1 00:00:01,280 --> 00:00:03,520 Speaker 1: This episode is brought to you by P and C Bank. 2 00:00:03,920 --> 00:00:06,680 Speaker 1: A lot of people think podcasts about work are boring, 3 00:00:06,880 --> 00:00:10,719 Speaker 1: and sure they definitely can be, but understanding of professionals 4 00:00:10,800 --> 00:00:14,080 Speaker 1: routine shows us how they achieve their success little by little, 5 00:00:14,440 --> 00:00:18,079 Speaker 1: day after day. It's like banking with P and C Bank. 6 00:00:18,560 --> 00:00:21,360 Speaker 1: It might seem boring to safe plan and make calculated 7 00:00:21,400 --> 00:00:24,479 Speaker 1: decisions with your bank, but keeping your money boring is 8 00:00:24,520 --> 00:00:27,840 Speaker 1: what helps you live or more happily fulfilled life. P 9 00:00:27,960 --> 00:00:32,800 Speaker 1: and C Bank Brilliantly Boring since eighteen sixty five. Brilliantly 10 00:00:32,840 --> 00:00:35,479 Speaker 1: Boring since eighteen sixty five is a service mark of 11 00:00:35,520 --> 00:00:38,760 Speaker 1: the PNC Financial Service Group, Inc. P and C Bank 12 00:00:39,159 --> 00:00:41,360 Speaker 1: National Association Member FDIC. 13 00:00:43,280 --> 00:00:47,640 Speaker 2: This is a very important segment. So can we bring 14 00:00:47,680 --> 00:00:48,279 Speaker 2: our guests up? 15 00:00:51,280 --> 00:00:57,360 Speaker 3: Hey, hey, how are you now? 16 00:00:58,480 --> 00:00:59,200 Speaker 4: We are good? 17 00:00:59,280 --> 00:01:00,000 Speaker 5: How you feel it? 18 00:01:01,320 --> 00:01:04,280 Speaker 6: I'm feeling great. I'm just a pleasure to be here 19 00:01:04,360 --> 00:01:07,600 Speaker 6: and so much fun to listen to you guys. This 20 00:01:07,680 --> 00:01:11,679 Speaker 6: is great. Not only is it good stuff, it's interesting 21 00:01:11,760 --> 00:01:13,600 Speaker 6: and fun. I love the way you present it and 22 00:01:14,080 --> 00:01:17,040 Speaker 6: I'm like, yes, okay, I'm right, I'm taking notes right here, 23 00:01:18,120 --> 00:01:18,840 Speaker 6: thank you. 24 00:01:18,640 --> 00:01:19,000 Speaker 7: Thank you. 25 00:01:19,080 --> 00:01:22,520 Speaker 2: So this is finally important. So okay, Lisa Blackwell. So, 26 00:01:22,760 --> 00:01:26,440 Speaker 2: all right, some backstory. If you following video, you know 27 00:01:26,520 --> 00:01:28,399 Speaker 2: that they every year they come out with a new 28 00:01:28,520 --> 00:01:32,640 Speaker 2: chip and they named the chip after a historical figure, 29 00:01:33,200 --> 00:01:35,480 Speaker 2: like one of the top scientists in the world, somebody 30 00:01:35,480 --> 00:01:38,200 Speaker 2: that has contributed to artificial intelligence, somebody that you know, 31 00:01:38,360 --> 00:01:43,400 Speaker 2: like a pioneer in the space. Last year chip was 32 00:01:43,480 --> 00:01:48,440 Speaker 2: named Blackwell. So Blackwell before Ruben is this year. Ruben 33 00:01:48,480 --> 00:01:51,200 Speaker 2: hasn't come out yet, So currently Blackwell is the most 34 00:01:51,240 --> 00:01:57,600 Speaker 2: advanced chip EVE ever created. Now, the interesting thing is 35 00:01:57,600 --> 00:02:03,840 Speaker 2: that Blackwell was named after David Blackwell. David Blackwell is 36 00:02:03,880 --> 00:02:10,240 Speaker 2: Lisa's grandfather. Correct, this is a very interesting story, and 37 00:02:10,280 --> 00:02:12,680 Speaker 2: we're gonna play a vision. We're gonna play a visual 38 00:02:13,080 --> 00:02:14,920 Speaker 2: to talk about it in that week. But before we 39 00:02:14,960 --> 00:02:17,280 Speaker 2: played a visual, can you just queue up? Can you 40 00:02:17,360 --> 00:02:18,680 Speaker 2: cute this up for us? 41 00:02:20,080 --> 00:02:24,720 Speaker 6: Sure? So you're about to see ten minutes of what 42 00:02:24,880 --> 00:02:28,640 Speaker 6: is a concept reel for the documentary that we're creating 43 00:02:28,919 --> 00:02:35,280 Speaker 6: about my grandfather's story. And it really has a little 44 00:02:35,280 --> 00:02:38,200 Speaker 6: bit of the sort of lynch pin that everything turned 45 00:02:38,240 --> 00:02:40,960 Speaker 6: around on, like when I start digging into the story 46 00:02:41,320 --> 00:02:43,600 Speaker 6: and what I discovered and you guys are getting ready 47 00:02:43,639 --> 00:02:47,640 Speaker 6: to see sort of what that looks like. And again 48 00:02:47,680 --> 00:02:50,600 Speaker 6: this is the concept reel for the documentary. 49 00:02:51,800 --> 00:02:53,120 Speaker 2: Let's play it? Shall We? 50 00:02:53,160 --> 00:02:53,359 Speaker 8: Thanks? 51 00:02:53,400 --> 00:02:54,120 Speaker 5: Do it? 52 00:02:54,240 --> 00:02:54,600 Speaker 3: Okay? 53 00:02:55,919 --> 00:03:00,120 Speaker 4: National Medal of Science too. David Blackwell, University of California, Berkeley. 54 00:03:00,560 --> 00:03:03,919 Speaker 5: I would like to introduce you to a very very 55 00:03:03,960 --> 00:03:07,760 Speaker 5: big GPU named after David Blackwell. 56 00:03:09,960 --> 00:03:15,480 Speaker 4: For fundamental contributions to probability theory, mathematical statistics, information theory, 57 00:03:15,760 --> 00:03:18,120 Speaker 4: mathematical logic, and Blackwell games. 58 00:03:18,919 --> 00:03:22,520 Speaker 8: David Blackwell was it a great amongst greats. He was 59 00:03:22,720 --> 00:03:26,240 Speaker 8: the great, great, great mathematician among great mathematicians. 60 00:03:26,520 --> 00:03:29,280 Speaker 9: Blackwell was a pioneer who was ahead of its time. 61 00:03:40,880 --> 00:03:44,400 Speaker 7: I thought, I knew my grandfather Eggs had done. My 62 00:03:44,560 --> 00:03:47,280 Speaker 7: team is at dusk in a five percent dividend man 63 00:03:47,360 --> 00:03:52,600 Speaker 7: all the way. Years later, Nvidia named their platform Blackwell. 64 00:03:53,320 --> 00:03:58,440 Speaker 7: That's when I started digging and discovered my grandfather. Professor Blackwell, 65 00:03:59,040 --> 00:04:02,600 Speaker 7: architect of the mathematics behind artificial intelligence. 66 00:04:15,320 --> 00:04:19,080 Speaker 9: A lot of his ideas really cause people to rethink 67 00:04:19,440 --> 00:04:23,400 Speaker 9: how do we interpret data? 68 00:04:23,960 --> 00:04:27,440 Speaker 8: There are many many standard tools that people use all 69 00:04:27,480 --> 00:04:30,719 Speaker 8: the time that are named after David. You know Blackwell 70 00:04:30,800 --> 00:04:33,240 Speaker 8: spaces and the raw Blackwell theorem. 71 00:04:33,279 --> 00:04:38,400 Speaker 9: Blackwell nowadays is known for what's called the Blackwell rawl theorem, 72 00:04:38,839 --> 00:04:42,760 Speaker 9: sort of like saying, if something happens frequently enough, you 73 00:04:42,760 --> 00:04:44,919 Speaker 9: should be able to kind of predict what's going to 74 00:04:44,960 --> 00:04:49,040 Speaker 9: happen next. However, there might be some bias in some 75 00:04:49,160 --> 00:04:53,800 Speaker 9: of that data. That's essentially what the Blackwell Rowl theorem 76 00:04:53,839 --> 00:04:55,640 Speaker 9: tells you is that there's a way you can kind 77 00:04:55,680 --> 00:04:59,200 Speaker 9: of estimate what should happen, but then minimize the bias 78 00:04:59,240 --> 00:05:01,520 Speaker 9: that you would have there in the data. And this 79 00:05:01,680 --> 00:05:06,680 Speaker 9: is an amazing approach to doing statistics. If black men 80 00:05:06,760 --> 00:05:09,400 Speaker 9: were alive today, he would certainly be the one to 81 00:05:09,520 --> 00:05:11,400 Speaker 9: raise up his hand and say, you know, look, there's 82 00:05:11,440 --> 00:05:14,120 Speaker 9: bias in AI, there's bias in the training data that 83 00:05:14,160 --> 00:05:17,159 Speaker 9: we're using, and we really need to think about minimizing 84 00:05:17,200 --> 00:05:19,600 Speaker 9: these as more and more people use these models. 85 00:05:22,720 --> 00:05:25,400 Speaker 10: I was very clear that I was going to be 86 00:05:25,440 --> 00:05:29,400 Speaker 10: a school teacher. My father had a friend who was 87 00:05:29,440 --> 00:05:33,279 Speaker 10: on the school board, and before I finished high school, 88 00:05:33,760 --> 00:05:36,039 Speaker 10: he already told my father that if I went to 89 00:05:36,120 --> 00:05:39,200 Speaker 10: college and got a degree, he would get me a 90 00:05:39,320 --> 00:05:42,400 Speaker 10: job teaching. And this was in the depression, and it 91 00:05:42,440 --> 00:05:46,720 Speaker 10: was a great relief to me and everybody else when 92 00:05:46,760 --> 00:05:49,599 Speaker 10: it was clear what my future was going to be. 93 00:05:50,520 --> 00:05:55,520 Speaker 10: After I got my bachelor's degree in three years, I 94 00:05:55,600 --> 00:05:59,200 Speaker 10: decided to spend my fourth year getting a master's degree. 95 00:06:00,000 --> 00:06:01,920 Speaker 10: But then at the end of my fourth year I 96 00:06:01,960 --> 00:06:05,039 Speaker 10: was encouraged to apply for a fellowship to go on 97 00:06:05,120 --> 00:06:07,799 Speaker 10: for a PhD. And I did, and then of course 98 00:06:07,839 --> 00:06:15,400 Speaker 10: my objective changed. There were perhaps twenty five awards, twenty 99 00:06:15,440 --> 00:06:21,400 Speaker 10: two of them required teaching assistant responsibilities. Three of them 100 00:06:21,400 --> 00:06:29,280 Speaker 10: were pure fellowships, and everybody applied for both things, and 101 00:06:29,320 --> 00:06:33,240 Speaker 10: the lucky three got the fellowships. And one of my 102 00:06:33,360 --> 00:06:37,680 Speaker 10: colleagues who also applied, told me that I was going 103 00:06:37,720 --> 00:06:42,040 Speaker 10: to get one of the fellowships, and I said, how 104 00:06:42,080 --> 00:06:45,200 Speaker 10: do you know? And he said, well, they're good enough 105 00:06:45,240 --> 00:06:48,200 Speaker 10: to be supported, and they're not going to put you 106 00:06:48,240 --> 00:06:49,680 Speaker 10: in front of a classroom. 107 00:06:52,120 --> 00:06:56,200 Speaker 9: He was right, they were not going to put you 108 00:06:56,240 --> 00:06:59,719 Speaker 9: in front of a classroom because because I'm black. 109 00:07:16,840 --> 00:07:22,000 Speaker 8: David, you know the I he he was s he 110 00:07:22,080 --> 00:07:24,960 Speaker 8: was a black mathematician. And there are no BA black 111 00:07:24,960 --> 00:07:27,560 Speaker 8: mathematicians that w would that it were, but I but 112 00:07:27,720 --> 00:07:30,600 Speaker 8: they they, they they are very few. Anyway, what was 113 00:07:30,640 --> 00:07:32,800 Speaker 8: it like to be David Blackwell? I never could get 114 00:07:32,840 --> 00:07:36,000 Speaker 8: him to talk about that, uh, anything about what his 115 00:07:36,160 --> 00:07:41,520 Speaker 8: experiences were in Berkeley. He had the problems of not 116 00:07:41,600 --> 00:07:45,560 Speaker 8: being hired in the nineteen fifties because the wife of 117 00:07:45,600 --> 00:07:48,280 Speaker 8: the chairman of the Berkeley Math Department said, well, I 118 00:07:48,280 --> 00:07:51,240 Speaker 8: wouldn't wanna h see us hire somebody that I couldn't 119 00:07:51,280 --> 00:07:56,040 Speaker 8: invite to my home yet Jersey name and realized that 120 00:07:56,120 --> 00:08:00,920 Speaker 8: David Blackwell was David Blackwell, and did hire 'em these 121 00:08:01,360 --> 00:08:05,280 Speaker 8: awful people's dead bodies. But to try to get David 122 00:08:05,440 --> 00:08:08,560 Speaker 8: to tell any story of that sort, he just wasn't 123 00:08:08,560 --> 00:08:09,560 Speaker 8: a negative person. 124 00:08:21,560 --> 00:08:25,200 Speaker 6: My grandfather came to UC Berkeley in nineteen fifty four. 125 00:08:26,120 --> 00:08:29,800 Speaker 6: That was the same year that the Brown versus the 126 00:08:29,880 --> 00:08:34,920 Speaker 6: Board of Education came down, nineteen fifty four, and my 127 00:08:35,000 --> 00:08:38,080 Speaker 6: grandparents are driving across the country with their seven kids 128 00:08:38,360 --> 00:08:40,760 Speaker 6: with the Green Book. There was this book that they 129 00:08:40,800 --> 00:08:44,040 Speaker 6: had to use to get across the country, so they 130 00:08:44,120 --> 00:08:47,800 Speaker 6: knew where they could sleep, safe places that they could sleep. 131 00:08:50,120 --> 00:08:54,240 Speaker 6: Then when he got here, he wasn't offered the customary hospitality, 132 00:08:55,440 --> 00:08:58,920 Speaker 6: and so he went and they didn't have housing. They 133 00:08:58,960 --> 00:09:02,200 Speaker 6: slept in park for two months well and he would 134 00:09:02,200 --> 00:09:05,480 Speaker 6: go commute and teach at the school. Seven children. 135 00:09:08,960 --> 00:09:11,320 Speaker 11: When we traveled, and we did travel a lot in 136 00:09:11,360 --> 00:09:15,040 Speaker 11: the car, in the station wagon. When we would travel, 137 00:09:15,080 --> 00:09:16,800 Speaker 11: he would have a map and he would give each 138 00:09:16,800 --> 00:09:20,400 Speaker 11: of us a map, and he would have gasoline estimates, 139 00:09:20,840 --> 00:09:23,079 Speaker 11: time estimates for how long I was going to take us, 140 00:09:23,640 --> 00:09:26,560 Speaker 11: estimates for how long we go to the bathroom, estimates 141 00:09:26,559 --> 00:09:30,040 Speaker 11: for what the traffic was going to be. But along 142 00:09:30,080 --> 00:09:34,319 Speaker 11: the way he kept us all engaged at our own level. 143 00:09:34,679 --> 00:09:35,120 Speaker 12: And it was. 144 00:09:35,160 --> 00:09:39,800 Speaker 11: Remarkable to have children ten years apart, one is very young, 145 00:09:39,880 --> 00:09:43,040 Speaker 11: the other one is much more mature, and still keep 146 00:09:43,080 --> 00:09:47,480 Speaker 11: them all engaged. They somehow felt equal to the challenge. 147 00:09:48,000 --> 00:09:53,800 Speaker 11: It was just reflective of his considerate manner of teaching. 148 00:09:55,440 --> 00:10:00,840 Speaker 7: Your grandfather was the most delightful, friendly, warmest person. 149 00:10:01,720 --> 00:10:04,080 Speaker 4: I just remember him with a smile in his face. 150 00:10:10,960 --> 00:10:14,439 Speaker 9: All the years I had heard people talk about David Blackwell, 151 00:10:15,200 --> 00:10:19,640 Speaker 9: they never talked about him being angry or bitter. Most importantly, 152 00:10:20,280 --> 00:10:24,679 Speaker 9: they never talked about him saying anything negative about anyone else. 153 00:10:25,640 --> 00:10:28,600 Speaker 9: And that I think truly is a testament to his character. 154 00:10:28,960 --> 00:10:31,839 Speaker 9: That's what I strive to do, is to really say, 155 00:10:32,200 --> 00:10:35,760 Speaker 9: maybe things have happened negatively, but let me not try 156 00:10:35,800 --> 00:10:37,640 Speaker 9: to dwell on that and let me try to move 157 00:10:37,679 --> 00:10:39,959 Speaker 9: forward to say what are positive things that are happening 158 00:10:40,240 --> 00:10:41,680 Speaker 9: and what are ways that I can make this world 159 00:10:41,720 --> 00:10:42,319 Speaker 9: a better place. 160 00:10:45,600 --> 00:10:51,800 Speaker 5: You have arrived at a developer's conference. There will be 161 00:10:51,840 --> 00:10:59,080 Speaker 5: a lot of science described algorithms, computer architecture, mathematics. 162 00:11:02,080 --> 00:11:06,640 Speaker 12: In March last year, Nvidia unveiled its most ambitious GPU architecture. 163 00:11:08,280 --> 00:11:14,240 Speaker 12: We named it Blackwell in honor of doctor Blackwell. Today 164 00:11:14,280 --> 00:11:16,800 Speaker 12: we're witnessing the dawn of the AI era, and the 165 00:11:16,840 --> 00:11:20,680 Speaker 12: Blackwell architecture is the engine that's powering this new industrial revolution. 166 00:11:22,320 --> 00:11:25,840 Speaker 12: We strive each day to honor doctor Blackwell's remarkable legacy 167 00:11:26,080 --> 00:11:30,920 Speaker 12: by bringing the promise of AI to industries, developers, and researchers. 168 00:11:30,320 --> 00:11:39,760 Speaker 10: Worldwide, specifically through African Americans. I would say, don't consider 169 00:11:39,840 --> 00:11:42,840 Speaker 10: your race as a handicap. If you want to go 170 00:11:42,920 --> 00:11:47,120 Speaker 10: in a direction, go in that direction. In other words, 171 00:11:47,120 --> 00:11:51,959 Speaker 10: make decisions on the same basis as if you were Chinese, right, 172 00:11:52,160 --> 00:11:56,880 Speaker 10: or anything else. I don't worry so much about race. 173 00:11:57,400 --> 00:12:01,600 Speaker 10: It's going to be a factor. Let other people worry 174 00:12:01,640 --> 00:12:03,520 Speaker 10: about it. Don't you worry about it. 175 00:12:06,040 --> 00:12:14,960 Speaker 9: David Blackwell was the epitome of excellence. We had a 176 00:12:15,080 --> 00:12:19,000 Speaker 9: booth at a math conference, an individual walked by. He 177 00:12:19,040 --> 00:12:23,760 Speaker 9: wasn't a black mathematician. He pauses at the table, looks 178 00:12:23,760 --> 00:12:27,400 Speaker 9: at the picture of Blackwell and is very confused and 179 00:12:27,440 --> 00:12:30,520 Speaker 9: then says, you know, I'm a statistician, but that picture 180 00:12:30,559 --> 00:12:35,120 Speaker 9: can't be right. David Blackwell wasn't black. So I asked him, so, 181 00:12:35,280 --> 00:12:37,719 Speaker 9: how are you convinced that he wasn't black? And he said, well, 182 00:12:39,280 --> 00:12:44,160 Speaker 9: there's this Blackwell Ralph theorem, and it's an amazing theorem. 183 00:12:44,280 --> 00:12:50,880 Speaker 9: It just never crossed my mind that Blackwell could be black. 184 00:12:52,200 --> 00:12:55,400 Speaker 6: So imagine if David Blackwell didn't have to be hidden 185 00:12:55,520 --> 00:12:58,840 Speaker 6: fifty years ago, if he wasn't hidden fifty years ago, 186 00:12:59,080 --> 00:13:02,679 Speaker 6: what would stim look like today? What's it going to 187 00:13:02,720 --> 00:13:06,280 Speaker 6: look like tomorrow? We don't uphold his face and those. 188 00:13:06,160 --> 00:13:24,840 Speaker 3: Like him, Yes, Wow, there, you're wow. 189 00:13:29,800 --> 00:13:35,960 Speaker 1: Amazing is incredible watching it, Talking to you, getting to 190 00:13:35,960 --> 00:13:38,199 Speaker 1: know you over the past few months has just been incredible. 191 00:13:39,000 --> 00:13:41,480 Speaker 1: H The chat is going crazy. You know, we don't 192 00:13:41,520 --> 00:13:43,400 Speaker 1: need a month, so we don't need a day to 193 00:13:43,440 --> 00:13:46,720 Speaker 1: celebrate our history. We make history every day. And I'm 194 00:13:46,760 --> 00:13:49,080 Speaker 1: so happy that you've put this peeple together. 195 00:13:49,000 --> 00:13:52,080 Speaker 2: And just for full context for people like, you know, 196 00:13:52,480 --> 00:13:54,640 Speaker 2: it's interesting because we talked about AI so much and 197 00:13:54,720 --> 00:13:58,360 Speaker 2: who really knew that one of the forefathers of artificial 198 00:13:58,400 --> 00:14:03,439 Speaker 2: intelligence was black and held in such high regard in 199 00:14:04,320 --> 00:14:07,280 Speaker 2: the world of science that the largest company in the 200 00:14:07,320 --> 00:14:10,920 Speaker 2: history of the world, which is in Vidia, named their 201 00:14:12,360 --> 00:14:18,080 Speaker 2: most advanced piece of software ever after a black man. 202 00:14:18,240 --> 00:14:20,440 Speaker 2: And you know, once again, it just goes back to 203 00:14:20,880 --> 00:14:22,720 Speaker 2: I think you said this had in video, like if 204 00:14:22,760 --> 00:14:25,600 Speaker 2: he was well known, would what would that do to 205 00:14:25,800 --> 00:14:28,880 Speaker 2: stem in our community? Because we aspire to be things 206 00:14:28,920 --> 00:14:31,600 Speaker 2: that we see success in. So we aspire to be 207 00:14:31,880 --> 00:14:34,480 Speaker 2: athletes and entertainments because we see success in that. It's 208 00:14:34,560 --> 00:14:37,880 Speaker 2: not just because we're talented in athletics and entertainment. We are, 209 00:14:38,280 --> 00:14:40,800 Speaker 2: but we're also talented in math and science. But we 210 00:14:40,920 --> 00:14:44,360 Speaker 2: don't see we don't. We haven't we haven't been exposed 211 00:14:44,360 --> 00:14:46,760 Speaker 2: to that. So that's not something that we aspire to be. 212 00:14:47,120 --> 00:14:50,320 Speaker 2: But what if what if his story was so well known? 213 00:14:50,560 --> 00:14:53,800 Speaker 2: How would that potentially change the trajectory of young people 214 00:14:53,920 --> 00:14:59,280 Speaker 2: wanting knowing that artificial intelligence was practically started by somebody 215 00:14:59,280 --> 00:14:59,800 Speaker 2: that was black. 216 00:15:01,160 --> 00:15:05,080 Speaker 1: Yeah, Lisa, it you know, we had this call and 217 00:15:05,280 --> 00:15:07,280 Speaker 1: just I mean, when we talked about relationships earlier in 218 00:15:07,320 --> 00:15:09,240 Speaker 1: the show. This is just one of these relationships that 219 00:15:09,360 --> 00:15:11,840 Speaker 1: was kind of just like God right, Like I remember 220 00:15:11,880 --> 00:15:14,000 Speaker 1: we had a call and it got emotional for a 221 00:15:14,080 --> 00:15:18,000 Speaker 1: sec because unbeknownst to you, we are already going to 222 00:15:18,040 --> 00:15:19,960 Speaker 1: in Vidio. Shout out to our brother too, and I 223 00:15:20,000 --> 00:15:22,840 Speaker 1: know Noel's on the check in. They were like, Hey, Troy, 224 00:15:22,840 --> 00:15:24,600 Speaker 1: this this person that you guys should meet. I think 225 00:15:24,640 --> 00:15:27,360 Speaker 1: should be incredible. I'm like, who is this person? I 226 00:15:27,400 --> 00:15:28,960 Speaker 1: saw the deck, I'm like, oh, this is this is 227 00:15:28,960 --> 00:15:32,880 Speaker 1: a great story. But when we connected, it was fresh 228 00:15:32,880 --> 00:15:34,560 Speaker 1: off in invest fast and I was like, there was 229 00:15:34,640 --> 00:15:37,000 Speaker 1: this message that I got from my brother Marcus Rose 230 00:15:37,200 --> 00:15:39,840 Speaker 1: about you know, being who you're going to be so 231 00:15:39,920 --> 00:15:41,840 Speaker 1: somebody else can be who they're going to be. And 232 00:15:41,840 --> 00:15:44,080 Speaker 1: when I relayed that message to you, I said, yes. 233 00:15:44,840 --> 00:15:48,480 Speaker 1: Jensen and Chris, the co founders of Nvidio named the 234 00:15:48,560 --> 00:15:51,480 Speaker 1: chip Blackwell, they did their part. They may not have 235 00:15:51,560 --> 00:15:54,720 Speaker 1: told his story, but they did their part so that 236 00:15:54,920 --> 00:15:57,480 Speaker 1: we could now do our part. I want to know 237 00:15:57,560 --> 00:16:00,480 Speaker 1: what this moment feels like for you. I've see being 238 00:16:00,520 --> 00:16:04,160 Speaker 1: on the campus, being there meeting the co founder. What 239 00:16:04,240 --> 00:16:10,040 Speaker 1: was this moment like for you. 240 00:16:10,040 --> 00:16:13,240 Speaker 6: You know, I want to say that this moment right 241 00:16:13,280 --> 00:16:17,960 Speaker 6: now is bigger to me than that moment. This moment 242 00:16:18,040 --> 00:16:21,960 Speaker 6: to me is bigger because this is my community, this 243 00:16:22,880 --> 00:16:27,000 Speaker 6: is what this is who my grandfather cared about, and 244 00:16:27,400 --> 00:16:29,680 Speaker 6: these are the people I care about, and so this 245 00:16:29,800 --> 00:16:33,680 Speaker 6: is bigger. And to see their excitement about this that 246 00:16:33,840 --> 00:16:38,640 Speaker 6: this moves me in a way I cannot express, But 247 00:16:38,760 --> 00:16:46,680 Speaker 6: to answer your question directly, it was interesting. It's a 248 00:16:46,720 --> 00:16:50,120 Speaker 6: full circle moment in so many ways. Because I just 249 00:16:50,200 --> 00:16:53,520 Speaker 6: want to put a finer point on something you said earlier, 250 00:16:53,720 --> 00:16:58,080 Speaker 6: where Nvidia named has a history of naming their products 251 00:16:58,120 --> 00:17:03,600 Speaker 6: after accomplished scientists and things like that. Well, Vera Ruben, 252 00:17:03,640 --> 00:17:07,360 Speaker 6: who's the next one, she's actually an astronomer, and then 253 00:17:07,440 --> 00:17:11,520 Speaker 6: another person like they're just different people who are who 254 00:17:11,600 --> 00:17:16,439 Speaker 6: have made profound contributions to science. He's the only one 255 00:17:16,560 --> 00:17:21,600 Speaker 6: who's really done something in AI specifically, And part of 256 00:17:21,640 --> 00:17:24,560 Speaker 6: the reason is there are only a few of them, 257 00:17:24,960 --> 00:17:29,160 Speaker 6: and most of them just like Claude Shannon is one 258 00:17:29,320 --> 00:17:32,560 Speaker 6: and I think that's what Claude is named after. But 259 00:17:33,200 --> 00:17:38,800 Speaker 6: so we came in there and saw what they had, 260 00:17:39,160 --> 00:17:42,359 Speaker 6: what they're creating with his work. It's just so powerful. 261 00:17:42,640 --> 00:17:47,480 Speaker 6: But it's also something else because nobody knows that he 262 00:17:47,680 --> 00:17:51,000 Speaker 6: was there doing it, you know, So it's two things 263 00:17:51,040 --> 00:17:51,480 Speaker 6: at once. 264 00:17:52,720 --> 00:17:53,400 Speaker 10: Ernest, what's up? 265 00:17:53,480 --> 00:17:55,959 Speaker 1: You ever walk into a small business and everything just 266 00:17:56,119 --> 00:17:58,760 Speaker 1: works like the checkout is fast, there are seats of 267 00:17:58,840 --> 00:18:02,480 Speaker 1: digital tips, is a breeze, and you're out the door 268 00:18:02,520 --> 00:18:06,400 Speaker 1: before the line even builds Odds are they're using Square. 269 00:18:06,880 --> 00:18:09,480 Speaker 1: We love supporting businesses that run on Square because it 270 00:18:09,560 --> 00:18:12,520 Speaker 1: just feels seamless. 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