1 00:00:15,076 --> 00:00:25,436 Speaker 1: Pushkin. This is solvable. I'm Ronald Young Junior. Every single 2 00:00:25,516 --> 00:00:29,556 Speaker 1: instance where something has transformed our society for good, there's 3 00:00:29,596 --> 00:00:32,396 Speaker 1: always been this fear. Some of the greatest advancements in 4 00:00:32,436 --> 00:00:36,876 Speaker 1: our society are directly linked to technological breakthroughs. Whether it 5 00:00:36,956 --> 00:00:41,036 Speaker 1: was the wheel, the printing press, or the microchip, the 6 00:00:41,116 --> 00:00:45,516 Speaker 1: world has been transformed from generation to generation. In many cases, 7 00:00:45,796 --> 00:00:49,596 Speaker 1: transformation was not greeted with acceptance and quick adoption, but 8 00:00:49,716 --> 00:00:54,556 Speaker 1: instead with anger, fear, and apprehension. And this is the 9 00:00:54,636 --> 00:01:00,836 Speaker 1: reception that has met artificial intelligence, that potential negative of 10 00:01:00,876 --> 00:01:06,836 Speaker 1: being reliant on this software, on AI, on robotics, that 11 00:01:06,876 --> 00:01:09,476 Speaker 1: the negatives are less than the post as it is. 12 00:01:09,796 --> 00:01:12,516 Speaker 1: But it's not just the fear of reliance on these technologies. 13 00:01:13,076 --> 00:01:15,996 Speaker 1: It's also the fear of the technology magnifying and multiplying 14 00:01:16,036 --> 00:01:20,316 Speaker 1: our worst two intendencies. There have been troubling instances where 15 00:01:20,356 --> 00:01:23,156 Speaker 1: bias and robotics and AI have been identified as a 16 00:01:23,156 --> 00:01:27,596 Speaker 1: potential disqualifying factor for their wide deployment. These systems have 17 00:01:28,036 --> 00:01:31,356 Speaker 1: some aspect of bias, time and time and time again, 18 00:01:31,796 --> 00:01:35,556 Speaker 1: they're still better than the human biases. Doctor Ayanna Howard 19 00:01:35,676 --> 00:01:37,876 Speaker 1: is the Dean of the College of engineering at the 20 00:01:38,036 --> 00:01:41,516 Speaker 1: Ohio State University and one of the few non white 21 00:01:41,596 --> 00:01:44,796 Speaker 1: male roboticists in the field. So I don't think that 22 00:01:44,836 --> 00:01:47,756 Speaker 1: these systems can ever get to zero bias, because there 23 00:01:47,796 --> 00:01:50,636 Speaker 1: will always be a group that the system has not 24 00:01:50,796 --> 00:01:53,676 Speaker 1: interacted with. It might be that it's perfect, as perfect 25 00:01:53,836 --> 00:01:59,356 Speaker 1: is perfect, and then there's an unknown community in South 26 00:01:59,396 --> 00:02:02,836 Speaker 1: Wales somewhere that had never interacted right, and now it 27 00:02:02,876 --> 00:02:06,196 Speaker 1: doesn't work with them. Doctor Howard founded a robotics company 28 00:02:06,236 --> 00:02:11,556 Speaker 1: called zy Robotics. They develop mobile therapies and educational products 29 00:02:11,556 --> 00:02:15,196 Speaker 1: for children with special needs. She firmly believes that the 30 00:02:15,236 --> 00:02:18,916 Speaker 1: proper use of AI and robotics is an invaluable asset 31 00:02:18,956 --> 00:02:29,716 Speaker 1: to humanity. Underrepresentation in robotics and AI is a solvable problem. 32 00:02:30,316 --> 00:02:32,396 Speaker 1: So when I was a kid, me and my sister 33 00:02:32,676 --> 00:02:35,276 Speaker 1: got left at home a lot during the summers, which 34 00:02:35,316 --> 00:02:38,316 Speaker 1: meant that we watched a lot of reruns on television, 35 00:02:38,636 --> 00:02:40,756 Speaker 1: and one of the things that we watched all the 36 00:02:40,796 --> 00:02:43,516 Speaker 1: time was The Biotic Woman, and I heard that that 37 00:02:43,636 --> 00:02:46,596 Speaker 1: was a favorite of yours as well. It was um 38 00:02:46,716 --> 00:02:50,596 Speaker 1: although I will tell you the Beyondic Woman when I 39 00:02:50,676 --> 00:02:54,116 Speaker 1: was young was current, so I'm sure you must have 40 00:02:54,116 --> 00:02:59,156 Speaker 1: saw the reruns, say but yeah, that was that was 41 00:02:59,196 --> 00:03:01,516 Speaker 1: my favorite, and I think what you know, I was 42 00:03:01,556 --> 00:03:05,796 Speaker 1: always into anything that was science fiction, everything from you know, 43 00:03:05,796 --> 00:03:10,356 Speaker 1: Battlestar Galactica, Star Wars. Do you mean the yeah, no, yeah, 44 00:03:10,396 --> 00:03:12,996 Speaker 1: the original, not the ones that they tried to remake 45 00:03:13,036 --> 00:03:16,236 Speaker 1: over and over and over again. Back then it was 46 00:03:16,276 --> 00:03:21,996 Speaker 1: all of it was like new right, it was like fascinating. Yeah. 47 00:03:22,556 --> 00:03:25,876 Speaker 1: So The Beondic Woman was this show where this this 48 00:03:26,156 --> 00:03:30,276 Speaker 1: woman was horribly mangled in like this this horrific car accident, 49 00:03:30,596 --> 00:03:35,756 Speaker 1: right like accident, it was a skydiving she would have died, right, 50 00:03:35,796 --> 00:03:42,756 Speaker 1: and instead the doctors took her and rebuilt her basically 51 00:03:42,956 --> 00:03:47,316 Speaker 1: added the Beiondic parts and she would go around saving 52 00:03:47,316 --> 00:03:49,716 Speaker 1: the world, which was was it was awesome, but she 53 00:03:49,836 --> 00:03:52,436 Speaker 1: was human, like she had a personality. That was the 54 00:03:52,876 --> 00:03:56,036 Speaker 1: nice thing about that. Yes, like she wasn't exactly a robot, 55 00:03:56,076 --> 00:03:58,876 Speaker 1: but she was like she was a robot. It was 56 00:03:58,916 --> 00:04:00,596 Speaker 1: actually if you think about it, there was only two 57 00:04:00,636 --> 00:04:03,756 Speaker 1: sci fi shows that showed women in like a positive 58 00:04:03,836 --> 00:04:07,876 Speaker 1: superhero light. One was wonder Woman and one was The 59 00:04:07,996 --> 00:04:10,556 Speaker 1: Roonic Woman. So like one of the earliest forms of 60 00:04:10,556 --> 00:04:12,636 Speaker 1: a superhero. I saw was the biotic was So I 61 00:04:12,676 --> 00:04:15,116 Speaker 1: appreciate that. But tell me a little bit, how about 62 00:04:15,156 --> 00:04:18,276 Speaker 1: how that watching that show kind of pique your interest 63 00:04:18,356 --> 00:04:21,396 Speaker 1: in STEM. Well, so when I was watching all of 64 00:04:21,436 --> 00:04:27,116 Speaker 1: these things, it also coincided with the time in middle 65 00:04:27,116 --> 00:04:29,796 Speaker 1: school where you have to define the rest of your life. 66 00:04:30,356 --> 00:04:31,996 Speaker 1: So they still do this, right, you have to write 67 00:04:31,996 --> 00:04:34,156 Speaker 1: these essays about what do you want to do when 68 00:04:34,156 --> 00:04:37,156 Speaker 1: you grow up? And so I wanted to build a 69 00:04:37,156 --> 00:04:39,916 Speaker 1: broonic women. So that's what I wanted to do. Now. 70 00:04:39,956 --> 00:04:43,476 Speaker 1: Of course, as my teacher said, well, building a bionic 71 00:04:43,556 --> 00:04:47,036 Speaker 1: woman is not actually a career, so you have to 72 00:04:47,036 --> 00:04:50,796 Speaker 1: think about, like what label do you want? And so 73 00:04:51,156 --> 00:04:53,636 Speaker 1: originally I thought I wanted to be a doctor because 74 00:04:53,836 --> 00:04:57,516 Speaker 1: that was who put her together. So what changed? I 75 00:04:57,596 --> 00:05:03,356 Speaker 1: took biology and we were dissecting frogs and I remember 76 00:05:03,396 --> 00:05:05,436 Speaker 1: we had to learn how to kill the frogs and 77 00:05:05,636 --> 00:05:08,196 Speaker 1: open them up, right, I mean this was this was 78 00:05:08,236 --> 00:05:11,156 Speaker 1: the days because you had to do this. Yeah, and 79 00:05:11,316 --> 00:05:13,956 Speaker 1: I hated it. I mean I absolutely And I was 80 00:05:13,996 --> 00:05:15,956 Speaker 1: always good at math and science, and I was like, 81 00:05:16,316 --> 00:05:19,116 Speaker 1: I do not like this course. And then I thought, man, 82 00:05:19,356 --> 00:05:21,636 Speaker 1: if I don't like biology, how am I going to 83 00:05:21,716 --> 00:05:24,516 Speaker 1: go to med? School, How am I gonna actually start 84 00:05:24,636 --> 00:05:28,076 Speaker 1: this whole process of being a doctor. But I had 85 00:05:28,076 --> 00:05:31,556 Speaker 1: a teacher who said, hey, why not think about engineering? 86 00:05:37,196 --> 00:05:39,596 Speaker 1: So what is it that you do now? I am 87 00:05:39,636 --> 00:05:43,396 Speaker 1: a roboticist, which means my research and my practice is 88 00:05:43,436 --> 00:05:49,596 Speaker 1: about building, designing, programming robots with the focus on the human, 89 00:05:50,156 --> 00:05:54,196 Speaker 1: improving the human quality of life, with an even more 90 00:05:54,316 --> 00:05:59,596 Speaker 1: focused initiative effort on children, so pediatrics. And you started 91 00:05:59,596 --> 00:06:05,996 Speaker 1: off designing rovers for NASA. So my very first job 92 00:06:06,636 --> 00:06:11,156 Speaker 1: as a roboticist, I was a robotics researcher and my 93 00:06:11,276 --> 00:06:15,836 Speaker 1: task was thinking about future rovers and how do we 94 00:06:16,316 --> 00:06:20,836 Speaker 1: enable them to navigate long range traversals on Mars. So 95 00:06:20,876 --> 00:06:24,156 Speaker 1: how did you go from rovers to the types of 96 00:06:24,276 --> 00:06:27,636 Speaker 1: robots that you're making today? You're really thinking about them 97 00:06:27,676 --> 00:06:32,276 Speaker 1: in a way of being assistive towards humans. Yeah, so 98 00:06:32,436 --> 00:06:35,276 Speaker 1: I think one of the things about my perception on 99 00:06:35,756 --> 00:06:39,676 Speaker 1: robotics systems is really the reason why I do it 100 00:06:39,716 --> 00:06:42,636 Speaker 1: is to assist us and improve our quality of life. 101 00:06:42,876 --> 00:06:45,436 Speaker 1: And you know, mind you, when I used to talk 102 00:06:45,436 --> 00:06:48,156 Speaker 1: about robotics, the first thing that people would say would 103 00:06:48,156 --> 00:06:51,076 Speaker 1: be like, oh, you're You're the one that is taking 104 00:06:51,076 --> 00:06:53,236 Speaker 1: over the jobs you're the one that took you know, 105 00:06:53,556 --> 00:06:56,716 Speaker 1: a lot of people in manufacturing. I had people say, yeah, 106 00:06:56,796 --> 00:06:59,716 Speaker 1: my grandfather got fired because of your robots, like that 107 00:06:59,876 --> 00:07:03,596 Speaker 1: was a common thing. And I understood that from very 108 00:07:03,796 --> 00:07:06,436 Speaker 1: very early on, and those were not the robots that 109 00:07:06,476 --> 00:07:09,596 Speaker 1: I wanted to build in design. But I think because 110 00:07:09,636 --> 00:07:14,036 Speaker 1: I always understood that I am part of an ecosystem, 111 00:07:14,076 --> 00:07:17,676 Speaker 1: I am part of a community, and my responsibility is 112 00:07:17,836 --> 00:07:21,916 Speaker 1: to become a contributing member of the community. And anything 113 00:07:21,956 --> 00:07:25,076 Speaker 1: I did, because my talent was my mind, anything I 114 00:07:25,116 --> 00:07:29,076 Speaker 1: did was about a overall positive net result versus a 115 00:07:29,116 --> 00:07:31,316 Speaker 1: negative you know. I was the kid that had to 116 00:07:31,316 --> 00:07:34,756 Speaker 1: go out into the neighborhood and paint over the graffiti 117 00:07:34,836 --> 00:07:37,156 Speaker 1: and pick up the like that was our Saturday. Like. 118 00:07:37,236 --> 00:07:38,876 Speaker 1: I didn't get to go out and play like the 119 00:07:38,916 --> 00:07:41,796 Speaker 1: other kids until I had done my chores, which was 120 00:07:42,076 --> 00:07:44,716 Speaker 1: you know, all of these community kind of things. And 121 00:07:44,756 --> 00:07:47,556 Speaker 1: so I think I just grew up realizing that it 122 00:07:47,636 --> 00:07:51,156 Speaker 1: was about community. But also my talent was my mind. 123 00:07:51,236 --> 00:07:54,916 Speaker 1: It was how I designed a thought about robotics. When 124 00:07:54,916 --> 00:07:57,116 Speaker 1: it comes to the types of robots you designed to 125 00:07:57,196 --> 00:08:01,236 Speaker 1: help people who have disabilities, is there anything personal that 126 00:08:01,276 --> 00:08:04,116 Speaker 1: happened to you that made you connect with them specifically. 127 00:08:04,956 --> 00:08:09,756 Speaker 1: I had always done these stems, so science, alogy, engineering, 128 00:08:09,756 --> 00:08:14,836 Speaker 1: and math camps primarily to engage girls and I'll say 129 00:08:14,876 --> 00:08:17,916 Speaker 1: underrepresented minority, since that's what we were called back in 130 00:08:17,996 --> 00:08:21,356 Speaker 1: the day. I had ran this one camp where there 131 00:08:21,396 --> 00:08:24,556 Speaker 1: was this young lady who had a visual impairment. She 132 00:08:24,716 --> 00:08:28,756 Speaker 1: was bright, bright, like smart, but the system did not 133 00:08:28,796 --> 00:08:32,116 Speaker 1: work for her because it wasn't accessible. And this was 134 00:08:32,156 --> 00:08:33,996 Speaker 1: the first time I'd heard this, were like, what is 135 00:08:34,036 --> 00:08:37,676 Speaker 1: accessible and what is assessively technology? Because I hadn't no idea, 136 00:08:37,756 --> 00:08:40,276 Speaker 1: And what it was is like I saw a problem. 137 00:08:40,356 --> 00:08:43,396 Speaker 1: I was like this, like makes no sense. Why is 138 00:08:43,396 --> 00:08:48,196 Speaker 1: our technology not able to be used by everyone? As 139 00:08:48,236 --> 00:08:51,836 Speaker 1: an engineer, you know, I see these kinds of problems 140 00:08:52,036 --> 00:08:54,916 Speaker 1: as basically a challenge to design a solution. It's just 141 00:08:54,956 --> 00:08:58,516 Speaker 1: the way I'm geared and think about it. And so 142 00:08:58,556 --> 00:09:02,196 Speaker 1: that started me down this rabbit hole of looking at 143 00:09:02,196 --> 00:09:07,676 Speaker 1: this target demographic understanding that we as engineers aren't really 144 00:09:07,716 --> 00:09:11,316 Speaker 1: addressing the needs of all the different populations. And I 145 00:09:11,396 --> 00:09:15,476 Speaker 1: was always a proponent of diversity, and I didn't really 146 00:09:15,556 --> 00:09:19,956 Speaker 1: understand until that moment that diversity includes raise includes ethnicity, 147 00:09:19,996 --> 00:09:23,996 Speaker 1: includes gender, but it also includes ability and disability. So 148 00:09:24,076 --> 00:09:27,956 Speaker 1: you have a number of apps, games, and toys all 149 00:09:28,076 --> 00:09:31,956 Speaker 1: under their company, zy Robotics. Is there a particular product 150 00:09:31,996 --> 00:09:35,836 Speaker 1: that you're like, you're especially proud of. I actually enjoy 151 00:09:35,996 --> 00:09:39,356 Speaker 1: all of them. I will say the one that I 152 00:09:39,516 --> 00:09:43,516 Speaker 1: especially like is one called Tommy the Turtle. Learn to Code. 153 00:09:43,796 --> 00:09:46,236 Speaker 1: Help your kids learn the basic of coding with Tommy 154 00:09:46,316 --> 00:09:49,356 Speaker 1: the Turtle. Your little one will absolutely love interacting with 155 00:09:49,396 --> 00:09:52,836 Speaker 1: Tommy and his colorful friends as they gain valuable skills 156 00:09:52,956 --> 00:09:55,636 Speaker 1: in programming. I can get a four year old to 157 00:09:55,716 --> 00:09:58,836 Speaker 1: learn how to code and love it. And you basically 158 00:09:59,036 --> 00:10:02,356 Speaker 1: have to do things like, you know, Tommy wants to 159 00:10:02,396 --> 00:10:06,036 Speaker 1: play with cat, but cats too far away. Can Tommy 160 00:10:06,156 --> 00:10:08,756 Speaker 1: move towards cats so they can play right? And so 161 00:10:08,796 --> 00:10:11,396 Speaker 1: there's a always these this you know, goofy rhyming and stuff, 162 00:10:11,636 --> 00:10:14,596 Speaker 1: and so then it'll go through like okay, in order 163 00:10:14,636 --> 00:10:16,076 Speaker 1: to do this, you know, you have to put in 164 00:10:16,116 --> 00:10:19,756 Speaker 1: this little button which is move one space right, and 165 00:10:19,796 --> 00:10:22,396 Speaker 1: so it walks you through this and at the end 166 00:10:22,436 --> 00:10:25,396 Speaker 1: it says, guess what you've done your first coding program. 167 00:10:25,756 --> 00:10:28,156 Speaker 1: Now let's try it with dog. And so it basically 168 00:10:28,156 --> 00:10:33,276 Speaker 1: builds up this sequential understanding sequences, understanding logic with the 169 00:10:33,356 --> 00:10:37,716 Speaker 1: subjective of again having friends, playing around and things like that. 170 00:10:38,076 --> 00:10:41,196 Speaker 1: And the Tommy to try to learn to code is 171 00:10:41,236 --> 00:10:44,236 Speaker 1: also accessible, so if you have a children within motor disability, 172 00:10:44,276 --> 00:10:47,836 Speaker 1: it actually has accessibility functions so you can use things 173 00:10:47,876 --> 00:10:51,676 Speaker 1: like switch devices. If you have slight visual impairments, there 174 00:10:51,676 --> 00:10:54,476 Speaker 1: are things you can do with the print. So I'm 175 00:10:54,516 --> 00:10:57,796 Speaker 1: most proud of that because I've actually seen like four 176 00:10:57,876 --> 00:11:02,316 Speaker 1: year olds like excited about coding, which is amazing and fascinating. 177 00:11:02,956 --> 00:11:06,076 Speaker 1: What types of benefits do you think children with disabilities 178 00:11:06,116 --> 00:11:09,836 Speaker 1: received from working with robots that you design? So one 179 00:11:09,836 --> 00:11:14,716 Speaker 1: of the things that we know is that with anything repetition, repetition, 180 00:11:14,716 --> 00:11:18,316 Speaker 1: repetition is good. The problem is is that the amount 181 00:11:18,316 --> 00:11:20,836 Speaker 1: of and I'll just call it exercise, which is repetition, 182 00:11:20,916 --> 00:11:23,156 Speaker 1: The amount of exercise you have to do has to 183 00:11:23,196 --> 00:11:26,396 Speaker 1: be done consistently, has to be done in a repeated fashion, 184 00:11:26,756 --> 00:11:29,436 Speaker 1: and typically you need someone to kind of guide you 185 00:11:29,476 --> 00:11:32,796 Speaker 1: because you know, they're kids, and we just don't have 186 00:11:33,036 --> 00:11:36,996 Speaker 1: enough resources. So most parents do not have are not 187 00:11:37,036 --> 00:11:40,196 Speaker 1: as privilege enough to bring in a human therapist into 188 00:11:40,236 --> 00:11:43,676 Speaker 1: the home every day to work with their child, And 189 00:11:43,756 --> 00:11:48,196 Speaker 1: so what we focused on was designing these robotic systems 190 00:11:48,236 --> 00:11:50,876 Speaker 1: that are adaptive so they have artificial intelligence in them 191 00:11:51,276 --> 00:11:54,956 Speaker 1: so that they can work with the kids basically every day. 192 00:11:55,276 --> 00:11:59,196 Speaker 1: So they are augmenting the services of a human therapist. 193 00:11:59,276 --> 00:12:02,556 Speaker 1: But in the home environment. Did you see an uptick 194 00:12:02,756 --> 00:12:07,316 Speaker 1: in people being interested in this type of technology during 195 00:12:07,356 --> 00:12:10,316 Speaker 1: COVID when you wasn't so easy to have like a 196 00:12:10,436 --> 00:12:14,156 Speaker 1: human aid in the house to help with children with disabilities? Yeah, 197 00:12:14,156 --> 00:12:17,356 Speaker 1: so I will say yes, and that goes across the 198 00:12:17,396 --> 00:12:22,276 Speaker 1: board in terms of AI, so artificial intelligence software as 199 00:12:22,316 --> 00:12:25,076 Speaker 1: well as robots. There was an uptick in the use 200 00:12:25,356 --> 00:12:29,716 Speaker 1: since March of twenty twenty, and it's primarily because of 201 00:12:29,756 --> 00:12:34,156 Speaker 1: the fact that you know, humans were dangerous, right, like 202 00:12:34,196 --> 00:12:35,836 Speaker 1: we know this. This was the whole thing is like 203 00:12:35,996 --> 00:12:38,876 Speaker 1: you're shut in. You're locked in because other humans who 204 00:12:38,876 --> 00:12:41,996 Speaker 1: are outside of your community, outside of your home are dangerous, 205 00:12:42,316 --> 00:12:45,156 Speaker 1: whereas robots were not. And so there was this whole 206 00:12:45,476 --> 00:12:50,156 Speaker 1: change and shift on the perception of robots, like robots 207 00:12:50,236 --> 00:12:54,996 Speaker 1: were more of like, oh, this actually enables me to 208 00:12:55,236 --> 00:12:59,556 Speaker 1: have a little bit of semblance of livelihood. And we 209 00:12:59,596 --> 00:13:03,756 Speaker 1: saw an uptick in companion robots because again, you couldn't 210 00:13:03,796 --> 00:13:07,036 Speaker 1: have you know, your young grandkids, for example, coming in, 211 00:13:07,356 --> 00:13:11,116 Speaker 1: or therapists necessarily come in, but robots could because they 212 00:13:11,156 --> 00:13:16,116 Speaker 1: were safer than people. There's folks that feel like our 213 00:13:16,156 --> 00:13:19,676 Speaker 1: addiction and technology is a problem, and building technology that 214 00:13:19,716 --> 00:13:24,636 Speaker 1: assists humans in whatever capacity kind of builds on that 215 00:13:24,716 --> 00:13:27,676 Speaker 1: addiction a little bit, or it feeds into that addiction. Rather, 216 00:13:28,116 --> 00:13:31,076 Speaker 1: what's your response to that to people that would would 217 00:13:31,076 --> 00:13:34,356 Speaker 1: say that maybe we need less technology and not more. Yeah, 218 00:13:34,396 --> 00:13:37,916 Speaker 1: So I actually have two answers to that. One is, yes, 219 00:13:37,996 --> 00:13:40,916 Speaker 1: it does feed on these addictions. Like my robots would 220 00:13:40,956 --> 00:13:44,316 Speaker 1: not work if I didn't model them based on humans 221 00:13:44,356 --> 00:13:47,796 Speaker 1: propensity to interact with these systems, right, it wouldn't work. 222 00:13:48,036 --> 00:13:50,756 Speaker 1: So we know that as a fact. But I would 223 00:13:50,796 --> 00:13:56,476 Speaker 1: say that that potential negative of being reliant on the 224 00:13:56,836 --> 00:14:01,756 Speaker 1: software on AI on robotis that the negatives are less 225 00:14:01,756 --> 00:14:05,836 Speaker 1: than the positives, and the positives are. It improves so 226 00:14:05,836 --> 00:14:10,236 Speaker 1: so many things. It makes access much more accessible in 227 00:14:10,396 --> 00:14:15,916 Speaker 1: terms of you know, equal opportunity about jobs, education, food sources, 228 00:14:15,916 --> 00:14:19,276 Speaker 1: and things like that. So the positives I think outweigh 229 00:14:19,356 --> 00:14:21,956 Speaker 1: the negatives over and over and over and over again. 230 00:14:23,196 --> 00:14:25,876 Speaker 1: When you see this type of interaction and you see, 231 00:14:26,796 --> 00:14:30,916 Speaker 1: you know, children interacted with the technology you've created and 232 00:14:30,916 --> 00:14:33,316 Speaker 1: the tools that you've created. How do you feel like, 233 00:14:33,396 --> 00:14:35,316 Speaker 1: how does that make you feel to see them interacting 234 00:14:35,316 --> 00:14:39,556 Speaker 1: with it successfully? My very first moment, I remember it 235 00:14:39,596 --> 00:14:41,836 Speaker 1: to this day. We were this is at Georgia Tech. 236 00:14:41,876 --> 00:14:44,996 Speaker 1: It was one of our very first studies. We had 237 00:14:45,036 --> 00:14:50,316 Speaker 1: a gamified therapy protocol called Superpop. We had to pop 238 00:14:50,356 --> 00:14:53,956 Speaker 1: these bubbles and it was linked to some movement therapy. 239 00:14:54,436 --> 00:14:56,036 Speaker 1: So I remember we go into the home and there 240 00:14:56,116 --> 00:14:59,516 Speaker 1: was this young child, child with cerebral palsy who was 241 00:14:59,516 --> 00:15:04,636 Speaker 1: in a wheelchair and severe sebesticity, so very limited movements. 242 00:15:05,036 --> 00:15:07,516 Speaker 1: And I remember the game because the game adapts to 243 00:15:07,676 --> 00:15:09,796 Speaker 1: the abilities of the child, and so you know, the 244 00:15:09,876 --> 00:15:12,276 Speaker 1: very first one, it started adapting and started adapting. And 245 00:15:12,316 --> 00:15:16,436 Speaker 1: I remember at one instance, this child touched the bubble 246 00:15:16,476 --> 00:15:19,516 Speaker 1: because it's popping bubbles, right, and I just I remember 247 00:15:19,716 --> 00:15:23,276 Speaker 1: like there was this smile. It was like the sun 248 00:15:23,516 --> 00:15:26,436 Speaker 1: like shone in this room. It was like all of 249 00:15:26,476 --> 00:15:28,716 Speaker 1: the wats that are out there, it just shone in 250 00:15:28,756 --> 00:15:31,956 Speaker 1: his face, pure joy, and it was like you you 251 00:15:31,956 --> 00:15:35,836 Speaker 1: couldn't have sold it. You couldn't have bottled it. You 252 00:15:35,876 --> 00:15:38,996 Speaker 1: couldn't have like bitcoined it right. It was like that 253 00:15:39,076 --> 00:15:42,756 Speaker 1: pure joy that only a child could show. And at 254 00:15:42,756 --> 00:15:44,996 Speaker 1: that point that was like my first moment going like, 255 00:15:45,116 --> 00:15:48,196 Speaker 1: oh my gosh, I feel really really really really good. 256 00:15:48,356 --> 00:15:51,036 Speaker 1: I like this feeling, I really do. It makes me 257 00:15:51,116 --> 00:15:54,956 Speaker 1: realize that I'm doing the right thing, impacting directly someone's life. 258 00:16:00,716 --> 00:16:04,716 Speaker 1: You're now working with children with disabilities, specifically in pediatrics, 259 00:16:04,836 --> 00:16:07,356 Speaker 1: and you know you said you got there because you 260 00:16:07,436 --> 00:16:10,796 Speaker 1: first wanted to work with with women, and being a 261 00:16:10,836 --> 00:16:14,196 Speaker 1: woman in STEM, I know, is not the easiest path 262 00:16:14,316 --> 00:16:17,276 Speaker 1: for most Do you find that you had any personal 263 00:16:17,316 --> 00:16:19,876 Speaker 1: experiences that kind of led you to say that I 264 00:16:19,916 --> 00:16:21,876 Speaker 1: want to kind of kick the ladder down for more 265 00:16:21,916 --> 00:16:26,076 Speaker 1: women to come into this field. Yes, when I started 266 00:16:26,636 --> 00:16:29,596 Speaker 1: at NASA and things like that, I was always passionate 267 00:16:29,636 --> 00:16:34,356 Speaker 1: about working in with young kids. Actually, even in undergrad 268 00:16:34,356 --> 00:16:37,316 Speaker 1: I tutored at the local high school and things like that. 269 00:16:37,676 --> 00:16:40,556 Speaker 1: So I'd always done that just because I remember growing up, 270 00:16:40,876 --> 00:16:43,116 Speaker 1: you know, engineers would come to my school and I 271 00:16:43,156 --> 00:16:45,036 Speaker 1: was inspired, and so I just felt that that was 272 00:16:45,076 --> 00:16:49,356 Speaker 1: a responsibility as an engineer. I didn't really feel that 273 00:16:49,636 --> 00:16:53,236 Speaker 1: I had a responsibility to be quote unquote, I guess 274 00:16:53,236 --> 00:16:57,196 Speaker 1: a role model until I was much older, and it 275 00:16:57,276 --> 00:17:01,156 Speaker 1: was because I suddenly realized that I was tired of 276 00:17:01,196 --> 00:17:06,076 Speaker 1: not seeing myself and therefore, like, I'm old, So it's 277 00:17:06,116 --> 00:17:08,756 Speaker 1: not like I can produce myself right now, that's my 278 00:17:08,836 --> 00:17:13,276 Speaker 1: age age, but I sure could start reaching and making 279 00:17:13,316 --> 00:17:17,756 Speaker 1: sure that the generation behind me saw themselves and didn't 280 00:17:17,756 --> 00:17:20,036 Speaker 1: have to go through the same things. And so it 281 00:17:20,116 --> 00:17:22,716 Speaker 1: then became more of a mission than you know, things 282 00:17:22,756 --> 00:17:24,396 Speaker 1: like oh I'm doing out right now here. No no, no no, no, 283 00:17:24,516 --> 00:17:26,996 Speaker 1: it's a mission because it sucks when you don't see yourself. 284 00:17:27,716 --> 00:17:31,676 Speaker 1: Are you speaking specifically about women? Are also specifically about 285 00:17:31,716 --> 00:17:36,636 Speaker 1: black women? So I actually have three. It's women marginalized community, 286 00:17:36,756 --> 00:17:40,316 Speaker 1: so black, Latin X, Indigenous, and it's also black women. 287 00:17:40,356 --> 00:17:44,316 Speaker 1: So it was actually three because those three there's not 288 00:17:44,396 --> 00:17:47,596 Speaker 1: a lot of us in any stages at the upper echelon, 289 00:17:47,676 --> 00:17:51,596 Speaker 1: it's not a lot. How do you think enfranchising those 290 00:17:51,596 --> 00:17:54,236 Speaker 1: groups that you're talking about, how do you think that's solved? 291 00:17:54,236 --> 00:17:57,156 Speaker 1: Do you think it's just solved with them just seeing 292 00:17:57,156 --> 00:17:59,076 Speaker 1: you in the room? Ward? What's the extra step you 293 00:17:59,076 --> 00:18:01,836 Speaker 1: feel that needs to be taken in order to draw 294 00:18:01,916 --> 00:18:04,756 Speaker 1: more of those people into the room. Yeah, so being 295 00:18:04,796 --> 00:18:07,836 Speaker 1: able to see yourself is only the first step, but 296 00:18:07,996 --> 00:18:10,236 Speaker 1: it is an important step because if you don't even 297 00:18:10,276 --> 00:18:12,316 Speaker 1: see yourself, you're not even going to think that it's 298 00:18:12,356 --> 00:18:14,516 Speaker 1: something for you. Right. So one is about, you know, 299 00:18:14,556 --> 00:18:18,556 Speaker 1: getting this motivation. The other is about ensuring that there's opportunities. 300 00:18:18,836 --> 00:18:22,156 Speaker 1: And so you know, if I see myself, but I 301 00:18:22,196 --> 00:18:25,876 Speaker 1: don't have access, I don't have the curriculum in the schools, 302 00:18:26,356 --> 00:18:28,316 Speaker 1: I don't know, you know, where am I supposed to 303 00:18:28,356 --> 00:18:30,796 Speaker 1: go in order to learn how to do artificial intelligence 304 00:18:30,876 --> 00:18:34,276 Speaker 1: or robotics? Then I might see myself, but I don't 305 00:18:34,276 --> 00:18:36,836 Speaker 1: have a path for it, right, And so it's about 306 00:18:36,876 --> 00:18:40,036 Speaker 1: seeing yourself, believing in that, and then having the opportunities 307 00:18:40,076 --> 00:18:44,756 Speaker 1: to excel. Irrespective of your environment or how you're growing up, 308 00:18:44,796 --> 00:18:54,516 Speaker 1: are those individuals who are around you. So there's a 309 00:18:54,516 --> 00:18:58,156 Speaker 1: lot of people that are afraid of a future with 310 00:18:58,316 --> 00:19:01,396 Speaker 1: robots at it. What would you say? How would you 311 00:19:02,036 --> 00:19:03,676 Speaker 1: talk to about and and you know what, I was 312 00:19:03,716 --> 00:19:05,796 Speaker 1: one of those people. I watched I robot and I 313 00:19:05,876 --> 00:19:08,676 Speaker 1: was like, no, no, not like that. That. What it's 314 00:19:08,716 --> 00:19:11,996 Speaker 1: easy to anything these days? What do you think the 315 00:19:12,036 --> 00:19:14,436 Speaker 1: future looks like with robots in it. As a roboticist, 316 00:19:14,476 --> 00:19:16,716 Speaker 1: what do you think that future? And actually, and I'm 317 00:19:16,756 --> 00:19:19,436 Speaker 1: sorry to take another step back, but those those Boston 318 00:19:19,516 --> 00:19:22,356 Speaker 1: Dynamics videos are really scary people when they see the 319 00:19:22,436 --> 00:19:24,676 Speaker 1: robots though in the backflips. And of course we've all 320 00:19:24,676 --> 00:19:27,316 Speaker 1: seen the Matrix. I'm now referencing all the movies. But 321 00:19:27,756 --> 00:19:29,476 Speaker 1: what would you say to people who have a fear 322 00:19:29,556 --> 00:19:33,556 Speaker 1: of the future with robots in it. The fear that 323 00:19:33,596 --> 00:19:39,116 Speaker 1: people have, honestly is the fear of the uncertainty, which 324 00:19:39,476 --> 00:19:42,196 Speaker 1: you're going to have irrespective of what it is. You know, 325 00:19:42,236 --> 00:19:44,396 Speaker 1: I think about you know, horse and carriage and the 326 00:19:44,396 --> 00:19:47,516 Speaker 1: cars came. I'm sure there were people like, oh, automobiles, 327 00:19:47,556 --> 00:19:50,356 Speaker 1: you know they kill people like people die in automobiles. 328 00:19:50,356 --> 00:19:52,756 Speaker 1: Why would you ever get it? Right? Like every single 329 00:19:52,876 --> 00:19:56,876 Speaker 1: instance where something has transformed our society for good, there's 330 00:19:56,916 --> 00:19:59,956 Speaker 1: always been this fear always always. We can go historically 331 00:19:59,956 --> 00:20:04,036 Speaker 1: when this has happened. And so robots in AI, artificial 332 00:20:04,036 --> 00:20:07,956 Speaker 1: intelligence is now that technology that is causing fear because 333 00:20:07,996 --> 00:20:12,076 Speaker 1: of the uncertainty. And this is happening and whether you 334 00:20:12,236 --> 00:20:15,116 Speaker 1: like it or not, whether you're afraid or not, it 335 00:20:15,196 --> 00:20:19,236 Speaker 1: is happening, and it will accelerate period guaranteed. And so 336 00:20:19,276 --> 00:20:22,996 Speaker 1: what I say is figure out how to become part 337 00:20:23,036 --> 00:20:26,316 Speaker 1: of the solution. If you're you know, fearful of the 338 00:20:26,436 --> 00:20:29,516 Speaker 1: I robots are feel full of, you know, bossing dynamics 339 00:20:29,596 --> 00:20:32,556 Speaker 1: kind of thing, well, become a computer scientist and go 340 00:20:32,636 --> 00:20:36,196 Speaker 1: work for those companies so that you don't have those 341 00:20:36,316 --> 00:20:39,076 Speaker 1: kind of robots being developed. Right, Like, this is like 342 00:20:39,316 --> 00:20:42,716 Speaker 1: part of the solution and also allows you to conquer 343 00:20:42,756 --> 00:20:46,356 Speaker 1: your fear but also take charge of your future. I 344 00:20:46,396 --> 00:20:48,476 Speaker 1: don't know why the idea of me taking years and 345 00:20:48,556 --> 00:20:50,756 Speaker 1: years of trigonometry just so I could go into box 346 00:20:50,796 --> 00:20:53,476 Speaker 1: of dynamics and be like, hey, what's going on in here? 347 00:20:53,596 --> 00:20:56,676 Speaker 1: What do you guys do these robots just sounded very, 348 00:20:56,796 --> 00:20:59,836 Speaker 1: very funny to me. But I love that. I think 349 00:20:59,836 --> 00:21:02,876 Speaker 1: that's a great solution. Well someone will hopefully someone's listening. 350 00:21:02,876 --> 00:21:06,356 Speaker 1: It's like, yeah, I love that idea. I'm doing it now. 351 00:21:12,836 --> 00:21:15,556 Speaker 1: Can we expand on the bias and racism forbid it? 352 00:21:15,996 --> 00:21:18,236 Speaker 1: Those stories do come to life, and I feel like 353 00:21:18,676 --> 00:21:20,836 Speaker 1: there has been some truth to some of them in 354 00:21:20,876 --> 00:21:23,076 Speaker 1: the research that I've done. Can you talk a little 355 00:21:23,116 --> 00:21:25,596 Speaker 1: bit about maybe dispel some of the some of the 356 00:21:25,636 --> 00:21:27,916 Speaker 1: fear that folks are having when it comes to bias 357 00:21:27,916 --> 00:21:31,316 Speaker 1: and racism from artificial intelligence and front robots, Like, what 358 00:21:31,676 --> 00:21:35,036 Speaker 1: exactly are we seeing there? Yeah, so there is there 359 00:21:35,116 --> 00:21:37,756 Speaker 1: is truth. So a lot of the systems that are 360 00:21:37,796 --> 00:21:42,196 Speaker 1: deployed that are out there have aspects of bias. And 361 00:21:42,716 --> 00:21:46,436 Speaker 1: what the definition of bias is that there are differences 362 00:21:46,476 --> 00:21:51,316 Speaker 1: in the results in their behaviors toward different types of 363 00:21:51,316 --> 00:21:57,996 Speaker 1: people based on gender, based on racial, ethnic identity, sexual orientation, religion, 364 00:21:58,236 --> 00:22:01,116 Speaker 1: like basically any type of attribute you can think about. 365 00:22:01,876 --> 00:22:06,316 Speaker 1: These systems have some aspect of bias. So that's the negative. 366 00:22:06,876 --> 00:22:11,636 Speaker 1: The positive, though, is that time and time and time again, 367 00:22:12,076 --> 00:22:15,236 Speaker 1: they're still better than the human biases. I think our 368 00:22:15,236 --> 00:22:17,836 Speaker 1: systems can be better. I honestly think we can design 369 00:22:17,956 --> 00:22:21,116 Speaker 1: and build robots that can make us better humans, that 370 00:22:21,156 --> 00:22:24,116 Speaker 1: can make us less biased humans. I totally believe that. 371 00:22:24,476 --> 00:22:27,476 Speaker 1: But unless we really focus on that, we're just going 372 00:22:27,556 --> 00:22:30,396 Speaker 1: to keep doing the same thing and propagate and just 373 00:22:30,476 --> 00:22:33,876 Speaker 1: continuing the biases that we have of the past. That's 374 00:22:33,996 --> 00:22:37,116 Speaker 1: what I'm afraid of. What do you say to the 375 00:22:37,156 --> 00:22:40,356 Speaker 1: activists who just heard this and heard you say that 376 00:22:40,596 --> 00:22:43,556 Speaker 1: robots are biased but less biased. How do you call 377 00:22:43,676 --> 00:22:46,316 Speaker 1: dib down to say, to get because I'm sure what 378 00:22:46,396 --> 00:22:47,876 Speaker 1: people are going to want to hear you say is 379 00:22:47,876 --> 00:22:49,996 Speaker 1: like they're not biased at all. Like we get there, 380 00:22:50,116 --> 00:22:52,156 Speaker 1: we get to a place where we're using these machines 381 00:22:52,436 --> 00:22:54,956 Speaker 1: and the data we're feeding into them and the way 382 00:22:54,956 --> 00:22:57,716 Speaker 1: that they're interacting with the world makes them get down 383 00:22:57,716 --> 00:23:00,516 Speaker 1: to zero bias. Is it even possible for that to 384 00:23:00,556 --> 00:23:03,556 Speaker 1: happen when they live in a world with biased humans 385 00:23:03,556 --> 00:23:06,196 Speaker 1: creating robots? Yeah? No. So I don't think that these 386 00:23:06,196 --> 00:23:09,116 Speaker 1: systems can ever get to zero bias because there will 387 00:23:09,196 --> 00:23:12,636 Speaker 1: always be a group that the system has not interacted with. 388 00:23:12,916 --> 00:23:15,436 Speaker 1: It might be that it's perfect, as perfect is perfect, 389 00:23:15,476 --> 00:23:21,436 Speaker 1: and then there's an unknown community in South Wales somewhere 390 00:23:21,916 --> 00:23:24,476 Speaker 1: that had never interacted, right, and now it doesn't work 391 00:23:24,476 --> 00:23:26,676 Speaker 1: with them, right. So I don't think that we can 392 00:23:26,716 --> 00:23:29,996 Speaker 1: ever get because as humans, we're unique and we're different, 393 00:23:30,076 --> 00:23:33,196 Speaker 1: Like there's an attribute that we're going to miss. So 394 00:23:33,236 --> 00:23:36,076 Speaker 1: I don't believe in zero bias. What I do believe, though, 395 00:23:36,236 --> 00:23:39,996 Speaker 1: is that because these systems are based on data and 396 00:23:40,036 --> 00:23:43,196 Speaker 1: they're not based on our lived experiences, it means that 397 00:23:43,276 --> 00:23:46,916 Speaker 1: you can basically quiz them. You can look at them, right, 398 00:23:46,956 --> 00:23:49,916 Speaker 1: So you can basically say, look, I have you know, 399 00:23:49,996 --> 00:23:54,516 Speaker 1: this person who is from a rural community that is 400 00:23:54,676 --> 00:23:58,596 Speaker 1: identifies as black. That's female, and I have the exact 401 00:23:58,636 --> 00:24:00,956 Speaker 1: same person. But now we change from female to male. 402 00:24:01,236 --> 00:24:03,516 Speaker 1: You know what's the outcome? Right, Like, you can ask 403 00:24:03,556 --> 00:24:05,956 Speaker 1: that and be like, oh wait, the differences are here, Okay, 404 00:24:06,036 --> 00:24:08,996 Speaker 1: we have some bias. Let's go fixes. Now you ask 405 00:24:09,356 --> 00:24:13,116 Speaker 1: human the same question, it'd be kind of hard to 406 00:24:13,156 --> 00:24:16,516 Speaker 1: figure out if they're biased or not, just in general. 407 00:24:16,556 --> 00:24:18,276 Speaker 1: And so that's what I'm saying is it's like you 408 00:24:18,316 --> 00:24:21,236 Speaker 1: can quiz the AI, and because it's based on data 409 00:24:21,276 --> 00:24:23,476 Speaker 1: and an algorithm, it can give you the answer you 410 00:24:23,476 --> 00:24:26,156 Speaker 1: ask a human and if they know they're biased, they're 411 00:24:26,156 --> 00:24:28,916 Speaker 1: gonna lie. And if they don't know they're biased, they're 412 00:24:28,956 --> 00:24:31,356 Speaker 1: just going to make up excuses because they don't realize 413 00:24:31,356 --> 00:24:34,956 Speaker 1: that they are putting their own lived experience in their decision. 414 00:24:35,796 --> 00:24:38,156 Speaker 1: So you would say the difference between robot bias and 415 00:24:38,236 --> 00:24:42,276 Speaker 1: human bias is the ability to be I guess, radically 416 00:24:42,276 --> 00:24:44,636 Speaker 1: transparent in the fact that we can open them up, 417 00:24:44,676 --> 00:24:47,556 Speaker 1: look at their code and see what the answer is. Correct. 418 00:24:47,596 --> 00:24:49,596 Speaker 1: Although we don't do that now. And that's why a 419 00:24:49,636 --> 00:24:52,156 Speaker 1: lot of this stuff is going on about AI and 420 00:24:52,196 --> 00:24:56,036 Speaker 1: biases because companies that are putting these out are not 421 00:24:56,396 --> 00:24:58,556 Speaker 1: opening it up. They're not doing the assessment, they're not 422 00:24:58,596 --> 00:25:02,356 Speaker 1: doing the analysis. It's other researchers like myself that are 423 00:25:02,476 --> 00:25:04,756 Speaker 1: you know, third party looking at and be like, hey, 424 00:25:05,076 --> 00:25:07,956 Speaker 1: there's something going on here, right it shouldn't be our 425 00:25:08,076 --> 00:25:10,596 Speaker 1: role and our responsibile. But that's what we're doing for 426 00:25:10,636 --> 00:25:13,876 Speaker 1: the community because the companies are not doing it for themselves. 427 00:25:19,836 --> 00:25:23,116 Speaker 1: What do you think our listeners can do to kind 428 00:25:23,116 --> 00:25:26,756 Speaker 1: of support this mission and improve the marketplace and kind 429 00:25:26,796 --> 00:25:30,596 Speaker 1: of help more with learn more about AI and assistive technologies, 430 00:25:30,796 --> 00:25:33,516 Speaker 1: but kind of also engage with it more. One thing 431 00:25:33,556 --> 00:25:36,796 Speaker 1: that I think everyone has to figure out is how 432 00:25:36,836 --> 00:25:40,476 Speaker 1: to code and how to program. Honestly, the jobs are changing, 433 00:25:40,716 --> 00:25:43,756 Speaker 1: and even the jobs you think are, you know, well, 434 00:25:43,756 --> 00:25:47,236 Speaker 1: what about law, what about policing? What about Guess what? 435 00:25:47,556 --> 00:25:49,236 Speaker 1: If you don't know how to code, there's going to 436 00:25:49,276 --> 00:25:51,356 Speaker 1: be a whole next generation that's going to come take 437 00:25:51,396 --> 00:25:53,356 Speaker 1: your job. It's going to be humans that take it. 438 00:25:53,436 --> 00:25:55,316 Speaker 1: But it's because they know how to do the new 439 00:25:55,356 --> 00:25:57,356 Speaker 1: types of jobs that are going to be out there, 440 00:25:57,756 --> 00:25:59,756 Speaker 1: So that's one thing everyone needs to learn how to 441 00:25:59,756 --> 00:26:03,076 Speaker 1: program and the code, not be a computer scientist, but 442 00:26:03,436 --> 00:26:06,116 Speaker 1: learn that as a skill set. Second is is I 443 00:26:06,156 --> 00:26:09,756 Speaker 1: think people need to do a little bit more investigation 444 00:26:10,116 --> 00:26:12,156 Speaker 1: of the things that are out there instead of just 445 00:26:12,316 --> 00:26:15,756 Speaker 1: listening to media, because a lot of things that are 446 00:26:15,836 --> 00:26:19,356 Speaker 1: out are the horror stories. So what types of publications 447 00:26:19,476 --> 00:26:21,676 Speaker 1: or media sources do you think that listeners can go 448 00:26:21,756 --> 00:26:26,916 Speaker 1: to in order to get accurate information about STEM, about robotics, 449 00:26:27,156 --> 00:26:31,476 Speaker 1: about science and technology generally. So I actually personally like Wired. 450 00:26:31,756 --> 00:26:37,356 Speaker 1: It's a technology magazine, but it is really accessible to 451 00:26:37,436 --> 00:26:39,796 Speaker 1: like a general audience, and it shows both sides. So 452 00:26:39,836 --> 00:26:43,396 Speaker 1: it has stories that you talk about the negatives of AI, 453 00:26:43,516 --> 00:26:46,396 Speaker 1: but it also in the same article we'll talk about 454 00:26:46,396 --> 00:26:49,956 Speaker 1: the positive. So I think in terms of being accessible, 455 00:26:50,076 --> 00:26:54,396 Speaker 1: that's one of my favorite Doctor Howard, thank you so 456 00:26:54,476 --> 00:27:06,076 Speaker 1: much for being with us. Thank you. Doctor Ayana Howard 457 00:27:06,156 --> 00:27:08,716 Speaker 1: is a roboticist and dean of the College of Engineering 458 00:27:08,836 --> 00:27:12,676 Speaker 1: at the Ohio State University. Be sure to check out 459 00:27:12,716 --> 00:27:14,356 Speaker 1: our show notes to find out ways you can learn 460 00:27:14,356 --> 00:27:17,996 Speaker 1: more about robotics and how to get involved. Next week, 461 00:27:17,996 --> 00:27:20,836 Speaker 1: we're talking about vaccine passes and how they might help 462 00:27:20,876 --> 00:27:23,396 Speaker 1: solve the problem of safe reentry and to some of 463 00:27:23,396 --> 00:27:29,316 Speaker 1: our busiest cities. Solvable Senior Producer is Jocelyn Frank, Research 464 00:27:29,396 --> 00:27:33,876 Speaker 1: by David Jah, Booking by Lisa Dunn. Our managing producer 465 00:27:33,916 --> 00:27:37,356 Speaker 1: is Sasha Matthias, and our executive producer is Mio Lobel. 466 00:27:38,556 --> 00:27:44,276 Speaker 1: Special thanks to Heather Fame, John Schnars, Carl Migliori, Christina Sullivan, 467 00:27:44,596 --> 00:27:50,276 Speaker 1: Eric Sandler, Maggie Taylor, Emily Rostick, Maya Kanik, and Kadijah Holland. 468 00:27:51,436 --> 00:27:54,436 Speaker 1: Solvable is a production of Pushkin Industries. If you like 469 00:27:54,556 --> 00:27:57,916 Speaker 1: the show, please remember to share, rate, and review. It 470 00:27:57,996 --> 00:28:00,716 Speaker 1: helps us find our way to the ears of new listeners. 471 00:28:01,676 --> 00:28:04,876 Speaker 1: You can find Pushkin podcasts wherever you listen, including on 472 00:28:04,916 --> 00:28:09,796 Speaker 1: the iHeartRadio app and Apple Podcasts. I'm Ronald Young Junior. 473 00:28:10,316 --> 00:28:24,036 Speaker 1: Thanks for listening. M