1 00:00:00,040 --> 00:00:03,600 Speaker 1: An illegal alien from Guatemala charged with raping a child 2 00:00:03,680 --> 00:00:07,480 Speaker 1: in Massachusetts. An MS thirteen gang member from Al Salvador 3 00:00:07,720 --> 00:00:11,840 Speaker 1: accused of murdering a Texas man of Venezuelan charged with 4 00:00:11,920 --> 00:00:15,840 Speaker 1: filming and selling child pornography in Michigan. These are just 5 00:00:15,920 --> 00:00:19,680 Speaker 1: some of the heinous migrant criminals caught because of President 6 00:00:19,720 --> 00:00:23,320 Speaker 1: Donald J. Trump's leadership. I'm Christy nom the United States 7 00:00:23,320 --> 00:00:28,120 Speaker 1: Secretary of Homeland Security. Under President Trump, attempted illegal border 8 00:00:28,160 --> 00:00:31,680 Speaker 1: crossings are at the lowest levels ever recorded, and over 9 00:00:31,760 --> 00:00:34,960 Speaker 1: one hundred thousand illegal aliens have been arrested. If you 10 00:00:35,040 --> 00:00:38,920 Speaker 1: are here illegally, your next you will be fined nearly 11 00:00:39,000 --> 00:00:43,000 Speaker 1: one thousand dollars a day, imprisoned, and deported. You will 12 00:00:43,040 --> 00:00:46,680 Speaker 1: never return. But if you register using our CBP home 13 00:00:46,720 --> 00:00:50,120 Speaker 1: app and leave now, you could be allowed to return legally. 14 00:00:50,479 --> 00:00:55,240 Speaker 1: Do what's right. Leave now. Under President Trump, America's laws, 15 00:00:55,400 --> 00:00:57,400 Speaker 1: border and families will. 16 00:00:57,200 --> 00:01:00,000 Speaker 2: Be protected sponsored by the United States Department of Homeland Security. 17 00:01:00,200 --> 00:01:01,720 Speaker 2: So let's talk about this well. 18 00:01:01,720 --> 00:01:03,040 Speaker 3: First and foremost, thank you for joining us. 19 00:01:03,040 --> 00:01:04,040 Speaker 2: Appreciate it. Thank you. 20 00:01:04,560 --> 00:01:06,880 Speaker 3: So you were working at I think when we first 21 00:01:06,920 --> 00:01:10,400 Speaker 3: met you were working at in Video, right, Yeah, what 22 00:01:10,440 --> 00:01:11,399 Speaker 3: were you doing at in Video. 23 00:01:12,160 --> 00:01:17,440 Speaker 4: So my title was a Senior Principal scientist primarily, So 24 00:01:18,560 --> 00:01:22,280 Speaker 4: for joining in Video, I was the chief Analytics Officer 25 00:01:22,360 --> 00:01:24,840 Speaker 4: for the City of New York. So I really did 26 00:01:24,840 --> 00:01:26,760 Speaker 4: a lot of my data and AI work in the 27 00:01:26,880 --> 00:01:31,720 Speaker 4: urban space within city and environments, and so I got 28 00:01:31,760 --> 00:01:36,120 Speaker 4: to reach out from Jensen and said, hey, look, we 29 00:01:36,160 --> 00:01:38,959 Speaker 4: want to bring your skill sets into in Video. So 30 00:01:39,080 --> 00:01:42,200 Speaker 4: a lot of my focus at in Video was on 31 00:01:42,240 --> 00:01:45,520 Speaker 4: how we use AI and super compute in cities to 32 00:01:45,600 --> 00:01:49,840 Speaker 4: solve a lot of the complex, intractable challenges in cities 33 00:01:50,920 --> 00:01:52,840 Speaker 4: in video. Now, if you want to think about from 34 00:01:52,880 --> 00:01:57,480 Speaker 4: a product perspective, there's two products at in Video that 35 00:01:57,640 --> 00:02:02,040 Speaker 4: primarily functioned with and so you have the video Supercompute, 36 00:02:02,040 --> 00:02:06,520 Speaker 4: which they referred to as superpods as the base infrastructure. 37 00:02:06,520 --> 00:02:09,640 Speaker 4: But then there was two products was Rapids, which was 38 00:02:09,680 --> 00:02:13,680 Speaker 4: like an advanced data science platform and AI platform, and 39 00:02:13,680 --> 00:02:16,799 Speaker 4: in the other is more well known, which is Omniverse, 40 00:02:16,840 --> 00:02:20,680 Speaker 4: which is their digital twin technology where they do physics 41 00:02:20,919 --> 00:02:27,400 Speaker 4: based simulations and three D viewing of anything in the world. 42 00:02:28,280 --> 00:02:32,040 Speaker 4: So I primarily functioned in that space, work with cities globally, 43 00:02:33,040 --> 00:02:37,679 Speaker 4: work with urban environments, globally to implement AI and supercompute. 44 00:02:37,880 --> 00:02:38,720 Speaker 5: All right, so I met. 45 00:02:38,880 --> 00:02:41,840 Speaker 6: I know you started at Motorola and then you were 46 00:02:41,919 --> 00:02:44,040 Speaker 6: at Video, but you're not there anymore. And one of 47 00:02:44,040 --> 00:02:47,600 Speaker 6: the conversations we had was about the correlations between college 48 00:02:47,680 --> 00:02:50,400 Speaker 6: athletics in the world of tech and the sense of 49 00:02:50,440 --> 00:02:53,120 Speaker 6: when you're really good at something, people are always trying 50 00:02:53,120 --> 00:02:56,160 Speaker 6: to find you as the next talent. Can you explain 51 00:02:56,200 --> 00:02:58,920 Speaker 6: that to the audience of how the tech world has 52 00:02:58,960 --> 00:03:00,880 Speaker 6: a correlation to college sports. 53 00:03:01,400 --> 00:03:04,080 Speaker 4: Yeah, I think there's two You know, that's such a 54 00:03:04,080 --> 00:03:05,840 Speaker 4: great question. I think there's two ways you want to 55 00:03:05,840 --> 00:03:08,919 Speaker 4: look at that. First one is in policon Valley, and 56 00:03:09,080 --> 00:03:12,080 Speaker 4: I spent time not only within Video but at a 57 00:03:12,120 --> 00:03:15,960 Speaker 4: startup engaging in and around Silicon Valley and Silicon Valley, 58 00:03:16,480 --> 00:03:18,919 Speaker 4: they don't if you're at a company, if you're a 59 00:03:18,960 --> 00:03:25,920 Speaker 4: tech genius, giant, even halfway decent at Silicon Valley, if 60 00:03:25,960 --> 00:03:28,800 Speaker 4: you stay at a company more than two years, you 61 00:03:29,000 --> 00:03:32,680 Speaker 4: basically are seen as somebody who's garbage like pass it 62 00:03:33,320 --> 00:03:37,040 Speaker 4: because they're constantly looking for the best of the best, 63 00:03:37,480 --> 00:03:39,600 Speaker 4: and they will do anything to pull you from one 64 00:03:39,640 --> 00:03:42,280 Speaker 4: company to another. You've seen it in biopicks, whether it's 65 00:03:42,320 --> 00:03:45,880 Speaker 4: the wee Work biopick or Uber's biopick. Success of a 66 00:03:45,920 --> 00:03:48,840 Speaker 4: company is taking the best talent from other companies and 67 00:03:48,880 --> 00:03:51,400 Speaker 4: bringing it to your companies. And oftentimes it causes drama 68 00:03:52,680 --> 00:03:55,800 Speaker 4: because you know, they got to do backhanded deals and 69 00:03:55,800 --> 00:03:57,600 Speaker 4: so on and so forth. But they're always looking for 70 00:03:57,640 --> 00:04:00,920 Speaker 4: the top company, so you're always looking to be the 71 00:04:00,960 --> 00:04:03,800 Speaker 4: best and the other way I put it in so 72 00:04:03,840 --> 00:04:06,240 Speaker 4: I spent a couple of years. I was faculty at 73 00:04:07,680 --> 00:04:11,160 Speaker 4: Johns Hopkins research scientists at Johns Hopkins for a while, 74 00:04:11,360 --> 00:04:14,520 Speaker 4: and I spent two years teaching it in Hong Kong 75 00:04:14,520 --> 00:04:17,880 Speaker 4: and Hong Kong University Science and Technology. And I used 76 00:04:17,920 --> 00:04:20,000 Speaker 4: to when I was faculty there, I used to go 77 00:04:20,080 --> 00:04:23,320 Speaker 4: around and go to a lot of the tech companies 78 00:04:23,680 --> 00:04:26,160 Speaker 4: and one of the CEOs of a tech company was like, yo, 79 00:04:26,839 --> 00:04:29,520 Speaker 4: you know, we recruit some of the top scientists from 80 00:04:29,520 --> 00:04:33,120 Speaker 4: around the world to come and work for our company. 81 00:04:33,120 --> 00:04:35,599 Speaker 4: But what we do is I said, well, how difficult 82 00:04:35,680 --> 00:04:37,440 Speaker 4: is that. He said, well, it's not that difficult because 83 00:04:37,480 --> 00:04:40,640 Speaker 4: they're all seventh and eighth graders. What they do is 84 00:04:40,680 --> 00:04:42,840 Speaker 4: they actually begin scouting you the same way you think 85 00:04:42,880 --> 00:04:46,600 Speaker 4: about AAU it is the same way they think about scientists. 86 00:04:46,640 --> 00:04:48,920 Speaker 4: They start at seventh and eighth grade and they start 87 00:04:48,960 --> 00:04:52,080 Speaker 4: grooming you to come to China. And what they do 88 00:04:52,200 --> 00:04:56,240 Speaker 4: is they put you into their universities and then the 89 00:04:56,279 --> 00:04:59,080 Speaker 4: pipeline is right into their companies. And his thing is like, look, 90 00:05:00,040 --> 00:05:01,880 Speaker 4: bring the best of the best over, and we start 91 00:05:01,880 --> 00:05:04,360 Speaker 4: looking at him, and we go to science fairs at seventh, 92 00:05:04,680 --> 00:05:07,560 Speaker 4: seventh and eighth grade and we bring them over. If 93 00:05:07,600 --> 00:05:11,120 Speaker 4: they don't come to our company, they still are what's 94 00:05:11,160 --> 00:05:16,320 Speaker 4: referred to as friendly alumni to the universities in China. 95 00:05:16,680 --> 00:05:18,479 Speaker 4: And so he's like, it's a win win for China 96 00:05:18,600 --> 00:05:22,120 Speaker 4: in general in terms of bringing the intellectual capital over. 97 00:05:22,440 --> 00:05:27,839 Speaker 4: But they treat it. They treat science and scientists the 98 00:05:27,960 --> 00:05:31,000 Speaker 4: same way we treat sports players. And I got one 99 00:05:31,000 --> 00:05:33,680 Speaker 4: more anecdote. So when I was at Hong Kong. So 100 00:05:33,720 --> 00:05:35,600 Speaker 4: I was at Hong Kong University of Science and Technology, 101 00:05:35,600 --> 00:05:37,760 Speaker 4: one of the most beautiful college campuses in the world, 102 00:05:38,520 --> 00:05:42,760 Speaker 4: and they have this dope soccer field. It almost looks 103 00:05:42,800 --> 00:05:45,200 Speaker 4: like it's sliding off into the water. 104 00:05:45,880 --> 00:05:47,960 Speaker 5: And we used to go and play soccer all. 105 00:05:47,880 --> 00:05:51,360 Speaker 4: The time, basketball, football or whatever, but mostly mostly football. 106 00:05:51,440 --> 00:05:55,320 Speaker 4: Soccer and there was one dude that was nice, like 107 00:05:55,440 --> 00:06:00,039 Speaker 4: I've rarely seen football players like that. He was nice, nice, 108 00:06:00,160 --> 00:06:01,880 Speaker 4: and he was in my class, and I went up 109 00:06:01,880 --> 00:06:04,520 Speaker 4: and spost like, Yo, do you play for the national 110 00:06:06,080 --> 00:06:09,240 Speaker 4: soccer team? He was like football team? He was like nah, 111 00:06:09,320 --> 00:06:13,239 Speaker 4: because in my family and in my culture, it's actually 112 00:06:13,360 --> 00:06:16,240 Speaker 4: more honorable to be a computer scientist. 113 00:06:16,160 --> 00:06:17,599 Speaker 5: Than the play sports. 114 00:06:17,920 --> 00:06:21,720 Speaker 4: So he was like, I'm actually winning more that I'm 115 00:06:21,760 --> 00:06:24,920 Speaker 4: a computer science major at college than I would if 116 00:06:24,920 --> 00:06:26,160 Speaker 4: I played sports. 117 00:06:27,640 --> 00:06:30,000 Speaker 6: Ernests, What's up? You ever walk into a small business 118 00:06:30,000 --> 00:06:33,120 Speaker 6: and everything just works like, the checkout is fast, the 119 00:06:33,200 --> 00:06:36,880 Speaker 6: re seats are digital, tipping is a breeze, and you're 120 00:06:36,920 --> 00:06:39,640 Speaker 6: out the door before the line even builds. Odds are 121 00:06:40,120 --> 00:06:43,680 Speaker 6: they're using Square? We love supporting businesses that run on 122 00:06:43,720 --> 00:06:46,440 Speaker 6: Square because it just feels seamless. Whether it's a local 123 00:06:46,480 --> 00:06:49,360 Speaker 6: coffee shop, a vendor at a pop up market, or 124 00:06:49,400 --> 00:06:52,600 Speaker 6: even one of our merch partners. Square makes it easy 125 00:06:52,640 --> 00:06:55,840 Speaker 6: for them to take payments, manage inventory, and run their 126 00:06:55,880 --> 00:06:59,840 Speaker 6: business with confidence, all from one simple system. If you're 127 00:06:59,839 --> 00:07:03,200 Speaker 6: a business owner or even just thinking about launching something soon, 128 00:07:03,520 --> 00:07:06,120 Speaker 6: Square is hands down one of the best tools out 129 00:07:06,120 --> 00:07:09,400 Speaker 6: there to help you start, run and grow. It's not 130 00:07:09,520 --> 00:07:12,680 Speaker 6: just about payments. It's about giving you time back so 131 00:07:12,760 --> 00:07:15,400 Speaker 6: you can focus on what matters most Ready. To see 132 00:07:15,440 --> 00:07:19,200 Speaker 6: how Square can transform your business, visit Square dot com 133 00:07:19,240 --> 00:07:24,080 Speaker 6: backslash go backslash eyl to learn more that Square dot 134 00:07:24,120 --> 00:07:29,080 Speaker 6: Com backslash, go backslash eyl. Don't wait, don't hesitate. Let's 135 00:07:29,080 --> 00:07:31,560 Speaker 6: Square handle the back end so you can keep pushing 136 00:07:31,600 --> 00:07:37,480 Speaker 6: your vision forward. This episode is brought to you by 137 00:07:37,560 --> 00:07:40,320 Speaker 6: P and C Bank. A lot of people think podcasts 138 00:07:40,320 --> 00:07:43,600 Speaker 6: about work are boring, and sure, they definitely can be, 139 00:07:44,080 --> 00:07:47,320 Speaker 6: but understanding a professional's routine shows us how they achieve 140 00:07:47,400 --> 00:07:51,600 Speaker 6: their success little by little, day after day. It's like 141 00:07:51,680 --> 00:07:54,480 Speaker 6: banking with P and C Bank. It might seem boring 142 00:07:54,520 --> 00:07:57,400 Speaker 6: to save, plan and make calculated decisions with your bank, 143 00:07:57,800 --> 00:08:00,240 Speaker 6: but keeping your money boring is what helps you live 144 00:08:00,400 --> 00:08:04,400 Speaker 6: or more happily fulfilled life. P and C Bank Brilliantly 145 00:08:04,480 --> 00:08:09,040 Speaker 6: Boring since eighteen sixty five. Brilliantly Boring since eighteen sixty 146 00:08:09,040 --> 00:08:12,320 Speaker 6: five is a service mark of the PNC Financial Service Group, Inc. 147 00:08:12,800 --> 00:08:16,200 Speaker 6: P and C Bank National Association Member FDIC. 148 00:08:17,960 --> 00:08:21,640 Speaker 1: An illegal alien from Guatemala charged with raping a child 149 00:08:21,640 --> 00:08:25,440 Speaker 1: in Massachusetts. An MS thirteen gang member from Al Salvador 150 00:08:25,720 --> 00:08:29,840 Speaker 1: accused of murdering a Texas man of Venezuelan charged with 151 00:08:29,920 --> 00:08:33,800 Speaker 1: filming and selling child pornography in Michigan. These are just 152 00:08:33,880 --> 00:08:37,640 Speaker 1: some of the heinous migrant criminals caught because of President 153 00:08:37,679 --> 00:08:41,280 Speaker 1: Donald J. Trump's leadership. I'm Christy nom the United States 154 00:08:41,280 --> 00:08:46,080 Speaker 1: Secretary of Homeland Security. Under President Trump, attempted illegal border 155 00:08:46,120 --> 00:08:49,680 Speaker 1: crossings are at the lowest levels ever recorded, and over 156 00:08:49,720 --> 00:08:52,960 Speaker 1: one hundred thousand illegal aliens have been arrested. If you 157 00:08:53,000 --> 00:08:56,880 Speaker 1: are here illegally, your next you will be fined nearly 158 00:08:56,960 --> 00:09:01,000 Speaker 1: one thousand dollars a day, imprisoned, and deported. You will 159 00:09:01,040 --> 00:09:04,640 Speaker 1: never return. But if you register using our CBP home 160 00:09:04,720 --> 00:09:08,079 Speaker 1: app and leave now, you could be allowed to return legally. 161 00:09:08,440 --> 00:09:13,199 Speaker 1: Do what's right. Leave now. Under President Trump, America's laws, 162 00:09:13,360 --> 00:09:15,360 Speaker 1: border and families. 163 00:09:14,960 --> 00:09:17,360 Speaker 2: Will be protected sponsored by the United States Department of 164 00:09:17,400 --> 00:09:20,880 Speaker 2: Homeland Security in the national National Team, So there's a 165 00:09:20,880 --> 00:09:22,440 Speaker 2: lot of similarities. 166 00:09:21,800 --> 00:09:27,000 Speaker 7: There for those who are aspiring to go on your 167 00:09:27,000 --> 00:09:28,920 Speaker 7: path and video has been one of my favorite stocks 168 00:09:28,920 --> 00:09:30,720 Speaker 7: for the last few years, but it's really taken off 169 00:09:31,040 --> 00:09:34,000 Speaker 7: this past year. For those of us that are black 170 00:09:34,000 --> 00:09:36,760 Speaker 7: and Brown that want to make it into these tech 171 00:09:36,840 --> 00:09:37,600 Speaker 7: power players. 172 00:09:38,040 --> 00:09:39,920 Speaker 2: What would be your process. 173 00:09:39,559 --> 00:09:41,920 Speaker 7: If you have to start over now for how to 174 00:09:41,960 --> 00:09:45,080 Speaker 7: break into an inn Video and Apple, Microsoft Bezos Foundation. 175 00:09:45,760 --> 00:09:47,600 Speaker 7: What tips can you give us to help break into 176 00:09:47,640 --> 00:09:50,320 Speaker 7: those landscapes that we are not often seen in. 177 00:09:52,120 --> 00:09:52,360 Speaker 5: Man. 178 00:09:55,360 --> 00:09:57,440 Speaker 4: I don't know if my tips would be helpful, but 179 00:09:57,480 --> 00:09:59,440 Speaker 4: I can share some of what I think. 180 00:09:59,480 --> 00:10:02,920 Speaker 5: But that's such great question for me. 181 00:10:03,320 --> 00:10:06,120 Speaker 4: My focus was always on the math of it all, 182 00:10:06,400 --> 00:10:10,120 Speaker 4: the science of it all, oftentimes what I've seen, and 183 00:10:10,160 --> 00:10:13,319 Speaker 4: I went to Old HBCUs. I went to Lincoln University undergrad, 184 00:10:13,559 --> 00:10:17,120 Speaker 4: Morgan Howard University for my master's, and Morgan State for 185 00:10:17,200 --> 00:10:22,160 Speaker 4: my doctorate. And I stayed at Old HBCUs. But what 186 00:10:22,240 --> 00:10:25,959 Speaker 4: I saw was a lot of my friends and colleagues. 187 00:10:26,640 --> 00:10:30,440 Speaker 4: They would come in and within the first couple of 188 00:10:30,480 --> 00:10:35,160 Speaker 4: classes shift out of the computer science realm into like 189 00:10:35,360 --> 00:10:40,560 Speaker 4: business information systems or project management, those sort of non 190 00:10:41,160 --> 00:10:50,319 Speaker 4: math intensive computational mathematics intensive programs companies like Motorola. When 191 00:10:50,320 --> 00:10:52,079 Speaker 4: I was at Motorola, I was at Motorola Lab, so 192 00:10:52,080 --> 00:10:55,560 Speaker 4: I was a research scientist there. My primary research over 193 00:10:55,600 --> 00:10:58,640 Speaker 4: there was when Bluetooth was being invented, I was a 194 00:10:58,640 --> 00:11:02,800 Speaker 4: part of that team. I had free patents there. It 195 00:11:02,880 --> 00:11:05,880 Speaker 4: wasn't just me, it was a group of us, and 196 00:11:05,880 --> 00:11:09,040 Speaker 4: we all got our names on those patent disclosures. 197 00:11:09,280 --> 00:11:14,559 Speaker 5: But the reason why I was able to go to Motorola. 198 00:11:14,240 --> 00:11:16,559 Speaker 4: Labs, which is the research drama of Motorola and work 199 00:11:16,600 --> 00:11:19,000 Speaker 4: on the projects that I did was because I had 200 00:11:19,000 --> 00:11:23,120 Speaker 4: a math background and so staying strong in the math 201 00:11:23,160 --> 00:11:27,080 Speaker 4: background and I never went literally at the besos are 202 00:11:27,160 --> 00:11:31,280 Speaker 4: fun is the least technical that I've ever been in 203 00:11:31,280 --> 00:11:34,679 Speaker 4: my career. But the reason why when so I had 204 00:11:34,720 --> 00:11:38,000 Speaker 4: an hour long interviews, As Troy you mentioned, part of 205 00:11:38,040 --> 00:11:40,920 Speaker 4: my interview process was an hour long one on one 206 00:11:40,960 --> 00:11:43,720 Speaker 4: interview with Jensen, and one of the things I requested 207 00:11:43,760 --> 00:11:45,880 Speaker 4: in an interview was I didn't want to be a VP. 208 00:11:45,960 --> 00:11:47,840 Speaker 4: I didn't want to have the title VP. I wanted 209 00:11:47,840 --> 00:11:49,760 Speaker 4: to have a technical title because I wanted to remain 210 00:11:49,920 --> 00:11:54,079 Speaker 4: very technical and so senior Principal Scientists and in video 211 00:11:54,760 --> 00:11:59,320 Speaker 4: is the technical equivalent to a VP, and video, I 212 00:11:59,360 --> 00:12:01,760 Speaker 4: was like, I want to all equity better that will 213 00:12:01,800 --> 00:12:04,480 Speaker 4: be because but not not the title. But I was 214 00:12:04,520 --> 00:12:09,160 Speaker 4: deliberate in staying in the math and lane, and in 215 00:12:09,200 --> 00:12:11,679 Speaker 4: the math space. To this day, I still program I 216 00:12:12,120 --> 00:12:15,040 Speaker 4: do a lot of programming with my son and some 217 00:12:15,080 --> 00:12:18,400 Speaker 4: personal projects, but to this day I still focused on 218 00:12:18,480 --> 00:12:19,400 Speaker 4: computation works. 219 00:12:20,679 --> 00:12:24,160 Speaker 3: So all right, so let's get into this. So for 220 00:12:24,720 --> 00:12:28,560 Speaker 3: where are we headed with artificial intelligence? Where where is 221 00:12:28,600 --> 00:12:31,920 Speaker 3: this this headed? We talked about, you know, a variety 222 00:12:31,920 --> 00:12:33,680 Speaker 3: of different things in the past, but I want to 223 00:12:33,679 --> 00:12:35,680 Speaker 3: hear from your perspective to somebody that's doing it and 224 00:12:35,720 --> 00:12:38,760 Speaker 3: has done it for a while, where are we at 225 00:12:38,840 --> 00:12:41,280 Speaker 3: right now as far as like, is this the infancy stages? 226 00:12:41,360 --> 00:12:44,760 Speaker 3: And where where do you expect it to be in 227 00:12:44,800 --> 00:12:45,520 Speaker 3: ten years from now? 228 00:12:45,720 --> 00:12:47,439 Speaker 5: Yeah, yeah, that's a good question. 229 00:12:47,559 --> 00:12:52,640 Speaker 4: I think that's such an important question today. 230 00:12:52,600 --> 00:12:53,840 Speaker 5: Yeah, it's just this morning. 231 00:12:53,880 --> 00:12:57,319 Speaker 4: The White House just released its executive Order on AI 232 00:12:58,080 --> 00:13:03,360 Speaker 4: and some time I had I saw it prior to 233 00:13:03,480 --> 00:13:07,800 Speaker 4: but I spent some time digging through it this past weekend, 234 00:13:08,280 --> 00:13:14,520 Speaker 4: and you know, it's it's gonna challenge innovation as most 235 00:13:14,640 --> 00:13:20,280 Speaker 4: most regulation tends to do. So I think six months 236 00:13:20,320 --> 00:13:25,240 Speaker 4: ago to a year ago, the trajectory may be different 237 00:13:25,320 --> 00:13:28,040 Speaker 4: if a lot of these regulations that just came out 238 00:13:28,800 --> 00:13:30,360 Speaker 4: through this executive Order take hold. 239 00:13:30,559 --> 00:13:33,120 Speaker 5: But that aside, let's let's let's assume, which is the. 240 00:13:33,080 --> 00:13:36,679 Speaker 4: Case that federal regulations take a long time to even. 241 00:13:38,000 --> 00:13:41,680 Speaker 5: Uh uh have any impact or that. But let's answer 242 00:13:41,679 --> 00:13:42,720 Speaker 5: your question straight away. 243 00:13:44,679 --> 00:13:49,920 Speaker 4: I think if you look at the different domains that 244 00:13:50,040 --> 00:13:53,839 Speaker 4: are using AI, what you're gonna wind up seeing our 245 00:13:54,040 --> 00:13:59,040 Speaker 4: few domains where it's most impactful, and then you'll begin 246 00:13:59,120 --> 00:14:02,440 Speaker 4: to see some domains it's least impactful. And so domains 247 00:14:02,600 --> 00:14:05,640 Speaker 4: like health is where you're going to see AI really 248 00:14:05,679 --> 00:14:10,480 Speaker 4: take off. Regulations and everything aside. But because those regulations 249 00:14:10,520 --> 00:14:13,680 Speaker 4: from the health space have been built in, you'll see 250 00:14:14,440 --> 00:14:18,480 Speaker 4: it take off there. You'll see AI takeoff in the 251 00:14:18,520 --> 00:14:24,720 Speaker 4: defense space, aerospace, You'll see you'll see a lot of 252 00:14:24,760 --> 00:14:29,680 Speaker 4: AI in the personal engagement space that is sort of 253 00:14:29,720 --> 00:14:31,800 Speaker 4: building out this concept and you guys have talked about 254 00:14:31,840 --> 00:14:34,840 Speaker 4: it on your shows, like this concept of sort of 255 00:14:34,880 --> 00:14:38,880 Speaker 4: your own personal chief of staff, your own personal senior advisor, 256 00:14:39,280 --> 00:14:41,880 Speaker 4: your own personal scheduler. You're going to see a lot 257 00:14:41,880 --> 00:14:44,000 Speaker 4: of AI in that space. 258 00:14:44,280 --> 00:14:47,920 Speaker 1: An illegal alien from Guatemala charged with raping a child 259 00:14:47,960 --> 00:14:51,760 Speaker 1: in Massachusetts. An MS thirteen gang member from El Salvador 260 00:14:52,000 --> 00:14:56,120 Speaker 1: accused of murdering a Texas man of Venezuelan charged with 261 00:14:56,200 --> 00:15:00,520 Speaker 1: filming and selling child pornography in Michigan. These are sum 262 00:15:00,600 --> 00:15:04,440 Speaker 1: of the heinous migrant criminals caught because of President Donald J. 263 00:15:04,520 --> 00:15:08,280 Speaker 1: Trump's leadership. I'm Christy Noman, the United States Secretary of 264 00:15:08,320 --> 00:15:13,120 Speaker 1: Homeland Security. Under President Trump, attempted illegal border crossings are 265 00:15:13,160 --> 00:15:16,560 Speaker 1: at the lowest levels ever recorded, and over one hundred 266 00:15:16,600 --> 00:15:18,280 Speaker 1: thousand illegal aliens. 267 00:15:17,840 --> 00:15:18,680 Speaker 2: Have been arrested. 268 00:15:18,960 --> 00:15:22,520 Speaker 1: If you are here illegally, your next you will be 269 00:15:22,560 --> 00:15:26,440 Speaker 1: fined nearly one thousand dollars a day, imprisoned, and deported. 270 00:15:26,880 --> 00:15:30,120 Speaker 1: You will never return. But if you register using our 271 00:15:30,160 --> 00:15:33,360 Speaker 1: CBP home app and leave now, you could be allowed 272 00:15:33,400 --> 00:15:38,360 Speaker 1: to return legally. Do what's right. Leave now. Under President Trump, 273 00:15:38,520 --> 00:15:42,080 Speaker 1: America's laws, border and families will be protected. 274 00:15:42,200 --> 00:15:44,320 Speaker 2: Sponsored by the United States Department of Homeland Security.