WEBVTT - Broadcasting from Bloomberg Government NEXT 2018 0:00:00.520 --> 0:00:03.040 This is Bloomberg Business Week. I'm Carol Masser and I'm 0:00:03.120 --> 0:00:05.560 Jason Kelly. We're here every day bringing you the latest 0:00:05.600 --> 0:00:09.880 news from the world of business and finance, plus technology, politics, economics, 0:00:09.920 --> 0:00:13.520 all harnessing the power of Bloomberg Business Week reporters and editors, 0:00:13.560 --> 0:00:16.840 not to mention our journalists, analysts, and more than a 0:00:16.880 --> 0:00:19.599 hundred and twenty countries. You can download Bloomberg Business Week 0:00:19.640 --> 0:00:22.480 on iTunes, SoundCloud, or Bloomberg dot com. You can also 0:00:22.560 --> 0:00:25.000 listen to our radio show weekdays at two pm Eastern 0:00:25.079 --> 0:00:28.800 only on Bloomberg Radio. Mentally, he's the CEO at city 0:00:28.880 --> 0:00:32.800 Link dot AI. He's here with me here at the 0:00:32.840 --> 0:00:37.800 Bloomberg Government next event in Washington. This concept of a 0:00:37.920 --> 0:00:40.760 smart city, you know, that's that's very much top of mind. 0:00:40.800 --> 0:00:42.720 It's easy to say, but what is it? Tell us 0:00:42.720 --> 0:00:46.599 what it is? You know, our version is a digital city, 0:00:46.760 --> 0:00:50.199 and it's all about the citizen, you know, creating a 0:00:50.320 --> 0:00:56.200 frictionalists commerce and improving the quality of life. So everything 0:00:56.240 --> 0:00:59.360 in the future is going to be digital, everything from 0:00:59.360 --> 0:01:04.560 your calendars to your activities, to actually having artificial intelligence 0:01:04.560 --> 0:01:09.000 to predict and support your intent and more importantly, you 0:01:09.040 --> 0:01:14.000 start to get a network of machine learning artificial intelligence bots, 0:01:14.040 --> 0:01:17.240 if you will, to optimize the supply chain to support 0:01:17.240 --> 0:01:20.880 those intents within a digital city. So city link ai 0:01:21.000 --> 0:01:27.480 is a technology company that linked the data around people, places, activities, 0:01:27.520 --> 0:01:30.800 and things, and it's an operating system to make that 0:01:30.920 --> 0:01:35.680 data more accessible and more useful using our we call 0:01:35.760 --> 0:01:41.040 our EVE your Virtual Enterprise Artificial Intelligence platform. So it's 0:01:41.040 --> 0:01:45.520 all about the citizen engagement. And you kind of stick 0:01:45.520 --> 0:01:48.600 a step back and think about Disney. Everything about Disney 0:01:48.880 --> 0:01:52.560 is about the guests experience. So what we're doing is 0:01:52.640 --> 0:01:56.680 taking that approach and bringing it to many cities, many 0:01:56.760 --> 0:02:02.560 university towns and create that environ seven to create. Basically, 0:02:02.640 --> 0:02:06.240 we call it simple, convenient, and fun. All right, So 0:02:06.360 --> 0:02:10.359 convenience comes at a cost to some extent, both in 0:02:10.440 --> 0:02:13.520 actual costs and also you talk about transparency. We've talked 0:02:13.520 --> 0:02:17.080 a lot at this conference about access, access to information, 0:02:17.760 --> 0:02:23.160 and access to data. How do you ensure that it's safe, secure, 0:02:24.320 --> 0:02:28.720 and that everyone's privacy is taken into account. Yeah, that's 0:02:28.760 --> 0:02:31.600 a very very big topic. You can imagine all the 0:02:31.720 --> 0:02:34.320 large company like Amazon that you mentioned in Google and 0:02:34.360 --> 0:02:37.960 all tackling this the same issue. We are actually building 0:02:38.040 --> 0:02:41.920 a privacy model first, So the idea of having the 0:02:42.000 --> 0:02:45.639 ability to opt in once will happen and own your 0:02:45.639 --> 0:02:49.720 own data, have your own private personal assistant, your private 0:02:49.760 --> 0:02:54.760 clouds that follow you using our platform EVE, your virtual enterprise, 0:02:55.160 --> 0:02:57.960 and the ability to actually archive and delete the data 0:02:58.600 --> 0:03:02.160 and manage your own data is where we're moving towards. 0:03:02.160 --> 0:03:04.079 So the idea that is, how do we level the 0:03:04.160 --> 0:03:09.040 playing field and push the information, the ownership and responsibility 0:03:09.240 --> 0:03:13.040 back to the citizens, back to the local environment versus 0:03:13.120 --> 0:03:16.720 a one place where everybody is going to know one 0:03:16.840 --> 0:03:20.760 or two very large corporation. So we call that hyperlocalization. 0:03:21.320 --> 0:03:24.920 I mean, I want to ask you too though about development, uh, 0:03:25.080 --> 0:03:27.960 in terms of building these cities much easier to kind 0:03:28.000 --> 0:03:31.000 of build a connected city from the ground up, And 0:03:31.040 --> 0:03:33.760 I'm just curious about the obstacles that need to be 0:03:33.800 --> 0:03:36.160 overcome to kind of take our old cities and bring 0:03:36.200 --> 0:03:38.720 them and make them new and make them much more 0:03:38.760 --> 0:03:42.760 connected than they have been. That's a great question. In fact, 0:03:42.880 --> 0:03:44.960 we we face that every day, and I think you 0:03:45.000 --> 0:03:47.480 have to take a step back and take a holistic 0:03:47.480 --> 0:03:50.680 approach to this. So think about it from a consortium 0:03:50.720 --> 0:03:53.960 and a partnership perspective. Who's going to pay for all 0:03:54.000 --> 0:03:56.480 of this. So that's how we think about it. So 0:03:56.520 --> 0:03:59.000 when we come in, we bring the capital. We have 0:03:59.200 --> 0:04:03.400 a practical, who market driven environment that has a real 0:04:03.480 --> 0:04:07.440 return on impact investing. At the same time, because we 0:04:07.480 --> 0:04:11.240 are also a commercial real estate investor and developer, we 0:04:11.360 --> 0:04:16.360 understand the hurdle to build, finance, operate an environment. In fact, 0:04:16.400 --> 0:04:20.039 we're building a smart district out in Grammacy District outside 0:04:20.040 --> 0:04:23.800 of the Washington d C. Coincidentally only eleven miles from 0:04:23.800 --> 0:04:27.080 where National Landing will be right on the silver line 0:04:27.120 --> 0:04:30.279 out in Loudon County. But more importantly, you actually have 0:04:30.480 --> 0:04:33.120 to bring in everybody. In this case, we bring the 0:04:33.160 --> 0:04:36.440 tool set, we bring the capital, we bring all partners. 0:04:37.040 --> 0:04:40.200 Wave Capital Partners is one of our capital providers. So 0:04:40.360 --> 0:04:43.320 that holistic approach is the only way you can actually 0:04:43.320 --> 0:04:47.360 move the ball because you have to optimize the resources 0:04:47.400 --> 0:04:50.600 available in the local environment. We can call it for 0:04:50.640 --> 0:04:52.480 a second, I just a quick question. I'm just curious 0:04:52.560 --> 0:04:57.279 geographically global ating. Is there someone, some country, some area 0:04:57.360 --> 0:04:59.560 that's going to lead in the development of these types 0:04:59.600 --> 0:05:04.080 of city you know, in the United States. I think 0:05:04.120 --> 0:05:08.200 it has to be capital driven, in commercial driven, with 0:05:08.279 --> 0:05:12.840 a partnership with university and governments. So unlike you know 0:05:12.920 --> 0:05:16.280 other countries, we actually have to have return on investments 0:05:16.320 --> 0:05:20.000 and we have to think about an economically viable solution. 0:05:20.880 --> 0:05:25.520 It is great to think you can go green, but 0:05:25.720 --> 0:05:29.520 you cannot underwrite it and finance it and attract the 0:05:29.600 --> 0:05:36.039 impact investors to dry feasible and sustainable business model. It 0:05:36.160 --> 0:05:39.560 just doesn't go. So we're focusing on the US. There's 0:05:39.600 --> 0:05:43.840 three point five trillion dollars in commercial real estate market 0:05:43.880 --> 0:05:47.360 in the US. All model is simple. We're actually zero 0:05:47.520 --> 0:05:51.719 and asset. But we want to digitize the people, the places, 0:05:51.760 --> 0:05:56.560 the things and activities in a hyper localized environment. UM 0:05:56.640 --> 0:05:59.719 and will actually give the power back to the citizen. 0:06:00.440 --> 0:06:04.440 That's that's only available now because the ubiquity of technology. 0:06:05.279 --> 0:06:07.080 You know, ten years ago you wouldn't be able to 0:06:07.080 --> 0:06:09.520 do this because it cost you tens of millions up 0:06:09.600 --> 0:06:12.400 dollars to achieve what we're able to do today in 0:06:12.440 --> 0:06:17.880 a small UM seven investment. Yeah, that's great, Manly CEO 0:06:17.960 --> 0:06:20.839 at City Link AI here with me in Washington at 0:06:20.839 --> 0:06:25.760 the Bloomberg Government next eighteen event. Because when you where 0:06:25.960 --> 0:06:33.040 your fist will from, that will bring everybody down all right, Carol, 0:06:33.160 --> 0:06:36.680 So I know you're not worried about anything. You're always happy. 0:06:36.760 --> 0:06:38.480 You always got a lot of sleep. On the way 0:06:38.520 --> 0:06:43.960 back from London, I'm joined now by someone who is 0:06:44.000 --> 0:06:46.560 helping us put AI into perspective. He's got a new 0:06:46.560 --> 0:06:49.800 book out. He's Daniel Wagner, founder and chief executive officer 0:06:49.839 --> 0:06:52.480 of Country Risk Solutions. Here with me at the Bloomberg 0:06:52.480 --> 0:06:56.320 Government next. Some of the book is called AI Supremacy, 0:06:56.480 --> 0:06:59.040 Winning in the Era of Machine Learning. It's got a 0:06:59.120 --> 0:07:01.640 very cool uh cover care. I'm going to bring it 0:07:01.680 --> 0:07:05.360 back to you people racing. They've got flags made, They've 0:07:05.360 --> 0:07:09.000 got heads made of flags. It's so cool. Uh, Daniel, 0:07:09.000 --> 0:07:10.920 thank you so much for joining us. Good to be 0:07:10.960 --> 0:07:15.680 with you so help us feel good about AI and 0:07:15.840 --> 0:07:18.040 how it's gonna make our lives better and we shouldn't 0:07:18.040 --> 0:07:20.120 be scared of all the robots. Well, I think what 0:07:20.240 --> 0:07:23.200 AI is really going to do is help the human 0:07:23.320 --> 0:07:27.400 race be all that it can be. There are pluses 0:07:27.400 --> 0:07:30.440 and minuses associated with this, of course, but when you 0:07:30.480 --> 0:07:33.720 think about unleashing human potential, this, in my mind is 0:07:33.760 --> 0:07:36.400 what AI is really about. You know, you have such 0:07:36.400 --> 0:07:38.440 a fascinating background. We were talking a little bit about 0:07:38.440 --> 0:07:40.120 it before we came on. Are you worked at GE, 0:07:40.280 --> 0:07:42.360 you worked at a I G. You worked for the 0:07:42.400 --> 0:07:45.120 Asian Development Bank. I believe you've worked all over the world, 0:07:45.400 --> 0:07:49.080 so you understand that this is not a US phenomenon. 0:07:49.400 --> 0:07:53.640 Far from it, and the Chinese especially have essentially said 0:07:54.000 --> 0:07:56.560 we're coming for you when it comes to AI. To 0:07:56.560 --> 0:07:59.520 to the US, how is it playing out globally and 0:07:59.520 --> 0:08:01.520 and who's got the edge at this point. First of all, 0:08:01.560 --> 0:08:04.040 I should say that the Chinese get it. They see 0:08:04.080 --> 0:08:07.200 this as the future. They're devoting up to a hundred 0:08:07.280 --> 0:08:10.440 fifty billion dollars over the next decade to make this 0:08:10.560 --> 0:08:13.000 a reality for them and to make them the premier 0:08:13.120 --> 0:08:15.920 power in the AI arena. If you look on the 0:08:15.960 --> 0:08:18.200 book cover, you see that there are five countries in 0:08:18.240 --> 0:08:22.920 the mix, including China, the US, Japan, Germany, Russia, and 0:08:23.280 --> 0:08:25.720 the US is currently kind of in the lead and 0:08:25.840 --> 0:08:28.720 China's nipping at its heels. There is no doubt in 0:08:28.760 --> 0:08:31.760 my mind, based on the amount of resources being devoted 0:08:31.760 --> 0:08:35.200 by China, that this is China's race to lose and 0:08:35.240 --> 0:08:39.200 that it will very quickly, uh you know, succeed the 0:08:39.320 --> 0:08:41.360 US as the leader. And I think it's going to 0:08:41.480 --> 0:08:44.360 stay there in big part because we're not devoting the 0:08:44.400 --> 0:08:46.680 resources and spending the money to make sure that we 0:08:46.720 --> 0:08:49.920 stay on top. So it's a great point, and I 0:08:49.960 --> 0:08:53.240 think everybody should, you know, kind of sit down and 0:08:53.280 --> 0:08:56.400 listen to this because we don't, I think, fully acknowledge 0:08:56.480 --> 0:08:59.240 the amount of money and effort and time that China 0:08:59.280 --> 0:09:02.120 is putting on a What will be the result if 0:09:02.280 --> 0:09:07.679 China is the leader when it comes to artificial intelligence? Um, 0:09:07.800 --> 0:09:10.080 I just think about you know, one HND one, one 0:09:10.120 --> 0:09:14.040 country dominating this world. Right, There's already been a global 0:09:14.040 --> 0:09:18.280 grad for AI engineers big time, and China's aggressively pursuing them. 0:09:18.320 --> 0:09:21.120 You know, Dan Daniel, what does this mean potentially on 0:09:21.200 --> 0:09:24.559 a geopolitical level. That's a great question, and it does 0:09:24.600 --> 0:09:29.160 have geopolitical implications because the country or set of countries 0:09:29.200 --> 0:09:33.320 that rules the AI arena is going to rule the 0:09:33.360 --> 0:09:37.880 future of the global economy. And my concern is that 0:09:38.000 --> 0:09:40.120 not only for the US, but the other countries that 0:09:40.160 --> 0:09:44.440 are not sprinting ahead fall further and further behind. So 0:09:44.559 --> 0:09:48.600 the question becomes, if China takes the lead and they 0:09:48.640 --> 0:09:50.959 stay in the lead, will they ever be caught up? 0:09:51.440 --> 0:09:53.160 I think there's a pretty good chance they will not. 0:09:53.679 --> 0:09:56.400 They understand the stakes that are involved, and they're putting 0:09:56.440 --> 0:09:58.360 their money where their mouth is. They're not just talking 0:09:58.400 --> 0:10:01.360 about it, They're doing it and are doing it very aggressively, 0:10:01.640 --> 0:10:04.160 through m and A, through the acquisition of talent, through 0:10:04.280 --> 0:10:07.719 making sure that their graduates understand what AI is all about, etcetera. 0:10:08.000 --> 0:10:11.120 What does that mean for us? That's a very good question. 0:10:11.200 --> 0:10:14.120 We're getting all excited about DARPA getting a couple of 0:10:14.120 --> 0:10:17.280 billion dollars, Meanwhile tens of billions are being spent by 0:10:17.360 --> 0:10:19.160 other countries in a single year. Well, and I do 0:10:19.240 --> 0:10:21.480 think about Hank Paulson has warned about this former U. S. 0:10:21.480 --> 0:10:25.400 Treasury secretary about an economic iron curtain, right if if 0:10:25.960 --> 0:10:27.480 he talks about if the U. S. And China can't 0:10:27.480 --> 0:10:29.160 get along. But I mean there's a there's a lot 0:10:29.200 --> 0:10:33.440 of ways of um the world kind of being divided 0:10:33.520 --> 0:10:35.719 up as a result of being kind of first mover 0:10:35.800 --> 0:10:39.120 advantage when it comes to technology. So my perspective is 0:10:39.120 --> 0:10:41.640 that this really is the Chinese century. I think people 0:10:41.640 --> 0:10:44.640 who don't acknowledge that, or maybe a little bit delusional 0:10:44.760 --> 0:10:46.640 or maybe having a hard time falling off of our 0:10:46.640 --> 0:10:51.160 own precipice and that's okay. We have distinct comparative advantages, 0:10:51.200 --> 0:10:53.439 and so do they. It's very easy for them to 0:10:53.480 --> 0:10:55.800 do what they're doing. They simply wave a magic wand, 0:10:56.080 --> 0:10:58.679 and being an authoritarian government, they make it happen. They 0:10:58.679 --> 0:11:00.960 don't have to have a lot of discussion, they don't 0:11:01.000 --> 0:11:04.320 have to talk about allocation of resources. It just happens. 0:11:04.600 --> 0:11:07.640 In this country, it's a much more different process. It's 0:11:07.720 --> 0:11:12.960 much more complicated, it's much more um convoluted, and that 0:11:12.960 --> 0:11:15.400 that's to our own disadvantage. It would be really nice 0:11:15.440 --> 0:11:19.000 if we had successive governments which acknowledged what it is 0:11:19.040 --> 0:11:22.959 that is at stake and we're simply proceeding apace so 0:11:23.000 --> 0:11:25.680 that we have a strategy and that strategy is going 0:11:25.720 --> 0:11:28.400 to be implemented on an ongoing basis. But that's not 0:11:28.440 --> 0:11:30.320 what we have. So let's go back to where you 0:11:30.520 --> 0:11:33.920 started this conversation, which was a pretty optimistic view of 0:11:34.080 --> 0:11:37.400 you know, how AI ultimately can make us better humans. 0:11:37.760 --> 0:11:39.840 One thing that we've covered in Bloomberg Business Week and 0:11:39.920 --> 0:11:43.160 a number of times, and relatively provocatively, and I think importantly, 0:11:43.360 --> 0:11:49.360 is understanding bias in machines and machine learning and trying 0:11:49.360 --> 0:11:51.920 to prevent that. I know it's something you deal with 0:11:52.559 --> 0:11:55.840 in your book. Are we worried enough about that? And 0:11:55.880 --> 0:11:59.040 are the right things being done to combat that? So 0:11:59.120 --> 0:12:01.840 we aren't concerned enough about it? People should understand that 0:12:01.880 --> 0:12:06.199 there's bias introduced in everything that we teach a robot 0:12:06.400 --> 0:12:09.880 or a machine to do. They inherit our biases. That 0:12:09.960 --> 0:12:12.400 includes racial bias, by the way, which has all sorts 0:12:12.400 --> 0:12:18.559 of unfortunate undertones when AI is put into practice. Uh, 0:12:18.600 --> 0:12:20.400 there's not much that we can do about that. The 0:12:20.400 --> 0:12:23.160 only way we lose bias, I think is when the 0:12:23.200 --> 0:12:26.160 computers don't need us anymore and they can teach themselves. 0:12:26.240 --> 0:12:28.960 But of course then they've already integrated the biases that 0:12:29.000 --> 0:12:31.920 we have into the process. That's part of human nature, 0:12:32.120 --> 0:12:34.240 that's part of who we are. It will be really 0:12:34.320 --> 0:12:38.040 interesting to watch the evolution about how bias either becomes 0:12:38.120 --> 0:12:42.440 enhanced or slowly disappears as AI develops in the future. 0:12:42.559 --> 0:12:46.640 Fascinating such big questions. Congratulations on this book. The book 0:12:46.720 --> 0:12:50.160 is called AI Supremacy, Winning in the Era of Machine Learning. 0:12:50.559 --> 0:12:54.160 Co author Daniel Wagner also the CEO of Country Risk 0:12:54.280 --> 0:12:56.640 Solutions based in Connecticut, but here with me at the 0:12:56.640 --> 0:13:00.959 Bloomberg Government Next Event and watched to Thank you so much, 0:13:01.160 --> 0:13:03.360 come back and join us. I know you're working on 0:13:03.400 --> 0:13:05.840 a new book. I won't spoil it, but it's going 0:13:05.880 --> 0:13:08.880 to be a good one as well. Back to you, Carol. Yeah, 0:13:08.960 --> 0:13:12.040 it's certainly gonna be a focus in twenty nineteen. Speaking 0:13:12.040 --> 0:13:14.920 more about artis intelligence. You are listening to Bloomberg Business Week, 0:13:14.920 --> 0:13:18.319 Carol Master Jason Kelly right here on Bloomberg Radio. You 0:13:18.440 --> 0:13:24.600 say you want a rebel little show. No, all right, 0:13:24.640 --> 0:13:28.080 So this is a very very cool topic we're about 0:13:28.080 --> 0:13:30.240 to get into, Carol. I'm very fortunate to be joined 0:13:30.240 --> 0:13:33.040 by Diana Cooper. She's the senior VP of Policy and 0:13:33.160 --> 0:13:37.079 Strategy at Precision Hawk. She's joining me here at the 0:13:37.080 --> 0:13:42.720 Bloomberg Government Next event in Washington. So Precision Hawk, Carol, 0:13:43.040 --> 0:13:46.320 it's about drone. It's about drones. We've been sitting here 0:13:46.880 --> 0:13:50.160 talking about drones, talking about a sort of rogue drone 0:13:50.200 --> 0:13:52.439 operation I was involved in a couple of years ago. 0:13:52.480 --> 0:13:56.120 I don't think you were there, Carol, out in San Francisco. Um, Diana, 0:13:56.160 --> 0:14:00.400 great to be with you. So tell us how drones 0:14:00.440 --> 0:14:02.640 are kind of coming into the mainstream because you're talking 0:14:02.640 --> 0:14:07.319 about using them for kind of business intelligence at this point. Yeah. Absolutely. 0:14:07.400 --> 0:14:09.880 So drones have been around on the commercial side for 0:14:09.960 --> 0:14:13.040 about four or five years now. Really started to take 0:14:13.080 --> 0:14:15.120 off about two years ago when d f A put 0:14:15.120 --> 0:14:17.800 out the first rule for commercial operations known as Part 0:14:17.840 --> 0:14:20.600 one oh seven, which kind of made it more accessible 0:14:20.640 --> 0:14:24.800 than through a very um expensive and lengthy waiverard process. 0:14:24.880 --> 0:14:27.720 And so the first applications were we we actually saw 0:14:27.760 --> 0:14:31.320 where India agriculture space, so a lot of farmers are 0:14:31.440 --> 0:14:35.320 using it to monitor their crop yield, look for UM 0:14:35.480 --> 0:14:38.840 bug infed stations, water damage and things like that, and 0:14:38.920 --> 0:14:42.960 really understand where to do precision UH interventions like nitrogen 0:14:43.000 --> 0:14:46.440 application and things like that. UM. More recently, we've seen 0:14:46.880 --> 0:14:49.920 energy companies as well as insurance companies doing things like 0:14:50.040 --> 0:14:54.000 roof inspections and all kinds of new applications coming out 0:14:54.040 --> 0:14:56.240 as well. I gotta jump in because so I know 0:14:56.320 --> 0:14:58.320 I keep taking it back to London and our blood 0:14:58.320 --> 0:15:02.000 breakaway of it. Well no, well I am. But what 0:15:02.040 --> 0:15:04.400 was interesting is UM. One of the sponsors was Dell, 0:15:04.560 --> 0:15:07.600 and they talked UM a lot about They actually showed 0:15:07.600 --> 0:15:09.800 a video clip of two people getting in a car 0:15:09.880 --> 0:15:14.120 accident and how in a connected world versus the traditional world. 0:15:14.120 --> 0:15:16.680 That kind of showed the two drivers and when you know, 0:15:17.080 --> 0:15:19.240 I had to call their insurance agent was on the 0:15:19.240 --> 0:15:22.040 phone waiting the other one the smart car said Hey, 0:15:22.120 --> 0:15:23.920 you're in an accident. Let me call your insured. The 0:15:23.960 --> 0:15:25.760 insure gets on Da da Da da, and then all 0:15:25.800 --> 0:15:27.600 of a sudden said hey, we're gonna send out a drone. 0:15:27.600 --> 0:15:29.360 They're gonna look at the damage. And the drone comes 0:15:29.360 --> 0:15:31.720 and checks it out. Now, it was a little bit extreme, 0:15:31.840 --> 0:15:35.000 but as they said, this will happen and you will 0:15:35.040 --> 0:15:38.120 like instantaneously be able to kind of get it all done, 0:15:38.240 --> 0:15:42.160 get an insurance adjustment or or assessment. Um, you know, 0:15:42.240 --> 0:15:43.920 like I said, courtesy of a drone coming out to 0:15:44.000 --> 0:15:46.720 check out the situation, look at the damage. Like we're 0:15:46.800 --> 0:15:49.280 we're getting ready for a very different type of world, 0:15:49.320 --> 0:15:52.160 are we not? Absolutely? And that's that application is already 0:15:52.160 --> 0:15:55.520 starting to happen today, not just for accidents. But you know, 0:15:55.600 --> 0:15:57.480 for example, our company did a lot of work after 0:15:57.560 --> 0:16:00.240 Hurricane Florence in North Carolina and a lot to the 0:16:00.240 --> 0:16:03.480 areas we're just devastated and fully flooded. You couldn't access 0:16:03.560 --> 0:16:06.480 them by foot, So traditional adjusters couldn't actually walk up 0:16:06.520 --> 0:16:09.000 to those neighborhoods and see what the damage was. There 0:16:09.000 --> 0:16:11.120 were t frs in place, it was difficult for manned 0:16:11.120 --> 0:16:14.200 aircraft to access and gather that data. So we're able 0:16:14.240 --> 0:16:16.920 to fly in very quickly with drones, gather really good data, 0:16:17.080 --> 0:16:20.840 help people's claims get processed faster and get back into 0:16:20.880 --> 0:16:22.840 their homes or get some sort of a remedy. So 0:16:22.880 --> 0:16:26.360 I gotta ask you because you arguably have, like the 0:16:26.400 --> 0:16:28.880 engineers have a tough job, but you arguably have an 0:16:28.920 --> 0:16:32.640 even tougher job, which is to ensure that sort of 0:16:32.680 --> 0:16:37.880 the regulatory piece keeps up with where technology is going. 0:16:37.920 --> 0:16:40.520 So tell us about the latest there and what you're 0:16:40.560 --> 0:16:43.680 hearing from, especially from the federal level you're based here 0:16:44.120 --> 0:16:47.920 in Washington, About how much that the government is or 0:16:48.000 --> 0:16:51.120 isn't sort of embracing where you're going. Yeah, absolutely, We're 0:16:51.160 --> 0:16:53.480 We're lucky to have a very forward leaning regulator, the 0:16:53.560 --> 0:16:55.880 f A that you know, had the foresight not only 0:16:55.960 --> 0:16:58.040 to create the first commercial rule for drones a couple 0:16:58.040 --> 0:17:00.480 of years ago, which is fair fairly forward meaning for 0:17:00.600 --> 0:17:03.080 given that time frame, but they also had the foresight 0:17:03.160 --> 0:17:06.119 to actually enter into partnerships with commercial companies like ours 0:17:06.160 --> 0:17:08.840 to have us test things like beyondline of site operations, 0:17:09.160 --> 0:17:11.520 to actually create the data so that they can have 0:17:11.640 --> 0:17:15.640 data driven rulemaking for expanded operations. Uh so we're pretty 0:17:15.680 --> 0:17:17.639 lucky in that respect. We want things to stay with 0:17:17.680 --> 0:17:20.000 the f A. Uh. What we want to do is 0:17:20.040 --> 0:17:22.120 try to keep the cities and the states from actively 0:17:22.200 --> 0:17:24.919 also piling on regulations on the drone industry. And so 0:17:24.960 --> 0:17:26.880 how do you do that? I mean that that would 0:17:26.920 --> 0:17:31.400 seem like a very real danger at this point. Yeah, absolutely. Uh. 0:17:31.520 --> 0:17:33.520 Fine Stein and Lewis each had bills on the hill 0:17:33.560 --> 0:17:36.320 over the last year trying to um get the ability 0:17:36.320 --> 0:17:38.480 to regulate up to two feet, which we think is 0:17:38.760 --> 0:17:41.639 damaging from an innovation perspective, you can't build your business 0:17:41.640 --> 0:17:43.560 across city or state lines if you have to deal 0:17:43.640 --> 0:17:48.000 with thirty six thousand counties regulating your operations. And also 0:17:48.080 --> 0:17:51.439 from a safety perspective, you know, we have a highly 0:17:51.880 --> 0:17:56.040 sophisticated regulator with a great aviation safety track record. Counties 0:17:56.080 --> 0:17:58.760 and states do not have that track record and would 0:17:58.760 --> 0:18:00.720 be you know, we would be seeing all of accidents 0:18:00.760 --> 0:18:03.159 take place. What are the tricky things that still have 0:18:03.240 --> 0:18:05.520 to Diana kind of be worked out as we move 0:18:05.560 --> 0:18:11.720 into a more drone prone world. Yeah. Absolutely, Um, you 0:18:11.760 --> 0:18:14.680 know the first is really remote identification and tracking. So 0:18:15.000 --> 0:18:17.880 there's been an informal hold over rulemaking for a flight 0:18:17.920 --> 0:18:21.199 over people or expanded operations because the security agencies have 0:18:21.320 --> 0:18:24.080 concerns today. If they see a drone flying over a 0:18:24.080 --> 0:18:26.640 crowd of people, they know that operation is likely illegal, 0:18:26.680 --> 0:18:29.000 and so that's a threat they need to mitigate. If 0:18:29.040 --> 0:18:31.160 everyone is allowed to do it under the rules, how 0:18:31.160 --> 0:18:33.639 will they know what's a good drone versus a roade drone. 0:18:34.040 --> 0:18:36.200 And so really getting the rule making out for remote 0:18:36.240 --> 0:18:39.320 identification and tracking is the next big thing that we're 0:18:39.320 --> 0:18:42.760 waiting for, and it's supposed to drop imminently. Great. Diana Cooper, 0:18:42.800 --> 0:18:46.080 Senior vice president of Policy and Strategy at Precision Hawk. 0:18:46.119 --> 0:18:50.000 Fun fact, Carol, original name wine Hawk because they used 0:18:50.040 --> 0:18:52.600 to look over vineyards. I knew you would like that, 0:18:57.359 --> 0:19:00.479 all right, Little Bruce Springsteen fourth a bit of New 0:19:00.600 --> 0:19:04.280 Jersey here in Washington, d C. We are Carol at 0:19:04.320 --> 0:19:07.760 the Bloomberg Government next event and as we've been talking 0:19:07.760 --> 0:19:12.040 about artificial intelligence, so top of mind here, but I 0:19:12.080 --> 0:19:14.320 feel like the conversations are getting in a good way, 0:19:14.840 --> 0:19:20.280 much more complex and a very special guest joining us Saska. 0:19:20.400 --> 0:19:24.760 Moissy Levich is head of AI Foundations at IBM, and 0:19:24.760 --> 0:19:26.359 I have to say, Carrol, if you didn't see this 0:19:26.440 --> 0:19:30.119 in our notes, has a really cool Twitter handled data Priestess. 0:19:30.840 --> 0:19:33.200 That is amazing. How did you come up at best? 0:19:33.520 --> 0:19:38.760 That is I thought about the data Jong keep Yeah, 0:19:38.880 --> 0:19:41.880 I like data preestiss. It feels a little more. It's 0:19:41.920 --> 0:19:45.600 a female power right there. You go, So tell us 0:19:45.640 --> 0:19:50.800 about this intersection of basically the human and the machine. 0:19:50.840 --> 0:19:52.960 You do so much work in this area. Help us 0:19:53.040 --> 0:19:57.080 understand kind of where we are and where we're going. Okay, 0:19:57.160 --> 0:20:00.760 so um, I'm going to start where we used to be, 0:20:00.840 --> 0:20:04.200 which was like in nineties fifties when the whole field started. 0:20:04.200 --> 0:20:08.600 There was like this whole big fascination with creating machines 0:20:08.680 --> 0:20:13.840 then that can think and on that journey, we've tried 0:20:14.119 --> 0:20:16.520 so many different things, and I think we're still very 0:20:16.640 --> 0:20:19.080 very early on that journey. But right now, we we 0:20:19.280 --> 0:20:22.280 came to a point where we suddenly had this huge 0:20:22.320 --> 0:20:25.320 access to massive amounts of data. We know how to 0:20:25.440 --> 0:20:28.639 analyze it, how to parts it, how to tease insights 0:20:28.680 --> 0:20:31.399 out of it, and we're actually beginning to think about 0:20:31.400 --> 0:20:35.200 how can this take us to be hopefully maybe better 0:20:35.320 --> 0:20:38.840 or maybe more efficient, And we are beginning to to 0:20:39.000 --> 0:20:41.439 kind of put it into some sort of decision making. 0:20:42.520 --> 0:20:47.000 Uh where we're going? I think there's an enormous evolution 0:20:47.040 --> 0:20:48.600 ahead of us, and I think I would like to 0:20:48.600 --> 0:20:50.560 think that we we should be the ones driving it. 0:20:50.600 --> 0:20:53.600 We should be the ones responsible for saying, hey, this 0:20:53.680 --> 0:20:55.639 is where I want to take AI next. These are 0:20:55.640 --> 0:20:57.639 the kinds of things that we want to create. So, 0:20:57.760 --> 0:21:02.679 for example, rather than maybe doing movie recommendations and taking 0:21:03.240 --> 0:21:05.280 us to the best restaurant, can we put into some 0:21:05.359 --> 0:21:09.160 really good use to humanity, you know, helping solve issues 0:21:09.320 --> 0:21:13.880 I love? Well, like what I love that idea? Uh? Yeah, 0:21:13.880 --> 0:21:16.840 So so I can give you plenty of examples, because 0:21:17.000 --> 0:21:20.679 um um, I run a program at IBM called Science 0:21:20.680 --> 0:21:23.560 for Social Goods. So I'll just pull a couple of examples. 0:21:23.560 --> 0:21:26.600 So one, for example, is how can we maybe mine 0:21:26.720 --> 0:21:30.600 massive amounts of prescription data or or healthcare data to 0:21:30.760 --> 0:21:35.639 understand how people get addicted to opioids because different doctors 0:21:35.400 --> 0:21:39.520 prescribed differently, different people behave differently, and if we can 0:21:39.520 --> 0:21:42.000 tease out these patterns, we can actually come up with 0:21:42.040 --> 0:21:47.440 the shot at creating more more responsible UH prescription guidelines 0:21:47.480 --> 0:21:51.639 and and curb the epidemic. Um. Another example is, okay, 0:21:51.680 --> 0:21:56.560 how about teaching computers to design new drugs that can 0:21:56.680 --> 0:22:01.000 um maybe help us deal with the antimicro build resistance. Like, 0:22:01.560 --> 0:22:04.840 designing new drugs is enormously expensive and it takes, like 0:22:04.880 --> 0:22:08.640 you know, decades and an enormous investment, and imagine being 0:22:08.680 --> 0:22:12.560 able to do that faster and cheaper and with a 0:22:12.560 --> 0:22:15.000 little bit more of help of kind of different type 0:22:15.000 --> 0:22:18.520 of creativity. They will take us to a really new direction. 0:22:18.920 --> 0:22:22.440 And So what's your biggest worry as you design these systems? 0:22:22.440 --> 0:22:27.399 As you think about this interaction between humans and machines, 0:22:27.440 --> 0:22:29.800 where are we most likely to kind of get it wrong? 0:22:30.240 --> 0:22:34.760