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 --> 00:00:14,240 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:14,240 --> 00:00:17,680 Speaker 1: My name is Robert Lamb and I'm Julie Douglas. Julie 4 00:00:17,720 --> 00:00:20,840 Speaker 1: coming this. Have you ever turned to I know sometimes 5 00:00:20,880 --> 00:00:23,319 Speaker 1: it's difficult to find babysitters in the last minute, but 6 00:00:23,360 --> 00:00:26,400 Speaker 1: have you ever turned to the room bo? I've thought 7 00:00:26,440 --> 00:00:29,120 Speaker 1: about it. I mean, obviously I thought about my cat first, 8 00:00:29,720 --> 00:00:31,200 Speaker 1: and then the rumba because I thought, you know what, 9 00:00:31,480 --> 00:00:33,360 Speaker 1: she could heard my daughter. I mean, she's a to 10 00:00:33,440 --> 00:00:37,680 Speaker 1: your old toddler running around. The rumba could just chase 11 00:00:37,680 --> 00:00:39,560 Speaker 1: her around. Opening a big deal. Is the cat of 12 00:00:39,560 --> 00:00:43,120 Speaker 1: female male well still, I mean male cats or at 13 00:00:43,200 --> 00:00:45,440 Speaker 1: least used to batten, you know, little kittens around and 14 00:00:45,479 --> 00:00:47,960 Speaker 1: keeping them in line, right so right right, yeah, between 15 00:00:48,040 --> 00:00:51,960 Speaker 1: the rumba and my cat, owen, I think it could work. Yeah, yeah, yeah. 16 00:00:51,960 --> 00:00:53,959 Speaker 1: The cat disciplined in the room that can just sort 17 00:00:53,960 --> 00:00:56,680 Speaker 1: of lay out rooms and occasionally bump into the child 18 00:00:56,920 --> 00:00:59,440 Speaker 1: and let it know that it's loved right exactly. Yeah, 19 00:00:59,440 --> 00:01:02,440 Speaker 1: they're they're that interaction. It would be really enriching for 20 00:01:02,520 --> 00:01:05,880 Speaker 1: my daughter. I think, yeah, well, this is exactly the 21 00:01:05,920 --> 00:01:08,480 Speaker 1: thing that we're going to talk about for twenty minutes 22 00:01:08,560 --> 00:01:11,840 Speaker 1: or so here uh or or longer. I don't know. 23 00:01:12,120 --> 00:01:14,640 Speaker 1: And and that's gonna be Can we turn to machines 24 00:01:14,720 --> 00:01:19,520 Speaker 1: to two babysit children, to raise children to uh to 25 00:01:19,680 --> 00:01:21,520 Speaker 1: just you know, can we just pretty much hand them 26 00:01:21,520 --> 00:01:24,680 Speaker 1: off to robots at birth? Or or or will robots 27 00:01:24,680 --> 00:01:27,120 Speaker 1: be doing those delivery as well? Who knows? And uh 28 00:01:27,160 --> 00:01:30,759 Speaker 1: It used to say, cool idea. Is this a frightening 29 00:01:30,920 --> 00:01:34,680 Speaker 1: prospect for the future. I don't know. I mean sometimes 30 00:01:34,720 --> 00:01:36,680 Speaker 1: I wonder if we're entering into the winter of our 31 00:01:36,760 --> 00:01:40,959 Speaker 1: robot discontent, because I mean it's a little bit yeah, 32 00:01:41,000 --> 00:01:43,920 Speaker 1: you know, robots rearing a child could be a little 33 00:01:43,959 --> 00:01:46,760 Speaker 1: bit scary. Yeah, And and yet it's it's one of 34 00:01:46,800 --> 00:01:48,520 Speaker 1: those things that we were talking about yesterday about just 35 00:01:48,560 --> 00:01:51,880 Speaker 1: about robots, like what can a robot not? Do? You know? 36 00:01:52,400 --> 00:01:55,280 Speaker 1: If it seems nothing, do we get enough negatives into 37 00:01:55,280 --> 00:01:59,560 Speaker 1: that into that for you? Um, there's a we were 38 00:01:59,560 --> 00:02:02,720 Speaker 1: just constant dreaming up. We've been just been dreaming up 39 00:02:02,760 --> 00:02:05,040 Speaker 1: ways that they can enter our lives and take over 40 00:02:05,080 --> 00:02:08,360 Speaker 1: different responsibilities for ages. I mean, the term robot comes 41 00:02:08,400 --> 00:02:12,640 Speaker 1: from I believe the check word for for slave for something. 42 00:02:12,800 --> 00:02:14,760 Speaker 1: You know, it's it's very tied to the idea of 43 00:02:15,120 --> 00:02:17,119 Speaker 1: let's make something that will do things that I don't 44 00:02:17,160 --> 00:02:20,639 Speaker 1: want to do. So child rearing, Uh, you know, we've 45 00:02:20,760 --> 00:02:23,800 Speaker 1: sort of reach there, reached that point by default. But 46 00:02:23,880 --> 00:02:26,840 Speaker 1: we've we've we've already handed off a lot of our 47 00:02:26,880 --> 00:02:29,960 Speaker 1: labor to robots, the vacuuming, for instance, UM, a lot 48 00:02:29,960 --> 00:02:34,760 Speaker 1: of industrialized functions. Um, We're we're making a lot of 49 00:02:34,800 --> 00:02:39,760 Speaker 1: headway when it comes to surgery. Um. Robots have been 50 00:02:39,760 --> 00:02:43,000 Speaker 1: able to demonstrate the ability to uh, to compose music, 51 00:02:43,040 --> 00:02:46,960 Speaker 1: to create art. There's play Jeopardy played Jeopardy. Is that 52 00:02:47,000 --> 00:02:49,040 Speaker 1: going on right now? I think it's about too. I 53 00:02:49,080 --> 00:02:51,280 Speaker 1: think it's around the corner. They can already beat me 54 00:02:51,320 --> 00:02:57,080 Speaker 1: at scrabble pretty easily if my iPhone is in any indication. So, um, 55 00:02:57,240 --> 00:02:59,160 Speaker 1: can they raise children? I mean it's not so much 56 00:02:59,160 --> 00:03:03,279 Speaker 1: of a stretch, right, mean they think about right now, television, 57 00:03:03,520 --> 00:03:06,000 Speaker 1: iPad iPod. I mean there's a ton of things that 58 00:03:06,040 --> 00:03:09,919 Speaker 1: we use to some of us and I'm guilty sometimes, 59 00:03:10,080 --> 00:03:13,560 Speaker 1: Um for the computer two put our kids in front 60 00:03:13,639 --> 00:03:16,839 Speaker 1: of and sort of babysit it for a couple of minutes, right, Um, 61 00:03:16,919 --> 00:03:18,720 Speaker 1: So it's not too much of a stretch of the 62 00:03:18,760 --> 00:03:21,160 Speaker 1: imagination that you would use a robot and that it 63 00:03:21,240 --> 00:03:24,840 Speaker 1: actually may be enriching and actually maybe program to actually 64 00:03:24,880 --> 00:03:27,920 Speaker 1: teach your children's stuff. Right. Yeah, it's like like Teddy Ruxpan, Right. 65 00:03:28,000 --> 00:03:31,840 Speaker 1: Teddy Ruxpin was like a robot cuddly bear that you 66 00:03:31,919 --> 00:03:35,280 Speaker 1: just put a tape cassette and it's stomach and then 67 00:03:35,280 --> 00:03:39,720 Speaker 1: it would blurk out all sorts of wonderful entertaining stuff. Right. 68 00:03:39,880 --> 00:03:42,680 Speaker 1: It was super creepy at the same time, right, So yeah, 69 00:03:42,800 --> 00:03:45,280 Speaker 1: which I guess is the whole point of this. So 70 00:03:45,680 --> 00:03:49,320 Speaker 1: there's also the idea that once you introduce technology into 71 00:03:49,320 --> 00:03:51,560 Speaker 1: the mainstream, we're not really going to go back, right, 72 00:03:51,680 --> 00:03:54,120 Speaker 1: Like we're gonna we're not gonna quit using our iPods 73 00:03:54,240 --> 00:03:58,080 Speaker 1: or iPads to entertain our children from time to time. Um, 74 00:03:58,120 --> 00:04:01,320 Speaker 1: if we have a robot introduced as a babysitter, most 75 00:04:01,360 --> 00:04:04,320 Speaker 1: likely we're not going to back off of that anytime soon. 76 00:04:04,400 --> 00:04:07,400 Speaker 1: And in fact, this is happening right like yes, well, 77 00:04:07,520 --> 00:04:12,680 Speaker 1: um in Japan. In Japan where robots are everywhere, Um there, 78 00:04:13,200 --> 00:04:16,160 Speaker 1: because they have a rich tradition of of of loving robots, 79 00:04:16,160 --> 00:04:19,000 Speaker 1: they've been far more into sci fi than than than 80 00:04:19,080 --> 00:04:21,160 Speaker 1: we are as a as a nation. For for ages, 81 00:04:21,200 --> 00:04:22,840 Speaker 1: they've they've grew up with They have all these guys 82 00:04:22,920 --> 00:04:24,880 Speaker 1: that grew up with the dream of robots and they 83 00:04:24,920 --> 00:04:26,720 Speaker 1: all talk about it in interviews that they grew up 84 00:04:26,720 --> 00:04:30,080 Speaker 1: watching like robots on cartoons and then they get into 85 00:04:30,200 --> 00:04:33,599 Speaker 1: robotics and they're creating that vision sort of piece by piece, 86 00:04:33,800 --> 00:04:36,480 Speaker 1: you know, nobody's creating you know, whammo, here's a robot 87 00:04:36,560 --> 00:04:38,919 Speaker 1: human that does all the things that human does. But 88 00:04:38,920 --> 00:04:43,240 Speaker 1: but like literally any tiny or even large even some 89 00:04:43,279 --> 00:04:45,440 Speaker 1: more complicated tasks that you can think of. Like there's 90 00:04:45,440 --> 00:04:50,960 Speaker 1: this huge uh food food industrialized food uh uh convention 91 00:04:51,000 --> 00:04:53,919 Speaker 1: that happens in Tokyo every year, and like there you 92 00:04:53,920 --> 00:04:57,480 Speaker 1: can go and you can see robots making sushi, robots 93 00:04:57,560 --> 00:05:01,560 Speaker 1: off you know, frying up. They are free the food 94 00:05:01,560 --> 00:05:04,359 Speaker 1: and so stuff like it is delicious, right, Like I 95 00:05:04,360 --> 00:05:07,039 Speaker 1: mean they interact with everybody. Yeah, yeah, that's of course. 96 00:05:07,040 --> 00:05:11,200 Speaker 1: That's the other huge thing in robots is making robots 97 00:05:11,240 --> 00:05:14,400 Speaker 1: that can interact with us and can and can interact 98 00:05:14,440 --> 00:05:20,960 Speaker 1: with a human environment socially intelligent machines. But but in Japan, 99 00:05:21,000 --> 00:05:24,200 Speaker 1: there are two particular examples of of of of these 100 00:05:24,320 --> 00:05:26,080 Speaker 1: robots that have come out that are they're very much 101 00:05:26,080 --> 00:05:29,240 Speaker 1: in line with the idea of a robot nursemaid or 102 00:05:29,400 --> 00:05:34,320 Speaker 1: robot babysitter. One is Reba. That's r I b A 103 00:05:34,480 --> 00:05:36,800 Speaker 1: not like not to be confused with a country singer, right, 104 00:05:36,839 --> 00:05:39,080 Speaker 1: and it doesn't have a big shock of red hair 105 00:05:39,680 --> 00:05:43,240 Speaker 1: or anything. And it's but it does look like a giant, 106 00:05:43,800 --> 00:05:47,640 Speaker 1: very cute, very cartoony, very Kauai. I believe it's a term. Yeah, 107 00:05:48,200 --> 00:05:51,560 Speaker 1: cuteness factor. Yeah, very very very cute looking, looks like 108 00:05:51,600 --> 00:05:54,280 Speaker 1: a giant teddy bear, except it's you know, it's not soft. 109 00:05:54,320 --> 00:05:56,479 Speaker 1: It's kind of hard looking. But we can lift patients. 110 00:05:56,520 --> 00:06:01,600 Speaker 1: In fact, it can. It's designed to lift up to 111 00:06:01,640 --> 00:06:05,880 Speaker 1: a dred thirty four pounds of of like old person 112 00:06:06,000 --> 00:06:08,159 Speaker 1: from a chair, I guess. And this is a child. 113 00:06:08,320 --> 00:06:11,960 Speaker 1: This is the robot that's like four pounds, right, four pounds. 114 00:06:12,000 --> 00:06:15,440 Speaker 1: Without the giant cute face, it would look horribly menacing 115 00:06:15,520 --> 00:06:17,520 Speaker 1: right coming at you to pick you up. Yeah, So 116 00:06:17,560 --> 00:06:20,400 Speaker 1: I guess that's the point of putting the Kauai face 117 00:06:20,440 --> 00:06:22,240 Speaker 1: all over it. Yeah, And and you know it's like 118 00:06:22,360 --> 00:06:24,920 Speaker 1: very it's that that hello kitty thing if if anybody's 119 00:06:25,200 --> 00:06:29,159 Speaker 1: having trouble like picturing this just hello kitty everywhere. And 120 00:06:29,279 --> 00:06:31,799 Speaker 1: but but even in Japan they recognize that that kauai 121 00:06:31,839 --> 00:06:34,400 Speaker 1: can be creepy because they have the whole uh kauai 122 00:06:34,480 --> 00:06:40,200 Speaker 1: now air um a thing going on where No, seriously, 123 00:06:40,240 --> 00:06:42,640 Speaker 1: it's like they have it's like cute stuff, but it's 124 00:06:42,640 --> 00:06:47,440 Speaker 1: also like really grim um. I wish I could think 125 00:06:47,440 --> 00:06:49,799 Speaker 1: there's a there's an artist by the name of Junko 126 00:06:49,960 --> 00:06:52,120 Speaker 1: or Junkio. I'll have to link to it on the 127 00:06:52,120 --> 00:06:54,840 Speaker 1: blog post that goes with us. But she she's really 128 00:06:54,920 --> 00:06:57,200 Speaker 1: heavy into like creating these cute things, but they're all 129 00:06:57,200 --> 00:06:59,680 Speaker 1: like doing horrible things as well. Or Gloomy Bear is 130 00:06:59,680 --> 00:07:04,839 Speaker 1: a bokay yea so um, but Reba is not gloomy. 131 00:07:04,880 --> 00:07:07,160 Speaker 1: Reba is very happy looking and as these big padded 132 00:07:07,160 --> 00:07:12,280 Speaker 1: claws to lift people and things. Another example is there 133 00:07:12,360 --> 00:07:15,280 Speaker 1: is a there's a major retailer in Japan called a 134 00:07:15,640 --> 00:07:19,200 Speaker 1: called Eon Company. Yeah, sort of like a Macyzer. Yeah. 135 00:07:19,440 --> 00:07:21,840 Speaker 1: And of course you go shopping, you have kids with you, 136 00:07:21,840 --> 00:07:23,920 Speaker 1: you want to, you know, put the kids somewhere while 137 00:07:23,960 --> 00:07:25,800 Speaker 1: you shop. It's like like here they do it at 138 00:07:25,800 --> 00:07:28,040 Speaker 1: Ikea all the time, right, you have the Ikea playland. 139 00:07:28,480 --> 00:07:30,840 Speaker 1: We drop the kids off and then either go shop 140 00:07:30,920 --> 00:07:33,960 Speaker 1: or like you've suggested, go to work for the day, 141 00:07:34,080 --> 00:07:36,280 Speaker 1: right and come back to yeah, yeah, yeah, that's my 142 00:07:36,320 --> 00:07:40,400 Speaker 1: other choice childcare. So that the the the company t 143 00:07:40,600 --> 00:07:44,280 Speaker 1: Musk came up with this, uh, this robot that looks 144 00:07:44,360 --> 00:07:47,400 Speaker 1: kind of like a large like yellow and white appliance 145 00:07:47,440 --> 00:07:49,720 Speaker 1: with kind of a wally head on top, you know, 146 00:07:50,480 --> 00:07:53,280 Speaker 1: very cute, uh and uh, and it can it can 147 00:07:53,320 --> 00:07:55,360 Speaker 1: interact with the children by like each child will have 148 00:07:55,400 --> 00:07:58,600 Speaker 1: like a badge on on his or her shirt, and 149 00:07:58,640 --> 00:08:00,960 Speaker 1: the robot can like scan badge. You know, it'll know 150 00:08:01,120 --> 00:08:04,040 Speaker 1: to interact with this person with its limited vocabulary, so 151 00:08:04,040 --> 00:08:07,280 Speaker 1: it would be like hello Robert. Yeah, hello Robert, your 152 00:08:07,320 --> 00:08:11,240 Speaker 1: parents are totally coming back, you know. Yeah. And and 153 00:08:11,280 --> 00:08:13,920 Speaker 1: then there are other awesome things. It has two u 154 00:08:14,120 --> 00:08:16,040 Speaker 1: two eyes. One of the eyes can be used to 155 00:08:16,080 --> 00:08:20,120 Speaker 1: project advertisements at the child, and the other is is 156 00:08:20,120 --> 00:08:21,920 Speaker 1: a camera so they can like record things and then 157 00:08:21,920 --> 00:08:23,920 Speaker 1: play them back for the kid. Okay, So that's the 158 00:08:24,120 --> 00:08:26,560 Speaker 1: super scary part, right, and not even that it's robot 159 00:08:26,560 --> 00:08:29,920 Speaker 1: interacting with your kids, that it's beaming like McDonald's advertisements 160 00:08:29,920 --> 00:08:32,480 Speaker 1: at your child and taking pictures of it at this time. Yeah, 161 00:08:32,480 --> 00:08:34,360 Speaker 1: Like I'm thinking of the scene from The Dark Crystal 162 00:08:34,400 --> 00:08:37,880 Speaker 1: where the they strapped the little gelfling in the chair 163 00:08:37,920 --> 00:08:39,680 Speaker 1: and then like the laser goes right into his eyes 164 00:08:39,760 --> 00:08:42,120 Speaker 1: or sort of a clockwork orange kind of thing with 165 00:08:42,160 --> 00:08:45,040 Speaker 1: cute robots and little Japanese kids. Okay, so it's there, 166 00:08:45,040 --> 00:08:49,720 Speaker 1: it's in existence. Japan is exploring it. And obviously, UM, 167 00:08:49,760 --> 00:08:51,959 Speaker 1: you know that the technology is going to be slower 168 00:08:52,000 --> 00:08:55,560 Speaker 1: to creep into the US, for instance, UM in the West, 169 00:08:55,679 --> 00:09:00,160 Speaker 1: but it's there's no denying it. It's it's possible, um, 170 00:09:00,240 --> 00:09:02,280 Speaker 1: and at some point we're probably going to be using 171 00:09:02,280 --> 00:09:06,280 Speaker 1: this technology to interact with our children, if not babysit, 172 00:09:06,400 --> 00:09:11,000 Speaker 1: at least teach them right right, But what ares I 173 00:09:11,000 --> 00:09:13,640 Speaker 1: mean we've talked about operating rooms. Um, We've got the 174 00:09:13,679 --> 00:09:18,600 Speaker 1: DaVinci robot right that that UM surgeons are using. And 175 00:09:18,679 --> 00:09:21,920 Speaker 1: uh yeah, in two thousand nine alone, seventy three thousand 176 00:09:21,960 --> 00:09:26,160 Speaker 1: American men underwent robot assisted prostate surgery. So I mean 177 00:09:26,160 --> 00:09:28,680 Speaker 1: it's robot assisted. It's not like, uh, you know, go 178 00:09:28,800 --> 00:09:31,679 Speaker 1: in drop your pants and a robot. I mean, it's 179 00:09:31,760 --> 00:09:35,440 Speaker 1: it's full about assisted. It's now the drop of your 180 00:09:35,480 --> 00:09:38,040 Speaker 1: pants just through me. Okay, um, I think what's cool 181 00:09:38,080 --> 00:09:40,240 Speaker 1: about that too, Um, not the drop your pants port, 182 00:09:40,320 --> 00:09:43,520 Speaker 1: but the robotic assistance is that it's got tremor control 183 00:09:43,600 --> 00:09:45,719 Speaker 1: on it, right, so if you know, if this or 184 00:09:46,080 --> 00:09:49,720 Speaker 1: it sort of recalibrates for any errors that the surgeon 185 00:09:49,920 --> 00:09:52,560 Speaker 1: might make in terms of the tremor that they're using 186 00:09:52,559 --> 00:09:55,160 Speaker 1: to control it. Yeah, and generally with the robotic us 187 00:09:55,280 --> 00:09:58,400 Speaker 1: or you're dealing with smaller incisions, a lot smaller skill 188 00:09:58,480 --> 00:10:01,880 Speaker 1: opera because nobody's having to like get their hand in there. Yeah, 189 00:10:01,960 --> 00:10:05,080 Speaker 1: it's three D vision, so you're able to actually look 190 00:10:05,120 --> 00:10:07,360 Speaker 1: at the area that you're operating on in a in 191 00:10:07,400 --> 00:10:10,040 Speaker 1: a full or way. Correct. So that's a plus. That's 192 00:10:10,040 --> 00:10:14,679 Speaker 1: a that's great robotics ya um. And then you spoke 193 00:10:14,720 --> 00:10:19,280 Speaker 1: of the REBA. And then there's also the Cody the 194 00:10:19,320 --> 00:10:23,920 Speaker 1: sponge bath. Oh yes, Cody, yeah, who works with the elderly. 195 00:10:24,559 --> 00:10:28,320 Speaker 1: And it can open doors and drawers and cabinets and 196 00:10:28,320 --> 00:10:31,440 Speaker 1: and give you a sponge bath. I think men like 197 00:10:31,440 --> 00:10:34,400 Speaker 1: like just figuratively can open doors, Like it just open 198 00:10:34,440 --> 00:10:38,160 Speaker 1: stores for itself by administering sponge baths. Oh yeah, like 199 00:10:38,200 --> 00:10:41,520 Speaker 1: the really important people, right, things just weird things start happening. Yeah, 200 00:10:41,559 --> 00:10:43,320 Speaker 1: next thing you know, it's it's no longer worked in 201 00:10:43,320 --> 00:10:47,200 Speaker 1: the hospital. It's got a it's got a secretarial position uptown. 202 00:10:47,360 --> 00:10:49,840 Speaker 1: And why not. I mean, who who doesn't need a 203 00:10:49,840 --> 00:10:53,400 Speaker 1: sponge bath at work sometimes? Um? And then there's the 204 00:10:53,400 --> 00:10:56,880 Speaker 1: baby seal pair of have you seen this one? Oh yeah, 205 00:10:56,960 --> 00:11:01,160 Speaker 1: I think I saw no Sharky referring to this. Okay, yeah, right, 206 00:11:01,480 --> 00:11:04,240 Speaker 1: and this is it's a little baby seal, right, and 207 00:11:04,280 --> 00:11:06,000 Speaker 1: it interacts with you. So if you look at it, 208 00:11:06,000 --> 00:11:08,840 Speaker 1: it starts cooing at you. And they're actually using it 209 00:11:08,880 --> 00:11:12,079 Speaker 1: with Alzheimer's patients, um. And and they think that it's 210 00:11:12,120 --> 00:11:15,000 Speaker 1: really helpful for the patient to bond with the animal, 211 00:11:15,320 --> 00:11:19,160 Speaker 1: and it's therapeutic. So I mean there's there's that. Yeah. 212 00:11:19,160 --> 00:11:20,839 Speaker 1: Well I like the idea of them just having cats 213 00:11:20,840 --> 00:11:23,439 Speaker 1: and dogs. I mean, that's that's been pretty successful in 214 00:11:23,720 --> 00:11:26,360 Speaker 1: various nursing comes. Yeah, we know if you pet an 215 00:11:26,360 --> 00:11:28,480 Speaker 1: animal that it already is going to learn your heart 216 00:11:28,559 --> 00:11:31,720 Speaker 1: rate and probably can tell your secrets to the cat 217 00:11:31,800 --> 00:11:34,360 Speaker 1: or dog and you know, interact with it. But I 218 00:11:34,360 --> 00:11:36,120 Speaker 1: guess what the thing about the seal is that it does. 219 00:11:36,320 --> 00:11:38,800 Speaker 1: Maybe maybe your cat runs away like mine does. Right, 220 00:11:39,080 --> 00:11:41,440 Speaker 1: the seal will actually interact with you and sort of 221 00:11:41,520 --> 00:11:44,920 Speaker 1: encourage you to have that bond as if it's listening 222 00:11:44,920 --> 00:11:48,280 Speaker 1: to you or understanding you. Yeah, and I won't say, 223 00:11:48,360 --> 00:11:52,040 Speaker 1: go to sleep on somebody's face, and yeah, it won't 224 00:11:52,080 --> 00:11:54,760 Speaker 1: pop a squat on your face, which is always good. Um. 225 00:11:54,800 --> 00:11:58,040 Speaker 1: And then we actually talked about this for about before. 226 00:11:58,040 --> 00:12:02,000 Speaker 1: It's Roxy the sex Spot. Yeah, yeah, Roxy with three 227 00:12:02,160 --> 00:12:05,160 Speaker 1: X yeah, three xs in case you didn't already get 228 00:12:05,440 --> 00:12:07,000 Speaker 1: or maybe more. I don't know. At this point they 229 00:12:07,040 --> 00:12:11,120 Speaker 1: may have added more exs. Yeah, the newer model. But 230 00:12:11,240 --> 00:12:14,559 Speaker 1: she's you know, she costs nine thousand dollars, she's anatomically correct. 231 00:12:14,679 --> 00:12:18,439 Speaker 1: She you can program her, she can interact with you. Again, 232 00:12:18,520 --> 00:12:23,440 Speaker 1: here's the technology in use right now. Yeah. And um, 233 00:12:23,520 --> 00:12:28,160 Speaker 1: and there's another actually interesting example. UM Tokyo University of 234 00:12:28,280 --> 00:12:34,720 Speaker 1: Science Professor Hiroshi Kobyashi and uh two thousand nine, he 235 00:12:35,280 --> 00:12:40,439 Speaker 1: was working on a classroom robot named Saya, and they 236 00:12:40,480 --> 00:12:43,240 Speaker 1: were experimenting with using a robot teacher the front of 237 00:12:43,240 --> 00:12:46,120 Speaker 1: the class. Uh. And this was not you know, they 238 00:12:46,120 --> 00:12:48,400 Speaker 1: didn't like roll in the teacher and like let it 239 00:12:48,480 --> 00:12:50,480 Speaker 1: loose to teach an entire year or anything. They just 240 00:12:50,480 --> 00:12:53,440 Speaker 1: sort of tested it out, and you know, it wasn't 241 00:12:53,440 --> 00:12:57,360 Speaker 1: really capable of doing much beyond calling roll and shushing 242 00:12:57,600 --> 00:13:01,160 Speaker 1: children and and on. Obviously, this would not work in 243 00:13:01,400 --> 00:13:06,439 Speaker 1: some of like America's worse schools to be unless it 244 00:13:06,679 --> 00:13:10,000 Speaker 1: actually had like missible watchers or something. But um, but 245 00:13:10,120 --> 00:13:12,240 Speaker 1: it's another example of people are figuring it out there, 246 00:13:12,280 --> 00:13:13,760 Speaker 1: like how can we take a machine, how can we 247 00:13:13,760 --> 00:13:16,679 Speaker 1: program it to interact with children, to teach them and 248 00:13:16,760 --> 00:13:21,360 Speaker 1: maintain their attention and and make them behave well. And 249 00:13:21,440 --> 00:13:23,920 Speaker 1: I think that's that's that's that's where the worst maybe 250 00:13:23,920 --> 00:13:26,800 Speaker 1: comes in when you're talking about robots and children. Is 251 00:13:26,840 --> 00:13:32,040 Speaker 1: this idea that they can teach children, particularly like toddlers. Um, 252 00:13:32,080 --> 00:13:35,600 Speaker 1: because repetition is so important when you're learning, not just 253 00:13:36,000 --> 00:13:39,960 Speaker 1: throughout your life, but at those early stages. So um, 254 00:13:40,000 --> 00:13:43,120 Speaker 1: having a robot um interact with the child, particularly with 255 00:13:43,160 --> 00:13:46,839 Speaker 1: the child with autism is actually, it's found found to 256 00:13:46,840 --> 00:13:49,960 Speaker 1: be pretty helpful. There's a two foot tall robot name now, 257 00:13:50,080 --> 00:13:53,760 Speaker 1: and you can introduce himself, extends his hand for a 258 00:13:53,800 --> 00:13:56,199 Speaker 1: shake and announces that children like to play with him. 259 00:13:56,240 --> 00:13:58,560 Speaker 1: So he's already putting that idea in the kid's brain 260 00:13:59,080 --> 00:14:00,880 Speaker 1: and it can take a bow. That's what you want 261 00:14:00,920 --> 00:14:05,640 Speaker 1: every robot to do. Um uh performs tai Chi routines 262 00:14:05,640 --> 00:14:09,960 Speaker 1: with the company music yeah. Um. But more importantly, it 263 00:14:09,960 --> 00:14:13,280 Speaker 1: can be programmed to incrementally increase the complexity of its 264 00:14:13,360 --> 00:14:18,439 Speaker 1: routines over time. So it's the children progressed through therapy. Um. 265 00:14:18,480 --> 00:14:21,240 Speaker 1: It actually helps them to go to different levels. And 266 00:14:21,280 --> 00:14:23,480 Speaker 1: so if you think of an autistic child, they actually 267 00:14:23,560 --> 00:14:25,800 Speaker 1: they need to log a lot of hours of therapy. 268 00:14:25,840 --> 00:14:28,400 Speaker 1: And this is where I think robots can actually be 269 00:14:28,440 --> 00:14:31,120 Speaker 1: really helpful because that it's pretty intensive, right, that this 270 00:14:31,160 --> 00:14:34,000 Speaker 1: sort of therapy the child with autism needs. So you 271 00:14:34,040 --> 00:14:36,840 Speaker 1: can have you can program this robot to work with 272 00:14:36,880 --> 00:14:39,720 Speaker 1: that child over and over again again that repetition, that 273 00:14:39,920 --> 00:14:43,160 Speaker 1: the burrowing of that neural pathway to learn a certain task, 274 00:14:43,840 --> 00:14:45,920 Speaker 1: and that can be helpful. I mean not just for 275 00:14:45,920 --> 00:14:47,840 Speaker 1: a child with autism, but you know, for a young 276 00:14:48,040 --> 00:14:50,400 Speaker 1: two three year old who is doing something over and 277 00:14:50,440 --> 00:14:52,960 Speaker 1: over again, robots maybe the way to go. I mean, 278 00:14:53,000 --> 00:14:56,800 Speaker 1: of course you still need the human element and human interaction, 279 00:14:57,040 --> 00:15:00,680 Speaker 1: but that sort of technology I think it's pretty intriguing. 280 00:15:06,200 --> 00:15:14,320 Speaker 1: This presentation is brought to you by Intel sponsors of Tomorrow. Now, 281 00:15:14,440 --> 00:15:16,240 Speaker 1: one thing about this is a particular study. I found 282 00:15:16,400 --> 00:15:18,640 Speaker 1: a quote where they mentioned that the children, the children 283 00:15:18,680 --> 00:15:20,880 Speaker 1: are not only connecting with the robot, but also with 284 00:15:20,920 --> 00:15:24,200 Speaker 1: the tester who controls the robot, right, yeah, and they're 285 00:15:24,200 --> 00:15:27,160 Speaker 1: both sharing this novel experience. Yeah, so I mean that 286 00:15:27,280 --> 00:15:29,960 Speaker 1: kind of I mean, not the I'm not poo pooing 287 00:15:30,000 --> 00:15:32,040 Speaker 1: the idea, but just playing Devil's addeviate here. You're you're 288 00:15:32,040 --> 00:15:35,960 Speaker 1: still not talking about a robot like completely taking on 289 00:15:36,040 --> 00:15:39,120 Speaker 1: this child. They're human in the mix, which is kind 290 00:15:39,120 --> 00:15:42,120 Speaker 1: of like it's I mean, if that is a robot 291 00:15:42,160 --> 00:15:45,440 Speaker 1: teaching a child, then is dad working on a remote 292 00:15:45,480 --> 00:15:49,040 Speaker 1: control truck with his son? Is that a robot teaching 293 00:15:49,040 --> 00:15:52,760 Speaker 1: a child? That's a good question. I think more specifically 294 00:15:52,840 --> 00:15:56,360 Speaker 1: with the kids with autism, what they were thinking is that, um, 295 00:15:56,400 --> 00:15:59,520 Speaker 1: the children are responding well to the robots because they 296 00:15:59,520 --> 00:16:04,000 Speaker 1: can the robot's behavior. So yeah, there's a there's uh 297 00:16:04,040 --> 00:16:07,800 Speaker 1: an adult human with them, but the sort of interaction 298 00:16:07,840 --> 00:16:11,120 Speaker 1: that they're having with a robot, they and and some 299 00:16:11,200 --> 00:16:14,240 Speaker 1: of this is speculative, right, but they think that there's 300 00:16:14,280 --> 00:16:16,760 Speaker 1: a comfort level and that they can say Okay, well 301 00:16:16,840 --> 00:16:18,760 Speaker 1: humans and I can't. I don't always know what they're 302 00:16:18,760 --> 00:16:21,160 Speaker 1: about to do. But this robot is it is so 303 00:16:21,240 --> 00:16:25,160 Speaker 1: repetitive and it's teaching these these things and I'm really 304 00:16:25,160 --> 00:16:28,920 Speaker 1: responding to it. That's the idea behind it. And I mean, 305 00:16:29,480 --> 00:16:33,120 Speaker 1: we can't eagineting this without talking about are just ridiculous 306 00:16:33,160 --> 00:16:37,120 Speaker 1: ability to anthem amorphize anything everything. Yeah, Like like if 307 00:16:37,160 --> 00:16:40,720 Speaker 1: anybody out there watches is a community. Um. I think 308 00:16:40,720 --> 00:16:43,600 Speaker 1: it's the first episode where Jeff Linger uses the example 309 00:16:43,640 --> 00:16:47,000 Speaker 1: of you name a pencil, like this pencil's name is 310 00:16:47,040 --> 00:16:48,640 Speaker 1: Carl or whatever, and then you snap it in half 311 00:16:48,680 --> 00:16:51,560 Speaker 1: and everybody's gonna feel a little part of themselves die 312 00:16:51,800 --> 00:16:54,640 Speaker 1: because I mean that's all it takes. It's like two 313 00:16:54,640 --> 00:16:56,800 Speaker 1: dots in a line makes a face and we can 314 00:16:56,840 --> 00:17:00,680 Speaker 1: instantly associate with it, you know if it's it's it's 315 00:17:00,720 --> 00:17:04,480 Speaker 1: just completely rampant everywhere. Well yeah, so it's it's just 316 00:17:04,680 --> 00:17:07,600 Speaker 1: ridic like like the idea that a person could could 317 00:17:07,640 --> 00:17:10,600 Speaker 1: feel like a machine is a robot is real and 318 00:17:10,720 --> 00:17:14,440 Speaker 1: uh and is and and actually maybe even feels for them. 319 00:17:14,840 --> 00:17:17,560 Speaker 1: Uh you know, I that's that's just a no brainer 320 00:17:17,600 --> 00:17:19,760 Speaker 1: because I mean we do that all the time with 321 00:17:19,880 --> 00:17:22,800 Speaker 1: things that were like dogs and cats. You know, it's like, 322 00:17:22,880 --> 00:17:26,919 Speaker 1: just my cat actually love me. Well no, not really, 323 00:17:27,160 --> 00:17:29,240 Speaker 1: but but there, I mean, there's a bond there, but 324 00:17:29,280 --> 00:17:32,879 Speaker 1: it's not, it's not. I definitely anthropomorphize that. I make 325 00:17:32,920 --> 00:17:34,879 Speaker 1: more of it out of it than it is. And 326 00:17:34,920 --> 00:17:39,760 Speaker 1: I sort of willingly partake of this, this worldview, this 327 00:17:39,880 --> 00:17:42,560 Speaker 1: fiction that I create in which my my cat thinks 328 00:17:42,560 --> 00:17:45,160 Speaker 1: more of me than say, just a warm food provider. 329 00:17:45,200 --> 00:17:46,399 Speaker 1: Oh yeah, I mean, I think we do it all 330 00:17:46,440 --> 00:17:48,199 Speaker 1: the time. We don't even know. Like I've noticed that 331 00:17:48,240 --> 00:17:51,040 Speaker 1: our I T mastermind um is he like will refer 332 00:17:51,080 --> 00:17:53,720 Speaker 1: to my computer as her all the time. And I 333 00:17:53,720 --> 00:17:56,199 Speaker 1: think it's really sweet. She she's great. Don't worry about her. 334 00:17:56,280 --> 00:17:59,479 Speaker 1: She's in good hands. Okay, yeah, I mean, you're right, 335 00:17:59,520 --> 00:18:02,760 Speaker 1: we can't. But and we have mentioned Sherry Turkle before. 336 00:18:02,880 --> 00:18:06,560 Speaker 1: She's the psychologist at m I T who was there 337 00:18:06,600 --> 00:18:12,840 Speaker 1: at the forefront of robotics development there and social robotics, right, 338 00:18:13,040 --> 00:18:15,879 Speaker 1: and she has talked about how there's a sort of 339 00:18:15,880 --> 00:18:18,800 Speaker 1: cautionary tale here because we can't help but connect with 340 00:18:18,960 --> 00:18:23,520 Speaker 1: another UM thing and and describe it as having a 341 00:18:23,640 --> 00:18:26,359 Speaker 1: sort of sort of humanness, And she saw that over 342 00:18:26,440 --> 00:18:29,919 Speaker 1: and over again with their studies, particularly with children and 343 00:18:30,000 --> 00:18:34,360 Speaker 1: even adults. The most basic primitive robot, someone might come 344 00:18:34,359 --> 00:18:38,280 Speaker 1: away feeling like they had some sort of deep relationship 345 00:18:38,320 --> 00:18:41,680 Speaker 1: with that robot, and she herself had developed one a 346 00:18:41,800 --> 00:18:45,480 Speaker 1: sort of crush on a robot. Um cog there. Yeah, 347 00:18:45,480 --> 00:18:47,800 Speaker 1: I think you mentioned in the previous podcast. Yeah, I 348 00:18:47,840 --> 00:18:53,679 Speaker 1: love hating robots. Yeah, and um she cites though specifically 349 00:18:53,720 --> 00:18:56,440 Speaker 1: this robot named Kismet that worked a lot with children, 350 00:18:57,000 --> 00:18:59,800 Speaker 1: and she talks about how the kids would interact with 351 00:19:00,080 --> 00:19:03,680 Speaker 1: is Meant. And one day kis Meant malfunctioned. The child 352 00:19:03,800 --> 00:19:06,240 Speaker 1: killed the child. That's right, and this was the twelve 353 00:19:06,320 --> 00:19:10,160 Speaker 1: year old child, and that the child thought that kiss 354 00:19:10,240 --> 00:19:14,240 Speaker 1: Meant was rejecting her. Now there are there's another adult 355 00:19:14,240 --> 00:19:18,399 Speaker 1: there sharing experience. The child didn't actually die. No, the 356 00:19:18,440 --> 00:19:20,719 Speaker 1: child did not die. In fact, she got so upset 357 00:19:20,760 --> 00:19:23,400 Speaker 1: and she just thought that she had done something to 358 00:19:23,600 --> 00:19:26,000 Speaker 1: um make the robot hate her that she started stuffing 359 00:19:26,119 --> 00:19:29,600 Speaker 1: her face full of snacks and crying. So I mean 360 00:19:29,760 --> 00:19:33,040 Speaker 1: she had like a food disorder after that. Um. But 361 00:19:33,280 --> 00:19:35,760 Speaker 1: to your point though about having the shared experience right there, 362 00:19:35,800 --> 00:19:38,600 Speaker 1: there's another adult there, and that adult is saying no, no, no, 363 00:19:38,680 --> 00:19:41,760 Speaker 1: it's it's you know, Kisman is broken. It's not you. 364 00:19:41,920 --> 00:19:44,400 Speaker 1: And yet she's the one that goes away feeling like 365 00:19:45,119 --> 00:19:47,480 Speaker 1: she's done something to this robot. And and this is 366 00:19:47,520 --> 00:19:49,880 Speaker 1: what Sherry Turkle says, is the problem is that we're 367 00:19:49,920 --> 00:19:53,200 Speaker 1: scribing all this meaning to something that can't relate back 368 00:19:53,240 --> 00:19:56,760 Speaker 1: to us, that doesn't have the sort of nuance or 369 00:19:56,800 --> 00:20:01,200 Speaker 1: social uh socialization, that sort of contact X to actually 370 00:20:01,320 --> 00:20:05,000 Speaker 1: interact with us in a meaningful way. Yeah, like it 371 00:20:05,119 --> 00:20:08,000 Speaker 1: can at a certain extent that the fiction can fall 372 00:20:08,040 --> 00:20:11,440 Speaker 1: apart that you create. I mean, at least with with animals, 373 00:20:11,480 --> 00:20:13,399 Speaker 1: there's a I mean, there's definitely a bond there, and 374 00:20:13,440 --> 00:20:15,760 Speaker 1: you're just kind of you're you're putting a whole bunch 375 00:20:15,800 --> 00:20:18,520 Speaker 1: of layers over it. But but yeah, with the with 376 00:20:18,560 --> 00:20:22,119 Speaker 1: the robot, it becomes a little more tricky. But I mean, 377 00:20:22,160 --> 00:20:23,840 Speaker 1: I also can't help but think of puppetry when we 378 00:20:23,840 --> 00:20:27,200 Speaker 1: talk about any of this, you know, I mean we're 379 00:20:27,200 --> 00:20:30,600 Speaker 1: talking about like one person using a machine to interact 380 00:20:30,640 --> 00:20:35,000 Speaker 1: with a second person, And I mean the the medium 381 00:20:35,040 --> 00:20:40,000 Speaker 1: of puppetry is essentially one person using um a a puppet, 382 00:20:40,040 --> 00:20:43,280 Speaker 1: a false little creature on the end of their hand 383 00:20:43,400 --> 00:20:46,840 Speaker 1: or hanging from some strings, etcetera, to to interact with 384 00:20:46,880 --> 00:20:50,119 Speaker 1: a person or a group of people. Um, there was 385 00:20:50,240 --> 00:20:51,919 Speaker 1: there was some study we were talking about the other 386 00:20:52,000 --> 00:20:57,360 Speaker 1: day about uh, children observing like a robot acting badly 387 00:20:57,600 --> 00:20:59,879 Speaker 1: or I believe Oh yeah, actually it wasn't. It was 388 00:20:59,880 --> 00:21:02,760 Speaker 1: a puppet show. Yeah. They were trying to figure out 389 00:21:02,760 --> 00:21:05,960 Speaker 1: whether or not eighteen months old eighteen month old children 390 00:21:06,359 --> 00:21:09,199 Speaker 1: had the ability to sniff out right and wrong. So 391 00:21:09,240 --> 00:21:13,160 Speaker 1: they want right and they're like that punch is horrible. Yeah, 392 00:21:13,240 --> 00:21:15,359 Speaker 1: they really that. There was like made mee a rabbit 393 00:21:15,400 --> 00:21:17,359 Speaker 1: that would come and there were three characters and one 394 00:21:17,400 --> 00:21:20,440 Speaker 1: was a rabbit I think, and the rabbit was pulling 395 00:21:20,440 --> 00:21:23,520 Speaker 1: all sorts of shenan again and not acting nice and so, um, 396 00:21:23,600 --> 00:21:26,840 Speaker 1: the child actually would react pretty strongly to that rabbit 397 00:21:26,880 --> 00:21:28,840 Speaker 1: and not like it. I mean it was it was 398 00:21:29,000 --> 00:21:32,639 Speaker 1: obvious in every single case that the kid was like, no, 399 00:21:32,760 --> 00:21:35,600 Speaker 1: that rabbits up to no good. So yeah, I mean 400 00:21:35,640 --> 00:21:38,679 Speaker 1: the kid has the capacity for that sort of Um. 401 00:21:38,920 --> 00:21:42,720 Speaker 1: I guess you could even say empathy, right, yeah, yeah, 402 00:21:42,840 --> 00:21:45,240 Speaker 1: some some form of empathy. Anyway, I understand that there's 403 00:21:45,240 --> 00:21:48,000 Speaker 1: a lot of stuff really still taking hold in the 404 00:21:48,040 --> 00:21:50,879 Speaker 1: early stages. But there is, but there's a lot more 405 00:21:50,920 --> 00:21:54,240 Speaker 1: that they're discovering that that babies and toddlers are um 406 00:21:54,280 --> 00:21:58,119 Speaker 1: able to detect and process um that we sometimes we 407 00:21:58,160 --> 00:22:01,040 Speaker 1: think that we're just faking it until we make it right. Um, 408 00:22:01,119 --> 00:22:03,000 Speaker 1: but there may be some sort on some sort of 409 00:22:03,080 --> 00:22:06,879 Speaker 1: level saying well that's just not good. You know, that 410 00:22:07,480 --> 00:22:09,960 Speaker 1: turned rabbit. So it's i mean sometimes we use that 411 00:22:09,960 --> 00:22:12,880 Speaker 1: stuff to explain away, uh wh wh my little kids 412 00:22:12,880 --> 00:22:15,280 Speaker 1: are such rats, you know. So it's kind of like 413 00:22:15,320 --> 00:22:17,800 Speaker 1: if they if they really can empathize, then they're just 414 00:22:17,880 --> 00:22:20,480 Speaker 1: all the more horrible they really are that self well 415 00:22:20,520 --> 00:22:23,080 Speaker 1: you know you kind of. I mean, there's the theory 416 00:22:23,160 --> 00:22:26,040 Speaker 1: that kids are little monsters, right, are little creatures and 417 00:22:26,080 --> 00:22:28,439 Speaker 1: they have to learn to be human. And so you've 418 00:22:28,480 --> 00:22:32,199 Speaker 1: got to maybe go through inflicting that pains you know, 419 00:22:32,240 --> 00:22:34,320 Speaker 1: as a child, are having it inflicted upon you in 420 00:22:34,400 --> 00:22:37,160 Speaker 1: order to understand that. Okay, So so if you're falling 421 00:22:37,200 --> 00:22:40,120 Speaker 1: along um in the car at home, children are monsters 422 00:22:40,240 --> 00:22:46,200 Speaker 1: and we need soulish robots to transform them to humans. Yeah, yes, um. 423 00:22:46,240 --> 00:22:48,920 Speaker 1: And of course the way that adults, I mean, there's 424 00:22:49,320 --> 00:22:52,000 Speaker 1: you know, children are watching what adults do, and the 425 00:22:52,040 --> 00:22:56,080 Speaker 1: way that we interact with with robots also has a 426 00:22:56,119 --> 00:23:00,000 Speaker 1: huge influence. Um. There's a study from Andrew melts Off 427 00:23:00,040 --> 00:23:02,119 Speaker 1: that the University of Washington and Seattle, they did a 428 00:23:02,160 --> 00:23:06,520 Speaker 1: study about gaze following, and they found that infant like, 429 00:23:06,640 --> 00:23:09,560 Speaker 1: you know, a robot turns his head and looks at something, 430 00:23:09,960 --> 00:23:11,840 Speaker 1: and then you look to see, oh, what's that robot 431 00:23:11,840 --> 00:23:14,760 Speaker 1: looking at? Um, Like, you wouldn't necessarily do that with 432 00:23:14,800 --> 00:23:17,880 Speaker 1: a with a security camera just sort of going back 433 00:23:17,880 --> 00:23:20,320 Speaker 1: and forth. You would recognize that as something that had 434 00:23:20,320 --> 00:23:21,719 Speaker 1: that some sort of right. You wouldn't be like, oh, 435 00:23:21,760 --> 00:23:24,040 Speaker 1: I wonder what that's looking at. But they found that 436 00:23:24,080 --> 00:23:27,240 Speaker 1: infants would follow the gaze of robots if adults had 437 00:23:27,240 --> 00:23:31,000 Speaker 1: treated the robots as people. So, um, so you know, 438 00:23:31,040 --> 00:23:34,520 Speaker 1: if we're everybody's anthropomorphizing, and then you have you have 439 00:23:34,880 --> 00:23:38,840 Speaker 1: adult humans treating the robots as if they're, if not humans, 440 00:23:38,880 --> 00:23:41,400 Speaker 1: at least something on par with a human, and then 441 00:23:41,480 --> 00:23:44,639 Speaker 1: you know children are growing up in that environment, then uh, 442 00:23:45,160 --> 00:23:48,679 Speaker 1: then yeah, it's it's completely feasible and believable that they 443 00:23:48,720 --> 00:23:51,800 Speaker 1: would see this robot is something. But then what happens 444 00:23:51,800 --> 00:23:54,520 Speaker 1: when it shuts down? Right? That had I mean, all 445 00:23:54,560 --> 00:23:58,400 Speaker 1: of a sudden, you're traumatized. Right. So you had mentioned 446 00:23:58,760 --> 00:24:03,359 Speaker 1: Harry Harlow some of his studies, and I read the 447 00:24:03,400 --> 00:24:05,119 Speaker 1: material and I have to say that I'm still a 448 00:24:05,119 --> 00:24:07,560 Speaker 1: little bit I'm reeling from it, but I think it's 449 00:24:07,600 --> 00:24:10,720 Speaker 1: interesting in this contact. Yeah, Harlow for those of you, 450 00:24:10,880 --> 00:24:13,520 Speaker 1: uh not familiar, this is back in the fifties and 451 00:24:13,560 --> 00:24:18,560 Speaker 1: he worked with Reese's monkeys and he u for instance, 452 00:24:18,600 --> 00:24:19,919 Speaker 1: one of his experiments that we're not going to go 453 00:24:19,960 --> 00:24:24,200 Speaker 1: into uh what's called the pit of despair. So that 454 00:24:24,200 --> 00:24:27,600 Speaker 1: that should tell you these were very controversial experiments because 455 00:24:27,840 --> 00:24:29,639 Speaker 1: like with the Pit of the Despair, he was just 456 00:24:29,640 --> 00:24:33,760 Speaker 1: gonna try and get like induced clinical depression in Reese's 457 00:24:33,760 --> 00:24:36,240 Speaker 1: monkeys by putting them in like this horrible little cage 458 00:24:36,320 --> 00:24:39,679 Speaker 1: with weird walls. It's a chamber and yeah, with no 459 00:24:40,160 --> 00:24:43,719 Speaker 1: absolute all sensory deprivation. Right, Yeah, it was years. It 460 00:24:43,760 --> 00:24:45,600 Speaker 1: was technically it was not called the pit of despair 461 00:24:45,600 --> 00:24:48,119 Speaker 1: in the experiment. They referred to it as a vertical 462 00:24:48,200 --> 00:24:50,840 Speaker 1: chamber apparatus. But that's right. And the monkey was hung 463 00:24:50,920 --> 00:24:54,240 Speaker 1: upside down anyway, So you know, we're dealing maybe with 464 00:24:54,240 --> 00:24:56,240 Speaker 1: a mann I depressive here, but he did do some 465 00:24:56,320 --> 00:25:01,080 Speaker 1: interesting research. Yeah, that the one involving love particularly. There's 466 00:25:01,080 --> 00:25:02,879 Speaker 1: some cool videos of this and I'll be sure to 467 00:25:02,880 --> 00:25:07,080 Speaker 1: put these into the blog post lit accompany this. Um 468 00:25:07,119 --> 00:25:09,480 Speaker 1: and you've probably seen clips of this before where he 469 00:25:09,560 --> 00:25:12,840 Speaker 1: had little baby monkeys and then he had to fake 470 00:25:13,080 --> 00:25:17,199 Speaker 1: mama monkeys. There's the metal monkey, which just looks like 471 00:25:17,240 --> 00:25:20,840 Speaker 1: this piece of scrap metal with a sort of monkey 472 00:25:20,840 --> 00:25:23,439 Speaker 1: looking head and like a single nipple coming out of 473 00:25:23,440 --> 00:25:28,080 Speaker 1: its chest with the milk. Yes, and then there's a 474 00:25:28,080 --> 00:25:31,760 Speaker 1: a furry mommy which is uh, this this all equally 475 00:25:31,760 --> 00:25:35,919 Speaker 1: fake looking monkey thing that is, but it's covered in 476 00:25:35,960 --> 00:25:40,040 Speaker 1: fur so it's soft to the touch. And so he 477 00:25:40,080 --> 00:25:43,360 Speaker 1: was curious is to how, um, you know, how interaction 478 00:25:43,440 --> 00:25:47,280 Speaker 1: with the mother affected the infants well being and as 479 00:25:47,320 --> 00:25:49,600 Speaker 1: in this this sort of came out of this idea 480 00:25:49,680 --> 00:25:52,600 Speaker 1: to that up until then, people thought that the balling 481 00:25:52,680 --> 00:25:56,880 Speaker 1: between a mother and child was completely um nutritive, right, 482 00:25:56,960 --> 00:25:59,920 Speaker 1: Like the the child was bonding with the mother because 483 00:26:00,080 --> 00:26:03,000 Speaker 1: knew it could get milk. So the idea is that 484 00:26:03,080 --> 00:26:06,280 Speaker 1: you have this wire mesh creature with milk and it'll 485 00:26:06,320 --> 00:26:08,280 Speaker 1: bond with it. Yeah, but he found that the baby's 486 00:26:08,480 --> 00:26:12,120 Speaker 1: rarely stayed with the wire model, with a wire monkey 487 00:26:12,160 --> 00:26:14,399 Speaker 1: longer than it took to get the food it needed, 488 00:26:14,520 --> 00:26:17,000 Speaker 1: and then it would scuttle off to the the cloth 489 00:26:17,400 --> 00:26:21,280 Speaker 1: soft monkey model, especially if it were scared, and it 490 00:26:21,280 --> 00:26:23,000 Speaker 1: would hang out with with that one most of the time. 491 00:26:23,200 --> 00:26:26,119 Speaker 1: If they switched the nipple to the soft monkey, it 492 00:26:26,160 --> 00:26:29,160 Speaker 1: wouldn't hang out with the metal monkey mom at all. Right, 493 00:26:29,280 --> 00:26:31,720 Speaker 1: and it's like I got everything I need right here. Yeah, okay, 494 00:26:31,760 --> 00:26:34,720 Speaker 1: And so we've got some obvious parallels with robots coming up, right, 495 00:26:35,400 --> 00:26:37,439 Speaker 1: which I mean it just to throw back to the 496 00:26:37,640 --> 00:26:39,560 Speaker 1: to some of the Japanese models we're looking at. You know, 497 00:26:39,600 --> 00:26:42,080 Speaker 1: it's the they know better than to make a a 498 00:26:42,280 --> 00:26:45,439 Speaker 1: you know, a nursing or babysitting robot that looks like, 499 00:26:45,920 --> 00:26:48,639 Speaker 1: you know, a water heater. They're they're creating them cute, 500 00:26:48,640 --> 00:26:52,880 Speaker 1: they're creating them. In the case of the uh, the 501 00:26:52,880 --> 00:26:55,960 Speaker 1: the rebud that lifts uh that looks like a bear 502 00:26:56,040 --> 00:26:58,000 Speaker 1: that lifts patient stuff. I mean, it's it's it's hands 503 00:26:58,000 --> 00:27:02,560 Speaker 1: are soft, so um so yeah. But what happens when 504 00:27:03,480 --> 00:27:05,679 Speaker 1: when monkeys are are raised by these things? He he 505 00:27:05,720 --> 00:27:09,560 Speaker 1: did some some additional studies, and he found that babies 506 00:27:09,640 --> 00:27:13,160 Speaker 1: raised with real mothers but no playmates were often fearful 507 00:27:13,240 --> 00:27:17,480 Speaker 1: or inappropriately aggressive. Baby monkeys without playmates or real mothers 508 00:27:17,520 --> 00:27:21,000 Speaker 1: became socially incompetent and uh. But when and when older 509 00:27:21,040 --> 00:27:24,320 Speaker 1: were often unsuccessful at mating. And he also found that 510 00:27:24,400 --> 00:27:27,480 Speaker 1: young monkeys reared with live mothers and young peers easily 511 00:27:27,520 --> 00:27:31,160 Speaker 1: learned to play and socialize with other young monkeys. Uh. 512 00:27:31,440 --> 00:27:34,400 Speaker 1: While the while, if you were if you grew up 513 00:27:34,400 --> 00:27:37,479 Speaker 1: with the cloth monkey the fake monkey, um, you were slower, 514 00:27:37,520 --> 00:27:39,800 Speaker 1: but you seem to catch up socially by about a 515 00:27:39,880 --> 00:27:42,919 Speaker 1: year if you were around other monkey babies. But if 516 00:27:42,960 --> 00:27:46,119 Speaker 1: you're deprived of any interaction with your species, then you 517 00:27:46,160 --> 00:27:50,000 Speaker 1: were a violent mess. Right. But then again we have 518 00:27:50,080 --> 00:27:52,480 Speaker 1: to look at the fact that these were very basically 519 00:27:52,480 --> 00:27:56,199 Speaker 1: these were not even robots. These are just the just 520 00:27:56,400 --> 00:27:59,760 Speaker 1: mimicking like the cloth monkey was just basically mimicking softness. 521 00:28:00,119 --> 00:28:01,800 Speaker 1: But see, what I think is interesting about this is 522 00:28:01,800 --> 00:28:04,520 Speaker 1: that if they had the interaction with their species, then 523 00:28:04,560 --> 00:28:08,560 Speaker 1: they could have that nuanced communication. And again this is 524 00:28:08,840 --> 00:28:10,720 Speaker 1: one of the problems with robots. Write that they don't 525 00:28:10,720 --> 00:28:14,280 Speaker 1: have the social context and they can't right not yet, 526 00:28:14,320 --> 00:28:17,280 Speaker 1: they can't interact with us on a level that's meaningful. 527 00:28:17,760 --> 00:28:21,640 Speaker 1: So we have this little thing called mirror neurons, and 528 00:28:21,920 --> 00:28:25,640 Speaker 1: those are activated when people observe an activity, and these 529 00:28:25,640 --> 00:28:28,800 Speaker 1: neurons resonate as if they were mimicking the activity. So 530 00:28:28,840 --> 00:28:32,120 Speaker 1: the brain learns about an activity by effectively copying what's 531 00:28:32,160 --> 00:28:35,840 Speaker 1: going on, and the brain processes the observed actions as 532 00:28:35,880 --> 00:28:38,640 Speaker 1: if it's doing itself. Right. So if I'm a monkey 533 00:28:38,680 --> 00:28:41,680 Speaker 1: and I'm watching my monkey mother, uh do this some 534 00:28:42,040 --> 00:28:44,960 Speaker 1: this sort of thing, uh, in a either social context 535 00:28:45,080 --> 00:28:47,840 Speaker 1: or maybe she's using a tool to do something, then 536 00:28:47,880 --> 00:28:50,600 Speaker 1: I'm able to mimic that in my brain. But but 537 00:28:50,600 --> 00:28:53,520 Speaker 1: but if I'm deprived of that, uh, then I as 538 00:28:53,560 --> 00:28:56,040 Speaker 1: a monkey and at a great disadvantage. So you look 539 00:28:56,040 --> 00:28:59,920 Speaker 1: at the obvious parallels here with robots. Yeah, and the 540 00:29:00,280 --> 00:29:02,360 Speaker 1: mimicking thing is is interesting too when you look at 541 00:29:02,400 --> 00:29:06,360 Speaker 1: the in the future of socially intelligent machines UM, just 542 00:29:06,400 --> 00:29:10,120 Speaker 1: as far as it concerns, say, more complicated industrial tasks. 543 00:29:10,480 --> 00:29:12,960 Speaker 1: There's the the idea that you would have a machine 544 00:29:13,200 --> 00:29:16,440 Speaker 1: learning from a human watching the human, observing human doing 545 00:29:16,440 --> 00:29:18,640 Speaker 1: the task and learning it. Or in a surgical situation, 546 00:29:19,240 --> 00:29:21,680 Speaker 1: human is using the robot, controlling the robot to conduct 547 00:29:21,680 --> 00:29:27,400 Speaker 1: a surgery, and if a sufficiently advanced machine, sufficiently socially 548 00:29:27,440 --> 00:29:31,920 Speaker 1: intelligent machine would learn from those, uh, those different techniques 549 00:29:31,920 --> 00:29:34,520 Speaker 1: in maneuvers that the human is using and be able 550 00:29:34,600 --> 00:29:37,880 Speaker 1: to replicate them. Uh. That's that's kind of the ultimate goal. 551 00:29:37,960 --> 00:29:40,600 Speaker 1: And then and will not the ultimate goal, but one 552 00:29:40,640 --> 00:29:42,680 Speaker 1: of the big goals. Yeah. And once we get there, 553 00:29:42,720 --> 00:29:44,000 Speaker 1: you know, then we have we have a robot that 554 00:29:44,040 --> 00:29:46,880 Speaker 1: can learn skills and then and then improve upon them 555 00:29:46,880 --> 00:29:49,520 Speaker 1: with machine precision. Yeah. And it's interesting that m I 556 00:29:49,560 --> 00:29:51,520 Speaker 1: T is actually looking at toddlers in the way that 557 00:29:51,560 --> 00:29:55,920 Speaker 1: they learn and develop and applying that to robots as 558 00:29:55,960 --> 00:29:59,960 Speaker 1: well physically, not so much with with emotions right now. 559 00:30:00,200 --> 00:30:02,320 Speaker 1: But you know, we were talking about tracking gays or 560 00:30:02,320 --> 00:30:04,280 Speaker 1: doing the same thing with robots and trying to figure 561 00:30:04,320 --> 00:30:07,000 Speaker 1: out how to make that more nuanced for them. Yeah, 562 00:30:07,040 --> 00:30:09,080 Speaker 1: And it comes down to like how complex the system 563 00:30:09,120 --> 00:30:12,680 Speaker 1: is too, Like assembling some you know, cogs on an 564 00:30:12,720 --> 00:30:15,200 Speaker 1: assembly line that is, you know, that's going to be 565 00:30:15,720 --> 00:30:19,440 Speaker 1: just so complicated, and then surgery you're you're dealing with 566 00:30:19,480 --> 00:30:21,800 Speaker 1: a with it, you know, a more complicated system, but 567 00:30:22,760 --> 00:30:26,240 Speaker 1: it's it's a system now how about child rearing, Like 568 00:30:26,280 --> 00:30:29,440 Speaker 1: how complex, how nuances that system? And then how much 569 00:30:29,520 --> 00:30:31,320 Speaker 1: how much effort has to go into it, how much 570 00:30:31,320 --> 00:30:34,440 Speaker 1: programming has to go into a robot capable of navigating 571 00:30:34,440 --> 00:30:37,320 Speaker 1: that system and dealing with these little monsters that behave 572 00:30:37,440 --> 00:30:41,560 Speaker 1: so erratically and and may have you know, other you know, 573 00:30:41,640 --> 00:30:45,080 Speaker 1: other things influencing their lives. Well in language is key, right, 574 00:30:45,200 --> 00:30:49,480 Speaker 1: I mean, if anything, that's what distinguishes us from creatures, um, 575 00:30:49,680 --> 00:30:51,880 Speaker 1: is that we have the ability to communicate with each 576 00:30:51,880 --> 00:30:56,920 Speaker 1: other close that close. Clothes are helpful, um, but and 577 00:30:57,000 --> 00:30:59,440 Speaker 1: you know some creatures do wear clothes. I'm not going 578 00:30:59,480 --> 00:31:02,280 Speaker 1: to go into specifics. That's for another podcast. Are you 579 00:31:02,280 --> 00:31:04,640 Speaker 1: talking about those TV shows with the monkeys in them? 580 00:31:04,680 --> 00:31:07,240 Speaker 1: Because I think it's hard to argue that that's consensual 581 00:31:07,880 --> 00:31:12,640 Speaker 1: that they're wearing squirrels too. But um, but language that's 582 00:31:12,640 --> 00:31:16,360 Speaker 1: the problem. Right. So AI developers they've been able to 583 00:31:17,240 --> 00:31:20,600 Speaker 1: develop robots physically, but they can't quite figure out how 584 00:31:20,640 --> 00:31:25,360 Speaker 1: to do this this mysterious thing which is too imbibe 585 00:31:25,400 --> 00:31:27,840 Speaker 1: them with language and to be able to communicate with us. 586 00:31:27,880 --> 00:31:30,040 Speaker 1: And again that's why they're looking at toddlers and babies 587 00:31:30,040 --> 00:31:34,600 Speaker 1: and seeing how their language centers develop um and in fact, 588 00:31:34,640 --> 00:31:39,640 Speaker 1: there's AI specialist Rao and he says, only by integrating 589 00:31:39,640 --> 00:31:43,120 Speaker 1: sensations from their own mechanical bodies will robots have a 590 00:31:43,160 --> 00:31:45,440 Speaker 1: shot at understanding what it means for a chair to 591 00:31:45,480 --> 00:31:48,840 Speaker 1: be soft and a person to be soft hearted. So 592 00:31:49,160 --> 00:31:52,080 Speaker 1: they have to have those experiences themselves. I think we've 593 00:31:52,120 --> 00:31:54,640 Speaker 1: touched on this before. How I think maybe it's in 594 00:31:54,640 --> 00:31:57,360 Speaker 1: the Pain podcast about like the the idea of creating 595 00:31:57,440 --> 00:32:00,640 Speaker 1: robots that can feel pain like part of sensation. You 596 00:32:00,680 --> 00:32:03,720 Speaker 1: would need to create a robot that can feel something 597 00:32:03,760 --> 00:32:07,760 Speaker 1: like pain. That that that has to uh, that makes 598 00:32:07,840 --> 00:32:11,080 Speaker 1: a task strenuous of its strenuous um you know. Yeah, 599 00:32:11,160 --> 00:32:14,640 Speaker 1: that's right to have the ability to empathize, right right, Yeah, 600 00:32:14,680 --> 00:32:16,680 Speaker 1: I mean they're getting closer there. There's some have you 601 00:32:16,680 --> 00:32:19,960 Speaker 1: heard about the I cub Okay, this is a robots 602 00:32:20,000 --> 00:32:22,760 Speaker 1: as tall as a three year old, It weighs fifty pounds, 603 00:32:22,800 --> 00:32:26,160 Speaker 1: it has a child's face, and it's a bear. No 604 00:32:27,640 --> 00:32:30,640 Speaker 1: only in your world, cub, I'm picturing like a small 605 00:32:30,720 --> 00:32:33,960 Speaker 1: robotic bear. Yeah, it is a little misleading. It's got 606 00:32:34,160 --> 00:32:38,000 Speaker 1: five fingered hands and uh, it's actually under the tuliage 607 00:32:38,080 --> 00:32:39,960 Speaker 1: right now of a computer scientist or one of them 608 00:32:40,040 --> 00:32:44,560 Speaker 1: is Georgio Metha and the Italian Institute of Technology in Genoa. 609 00:32:44,880 --> 00:32:50,480 Speaker 1: So if you're not seeing the geppetto and similarities, here 610 00:32:50,520 --> 00:32:53,280 Speaker 1: there you go. But when not crawling, I cub can 611 00:32:53,360 --> 00:32:57,280 Speaker 1: sit up and grasp objects. It possesses the robotic equivalent 612 00:32:57,320 --> 00:32:59,400 Speaker 1: of sight, hearing, and touch and has a sense of 613 00:32:59,440 --> 00:33:01,720 Speaker 1: balance and flexible skin. Is now in the works for 614 00:33:01,840 --> 00:33:05,040 Speaker 1: it um that the idea is that eventually it can talk, 615 00:33:05,320 --> 00:33:08,400 Speaker 1: and they're they're getting there again. They're they're studying patterns 616 00:33:08,440 --> 00:33:12,120 Speaker 1: and ways that they can try to create the same 617 00:33:12,160 --> 00:33:16,800 Speaker 1: neural circuitry that we have in terms of mirror neurons UM. 618 00:33:16,840 --> 00:33:21,479 Speaker 1: And it's a super creepy robot. Well, that's another thing 619 00:33:21,480 --> 00:33:22,600 Speaker 1: we have to deal with, is the you know, the 620 00:33:22,600 --> 00:33:25,200 Speaker 1: the whole uncanny valley, the idea that the closer you 621 00:33:25,760 --> 00:33:28,440 Speaker 1: and I guess that's why making a cute robot look 622 00:33:28,480 --> 00:33:31,320 Speaker 1: like a cartoon bear is is far better than trying 623 00:33:31,320 --> 00:33:33,000 Speaker 1: to make it look like a person. Because the closer 624 00:33:33,040 --> 00:33:36,640 Speaker 1: you come to making um an artificial structure or creation 625 00:33:36,640 --> 00:33:39,280 Speaker 1: looks like a human is the idea, the more creepy 626 00:33:39,280 --> 00:33:41,560 Speaker 1: it becomes. It's the Yeah, the more grotesque it is 627 00:33:41,600 --> 00:33:43,480 Speaker 1: because you we we can look at it and say, 628 00:33:43,520 --> 00:33:46,040 Speaker 1: there's something wrong with that. Right, it looks just like 629 00:33:46,120 --> 00:33:49,480 Speaker 1: me except where it's not me um and and that's 630 00:33:49,640 --> 00:33:53,000 Speaker 1: where the alienation comes from. Right, So better off to 631 00:33:53,080 --> 00:33:55,400 Speaker 1: just make it look like you know, Yogi bear and 632 00:33:55,640 --> 00:34:00,560 Speaker 1: have it change divers that way. But it's it's interesting, 633 00:34:00,840 --> 00:34:03,880 Speaker 1: especially after last week's podcast where we talked about love 634 00:34:03,920 --> 00:34:05,320 Speaker 1: and about like what's going on in the in the 635 00:34:05,400 --> 00:34:09,080 Speaker 1: mind with love and like what love is, because we're 636 00:34:09,080 --> 00:34:12,040 Speaker 1: talking about creating a machine that can essentially like no 637 00:34:12,080 --> 00:34:15,839 Speaker 1: matter how complicated you you talk about the the AI 638 00:34:16,280 --> 00:34:18,239 Speaker 1: and and in the way it interacts for this, no 639 00:34:18,280 --> 00:34:22,600 Speaker 1: matter how socially intelligent it is, it is essentially faking it. 640 00:34:22,600 --> 00:34:26,040 Speaker 1: It's it's faking being human. It's faking it's compassion for 641 00:34:26,160 --> 00:34:28,960 Speaker 1: the for this child, it's faking it's love for the 642 00:34:29,560 --> 00:34:33,080 Speaker 1: lonely dude who buys um you know, the future version 643 00:34:33,080 --> 00:34:35,200 Speaker 1: of Roxy. You know that there, it's faking all of 644 00:34:35,239 --> 00:34:37,480 Speaker 1: these things that are human and it and it's easy 645 00:34:37,560 --> 00:34:39,400 Speaker 1: to to sort of get on a high horse and 646 00:34:39,520 --> 00:34:41,040 Speaker 1: about that and be like, oh, it's just this is 647 00:34:41,320 --> 00:34:44,000 Speaker 1: so disgusting it's just gonna fake all these things that 648 00:34:44,080 --> 00:34:47,319 Speaker 1: matter to this. But when you look like at the 649 00:34:46,880 --> 00:34:52,400 Speaker 1: at the the neural activity behind the real organic realities 650 00:34:52,440 --> 00:34:55,680 Speaker 1: of love and compassion um or, I mean, it all 651 00:34:55,719 --> 00:35:00,000 Speaker 1: boils down to kind of things that are fake as well, uh, 652 00:35:00,040 --> 00:35:02,200 Speaker 1: because I mean it comes down to issues of like, 653 00:35:02,239 --> 00:35:04,680 Speaker 1: all right, well, this organism that we call mom, you know, 654 00:35:04,719 --> 00:35:08,040 Speaker 1: it just wants to uh, to carry on it's it's genes. 655 00:35:08,160 --> 00:35:11,880 Speaker 1: It wants to to eat food and and uh. And 656 00:35:11,920 --> 00:35:14,000 Speaker 1: you know, it's like there are a number of very robotic, 657 00:35:14,160 --> 00:35:17,160 Speaker 1: robotic tasks and on top of that, there's this neural 658 00:35:17,160 --> 00:35:20,680 Speaker 1: complexity that it creates these things that we call love 659 00:35:20,760 --> 00:35:24,080 Speaker 1: and and uh. And so by by looking at a 660 00:35:24,160 --> 00:35:25,800 Speaker 1: robot faking, we have to look at how much of 661 00:35:25,840 --> 00:35:28,600 Speaker 1: it is fake in our lives general. Unless well, that's 662 00:35:28,600 --> 00:35:31,440 Speaker 1: interesting to say that because I had read something about 663 00:35:31,480 --> 00:35:36,400 Speaker 1: how some programmers at Georgia Tech had given some robots 664 00:35:36,400 --> 00:35:39,799 Speaker 1: the ability to deceive, and I was horrified by that. 665 00:35:40,040 --> 00:35:43,600 Speaker 1: But you're right, I mean, that's that's a purely human thing, deception. 666 00:35:44,080 --> 00:35:45,719 Speaker 1: I guess. I was just horrified in the fact that 667 00:35:45,760 --> 00:35:47,719 Speaker 1: you would have a robot that would have that ability 668 00:35:48,280 --> 00:35:53,279 Speaker 1: and specifically, they programmed it um in cases of warfare. 669 00:35:53,640 --> 00:35:55,680 Speaker 1: So if you're in the battlefield and you have the 670 00:35:55,680 --> 00:35:59,440 Speaker 1: power of deception, you could you know, successfully hide and 671 00:35:59,480 --> 00:36:03,600 Speaker 1: mislead enemy if you are a robot, and also give 672 00:36:03,680 --> 00:36:08,520 Speaker 1: false information. But take it back down to your robo nanny. 673 00:36:08,719 --> 00:36:12,040 Speaker 1: You know, do you want your robo nanny to be deceptive? Well, 674 00:36:12,080 --> 00:36:14,279 Speaker 1: what if the kid asks, Santa Claus is real? Yes, 675 00:36:14,400 --> 00:36:15,920 Speaker 1: you don't. You don't want the robot to be like 676 00:36:16,080 --> 00:36:19,719 Speaker 1: Santa Claus is a cultural construct that means nothing. You 677 00:36:19,760 --> 00:36:23,000 Speaker 1: know that kids crying and then I don't know what 678 00:36:23,040 --> 00:36:25,520 Speaker 1: happens next. Yeah, but maybe I mean, what does Robin 679 00:36:25,600 --> 00:36:28,560 Speaker 1: Nanny doing. I mean, Robinani could then turn to you 680 00:36:28,640 --> 00:36:31,280 Speaker 1: and say, we had a great afternoon, we did enriching 681 00:36:31,320 --> 00:36:34,160 Speaker 1: activities and really like Robin Nanny was, you know, watching 682 00:36:34,680 --> 00:36:39,680 Speaker 1: watching the soaps, you still need a nanny cam to 683 00:36:39,800 --> 00:36:42,040 Speaker 1: watch then, and then the nanny camp itself is a robot. 684 00:36:42,080 --> 00:36:45,080 Speaker 1: You end up with all these levels. Uh, it becomes 685 00:36:45,120 --> 00:36:47,800 Speaker 1: a quagmire pretty quickly, and you start talking about a 686 00:36:48,200 --> 00:36:51,839 Speaker 1: robot filled future. It's it's a little bit. Uh, it's 687 00:36:51,920 --> 00:36:53,959 Speaker 1: it's dim, right. There are aspects of it that seems 688 00:36:54,000 --> 00:36:56,879 Speaker 1: kind of dim, but I will say that they're there. 689 00:36:58,239 --> 00:37:01,160 Speaker 1: There's possibilities in terms of teaching children. Yeah, I mean 690 00:37:01,280 --> 00:37:03,200 Speaker 1: lots of children already. I mean not that this is 691 00:37:03,239 --> 00:37:06,120 Speaker 1: like there's not necessarily a pro into a large extent. 692 00:37:06,160 --> 00:37:07,960 Speaker 1: It's maybe a negative in some cases, but I mean 693 00:37:08,000 --> 00:37:11,280 Speaker 1: children are already depending on computers more and more. They're 694 00:37:11,320 --> 00:37:14,280 Speaker 1: they're turning the video games and social networking for their 695 00:37:14,320 --> 00:37:18,040 Speaker 1: their community. So, um, you know this is we're just 696 00:37:18,080 --> 00:37:21,680 Speaker 1: talking in a way an extension of this, right. You know. 697 00:37:21,800 --> 00:37:24,239 Speaker 1: I think that um, everyone's want. I think we try 698 00:37:24,280 --> 00:37:26,600 Speaker 1: to figure out like what the next niche thing is. 699 00:37:26,880 --> 00:37:30,920 Speaker 1: And I really think that robot camps, yes, for anybody 700 00:37:30,960 --> 00:37:33,160 Speaker 1: who's who's got the equipment out there that E mean 701 00:37:33,239 --> 00:37:35,719 Speaker 1: like a camp where people go and build robots. Yeah, yeah, 702 00:37:35,760 --> 00:37:37,680 Speaker 1: the Moxie. Yeah, I mean I'm thinking about I mean, 703 00:37:37,680 --> 00:37:39,360 Speaker 1: my daughter, I think I need to enroll her in 704 00:37:39,400 --> 00:37:41,520 Speaker 1: some AI so she can control her Oh well, I mean, 705 00:37:41,560 --> 00:37:44,520 Speaker 1: well they have things like first and all. No, no, yeah, 706 00:37:44,520 --> 00:37:47,359 Speaker 1: but I mean like we're talking like early childhood education here, 707 00:37:47,520 --> 00:37:51,000 Speaker 1: like never mind music garden. Okay, you know, I think 708 00:37:52,120 --> 00:37:53,719 Speaker 1: I think we should all aspire to maybe have our 709 00:37:53,800 --> 00:37:56,680 Speaker 1: children be able to take a part, put together and 710 00:37:56,719 --> 00:37:59,480 Speaker 1: manipulate their own robots by age four. I mean, robots 711 00:37:59,520 --> 00:38:01,560 Speaker 1: are going to be making all the music anyway, right, 712 00:38:01,680 --> 00:38:06,359 Speaker 1: so true? Yeah, just you know, when the technological singularity comes, 713 00:38:06,360 --> 00:38:11,840 Speaker 1: they're prepared. Yeah. So uh, you know, I guess you know. 714 00:38:11,840 --> 00:38:13,440 Speaker 1: We put it out to you guys to let us 715 00:38:13,440 --> 00:38:15,960 Speaker 1: know what you think about the prospect of future generations 716 00:38:16,000 --> 00:38:20,640 Speaker 1: being raised by robots, babysitted by robots. Um, if anybody 717 00:38:20,640 --> 00:38:22,360 Speaker 1: out there was raised by robot, let us know. I 718 00:38:22,360 --> 00:38:26,280 Speaker 1: don't know, it's possible some you know, secluded uh family 719 00:38:26,320 --> 00:38:29,600 Speaker 1: situation where dad's a mad scientist and and uh and 720 00:38:29,719 --> 00:38:32,080 Speaker 1: mom is addicted to the home shopping network and isn't 721 00:38:32,200 --> 00:38:35,520 Speaker 1: all that available or some documents have recently become declassified 722 00:38:35,600 --> 00:38:39,000 Speaker 1: and you can speak about it. Yes, yeah, let let 723 00:38:39,080 --> 00:38:41,640 Speaker 1: us know. Send us this document and speaking which I 724 00:38:41,680 --> 00:38:44,040 Speaker 1: do have one listener mail to read here And this 725 00:38:44,160 --> 00:38:51,279 Speaker 1: comes from Caitlin, and Caitlyn writes, Hey, Robert love the podcast. 726 00:38:51,560 --> 00:38:53,759 Speaker 1: As an English teacher, I often recommend it to my 727 00:38:53,840 --> 00:38:56,840 Speaker 1: adult students to train their ear to the American accent. 728 00:38:57,640 --> 00:39:02,480 Speaker 1: Which this is kind of funny. Um, But Caitlin is 729 00:39:02,840 --> 00:39:05,200 Speaker 1: writing to us from Madrid, staining by the way she 730 00:39:05,239 --> 00:39:07,759 Speaker 1: continues just writing because something occurred to me today as 731 00:39:07,800 --> 00:39:10,719 Speaker 1: I was listening to you talk about terraforming in the 732 00:39:10,760 --> 00:39:13,120 Speaker 1: beginning of the Werwolf Principle podcast where we talk about 733 00:39:13,160 --> 00:39:16,279 Speaker 1: engineering humans for space. I'm sure you have read Kim 734 00:39:16,400 --> 00:39:20,319 Speaker 1: Stanley Robinson's excellent Red Mars, Green Mars, Blue Mars trilogy, 735 00:39:20,600 --> 00:39:22,640 Speaker 1: but there are so many times when I expect to 736 00:39:22,640 --> 00:39:24,360 Speaker 1: hear you bring it up and you don't. You have 737 00:39:24,560 --> 00:39:27,880 Speaker 1: read it, right. All of Robin Robinson's work is excellent, 738 00:39:27,920 --> 00:39:29,960 Speaker 1: but the Mars trilogy is probably the best work of 739 00:39:30,000 --> 00:39:32,480 Speaker 1: fiction I've read of any genre. I really enjoy your 740 00:39:32,480 --> 00:39:35,440 Speaker 1: book recommendations, especially since I got a kindle, and thanks 741 00:39:35,440 --> 00:39:37,840 Speaker 1: to you, I've decided to read some more Ian Banks, 742 00:39:38,160 --> 00:39:40,560 Speaker 1: although after reading The Wasps Factory I was a bit 743 00:39:40,600 --> 00:39:42,920 Speaker 1: turned off. But it was his first work, or one 744 00:39:42,920 --> 00:39:45,600 Speaker 1: of his early works, right, and I've been told later 745 00:39:45,640 --> 00:39:48,439 Speaker 1: works are more different. Keep up the good work, both 746 00:39:48,480 --> 00:39:50,400 Speaker 1: of you. Uh So, first of all, I do have 747 00:39:50,440 --> 00:39:55,120 Speaker 1: to admit that I have not read the Mars trilogy. Yeah, 748 00:39:55,320 --> 00:39:57,000 Speaker 1: it's it's been on the list for a while, but 749 00:39:57,040 --> 00:40:00,239 Speaker 1: I have the habit of finding authors I like, and 750 00:40:00,280 --> 00:40:02,839 Speaker 1: then I got compulsively read that authorn till I either 751 00:40:02,880 --> 00:40:04,600 Speaker 1: read everything they've written or I just get really sick 752 00:40:04,640 --> 00:40:08,239 Speaker 1: of them, which you know is good when I'm onto 753 00:40:08,440 --> 00:40:11,160 Speaker 1: a good author, but it means I'm not necessarily getting 754 00:40:11,200 --> 00:40:13,279 Speaker 1: to some of these other works that I should read. 755 00:40:13,920 --> 00:40:17,759 Speaker 1: And as for banks work, yeah, wasp Factory was his 756 00:40:17,840 --> 00:40:21,480 Speaker 1: first novel, and uh it is it's rather different than 757 00:40:21,520 --> 00:40:24,440 Speaker 1: a sci fi It's it's a rather dark story about 758 00:40:24,440 --> 00:40:29,799 Speaker 1: really really messed up Scottish family, uh that lives on 759 00:40:29,840 --> 00:40:32,359 Speaker 1: the beach, and it's more of a it's like sort 760 00:40:32,400 --> 00:40:34,960 Speaker 1: of a psychological horror kind of a thing. But his 761 00:40:35,120 --> 00:40:37,239 Speaker 1: his later books are like, they're the sci fi books 762 00:40:37,239 --> 00:40:39,520 Speaker 1: that I've been talking about. And so if one word 763 00:40:39,520 --> 00:40:43,040 Speaker 1: to pick up their first u in in banks sci 764 00:40:43,080 --> 00:40:45,840 Speaker 1: fi book, I would say The Player of Games is 765 00:40:45,880 --> 00:40:49,479 Speaker 1: probably a good starting point. So Caitlin, thanks for writing 766 00:40:49,560 --> 00:40:51,160 Speaker 1: us and for the rest of you, if you have 767 00:40:51,200 --> 00:40:53,359 Speaker 1: anything you want to share with us, you can find 768 00:40:53,440 --> 00:40:56,560 Speaker 1: us on Facebook and Twitter both as blow the Mind. 769 00:40:57,440 --> 00:41:01,080 Speaker 1: We regularly update that feed with the cool links to 770 00:41:01,520 --> 00:41:04,800 Speaker 1: how stuff work stuff as well as uh curios from 771 00:41:04,800 --> 00:41:07,640 Speaker 1: throughout the web, and you can also drop us a 772 00:41:07,680 --> 00:41:14,680 Speaker 1: line at blow the mind at how stuff works dot com. 773 00:41:14,719 --> 00:41:17,200 Speaker 1: For more on this and thousands of other topics, visit 774 00:41:17,239 --> 00:41:20,040 Speaker 1: how stuff works dot com. To learn more about the podcast, 775 00:41:20,239 --> 00:41:22,760 Speaker 1: click on the podcast icon in the upper right corner 776 00:41:22,800 --> 00:41:25,879 Speaker 1: of our homepage. The how stuff Works iPhone app has 777 00:41:25,880 --> 00:41:28,400 Speaker 1: a ride. Download it today on iTunes.