1 00:00:03,400 --> 00:00:05,640 Speaker 1: What's up Its way up at Angela Yee. I'm Angela 2 00:00:05,680 --> 00:00:07,800 Speaker 1: Yee and we have a special gift for you guys today. 3 00:00:08,280 --> 00:00:12,920 Speaker 1: Doctor Hassan Teta is here with us. And I actually 4 00:00:12,960 --> 00:00:15,640 Speaker 1: requested for you to come on the show because I 5 00:00:15,680 --> 00:00:19,120 Speaker 1: want to make sure that we talk about artificial intelligence, 6 00:00:19,200 --> 00:00:21,800 Speaker 1: we talk about healthcare, and talk about it with somebody 7 00:00:21,840 --> 00:00:24,160 Speaker 1: who is a leader in that field. 8 00:00:24,640 --> 00:00:26,480 Speaker 2: And so, first off, author. 9 00:00:26,200 --> 00:00:30,639 Speaker 1: Of Gifts of the Hearts, okay, but now author of 10 00:00:30,680 --> 00:00:33,880 Speaker 1: the Art of Human Care with AI artificial intelligence. 11 00:00:33,960 --> 00:00:34,920 Speaker 2: So welcome to the show. 12 00:00:35,040 --> 00:00:35,960 Speaker 3: Thank you, Angela. 13 00:00:36,440 --> 00:00:39,320 Speaker 1: And I just want to run down your background a 14 00:00:39,360 --> 00:00:41,120 Speaker 1: little bit. You have so much that I was like, 15 00:00:41,159 --> 00:00:42,839 Speaker 1: I don't even I'm going to let you do it 16 00:00:42,880 --> 00:00:44,720 Speaker 1: because I don't want to miss anything. 17 00:00:45,640 --> 00:00:48,400 Speaker 3: Oh you let me Yeah, okay, Well, let's start with 18 00:00:48,440 --> 00:00:52,720 Speaker 3: something we have a lot in common with Fromkoklyn, well, 19 00:00:52,720 --> 00:00:56,200 Speaker 3: from Brooklyn, absolutely, So I think you could start with 20 00:00:56,280 --> 00:00:58,160 Speaker 3: that and you can end with that, and you probably 21 00:00:58,200 --> 00:00:59,880 Speaker 3: need to know everything you need to know about me. 22 00:01:00,200 --> 00:01:02,600 Speaker 1: No, but even just how you got into healthcare I 23 00:01:02,600 --> 00:01:05,880 Speaker 1: think is an interesting story, right because you were on 24 00:01:05,920 --> 00:01:08,720 Speaker 1: a plane, and so just tell us what happened while 25 00:01:08,720 --> 00:01:09,479 Speaker 1: you were on that flight. 26 00:01:09,720 --> 00:01:13,000 Speaker 3: Yeah, so that takes me back to undergrad I went 27 00:01:13,040 --> 00:01:15,520 Speaker 3: to Brooklyn Tech. We were just talking about high school. 28 00:01:15,640 --> 00:01:17,880 Speaker 3: I went to a very small arts and science college, 29 00:01:17,880 --> 00:01:22,000 Speaker 3: one of the Sunies upstate Sunny Plattsburgh, and was a 30 00:01:22,040 --> 00:01:25,240 Speaker 3: pre med major. Wanted to become a doctor because my 31 00:01:25,400 --> 00:01:28,679 Speaker 3: parents are West African immigrants and that's what they want 32 00:01:28,720 --> 00:01:34,440 Speaker 3: their sons to be. So I was down that path. 33 00:01:34,520 --> 00:01:36,240 Speaker 3: And when I was in Brooklyn Tech, I made it. 34 00:01:36,280 --> 00:01:43,000 Speaker 4: In bio biochemistry and biomedical engineering and got to Brooklyn Tech, graduated, 35 00:01:43,040 --> 00:01:50,680 Speaker 4: went to Sunney Plattsburgh, and got admitted to medical school eventually, 36 00:01:50,760 --> 00:01:54,840 Speaker 4: But that's not what happened first. What happened first was 37 00:01:54,880 --> 00:01:58,160 Speaker 4: I got an early decision interviewed down in Johns Hopkins, 38 00:01:58,440 --> 00:02:01,080 Speaker 4: and I was on that plane, like you said, and 39 00:02:01,280 --> 00:02:04,040 Speaker 4: when that happened, I was sitting next to an individual 40 00:02:04,240 --> 00:02:05,639 Speaker 4: who had a very bad cough. 41 00:02:06,600 --> 00:02:09,799 Speaker 3: Now, you know, way before COVID, so you know back then, 42 00:02:09,880 --> 00:02:12,919 Speaker 3: you know obviously you know behaviors and times of change. 43 00:02:13,040 --> 00:02:16,079 Speaker 3: But I went to Baltimore, I interviewed at Hopkins and 44 00:02:16,480 --> 00:02:19,480 Speaker 3: was really really confident I was gonna get accepted to 45 00:02:19,639 --> 00:02:21,720 Speaker 3: medical school, which is a huge deal, by the way, 46 00:02:21,800 --> 00:02:24,440 Speaker 3: which is a huge deal at Hopkins and early decision 47 00:02:24,480 --> 00:02:26,760 Speaker 3: on top of that, and I got back in about 48 00:02:26,760 --> 00:02:30,359 Speaker 3: a week later. Angela. I was deathly ill, had a fever, 49 00:02:31,440 --> 00:02:33,760 Speaker 3: had a headache. I felt like someone had my head 50 00:02:33,760 --> 00:02:36,679 Speaker 3: in a vice script and was just squeezing, and I 51 00:02:36,720 --> 00:02:40,200 Speaker 3: had chills. And I went to the infirmary and you know, 52 00:02:40,480 --> 00:02:42,200 Speaker 3: told them my symptoms and they said, well, you probably 53 00:02:42,200 --> 00:02:44,120 Speaker 3: have a stomach food. Just go back to your doring room. 54 00:02:44,160 --> 00:02:45,640 Speaker 3: I was an ra so I had a single. I 55 00:02:45,639 --> 00:02:48,639 Speaker 3: was by myself. I'm a member of five Beta Sigma 56 00:02:48,639 --> 00:02:52,280 Speaker 3: for Attorney. So fortunately my my fraternity brothers came looking 57 00:02:52,320 --> 00:02:54,640 Speaker 3: for me on a Friday night and found me, you know, 58 00:02:54,840 --> 00:02:57,840 Speaker 3: half dead pretty much, and took me to the hospital. 59 00:02:58,080 --> 00:03:02,359 Speaker 3: And it turned out that what I had was bacterial meningitis. 60 00:03:02,480 --> 00:03:06,000 Speaker 3: Oh it is. And you know, for your listeners or 61 00:03:06,000 --> 00:03:09,600 Speaker 3: anybody who knows about bacterial meningitis, this happens almost annually. 62 00:03:09,639 --> 00:03:12,000 Speaker 3: There's a couple of kids in colleges all across the 63 00:03:12,000 --> 00:03:16,720 Speaker 3: country that you know, succumbed to meningitis and die. And 64 00:03:16,760 --> 00:03:18,400 Speaker 3: in my case, I was fortunate. I got to the 65 00:03:18,400 --> 00:03:21,560 Speaker 3: hospital time, I got the treatment I needed. I met 66 00:03:21,639 --> 00:03:25,160 Speaker 3: a physician in the er that took care of me. 67 00:03:25,560 --> 00:03:27,519 Speaker 3: I spent about two weeks in the hospital, a week 68 00:03:27,560 --> 00:03:30,040 Speaker 3: in IC, you tube and every office of my body, 69 00:03:30,160 --> 00:03:32,960 Speaker 3: so all before I became a doctor. And when I 70 00:03:33,000 --> 00:03:36,000 Speaker 3: finally got out of the hospital, I found out that 71 00:03:36,080 --> 00:03:37,360 Speaker 3: Johns Hopkins rejected me. 72 00:03:38,040 --> 00:03:38,680 Speaker 2: Oh my god. 73 00:03:38,840 --> 00:03:42,200 Speaker 3: But that was all okay, because ultimately it worked out. 74 00:03:42,400 --> 00:03:44,520 Speaker 3: I think what kept me alive was the knowledge and 75 00:03:44,600 --> 00:03:46,360 Speaker 3: the feeling and the confidence that I was going to 76 00:03:46,560 --> 00:03:48,880 Speaker 3: become a doctor. So I think that that kept me 77 00:03:48,920 --> 00:03:51,360 Speaker 3: a lot, and it was my purpose back then. So 78 00:03:51,440 --> 00:03:54,320 Speaker 3: ultimately I wound up going to Downstate Medical School, came 79 00:03:54,360 --> 00:03:56,400 Speaker 3: back to Brooklyn. That's why I say it begins and 80 00:03:56,440 --> 00:03:58,880 Speaker 3: starts in Brooklyn. Came it came back to Brooklyn for 81 00:03:59,480 --> 00:04:02,040 Speaker 3: medical schoo school. I spent nine years at Downstate. I 82 00:04:02,040 --> 00:04:04,120 Speaker 3: did four years of medical school and then I did 83 00:04:04,160 --> 00:04:06,920 Speaker 3: five years of a general surgery residency. That's where I 84 00:04:06,960 --> 00:04:10,000 Speaker 3: got to become a surgeon. I joined the Navy after 85 00:04:10,080 --> 00:04:10,800 Speaker 3: medical school. 86 00:04:11,120 --> 00:04:11,800 Speaker 2: You did it all. 87 00:04:12,880 --> 00:04:15,440 Speaker 3: Joined the Navy after medical school, traveled the world after 88 00:04:15,480 --> 00:04:18,000 Speaker 3: I finished my time at Downstate, and then I went 89 00:04:18,040 --> 00:04:21,400 Speaker 3: to Minnesota and that's where I became trained to be 90 00:04:21,520 --> 00:04:27,200 Speaker 3: a heart and lung surgeon specialized in transplant, and I 91 00:04:27,320 --> 00:04:28,520 Speaker 3: spent some time in Boston. 92 00:04:28,720 --> 00:04:30,400 Speaker 2: And while I was in Boston, I had the bus 93 00:04:30,520 --> 00:04:31,440 Speaker 2: and axent just now too. 94 00:04:31,720 --> 00:04:33,760 Speaker 3: I went to Boston and spent the year in Boston, 95 00:04:33,839 --> 00:04:36,719 Speaker 3: worked in the hospital there, and then ultimately the Navy 96 00:04:37,480 --> 00:04:42,239 Speaker 3: transplanted me to but that's the Maryland right outside of Washington, 97 00:04:42,320 --> 00:04:44,680 Speaker 3: d C. And I was, you know, I was a 98 00:04:44,720 --> 00:04:48,160 Speaker 3: practicing cardiothorastic surgeon still am. That's what I did for 99 00:04:48,279 --> 00:04:51,440 Speaker 3: most of my time in the Navy, and I had 100 00:04:51,480 --> 00:04:54,880 Speaker 3: an opportunity to learn about informatics. Okay, and you know, 101 00:04:54,920 --> 00:04:58,719 Speaker 3: for your listeners, what informatics is is it's the constellation 102 00:04:59,080 --> 00:05:04,719 Speaker 3: of data, technology and clinical science and we bring those 103 00:05:04,760 --> 00:05:09,640 Speaker 3: together to help doctors do their job better. Okay, do 104 00:05:09,680 --> 00:05:11,920 Speaker 3: their work better. And if you think about it, we 105 00:05:12,000 --> 00:05:12,960 Speaker 3: need technology. 106 00:05:13,279 --> 00:05:13,880 Speaker 2: Absolutely. 107 00:05:13,960 --> 00:05:15,960 Speaker 3: I wouldn't be able to do open heart surgery without 108 00:05:16,000 --> 00:05:20,640 Speaker 3: great technology, without those innovations that have pioneered our field. 109 00:05:21,960 --> 00:05:25,039 Speaker 3: You need data because you need information, and you combine 110 00:05:25,120 --> 00:05:30,800 Speaker 3: data and technology, you actually enhanced the clinical expertise of 111 00:05:30,880 --> 00:05:34,360 Speaker 3: the doctor that is taking care of you. And so 112 00:05:34,440 --> 00:05:38,320 Speaker 3: that specialty actually became a subspecialty that became a board 113 00:05:38,360 --> 00:05:42,800 Speaker 3: certified subspecialty. I became board certified and that almost ten 114 00:05:42,880 --> 00:05:46,720 Speaker 3: years ago, and that provide an opportunity for me to 115 00:05:46,839 --> 00:05:50,920 Speaker 3: sort of have another dimension of my clinical practice beyond 116 00:05:51,000 --> 00:05:52,120 Speaker 3: just heart and lung surgery. 117 00:05:52,440 --> 00:05:52,760 Speaker 2: Sheeeesh. 118 00:05:53,160 --> 00:05:54,960 Speaker 1: And I'm glad that you ran all this down because 119 00:05:54,960 --> 00:05:57,120 Speaker 1: there's no way that I would have been able to. 120 00:05:57,480 --> 00:06:02,960 Speaker 3: But it does have well, I appreciate that that a 121 00:06:03,000 --> 00:06:07,040 Speaker 3: law too, because it gives I think your listeners and 122 00:06:07,920 --> 00:06:11,080 Speaker 3: and folks that are listening to this, the perspective that 123 00:06:11,160 --> 00:06:13,120 Speaker 3: I come to just work with. 124 00:06:13,279 --> 00:06:15,440 Speaker 1: Yeah, because it's about caring. It's about what you went 125 00:06:15,480 --> 00:06:19,480 Speaker 1: through also, right and their death experience, so health wellness, 126 00:06:19,480 --> 00:06:23,240 Speaker 1: but then also technology and also artificial intelligence. 127 00:06:23,279 --> 00:06:24,800 Speaker 2: It brings all of these things together. 128 00:06:25,040 --> 00:06:29,400 Speaker 3: Yeah. So that passion for not only being on the 129 00:06:30,160 --> 00:06:32,840 Speaker 3: you know, patient side, and knowing what that felt like, 130 00:06:32,920 --> 00:06:37,040 Speaker 3: that vulnerability, that that fear, that anxiety, having a tube 131 00:06:37,080 --> 00:06:39,880 Speaker 3: and aary orphice of your body totally made me a 132 00:06:39,920 --> 00:06:42,479 Speaker 3: different kind of doctor. You know, when most people meet me, 133 00:06:42,600 --> 00:06:44,440 Speaker 3: especially in the operating room, they're like, You're not like 134 00:06:44,480 --> 00:06:45,440 Speaker 3: most cardiac surgent. 135 00:06:45,560 --> 00:06:48,120 Speaker 1: Yeah, because I feel like doctors are supposed to be 136 00:06:48,279 --> 00:06:49,679 Speaker 1: like very emotionless. 137 00:06:50,880 --> 00:06:52,560 Speaker 2: You know, that's what I've always seen. 138 00:06:52,440 --> 00:06:54,599 Speaker 1: And heard that because there's a lot of things that 139 00:06:54,640 --> 00:06:56,200 Speaker 1: can happen, and they can go wrong, and they tell you, 140 00:06:56,279 --> 00:06:58,120 Speaker 1: don't get too attached, don't do this, don't do that. 141 00:06:58,279 --> 00:07:00,680 Speaker 1: You know, while they do care for you, at a 142 00:07:00,680 --> 00:07:03,799 Speaker 1: certain point, it's like it can be really hard mentally 143 00:07:03,880 --> 00:07:05,440 Speaker 1: for a doctor also to become. 144 00:07:05,680 --> 00:07:08,120 Speaker 3: Well, that's unfortunate that you had that experience, because I've 145 00:07:08,120 --> 00:07:12,640 Speaker 3: been fortunate to have certainly that experience, but many experiences 146 00:07:12,680 --> 00:07:17,040 Speaker 3: of doctors that are very compassionate, very passionate, and very 147 00:07:17,040 --> 00:07:21,040 Speaker 3: empathetic and also sort of put themselves in the shoes 148 00:07:21,080 --> 00:07:21,360 Speaker 3: of the. 149 00:07:21,280 --> 00:07:22,760 Speaker 2: Patient because you have to give bad news. 150 00:07:22,840 --> 00:07:26,720 Speaker 3: Sometimes you do a lot, you do right absolutely. 151 00:07:26,320 --> 00:07:28,680 Speaker 2: And then good news, which is amazing happen. 152 00:07:28,640 --> 00:07:31,840 Speaker 3: And then good news. So there's definitely a balance. But 153 00:07:32,000 --> 00:07:35,200 Speaker 3: that sort of entree into the technology space led to 154 00:07:35,880 --> 00:07:39,120 Speaker 3: many opportunities for me within the military. I became the 155 00:07:39,200 --> 00:07:42,000 Speaker 3: chief Medical Informatics Officer for the military for the Navy 156 00:07:42,360 --> 00:07:46,360 Speaker 3: our Department. I went to the War College and studies 157 00:07:46,520 --> 00:07:50,400 Speaker 3: National Security strategy, and while I was there, as the 158 00:07:50,400 --> 00:07:53,320 Speaker 3: only doctor in that class, I had an opportunity to 159 00:07:53,320 --> 00:07:56,520 Speaker 3: work with some really smart people that were on the 160 00:07:56,560 --> 00:08:00,680 Speaker 3: intel side that were AI experts, and I proposed a 161 00:08:00,720 --> 00:08:04,520 Speaker 3: project for them, and I wanted to work on a 162 00:08:04,600 --> 00:08:09,640 Speaker 3: thesis blending artificial intelligence and military medicine. And so I 163 00:08:09,680 --> 00:08:12,160 Speaker 3: spent a few years working on that, and then I 164 00:08:12,240 --> 00:08:16,200 Speaker 3: had one of the greatest opportunities that probably ever came 165 00:08:16,240 --> 00:08:18,800 Speaker 3: before me, and that was I was recruited to the 166 00:08:18,800 --> 00:08:22,560 Speaker 3: Pentagon to lead the war Fighter Health Mission at a 167 00:08:22,640 --> 00:08:25,679 Speaker 3: new organization that was established in the Department of Defense 168 00:08:26,160 --> 00:08:29,720 Speaker 3: exclusively for advancing AI in the military. It's a Joint 169 00:08:29,760 --> 00:08:31,040 Speaker 3: Artificial Intelligence Center. 170 00:08:32,040 --> 00:08:33,920 Speaker 1: All right, well, good, and this is where and by 171 00:08:33,920 --> 00:08:35,560 Speaker 1: the way, I know your parents are so proud because 172 00:08:35,559 --> 00:08:38,199 Speaker 1: this is exactly they could not have even imagined that 173 00:08:38,240 --> 00:08:39,800 Speaker 1: you would be when they wanted you to go to 174 00:08:39,840 --> 00:08:41,959 Speaker 1: school and become a doctor, that this is where we 175 00:08:42,000 --> 00:08:42,880 Speaker 1: would be right now. 176 00:08:43,320 --> 00:08:46,480 Speaker 3: Well, definitely an emotional cord that you hit there. My 177 00:08:46,600 --> 00:08:51,440 Speaker 3: dad regrettably passed away after I graduated from med school, 178 00:08:52,080 --> 00:08:56,000 Speaker 3: and just about a month ago, Angela, my mom passed away. 179 00:08:56,120 --> 00:08:57,280 Speaker 2: I'm so sorry to hear that. 180 00:08:57,559 --> 00:09:01,760 Speaker 3: But I do believe I liked to think that. You know, 181 00:09:02,040 --> 00:09:05,880 Speaker 3: I just wrote something and I quoted John F. Kennedy. 182 00:09:05,720 --> 00:09:09,240 Speaker 3: He said that you are not a true adult until 183 00:09:09,280 --> 00:09:12,440 Speaker 3: you lose both of your parents. I am now a 184 00:09:12,440 --> 00:09:15,600 Speaker 3: true adult, okay. And I don't know if it feels 185 00:09:15,640 --> 00:09:17,720 Speaker 3: very good to be a true adult because of losing 186 00:09:17,720 --> 00:09:22,880 Speaker 3: both parents. But one thing that I am pretty confident 187 00:09:22,880 --> 00:09:25,720 Speaker 3: about is you have a mother and a father like 188 00:09:25,800 --> 00:09:28,240 Speaker 3: I did. They always live within you, and they're in 189 00:09:28,280 --> 00:09:33,440 Speaker 3: your heart. And my mom, I believe, is very happy, 190 00:09:33,440 --> 00:09:34,960 Speaker 3: and I'm going to continue to make her proud. 191 00:09:35,040 --> 00:09:38,880 Speaker 1: So listen, I have no doubts about that at all 192 00:09:38,920 --> 00:09:40,920 Speaker 1: from everything that I've just heard up to this point. 193 00:09:41,800 --> 00:09:44,800 Speaker 1: And I do want to talk now about artificial intelligence 194 00:09:44,920 --> 00:09:47,560 Speaker 1: and about health care and wellness and in that space 195 00:09:47,880 --> 00:09:50,040 Speaker 1: because I feel like the reason why I wanted to 196 00:09:50,040 --> 00:09:52,920 Speaker 1: hear is because a lot of people get nervous when 197 00:09:52,960 --> 00:09:55,640 Speaker 1: they hear about artificial intelligence. Right, We've been watching what's 198 00:09:55,640 --> 00:09:58,760 Speaker 1: been going on with sag after with the writers striking. 199 00:10:00,400 --> 00:10:03,320 Speaker 1: Also in other spaces where people are like, okay, is 200 00:10:03,440 --> 00:10:05,760 Speaker 1: AI going to take over a job? We've seen articles 201 00:10:05,760 --> 00:10:07,840 Speaker 1: where it's like these are the jobs that won't exist 202 00:10:07,880 --> 00:10:11,200 Speaker 1: anymore thanks to artificial intelligence. But then we also see 203 00:10:11,280 --> 00:10:14,360 Speaker 1: the benefits of artificial intelligence. And my main thing is 204 00:10:14,360 --> 00:10:16,839 Speaker 1: that people need to understand it and to understand how 205 00:10:16,840 --> 00:10:19,360 Speaker 1: they can also be involved in that space and know 206 00:10:19,400 --> 00:10:21,080 Speaker 1: how this will affect their life, but how you can 207 00:10:21,120 --> 00:10:22,960 Speaker 1: make sure that it affects you in a positive way. 208 00:10:23,440 --> 00:10:29,040 Speaker 1: So let's first start with in the healthcare space artificial intelligence. 209 00:10:29,080 --> 00:10:31,760 Speaker 1: What are some of the benefits, Like you said, for 210 00:10:31,880 --> 00:10:34,760 Speaker 1: you being a heart surgeon and doing what you've done, 211 00:10:34,800 --> 00:10:37,560 Speaker 1: you couldn't have done that without the technology that you have, 212 00:10:37,640 --> 00:10:40,920 Speaker 1: and with artificial intelligence that brings it to another level. 213 00:10:40,920 --> 00:10:42,720 Speaker 1: So let's talk about some of the benefits that that 214 00:10:42,760 --> 00:10:43,720 Speaker 1: brings to that space. 215 00:10:44,040 --> 00:10:47,360 Speaker 3: That's a great question. So first and foremost, I think 216 00:10:47,600 --> 00:10:50,959 Speaker 3: you would want to kind of categorize yourself, and you 217 00:10:51,160 --> 00:10:54,000 Speaker 3: put yourself in sort of a category. Are you a 218 00:10:54,000 --> 00:10:59,320 Speaker 3: AI optimist, are you an AI pessimist? Or are you 219 00:10:59,400 --> 00:11:01,280 Speaker 3: sort of in the friend and agnostic to it, like 220 00:11:01,320 --> 00:11:04,040 Speaker 3: it doesn't matter to you. And I think that, you know, 221 00:11:04,240 --> 00:11:06,800 Speaker 3: almost everyone can kind of put themselves, depending on the 222 00:11:06,880 --> 00:11:10,120 Speaker 3: subject matter, in one of those camps. Very enthusiastic about it, 223 00:11:10,240 --> 00:11:14,520 Speaker 3: very you know, not so enthusiastic about very pessimistic, anxious 224 00:11:14,520 --> 00:11:16,880 Speaker 3: and scared about it, and then kind of think that 225 00:11:16,920 --> 00:11:20,160 Speaker 3: doesn't matter, so kind of like, let's start with that 226 00:11:21,120 --> 00:11:24,160 Speaker 3: sort of position. So if you're listening to this, kind 227 00:11:24,160 --> 00:11:26,480 Speaker 3: of put yourself in one of those those categories and 228 00:11:26,520 --> 00:11:28,520 Speaker 3: then and then listen to what I say, and then 229 00:11:28,559 --> 00:11:29,920 Speaker 3: let's see, if you know, if. 230 00:11:30,440 --> 00:11:33,840 Speaker 1: Things change a little bit, and put myself in a category. Okay, 231 00:11:33,960 --> 00:11:38,120 Speaker 1: I would say in between an agnostic and optimists, because 232 00:11:38,720 --> 00:11:40,880 Speaker 1: I feel like it's really important to be educated, but 233 00:11:40,920 --> 00:11:43,240 Speaker 1: I need to work on that. And so for me 234 00:11:43,280 --> 00:11:45,240 Speaker 1: to be to the place where I can be an optimist, 235 00:11:45,280 --> 00:11:46,559 Speaker 1: I need to understand. 236 00:11:46,160 --> 00:11:50,080 Speaker 3: More absolutely, And I think that is spot on for 237 00:11:50,200 --> 00:11:54,600 Speaker 3: where you know, so we sort of begin our understanding 238 00:11:54,640 --> 00:11:59,960 Speaker 3: of AI and artificial intelligence has evolved a lot, you know, 239 00:12:00,120 --> 00:12:01,920 Speaker 3: and why is it sort of at the forefront of 240 00:12:01,960 --> 00:12:06,160 Speaker 3: everyone's mind? Artificial intelligence was described decades ago. No one 241 00:12:06,240 --> 00:12:08,079 Speaker 3: was talking about it, right, It was like, you know, 242 00:12:08,320 --> 00:12:09,520 Speaker 3: so a lot of us that have been in this 243 00:12:09,600 --> 00:12:11,920 Speaker 3: space for years kind of look at this as sort 244 00:12:11,960 --> 00:12:14,360 Speaker 3: of a novel time in a way, because. 245 00:12:14,200 --> 00:12:18,160 Speaker 2: There's been movies it like Long Far Away Scientistion. 246 00:12:17,800 --> 00:12:19,960 Speaker 3: Right, And maybe it's now more at the forefront because 247 00:12:20,000 --> 00:12:23,720 Speaker 3: obviously information gets transferred and disseminated a lot more efficiently 248 00:12:23,760 --> 00:12:25,880 Speaker 3: now than it did in the past. But there is 249 00:12:25,920 --> 00:12:30,559 Speaker 3: really kind of a critical space that we're in right 250 00:12:30,600 --> 00:12:33,360 Speaker 3: now that I think has brought artificial intelligence sort of 251 00:12:33,400 --> 00:12:35,960 Speaker 3: to the apagy and sort of to this you know, 252 00:12:36,080 --> 00:12:39,199 Speaker 3: this szeitgeist of everybody talking about it and knowing about 253 00:12:39,240 --> 00:12:42,840 Speaker 3: it and being fearful of it, being optimistic about it, 254 00:12:42,920 --> 00:12:45,000 Speaker 3: or still not understanding what it is, but hearing a 255 00:12:45,080 --> 00:12:48,880 Speaker 3: lot about it. And if you think about what time 256 00:12:48,960 --> 00:12:51,120 Speaker 3: and period we're in, we're sort of in this era 257 00:12:51,280 --> 00:12:59,120 Speaker 3: right We're in this era of technology advancement, data, network connectivity, 258 00:12:59,200 --> 00:13:02,200 Speaker 3: and a lot of power that we never had before. 259 00:13:02,240 --> 00:13:05,040 Speaker 3: And what I mean by that is knowledge of power. 260 00:13:05,240 --> 00:13:09,600 Speaker 3: You're not knowledge within the power of knowledge, but also 261 00:13:09,679 --> 00:13:13,520 Speaker 3: computing power that we didn't have before. And we also 262 00:13:13,600 --> 00:13:17,880 Speaker 3: have a capacity right now to exchange information at a 263 00:13:18,320 --> 00:13:21,720 Speaker 3: pace that we never had before. You know, you and 264 00:13:21,760 --> 00:13:24,360 Speaker 3: I can now talk to each other here obviously, we 265 00:13:24,360 --> 00:13:26,440 Speaker 3: can reach out across the world and talk to somebody 266 00:13:26,440 --> 00:13:30,280 Speaker 3: and collaborate with someone in another country in real time 267 00:13:30,679 --> 00:13:33,120 Speaker 3: on a project. Right. I mean, think about how you 268 00:13:33,160 --> 00:13:35,280 Speaker 3: have to exchange we think about how you have to 269 00:13:35,280 --> 00:13:37,800 Speaker 3: exchange information. In the past, you would have to send 270 00:13:37,840 --> 00:13:40,480 Speaker 3: a fact documents going back and forth. Now you just 271 00:13:40,520 --> 00:13:43,240 Speaker 3: attach a pdf. You can attach a thousand page PDF 272 00:13:43,240 --> 00:13:45,480 Speaker 3: and just send it in a minute on the second 273 00:13:45,520 --> 00:13:48,840 Speaker 3: phone on your phone. So think about that. So if 274 00:13:48,840 --> 00:13:51,880 Speaker 3: you think about we have a lot more data, we 275 00:13:51,920 --> 00:13:54,520 Speaker 3: have computing power we didn't have before, we have a 276 00:13:54,559 --> 00:13:58,680 Speaker 3: network of connectivity, we have really really bright people. And 277 00:13:58,800 --> 00:14:01,480 Speaker 3: for many years people have been working in this space, 278 00:14:02,760 --> 00:14:08,960 Speaker 3: developing algorithms, developing the technology, building the infrastructure to make 279 00:14:09,120 --> 00:14:16,000 Speaker 3: AI a lot more effective, a lot more visible, and 280 00:14:16,280 --> 00:14:19,920 Speaker 3: a lot more impactful. So that's a good thing in 281 00:14:20,000 --> 00:14:23,000 Speaker 3: many ways, right, but it also brings a lot of concern. 282 00:14:23,080 --> 00:14:25,640 Speaker 3: It brings a lot of anxiety because we are also 283 00:14:25,680 --> 00:14:29,800 Speaker 3: seeing an impact, the real impact on people's lives. So 284 00:14:30,440 --> 00:14:32,960 Speaker 3: I just wanted to kind of give that background in context. 285 00:14:33,320 --> 00:14:35,880 Speaker 3: But then I want to say this one thing and 286 00:14:35,920 --> 00:14:40,840 Speaker 3: then we'll continue the conversation, and that is AI will 287 00:14:40,960 --> 00:14:45,840 Speaker 3: impact everything. I'm going to repeat that so you understand it, 288 00:14:45,880 --> 00:14:50,080 Speaker 3: and so everyone else understands it. AI will impact everything 289 00:14:51,920 --> 00:14:54,680 Speaker 3: except for the things that matter most. 290 00:14:56,200 --> 00:14:59,040 Speaker 4: Okay, the rest is going to be up to us. 291 00:15:00,080 --> 00:15:00,800 Speaker 3: What do I mean by this? 292 00:15:01,160 --> 00:15:01,640 Speaker 2: Explain that. 293 00:15:01,880 --> 00:15:08,080 Speaker 3: Explain that at the time that electricity was kind of 294 00:15:08,080 --> 00:15:11,080 Speaker 3: coming on the scene. You think about New York, right, 295 00:15:11,080 --> 00:15:15,400 Speaker 3: our big city here. Can you imagine New York without electricity? 296 00:15:15,600 --> 00:15:16,960 Speaker 2: Absolutely, we wouldn't be here. 297 00:15:17,040 --> 00:15:19,120 Speaker 3: Wouldn't be here. We were just talking in the green 298 00:15:19,160 --> 00:15:21,400 Speaker 3: room before we got in here about an event we 299 00:15:21,560 --> 00:15:24,280 Speaker 3: just had where the electricity went out. Power went out. 300 00:15:24,360 --> 00:15:27,520 Speaker 1: This was just we had a power outage that was 301 00:15:27,560 --> 00:15:29,560 Speaker 1: a huge deal around the city. People couldn't get their 302 00:15:29,600 --> 00:15:33,160 Speaker 1: cars out of the parking garages, like, everything was shut down. 303 00:15:33,560 --> 00:15:38,800 Speaker 3: And no doubt people were harmed. Right. Uh, air conditioning 304 00:15:38,880 --> 00:15:44,160 Speaker 3: that work, and heat not working if it was winter time, refrigerators, Yeah, 305 00:15:44,240 --> 00:15:46,760 Speaker 3: lots of things, right, But at the end of the day, 306 00:15:46,920 --> 00:15:49,960 Speaker 3: people adapted and they survived. They kind of got through it. 307 00:15:50,280 --> 00:15:54,280 Speaker 3: But just think about how electricity was introduced into the world. 308 00:15:54,920 --> 00:15:58,080 Speaker 3: Let's just be specific about America, and if you go 309 00:15:58,160 --> 00:15:59,880 Speaker 3: back to that time and you read about what was 310 00:16:00,080 --> 00:16:02,560 Speaker 3: going on, all the same kinds of things that are 311 00:16:02,560 --> 00:16:08,440 Speaker 3: happening right now. Are absolutely the people that make oil 312 00:16:08,600 --> 00:16:11,560 Speaker 3: to put oil in all those lamps, those gas lamps, 313 00:16:12,160 --> 00:16:14,760 Speaker 3: what are we going to do our business is our livelihood? 314 00:16:14,760 --> 00:16:17,280 Speaker 3: We're going to go the people that were lighting those lights, 315 00:16:17,680 --> 00:16:19,240 Speaker 3: What are we going to do our jobs? Are going 316 00:16:19,320 --> 00:16:23,080 Speaker 3: to be gone. And what happened Angela. They adapted and 317 00:16:23,120 --> 00:16:26,880 Speaker 3: they evolved. And that's the great thing about our human species. 318 00:16:26,920 --> 00:16:29,560 Speaker 3: We can adapt and we can evolve. So my point 319 00:16:29,640 --> 00:16:34,080 Speaker 3: is electricity absolutely impacts everything that we do. Right now, 320 00:16:34,280 --> 00:16:36,560 Speaker 3: we wouldn't be able to have this dialogue and this 321 00:16:36,680 --> 00:16:39,520 Speaker 3: exchange and have it delivered and disseminated to everybody if 322 00:16:39,520 --> 00:16:41,280 Speaker 3: it wasn't for electricity. 323 00:16:40,760 --> 00:16:42,160 Speaker 4: Right now, right we would be in the dark. 324 00:16:42,480 --> 00:16:45,080 Speaker 3: Right, We wouldn't be able to have this conversation, not 325 00:16:45,200 --> 00:16:47,160 Speaker 3: in the way that we're having right now. But you 326 00:16:47,200 --> 00:16:49,280 Speaker 3: and I could still exchange ideas if we were in 327 00:16:49,320 --> 00:16:51,080 Speaker 3: the dark and we were just sitting here by candle light. 328 00:16:52,200 --> 00:16:54,800 Speaker 3: And that's really the juxt of what matters. Right, So, 329 00:16:54,880 --> 00:17:00,520 Speaker 3: your your relationships, caring for your family, taking care of yourself, 330 00:17:00,560 --> 00:17:04,000 Speaker 3: those things are still going to happen with or without AI. Now, 331 00:17:04,040 --> 00:17:07,679 Speaker 3: can you use AI to leverage those things, to leverage 332 00:17:07,720 --> 00:17:14,000 Speaker 3: those interactions to improve care, to improve education, to fitness, fitness, 333 00:17:14,040 --> 00:17:18,560 Speaker 3: to do all these other things. Absolutely, But let's regard 334 00:17:18,600 --> 00:17:20,639 Speaker 3: AI for what it is. It is a tool, it 335 00:17:20,720 --> 00:17:25,399 Speaker 3: is a technology, it is an innovation that undoubtedly is 336 00:17:25,520 --> 00:17:28,080 Speaker 3: impacting every aspect of our life right now. It's going 337 00:17:28,160 --> 00:17:32,280 Speaker 3: to continue to impact every aspect of all life. But 338 00:17:32,640 --> 00:17:36,440 Speaker 3: there is great benefit but also great potential for there 339 00:17:36,480 --> 00:17:39,800 Speaker 3: to be harm with it, just like every technology. 340 00:17:39,920 --> 00:17:41,960 Speaker 1: Now, what do you think about people who are in 341 00:17:42,040 --> 00:17:44,359 Speaker 1: specific fields that are like, Look, we want to make 342 00:17:44,359 --> 00:17:49,240 Speaker 1: sure that our jobs and our futures are also stable 343 00:17:49,400 --> 00:17:51,400 Speaker 1: because we want to make sure that there's things put 344 00:17:51,440 --> 00:17:55,200 Speaker 1: into place, especially like say SAG after everything that's going 345 00:17:55,240 --> 00:17:58,000 Speaker 1: on with the writers strike with the actors, and people 346 00:17:58,080 --> 00:17:59,879 Speaker 1: are very concerned about what are you going to be 347 00:18:00,080 --> 00:18:02,960 Speaker 1: using AI to write scripts where you no longer need 348 00:18:03,080 --> 00:18:06,320 Speaker 1: humans for that, you know, for a lot of different things. 349 00:18:06,400 --> 00:18:09,800 Speaker 1: People have been concerned about whether or not. And I 350 00:18:09,880 --> 00:18:12,120 Speaker 1: understand what you're saying, because there'll be different jobs available, 351 00:18:12,200 --> 00:18:15,480 Speaker 1: right will adapt and there'll be another need for us 352 00:18:15,760 --> 00:18:18,160 Speaker 1: to be able to somehow you know, and we don't 353 00:18:18,160 --> 00:18:19,879 Speaker 1: even know what that is completely yet, or. 354 00:18:19,880 --> 00:18:20,400 Speaker 2: Maybe you do. 355 00:18:22,240 --> 00:18:22,879 Speaker 3: From the future. 356 00:18:25,080 --> 00:18:28,440 Speaker 1: Think about corporations having to put things in place because 357 00:18:28,440 --> 00:18:31,240 Speaker 1: we see like with Netflix, you know, while people are 358 00:18:31,280 --> 00:18:35,040 Speaker 1: having issues and there's a strike going on, they're also 359 00:18:35,119 --> 00:18:36,520 Speaker 1: hiring for people in AI. 360 00:18:36,840 --> 00:18:40,080 Speaker 3: Yeah, that's a that's a great question, and I'll answer 361 00:18:40,080 --> 00:18:43,359 Speaker 3: the question with given a story and a bit of 362 00:18:44,280 --> 00:18:47,199 Speaker 3: context from from the work that I did UH and 363 00:18:47,320 --> 00:18:50,000 Speaker 3: continue to do in health in the healthcare space, so 364 00:18:50,040 --> 00:18:52,360 Speaker 3: you can kind of make some parallels and I think 365 00:18:52,359 --> 00:18:55,720 Speaker 3: it'll answer the question in this way. So when we 366 00:18:55,720 --> 00:18:57,639 Speaker 3: were at the JAKE, when I first got it to 367 00:18:57,680 --> 00:18:59,720 Speaker 3: join our official intelligence center. By the way, that that 368 00:18:59,840 --> 00:19:02,960 Speaker 3: orization has evolved and has matured into a different organization 369 00:19:03,040 --> 00:19:05,560 Speaker 3: within the Department now, But when we got there, we 370 00:19:05,720 --> 00:19:08,880 Speaker 3: just think about the timing. It started in twenty twenty eighteen, 371 00:19:09,440 --> 00:19:11,639 Speaker 3: it was before the pandemic, and in twenty nineteen is 372 00:19:11,640 --> 00:19:14,440 Speaker 3: when I arrived. So it's brand new organization, huge budget, 373 00:19:14,680 --> 00:19:18,560 Speaker 3: lots of resources, with the mandate to advance and accelerate 374 00:19:18,880 --> 00:19:21,640 Speaker 3: artificial intelligence within the Department of Defense, one of the 375 00:19:21,760 --> 00:19:27,200 Speaker 3: largest organizations on the planet. And I was privileged and 376 00:19:28,160 --> 00:19:31,680 Speaker 3: had this opportunity to lead war fighter health a huge 377 00:19:31,720 --> 00:19:35,440 Speaker 3: portfolio and how do we bring AI to the point 378 00:19:35,480 --> 00:19:38,000 Speaker 3: of care and help our war fighters and those that 379 00:19:38,080 --> 00:19:41,000 Speaker 3: are supporting war fighters. And we had a number of projects. 380 00:19:41,000 --> 00:19:44,600 Speaker 3: There were dozens and dozens of projects that our team 381 00:19:44,680 --> 00:19:48,359 Speaker 3: and myself were shepherding through a process of maturity and delivery. 382 00:19:49,240 --> 00:19:52,840 Speaker 3: I'm going to highlight two in particular, and I'm going 383 00:19:52,920 --> 00:19:57,159 Speaker 3: to kind of give you some context for how this 384 00:19:57,280 --> 00:19:59,600 Speaker 3: experience that I had answers the question that you're asking 385 00:20:00,000 --> 00:20:02,760 Speaker 3: specifically about the writers and other people that may be 386 00:20:02,840 --> 00:20:06,639 Speaker 3: listening right now. So it was a macro and a 387 00:20:07,280 --> 00:20:09,960 Speaker 3: micro and a macro example, if you think about it 388 00:20:10,000 --> 00:20:14,600 Speaker 3: that way. So on a microscopic level, we have slides. 389 00:20:14,640 --> 00:20:16,640 Speaker 3: So if you get a biopsy, or if we take 390 00:20:16,640 --> 00:20:19,879 Speaker 3: a piece of tissue from your body, or we do 391 00:20:19,960 --> 00:20:22,359 Speaker 3: surgery and resect something, we look at it on a 392 00:20:22,359 --> 00:20:25,040 Speaker 3: microscope to make a diagnosis. Is this cancer? Is this 393 00:20:25,119 --> 00:20:27,680 Speaker 3: something that's not cancer, or is this something that's benign. 394 00:20:28,200 --> 00:20:31,840 Speaker 3: And when we look onto those microscopes, we're looking at slides. Well, 395 00:20:31,840 --> 00:20:34,600 Speaker 3: we can digitize, and we did. We digitized those slides. 396 00:20:34,640 --> 00:20:38,520 Speaker 3: We digitized thousands, hundreds of thousands of those slides. And 397 00:20:38,560 --> 00:20:41,639 Speaker 3: what we did is used computer vision, built an interface, 398 00:20:41,960 --> 00:20:46,160 Speaker 3: and developed algorithms so that we could help detect cancer. 399 00:20:46,640 --> 00:20:49,720 Speaker 3: And without going into a whole lot of tech stuff, 400 00:20:50,359 --> 00:20:53,080 Speaker 3: I'll just tell you that what would happen is when 401 00:20:53,640 --> 00:20:57,920 Speaker 3: the pathologist or the physician looks into the microscope, this 402 00:20:58,600 --> 00:21:02,119 Speaker 3: technology that we were helping to implement with draw a 403 00:21:02,119 --> 00:21:06,119 Speaker 3: circle around the abnormal cells. Now, think about how that 404 00:21:06,160 --> 00:21:11,480 Speaker 3: could make the job of the pathologists that much better, right, 405 00:21:11,520 --> 00:21:17,480 Speaker 3: because we're looking at this slide and very trained, very skilled, 406 00:21:17,560 --> 00:21:21,480 Speaker 3: very seasoned professionals are looking at this information and they 407 00:21:21,480 --> 00:21:24,200 Speaker 3: have to make a call. And sometimes it's a judgment 408 00:21:24,240 --> 00:21:26,960 Speaker 3: call is this a cancer or is it not a cancer? 409 00:21:27,000 --> 00:21:30,159 Speaker 3: And sometimes we have to refer to an expert. Well, 410 00:21:31,280 --> 00:21:36,280 Speaker 3: we were implementing that technology, and we delivered those microscopes 411 00:21:36,280 --> 00:21:39,080 Speaker 3: to different centers. Now here's the neat thing. What we 412 00:21:39,240 --> 00:21:44,280 Speaker 3: found is that those microscopes in that interface, when we 413 00:21:44,359 --> 00:21:47,879 Speaker 3: compare them to just physicians looking at it on their 414 00:21:47,920 --> 00:21:52,080 Speaker 3: own without that, we were getting a high diagnostic. 415 00:21:51,480 --> 00:21:53,480 Speaker 4: Accuracy with that tool. 416 00:21:54,400 --> 00:21:56,800 Speaker 3: That's one example. Right, Let's like kind of park that 417 00:21:56,880 --> 00:22:00,359 Speaker 3: over there. Now look in a population, Like I said, 418 00:22:00,560 --> 00:22:04,200 Speaker 3: that's a microscopic way. Now let's zoom out. Let's zoom 419 00:22:04,200 --> 00:22:05,960 Speaker 3: out all the way to the rest of the country. 420 00:22:06,040 --> 00:22:10,399 Speaker 3: So in the middle of the pandemic in twenty twenty, 421 00:22:10,560 --> 00:22:13,919 Speaker 3: early twenty twenty, right, think back to where you are 422 00:22:14,080 --> 00:22:15,840 Speaker 3: watch thirteenth. Yeah, twenty twenty, I. 423 00:22:15,840 --> 00:22:18,639 Speaker 2: Was in Detroit. It was three one, three day. 424 00:22:18,920 --> 00:22:22,280 Speaker 3: I know that three one three day. I remember where 425 00:22:22,280 --> 00:22:24,480 Speaker 3: I was. I was in the hallway of the Pentagon, 426 00:22:25,160 --> 00:22:27,920 Speaker 3: and I was one of these, you know folks, kind 427 00:22:27,920 --> 00:22:30,639 Speaker 3: of like I want to say, crying wolf, but definitely saying, 428 00:22:30,720 --> 00:22:34,960 Speaker 3: every day something is happening. Look at the because every 429 00:22:35,040 --> 00:22:37,520 Speaker 3: day I would read the paper, and on the above 430 00:22:37,640 --> 00:22:40,240 Speaker 3: or below the fold of the Wall Street Journal, which 431 00:22:40,280 --> 00:22:42,920 Speaker 3: is one of my favorite things I read every day, 432 00:22:43,359 --> 00:22:45,800 Speaker 3: I would see something a headline of COVID. And I 433 00:22:45,920 --> 00:22:48,879 Speaker 3: was seeing this back in October of twenty twenty of 434 00:22:48,920 --> 00:22:51,399 Speaker 3: twenty nineteen. So now we're in the spring of twenty 435 00:22:51,440 --> 00:22:54,359 Speaker 3: twenty and things are happening, and I'll never forget because 436 00:22:54,400 --> 00:22:59,000 Speaker 3: in this I was out there evangelizing this COVID thing again. 437 00:22:59,160 --> 00:23:01,560 Speaker 1: Yeah right, all right, because I was in Detroit and 438 00:23:02,400 --> 00:23:04,000 Speaker 1: I was supposed to come back to New York, but 439 00:23:04,040 --> 00:23:06,919 Speaker 1: New York was shutting down, and so I was like, well, 440 00:23:07,000 --> 00:23:08,480 Speaker 1: let me just stay out here because they hadn't found 441 00:23:08,480 --> 00:23:10,240 Speaker 1: any cases in Detroit yet. 442 00:23:10,440 --> 00:23:11,840 Speaker 2: Yeah, exactly yet. 443 00:23:12,000 --> 00:23:13,679 Speaker 1: So I'm like, is this something that's just happening in 444 00:23:13,800 --> 00:23:16,000 Speaker 1: LA and in New York and it's not, you know, 445 00:23:16,040 --> 00:23:18,600 Speaker 1: should I just stay here for a little while longer? 446 00:23:19,000 --> 00:23:20,160 Speaker 1: But then after a while, I was like, I need 447 00:23:20,160 --> 00:23:22,080 Speaker 1: to just get home just in case. 448 00:23:22,240 --> 00:23:22,480 Speaker 2: Yeah. 449 00:23:22,680 --> 00:23:24,720 Speaker 3: Right, So I was in the hallway, we're talking. My 450 00:23:24,800 --> 00:23:28,359 Speaker 3: colleagues passed me. Now I was again, I was in 451 00:23:28,359 --> 00:23:32,280 Speaker 3: a minority. I'm a physician in a department talking about 452 00:23:32,280 --> 00:23:35,800 Speaker 3: AI and the department defense and people. My friends walked 453 00:23:35,800 --> 00:23:37,800 Speaker 3: by and they were like, Oh, there's that crazy doctor 454 00:23:37,840 --> 00:23:39,879 Speaker 3: talking about this COVID thing again. I said, all right, 455 00:23:39,920 --> 00:23:43,080 Speaker 3: you guys think I'm crazy. There was lunchtime. Do you 456 00:23:43,080 --> 00:23:48,160 Speaker 3: remember what happened around the four o'clock late afternoon they 457 00:23:48,200 --> 00:23:50,760 Speaker 3: canceled the NBA season. Oh, and that was the last 458 00:23:50,800 --> 00:23:53,320 Speaker 3: day we went to the office. That's the context I'm 459 00:23:53,320 --> 00:23:55,879 Speaker 3: going to tell you the Macro story about. Okay, Right, 460 00:23:56,040 --> 00:24:00,960 Speaker 3: so now we're all at home. Angela and our team 461 00:24:01,240 --> 00:24:07,560 Speaker 3: that has a portfolio to advance AI and help people 462 00:24:08,359 --> 00:24:10,160 Speaker 3: are stuck at home and what we're going to do. 463 00:24:11,040 --> 00:24:14,280 Speaker 3: And we're on the phone, and we're on zoom calls, 464 00:24:14,320 --> 00:24:18,080 Speaker 3: and we're on teams, and we're we're desperate, right because 465 00:24:18,119 --> 00:24:21,960 Speaker 3: we feel like impassion to try and help. No one 466 00:24:22,040 --> 00:24:26,800 Speaker 3: knew what to look for with this, right, this disease. 467 00:24:26,880 --> 00:24:29,560 Speaker 3: I mean, we're learning, like literally on the fly Learning. 468 00:24:30,520 --> 00:24:33,280 Speaker 3: I'm talking to my colleagues there in the ER, I 469 00:24:33,320 --> 00:24:35,080 Speaker 3: a lot of friends in New York. I'm in DC, 470 00:24:35,720 --> 00:24:40,040 Speaker 3: and they're getting run over by cases. And this is 471 00:24:40,080 --> 00:24:42,520 Speaker 3: a desperate time, right. We didn't have a lot of 472 00:24:42,560 --> 00:24:43,400 Speaker 3: testing back then. 473 00:24:43,600 --> 00:24:46,320 Speaker 1: It was impossible to get tested unless you pretty much 474 00:24:46,320 --> 00:24:46,680 Speaker 1: had it. 475 00:24:46,840 --> 00:24:49,879 Speaker 3: Yeah, And we started to think about what can we 476 00:24:49,920 --> 00:24:55,560 Speaker 3: do to help, How can we provide something that could help, 477 00:24:56,160 --> 00:24:59,919 Speaker 3: some tools, some technology, some way of informing people how 478 00:25:00,000 --> 00:25:03,880 Speaker 3: how to deal with this problem. And our great team 479 00:25:04,040 --> 00:25:06,919 Speaker 3: came up with these ideas and we're just brainstorming and 480 00:25:06,960 --> 00:25:09,000 Speaker 3: we're like, I mean, we're on calls. Until you think 481 00:25:09,040 --> 00:25:11,720 Speaker 3: about it, We're not in rooms doing this like in 482 00:25:11,800 --> 00:25:15,160 Speaker 3: real time. We're like doing it all virtually. But our team, 483 00:25:15,240 --> 00:25:17,040 Speaker 3: what we did is we came up with a really 484 00:25:17,119 --> 00:25:21,199 Speaker 3: neat concept and what we did have access to was 485 00:25:21,280 --> 00:25:24,560 Speaker 3: claims data. If you think about you go to the hospital, 486 00:25:24,680 --> 00:25:28,040 Speaker 3: you go see your doctor, you go see you visit 487 00:25:28,119 --> 00:25:31,960 Speaker 3: the clinic. They make a diagnosis on you and they 488 00:25:32,040 --> 00:25:35,840 Speaker 3: write down what you had, fever, chills, flu like symptoms, 489 00:25:36,400 --> 00:25:39,119 Speaker 3: sprained ankle, broken toe, whatever it is, and there's a 490 00:25:39,160 --> 00:25:41,760 Speaker 3: code for that. That code gets sent to the insurance 491 00:25:41,760 --> 00:25:45,440 Speaker 3: companies and in this case CMS, and that claim gets 492 00:25:45,600 --> 00:25:47,959 Speaker 3: entered and that's how you can track. You couldn't actually 493 00:25:48,000 --> 00:25:51,320 Speaker 3: go back and look to the code how many broken 494 00:25:51,520 --> 00:25:54,159 Speaker 3: you know, fhibulas that we have this year. So we 495 00:25:54,240 --> 00:25:56,840 Speaker 3: didn't have an ICD ten, which is what they're called 496 00:25:56,880 --> 00:26:00,119 Speaker 3: code for COVID in the spring of twenty twenty, not 497 00:26:00,240 --> 00:26:03,400 Speaker 3: yet anyway. But what we did have is flu like symptoms. 498 00:26:03,840 --> 00:26:06,480 Speaker 3: And we knew that people that of a certain age, 499 00:26:06,720 --> 00:26:10,280 Speaker 3: particularly a Medicaid age Medicare age was sixty five and older, 500 00:26:10,600 --> 00:26:14,680 Speaker 3: those folks were particularly susceptible to getting sick, right, because 501 00:26:14,680 --> 00:26:16,760 Speaker 3: we saw a lot of those folks coming into the hospitals, 502 00:26:17,280 --> 00:26:19,280 Speaker 3: and we said, what if we can use that data 503 00:26:19,320 --> 00:26:22,400 Speaker 3: as a surrogate to really track COVID cases, because if 504 00:26:22,440 --> 00:26:25,720 Speaker 3: these people are coming into the hospital with flu like symptoms, 505 00:26:25,960 --> 00:26:29,159 Speaker 3: there could be a really good chance that that was 506 00:26:29,440 --> 00:26:32,959 Speaker 3: not necessarily the flu, but it was COVID, right, especially 507 00:26:32,960 --> 00:26:36,280 Speaker 3: when you see surges. And so we had this database, 508 00:26:36,640 --> 00:26:42,280 Speaker 3: and we built these cooperative agreements with people that usually 509 00:26:42,320 --> 00:26:46,560 Speaker 3: don't ordinarily share information and share data, and we had 510 00:26:46,600 --> 00:26:50,520 Speaker 3: teams of experts from like you know, big companies, IBM, Amazon, 511 00:26:50,600 --> 00:26:54,199 Speaker 3: like all these great people, talented folks working with us 512 00:26:54,240 --> 00:26:59,600 Speaker 3: to build what we called the Salas platform, and a 513 00:26:59,640 --> 00:27:03,240 Speaker 3: public about both of these projects. Anyone's interested about learning 514 00:27:03,240 --> 00:27:05,520 Speaker 3: more about the micro example I talked about earlier, this 515 00:27:05,600 --> 00:27:08,320 Speaker 3: macro example, you can go look it up. It's in 516 00:27:08,320 --> 00:27:11,720 Speaker 3: the public domain. And what we were able to do 517 00:27:11,840 --> 00:27:17,840 Speaker 3: is develop a model and build out a dashboard that 518 00:27:17,880 --> 00:27:19,960 Speaker 3: could go down to the zip code level and tell 519 00:27:20,000 --> 00:27:23,400 Speaker 3: you and show you where the COVID cases were. Why 520 00:27:23,480 --> 00:27:26,600 Speaker 3: is that important? Because what were we trying to do 521 00:27:26,720 --> 00:27:30,600 Speaker 3: back then? We're trying to contain the spread of COVID, 522 00:27:31,240 --> 00:27:39,199 Speaker 3: but we were also trying to deliver really needed not 523 00:27:39,280 --> 00:27:43,720 Speaker 3: only medicines, but you know ppe protective you know, protective 524 00:27:43,760 --> 00:27:46,920 Speaker 3: equipment and gear to people where they needed it. Well, 525 00:27:47,480 --> 00:27:51,119 Speaker 3: now we had a tool that could show those policymakers, 526 00:27:51,200 --> 00:27:54,959 Speaker 3: those people that were delivering resources in personnel where they 527 00:27:54,960 --> 00:27:57,720 Speaker 3: should go. We could predict where the cases were, and 528 00:27:58,200 --> 00:28:01,359 Speaker 3: we had a way of you know, developing predictive models. 529 00:28:01,359 --> 00:28:04,679 Speaker 3: So this could this was this was data that was 530 00:28:04,720 --> 00:28:07,119 Speaker 3: sort of old because we're going by claims data. But 531 00:28:07,200 --> 00:28:09,280 Speaker 3: we can build models and you can you know, you 532 00:28:09,320 --> 00:28:12,000 Speaker 3: can develop like a system and a way to show 533 00:28:12,000 --> 00:28:15,080 Speaker 3: that today this is where those cases are, but tomorrow 534 00:28:15,080 --> 00:28:18,080 Speaker 3: are they going to be a Yeah, that's what we 535 00:28:18,080 --> 00:28:22,320 Speaker 3: were doing. It was advanced analytics, using data building algorithms 536 00:28:23,119 --> 00:28:27,639 Speaker 3: and developing a visual display that could help people that 537 00:28:27,720 --> 00:28:31,119 Speaker 3: weren't the programmers to see on a map, you know, 538 00:28:32,280 --> 00:28:34,680 Speaker 3: state by state, county by counties, down to the zip 539 00:28:34,720 --> 00:28:37,680 Speaker 3: code level where they could deploy resources. And it became 540 00:28:38,240 --> 00:28:40,840 Speaker 3: a needed tool and something that was effective in the 541 00:28:40,920 --> 00:28:45,600 Speaker 3: response to sort of save lives, to help ease pain 542 00:28:45,680 --> 00:28:49,640 Speaker 3: and suffering, and to deploy needed resources personnel, equipment, et cetera, 543 00:28:49,720 --> 00:28:53,440 Speaker 3: to where they needed the most. So that's a macro example, 544 00:28:53,480 --> 00:28:56,280 Speaker 3: and that's a macro example. How does that pertain to 545 00:28:56,320 --> 00:28:58,920 Speaker 3: the writers? How does that pertain to people in music industry? 546 00:28:58,960 --> 00:29:01,360 Speaker 3: How does that pertain to anyone that's listening right now 547 00:29:01,360 --> 00:29:05,080 Speaker 3: that may be in any field of work. My message 548 00:29:05,120 --> 00:29:09,840 Speaker 3: is sort of cases, think about what the problem set 549 00:29:09,920 --> 00:29:15,400 Speaker 3: is in your particular industry, your particular world, right what 550 00:29:15,480 --> 00:29:18,880 Speaker 3: are the pain points? Where is it that you see 551 00:29:19,080 --> 00:29:25,880 Speaker 3: an opportunity to improve something. In the field of pathology surgery, 552 00:29:27,200 --> 00:29:34,120 Speaker 3: we saw an opportunity to enhance the diagnostic capability of 553 00:29:34,640 --> 00:29:38,120 Speaker 3: those that are making that judgment call with providing this 554 00:29:38,280 --> 00:29:41,000 Speaker 3: tool that happened to leverage AI. 555 00:29:41,240 --> 00:29:42,320 Speaker 2: Yes, so. 556 00:29:43,880 --> 00:29:49,640 Speaker 3: Now take that out. Now we have policy makers, people 557 00:29:49,640 --> 00:29:53,680 Speaker 3: that are in charge of deploying resources, people that are 558 00:29:53,680 --> 00:29:57,560 Speaker 3: caring for patients, that all are dealing with something that's 559 00:29:57,600 --> 00:30:00,480 Speaker 3: a bit unknown. COVID was very novel, right, That's why 560 00:30:00,560 --> 00:30:02,640 Speaker 3: they don't call it novel anymore. But remember in the beginning, 561 00:30:02,680 --> 00:30:06,000 Speaker 3: it's called novel coronavirus. No, we don't know what's going on. 562 00:30:08,240 --> 00:30:12,280 Speaker 3: But what we did was we had identified a problem 563 00:30:12,360 --> 00:30:17,160 Speaker 3: and then we used a tool call it artificial intelligence 564 00:30:17,240 --> 00:30:20,920 Speaker 3: data analysis. You know, again, the definition of AI is 565 00:30:20,960 --> 00:30:24,440 Speaker 3: going to continue to evolve, but then we applied that 566 00:30:24,520 --> 00:30:29,640 Speaker 3: innovation to address a problem. So I think what what 567 00:30:29,640 --> 00:30:32,520 Speaker 3: what I would encourage everyone to do is don't think 568 00:30:32,560 --> 00:30:39,240 Speaker 3: of the technology as justice a morphous, ambiguous thing. Think 569 00:30:39,240 --> 00:30:41,200 Speaker 3: of it and it's something evil, or think of it 570 00:30:41,200 --> 00:30:45,040 Speaker 3: as something it's it's it's quite agnostic. I think, you know, 571 00:30:45,080 --> 00:30:48,200 Speaker 3: whether it's good or bad or evil, it's really it 572 00:30:48,320 --> 00:30:51,240 Speaker 3: depends on who's using it, right, because there could be 573 00:30:51,360 --> 00:30:54,120 Speaker 3: nefarious people out there that are using tools to do 574 00:30:54,240 --> 00:30:57,400 Speaker 3: things that are not so good. But there's also people 575 00:30:57,440 --> 00:30:59,400 Speaker 3: out there that are using tools to do something that 576 00:30:59,520 --> 00:31:04,040 Speaker 3: is good. So I think if you approach this technology 577 00:31:04,680 --> 00:31:07,320 Speaker 3: much like the people that started to be the early 578 00:31:07,320 --> 00:31:11,560 Speaker 3: adopters for electricity, they started to use it in a 579 00:31:11,640 --> 00:31:13,960 Speaker 3: way that was addressing problems. 580 00:31:14,360 --> 00:31:17,960 Speaker 1: But every day and for everyday average person, Like I've 581 00:31:17,960 --> 00:31:21,320 Speaker 1: seen people talking about how with fitness, artificial intelligence has 582 00:31:21,360 --> 00:31:23,880 Speaker 1: helped them come up with like a running plan or 583 00:31:24,000 --> 00:31:26,640 Speaker 1: come up with different diets and things like that that 584 00:31:26,680 --> 00:31:28,720 Speaker 1: can actually help you get back on the path to 585 00:31:29,200 --> 00:31:32,840 Speaker 1: being more healthy and more in shape. And so there's 586 00:31:32,880 --> 00:31:35,480 Speaker 1: a lot of ways that it can actually benefit you. 587 00:31:35,560 --> 00:31:38,920 Speaker 1: But I just think it is you know, we see 588 00:31:39,000 --> 00:31:42,880 Speaker 1: chat GPT and people being nervous about that too, about 589 00:31:43,120 --> 00:31:45,640 Speaker 1: you know, that being used. But I guess anything can 590 00:31:45,680 --> 00:31:49,360 Speaker 1: be used as a tool that's not beneficial or that's negative, 591 00:31:49,560 --> 00:31:51,680 Speaker 1: or it can be used for a good purpose, depending on, 592 00:31:51,760 --> 00:31:52,800 Speaker 1: like you said, who's using it. 593 00:31:53,120 --> 00:31:58,480 Speaker 3: Absolutely, I think there's great potential for AI to do 594 00:31:58,640 --> 00:32:00,960 Speaker 3: a lot of good. There's also potential for it to 595 00:32:01,000 --> 00:32:05,160 Speaker 3: do harm, and so understandably, I think people are right 596 00:32:05,200 --> 00:32:10,040 Speaker 3: to be anxious about this particular technology, but they should also, 597 00:32:10,800 --> 00:32:13,040 Speaker 3: you know, just kind of step back and just have 598 00:32:13,120 --> 00:32:16,280 Speaker 3: a perspective and then you know, if nothing else kind 599 00:32:16,280 --> 00:32:18,160 Speaker 3: of go back to those words that I said, right, 600 00:32:18,800 --> 00:32:22,920 Speaker 3: AI will impact everything except the things that matter most. 601 00:32:23,520 --> 00:32:27,560 Speaker 1: And you know, and I also have been saying, it's 602 00:32:27,560 --> 00:32:29,240 Speaker 1: something that is like you said, it's going to happen. 603 00:32:29,320 --> 00:32:32,160 Speaker 1: It is going to impact everything. So it's in your 604 00:32:32,200 --> 00:32:35,800 Speaker 1: best interest to figure out how can you use that 605 00:32:36,000 --> 00:32:37,880 Speaker 1: and make sure that you're informed on it. And that's 606 00:32:37,880 --> 00:32:39,280 Speaker 1: why I wanted to have you up here so that 607 00:32:39,280 --> 00:32:43,080 Speaker 1: people can understand that this is something that either like, 608 00:32:43,120 --> 00:32:45,040 Speaker 1: look at streaming because you know, I always talk about 609 00:32:45,120 --> 00:32:47,200 Speaker 1: music when it comes to the music business, but you know, 610 00:32:47,280 --> 00:32:50,880 Speaker 1: you went from having cassette tapes to having CDs. People 611 00:32:50,920 --> 00:32:53,760 Speaker 1: had an issue with CDs, then you went from then 612 00:32:53,800 --> 00:32:55,400 Speaker 1: you went from CDs. 613 00:32:54,920 --> 00:32:56,080 Speaker 3: You missed a tracks. 614 00:32:57,840 --> 00:32:59,120 Speaker 2: Tras, and then go that far back. 615 00:33:00,200 --> 00:33:04,440 Speaker 3: But I'll go back there for I got some tracks cats, Yeah, 616 00:33:04,720 --> 00:33:07,360 Speaker 3: Beta and VHS streaming services. 617 00:33:07,440 --> 00:33:09,760 Speaker 1: Right, People were really like, Okay, how are artists going 618 00:33:09,800 --> 00:33:12,080 Speaker 1: to get paid from that? How are labels going to 619 00:33:12,120 --> 00:33:14,000 Speaker 1: make sure that they make money? Well? Is this something 620 00:33:14,040 --> 00:33:17,240 Speaker 1: that And even before that, LimeWire was an issue for 621 00:33:17,280 --> 00:33:20,160 Speaker 1: people because it was illegal and people were not getting 622 00:33:20,160 --> 00:33:22,080 Speaker 1: any type of royalties from that, but they have to 623 00:33:22,080 --> 00:33:23,720 Speaker 1: figure out a way so that it would be fair 624 00:33:23,760 --> 00:33:26,200 Speaker 1: for people that they could actually benefit and make money 625 00:33:26,360 --> 00:33:28,960 Speaker 1: from streaming services. So now they have different streaming services, 626 00:33:28,960 --> 00:33:30,840 Speaker 1: the labels are on board, and. 627 00:33:31,120 --> 00:33:33,040 Speaker 2: Things have changed. You don't have to buy a CD anymore. 628 00:33:33,040 --> 00:33:34,200 Speaker 1: You don't have to go out the day that the 629 00:33:34,240 --> 00:33:36,840 Speaker 1: album's coming out to go and get it, you know. 630 00:33:37,000 --> 00:33:40,080 Speaker 1: So I think in that way because we also see 631 00:33:40,320 --> 00:33:41,640 Speaker 1: labels signing. 632 00:33:42,000 --> 00:33:43,360 Speaker 2: AI artists too. 633 00:33:43,960 --> 00:33:48,320 Speaker 1: So now these songs are being created through artificial intelligence, 634 00:33:48,320 --> 00:33:51,680 Speaker 1: and there's artists doing videos and making songs that are 635 00:33:51,720 --> 00:33:54,760 Speaker 1: strictly through AI, and it sounds really good, but it's 636 00:33:54,800 --> 00:33:57,640 Speaker 1: also making people nervous because then it's like, Okay, what 637 00:33:57,720 --> 00:34:00,360 Speaker 1: are we going to need? You know, is going to 638 00:34:00,360 --> 00:34:02,000 Speaker 1: take the place of artist who's making the money from 639 00:34:02,000 --> 00:34:02,920 Speaker 1: this and things like that. 640 00:34:04,520 --> 00:34:07,160 Speaker 3: I always share a story with you that I think 641 00:34:07,280 --> 00:34:12,320 Speaker 3: will be appropriate for for everyone listening, and it also 642 00:34:12,480 --> 00:34:14,719 Speaker 3: has a bit of a lesson and sort of a 643 00:34:14,760 --> 00:34:19,920 Speaker 3: moral behind it, and I'll kind of say at the outset, 644 00:34:20,800 --> 00:34:26,320 Speaker 3: don't let AI be something that takes over you. And 645 00:34:27,120 --> 00:34:31,200 Speaker 3: if that makes sense, use this time, this opportunity to 646 00:34:31,280 --> 00:34:33,560 Speaker 3: learn as much as you can about AI and how 647 00:34:33,600 --> 00:34:39,359 Speaker 3: you can help yourself and the work that you do 648 00:34:41,040 --> 00:34:41,720 Speaker 3: become better. 649 00:34:41,920 --> 00:34:43,000 Speaker 4: Okay, Right, so. 650 00:34:43,040 --> 00:34:46,000 Speaker 3: Let's start with that, right. But you talked about the 651 00:34:46,080 --> 00:34:50,319 Speaker 3: music and streaming and and you know, for certain things, 652 00:34:50,320 --> 00:34:54,359 Speaker 3: I'm a late adopter, right, I really enjoy That's why 653 00:34:54,400 --> 00:34:55,799 Speaker 3: I went back to eight tracks for you. Right. I 654 00:34:55,840 --> 00:34:58,160 Speaker 3: have vinyl, I have cassets, I have you know, all 655 00:34:58,200 --> 00:35:01,279 Speaker 3: those different things. And I knew there were streaming and 656 00:35:01,320 --> 00:35:03,480 Speaker 3: all this, obviously, But one of the things I used 657 00:35:03,480 --> 00:35:06,399 Speaker 3: to love doing was going into the music store when 658 00:35:06,440 --> 00:35:10,719 Speaker 3: they existed, looking at records, right, and Tower Records and 659 00:35:10,719 --> 00:35:14,000 Speaker 3: those kinds of you know, those iconic places that existed 660 00:35:14,080 --> 00:35:18,280 Speaker 3: right back then, Right, that was like the greatest pastime 661 00:35:19,160 --> 00:35:21,520 Speaker 3: when I moved to DC. When I first got to DC, 662 00:35:21,640 --> 00:35:23,239 Speaker 3: this was in two thousand and nine. I'm going to 663 00:35:23,280 --> 00:35:26,920 Speaker 3: tell you something, Angela. Don't judge me, you know, I 664 00:35:27,800 --> 00:35:29,520 Speaker 3: don't judge me. This is just the truth. This is 665 00:35:29,520 --> 00:35:32,280 Speaker 3: what happened. I got there and I wanted a song, 666 00:35:33,320 --> 00:35:36,120 Speaker 3: and I foolishly got it. And I say foolishly in 667 00:35:36,120 --> 00:35:38,000 Speaker 3: two thousand and I foolishly got into my car and 668 00:35:38,040 --> 00:35:41,120 Speaker 3: I drove to the mall because I was like, well, 669 00:35:41,160 --> 00:35:43,640 Speaker 3: obviously it was a CD. I could just go into 670 00:35:43,680 --> 00:35:47,480 Speaker 3: the store. And I walked the entire mall and I 671 00:35:47,520 --> 00:35:52,600 Speaker 3: looked at the directory. There was no music store, and 672 00:35:52,640 --> 00:35:54,600 Speaker 3: I was like, how is this possible? And now I'd 673 00:35:54,600 --> 00:35:56,920 Speaker 3: been a while since I actually went to go buy music, 674 00:35:57,120 --> 00:35:59,799 Speaker 3: right because I had a pretty good collection. I hate 675 00:35:59,800 --> 00:36:03,040 Speaker 3: trying because I could listen to so I'm good, right, right, 676 00:36:03,360 --> 00:36:08,120 Speaker 3: But there was this one song that I wanted. Yeah, 677 00:36:08,880 --> 00:36:11,319 Speaker 3: I'm not going to say, but I just I just 678 00:36:11,400 --> 00:36:14,400 Speaker 3: I couldn't find it. And that's when it dawned on 679 00:36:14,480 --> 00:36:21,400 Speaker 3: me that what I had not fully embraced was a 680 00:36:21,520 --> 00:36:26,160 Speaker 3: change had happened, an evolution had happened, something had occurred, 681 00:36:26,480 --> 00:36:29,719 Speaker 3: and I didn't get fully on board. Now quickly, I 682 00:36:29,760 --> 00:36:34,040 Speaker 3: got my iTunes and I figured it out. Excuse me, 683 00:36:34,040 --> 00:36:35,799 Speaker 3: and I figured out and I knew how to get it, 684 00:36:35,840 --> 00:36:39,879 Speaker 3: and I was like, okay, cool, Now it's really easy. 685 00:36:39,920 --> 00:36:41,680 Speaker 3: I can look up any song I want and I 686 00:36:41,719 --> 00:36:46,240 Speaker 3: can download it. But that experience kind of was reinforcing 687 00:36:46,320 --> 00:36:49,080 Speaker 3: again what I'm trying to, you know, sort of convey 688 00:36:49,120 --> 00:36:52,719 Speaker 3: to everyone, which is, don't be the one that gets 689 00:36:52,760 --> 00:36:54,759 Speaker 3: into the car in two thousand and nine and drive 690 00:36:54,880 --> 00:36:56,920 Speaker 3: to the mall to try and find get right or 691 00:36:56,920 --> 00:37:01,320 Speaker 3: get left your CD like, you know, I should have 692 00:37:01,400 --> 00:37:03,279 Speaker 3: been learning about it. I should have you know, I 693 00:37:03,280 --> 00:37:05,560 Speaker 3: should have talked to you know, someone else that knew 694 00:37:05,560 --> 00:37:08,879 Speaker 3: how to download music back then or figured it out, 695 00:37:08,960 --> 00:37:12,239 Speaker 3: you know, And I would have been the beneficiary, right 696 00:37:12,239 --> 00:37:13,920 Speaker 3: because I wouldn't have wasted my time when I got 697 00:37:13,960 --> 00:37:17,239 Speaker 3: to the mall. I wouldn't have had this rude awakening. 698 00:37:17,800 --> 00:37:20,120 Speaker 1: Let me ask you this, doctor Hassantet, because I know 699 00:37:20,160 --> 00:37:22,560 Speaker 1: people right now are listening and they're going to be uh, 700 00:37:22,800 --> 00:37:24,759 Speaker 1: they're going to be inspired to be like okay, well 701 00:37:24,840 --> 00:37:26,800 Speaker 1: let me go, like where do I start? You know, 702 00:37:26,960 --> 00:37:29,680 Speaker 1: how can this be beneficial for me in my everyday life? 703 00:37:30,239 --> 00:37:32,359 Speaker 1: So what would you tell a person who's listening right 704 00:37:32,400 --> 00:37:35,480 Speaker 1: now that's like, okay, I don't know anything about artificial intelligence, 705 00:37:35,520 --> 00:37:37,399 Speaker 1: but now I'm inspired and I want to make sure 706 00:37:37,440 --> 00:37:40,200 Speaker 1: that I'm not driving to the mall looking for a 707 00:37:40,280 --> 00:37:44,360 Speaker 1: CD single. So what would you say are some places 708 00:37:44,400 --> 00:37:46,120 Speaker 1: that are good to get started and what are some 709 00:37:46,239 --> 00:37:48,800 Speaker 1: things in everyday life of the average person that this 710 00:37:48,960 --> 00:37:50,040 Speaker 1: can benefit them. 711 00:37:50,200 --> 00:37:52,160 Speaker 3: Yeah, that's a great question. So first of all, I 712 00:37:52,200 --> 00:37:54,520 Speaker 3: would like say selfish plug, I would say read my 713 00:37:54,600 --> 00:37:58,920 Speaker 3: book Art of Human Care for with Artificial Intelligence is 714 00:37:59,040 --> 00:38:05,080 Speaker 3: purposely written in a way that really simplifies the science 715 00:38:05,120 --> 00:38:08,839 Speaker 3: of artificial intelligence to the sort of place that we're 716 00:38:08,840 --> 00:38:11,800 Speaker 3: at right now this point in time. It is definitely 717 00:38:12,600 --> 00:38:15,719 Speaker 3: UH in the context of healthcare, but that has wide 718 00:38:15,719 --> 00:38:17,759 Speaker 3: applicability to any field that you're in. 719 00:38:18,120 --> 00:38:20,760 Speaker 2: Human partnerships determine AI success. 720 00:38:21,560 --> 00:38:23,680 Speaker 3: And UH, and I think that's a that's that's a 721 00:38:23,840 --> 00:38:26,720 Speaker 3: that's a place where I think in a very brief 722 00:38:27,280 --> 00:38:30,279 Speaker 3: and and very comprehensive way you can get a good 723 00:38:30,640 --> 00:38:32,759 Speaker 3: glimpse of it, particularly if you're interested in healthcare and 724 00:38:32,840 --> 00:38:38,279 Speaker 3: how it may impact AI. Beyond that, the resources are many, right, 725 00:38:38,680 --> 00:38:43,960 Speaker 3: and I would just say that when you do look 726 00:38:44,160 --> 00:38:48,440 Speaker 3: for information anywhere, just do your best to verify the 727 00:38:48,600 --> 00:38:51,520 Speaker 3: veracity and the truth of what that is. And so 728 00:38:51,840 --> 00:38:54,400 Speaker 3: I and and and for that, I would say for 729 00:38:54,680 --> 00:38:56,360 Speaker 3: for those that are sort of like I want to 730 00:38:56,480 --> 00:39:00,920 Speaker 3: get like a good AI one oh one, like courses 731 00:39:00,960 --> 00:39:03,040 Speaker 3: online that you can go to a test street when 732 00:39:03,080 --> 00:39:05,600 Speaker 3: there from institutions like Crocera if they were like you know, 733 00:39:05,680 --> 00:39:09,560 Speaker 3: platforms like that. A lot of the universities have free 734 00:39:09,640 --> 00:39:13,040 Speaker 3: courses go to one of those and they have brilliant professors. 735 00:39:13,080 --> 00:39:15,320 Speaker 3: I've been the beneficiary of those. I mean, before I 736 00:39:15,400 --> 00:39:20,000 Speaker 3: got this opportunity to do this work, I had I 737 00:39:20,080 --> 00:39:22,759 Speaker 3: took classes at Stanford, I took classes in MIT, I 738 00:39:22,800 --> 00:39:25,200 Speaker 3: took all these different places. You know, I learned. I 739 00:39:26,080 --> 00:39:30,160 Speaker 3: learned from experts, but I also had an opportunity to 740 00:39:30,239 --> 00:39:33,120 Speaker 3: get this information and the process it, internalize it for 741 00:39:33,239 --> 00:39:36,200 Speaker 3: myself and then figure out I figure out a way 742 00:39:36,239 --> 00:39:39,239 Speaker 3: of how I can apply it in my in my worksplace, 743 00:39:39,800 --> 00:39:41,880 Speaker 3: and then that led to the opportunity to sort of 744 00:39:41,960 --> 00:39:44,560 Speaker 3: lead this division in that in that in that organization. 745 00:39:44,680 --> 00:39:46,360 Speaker 1: I even think about real estate because I'm in the 746 00:39:46,360 --> 00:39:47,840 Speaker 1: process of getting my real estate license. 747 00:39:48,000 --> 00:39:49,719 Speaker 3: Congrat but thank you, But even just. 748 00:39:49,719 --> 00:39:51,920 Speaker 1: Thinking about that, and you know, when you have to 749 00:39:52,200 --> 00:39:54,680 Speaker 1: pull coms from an area, how AI can be helpful 750 00:39:54,800 --> 00:39:59,239 Speaker 1: with that and also cut back on the amount of 751 00:39:59,280 --> 00:40:01,080 Speaker 1: work that you have to do as far as administrative, 752 00:40:01,120 --> 00:40:03,200 Speaker 1: so you can be out there doing what it is 753 00:40:03,239 --> 00:40:05,000 Speaker 1: that you need to do, because sometimes things are just 754 00:40:05,120 --> 00:40:06,920 Speaker 1: tedious work, you know. 755 00:40:07,080 --> 00:40:09,160 Speaker 2: And I think AI can be really helpful. 756 00:40:09,440 --> 00:40:12,120 Speaker 1: And in healthcare too, like you said, with administrative things 757 00:40:12,520 --> 00:40:14,600 Speaker 1: for our prevention, that's something that comes up a lot 758 00:40:14,719 --> 00:40:17,080 Speaker 1: as far as healthcare and AI because you know, I'm. 759 00:40:16,880 --> 00:40:19,279 Speaker 3: Glad it's weird in there, because I'll get alerts. Did 760 00:40:19,360 --> 00:40:21,880 Speaker 3: you try there's no ideentn't And it turned out this 761 00:40:22,080 --> 00:40:24,440 Speaker 3: happened just a few weeks ago. Someone had stole my 762 00:40:24,880 --> 00:40:27,759 Speaker 3: son's credit card and I got an alert because of 763 00:40:27,920 --> 00:40:30,840 Speaker 3: an algorithm that said, this doesn't make sense, this is 764 00:40:30,960 --> 00:40:33,719 Speaker 3: not you know, and they called me and I asked 765 00:40:33,760 --> 00:40:36,759 Speaker 3: my son, where are you. He's like, I'm out doing 766 00:40:36,800 --> 00:40:40,080 Speaker 3: whatever you're wild with, you know, okay, And so yeah, 767 00:40:40,200 --> 00:40:42,520 Speaker 3: so there's a there's a there's a place where I'm 768 00:40:42,520 --> 00:40:44,719 Speaker 3: sure people were like, oh, this is a good thing, right, 769 00:40:44,840 --> 00:40:48,560 Speaker 3: because that actually helped prevent further loss and further damage. 770 00:40:48,600 --> 00:40:51,759 Speaker 3: So it is interesting in healthcare just because you brought 771 00:40:51,840 --> 00:40:56,000 Speaker 3: up the banking thing. Understandably, in healthcare, some of our 772 00:40:56,040 --> 00:41:00,920 Speaker 3: advances are a little bit behind where they are another industry. Yeah, right, 773 00:41:01,520 --> 00:41:04,840 Speaker 3: But that that that makes sense because you know, the 774 00:41:04,880 --> 00:41:07,200 Speaker 3: stakes are high in healthcare because if you're you know, 775 00:41:07,320 --> 00:41:10,160 Speaker 3: you're not doing something right or or or things are 776 00:41:10,200 --> 00:41:13,320 Speaker 3: not quite what they should be, people can get harmed 777 00:41:13,360 --> 00:41:19,799 Speaker 3: and worse. So that makes sense. But clearly, going back 778 00:41:19,840 --> 00:41:22,919 Speaker 3: again to sort of the central premise. AI is going 779 00:41:23,000 --> 00:41:26,680 Speaker 3: to impact and will impact everything except the things that 780 00:41:26,800 --> 00:41:30,400 Speaker 3: matter most. The rest is up to us. For your listeners. 781 00:41:30,440 --> 00:41:33,920 Speaker 3: For anybody that's listening here, I would encourage them to 782 00:41:34,400 --> 00:41:39,080 Speaker 3: really seek out sources and get educated and informed. And 783 00:41:39,200 --> 00:41:44,040 Speaker 3: I would imagine anybody listening to this podcast right now 784 00:41:44,800 --> 00:41:47,720 Speaker 3: is already making that step right there. They are people 785 00:41:47,840 --> 00:41:50,560 Speaker 3: that are looking for information. There's a lot of people 786 00:41:50,560 --> 00:41:53,920 Speaker 3: who don't look for information, and you know, that's going 787 00:41:53,960 --> 00:41:54,440 Speaker 3: to be a problem. 788 00:41:55,040 --> 00:41:56,800 Speaker 2: That's to your detriment if you're not looking for information. 789 00:41:56,880 --> 00:41:58,560 Speaker 3: I think that's going to be a problem. And as 790 00:41:58,600 --> 00:42:00,880 Speaker 3: you look for that information, just try and you know, 791 00:42:01,320 --> 00:42:04,600 Speaker 3: check the veracity of the information, because that is also 792 00:42:04,760 --> 00:42:07,600 Speaker 3: something that is going to be a lot more common, 793 00:42:07,800 --> 00:42:12,400 Speaker 3: you know, misinformation. Misinformation, Yeah, yeah, but I think you know, 794 00:42:12,520 --> 00:42:15,239 Speaker 3: there's there's lots of ways to check the veracity and 795 00:42:15,440 --> 00:42:17,759 Speaker 3: just go to the sources that you trust, you know, 796 00:42:17,800 --> 00:42:20,399 Speaker 3: and I think a lot of people have a good 797 00:42:20,520 --> 00:42:22,040 Speaker 3: concept of a trusted source. 798 00:42:22,280 --> 00:42:24,560 Speaker 2: Well, I trust anybody whose last name rhymes with meta. 799 00:42:25,239 --> 00:42:30,840 Speaker 1: Okay, that is doctor Hassan A Teta the Art of 800 00:42:30,920 --> 00:42:33,360 Speaker 1: Human Care with AI with artificial intelligence. 801 00:42:33,400 --> 00:42:34,520 Speaker 2: So make sure you pick this up. 802 00:42:35,000 --> 00:42:36,960 Speaker 1: And I do appreciate you so much for coming through 803 00:42:37,040 --> 00:42:40,080 Speaker 1: because there's so many questions that I have, so hopefully 804 00:42:40,719 --> 00:42:42,040 Speaker 1: us being fellow Brooklyn. 805 00:42:41,800 --> 00:42:44,120 Speaker 2: Nights, Yes, you know, if we ever have anything that 806 00:42:44,200 --> 00:42:44,600 Speaker 2: we need. 807 00:42:44,560 --> 00:42:48,239 Speaker 1: To verify or we need an expert to at least 808 00:42:48,320 --> 00:42:50,320 Speaker 1: give us a drop, us a word. You know, I 809 00:42:50,440 --> 00:42:52,800 Speaker 1: appreciate that because I never want to act like I 810 00:42:52,960 --> 00:42:55,719 Speaker 1: know certain things that I am not well versed in. 811 00:42:55,880 --> 00:42:57,560 Speaker 1: And clearly you are the source for this. 812 00:42:57,800 --> 00:42:58,360 Speaker 3: Oh, thank you. 813 00:42:59,080 --> 00:43:01,479 Speaker 1: All right, So, doctor Teta, And how can people also 814 00:43:01,560 --> 00:43:03,440 Speaker 1: find you if they want to reach out or follow you? 815 00:43:03,600 --> 00:43:05,400 Speaker 3: Yeah? The best place to see all the kind of 816 00:43:05,440 --> 00:43:08,960 Speaker 3: activities I'm doing right now at Teta dot com. That's 817 00:43:09,040 --> 00:43:12,400 Speaker 3: the website and that has our various projects and suite 818 00:43:12,440 --> 00:43:15,480 Speaker 3: of businesses that we have, and then the Art of 819 00:43:15,560 --> 00:43:18,879 Speaker 3: Human Cares where the books are available. This is one 820 00:43:18,920 --> 00:43:20,400 Speaker 3: of a few of the ones that I have in 821 00:43:20,480 --> 00:43:25,800 Speaker 3: the series. And yeah, I'm all about inspiring and serving 822 00:43:25,880 --> 00:43:30,200 Speaker 3: and healing and teaching and creating the greatest good for 823 00:43:30,280 --> 00:43:32,520 Speaker 3: humankinds empathy and empathy we. 824 00:43:32,600 --> 00:43:34,719 Speaker 1: Discussed all right, And just so you guys know, he's 825 00:43:34,719 --> 00:43:37,040 Speaker 1: the founder of Human Care Technologies, Inc. And also an 826 00:43:37,080 --> 00:43:40,040 Speaker 1: official member of Forbes Technology Council, and we are blessed 827 00:43:40,040 --> 00:43:41,040 Speaker 1: and honored to have you up here. 828 00:43:41,239 --> 00:43:41,960 Speaker 3: Thank you so much. 829 00:43:42,000 --> 00:43:43,640 Speaker 2: All right, it's way up at Angela yee