16:28,680 Speaker 1: feeling of a human hand on its manipulators. It would 317 00:16:28,800 --> 00:16:32,680 Speaker 1: enable it to be far more delicate with something. Uh. 318 00:16:32,760 --> 00:16:35,520 Speaker 1: And in this case, it would be handling delicate things. 319 00:16:35,520 --> 00:16:38,400 Speaker 1: It would be handling us sponge bath for instance. Right, 320 00:16:38,520 --> 00:16:42,120 Speaker 1: you don't want something that's just a big metal um 321 00:16:42,360 --> 00:16:44,440 Speaker 1: claw on the end of a of an arm. You 322 00:16:44,480 --> 00:16:47,160 Speaker 1: want something that has a certain amount of sensitivity to it. Right, 323 00:16:47,160 --> 00:16:48,400 Speaker 1: you don't want to get bashed over the head with 324 00:16:48,400 --> 00:16:50,840 Speaker 1: a bar of soap. Yeah. Um. So yeah, there's this 325 00:16:50,880 --> 00:16:53,080 Speaker 1: thing called robot skin. And this says this is from 326 00:16:53,080 --> 00:16:57,360 Speaker 1: a Fast Companies article. Robot skin can feel touched, sense chemicals, 327 00:16:57,360 --> 00:17:00,120 Speaker 1: and soak up solar power. It says, what we're talking 328 00:17:00,120 --> 00:17:04,040 Speaker 1: about is a super intelligent skin designed by Stanford University researcher. 329 00:17:04,080 --> 00:17:07,360 Speaker 1: It's solar powered and when the skin is subjected to pressure, 330 00:17:07,400 --> 00:17:10,800 Speaker 1: the current flowing through the embedded transistors is modified as 331 00:17:10,840 --> 00:17:15,400 Speaker 1: tiny pyramid shapes molded into the polymer layer compress, resulting 332 00:17:15,440 --> 00:17:19,320 Speaker 1: in a super sensitive transducer that can apparently detect the 333 00:17:19,359 --> 00:17:23,439 Speaker 1: pressure from a house flies feet So cool criss cross 334 00:17:23,520 --> 00:17:26,000 Speaker 1: nanimator scale wires talk with a thin rubber sheet. I 335 00:17:26,040 --> 00:17:28,199 Speaker 1: like it. Yeah, yeah, I mean, I just think that 336 00:17:28,240 --> 00:17:31,760 Speaker 1: it's it's an amazing technology in and of itself. And 337 00:17:31,760 --> 00:17:33,479 Speaker 1: of course they think that too, because it's not just 338 00:17:33,880 --> 00:17:37,399 Speaker 1: for androids all that that's what was intended for um, 339 00:17:37,640 --> 00:17:40,920 Speaker 1: they can also use it to coat cars or military 340 00:17:41,000 --> 00:17:45,280 Speaker 1: vehicles or even soldiers uniforms could act as biosensors and 341 00:17:45,480 --> 00:17:50,359 Speaker 1: solar power generators. And also think of the possibilities for prosthetics. Yeah, 342 00:17:50,400 --> 00:17:54,120 Speaker 1: and again for um, for astronaut gloves or or even 343 00:17:54,400 --> 00:17:58,879 Speaker 1: like forget the astronaut, for um, like a robotic exploring 344 00:17:58,920 --> 00:18:01,400 Speaker 1: probe where a person could say, put on a pair 345 00:18:01,440 --> 00:18:05,840 Speaker 1: of a pair of gloves on Earth and feel with 346 00:18:06,000 --> 00:18:10,400 Speaker 1: the very human tactile response. Uh An object on another planet, right, 347 00:18:10,440 --> 00:18:12,800 Speaker 1: and try to describe that in the texture of it 348 00:18:12,840 --> 00:18:15,800 Speaker 1: and so on and so forth. Um, of course I 349 00:18:15,840 --> 00:18:17,960 Speaker 1: can't help but think that it's going to be used 350 00:18:18,000 --> 00:18:22,520 Speaker 1: on Roxy the robot. This is the sex spot. Yeah, 351 00:18:22,560 --> 00:18:25,680 Speaker 1: but that's on the market right now. Yeah, but that's 352 00:18:25,720 --> 00:18:31,160 Speaker 1: in the words of of Arkin, the professor. Arkin, that's 353 00:18:31,160 --> 00:18:33,919 Speaker 1: a bad robot. That's just not like a very good robot. 354 00:18:34,160 --> 00:18:37,679 Speaker 1: I know, but it's something that could sor But I 355 00:18:37,720 --> 00:18:41,480 Speaker 1: don't see Roxy getting the kind of funding where she 356 00:18:41,600 --> 00:18:43,600 Speaker 1: can benefit from this kind of Seah. They're going to 357 00:18:43,640 --> 00:18:48,200 Speaker 1: have to do something about that tinny voice first. Yeah, 358 00:18:48,240 --> 00:18:50,960 Speaker 1: you know it says things like that gets me hot. Well, 359 00:18:51,080 --> 00:18:53,199 Speaker 1: well you bring up Roxy. As I recall, Roxy was 360 00:18:53,240 --> 00:18:56,480 Speaker 1: originally a nursing project and then they decided that they 361 00:18:56,560 --> 00:19:00,240 Speaker 1: wanted to make some fast cash on it. Yeah, that's 362 00:19:00,320 --> 00:19:03,840 Speaker 1: how all innovation seems to be generated these days. But 363 00:19:03,920 --> 00:19:06,000 Speaker 1: let's talk about let's talk about this thing called the 364 00:19:06,080 --> 00:19:10,480 Speaker 1: jazz robot and mirror neurons, because this is really important, um, 365 00:19:10,520 --> 00:19:13,960 Speaker 1: in terms of how we communicate with one another, right, Yeah, 366 00:19:14,080 --> 00:19:16,120 Speaker 1: and this comes down to it again. We're talking about 367 00:19:16,119 --> 00:19:18,040 Speaker 1: you want to robot that can live in a human environment, 368 00:19:18,040 --> 00:19:20,639 Speaker 1: which is not just dealing with a human space in 369 00:19:20,640 --> 00:19:22,560 Speaker 1: a safe way, but also dealing with the presence of 370 00:19:22,640 --> 00:19:27,000 Speaker 1: humans and being able to read them and uh and 371 00:19:27,040 --> 00:19:29,639 Speaker 1: so this this is something that just comes naturally to 372 00:19:29,720 --> 00:19:32,520 Speaker 1: humans because we have we we grow up around other humans, 373 00:19:32,560 --> 00:19:35,560 Speaker 1: most humans anyway. Um, we we have this theory of mind. 374 00:19:35,920 --> 00:19:38,399 Speaker 1: And anytime you're encountering somebody, you're you're picking up in 375 00:19:38,440 --> 00:19:41,120 Speaker 1: so many different things. You're picking up on on what 376 00:19:41,160 --> 00:19:45,040 Speaker 1: they're saying, their tone there there, what their eyes are doing, 377 00:19:45,080 --> 00:19:47,480 Speaker 1: what their hands are doing, what they're you know, their 378 00:19:47,520 --> 00:19:51,960 Speaker 1: basic expression, their body language, all these different factories because 379 00:19:51,960 --> 00:19:55,320 Speaker 1: you're trying to get into their head, right, which inadvertently 380 00:19:55,440 --> 00:19:58,680 Speaker 1: allows you to empathize with another person. Right, yeah, right, 381 00:19:58,720 --> 00:20:01,280 Speaker 1: And so like if you smile, than then you know, 382 00:20:01,359 --> 00:20:04,080 Speaker 1: somewhere in my muscles I start to smile back, and 383 00:20:04,080 --> 00:20:05,840 Speaker 1: I started to start to send these signals to my 384 00:20:05,960 --> 00:20:08,240 Speaker 1: brain that this is funny or we're having fun or 385 00:20:08,760 --> 00:20:11,440 Speaker 1: or something along those lines, right, And that's how that's 386 00:20:11,440 --> 00:20:13,560 Speaker 1: how we communicate with one another, right, We we read 387 00:20:13,640 --> 00:20:16,800 Speaker 1: so many different signals. So could you could you create 388 00:20:16,840 --> 00:20:19,280 Speaker 1: a robot create more to the point, could you create 389 00:20:19,320 --> 00:20:23,040 Speaker 1: an artificial intelligence, so it's capable of quickly analyzing all 390 00:20:23,080 --> 00:20:25,920 Speaker 1: that data and at least getting some somewhats of what 391 00:20:26,040 --> 00:20:30,359 Speaker 1: the mood is because uh, and imagine in a nursing environment. Uh, 392 00:20:30,440 --> 00:20:33,280 Speaker 1: this would in theory enable a robot to tell if 393 00:20:33,400 --> 00:20:36,520 Speaker 1: an individual was having some sort of an episode, if 394 00:20:36,560 --> 00:20:38,760 Speaker 1: they were they were distraught over something, if they were 395 00:20:38,800 --> 00:20:42,879 Speaker 1: in pain. Uh. I mean, especially with I'm thinking of 396 00:20:43,240 --> 00:20:46,240 Speaker 1: like Alzheimer's patients. Uh, you know, off hand, because a 397 00:20:46,240 --> 00:20:48,800 Speaker 1: lot of times these are people who aren't in a 398 00:20:48,960 --> 00:20:53,000 Speaker 1: condition anymore to really express what they're feelings, you know, 399 00:20:53,040 --> 00:20:54,560 Speaker 1: so you really need a human to be able to 400 00:20:54,640 --> 00:20:57,680 Speaker 1: check on them. But if the human is not immediately available, 401 00:20:57,760 --> 00:20:58,960 Speaker 1: then it would be nice to have a robot that 402 00:20:59,000 --> 00:21:00,480 Speaker 1: would be able to say, oh, well, this person is 403 00:21:00,600 --> 00:21:03,800 Speaker 1: experiencing some degree of pain. Um, we need to get 404 00:21:03,800 --> 00:21:05,679 Speaker 1: somebody here to check on them, right, right. And so 405 00:21:05,800 --> 00:21:08,560 Speaker 1: this is the the idea of this Jazz robot, which 406 00:21:08,600 --> 00:21:11,160 Speaker 1: is by the way funded by the European Union because 407 00:21:11,160 --> 00:21:13,760 Speaker 1: they've got some interest in this right into creating this 408 00:21:13,800 --> 00:21:17,040 Speaker 1: technology to help the population as a whole. And it 409 00:21:17,480 --> 00:21:21,480 Speaker 1: is um it's predicated on the notion of mirror neurons, right, 410 00:21:21,920 --> 00:21:26,639 Speaker 1: it's trying to use the same sort of circuitry model, okay, 411 00:21:26,680 --> 00:21:30,480 Speaker 1: and the way that the robot can predict behavior. So 412 00:21:30,520 --> 00:21:33,840 Speaker 1: it's actually trying to analyze the person's behavior and figure 413 00:21:33,880 --> 00:21:35,720 Speaker 1: out when there's been an error. Of course, when we 414 00:21:35,720 --> 00:21:39,520 Speaker 1: talk about UM, you know the more complex things like 415 00:21:39,600 --> 00:21:41,920 Speaker 1: checking in on a person and seeing if they're emotionally okay, 416 00:21:41,960 --> 00:21:44,280 Speaker 1: this is not something that the robot can do quite yet, 417 00:21:44,720 --> 00:21:47,399 Speaker 1: but these are the first steps. And so, for instance, 418 00:21:47,440 --> 00:21:49,879 Speaker 1: the robots were programmed to help a person assemble a 419 00:21:49,920 --> 00:21:53,959 Speaker 1: complicated toy UM. These robots could analyze the person's actions 420 00:21:53,960 --> 00:21:56,679 Speaker 1: as a person worked on the assemblage and then offer 421 00:21:56,760 --> 00:21:59,840 Speaker 1: suggestions by locating the proper tool to complete the assembly. 422 00:21:59,880 --> 00:22:02,879 Speaker 1: So at this point it's more like a helper a 423 00:22:02,920 --> 00:22:05,120 Speaker 1: partner robot, which I think is really interesting by the way, 424 00:22:05,119 --> 00:22:08,160 Speaker 1: the semantics of that a partner robot UM. But it's 425 00:22:08,200 --> 00:22:09,959 Speaker 1: not necessarily something that can be like, hey, how are 426 00:22:10,000 --> 00:22:13,000 Speaker 1: you feeling today? You know, would you like some soup? 427 00:22:13,520 --> 00:22:16,600 Speaker 1: It's cold outside? But maybe I mean these again, these 428 00:22:16,600 --> 00:22:19,080 Speaker 1: are the first steps to try and to get robots 429 00:22:19,119 --> 00:22:21,800 Speaker 1: to be able to deal with the complex machines that 430 00:22:21,880 --> 00:22:24,720 Speaker 1: we are right, of course, this would make us want 431 00:22:24,720 --> 00:22:27,520 Speaker 1: to dress up our robots, right well, yeah, and you 432 00:22:27,520 --> 00:22:30,359 Speaker 1: can already dress up your household robots to certain degree. 433 00:22:30,440 --> 00:22:33,080 Speaker 1: You can buy these, uh like furry covers to put 434 00:22:33,119 --> 00:22:36,679 Speaker 1: over your yea, And we're going to talk about that 435 00:22:36,720 --> 00:22:41,840 Speaker 1: and a bit right after this. This podcast is brought 436 00:22:41,880 --> 00:22:45,240 Speaker 1: to you by Intel, the sponsors of Tomorrow and the 437 00:22:45,320 --> 00:22:49,639 Speaker 1: Discovery Channel. At Intel, we believe curiosity is the spark 438 00:22:49,680 --> 00:22:53,919 Speaker 1: which drives innovation. Join us at curiosity dot com and 439 00:22:54,000 --> 00:22:59,679 Speaker 1: explore the answers to life's questions. So we're dressing up 440 00:22:59,680 --> 00:23:03,160 Speaker 1: our robe, yes, putting furry costumes in our room bas 441 00:23:03,200 --> 00:23:08,520 Speaker 1: so that they look like circular mice or something I 442 00:23:08,520 --> 00:23:11,360 Speaker 1: don't know, with faces on top. Maybe we just want 443 00:23:11,400 --> 00:23:12,800 Speaker 1: them to you know, buddy up to the cat more. 444 00:23:12,840 --> 00:23:14,920 Speaker 1: I don't know, Yeah, a little friend for the cat. 445 00:23:15,080 --> 00:23:17,760 Speaker 1: Let's not forget the pets out there, right And and 446 00:23:17,880 --> 00:23:19,439 Speaker 1: there is the you know, the whole idea. Do you 447 00:23:19,440 --> 00:23:21,560 Speaker 1: want to make the to make it more the ro 448 00:23:21,640 --> 00:23:24,760 Speaker 1: about more relatable? A do we make it look more 449 00:23:24,800 --> 00:23:27,679 Speaker 1: human and be does the the operator, the owner or 450 00:23:27,720 --> 00:23:30,840 Speaker 1: the beneficiary of its service have some input into how 451 00:23:30,840 --> 00:23:34,439 Speaker 1: it dresses well, Okay, so the Swedish Institute of Computer 452 00:23:34,480 --> 00:23:36,399 Speaker 1: Science has actually been studying how much we have the 453 00:23:36,440 --> 00:23:43,439 Speaker 1: ability to anthropomorphize robots by dressing up some off the 454 00:23:43,480 --> 00:23:46,640 Speaker 1: shelf bots they are available right now, which I think 455 00:23:46,720 --> 00:23:50,399 Speaker 1: is funny. Right, So they're taking um, you know, something 456 00:23:50,480 --> 00:23:56,200 Speaker 1: like the pleio the pet dinosaur, and they've been giving 457 00:23:56,520 --> 00:23:59,119 Speaker 1: or that actually have been programming it so that you know, 458 00:23:59,160 --> 00:24:01,920 Speaker 1: if you if you change the outfit, it will actually 459 00:24:02,080 --> 00:24:05,200 Speaker 1: act differently accordingly to how you dress up your your 460 00:24:05,280 --> 00:24:07,840 Speaker 1: pet dinosaur. So also, you know, just keep in mind 461 00:24:07,920 --> 00:24:10,600 Speaker 1: that the that pleo is able to learn as you play, 462 00:24:11,160 --> 00:24:14,640 Speaker 1: and uh so you know, you can manipulate its behavior 463 00:24:14,680 --> 00:24:17,800 Speaker 1: a bit. So if you give Cleo a special watchdog necklace, 464 00:24:17,800 --> 00:24:20,840 Speaker 1: the robot remains active and on guard and putting you know, 465 00:24:20,880 --> 00:24:23,560 Speaker 1: air quotes on on guard. You can change the costume 466 00:24:23,760 --> 00:24:27,920 Speaker 1: from um that necklace to pajamas and the robot would 467 00:24:28,000 --> 00:24:32,639 Speaker 1: slowly switch into sleep mode. The costumes or accessors you 468 00:24:32,760 --> 00:24:38,040 Speaker 1: choose communicate electronically with robots program and just behavior follows suit. Okay, 469 00:24:38,080 --> 00:24:40,199 Speaker 1: so these are just a couple of things that you 470 00:24:40,240 --> 00:24:43,880 Speaker 1: can do. And they've also experimented with the roomba, which 471 00:24:43,920 --> 00:24:46,080 Speaker 1: I think is really interesting. They have these patches that 472 00:24:46,119 --> 00:24:48,639 Speaker 1: they put on the roomba and again it will behave 473 00:24:49,640 --> 00:24:53,400 Speaker 1: accordingly to to whatever this patch is displaying. So they've 474 00:24:53,400 --> 00:24:56,760 Speaker 1: got a patch that can make the ruma seem aggressive 475 00:24:57,840 --> 00:25:02,320 Speaker 1: or curious or shy. It'll actually like hide under the sofa. 476 00:25:02,400 --> 00:25:04,680 Speaker 1: And the whole idea is that robots can seem more 477 00:25:04,800 --> 00:25:07,000 Speaker 1: natural like these are the first steps again to trying 478 00:25:07,000 --> 00:25:09,280 Speaker 1: to you know, make us feel a little bit more 479 00:25:09,320 --> 00:25:12,240 Speaker 1: comfortable with robots in our atmosphere and too, and to 480 00:25:13,080 --> 00:25:16,000 Speaker 1: study how we behave and how we really project our 481 00:25:16,040 --> 00:25:19,760 Speaker 1: feelings onto these inanimate objects. And as we discussed in 482 00:25:19,760 --> 00:25:22,480 Speaker 1: the last podcasts, sometimes that is people just taking out 483 00:25:22,480 --> 00:25:26,200 Speaker 1: their aggressions on the robots. Yes, yeah, yeah, And again 484 00:25:26,240 --> 00:25:28,880 Speaker 1: I think that this is also in conjunction with lyric program, 485 00:25:28,960 --> 00:25:32,080 Speaker 1: which is the living with Robots program that we talked 486 00:25:32,080 --> 00:25:35,000 Speaker 1: about before. Robot House. Robot House. Yeah again, that a 487 00:25:35,119 --> 00:25:37,840 Speaker 1: great reality series just you know, waiting to be made. 488 00:25:37,960 --> 00:25:41,040 Speaker 1: Well if this leads us in inevitably to the Uncanny 489 00:25:41,119 --> 00:25:44,600 Speaker 1: Valley and discussion of the Uncanny Valley um, This term, 490 00:25:44,640 --> 00:25:47,840 Speaker 1: of course, is thrown around a lot, especially in terms 491 00:25:48,000 --> 00:25:53,400 Speaker 1: of Hollywood animated films, video games. We we've all seen 492 00:25:53,440 --> 00:25:56,480 Speaker 1: it before. You're they're trying to create a lifelike semblance 493 00:25:56,520 --> 00:25:58,480 Speaker 1: of a human being saying, you know, a cut scene 494 00:25:58,480 --> 00:26:01,520 Speaker 1: in a game or or what was it the Polar 495 00:26:01,560 --> 00:26:04,280 Speaker 1: Express movie with Tom Hanks? Oh yeah, which apparently that 496 00:26:04,400 --> 00:26:09,680 Speaker 1: was just a tragedy, a huge mistake, right because everybody 497 00:26:09,960 --> 00:26:13,720 Speaker 1: or most people know who Tom Hanks is very warm 498 00:26:13,720 --> 00:26:16,320 Speaker 1: and engaging person, right at least that's the perstpona that 499 00:26:16,720 --> 00:26:18,399 Speaker 1: comes off, and all of a sudden you have a 500 00:26:18,400 --> 00:26:22,840 Speaker 1: dead eyed Tom Hanks and it's very wearing his face 501 00:26:22,960 --> 00:26:26,880 Speaker 1: or something. Yeah, yes, yes, leather face in a Christmas tail. Yeah, 502 00:26:26,920 --> 00:26:29,400 Speaker 1: it's uh. This this all goes back to the paper 503 00:26:29,640 --> 00:26:34,280 Speaker 1: in the journal Energy where roboticist Masahiro Mori proposed that 504 00:26:34,359 --> 00:26:37,240 Speaker 1: robots that are too human light can veer into unsettling 505 00:26:37,400 --> 00:26:42,640 Speaker 1: territory and basically to get real like number centric about it, Um, 506 00:26:42,680 --> 00:26:46,600 Speaker 1: you can make a robot up to realistic theory, says 507 00:26:47,280 --> 00:26:50,359 Speaker 1: and it's great, like, oh, this robot is looks like 508 00:26:50,359 --> 00:26:53,439 Speaker 1: a person. That's amazing, that's awesome. But then once you 509 00:26:53,480 --> 00:26:58,159 Speaker 1: hit that the like the the the space between and 510 00:26:58,200 --> 00:27:02,280 Speaker 1: a human look is just creepy that's the that's the theory. 511 00:27:02,640 --> 00:27:04,280 Speaker 1: That's the theory. And the theory is that on a 512 00:27:04,320 --> 00:27:07,400 Speaker 1: psychological level of minds are perceiving the robot human effects 513 00:27:07,400 --> 00:27:12,240 Speaker 1: similar as as dead right or an unhealthy human or 514 00:27:12,280 --> 00:27:15,960 Speaker 1: you know, on some I guess primal level, like it's decaying, 515 00:27:16,040 --> 00:27:19,400 Speaker 1: it's not alive, and so we back away. Again. This 516 00:27:19,480 --> 00:27:22,280 Speaker 1: is the theory. There's so there's some disagreements on it. 517 00:27:22,480 --> 00:27:26,119 Speaker 1: I mean, especially when you get outside of its applications 518 00:27:26,119 --> 00:27:29,160 Speaker 1: in entertainment and really start looking at robotics again where 519 00:27:29,160 --> 00:27:33,560 Speaker 1: where the term was originally um, you know, discussed and 520 00:27:33,960 --> 00:27:36,359 Speaker 1: there there's some studies that have found that that really 521 00:27:36,400 --> 00:27:39,520 Speaker 1: it's more about the mismatch. It's about it has really 522 00:27:39,560 --> 00:27:42,000 Speaker 1: cool looking skin scan looks very human, but they the 523 00:27:42,160 --> 00:27:46,280 Speaker 1: eyes are obviously mechanical, are too big and so they're 524 00:27:46,280 --> 00:27:48,840 Speaker 1: not matching up to the rest of your expectation, and 525 00:27:48,880 --> 00:27:52,879 Speaker 1: that's what's unsettling. There's a there's an interesting book called 526 00:27:53,280 --> 00:27:55,080 Speaker 1: The wind Up Girl. I think I may mention it 527 00:27:55,119 --> 00:27:58,440 Speaker 1: before Sci Fi book tied for Hugo Award last year, 528 00:27:58,480 --> 00:28:02,040 Speaker 1: I think and uh. The title character, the wind Up Girl, 529 00:28:02,160 --> 00:28:05,400 Speaker 1: is an artificial human that is created by Japanese scientists, 530 00:28:05,400 --> 00:28:06,919 Speaker 1: and this is in the future and all. But they 531 00:28:07,000 --> 00:28:09,720 Speaker 1: end up programming um they call him heard the wind 532 00:28:09,760 --> 00:28:12,240 Speaker 1: wind up girl, or they call them wind up girls 533 00:28:12,680 --> 00:28:15,400 Speaker 1: because they have programmed in and um it kind of 534 00:28:15,480 --> 00:28:20,000 Speaker 1: wind up movement to their bodies that they can't quite control. 535 00:28:20,240 --> 00:28:24,280 Speaker 1: Oh so they don't have smooth motor coordinations, right, because 536 00:28:24,320 --> 00:28:26,560 Speaker 1: they didn't want them to look too human. They wanted 537 00:28:26,600 --> 00:28:30,080 Speaker 1: to keep the uncanny valley um in place to a 538 00:28:30,080 --> 00:28:35,200 Speaker 1: certain degree so that they would be marked. Uh uh okay. 539 00:28:35,240 --> 00:28:36,760 Speaker 1: So this is sort of like the whole problem in 540 00:28:36,840 --> 00:28:40,840 Speaker 1: Blade Runner with replicants, right, figure out which is a replicant? Yeah, 541 00:28:40,840 --> 00:28:43,280 Speaker 1: who's the human? Who's They said, instead of giving it 542 00:28:43,320 --> 00:28:46,000 Speaker 1: a complicated test about a turtle on offense post to 543 00:28:46,040 --> 00:28:48,480 Speaker 1: bring the whole empathy thing into play, let's just watch 544 00:28:48,560 --> 00:28:50,320 Speaker 1: him walk down the street and you'll be able to 545 00:28:50,320 --> 00:28:53,600 Speaker 1: tell right away. Um. So, So anyway, there's the one 546 00:28:53,640 --> 00:28:55,400 Speaker 1: idea that it's a mismatch. It's like, oh, it looks 547 00:28:55,440 --> 00:28:59,280 Speaker 1: like a person, but it's moving weird uncanny valley um. 548 00:28:59,320 --> 00:29:01,640 Speaker 1: But then there have been some other recent studies, like 549 00:29:01,640 --> 00:29:04,600 Speaker 1: there was one study by Carl McDorman, director of Android 550 00:29:04,640 --> 00:29:08,200 Speaker 1: Science Center at Indiana University, and uh, he found that 551 00:29:08,240 --> 00:29:11,520 Speaker 1: the uncanny effects seemed to be tied to gender. They 552 00:29:11,520 --> 00:29:15,000 Speaker 1: found that they yeah, they rolled out some various robots 553 00:29:15,520 --> 00:29:18,560 Speaker 1: and uh, and they found that women subjects were sympathetic 554 00:29:18,640 --> 00:29:23,920 Speaker 1: to UM the the to the requests of the robot um, 555 00:29:24,040 --> 00:29:27,320 Speaker 1: whether they were represented as a person or poorly poorly 556 00:29:27,400 --> 00:29:30,760 Speaker 1: rendered computer animation. Okay, this is this the scenario where 557 00:29:30,760 --> 00:29:33,880 Speaker 1: they're they're acting at this role as doctor and these 558 00:29:33,880 --> 00:29:36,040 Speaker 1: are patients. So one is a human patient on one 559 00:29:36,160 --> 00:29:40,560 Speaker 1: is UM a computer value. Well that's right, they're not 560 00:29:40,560 --> 00:29:42,360 Speaker 1: really robots, but it's you know, it's the idea of 561 00:29:42,400 --> 00:29:46,480 Speaker 1: here's here's a person, here's a facsimile of a person UM, 562 00:29:46,560 --> 00:29:48,520 Speaker 1: and then let's see how they respond to it. And 563 00:29:48,760 --> 00:29:51,080 Speaker 1: the men sided with the real patient, but not the 564 00:29:51,160 --> 00:29:53,680 Speaker 1: uncanny computer generated one, where the women were far more 565 00:29:53,720 --> 00:29:56,480 Speaker 1: likely to side with the sympathetic one because they had 566 00:29:56,720 --> 00:30:00,560 Speaker 1: empathy across the board, right, which interesting enough, I think 567 00:30:00,560 --> 00:30:04,080 Speaker 1: ties in nicely something we discussed in the one of 568 00:30:04,080 --> 00:30:06,920 Speaker 1: our early episodes about a year ago. Uh, the idea 569 00:30:07,040 --> 00:30:10,160 Speaker 1: that in the future the human race will be composed 570 00:30:10,360 --> 00:30:14,400 Speaker 1: entirely of women in their robotic partners that's right, ready 571 00:30:14,400 --> 00:30:18,360 Speaker 1: to talk about that, yes, Uh, and for for many, 572 00:30:18,400 --> 00:30:21,719 Speaker 1: many many reasons. Uh, this is not actually probably going 573 00:30:21,760 --> 00:30:24,240 Speaker 1: to come to fruition, but I think I think it might. 574 00:30:25,560 --> 00:30:27,960 Speaker 1: I think I think it will because look at it. 575 00:30:27,960 --> 00:30:29,680 Speaker 1: The men can't get along with anybody. They can't get 576 00:30:29,680 --> 00:30:31,040 Speaker 1: along with the men, they can't get along with the 577 00:30:31,040 --> 00:30:32,600 Speaker 1: women all that well, and they can't get along with 578 00:30:32,640 --> 00:30:34,880 Speaker 1: the robots. Well. But you know, I always take issue 579 00:30:34,920 --> 00:30:36,600 Speaker 1: with it because I think, well, yeah, women are great. 580 00:30:36,720 --> 00:30:39,040 Speaker 1: Don't get me wrong, I'm one of them. But that 581 00:30:39,080 --> 00:30:42,120 Speaker 1: does not make us like altruistic, like you know, the 582 00:30:42,160 --> 00:30:45,040 Speaker 1: best humans on the earth. I mean, I'm going to 583 00:30:45,160 --> 00:30:47,360 Speaker 1: say it, we're a little bit flawed. Well wait, way 584 00:30:47,360 --> 00:30:52,720 Speaker 1: to cover up for the female in so many different ways. Anyway, 585 00:30:53,400 --> 00:30:55,840 Speaker 1: I'm I'm digging myself in here a little bit. Agreading 586 00:30:55,880 --> 00:30:58,120 Speaker 1: a really interesting article in Popular Mechanics called the Truth 587 00:30:58,120 --> 00:31:01,240 Speaker 1: about Robotic Robotics on Kenny Valley, and they discussed several 588 00:31:01,240 --> 00:31:04,680 Speaker 1: of these different studies and scenarios, and they ended up 589 00:31:04,880 --> 00:31:07,520 Speaker 1: making the case that that when it comes to robots, 590 00:31:08,080 --> 00:31:11,800 Speaker 1: this the Uncanny Valley is largely hypothetical, and it largely 591 00:31:12,880 --> 00:31:15,600 Speaker 1: becomes less of an issue when humans are actually face 592 00:31:15,680 --> 00:31:17,560 Speaker 1: to face with the robots and dealing with the robots 593 00:31:17,600 --> 00:31:19,760 Speaker 1: on a on a on a one on one basis. Yeah, 594 00:31:19,760 --> 00:31:21,320 Speaker 1: that's what I read too, is that you know, it's 595 00:31:21,360 --> 00:31:24,000 Speaker 1: just the uncanny Valley is just a big sweeping generalization 596 00:31:24,240 --> 00:31:27,920 Speaker 1: and that um, you know, it's basically a short lived 597 00:31:28,040 --> 00:31:31,440 Speaker 1: psychological state that we're in when we're looking at the ropebot. 598 00:31:31,480 --> 00:31:33,640 Speaker 1: But once you get comfortable with it, that all of 599 00:31:33,680 --> 00:31:36,200 Speaker 1: that sort of falls away and you begin to project 600 00:31:36,200 --> 00:31:38,440 Speaker 1: your feelings onto it no matter what it's I mean, 601 00:31:38,440 --> 00:31:42,280 Speaker 1: it's like anytime we humans deal with with other humans 602 00:31:42,280 --> 00:31:44,920 Speaker 1: and encounter a little bit of the uncanny valley, which 603 00:31:45,880 --> 00:31:47,720 Speaker 1: if you if you're ever talking to somebody for the 604 00:31:47,720 --> 00:31:49,840 Speaker 1: first time and they maybe have like one of their 605 00:31:49,840 --> 00:31:52,200 Speaker 1: eyes is you know, pointing out a little to the side, 606 00:31:52,280 --> 00:31:54,239 Speaker 1: kind of like a Forest Whittaker type of thing. You know, 607 00:31:55,160 --> 00:31:57,000 Speaker 1: you know, it can be it can be a little 608 00:31:57,080 --> 00:31:58,800 Speaker 1: like off putting it first, but then you get used 609 00:31:58,840 --> 00:32:00,959 Speaker 1: to it, and because it's as the person you know 610 00:32:01,080 --> 00:32:04,040 Speaker 1: and uh and you uh, you sort of jive with 611 00:32:04,080 --> 00:32:07,600 Speaker 1: their particular appearance and their particular energy, or even if 612 00:32:07,600 --> 00:32:09,200 Speaker 1: it's just something as weird as like somebody's got a 613 00:32:09,240 --> 00:32:14,400 Speaker 1: strange scar or strange blemish on their face, or you know, 614 00:32:14,440 --> 00:32:17,040 Speaker 1: a satan goatee or something, I don't know. Yeah, so 615 00:32:17,080 --> 00:32:20,000 Speaker 1: you think that Unkenny Valley underestimates our ability to to 616 00:32:20,520 --> 00:32:25,560 Speaker 1: be adaptable and flexible and and actually our empathy to right. Yeah, yeah, 617 00:32:25,600 --> 00:32:28,320 Speaker 1: I think we tend to overcome a lot of these things. 618 00:32:28,360 --> 00:32:30,640 Speaker 1: It's kind of like with noise. You know, people were like, oh, 619 00:32:30,800 --> 00:32:33,440 Speaker 1: how can somebody live next to train tracks or how 620 00:32:33,600 --> 00:32:35,480 Speaker 1: or next to an airport, But people do live next 621 00:32:35,480 --> 00:32:38,800 Speaker 1: train stations and airports, etcetera. And they get used to 622 00:32:38,840 --> 00:32:42,280 Speaker 1: it because humans can adapt to to not everything, but 623 00:32:42,400 --> 00:32:47,200 Speaker 1: to almost anything. So this, this is something that I've 624 00:32:47,240 --> 00:32:50,720 Speaker 1: been wondering about. I know that the voice recognition is 625 00:32:50,760 --> 00:32:52,720 Speaker 1: a big problem with robots right now, at least a 626 00:32:52,760 --> 00:32:58,200 Speaker 1: technology that exists that something along the lines of of 627 00:32:58,280 --> 00:33:02,440 Speaker 1: these programs can success fully take what you're saying and 628 00:33:02,520 --> 00:33:06,520 Speaker 1: actually understand it and respond accordingly. So that's something that 629 00:33:06,680 --> 00:33:11,520 Speaker 1: in the future, obviously that it's gonna will be, you know, solved. Yeah, well, 630 00:33:11,560 --> 00:33:14,880 Speaker 1: I feel like maybe I only here what I'm listening to, 631 00:33:15,000 --> 00:33:17,160 Speaker 1: so well, yeah, exactly, I know, right, So it could 632 00:33:17,160 --> 00:33:20,680 Speaker 1: be as good as any caregiver out there. Um, but 633 00:33:20,760 --> 00:33:24,320 Speaker 1: I sort of wondered, like now that that Surrey is 634 00:33:25,080 --> 00:33:29,080 Speaker 1: on the new iPhone, like, is this you know apparently 635 00:33:29,400 --> 00:33:33,240 Speaker 1: like this voice of recognition technology is great and we'll 636 00:33:33,240 --> 00:33:35,000 Speaker 1: only get better. So I would like to hear from 637 00:33:35,000 --> 00:33:37,320 Speaker 1: people who have Surrey right now and see, you know, 638 00:33:37,440 --> 00:33:40,280 Speaker 1: would you say that she has an eight or better 639 00:33:41,280 --> 00:33:43,720 Speaker 1: chance at guessing what you're saying? And why is she 640 00:33:43,800 --> 00:33:47,440 Speaker 1: named after Tom Cruise's child right exactly? Why why his daughter? 641 00:33:48,440 --> 00:33:50,560 Speaker 1: I'm sure there's some sort of Steve Jobs joke in 642 00:33:50,600 --> 00:33:54,040 Speaker 1: there that that that's a great unsolved mystery. So yeah, 643 00:33:54,040 --> 00:33:58,280 Speaker 1: so there's some ideas about where caregiving robots are going 644 00:33:58,280 --> 00:34:00,240 Speaker 1: in the future, what the technology is looking like now, 645 00:34:00,280 --> 00:34:02,600 Speaker 1: how it's shaping up, and what it might look like. 646 00:34:02,640 --> 00:34:05,960 Speaker 1: So I would be very interested to hear from everyone 647 00:34:05,960 --> 00:34:08,040 Speaker 1: out there, I mean, what you think about this, uh, 648 00:34:08,400 --> 00:34:12,880 Speaker 1: through yourselves going forward, and especially anybody who has dealt 649 00:34:12,920 --> 00:34:16,600 Speaker 1: with caregiving situations, you know, be it a you know, 650 00:34:16,960 --> 00:34:20,600 Speaker 1: a parent or a grandparent, um, you know for yourself 651 00:34:20,719 --> 00:34:23,760 Speaker 1: or I mean yeah, so, um, and also your robot, 652 00:34:24,000 --> 00:34:27,960 Speaker 1: your caregiving robot naked or dressed up. That's what I 653 00:34:28,000 --> 00:34:30,000 Speaker 1: want to know well or yeah, or more to the point, 654 00:34:30,160 --> 00:34:32,680 Speaker 1: So a robot, say, robots helping you use the restroom 655 00:34:32,960 --> 00:34:35,640 Speaker 1: in your in your your golden years. Do you do 656 00:34:35,680 --> 00:34:37,279 Speaker 1: you want it to look like a person? Do you 657 00:34:37,320 --> 00:34:39,560 Speaker 1: want it to look like a bear? You wanted to 658 00:34:39,600 --> 00:34:42,520 Speaker 1: look like just a thing? Do you want it to 659 00:34:42,560 --> 00:34:45,120 Speaker 1: basically look like a you know, a side table that 660 00:34:45,719 --> 00:34:48,839 Speaker 1: you know that lifts up and down. I don't know well, 661 00:34:48,920 --> 00:34:50,719 Speaker 1: and you know, do you want the sort of end 662 00:34:50,800 --> 00:34:53,000 Speaker 1: user features that your iPhone has so that you can change, 663 00:34:53,239 --> 00:34:56,160 Speaker 1: you know, certain aspects of its appearance. You know it's 664 00:34:56,160 --> 00:34:59,799 Speaker 1: screen um, particularly when you go to the to the 665 00:34:59,800 --> 00:35:02,239 Speaker 1: red stroum. Yeah. And then and then this also ties 666 00:35:02,280 --> 00:35:03,680 Speaker 1: in you have a robot is helping you go to 667 00:35:03,719 --> 00:35:07,040 Speaker 1: the restroom. Yeah. Privacy issues coming to play too, because 668 00:35:07,080 --> 00:35:08,960 Speaker 1: if you have a robot that, in order to navigate 669 00:35:09,000 --> 00:35:12,760 Speaker 1: your space is constantly recording, constantly taking note of everything, 670 00:35:13,040 --> 00:35:15,840 Speaker 1: you don't want your bathroom helper robot to get a 671 00:35:15,920 --> 00:35:19,600 Speaker 1: virus or get hated. You know that's right, Ryan, Then 672 00:35:19,640 --> 00:35:22,560 Speaker 1: just you know, all of a sudden updates your Facebook 673 00:35:22,760 --> 00:35:25,240 Speaker 1: page and whatever that's going to be in the future 674 00:35:25,360 --> 00:35:28,600 Speaker 1: to say, yeah, my charge just went to the bathroom. 675 00:35:28,640 --> 00:35:30,920 Speaker 1: Ha ha ha, Yeah, you don't want it tweeting about like, 676 00:35:31,120 --> 00:35:32,600 Speaker 1: I don't know what I mean. Maybe people was coming 677 00:35:32,600 --> 00:35:35,120 Speaker 1: from the right place. The robots like yeah, another successful 678 00:35:35,120 --> 00:35:37,440 Speaker 1: batt movement, and then it tweets it. I don't know, 679 00:35:37,880 --> 00:35:39,200 Speaker 1: I don't know. I don't know if that these are 680 00:35:39,239 --> 00:35:42,040 Speaker 1: missives that we need to care about. Maybe in the 681 00:35:42,080 --> 00:35:44,879 Speaker 1: future that's what people will want. All right, Well, let's 682 00:35:44,920 --> 00:35:49,239 Speaker 1: have our helper bought hand us a listener mail. Thank you, 683 00:35:49,280 --> 00:35:53,160 Speaker 1: helper boy. So we heard this time from I listened 684 00:35:53,200 --> 00:35:55,600 Speaker 1: by the name of Mark Healing, and I'm using his 685 00:35:55,680 --> 00:35:58,319 Speaker 1: full name because Mark is an artist and uh he 686 00:35:58,400 --> 00:36:00,279 Speaker 1: has a fan page on Facebook. You can check it 687 00:36:00,280 --> 00:36:02,640 Speaker 1: out and as all all his art shared there. It's 688 00:36:02,640 --> 00:36:06,239 Speaker 1: pretty awesome, uh Mark Mark Wrightson and says, I just 689 00:36:06,360 --> 00:36:08,719 Speaker 1: finished listening to your most recent episode about art. This 690 00:36:08,760 --> 00:36:10,640 Speaker 1: would be this. U is your brand on art, and 691 00:36:10,680 --> 00:36:12,440 Speaker 1: I was very pleased to hear a different perspective on 692 00:36:12,480 --> 00:36:13,840 Speaker 1: some of the artists that I've been setting for the 693 00:36:13,840 --> 00:36:16,120 Speaker 1: past few years. Currently, I'm a student at Emily car 694 00:36:16,200 --> 00:36:18,600 Speaker 1: University of Art and Design in Vancouver, where I'm in 695 00:36:18,640 --> 00:36:20,560 Speaker 1: my final year. For a while now, I've been thinking 696 00:36:20,560 --> 00:36:23,280 Speaker 1: about the idea of an eight beauty. A concept like symmetry, 697 00:36:23,280 --> 00:36:26,000 Speaker 1: for example, expresses itself as a visual that has been 698 00:36:26,200 --> 00:36:29,160 Speaker 1: that has an inherent appeal in biological terms, a mate, 699 00:36:29,160 --> 00:36:32,640 Speaker 1: of course, is more attractive if their features appear more symmetrical. Uh. 700 00:36:32,680 --> 00:36:35,800 Speaker 1: This idea is pretty straightforward. Beyond just the sexual appeal 701 00:36:35,800 --> 00:36:39,000 Speaker 1: of symmetry, however, there is also the specific optical effect 702 00:36:39,000 --> 00:36:41,840 Speaker 1: which occurs when an image, whether very simply or complex, 703 00:36:41,920 --> 00:36:44,400 Speaker 1: is near. In regards to my practice, I attempt to 704 00:36:44,400 --> 00:36:47,680 Speaker 1: be very conscious of these notions notions of an eight beauty. 705 00:36:48,040 --> 00:36:50,239 Speaker 1: Um and he goes on. He shares a lot of 706 00:36:50,239 --> 00:36:52,359 Speaker 1: really cool ideas with us, and he says that I'd 707 00:36:52,360 --> 00:36:53,759 Speaker 1: be very pleased to be able to look at my 708 00:36:53,760 --> 00:36:56,279 Speaker 1: Facebook page. Hopefully some of what I am talking about 709 00:36:56,320 --> 00:36:58,360 Speaker 1: will become a little more apparent. If you go to 710 00:36:58,400 --> 00:37:00,239 Speaker 1: the first image in my galery, you will find find 711 00:37:00,560 --> 00:37:02,640 Speaker 1: my biggest project so far, which is taking about a 712 00:37:02,680 --> 00:37:04,920 Speaker 1: hundred and seventy four hours at this point. What he's 713 00:37:04,960 --> 00:37:08,839 Speaker 1: done with point zero zero three millimeter dots in order 714 00:37:08,880 --> 00:37:12,160 Speaker 1: to further pursue visual complexity, which has its own specific 715 00:37:12,239 --> 00:37:15,280 Speaker 1: capacity for drawing interviewer at least I hope. So anyways, 716 00:37:15,480 --> 00:37:18,239 Speaker 1: that is it. I really appreciate your podcast and I 717 00:37:18,239 --> 00:37:20,400 Speaker 1: have been a regular listener since way back when you 718 00:37:20,440 --> 00:37:22,520 Speaker 1: were still stuff in the Science leave. The show just 719 00:37:22,560 --> 00:37:25,160 Speaker 1: gets getting better and better. Thank you very much, Mark. 720 00:37:25,560 --> 00:37:28,000 Speaker 1: Thanks Mark. I was so pleased that we heard from 721 00:37:28,000 --> 00:37:30,960 Speaker 1: an artist on this because you know, you and I 722 00:37:31,000 --> 00:37:34,720 Speaker 1: are artists with words and the written page. I guess 723 00:37:34,840 --> 00:37:36,719 Speaker 1: I don't know that might be going a little too far, 724 00:37:37,120 --> 00:37:39,080 Speaker 1: but I did want to hear from a visual artist, 725 00:37:39,160 --> 00:37:42,719 Speaker 1: to hear their take on what beauty is and symmetry 726 00:37:42,880 --> 00:37:45,960 Speaker 1: and some of these different ideas that we were talking about. Yeah, totally, 727 00:37:46,239 --> 00:37:48,320 Speaker 1: And and do check out his Facebook page if you 728 00:37:48,360 --> 00:37:50,920 Speaker 1: want to see his art. I think it's like facebook 729 00:37:50,960 --> 00:37:55,279 Speaker 1: dot com slash m r k uh dot I l 730 00:37:55,480 --> 00:37:59,600 Speaker 1: l I check that out, and certainly any other artists 731 00:37:59,600 --> 00:38:01,920 Speaker 1: out there that are listening to us. So we'd love 732 00:38:01,960 --> 00:38:03,919 Speaker 1: to hear what you had to say about the art 733 00:38:03,920 --> 00:38:06,120 Speaker 1: on the Brain podcast. And you know, we'd love to 734 00:38:06,120 --> 00:38:08,120 Speaker 1: share our examples of your work on our Facebook page, 735 00:38:08,200 --> 00:38:11,239 Speaker 1: so let us know. And if you would like to 736 00:38:11,320 --> 00:38:14,200 Speaker 1: share any of that u with us, be it on 737 00:38:14,239 --> 00:38:15,960 Speaker 1: Facebook or Twitter. And if you have anything to say 738 00:38:16,000 --> 00:38:20,400 Speaker 1: about help our robots and the future against Facebook, Twitter. 739 00:38:20,800 --> 00:38:22,640 Speaker 1: Blow the mind on both of this, and you can 740 00:38:22,680 --> 00:38:24,799 Speaker 1: always drop us a line at blow of the Mind 741 00:38:24,800 --> 00:38:32,160 Speaker 1: at how stuff worth dot com. Be sure to check 742 00:38:32,200 --> 00:38:35,319 Speaker 1: out our new video podcast, Stuff from the Future. Join 743 00:38:35,400 --> 00:38:37,920 Speaker 1: how Stuff Work staff as we explore the most promising 744 00:38:38,000 --> 00:38:40,320 Speaker 1: and perplexing possibilities of tomorrow.