Full Transcript for Recognizing a technology that is here. AI & Education - with Andrew Rasiej

And by 1994, I was streaming, we didn't call it streaming, and we were called at a webcast in concerts. 03:53.315 --> 03:54.456 [SPEAKER_00]: Wow. 03:54.616 --> 03:57.678 [SPEAKER_00]: And we didn't really have a lot of technological tools. 03:57.838 --> 03:59.379 [SPEAKER_00]: I mean, the bandwidth wasn't there. 03:59.999 --> 04:04.062 [SPEAKER_00]: We would take pictures of bands, put it on the internet. 04:04.942 --> 04:09.325 [SPEAKER_00]: But I also became part of the New York Tech community and part of the digital transformation of the music industry. 04:09.965 --> 04:23.469 [SPEAKER_00]: But my life changed dramatically in 1997 because across the street from the club was a Washington every high school where 3,000 kids were going to school every day, 97 percent of the month, school lunch. 04:24.429 --> 04:26.950 [SPEAKER_00]: And it's part of a business group I went in to try to help out. 04:28.990 --> 04:31.531 [SPEAKER_00]: And I walked in and it was in a single computer anywhere in a school. 04:33.771 --> 04:37.032 [SPEAKER_00]: There was a Wangward processor on the principal assistant principal's desk. 04:40.133 --> 04:45.516 [SPEAKER_00]: And I, across the street, was already putting concerts online and part of your tech community and I was like, this doesn't work. 04:45.797 --> 04:45.917 [SPEAKER_01]: Right. 04:46.197 --> 04:58.324 [SPEAKER_00]: So I sent an email with him, friends, and saying, we should build a computer lab in the school, and I got a T1 line ordered from Verizon, and it cost like $6,000, found some used computers from a bank, and we fixed them up to be able to get on the internet. 04:59.064 --> 05:04.387 [SPEAKER_00]: And on a Saturday, 200 people showed up, which surprised me, and I started, and as a result 05:10.470 --> 05:15.953 [SPEAKER_00]: But we realized pretty quickly that even if we were doing that, there weren't any teachers or any administrators who even knew what to do with it. 05:16.453 --> 05:20.454 [SPEAKER_00]: So mouse developed a program training of the kids to do the work themselves. 05:21.094 --> 05:22.055 [SPEAKER_00]: And to train the teachers. 05:22.115 --> 05:26.897 [SPEAKER_00]: And so when you and I went to school, remember the AV kids, the kids who were responsible for the idea. 05:26.917 --> 05:29.738 [SPEAKER_00]: So that we basically created kids that were responsible for the internet. 05:30.098 --> 05:30.418 [SPEAKER_02]: Wow. 05:30.638 --> 05:32.099 [SPEAKER_00]: And a mouse will exist. 05:32.139 --> 05:35.280 [SPEAKER_00]: It runs an AI and robotics program in New York City Public Schools today. 05:36.580 --> 05:44.726 [SPEAKER_00]: But that led me to sort of spend a lot of time talking to political leaders and policy makers about the need for technology and public education. 05:44.746 --> 05:45.807 [SPEAKER_00]: Right. 05:46.467 --> 05:58.956 [SPEAKER_00]: Pretty soon I was being asked by some of them, including Hillary Clinton and her husband and some senators like Tom Dashall and and Ron Wyden to help explain it to the to the to the Democrats and to their colleagues. 05:59.156 --> 05:59.376 [SPEAKER_02]: Right. 06:01.327 --> 06:04.549 [SPEAKER_00]: And I, you know, I tried, and unfortunately I didn't succeed. 06:04.569 --> 06:17.019 [SPEAKER_00]: I thought the Democrats could really use the Internet as a tool to help them communicate to the American people and to build political power, because I always do technology as a democratization tool, right? 06:17.299 --> 06:26.386 [SPEAKER_00]: If you can provide people with better information, they could create demand more transparency, more accountability, and so access and bridging the digital divide. 06:26.946 --> 06:34.211 [SPEAKER_00]: was really important to me, but in those days, you know, even people like Al Gore didn't quite get it to be honest to, you know, you know, really. 06:35.592 --> 06:40.555 [SPEAKER_00]: In those days, he would consider a school wire to the internet if one teacher had an AOL account. 06:40.735 --> 06:40.995 [SPEAKER_02]: Right. 06:41.276 --> 06:41.536 [SPEAKER_02]: Right. 06:42.156 --> 06:43.857 [SPEAKER_02]: People did not appreciate it. 06:43.877 --> 06:44.618 [SPEAKER_02]: People understand it. 06:44.638 --> 06:48.841 [SPEAKER_00]: So I spent some time trying to convince the political leaders about it. 06:49.941 --> 06:52.163 [SPEAKER_00]: And, and I got lots of nods. 06:52.563 --> 06:53.444 [SPEAKER_00]: And that's on the shoulder. 06:54.024 --> 06:54.684 [SPEAKER_00]: And then phone calls 06:56.153 --> 06:57.674 [SPEAKER_02]: Right, right, donation, right, yeah. 06:57.814 --> 07:04.278 [SPEAKER_00]: And I learned something which is sad, and I think the tool I should be still true, which is that politics is a transactional business. 07:04.358 --> 07:05.378 [SPEAKER_01]: Yes. 07:05.558 --> 07:08.920 [SPEAKER_00]: And it most leaders, political leaders, out of necessity. 07:09.401 --> 07:13.243 [SPEAKER_00]: And sometimes out of greed, really only was in the two types of people. 07:13.303 --> 07:16.444 [SPEAKER_00]: People who give them a lot of money, or people who give a lot of money too. 07:16.645 --> 07:17.245 [SPEAKER_00]: They're consultants. 07:18.125 --> 07:22.307 [SPEAKER_00]: What I have a lot of money, so I was just trying to give them good advice and I had lots of nods and didn't get anywhere. 07:22.327 --> 07:22.848 [SPEAKER_02]: Right. 07:23.148 --> 07:33.773 [SPEAKER_00]: So anyway, in 2003, I noticed that Howard Dean was raising lots of money online through small donations. 07:34.793 --> 07:59.867 [SPEAKER_00]: mostly through blogs and meetups and the reason why was because there were a lot of people interested in him because he was the only Democrat running for president against in 2003 when the Iraqi war was being uh was going on with George Bush and he was the only Democrat willing to say that he was against the war and so a lot of people were supporting him so that was really interesting how the internet was actually

feeling his campaign this was one before social media that's right 08:00.727 --> 08:19.464 [SPEAKER_00]: Right, so when his campaign ended, we talked about that another time about why, but you know, I started a conference on the intersection of technology and politics, some of the personal democracy forum which I brought together Democrats and Republicans to talk about how does this technology work and how is it going to make it easier for people to vote, how it's going to make it easier for people to understand who to vote for, what are the issues? 08:20.185 --> 08:20.825 [SPEAKER_00]: And it was in those days. 08:20.845 --> 08:22.246 [SPEAKER_00]: It was easy to get everybody together. 08:22.286 --> 08:29.409 [SPEAKER_00]: So the personal democracy forum sort of became like a TED conference for people interested in that topic. 08:29.969 --> 08:31.690 [SPEAKER_00]: We ran it for 15 years. 08:32.730 --> 08:44.695 [SPEAKER_00]: You know, famous senators and congressmen don't Ron Wyden, Newt Kingrich, the mayor, Mayor Bloomberg, Spoon, even Edward Snowden. 08:44.735 --> 08:45.075 [SPEAKER_00]: Wow. 08:45.155 --> 08:45.735 [SPEAKER_00]: Wow. 08:45.835 --> 08:49.337 [SPEAKER_00]: Eric Schmidt, the CEO of Google, people like that, 08:50.297 --> 08:57.099 [SPEAKER_00]: And we sort of predicted that, you know, there would be somebody like an Obama in 2008 who used social media as a major tool to get elected. 08:57.299 --> 08:57.519 [SPEAKER_01]: Right. 08:58.199 --> 09:11.242 [SPEAKER_00]: But after 2008 and after Obama did that, I kind of saw a shift where there was a, you know, you couldn't get really Republicans at Democrats into the same room to talk about it because people really angry. 09:11.282 --> 09:13.783 [SPEAKER_00]: A lot of Republicans were angry about a black president. 09:15.223 --> 09:22.389 [SPEAKER_00]: And unfortunately for Obama, you know, the Democratic Party still was sort of thinking about this conventionally. 09:24.871 --> 09:37.562 [SPEAKER_00]: Anyway, I shifted my focus not so much to using technology for voting or for democracy, but rather for something I call civic tech any technology for the public good. 09:38.102 --> 09:43.726 [SPEAKER_00]: Because I started noticing that there were nonprofits that were using technology to make it easier to apply for benefits. 09:43.746 --> 09:43.867 [SPEAKER_00]: Right. 09:43.887 --> 09:49.751 [SPEAKER_00]: We'll apply for food stamps, or to clean up the environment. 09:49.771 --> 09:50.411 [SPEAKER_00]: Whatever it may be. 09:52.473 --> 10:01.039 [SPEAKER_00]: And so, by 2015, I launched a facility called Civic Hall, which brought together nonprofits, government agencies. 10:02.660 --> 10:08.827 [SPEAKER_00]: And we successfully launched maybe six years, 70 new nonprofits out of a collaborative workspace. 10:09.728 --> 10:19.079 [SPEAKER_00]: And then in 2017, 2018, the city of New York put out a request for proposals to build a office building a union square, literally across the streets when we're living Plaza was. 10:19.960 --> 10:45.290 [SPEAKER_00]: Can I pay you right back home and I was like really just going to build an office building guys come on you to better than that and so I made it proposal that the city Consider building the office building, but the designated third of it for a new and larger civic hall Because I wanted to create a facility where any New Yorker regardless of their background could get off the subway New York Square walk into our facility and get into to a digital skills training program to get a job 10:46.210 --> 11:01.236 [SPEAKER_00]: to be working in a tech industry, and this is really important to say, which is that I really believe that diversification of the tech industry was paramount to be making sure that technology was used for good. 11:01.596 --> 11:01.796 [SPEAKER_02]: Right. 11:02.137 --> 11:02.937 [SPEAKER_00]: Right. 11:02.957 --> 11:09.720 [SPEAKER_00]: Because if we don't change the color of the faces designing the future, people design the future, but we're going to get the same thing we have now. 11:09.780 --> 11:10.060 [SPEAKER_02]: Right. 11:10.240 --> 11:10.520 [SPEAKER_02]: Right. 11:10.780 --> 11:15.802 [SPEAKER_00]: So, fortunately, the city said yes, and then I spent six years raising the money to get that thing done. 11:15.862 --> 11:17.122 [SPEAKER_00]: You put it in everything else. 11:17.142 --> 11:17.442 [SPEAKER_02]: Yes. 11:18.182 --> 11:19.803 [SPEAKER_00]: And so, Civic Hall opened in 2024. 11:19.863 --> 11:25.945 [SPEAKER_00]: And it's acting as a, as a, as a, as a largest digital skills training center of its kind anywhere in the country. 11:25.965 --> 11:27.365 [SPEAKER_00]: It's an amazing, very proud of it. 11:27.645 --> 11:27.965 [SPEAKER_02]: Yes. 11:28.185 --> 11:44.232 [SPEAKER_00]: But with the caveat, which is that it was designed and built at a time when digital skills training was thought of as STEM, education, cyber security, digital marketing, sort of the mechanics of how technology was used in business or in jobs. 11:45.612 --> 11:57.419 [SPEAKER_00]: um, it didn't anticipate the speed at which AI was uh, was, uh, was moving and now because of AI, most of the pedagogy that would be taught at Civic Hall is obsolete. 11:57.680 --> 11:57.860 [SPEAKER_01]: Right. 11:58.100 --> 11:58.860 [SPEAKER_01]: Isn't that amazing? 11:58.880 --> 11:59.341 [SPEAKER_01]: Incredible. 11:59.701 --> 12:04.264 [SPEAKER_00]: And there's a huge challenge because a lot of those organizations don't have a new pedagogy. 12:04.524 --> 12:04.724 [SPEAKER_02]: Right. 12:04.844 --> 12:11.928 [SPEAKER_00]: And the reason why is because the employers themselves don't even know what they're going to be hiring for because we all know that AI is moving so fast that coding is no longer 12:14.970 --> 12:15.751 [SPEAKER_00]: What are those skills? 12:16.172 --> 12:16.952 [SPEAKER_00]: How do we teach them? 12:17.193 --> 12:17.473 [SPEAKER_02]: Right. 12:17.593 --> 12:18.514

[SPEAKER_00]: Who should be taught them? 12:19.055 --> 12:19.776 [SPEAKER_00]: How do we measure? 12:19.956 --> 12:20.196 [SPEAKER_02]: Right. 12:20.577 --> 12:21.017 [SPEAKER_00]: We don't know. 12:21.217 --> 12:21.478 [SPEAKER_02]: Right. 12:21.638 --> 12:22.459 [SPEAKER_02]: Right. 12:22.479 --> 12:26.123 [SPEAKER_00]: So we're in another huge public policy debate around technology. 12:27.505 --> 12:29.987 [SPEAKER_00]: This is repeating the internet first in social media. 12:30.468 --> 12:31.109 [SPEAKER_00]: And now AI. 12:32.290 --> 12:36.051 [SPEAKER_00]: And so, let me just say, I've devoted my life to bridging the digital divide. 12:36.071 --> 12:36.211 [SPEAKER_00]: Mm-hmm. 12:36.371 --> 12:40.813 [SPEAKER_00]: Oh, and I should have mentioned the longer way than 2005 Iran for public advocacy. 12:40.853 --> 12:41.753 [SPEAKER_02]: Yes, yes, right. 12:41.793 --> 12:42.213 [SPEAKER_02]: Right. 12:42.273 --> 12:47.295 [SPEAKER_00]: On a platform in New York City, a wireless city, because I mean that, you know, you couldn't actually 12:48.055 --> 12:50.937 [SPEAKER_00]: Connect people couldn't use the internet if they didn't have it. 12:50.957 --> 12:52.077 [SPEAKER_00]: Right. 12:52.337 --> 12:52.657 [SPEAKER_00]: Right. 12:52.697 --> 12:53.018 [SPEAKER_00]: Right. 12:53.078 --> 12:54.298 [SPEAKER_00]: So, what was Betsy Gapam? 12:54.318 --> 12:56.880 [SPEAKER_00]: She was a very nice woman who was the incumbent. 12:57.760 --> 13:02.022 [SPEAKER_00]: And, you know, and I, you know, tried really hard to get people to understand. 13:02.802 --> 13:04.343 [SPEAKER_00]: But, to be honest, it was very difficult. 13:04.363 --> 13:10.166 [SPEAKER_00]: In your times at a Toyota Board in 2005, when I went in for my endorsement interview, asked me what Wi-Fi was. 13:11.006 --> 13:11.707 [SPEAKER_02]: Right. 13:11.747 --> 13:15.649 [SPEAKER_02]: There's so much technological ignorance. 13:17.030 --> 13:23.317 [SPEAKER_02]: But Andrew, you're, I mean, you could be running a tech company making, you know, Bill is like, why? 13:23.437 --> 13:30.265 [SPEAKER_02]: Why are you so focused on bridging digital divide and democratizing technology? 13:30.305 --> 13:31.687 [SPEAKER_02]: Like, what, what's in you? 13:31.747 --> 13:33.269 [SPEAKER_02]: What, you know, who are your parents? 13:33.349 --> 13:34.030 [SPEAKER_02]: Like, how did you? 13:34.550 --> 13:42.837 [SPEAKER_00]: become this person I really want to spend some time talking about AI and how we can help people understand how to use it But I'll answer your question, China. 13:43.598 --> 13:51.524 [SPEAKER_00]: So when I was in my 40s, well, I was growing up I grew up my parents are Polish and They were forced labor camps survivors. 13:51.644 --> 13:55.868 [SPEAKER_00]: They were 13 years old when they were sent to Siberia and they came after the war. 13:55.948 --> 14:01.933 [SPEAKER_00]: They're Their their fathers were actually killed in a famous massacre in Poland for the cat and force massacre where 14:02.833 --> 14:05.974 [SPEAKER_00]: of the Soviets killed 20,000 Polish officers in one day. 14:05.994 --> 14:08.674 [SPEAKER_00]: Oh, my goodness. 14:08.834 --> 14:11.555 [SPEAKER_00]: They didn't know that at the time, but it was all found out later. 14:12.315 --> 14:24.257 [SPEAKER_00]: But my parents raised me basically paying attention to social justice, and just this period, so I heard the stories of the hardships and the struggles and my parents came to the United States. 14:24.777 --> 14:27.418 [SPEAKER_00]: My dad got a job as an engineer, worked his way up, 14:27.958 --> 14:37.426 [SPEAKER_00]: And they eventually were able to buy a house and where I was growing up as a kid, they were taking me to, you know, the Polish-American Congress or to some other event, my parents were like organizers. 14:37.667 --> 14:38.127 [SPEAKER_02]: Right. 14:38.728 --> 14:39.769 [SPEAKER_00]: Of the community. 14:39.889 --> 14:41.290 [SPEAKER_00]: And I would just want to go out and play baseball. 14:41.330 --> 14:41.630 [SPEAKER_00]: I didn't. 14:41.690 --> 14:43.772 [SPEAKER_00]: I didn't. 14:44.172 --> 14:44.392 [SPEAKER_00]: Right. 14:45.133 --> 14:45.513 [SPEAKER_00]: So anyway. 14:46.294 --> 14:52.055 [SPEAKER_00]: Fast-forward, I started Irving Plaza, I started Mouse, I really did give it much thought as to why your question. 14:52.716 --> 14:57.237 [SPEAKER_00]: And then when I was in my 40s I took my dad back to Poland to visit the town he was born in. 14:58.257 --> 15:01.778 [SPEAKER_00]: And so we went to visit the two bedroom home without running water where he was born. 15:02.638 --> 15:06.199 [SPEAKER_00]: And he then took me on the car to take me to a church where he was an altar boy. 15:06.859 --> 15:10.040 [SPEAKER_00]: And on the way we were driving this like two mile road. 15:10.680 --> 15:11.800 [SPEAKER_00]: And it was a building that we 15:16.341 --> 15:19.843 [SPEAKER_00]: I laid the bricks for that building, and I'm like, what? 15:20.083 --> 15:21.444 [SPEAKER_00]: That was 1936, 1936. 15:21.904 --> 15:22.924 [SPEAKER_00]: So a stop-to-car. 15:24.005 --> 15:26.126 [SPEAKER_00]: So my father tells me a story. 15:26.146 --> 15:41.033 [SPEAKER_00]: So it turns out that his father, who he hadn't seen since he was 13, because he was killed in the masquerade, just told you about, was the mayor of this small town, Hamlet, and that there were Jews and Muslims and Russian Orthodox and Catholics living in his district. 15:42.334 --> 15:43.795 [SPEAKER_00]: But they didn't have any secular place to meet. 15:43.815 --> 15:45.836 [SPEAKER_00]: They were going to their churches and temples and synagogues. 15:45.856 --> 15:47.057 [SPEAKER_00]: But they didn't have any secular place to meet. 15:47.538 --> 15:53.342 [SPEAKER_00]: So he managed to get the money from the federal government of Poland at the time. 15:53.362 --> 15:57.345 [SPEAKER_00]: This is between the wars for the materials to build this community center. 15:58.526 -->

15:59.266 [SPEAKER_00]: I did have the labor. 15:59.426 --> 16:05.511 [SPEAKER_00]: So on weekends, my grandfather and his cousins and neighbors built this community center. 16:05.531 --> 16:06.452 [SPEAKER_00]: My father told me this story. 16:06.492 --> 16:07.793 [SPEAKER_00]: He remembers it nine years old. 16:08.213 --> 16:11.137 [SPEAKER_00]: having a toy wheelbarrow delivering three bricks. 16:11.297 --> 16:11.858 [SPEAKER_02]: Wow. 16:12.078 --> 16:14.101 [SPEAKER_00]: To his uncle who was laying bricks on with some weak. 16:14.161 --> 16:15.002 [SPEAKER_02]: I love that. 16:15.263 --> 16:15.663 [SPEAKER_02]: Yeah. 16:16.164 --> 16:16.845 [SPEAKER_00]: And it's still there. 16:16.885 --> 16:17.526 [SPEAKER_00]: It's community center. 16:17.963 --> 16:18.183 [SPEAKER_02]: I am. 16:18.603 --> 16:23.325 [SPEAKER_00]: And all of a sudden it dawned on me that the reason I do what I do is because it's in my DNA. 16:23.345 --> 16:24.146 [SPEAKER_00]: That's right. 16:24.166 --> 16:24.386 [SPEAKER_00]: Right. 16:24.526 --> 16:24.806 [SPEAKER_00]: Right. 16:24.886 --> 16:25.126 [SPEAKER_00]: Right. 16:25.366 --> 16:27.767 [SPEAKER_02]: They are the beacon and the blueprint for you. 16:27.847 --> 16:30.168 [SPEAKER_00]: So Stephen Paul is that community center. 16:30.248 --> 16:30.868 [SPEAKER_00]: Right. 16:30.888 --> 16:31.329 [SPEAKER_00]: Right. 16:31.349 --> 16:31.789 [SPEAKER_00]: Right. 16:31.849 --> 16:32.269 [SPEAKER_00]: Right. 16:32.589 --> 16:34.050 [SPEAKER_00]: So so I'm still doing it. 16:34.230 --> 16:38.932 [SPEAKER_00]: So I figured out early in my life for 40 and so you know, sort of why. 16:40.245 --> 16:45.668 [SPEAKER_00]: So now whenever I see the vibe or something broken, I go in and try to fix it. 16:45.868 --> 16:46.168 [SPEAKER_02]: Right. 16:46.789 --> 16:52.432 [SPEAKER_00]: You know, I could talk to my shrink about, you know, Well, thank God, thank God for the time trying to fix things. 16:53.032 --> 17:03.418 [SPEAKER_00]: But, but thank God, but seriously, I've never seen the digital vibe greater, even though I walked into a school without a computer in 1997. 17:04.319 --> 17:05.220 [SPEAKER_02]: and it's 2026. 17:05.340 --> 17:11.683 [SPEAKER_00]: It's 2026 and the digital vibe is now greater and worse than ever before. 17:11.763 --> 17:14.865 [SPEAKER_02]: And getting more and more every minute every second. 17:15.045 --> 17:22.669 [SPEAKER_00]: And again, public policy makers are failing to understand how the technology works. 17:23.810 --> 17:28.333 [SPEAKER_00]: What the real challenges are, or more importantly, what the opportunities could be. 17:28.413 --> 17:28.613 [SPEAKER_00]: Right. 17:29.673 --> 17:31.375 [SPEAKER_01]: So talk to us about some of this stuff. 17:31.435 --> 17:41.082 [SPEAKER_01]: I mean, yeah, AI, what is AI, what, you know, most of our listeners are many policy makers who are listening. 17:41.482 --> 17:46.926 [SPEAKER_01]: But we have a lot of families and parents and people from the community who are hearing so much about AI. 17:49.528 --> 17:52.471 [SPEAKER_01]: I don't think fully appreciate that AI is already in front of them. 17:52.491 --> 17:54.272 [SPEAKER_01]: They may not recognize it as such. 17:54.873 --> 17:57.535 [SPEAKER_01]: But talk to us about what is this moment that we're doing? 17:57.775 --> 18:00.798 [SPEAKER_00]: Artificial intelligence has been around for a really long time. 18:01.038 --> 18:04.781 [SPEAKER_00]: You know, people use it on their maps in their cars, right? 18:04.861 --> 18:05.662 [SPEAKER_00]: Right. 18:06.242 --> 18:11.727 [SPEAKER_00]: And the answers that you get on the internet when you search on Google is essentially artificial intelligence. 18:11.767 --> 18:12.027 [SPEAKER_00]: Right. 18:13.748 --> 18:34.905 [SPEAKER_00]: It's really, it really exploded onto the public consciousness in 2022 when Chatchee PT released its large language model, which at LLM, which is, you know, basically has collected and somewhat controversially, lots and lots of information from search engines, from the New York Times, from publishers, from any information that might be available at the internet, 18:35.605 --> 18:48.213 [SPEAKER_00]: and is building a predictive engine that if you ask it a question could relatively accurately respond to the point where it could actually draft an email for you or draft a letter for you, etc. 18:48.233 --> 18:49.393 [SPEAKER_00]: Right. 18:49.594 --> 18:50.514 [SPEAKER_00]: That was 2022. 18:50.854 --> 18:56.298 [SPEAKER_00]: And so I think that was the kind of the milestone moment where everybody was like, oh, 18:57.238 --> 18:58.098 [SPEAKER_00]: Oh, I get it now. 18:58.739 --> 19:01.840 [SPEAKER_00]: I can use this, but also people are like, oh, it's dangerous. 19:01.860 --> 19:02.100 [SPEAKER_00]: It's great. 19:02.120 --> 19:02.900 [SPEAKER_00]: It's gonna create Terminator. 19:02.920 --> 19:14.625 [SPEAKER_00]: No, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, 19:15.025 --> 19:16.487 [SPEAKER_00]: not Arnold Schwarzenegger himself, of course. 19:16.947 --> 19:30.282 [SPEAKER_00]: But the robots and so since 2022, there's been massive advancements, the speed at which this is moving is accelerated dramatically. 19:31.743 --> 19:58.609 [SPEAKER_00]: to the point where now clogged, which is a or an anthropic LLM has a frontier model called Frontier, usually means at the front edge of emerging technology, if I use that term Frontier, that's what it means, called Maito's preview, which is so powerful that it can find any flaw in the software system that could take years or be hidden from humans without them even knowing it, 20:02.010 --> 20:20.196

[SPEAKER_00]: concerning but this technology is now can write the bill websites can write code like that instantly so as I mentioned before digital skills training is now something else but what it is not anthropic because people have heard that you know as a company you know is it a good guy is it a bad guy like what anthropic 20:20.396 --> 20:41.545 [SPEAKER_00]: So it seems like there's a lot, the team in the anthropic is highly concerned about the implications of these technologies on society and have been, as you know, you may have read in the last couple of days, one of their leaders and founders spend some time with the pope who just issued an encyclical about these technologies and health, society needs to recognize how they work and engage with them. 20:42.105 --> 20:45.349 [SPEAKER_00]: intelligently and so anthropic is a part of that discussion. 20:45.609 --> 20:45.849 [SPEAKER_02]: Right. 20:46.009 --> 20:52.697 [SPEAKER_00]: So and they, you know, when they when they discovered that they're frontier model, this mythos preview is so powerful. 20:52.717 --> 20:56.601 [SPEAKER_00]: They actually gave it to their competitors and gave it to the government. 20:56.781 --> 20:57.061 [SPEAKER_02]: Right. 20:57.302 --> 21:01.646 [SPEAKER_00]: So they've got a ready kind of a profile of being a more responsible AI company. 21:01.726 --> 21:02.007 [SPEAKER_00]: Right. 21:02.367 --> 21:06.230 [SPEAKER_00]: But the challenge is that there are lots of other AI companies that aren't thinking like this. 21:06.250 --> 21:06.610 [SPEAKER_00]: That's right. 21:06.910 --> 21:09.793 [SPEAKER_00]: And certainly bad actors and other parts of the world aren't thinking like this. 21:09.813 --> 21:10.273 [SPEAKER_01]: That's right. 21:10.593 --> 21:11.954 [SPEAKER_00]: This technology is so powerful. 21:12.014 --> 21:15.997 [SPEAKER_00]: It's sort of like giving away a nuclear grade uranium. 21:17.078 --> 21:20.681 [SPEAKER_00]: And then IKEA instructions on how to build a nuclear bomb. 21:20.821 --> 21:21.121 [SPEAKER_02]: Right. 21:21.561 --> 21:21.921 [SPEAKER_00]: For free. 21:22.302 --> 21:22.502 [SPEAKER_02]: Right. 21:24.243 --> 21:29.206 [SPEAKER_00]: So, so we have a problem because there aren't enough companies like Anthropica we're thinking about this. 21:29.226 --> 21:29.546 [SPEAKER_00]: Right. 21:30.006 --> 21:35.329 [SPEAKER_00]: And then the government to largely agree is advocated its responsibilities around regulating these technologies. 21:36.310 --> 21:36.550 [SPEAKER_00]: Right. 21:36.710 --> 21:37.531 [SPEAKER_02]: Which is crazy? 21:37.831 --> 21:39.632 [SPEAKER_02]: So, and I mean, that's a question. 21:39.772 --> 21:40.132 [SPEAKER_02]: Why? 21:40.272 --> 21:50.518 [SPEAKER_02]: Why has government been so irresponsible and regulating something that is so powerful and can have such a huge impact on our lives? 21:50.778 --> 21:55.502 [SPEAKER_00]: Well, I think that, you know, the Biden administration tried, it was a task force. 21:55.542 --> 22:00.286 [SPEAKER_00]: I think that there were people in government that understood the implications in tried. 22:00.387 --> 22:13.338 [SPEAKER_00]: Unfortunately, our electoral process delivered us a different administration with the new Trump's second administration and basically they took all the guardrails off and those and all of the policies related to what the government was trying to do, the federal government. 22:14.078 --> 22:25.922 [SPEAKER_00]: So now and that's because and what do you think that is because of just financial interest not one in having financial interest I mean, there's a huge amount of money at stake and I think millions of dollars. 22:25.942 --> 22:37.506 [SPEAKER_00]: I mean, oh, you had to do as look at who was in the audience at the inauguration right all the tech company every single tech leader was there Because you know the ultimate there their their responsibility is to their shareholders 22:38.106 --> 22:41.649 [SPEAKER_00]: And so they basically were doing their jobs. 22:42.550 --> 22:50.756 [SPEAKER_00]: But I think that the real challenge now is, you know, the state search starting to basically pick up the ball. 22:50.916 --> 22:51.157 [SPEAKER_02]: Right. 22:51.517 --> 22:56.381 [SPEAKER_00]: And interestingly, after Michael's preview, this is actually important. 22:57.081 --> 22:58.943 [SPEAKER_00]: Anthropic was working with the government. 22:59.423 --> 23:10.153 [SPEAKER_00]: with the Department of Defense and the Anthropoc basically drew a line in the sand at one point and said We will not let you use our models to build robots or to do surveillance, right? 23:11.094 --> 23:18.461 [SPEAKER_00]: And the defense department said oh, yet really so we're gonna now make essentially Name you as an enemy of the state 23:20.723 --> 23:24.384 [SPEAKER_00]: And, uh, ban your use of your technology, but we're going to go after you, essentially. 23:24.424 --> 23:24.664 [SPEAKER_02]: Right. 23:24.844 --> 23:26.945 [SPEAKER_00]: Two weeks later, it might as well as preview comes out. 23:27.545 --> 23:29.686 [SPEAKER_00]: And the White House is like, well, I think you guys should come back in. 23:29.926 --> 23:30.427 [SPEAKER_02]: Right. 23:30.467 --> 23:31.047 [SPEAKER_02]: We need you. 23:31.847 --> 23:36.349 [SPEAKER_00]: So, and now I think that there are some people realizing that that something needs to be done. 23:36.569 --> 23:43.572 [SPEAKER_00]: And Trump went from administration went from basically past trying to pass an executive order that would ban states from regulating AI. 23:44.292 --> 23:45.894 [SPEAKER_02]: for 10 years or something crazy, right? 23:45.914 --> 23:50.579 [SPEAKER_00]: Yeah, but that didn't happen to now basically saying that yeah, we have to probably put in some some guardrails. 23:51.039 --> 23:51.740 [SPEAKER_00]: Right. 23:52.440 --> 23:55.263 [SPEAKER_00]: But, you know, they have a huge problem whether you agree with him politically or not. 23:55.303 -->

23:56.945 [SPEAKER_00]: China is moving way ahead on AI. 23:56.985 --> 23:57.405 [SPEAKER_02]: That's right. 23:57.465 --> 23:58.646 [SPEAKER_00]: And robotics and so forth. 23:58.666 --> 24:01.369 [SPEAKER_00]: So, you know, the train, the horses left the barn. 24:01.389 --> 24:02.170 [SPEAKER_02]: Yes, yes, yes. 24:02.790 --> 24:16.521 [SPEAKER_00]: So, again, I think to answer your question, why is because I think public policy people in government general don't really understand how these technologies work and so as a result they are laid to the game. 24:17.982 --> 24:22.786 [SPEAKER_00]: But I think there's more recognition now that they need to pay attention, so hopefully that they'll catch up. 24:23.007 --> 24:24.288 [SPEAKER_00]: Yeah. 24:24.968 --> 24:36.579 [SPEAKER_00]: But there's probably no better place to talk about where AI has the potential to create harm, but also create lots of good as in public education, which is David's arena. 24:37.840 --> 24:38.561 [SPEAKER_00]: So let's talk about that. 24:38.721 --> 24:40.803 [SPEAKER_01]: Yeah, yeah, let's talk about it because, you know, 24:42.144 --> 24:56.685 [SPEAKER_01]: prior to my leaving as Chancellor, it became clear to me that this was kind of the next frontier, if you will, I organized a huge conference around it and got some of the top tech leaders from around the country, you know, involved. 24:57.166 --> 25:08.473 [SPEAKER_01]: I just wasn't there, you know, I left shortly thereafter, so didn't have a chance to dig in in the way that I would have, but had I remain in that seat as chance of New York City Public Schools. 25:10.414 --> 25:25.002 [SPEAKER_01]: I would have been a huge champion for technology, for AI, and certainly for us understanding much more deeply, the implications of it, putting the right guard rails in place, but recognizing that it is a technology that is here. 25:26.283 --> 25:31.164 [SPEAKER_01]: and you cannot put your head in the sand and think that that's leadership. 25:31.524 --> 25:33.445 [SPEAKER_01]: You've got to figure out how to get out in front of it. 25:33.905 --> 25:43.107 [SPEAKER_01]: How do you use technology to create more efficient systems for an institution as large as the New York City public school system? 25:43.608 --> 25:51.150 [SPEAKER_01]: How do you help teachers so that they understand that the use of technology can really help them to become better teachers? 25:52.850 --> 25:54.535 [SPEAKER_01]: And how do you lay the fears of people? 25:54.576 --> 26:00.112 [SPEAKER_01]: Well, the fears are that the technologies could become so advanced that it replaces teachers. 26:00.755 --> 26:05.576 [SPEAKER_01]: You know, I talked to my doctor recently and told him, you know, that was a great fear and education. 26:06.196 --> 26:08.077 [SPEAKER_01]: He's that's a great fear in the medical school. 26:08.097 --> 26:10.217 [SPEAKER_02]: The law space is accounting. 26:10.237 --> 26:11.318 [SPEAKER_01]: Yeah, he got his education. 26:11.418 --> 26:12.098 [SPEAKER_01]: It can be a... 26:12.118 --> 26:14.918 [SPEAKER_01]: It can be a... Is this a real fear? 26:15.019 --> 26:16.519 [SPEAKER_00]: Is it, well, let's separate. 26:16.539 --> 26:18.559 [SPEAKER_00]: I think there's a fear about it's impact on jobs. 26:18.819 --> 26:21.300 [SPEAKER_00]: Whether it's teacher jobs or legal jobs, right? 26:21.740 --> 26:24.501 [SPEAKER_00]: There's definitely something to be talked about related to. 26:24.561 --> 26:27.862 [SPEAKER_00]: It's impact on the economy and how we redesign our economy in an AI era. 26:29.462 --> 26:29.662 [SPEAKER_01]: Right. 26:30.443 --> 26:52.355 [SPEAKER_00]: But there's a bigger challenge when the education, which is that people are really worried about it, students using it, to offload cognitive development and I think it is a reasonable argument that no technology should be given to students under the age of 10, fifth grade, sixth grade. 26:54.036 --> 27:03.824 [SPEAKER_00]: Because it's, you know, we've already learned that social media has created huge problems, mental health problems, educational problems for students who have access to phones. 27:03.904 --> 27:09.069 [SPEAKER_00]: Because, you know, we went for having the internet and computers to, you know, basically the world in your hand, right? 27:09.549 --> 27:13.652 [SPEAKER_00]: So I'm not suggesting that this shouldn't have been a ban, this should not be a ban. 27:13.673 --> 27:14.013 [SPEAKER_00]: Of course, 27:15.234 --> 27:27.761 [SPEAKER_00]: cell phones in classrooms, and it's been proven that now, by the way, that there have been banned, that the performance of the students has improved, as a result, there's an actual evidence around it, right? 27:28.181 --> 27:37.727 [SPEAKER_00]: But it's an example of where the system and policymakers didn't understand the technology from the get-go, and so at the end of the day, got so far ahead that they've banned it. 27:38.327 --> 27:48.573 [SPEAKER_00]: So where we are now with this AI is that there's a new technology that people are remembering the experience that they had with social media and saying, okay, this is moving so fast. 27:48.593 --> 27:49.494 [SPEAKER_00]: Let's stop this. 27:50.374 --> 27:51.735 [SPEAKER_00]: And as you just said, you can't stop it. 27:51.775 --> 27:52.376 [SPEAKER_02]: Can't stop it. 27:52.396 --> 27:54.417 [SPEAKER_00]: Can't stick your head in the sense of where do we go, right? 27:55.157 --> 28:01.902 [SPEAKER_00]: So, we need a conversation with the public about what these technologies are. 28:02.122 --> 28:23.575 [SPEAKER_00]: One of the things I'm actually working on right now, very hard, is to create an education for all program, literally educate the public on QR code on bus stops, Lincoln, YC, Kiosks, in MTA subways, wherever, basically offering the public a basic understanding of how these tools 28:24.716 --> 28:34.701 [SPEAKER_00]: The finished government in 2018 published something called the

Elements of AI, which was to train 1% of the finished population, the basics of AI. 28:35.862 --> 28:42.085 [SPEAKER_00]: And it was so successful that they re-released it in 30 languages and it's reached over 170 countries. 28:42.985 --> 28:46.187 [SPEAKER_00]: So we need to educate the public about how these technologies work. 28:46.227 --> 28:47.167 [SPEAKER_00]: What is an LLM? 28:47.848 --> 28:48.388 [SPEAKER_02]: What is machine? 28:51.910 --> 29:00.492 [SPEAKER_00]: What is the future of AI when it comes to, you know, when it really becomes smarter than we are? 29:00.732 --> 29:01.112 [SPEAKER_01]: Yeah. 29:01.152 --> 29:01.292 [SPEAKER_00]: Right. 29:02.112 --> 29:07.453 [SPEAKER_01]: And what Finland did was to really educate just the broader population of jobs. 29:07.473 --> 29:12.114 [SPEAKER_00]: The broader population in my argument is, you can't develop public policy unless you bring the public along. 29:12.714 --> 29:12.974 [SPEAKER_00]: Right. 29:13.054 --> 29:14.495 [SPEAKER_00]: And they know what's happening. 29:14.875 --> 29:16.175 [SPEAKER_00]: You know, if there was another pandemic. 29:16.815 --> 29:18.656 [SPEAKER_00]: the government would educate the public, right? 29:18.937 --> 29:19.217 [SPEAKER_00]: Right. 29:19.257 --> 29:19.817 [SPEAKER_00]: This is what it is. 29:19.837 --> 29:21.739 [SPEAKER_00]: This is what the disease is what you do. 29:21.779 --> 29:26.823 [SPEAKER_00]: You pandemic, you have, you know, vaccinations, masking, social distancing, whatever it may be. 29:27.603 --> 29:35.649 [SPEAKER_00]: AI, and I'm not a negative person on AI, I'm kind of more of an optimist about it's potential, but I kind of view it as a digital pandemic, right? 29:35.749 --> 29:36.790 [SPEAKER_00]: It's a public safety issue. 29:37.030 --> 29:40.313 [SPEAKER_01]: Hey grandma, that's probably not me calling you up. 29:40.333 --> 29:41.133 [SPEAKER_01]: That's good for money. 29:41.233 --> 29:41.774 [SPEAKER_02]: That's right. 29:42.234 --> 30:00.236 [SPEAKER_00]: right because we talk about you should be scams you get a letter from the some social security administration agency and your your grandma would open and go oh they're sending me a check if I sent them 250 dollars right and you're like mom this is garbage your grandma is garbage scam right well that on AI it's much more sophisticated 30:00.356 --> 30:00.716 [SPEAKER_01]: Oh, yeah. 30:01.197 --> 30:03.999 [SPEAKER_01]: That's somebody calling that sounds just like a random one. 30:04.059 --> 30:06.381 [SPEAKER_00]: Well, business owners, we don't do two-factor authentication. 30:06.401 --> 30:08.243 [SPEAKER_00]: Their data is all exposed. 30:08.643 --> 30:13.928 [SPEAKER_00]: So now with my thought previews, bad actors can basically go inside and then take all their data or hold it for ransom. 30:13.948 --> 30:16.911 [SPEAKER_00]: I mean, there needs to be public education around you. 30:17.191 --> 30:22.035 [SPEAKER_02]: So I think one of the things that we are very focused on in this podcast are action items. 30:22.495 --> 30:25.258 [SPEAKER_02]: And so it sounds like one of the action items is get educated. 30:25.618 --> 30:31.983 [SPEAKER_02]: people can go out and get educated about what AI is, how does it work, how it can impact you. 30:32.003 --> 30:34.405 [SPEAKER_02]: That's something that everyone can do and should do. 30:34.445 --> 30:34.726 [SPEAKER_00]: You should. 30:34.806 --> 30:41.071 [SPEAKER_00]: Yeah, interestingly, I won't give out a name, but I'd like to leader who I know asked me. 30:41.091 --> 30:45.274 [SPEAKER_00]: He goes, how do I go to learn about how to use Claude, which is Anthropics? 30:45.655 --> 30:45.975 [SPEAKER_02]: Mm-hmm. 30:46.495 --> 30:47.076 [SPEAKER_00]: It's a Claude. 30:47.116 --> 30:48.057 [SPEAKER_00]: That's Claude. 30:48.137 --> 30:48.597 [SPEAKER_02]: Exactly. 30:48.817 --> 30:49.778 [SPEAKER_02]: And you can actually go on. 30:50.018 --> 30:54.181 [SPEAKER_02]: I think, go and say, how do I learn the fundamentals of ASP people? 30:54.221 --> 30:54.682 [SPEAKER_02]: It can occur. 30:54.822 --> 30:57.904 [SPEAKER_00]: Yeah, so most people are using chatGPT right now or amazed by it. 30:58.885 --> 31:00.366 [SPEAKER_00]: We'll get back to the challenges for a second. 31:00.386 --> 31:02.708 [SPEAKER_00]: But they don't know that you can actually set it up. 31:03.008 --> 31:04.429 [SPEAKER_00]: You can set preferences for it. 31:04.890 --> 31:07.211 [SPEAKER_00]: You can say, no, don't praise me. 31:07.912 --> 31:08.112 [SPEAKER_00]: Right? 31:08.132 --> 31:10.794 [SPEAKER_00]: I want you to be ruthless and giving me my answers. 31:10.974 --> 31:12.936 [SPEAKER_00]: Or I want you to be very specific and direct. 31:13.977 --> 31:19.861 [SPEAKER_00]: You can train it to be an assistant essentially, but your choice is as opposed to letting it just decide. 31:20.221 --> 31:25.385 [SPEAKER_00]: Because the companies that are built these have designed it to please you because then you'll use it more. 31:25.946 --> 31:26.746 [SPEAKER_02]: And a good more data. 31:27.147 --> 31:27.807 [SPEAKER_00]: And exactly. 31:27.887 --> 31:29.729 [SPEAKER_00]: And so there's a big challenge here. 31:29.809 --> 31:37.174 [SPEAKER_00]: And one of the things from a public policy perspective related to jobs is that there should, you know, people are talking about wealth. 31:37.394 --> 31:40.617 [SPEAKER_00]: This technology is going to take all of our data and try to monetize it. 31:41.598 --> 31:46.582 [SPEAKER_00]: The public should be getting some sort of benefit, and some people call it a tax. 31:47.343 --> 31:55.791 [SPEAKER_00]: I kind of think it should be like a dividend, I think that there should be shared ownership of those companies in the public interest, and there have been some people talking about how to do that. 31:56.351 --> 31:57.032 [SPEAKER_00]: I mean, think about it. 31:57.692 --> 32:00.155 [SPEAKER_00]: If these technologies really start disintermediating jobs.

32:00.795 --> 32:01.936 [SPEAKER_00]: And there hasn't been evidence of that yet. 32:01.956 --> 32:14.981 [SPEAKER_00]: If you look at the statistics around our unemployment rate, AI has been around now since, I mean, in this form, since 2022, we have not seen a drop in the job market. 32:15.342 --> 32:17.903 [SPEAKER_00]: We've seen young people having hard time getting. 32:18.703 --> 32:20.284 [SPEAKER_00]: entry-level jobs, right? 32:20.304 --> 32:26.809 [SPEAKER_00]: We've seen tech companies fire lots of workers, but you could argue that a lot of them are being fired because they over hired during the pandemic. 32:27.770 --> 32:31.553 [SPEAKER_00]: There's a lot of arguments over whether or not AI is actually disintegrating jobs. 32:31.773 --> 32:38.758 [SPEAKER_02]: But the controller just released a report saying 2027 is when we start to see huge impacts in New York, like 100,000 jobs. 32:38.978 --> 32:43.062 [SPEAKER_00]: Actually, I think it was he tried very hard to show a number of scenarios that could happen. 32:43.862 --> 32:46.424 [SPEAKER_00]: And that's a work of some of some of the more worst cases and some of the 32:47.825 --> 32:57.419 [SPEAKER_01]: So I think we have to see the head of Goldman Sachs recently came out and said that, you know, he thinks this is overblown. 32:58.381 --> 33:03.889 [SPEAKER_01]: If you see job laws, you're going to see the creation of new types of jobs, so we've got to figure out how to get out in front. 33:04.269 --> 33:13.179 [SPEAKER_00]: So many things exactly but what are those new types of jobs and so one of the things I'm trying to do now actually in addition to public you know promoting The try to get the government to do public education. 33:13.199 --> 33:13.680 [SPEAKER_02]: Right. 33:13.800 --> 33:14.541 [SPEAKER_00]: I wrote it up. 33:14.601 --> 33:17.384 [SPEAKER_00]: I'm asking the governor to take this on. 33:17.424 --> 33:21.048 [SPEAKER_00]: I wrote They're asking the mayor to take this on I'm making some small progress. 33:21.068 --> 33:22.170 [SPEAKER_00]: Hopefully they'll get it 33:23.130 --> 33:46.699 [SPEAKER_00]: But, I'm trying to create a center, I'm calling it the future of work commons, which is to create a place where you bring together the technology trainers and workforce development, the employers, the policy makers, the philanthropists who basically fund workforce development, and researchers, to actually talk to each other about, okay, so what is the future going to look like? 33:46.779 --> 33:48.219 [SPEAKER_00]: What are the jobs of the future? 33:48.399 --> 33:49.980 [SPEAKER_00]: How do we measure what success is? 33:50.680 --> 33:51.801 [SPEAKER_00]: You know, what is it digitally? 33:51.841 --> 33:54.944 [SPEAKER_00]: What is it digitally at AI enabled job mean? 33:55.144 --> 33:55.404 [SPEAKER_00]: Right. 33:55.584 --> 33:56.705 [SPEAKER_00]: I mean, this is a cliche. 33:57.306 --> 33:59.248 [SPEAKER_00]: I'm sure people have heard this, but I'm worth repeating. 33:59.868 --> 34:01.269 [SPEAKER_00]: AI isn't going to take your job. 34:01.390 --> 34:03.612 [SPEAKER_00]: A human being who knows how to use AI is right. 34:03.632 --> 34:03.912 [SPEAKER_00]: Right. 34:03.952 --> 34:04.432 [SPEAKER_00]: Right. 34:04.853 --> 34:08.376 [SPEAKER_00]: But to David to your point, we don't know what the jobs are. 34:08.756 --> 34:09.116 [SPEAKER_00]: Yeah. 34:09.277 --> 34:15.142 [SPEAKER_00]: You know, if you went to a farmer in 1860 who was working 16 hours a day and shown him the steam engine, 34:15.682 --> 34:17.664 [SPEAKER_00]: And said, guess what, you're only going to be working 40 hours a week. 34:18.545 --> 34:19.786 [SPEAKER_00]: They were like, what are you crazy? 34:20.306 --> 34:21.207 [SPEAKER_00]: They wouldn't be able to imagine it. 34:21.927 --> 34:23.429 [SPEAKER_02]: But even 15 years ago, right? 34:23.849 --> 34:25.991 [SPEAKER_02]: There's so many jobs we have now that we didn't have them. 34:26.271 --> 34:28.213 [SPEAKER_02]: But the advent of social media and all that. 34:28.233 --> 34:37.481 [SPEAKER_00]: So the question is, okay, so things are changing, but we don't have the economic, political, social, or educational institutions capable of dealing with this rate of change. 34:37.601 --> 34:38.382 [SPEAKER_02]: Absolutely not. 34:38.722 --> 34:50.948 [SPEAKER_00]: So there's something happening and people are concerned that it's moving so fast that a lot of people are going to be left behind, like parallegals or Uber drivers or potentially teachers. 34:50.968 --> 34:52.209 [SPEAKER_00]: Let's go back to education, right? 34:52.689 --> 34:57.932 [SPEAKER_00]: So yes, David, it could make the lives of teachers better, easier. 34:58.812 --> 35:03.535 [SPEAKER_00]: And there are examples where it's being used in education effectively. 35:04.655 --> 35:11.139 [SPEAKER_00]: I'll give you one example, there's an organization called Quill that uses AI to help students become better writers. 35:11.179 --> 35:12.300 [SPEAKER_00]: It doesn't write for them. 35:13.880 --> 35:20.004 [SPEAKER_00]: It's like a tool that students can use to essentially test their writing skills and give them feedback. 35:20.764 --> 35:23.426 [SPEAKER_00]: That's what I see too, but it works. 35:24.006 --> 35:29.129 [SPEAKER_00]: But again, it's kids who are older and are comfortable with the technology. 35:30.050 --> 35:35.234 [SPEAKER_00]: And there are examples where it's being used to help tutor kids in math with oversight. 35:36.695 --> 35:38.316 [SPEAKER_00]: So we just don't have enough examples. 35:38.597 --> 35:38.797 [SPEAKER_01]: Right. 35:40.098 --> 35:48.945 [SPEAKER_00]: But there's another bigger challenge, right, which is that these technologies are basically dismantling the industrial model of education. 35:49.225 --> 35:59.269 [SPEAKER_00]: Right, let's think about this, how many hours in a day, oh, sorry, in a year, what, I'm sorry, better way to ask it, what percentage of time are schools

physically open? 36:00.009 --> 36:14.095 [SPEAKER_00]: If you add up all 365 days a year, 24 hours in a day, vacations, weekends, you know, when schools are open from 8 in the morning to 2, 30 or 3 o'clock, it turns out that they're physically open 15% of all the time in the year. 36:14.235 --> 36:14.475 [SPEAKER_02]: Right. 36:14.695 --> 36:16.976 [SPEAKER_00]: Now, obviously education doesn't just happen in schools, 36:18.737 --> 36:20.458 [SPEAKER_00]: homework, other experiences, right? 36:20.518 --> 36:22.539 [SPEAKER_02]: But formal education is only 15%. 36:22.979 --> 36:28.401 [SPEAKER_00]: What productive activities could succeed if it's only really accessible 15% of all the time in the year? 36:28.982 --> 36:29.162 [SPEAKER_00]: Right. 36:29.522 --> 36:39.566 [SPEAKER_00]: So these technologies, the internet, AI, so forth, are creating an opportunity for lifelong education to happen, not just under the rubric of a classroom. 36:39.586 --> 36:40.046 [SPEAKER_00]: That's right. 36:40.126 --> 36:41.847 [SPEAKER_00]: And that's sage on the stage, right? 36:42.027 --> 36:46.629 [SPEAKER_00]: But rather opening it up to far more possibilities and form more participation by the community as a whole. 36:48.810 --> 36:53.134 [SPEAKER_01]: Well, because that takes vision, that takes leadership, and takes courage. 36:53.554 --> 36:55.636 [SPEAKER_01]: That very notion scares a lot of people. 36:56.456 --> 36:59.218 [SPEAKER_01]: It scares people who are teachers and teacher leaders. 37:00.680 --> 37:03.582 [SPEAKER_01]: That you would expand beyond a traditional day. 37:04.323 --> 37:09.146 [SPEAKER_01]: And who's going to be with the kids beyond the traditional day? 37:09.166 --> 37:13.210 [SPEAKER_01]: You know, unions have this certain amount of regulated hours that they want to 37:13.830 --> 37:15.731 [SPEAKER_01]: you know that they are committed to being with kids. 37:16.091 --> 37:20.232 [SPEAKER_01]: If you're saying hey, well kids should have access well beyond that. 37:21.692 --> 37:25.693 [SPEAKER_01]: That's going against what the norm has been and it scares people. 37:26.133 --> 37:27.713 [SPEAKER_01]: This whole technology scares folks. 37:27.974 --> 37:37.336 [SPEAKER_01]: Look at the amount of folks at your who are giving keynote speeches around the country and being bored almost off the stage at the mere mention of AI. 37:38.331 --> 37:48.679 [SPEAKER_01]: I mean, the mere mention of it on college campuses, and kids are saying, you know, the AI is going to take the jobs that they thought that they had been preparing for. 37:50.180 --> 37:56.505 [SPEAKER_01]: You talk about Quill, Quill's probably a really great technology that's helping kids learn to become better writers. 37:57.285 --> 38:05.452 [SPEAKER_01]: But we had Dr. Pedro Nagarra on, you know, how do you see a recently, one of the things he said is that one of college campuses, 38:05.992 --> 38:21.995 [SPEAKER_01]: You know, the use of the AI, so many kids are using it to

actually do their writing and so it just seems to be so many challenges that it takes visionary leadership to get out in front of this because nobody really knows what tomorrow holds. 38:22.280 --> 38:41.863 [SPEAKER_00]: right exactly and you know the unions are working with us with anthropic with open AI to you know try to develop mechanisms where teachers can learn about it and use it effectively and safely right so there is some dialogue going on sure but we're on wasn't built in a day right so people's fears are not completely unfounded that's right 38:42.183 --> 38:48.449 [SPEAKER_00]: And so I think it's important to acknowledge those fears and validate them and give people a sense that they've been heard. 38:49.110 --> 38:52.273 [SPEAKER_00]: But then also show them examples of how it can be applied and used properly. 38:52.473 --> 38:52.693 [SPEAKER_02]: Right. 38:52.813 --> 38:53.414 [SPEAKER_00]: Right. 38:53.594 --> 38:56.157 [SPEAKER_00]: And now that we're in an era of agentic AI. 38:56.517 --> 39:04.505 [SPEAKER_00]: And what does agentic AI basically means that you can basically, instead of just asking NLM to give you a, you know, help you write something or help you write 39:09.489 --> 39:14.734 [SPEAKER_00]: You can ask it to actually do things for you, like read all your emails and tell you which ones you really need to respond to, or... 39:14.955 --> 39:15.655 [SPEAKER_00]: I like that one. 39:15.675 --> 39:16.216 [SPEAKER_00]: I like that one. 39:16.236 --> 39:18.158 [SPEAKER_00]: I like that one. 39:18.218 --> 39:18.738 [SPEAKER_00]: I like that one. 39:18.818 --> 39:23.082 [SPEAKER_00]: People have created agents that are basically giving them access, you know, passwords and everything to everything they do. 39:23.123 --> 39:28.528 [SPEAKER_00]: Now it's looking, you know, first class tickets for them to go to, you know, to, you know, Bali without you. 39:28.628 --> 39:29.469 [SPEAKER_00]: Go grab their permission. 39:29.629 --> 39:29.909 [SPEAKER_00]: Wow. 39:31.070 --> 39:35.112 [SPEAKER_00]: So there's some, you know, but, but I'll give you an example of an agent. 39:35.192 --> 39:55.344 [SPEAKER_00]: So, you know, an immigration lawyer has maybe 200, 300 clients and they only various papers to be put together and they need to schedule their appointments with the immigration with the court or with, you know, with some process oriented, the organization and they spend hours and hours and hours on the phone. 39:56.144 --> 39:56.345 [SPEAKER_00]: Right. 39:56.765 --> 40:15.825 [SPEAKER_00]: So an agent has been built that helps immigration lawyers basically interact with their clients and when they get stuck that's when, you know, human being comes along saving the hours and hours of time and actually in many cases much more efficient for the user too because they're getting the answer and they're getting directed 40:16.345 --> 40:21.609 [SPEAKER_00]: And so there are examples where agents can actually be really, really helpful in public safety arena. 40:23.271 --> 40:27.714 [SPEAKER_00]: I know of a company that is helping 911 calls. 40:27.734 --> 40:30.776 [SPEAKER_00]: It listens to the 911 call with a live person. 40:31.177 --> 40:35.600 [SPEAKER_00]: But as soon as the agent, the live agent realizes that it's not an urgent emergency. 40:36.961 --> 40:42.844 [SPEAKER_00]: It offloads it to an AI, which basically turns it into a 3-1-1 call, or something else. 40:42.864 --> 40:48.627 [SPEAKER_00]: So now it saves time and lives because you've got more humans actually interacting with it. 40:48.647 --> 40:54.469 [SPEAKER_00]: So there are examples of where Agent AI can save time and save life and save money. 40:54.649 --> 40:55.390 [SPEAKER_00]: Sure. 40:55.490 --> 40:57.451 [SPEAKER_00]: And more and more of them is going to be more and more evident. 40:57.951 --> 40:59.592 [SPEAKER_00]: But again, those agents are replacing. 41:00.092 --> 41:00.813 [SPEAKER_00]: people. 41:00.893 --> 41:01.593 [SPEAKER_00]: That's like a law. 41:02.013 --> 41:03.054 [SPEAKER_00]: Parallel equals, right? 41:03.094 --> 41:04.575 [SPEAKER_00]: So you have a big case of lawsuit. 41:05.015 --> 41:11.640 [SPEAKER_00]: Moving to criminal investigation, and you go through what's known as discovery, thousands and thousands and thousands of documents, right? 41:12.080 --> 41:16.683 [SPEAKER_00]: Well, and with the Epstein files, the recent Epstein files, those files have been sent into... 41:16.764 --> 41:18.345 [SPEAKER_02]: Millions of pages. 41:18.745 --> 41:23.068 [SPEAKER_00]: And you're basically asking AI to parse them and point out where to look, where the problems are. 41:23.348 --> 41:23.568 [SPEAKER_00]: Right? 41:23.628 --> 41:23.828 [SPEAKER_00]: Right. 41:24.069 --> 41:24.249 [SPEAKER_00]: Right. 41:24.289 --> 41:24.469 [SPEAKER_00]: So, 41:25.069 --> 41:37.559 [SPEAKER_00]: There are examples of this all over again, and that's why those kids are worried because if you're taking it in law school You're gonna go work as a as a junior associate's in law firm doing the you know, they have you lifting right and AIs now doing it for the law firm You're not gonna have a job. 41:37.699 --> 41:38.099 [SPEAKER_02]: That's right. 41:38.319 --> 41:39.360 [SPEAKER_02]: That's right. 41:39.380 --> 41:40.201 [SPEAKER_00]: So what does that mean? 41:40.841 --> 41:41.262 [SPEAKER_00]: We don't know 41:42.022 --> 41:57.127 [SPEAKER_00]: So we need public policy that addresses it and I think there's a much bigger issue here, much much bigger issue, which is that this has to do with the failure of public policy to address affordability. 41:58.603 --> 42:16.931 [SPEAKER_00]: right so what does that mean well so if the industry if you follow my theory about the industrial age so we took a hundred years to get from the agricultural age to the industrial age and by 1950 the benefits of the industrial age accrued to the worker essentially you know this is over a simplification of course but

42:18.452 --> 42:23.476 [SPEAKER_00]: 40 hour work we could get you a house, college education for your kids, and a pension. 42:23.656 --> 42:25.878 [SPEAKER_00]: And it was some optimism that your kids would have a better life than you. 42:27.118 --> 42:34.564 [SPEAKER_00]: But by the time we got to globalization and maybe capital capital capital capitalism unfettered. 42:36.565 --> 42:44.370 [SPEAKER_00]: or, you know, just plain greed by the 80s and 90s, trickle down economics, whatever, that contract's been broken. 42:44.490 --> 42:44.890 [SPEAKER_02]: Totally. 42:45.311 --> 42:50.314 [SPEAKER_00]: And so you've got, you know, the reaction on one side is, Donald Trump saying, it's broken, I'm going to fix it. 42:50.434 --> 42:53.016 [SPEAKER_00]: And then you've got Bernie Sanders on the other side saying, it's broken. 42:53.116 --> 42:54.837 [SPEAKER_00]: And here's why I'm going to fix it. 42:54.937 --> 42:56.277 [SPEAKER_01]: You need to revive the whole time. 42:56.297 --> 42:58.278 [SPEAKER_00]: The whole thing has to be completely revamped, right? 42:58.338 --> 43:00.419 [SPEAKER_00]: So what does that mean? 43:01.219 --> 43:09.482 [SPEAKER_00]: So there's a Senator running for Senate main, by the Grand Platner. 43:09.642 --> 43:09.842 [SPEAKER_01]: Yeah. 43:10.582 --> 43:15.604 [SPEAKER_00]: And I listened to his thumb speech and I was very impressed by he said something that really struck me. 43:16.144 --> 43:18.345 [SPEAKER_00]: He was talking about how everybody in his town. 43:18.485 --> 43:20.966 [SPEAKER_00]: He's a harbor master and an oyster farmer. 43:21.686 --> 43:23.346 [SPEAKER_00]: He and his wife and his friends in his town. 43:24.987 --> 43:28.169 [SPEAKER_00]: to put the roof over the heads, to have food on the table, right? 43:29.150 --> 43:35.054 [SPEAKER_00]: And he said that he's running for office because he wants to give people back their time, the freedom of time. 43:35.415 --> 43:35.695 [SPEAKER_02]: Right. 43:36.799 --> 43:38.581 [SPEAKER_00]: And I thought, wow, that's a really simplification. 43:38.601 --> 43:39.842 [SPEAKER_00]: Because we talk about freedom of speech. 43:40.362 --> 43:43.345 [SPEAKER_00]: We talk about freedom of religion, freedom of who you want to marry. 43:44.025 --> 43:49.009 [SPEAKER_00]: But we, you know, all kinds of things in a political or civic context. 43:49.450 --> 43:52.713 [SPEAKER_00]: But we don't talk about it in terms of like just pure time. 43:52.873 --> 43:54.995 [SPEAKER_00]: Time to be with your kids, time to do what you love. 43:56.017 --> 44:02.323 [SPEAKER_00]: And so, if everybody's working two jobs, they don't have time to read the book, or walk on a beach, or teach their kid how to play basketball. 44:02.443 --> 44:02.603 [SPEAKER_00]: Right? 44:03.204 --> 44:06.587 [SPEAKER_00]: You know, when we got to a 40-hour work week, all kinds of industries were created. 44:07.147 --> 44:09.450 [SPEAKER_00]: Soccer practice, fake history, that's the world. 44:09.650 --> 44:09.850 [SPEAKER_00]: Right? 44:10.090 --> 44:13.073 [SPEAKER_00]: You know, whatever it may be, new industries were created. 44:13.553 --> 44:15.075 [SPEAKER_00]: We don't know what those new industries are. 44:15.675 --> 44:26.664 [SPEAKER_00]: But the ultimate question really is, is through these technologies, shift power from the few to the many, or what it's doing right now, is the many to the few, right? 44:27.005 --> 44:36.472 [SPEAKER_00]: And so we need to have a big discussion with the public about what is life supposed to be like if AI and agents are doing all of our work. 44:36.953 --> 44:38.174 [SPEAKER_01]: And who calls for the meeting? 44:38.214 --> 44:40.836 [SPEAKER_01]: Who calls for the big meeting with the public? 44:41.242 --> 44:45.944 [SPEAKER_00]: Well, my point is you can't even call for the meeting if you don't educate the public about what the meeting is about. 44:47.584 --> 44:58.488 [SPEAKER_00]: So we need to educate the public first about how these technologies work, but it's going to require visionary leaders to explain this to people and develop mechanisms that universal-based income is going to solve this problem. 44:58.528 --> 45:09.812 [SPEAKER_00]: As I mentioned, so what is the way do we ensure that people can have a roof over their head can have food on the table and that their kids have a better future with these technologies? 45:10.987 --> 45:20.497 [SPEAKER_02]: But I also think, you know, it's, you know, we could, we should have a big meeting and people should be educated and, you know, we should have vision to get in front of it. 45:20.557 --> 45:21.838 [SPEAKER_02]: But it's almost too late. 45:21.918 --> 45:22.979 [SPEAKER_02]: Like we can't get in front of it. 45:23.019 --> 45:25.261 [SPEAKER_02]: We are behind it completely behind it. 45:26.042 --> 45:29.465 [SPEAKER_02]: And we also have these factors like racism and sexism. 45:29.485 --> 45:34.730 [SPEAKER_02]: So when you talk about that 40 hour work week and you could get your pension and buy your house. 45:35.010 --> 45:35.551 [SPEAKER_02]: And that, that 45:36.211 --> 45:42.334 [SPEAKER_02]: That dream was not able to be realized because of all these other issues and isms that we have certainly in our society. 45:42.934 --> 45:44.655 [SPEAKER_02]: And it's only going to get worse. 45:44.775 --> 45:48.016 [SPEAKER_02]: So it is a pretty challenging place that we're in. 45:48.997 --> 45:55.399 [SPEAKER_02]: And people don't have a lot of confidence in face in our government that they're going to be regulating it. 45:55.419 --> 45:57.600 [SPEAKER_02]: They're going to have the conversations and going to do the things. 45:58.200 --> 46:01.962 [SPEAKER_02]: But part of what, and I know a part of a lot of your work was like, we have to make that. 46:02.442 --> 46:09.747 [SPEAKER_02]: So part of it is like, how do we put pressure because we do have some candidates, there is a candidate in New York, who's platform is about. 46:09.767 --> 46:12.068

[SPEAKER_02]: I'll express a regulation. 46:12.449 --> 46:15.230 [SPEAKER_02]: Like, we've got to ask the questions. 46:15.471 --> 46:18.613 [SPEAKER_02]: We have to make people kind of create an agenda. 46:18.633 --> 46:23.696 [SPEAKER_02]: Like you said, the states now are really taking some power to advance it. 46:24.056 --> 46:25.617 [SPEAKER_02]: So there's a sense of urgency here. 46:27.298 --> 46:29.900 [SPEAKER_02]: that we've got to be politically involved in having evidence. 46:29.920 --> 46:35.242 [SPEAKER_00]: There needs to be, that definitely needs to be able to see, I want to just, you brought up something I think that's really important, is it? 46:35.323 --> 46:44.388 [SPEAKER_00]: I think that that promise of the 40-hour work we give you a house and a food on the table and a college education and a pension is somewhat of a myth. 46:45.228 --> 46:52.732 [SPEAKER_00]: At least in, you know, it's mostly successful for mostly white Americans and not necessarily for those from marginalized communities. 46:53.673 --> 46:53.833 [SPEAKER_00]: Mm-hmm. 46:54.513 --> 46:55.554 [SPEAKER_00]: But our democracy is a myth. 46:55.634 --> 46:57.115 [SPEAKER_00]: You know, we don't actually elect anybody. 46:57.155 --> 46:57.935 [SPEAKER_00]: They choose us. 46:57.975 --> 46:59.896 [SPEAKER_00]: Look at the way everything's jerry-mandered, right? 46:59.976 --> 47:03.779 [SPEAKER_00]: So we live a lot of mythology that's no longer relevant. 47:04.159 --> 47:04.299 [SPEAKER_01]: Right. 47:04.659 --> 47:05.780 [SPEAKER_00]: So it's right. 47:05.860 --> 47:11.964 [SPEAKER_00]: But to your question, I think we needed demand that our elected leaders address these issues. 47:13.024 --> 47:24.493 [SPEAKER_00]: I can tell you're for sure that the, you know, the, the billionaires who are a lot of them who are making a lot of money and that's just one of the AI, but from other technologies are building bunkers for themselves, Montana, or what are you doing? 47:24.754 --> 47:26.495 [SPEAKER_00]: Right because they know the pitchforks are coming. 47:26.515 --> 47:27.035 [SPEAKER_02]: That's right. 47:28.817 --> 47:33.440 [SPEAKER_00]: And so I think they're preparing themselves for, you know, a reaction, and there may be a revolution. 47:33.480 --> 47:34.120 [SPEAKER_00]: It may take that. 47:34.621 --> 47:42.866 [SPEAKER_00]: Hopefully, hopefully we won't have it because, you know, even though you're moving from the industrial age to the industrial age, was a lot of blood sweat in your life. 47:42.986 --> 47:51.212 [SPEAKER_00]: A lot of violence, a lot of labor, and you know, union busting, and job labor laws, you could just go on and on. 47:51.272 --> 47:52.393 [SPEAKER_00]: It's not so smooth. 47:52.433 --> 47:54.134 [SPEAKER_00]: I mean, this is like smooth process. 47:54.174 --> 47:54.975 [SPEAKER_00]: It wasn't smooth at all. 47:55.055 --> 47:55.575 [SPEAKER_00]: That's right. 47:55.835 --> 48:00.498 [SPEAKER_00]: So we may not go through a smooth process from the industrial age to the information age. 48:00.678 --> 48:03.600 [SPEAKER_00]: But maybe historically, we should be a little bit more aware. 48:03.620 --> 48:04.001 [SPEAKER_02]: Right. 48:04.021 --> 48:04.621 [SPEAKER_02]: We should learn. 48:04.741 --> 48:05.402 [SPEAKER_02]: We should learn. 48:05.422 --> 48:05.962 [SPEAKER_02]: Right. 48:06.102 --> 48:06.582 [SPEAKER_02]: We should demand. 48:06.622 --> 48:09.504 [SPEAKER_00]: We should also question the biases that are built into the data. 48:09.985 --> 48:10.185 [SPEAKER_00]: Right. 48:10.245 --> 48:17.690 [SPEAKER_00]: That's the, you know, there's, there was an example of, you know, facial recognition, technology, AI, usually facial recognition, which, 48:18.050 --> 48:21.612 [SPEAKER_00]: which was, you know, dead set against, you know, basically profiling people of color. 48:21.752 --> 48:22.533 [SPEAKER_00]: Right. 48:23.033 --> 48:36.121 [SPEAKER_00]: So there are groups of organizations that are, you know, calling this out and trying to make sure that those models as they're built are as biased free as possible, but it's not an exact science. 48:36.642 --> 48:36.822 [SPEAKER_00]: Right. 48:36.942 --> 48:38.042 [SPEAKER_00]: It's going to require work. 48:38.082 --> 48:42.245 [SPEAKER_00]: So back to my point about changing the color or the face of the people designing the future. 48:42.825 --> 48:43.045 [SPEAKER_00]: Right. 48:43.426 --> 48:46.988 [SPEAKER_00]: Unless we have diverse perspectives on how these technologies are built, 48:47.308 --> 48:49.009 [SPEAKER_00]: We're going to end up repeating the same problems. 48:49.029 --> 48:58.256 [SPEAKER_01]: And I think we are going to repeat a lot of the same problems in spite of the fact that we do have some groups who are out there fighting the good fight as they should about racial biases. 48:58.276 --> 49:01.999 [SPEAKER_01]: She's talking about your own DNA, what's kind of led you into this work in this space. 49:02.819 --> 49:06.082 [SPEAKER_01]: And I look at issues around racing class as being built into the... 49:06.442 --> 49:24.188 [SPEAKER_01]: very DNA of the birth of this whole nation and that is going to be a made that will be a major challenge because this technology is here, it is moving, it is advancing at the speed of light and a lot of those biases are already being built in and it is being designed to benefit 49:25.289 --> 49:25.769 [SPEAKER_01]: the few. 49:26.329 --> 49:29.472 [SPEAKER_01]: And so it may in fact take a revolution for people. 49:29.492 --> 49:43.305 [SPEAKER_01]: So those bunkers are probably a good investment for some of those folks because because to the degree that lots of people find themselves out of jobs and just out of what this new industry will be. 49:44.085 --> 49:46.648 [SPEAKER_01]: You'll have a lot of very angry people. 49:47.268 --> 50:03.910 [SPEAKER_01]: and if they don't see hope for themselves and if they've not been part of this process and right now I just

don't see the leadership and you said room is not built in the day so we keep you hope alive but you know we get in closer to the end on this right but I want to ask you 50:05.898 --> 50:09.480 [SPEAKER_01]: What are the things that the average person can do? 50:10.160 --> 50:16.163 [SPEAKER_01]: Number one, and then number two, what is the call to action also for the policy makers again? 50:16.483 --> 50:18.264 [SPEAKER_01]: Just to clarify that a little bit. 50:18.284 --> 50:27.108 [SPEAKER_00]: So for every single person who is aware of how these technologies are now advancing, they need to learn about it themselves. 50:27.708 --> 50:31.411 [SPEAKER_00]: they need to understand it, they need to think about how it applies to their lives. 50:31.912 --> 50:33.814 [SPEAKER_00]: They should think about whether it might affect their jobs. 50:35.035 --> 50:37.377 [SPEAKER_00]: And most importantly, how it may affect their kids. 50:38.298 --> 50:39.899 [SPEAKER_02]: And this is such an important point. 50:40.199 --> 50:42.061 [SPEAKER_02]: I mean, I want to triple underline it. 50:42.081 --> 50:43.742 [SPEAKER_02]: We cannot put our heads in the sand. 50:43.822 --> 50:44.043 [SPEAKER_02]: No, no. 50:44.083 --> 50:46.365 [SPEAKER_02]: You can't say, we're going to stop everything. 50:46.425 --> 50:48.447 [SPEAKER_02]: Or know why I am in schools for two years or no. 50:48.487 --> 50:49.267 [SPEAKER_02]: We can't do that. 50:49.367 --> 50:52.290 [SPEAKER_02]: We've got to get educated as much as possible. 50:52.570 --> 51:05.821 [SPEAKER_00]: And so that brings up the second point, which is that the public should, what the public can do is demand that the government and other institutions align with government and including the AI companies. 51:07.274 --> 51:10.418 [SPEAKER_00]: do their part educating the public about how these technologies work. 51:10.678 --> 51:11.839 [SPEAKER_00]: I mean, just think about it. 51:12.400 --> 51:15.063 [SPEAKER_00]: When you click on an app, you download on your phone. 51:15.944 --> 51:17.066 [SPEAKER_00]: You don't read the Terms of Service? 51:17.286 --> 51:17.506 [SPEAKER_00]: No. 51:18.027 --> 51:21.090 [SPEAKER_00]: You just say, except we just gave away all your data. 51:21.371 --> 51:21.771 [SPEAKER_02]: All of it. 51:22.011 --> 51:22.172 [SPEAKER_00]: Right? 51:22.632 --> 51:23.093 [SPEAKER_00]: Right? 51:23.193 --> 51:25.074 [SPEAKER_00]: I think they shouldn't be called privacy policies. 51:25.094 --> 51:27.196 [SPEAKER_00]: This should be called data use policies, right? 51:27.456 --> 51:31.840 [SPEAKER_00]: And it should also be law that says that this should be written in plain English, so you can understand it. 51:31.860 --> 51:32.180 [SPEAKER_02]: Right. 51:32.260 --> 51:41.608 [SPEAKER_00]: But I've built a cycle of education for all.org, which is basically the elements of AI upgraded to an American context. 51:41.788 --> 51:42.970 [SPEAKER_00]: Wonderful. 51:43.230 --> 51:45.191 [SPEAKER_00]: With at a

ninth grade reading level. 51:47.272 --> 51:59.074 [SPEAKER_00]: And I put in a dozen papers that are pro-AI with along with a dozen research papers against AI and asked it to basically use the elements of AI as a model to develop an American public education. 51:59.794 --> 52:00.314 [SPEAKER_01]: That's great. 52:00.454 --> 52:02.475 [SPEAKER_01]: How do people like access that? 52:02.495 --> 52:03.615 [SPEAKER_00]: So educationforall.org. 52:03.835 --> 52:05.135 [SPEAKER_01]: That's great. 52:06.235 --> 52:10.616 [SPEAKER_00]: But the thing that I wanted to make clear is that I shouldn't be doing this. 52:10.636 --> 52:13.337 [SPEAKER_00]: The government should be doing this. 52:13.357 --> 52:15.317 [SPEAKER_00]: So this program isn't the Bual and Endwall. 52:15.837 --> 52:16.758 [SPEAKER_00]: I'm not claiming it is. 52:17.318 --> 52:19.920 [SPEAKER_00]: I'm using it as a demonstration of what's needed. 52:20.320 --> 52:25.324 [SPEAKER_00]: And so to answer your question, the public should be, you have to engage to your point of view, can't stick your head in the sand. 52:25.884 --> 52:40.214 [SPEAKER_00]: And then you need to demand accountability on the part of elected leaders and people responsible for things like public education to also not stick their heads in the sand and to understand this and identify ways that these technologies can help solve problems 52:40.714 --> 52:55.540 [SPEAKER_00]: not just cause problems, fight climate change, create better results, take the workload off teachers, but give them time to actually spend a meaningful interaction with students where their intervention can make a difference in the child's life. 52:57.561 --> 53:06.684 [SPEAKER_00]: Or in healthcare where we can see huge examples of where AI can be really, you know, and by the way, I wanted this is really important to point out, we're talking about AI today and everybody's talking about maybe robots. 53:07.124 --> 53:07.444 [SPEAKER_00]: We are on 53:09.645 --> 53:15.749 [SPEAKER_00]: Which is so powerful, give you an example, there's a, uh, I, I witnessed the quantum computer that was built by IBM. 53:17.070 --> 53:17.991 [SPEAKER_00]: It is so powerful. 53:18.191 --> 53:30.158 [SPEAKER_00]: It is, if you took all the computing and all the computing power that was ever created by man, today, half of it would fit in the quantum computer. 53:30.238 --> 53:30.539 [SPEAKER_02]: Hmm. 53:32.557 --> 53:32.837 [SPEAKER_02]: Wow. 53:33.058 --> 53:39.023 [SPEAKER_00]: So, we're talking about a super power beyond our imaginations. 53:39.504 --> 53:47.932 [SPEAKER_00]: And they're starting to use it for things like predictive modeling around proteins for healthcare, for speeding up the speed at which you can get to a vaccine. 53:48.453 --> 53:51.536 [SPEAKER_00]: I mean, there are all kinds of ways it can be used, but it also can break encryption. 53:52.136 --> 53:52.336 [SPEAKER_00]: Right. 53:52.376 --> 53:54.458 [SPEAKER_00]: So security is going to be blown out the window. 53:55.118 --> 53:55.299 [SPEAKER_00]: Right. 53:55.359 --> 53:57.640 [SPEAKER_00]: So we, the public, back to my point. 53:58.281 --> 54:06.507 [SPEAKER_00]: But if these things are moving this fast, the public needs to demand that they're elected leaders understand the technology and develop the public policies to protect the public first of all. 54:07.287 --> 54:22.870 [SPEAKER_00]: But then that the benefits of these technologies accrue to the public and not just to the few who have the money or the power to give money to the politicians to keep them from their regulating them in order to not have to share the wealth of this technology with others. 54:23.250 --> 54:23.370 [SPEAKER_02]: Right. 54:23.490 --> 54:27.811 [SPEAKER_02]: And I think the other thing that you said earlier was that AI is not going to take your job. 54:27.931 --> 54:30.071 [SPEAKER_02]: Someone who knows AI is going to take your job. 54:30.431 --> 54:37.333 [SPEAKER_02]: So what people can also do is be users, try to educate yourself as much as possible. 54:37.353 --> 54:39.493 [SPEAKER_02]: So you can be someone who knows AI. 54:39.753 --> 54:43.574 [SPEAKER_02]: So you can be more marketable in the marketplace as it was before. 54:43.594 --> 54:47.655 [SPEAKER_01]: So just being loud on the sidelines and screaming, no AI. 54:48.495 --> 54:58.000 [SPEAKER_01]: that's not the answer, being someone who is just afraid of what is happening and putting that in the sand, that's not the answer. 54:59.201 --> 55:07.786 [SPEAKER_01]: So we need people, as you said, educate themselves and to recognize that we are in a place in space right now where no one has all of the answers. 55:08.186 --> 55:08.466 [SPEAKER_01]: Right. 55:08.826 --> 55:11.228 [SPEAKER_01]: And so this is a lot of gray area. 55:12.108 --> 55:14.049 [SPEAKER_01]: and gray area scares people. 55:14.530 --> 55:16.691 [SPEAKER_01]: People like things when it's simple, it's black and white. 55:17.071 --> 55:29.678 [SPEAKER_01]: So you have a lot of people who are very much afraid, this is where leaders have to rise up with good intentions because you have a lot of bad actors who sees the gray area as well and will take advantage. 55:30.098 --> 55:33.060 [SPEAKER_01]: And so we know that that is fully activated, right? 55:33.240 --> 55:35.041 [SPEAKER_00]: Yeah, these issues actually are not technological. 55:35.061 --> 55:37.922 [SPEAKER_00]: They're sociological and interpolitical, right? 55:38.002 --> 55:40.624 [SPEAKER_00]: Some people compare AI to the electricity. 55:41.244 --> 55:44.566 [SPEAKER_00]: I compare it to the discovery of fire by humans. 55:44.886 --> 55:45.506 [SPEAKER_01]: Mm-hmm. 55:46.026 --> 55:46.927 [SPEAKER_00]: It's that powerful. 55:47.307 --> 55:47.547 [SPEAKER_01]: Right. 55:47.687 --> 55:50.228 [SPEAKER_00]: And so, we have to engage with it. 55:50.468 --> 55:51.369 [SPEAKER_00]: Yeah. 55:51.509 --> 55:52.529 [SPEAKER_02]: And there's opportunity. 55:52.629 -->

55:53.710 [SPEAKER_00]: There's huge opportunity. 55:53.970 --> 55:54.810 [SPEAKER_00]: Huge opportunity. 55:54.850 --> 56:00.013 [SPEAKER_00]: And I think that, you know, we may find a silver lining, which is that it may actually get us to focus on the things that matter most to us. 56:00.873 --> 56:11.157 [SPEAKER_00]: The things that technology can't do is they can't feel, they can't, you know, they can't emulate empathy or compassion, but they can't actually have compassion or empathy. 56:11.937 --> 56:14.758 [SPEAKER_00]: And so we have forgotten about teaching empathy to kids. 56:15.318 --> 56:18.960 [SPEAKER_00]: We forgot about sort of the soft skills that make us human. 56:19.640 --> 56:22.621 [SPEAKER_00]: So even though we're angry about this technology and how it's advancing, 56:23.181 --> 56:41.198 [SPEAKER_00]: I am optimistic that we may end up getting a silver lining, which is that these technologies will actually give us time to focus on what the meaning of life and the meaning of happiness is, that there may be another currency other than money, other than your title of your job which is the currency of happiness. 56:41.399 --> 56:41.779 [SPEAKER_01]: Come on. 56:42.079 --> 57:05.131 [SPEAKER_00]: That to me is my North Star and why I'm fighting as hard as hell to make sure that we don't make the same mistakes with this technology that we've already done with some of the previous versions of digitalization, the internet, social media, and actually push very hard so that this battle between good and evil is that the people who responsible for good in the world have the same skills and tools that the bad guys have. 57:05.931 --> 57:06.211 [SPEAKER_01]: Right. 57:06.352 --> 57:07.032 [SPEAKER_00]: That's what I'm working on. 57:07.292 --> 57:10.335 [SPEAKER_01]: Well, listen, no better way to end on that. 57:10.375 --> 57:11.937 [SPEAKER_02]: We all need to join that fight. 57:12.037 --> 57:12.177 [SPEAKER_02]: Yeah. 57:12.197 --> 57:17.743 [SPEAKER_01]: We all need to join the fight for happiness and for empathy and for all the things that make us human. 57:18.043 --> 57:20.946 [SPEAKER_01]: Congratulations on your one-year-old. 57:21.427 --> 57:21.687 [SPEAKER_01]: Oh, yeah. 57:21.707 --> 57:22.448 [SPEAKER_00]: I've never been mentioned that. 57:22.468 --> 57:22.868 [SPEAKER_00]: Yeah, yeah. 57:23.028 --> 57:24.350 [SPEAKER_00]: I have another project that I have a one-year-old. 57:24.570 --> 57:25.010 [UNKNOWN]: Yeah. 57:25.191 --> 57:25.991 [SPEAKER_00]: It's a good way. 57:26.492 --> 57:28.214 [SPEAKER_00]: We'll probably never learn to drive. 57:28.737 --> 57:30.398 [SPEAKER_02]: Right, because they'll be in it. 57:30.418 --> 57:32.000 [SPEAKER_02]: Because they'll be coming up in a draw. 57:32.060 --> 57:32.740 [SPEAKER_00]: Right, no, no. 57:32.780 --> 57:38.104 [SPEAKER_00]: It's a big challenge to try to figure out where do we educate them. 57:38.304 --> 57:41.026 [SPEAKER_00]: What is the public education system going to look like five years from now? 57:41.046 --> 57:51.515 [SPEAKER_00]: I mean, now it's easy to think, OK, we'll put them in a Montessori program or some sort of pre-school program, hopefully, you know, they will find one that matches our needs and our schedules. 57:52.035 --> 57:55.598 [SPEAKER_00]: But now when we get to kindergarten, grammar school, public school. 57:56.558 --> 58:02.102 [SPEAKER_00]: Like, really, so, knowing what I know, I'm like, I have to ask myself, like, what is it? 58:02.443 --> 58:03.624 [SPEAKER_00]: Right, that's right. 58:03.644 --> 58:04.004 [SPEAKER_00]: What is it? 58:04.104 --> 58:04.684 [SPEAKER_00]: What's it gonna be? 58:05.045 --> 58:05.305 [SPEAKER_00]: Yeah. 58:06.746 --> 58:21.617 [SPEAKER_00]: I certainly don't want my five-year-old sixth year-old being interacting with AI as a replacement for a teacher, but I'm waiting for examples where people are thinking critically about it and applying it, 58:22.297 --> 58:33.183 [SPEAKER_00]: with evidence where it might actually make it easier for a community of people to be engaged in a child's life, not just the physical classroom where they go. 58:33.824 --> 58:46.191 [SPEAKER_00]: One more thing to say, which is an example of what I mean by society changing, you could have a school on one side of the street and a nursing home on the other side of the street and a human beings and those two buildings never interact with each other. 58:47.593 --> 58:55.097 [SPEAKER_00]: Because we live, we have lived through an era of factorization, the factory schools, factory nursing home. 58:55.117 --> 58:55.577 [SPEAKER_01]: That's right. 58:55.777 --> 58:55.957 [SPEAKER_00]: Right. 58:56.278 --> 58:57.538 [SPEAKER_00]: The efficiency of the industrial age. 58:58.279 --> 59:15.048 [SPEAKER_00]: A lot of those systems, a lot of our thinking around it has to break down and in my mind it really comes down to civic engagement, not being about just voting or jury duty or community board involvement, but actually being present in your community and responsibility for yourself in your neighbors. 59:15.248 --> 59:15.528 [SPEAKER_00]: Yes. 59:16.429 --> 59:30.243 [SPEAKER_00]: And so this technology, this era gives us an opportunity to reinvent the human experience in a way that I think might surprise us. 59:31.762 --> 59:33.363 [SPEAKER_01]: Andrew, we say thank you so much, man. 59:33.383 --> 59:39.307 [SPEAKER_01]: This is, uh, we're heading to some, um, uncharted, uncharted waters. 59:39.387 --> 59:43.610 [SPEAKER_01]: Yeah, and that's, that's, it's scary for a lot of people, but it's also very exciting for others. 59:43.770 --> 59:47.012 [SPEAKER_00]: Well, and, uh, even in uncharted waters, things are discovered. 59:47.293 --> 59:47.953 [SPEAKER_00]: Yes, they are. 59:47.973 --> 59:48.874 [SPEAKER_00]: Yes, that's right. 59:48.894 --> 59:53.297 [SPEAKER_00]: So, let's, let's keep our eyes open in our ears open and our mind, most importantly, our minds and our hearts open. 59:53.577 --> 59:54.297 [SPEAKER_01]: Thank you, Andrew. 59:54.317 --> 59:55.058 [SPEAKER_01]: We appreciate you. 59:55.078 --> 59:57.860 [SPEAKER_01]: Thanks for taking a time and coming in sharing without audience. 59:57.980 --> 59:58.980 [SPEAKER_02]: Thank you so much. 59:59.060 --> 59:59.941 [SPEAKER_02]: All right. 59:59.961 --> 01:00:00.201 [SPEAKER_01]: Great. 01:00:00.221 --> 01:00:01.282 [SPEAKER_02]: Yeah, that's great.