WEBVTT 00:00.000 --> 00:02.800 The following is a conversation with Eric Schmidt. 00:02.800 --> 00:07.640 He was the CEO of Google for ten years and a chairman for six more, guiding the company 00:07.640 --> 00:13.080 through an incredible period of growth and a series of world changing innovations. 00:13.080 --> 00:19.280 He is one of the most impactful leaders in the era of the internet and the powerful voice 00:19.280 --> 00:22.400 for the promise of technology in our society. 00:22.400 --> 00:27.760 It was truly an honor to speak with him as part of the MIT course on artificial general 00:27.760 --> 00:32.040 intelligence and the artificial intelligence podcast. 00:32.040 --> 00:37.120 And now here's my conversation with Eric Schmidt. 00:37.120 --> 00:40.840 What was the first moment when you fell in love with technology? 00:40.840 --> 00:47.080 I grew up in the 1960s as a boy where every boy wanted to be an astronaut and part of 00:47.080 --> 00:49.000 the space program. 00:49.000 --> 00:54.400 So like everyone else of my age, we would go out to the cow pasture behind my house, 00:54.400 --> 00:58.640 which was literally a cow pasture, and we would shoot model rockets off. 00:58.640 --> 01:00.200 And that I think is the beginning. 01:00.200 --> 01:05.680 And of course, generationally, today, it would be video games and all the amazing things 01:05.680 --> 01:09.280 that you can do online with computers. 01:09.280 --> 01:15.080 There's a transformative, inspiring aspect of science and math that maybe rockets would 01:15.080 --> 01:17.560 bring, would instill in individuals. 01:17.560 --> 01:21.720 You've mentioned yesterday that eighth grade math is where the journey through Mathematical 01:21.720 --> 01:24.520 University diverges from many people. 01:24.520 --> 01:27.080 It's this fork in the roadway. 01:27.080 --> 01:31.160 There's a professor of math at Berkeley, Edward Franco. 01:31.160 --> 01:32.720 I'm not sure if you're familiar with him. 01:32.720 --> 01:33.720 I am. 01:33.720 --> 01:35.400 He has written this amazing book. 01:35.400 --> 01:41.960 I recommend to everybody called Love and Math, two of my favorite words. 01:41.960 --> 01:49.680 He says that if painting was taught like math, then students would be asked to paint a fence, 01:49.680 --> 01:54.520 which is his analogy of essentially how math is taught, and you never get a chance to discover 01:54.520 --> 01:59.400 the beauty of the art of painting or the beauty of the art of math. 01:59.400 --> 02:05.240 So how, when, and where did you discover that beauty? 02:05.240 --> 02:12.040 I think what happens with people like myself is that your math enabled pretty early, and 02:12.040 --> 02:16.640 all of a sudden you discover that you can use that to discover new insights. 02:16.640 --> 02:22.120 The great scientists will all tell a story, the men and women who are fantastic today, 02:22.120 --> 02:25.560 that somewhere when they were in high school or in college, they discovered that they could 02:25.560 --> 02:30.760 discover something themselves, and that sense of building something, of having an impact 02:30.760 --> 02:35.520 that you own drives knowledge, acquisition, and learning. 02:35.520 --> 02:41.000 In my case, it was programming, and the notion that I could build things that had not existed 02:41.000 --> 02:46.560 that I had built, that it had my name on it, and this was before open source, but you could 02:46.560 --> 02:49.160 think of it as open source contributions. 02:49.160 --> 02:53.760 So today, if I were a 16 or 17 year old boy, I'm sure that I would aspire as a computer 02:53.760 --> 02:59.000 scientist to make a contribution like the open source heroes of the world today. 02:59.000 --> 03:03.760 That would be what would be driving me, and I'd be trying and learning and making mistakes, 03:03.760 --> 03:06.720 and so forth, in the ways that it works. 03:06.720 --> 03:12.360 The repository that GitHub represents and that open source libraries represent is an 03:12.360 --> 03:17.840 enormous bank of knowledge of all of the people who are doing that, and one of the lessons 03:17.840 --> 03:22.240 that I learned at Google was that the world is a very big place, and there's an awful 03:22.240 --> 03:26.360 lot of smart people, and an awful lot of them are underutilized. 03:26.360 --> 03:32.240 So here's an opportunity, for example, building parts of programs, building new ideas to contribute 03:32.240 --> 03:36.640 to the greater of society. 03:36.640 --> 03:41.000 So in that moment in the 70s, the inspiring moment where there was nothing, and then you 03:41.000 --> 03:44.800 created something through programming, that magical moment. 03:44.800 --> 03:50.360 So in 1975, I think you've created a program called Lex, which I especially like because 03:50.360 --> 03:51.560 my name is Lex. 03:51.560 --> 03:52.560 So thank you. 03:52.560 --> 03:58.240 Thank you for creating a brand that established a reputation that's long lasting reliable 03:58.240 --> 04:01.240 and has a big impact on the world and still used today. 04:01.240 --> 04:03.000 So thank you for that. 04:03.000 --> 04:11.880 But more seriously, in that time, in the 70s, as an engineer, personal computers were being 04:11.880 --> 04:12.880 born. 04:12.880 --> 04:17.800 Do you think you'd be able to predict the 80s, 90s, and the aughts of where computers 04:17.800 --> 04:18.800 would go? 04:18.800 --> 04:23.160 I'm sure I could not and would not have gotten it right. 04:23.160 --> 04:27.960 I was the beneficiary of the great work of many, many people who saw it clearer than I 04:27.960 --> 04:29.160 did. 04:29.160 --> 04:34.760 With Lex, I worked with a fellow named Michael Lesk, who was my supervisor, and he essentially 04:34.760 --> 04:39.400 helped me architect and deliver a system that's still in use today. 04:39.400 --> 04:43.800 After that, I worked at Xerox Palo Alto Research Center, where the Alto was invented, and the 04:43.800 --> 04:50.600 Alto is the predecessor of the modern personal computer, or Macintosh, and so forth. 04:50.600 --> 04:55.360 And the altos were very rare, and I had to drive an hour from Berkeley to go use them, 04:55.360 --> 05:01.160 but I made a point of skipping classes and doing whatever it took to have access to this 05:01.160 --> 05:02.480 extraordinary achievement. 05:02.480 --> 05:05.000 I knew that they were consequential. 05:05.000 --> 05:08.240 What I did not understand was scaling. 05:08.240 --> 05:12.960 I did not understand what would happen when you had 100 million as opposed to 100. 05:12.960 --> 05:17.360 And so since then, and I have learned the benefit of scale, I always look for things 05:17.360 --> 05:23.160 which are going to scale to platforms, so mobile phones, Android, all those things. 05:23.160 --> 05:27.560 There are many, many people in the world. 05:27.560 --> 05:28.560 People really have needs. 05:28.560 --> 05:32.600 They really will use these platforms, and you can build big businesses on top of them. 05:32.600 --> 05:33.600 So it's interesting. 05:33.600 --> 05:36.880 So when you see a piece of technology, now you think, what will this technology look 05:36.880 --> 05:39.080 like when it's in the hands of a billion people? 05:39.080 --> 05:40.160 That's right. 05:40.160 --> 05:46.520 So an example would be that the market is so competitive now that if you can't figure 05:46.520 --> 05:51.880 out a way for something to have a million users or a billion users, it probably is not 05:51.880 --> 05:57.280 going to be successful because something else will become the general platform, and your 05:57.280 --> 06:04.360 idea will become a lost idea or a specialized service with relatively few users. 06:04.360 --> 06:06.000 So it's a path to generality. 06:06.000 --> 06:07.720 It's a path to general platform use. 06:07.720 --> 06:09.400 It's a path to broad applicability. 06:09.400 --> 06:15.000 Now, there are plenty of good businesses that are tiny, so luxury goods, for example. 06:15.000 --> 06:20.360 But if you want to have an impact at scale, you have to look for things which are of 06:20.360 --> 06:25.200 common value, common pricing, common distribution, and solve common problems, the problems that 06:25.200 --> 06:26.200 everyone has. 06:26.200 --> 06:30.440 And by the way, people have lots of problems, information, medicine, health, education, 06:30.440 --> 06:31.440 and so forth. 06:31.440 --> 06:32.440 Work on those problems. 06:32.440 --> 06:40.240 Like you said, you're a big fan of the middle class because there's so many of them by definition. 06:40.240 --> 06:46.600 So any product, any thing that has a huge impact, it improves their lives is a great 06:46.600 --> 06:48.960 business decision and it's just good for society. 06:48.960 --> 06:53.520 And there's nothing wrong with starting off in the high end as long as you have a plan 06:53.520 --> 06:55.520 to get to the middle class. 06:55.520 --> 06:59.280 There's nothing wrong with starting with a specialized market in order to learn and to 06:59.280 --> 07:01.080 build and to fund things. 07:01.080 --> 07:04.520 So you start a luxury market to build a general purpose market. 07:04.520 --> 07:09.640 But if you define yourself as only a narrow market, someone else can come along with a 07:09.640 --> 07:14.320 general purpose market that can push you to the corner, can restrict the scale of operation, 07:14.320 --> 07:17.320 can force you to be a lesser impact than you might be. 07:17.320 --> 07:22.800 So it's very important to think in terms of broad businesses and broad impact, even if 07:22.800 --> 07:26.360 you start in a little corner somewhere. 07:26.360 --> 07:33.200 So as you look to the 70s, but also in the decades to come, and you saw computers, did 07:33.200 --> 07:40.240 you see them as tools or was there a little element of another entity? 07:40.240 --> 07:46.240 I remember a quote saying AI began with our dream to create the gods. 07:46.240 --> 07:51.520 Is there a feeling when you wrote that program that you were creating another entity, giving 07:51.520 --> 07:52.800 life to something? 07:52.800 --> 07:58.880 I wish I could say otherwise, but I simply found the technology platforms so exciting. 07:58.880 --> 08:00.400 That's what I was focused on. 08:00.400 --> 08:04.640 I think the majority of the people that I've worked with, and there are a few exceptions, 08:04.640 --> 08:09.960 Steve Jobs being an example, really saw this as a great technological play. 08:09.960 --> 08:15.520 I think relatively few of the technical people understood the scale of its impact. 08:15.520 --> 08:19.680 So I used NCP, which is a predecessor to TCPIP. 08:19.680 --> 08:21.240 It just made sense to connect things. 08:21.240 --> 08:26.240 We didn't think of it in terms of the internet, and then companies, and then Facebook, and 08:26.240 --> 08:29.200 then Twitter, and then politics, and so forth. 08:29.200 --> 08:30.800 We never did that build. 08:30.800 --> 08:32.920 We didn't have that vision. 08:32.920 --> 08:38.200 And I think most people, it's a rare person who can see compounding at scale. 08:38.200 --> 08:41.520 Most people can see, if you ask people to predict the future, they'll say, they'll give 08:41.520 --> 08:44.080 you an answer of six to nine months or 12 months. 08:44.080 --> 08:47.560 Because that's about as far as people can imagine. 08:47.560 --> 08:51.020 But there's an old saying, which actually was attributed to a professor at MIT a long 08:51.020 --> 08:58.120 time ago, that we overestimate what can be done in one year, and we underestimate what 08:58.120 --> 09:00.280 can be done in a decade. 09:00.280 --> 09:05.560 And there's a great deal of evidence that these core platforms at hardware and software 09:05.560 --> 09:07.800 take a decade. 09:07.800 --> 09:09.600 So think about self driving cars. 09:09.600 --> 09:12.160 Self driving cars were thought about in the 90s. 09:12.160 --> 09:17.160 Over projects around them, the first DARPA Durand Challenge was roughly 2004. 09:17.160 --> 09:19.760 So that's roughly 15 years ago. 09:19.760 --> 09:25.400 And today we have self driving cars operating in a city in Arizona, right, so 15 years. 09:25.400 --> 09:31.720 And we still have a ways to go before they're more generally available. 09:31.720 --> 09:33.840 So you've spoken about the importance. 09:33.840 --> 09:37.080 You just talked about predicting into the future. 09:37.080 --> 09:41.640 You've spoken about the importance of thinking five years ahead and having a plan for those 09:41.640 --> 09:42.640 five years. 09:42.640 --> 09:47.840 And the way to say it is that almost everybody has a one year plan. 09:47.840 --> 09:50.960 Almost no one has a proper five year plan. 09:50.960 --> 09:55.160 And the key thing to having a five year plan is having a model for what's going to happen 09:55.160 --> 09:57.000 under the underlying platforms. 09:57.000 --> 09:59.840 So here's an example. 09:59.840 --> 10:05.120 Computer Moore's Law, as we know it, the thing that powered improvements in CPUs has largely 10:05.120 --> 10:10.400 halted in its traditional shrinking mechanism, because the costs have just gotten so high. 10:10.400 --> 10:12.200 It's getting harder and harder. 10:12.200 --> 10:16.600 But there's plenty of algorithmic improvements and specialized hardware improvements. 10:16.600 --> 10:21.240 So you need to understand the nature of those improvements and where they'll go in order 10:21.240 --> 10:24.360 to understand how it will change the platform. 10:24.360 --> 10:28.000 In the area of network connectivity, what are the gains that are going to be possible 10:28.000 --> 10:29.480 in wireless? 10:29.480 --> 10:35.720 It looks like there's an enormous expansion of wireless connectivity at many different 10:35.720 --> 10:36.960 bands, right? 10:36.960 --> 10:40.520 And that we will primarily, historically, I've always thought that we were primarily 10:40.520 --> 10:45.040 going to be using fiber, but now it looks like we're going to be using fiber plus very 10:45.040 --> 10:51.560 powerful high bandwidth sort of short distance connectivity to bridge the last mile, right? 10:51.560 --> 10:53.100 That's an amazing achievement. 10:53.100 --> 10:56.880 If you know that, then you're going to build your systems differently. 10:56.880 --> 10:59.800 By the way, those networks have different latency properties, right? 10:59.800 --> 11:05.040 Because they're more symmetric, the algorithms feel faster for that reason. 11:05.040 --> 11:09.920 And so when you think about whether it's a fiber or just technologies in general. 11:09.920 --> 11:15.920 So there's this barber, wooden poem or quote that I really like. 11:15.920 --> 11:20.400 It's from the champions of the impossible rather than the slaves of the possible that 11:20.400 --> 11:23.280 evolution draws its creative force. 11:23.280 --> 11:27.840 So in predicting the next five years, I'd like to talk about the impossible and the 11:27.840 --> 11:28.840 possible. 11:28.840 --> 11:34.720 Well, and again, one of the great things about humanity is that we produce dreamers, right? 11:34.720 --> 11:37.760 We literally have people who have a vision and a dream. 11:37.760 --> 11:43.400 They are, if you will, disagreeable in the sense that they disagree with the, they disagree 11:43.400 --> 11:46.240 with what the sort of zeitgeist is. 11:46.240 --> 11:48.040 They say there is another way. 11:48.040 --> 11:49.040 They have a belief. 11:49.040 --> 11:50.320 They have a vision. 11:50.320 --> 11:56.560 If you look at science, science is always marked by such people who, who went against 11:56.560 --> 12:01.360 some conventional wisdom, collected the knowledge at the time and assembled it in a way that 12:01.360 --> 12:03.760 produced a powerful platform. 12:03.760 --> 12:11.120 And you've been amazingly honest about in an inspiring way about things you've been wrong 12:11.120 --> 12:14.800 about predicting and you've obviously been right about a lot of things. 12:14.800 --> 12:23.880 But in this kind of tension, how do you balance as a company in predicting the next five years, 12:23.880 --> 12:26.520 the impossible, planning for the impossible. 12:26.520 --> 12:32.720 So listening to those crazy dreamers, letting them do, letting them run away and make the 12:32.720 --> 12:38.760 impossible real, make it happen and slow, you know, that's how programmers often think 12:38.760 --> 12:44.800 and slowing things down and saying, well, this is the rational, this is the possible, 12:44.800 --> 12:49.160 the pragmatic, the, the dreamer versus the pragmatist. 12:49.160 --> 12:56.680 So it's helpful to have a model which encourages a predictable revenue stream as well as the 12:56.680 --> 12:58.720 ability to do new things. 12:58.720 --> 13:03.120 So in Google's case, we're big enough and well enough managed and so forth that we have 13:03.120 --> 13:06.600 a pretty good sense of what our revenue will be for the next year or two, at least for 13:06.600 --> 13:07.960 a while. 13:07.960 --> 13:13.720 And so we have enough cash generation that we can make bets. 13:13.720 --> 13:18.760 And indeed, Google has become alphabet so the corporation is organized around these 13:18.760 --> 13:19.760 bets. 13:19.760 --> 13:25.560 And these bets are in areas of fundamental importance to, to the world, whether it's 13:25.560 --> 13:31.920 digital intelligence, medical technology, self driving cars, connectivity through balloons, 13:31.920 --> 13:33.440 on and on and on. 13:33.440 --> 13:36.080 And there's more coming and more coming. 13:36.080 --> 13:41.480 So one way you could express this is that the current business is successful enough 13:41.480 --> 13:44.720 that we have the luxury of making bets. 13:44.720 --> 13:48.960 And another one that you could say is that we have the, the wisdom of being able to see 13:48.960 --> 13:53.920 that a corporate structure needs to be created to enhance the likelihood of the success of 13:53.920 --> 13:55.320 those bets. 13:55.320 --> 13:59.760 So we essentially turned ourselves into a conglomerate of bets. 13:59.760 --> 14:04.360 And then this underlying corporation Google, which is itself innovative. 14:04.360 --> 14:08.160 So in order to pull this off, you have to have a bunch of belief systems. 14:08.160 --> 14:12.080 And one of them is that you have to have bottoms up and tops down the bottoms up. 14:12.080 --> 14:13.600 We call 20% time. 14:13.600 --> 14:17.040 And the idea is that people can spend 20% of the time on whatever they want. 14:17.040 --> 14:21.960 And the top down is that our founders in particular have a keen eye on technology and they're 14:21.960 --> 14:24.000 reviewing things constantly. 14:24.000 --> 14:27.520 So an example would be they'll, they'll hear about an idea or I'll hear about something 14:27.520 --> 14:28.880 and it sounds interesting. 14:28.880 --> 14:34.920 Let's go visit them and then let's begin to assemble the pieces to see if that's possible. 14:34.920 --> 14:39.920 And if you do this long enough, you get pretty good at predicting what's likely to work. 14:39.920 --> 14:42.120 So that's, that's a beautiful balance that struck. 14:42.120 --> 14:44.560 Is this something that applies at all scale? 14:44.560 --> 14:53.960 So seems seems to be that the Sergei, again, 15 years ago, came up with a concept that 14:53.960 --> 14:59.040 called 10% of the budget should be on things that are unrelated. 14:59.040 --> 15:05.040 It was called 70, 20, 10, 70% of our time on core business, 20% on adjacent business 15:05.040 --> 15:06.920 and 10% on other. 15:06.920 --> 15:11.200 And he proved mathematically, of course, he's a brilliant mathematician, that you needed 15:11.200 --> 15:18.800 that 10% right to make the sum of the growth work and it turns out he was right. 15:18.800 --> 15:24.320 So getting into the world of artificial intelligence, you've, you've talked quite extensively and 15:24.320 --> 15:32.160 effectively to the impact in the near term, the positive impact of artificial intelligence, 15:32.160 --> 15:38.720 whether it's machine, especially machine learning in medical applications and education 15:38.720 --> 15:44.160 and just making information more accessible, right in the AI community, there is a kind 15:44.160 --> 15:49.520 of debate, so there's this shroud of uncertainty as we face this new world with artificial 15:49.520 --> 15:50.800 intelligence in it. 15:50.800 --> 15:57.560 And there is some people like Elon Musk, you've disagreed on at least on the degree of emphasis 15:57.560 --> 16:00.800 he places on the existential threat of AI. 16:00.800 --> 16:07.120 So I've spoken with Stuart Russell, Max Tagmark, who share Elon Musk's view, and Yoshio Benjio, 16:07.120 --> 16:09.240 Steven Pinker, who do not. 16:09.240 --> 16:13.320 And so there's a, there's a, there's a lot of very smart people who are thinking about 16:13.320 --> 16:17.280 this stuff, disagreeing, which is really healthy, of course. 16:17.280 --> 16:22.800 So what do you think is the healthiest way for the AI community to, and really for the 16:22.800 --> 16:30.880 general public to think about AI and the concern of the technology being mismanaged 16:30.880 --> 16:33.000 in some, in some kind of way. 16:33.000 --> 16:37.560 So the source of education for the general public has been robot killer movies. 16:37.560 --> 16:38.760 Right. 16:38.760 --> 16:44.840 And Terminator, et cetera, and the one thing I can assure you we're not building are those 16:44.840 --> 16:46.200 kinds of solutions. 16:46.200 --> 16:51.240 Furthermore, if they were to show up, someone would notice and unplug them, right? 16:51.240 --> 16:57.760 So as exciting as those movies are, and they're great movies, where the killer robots to start, 16:57.760 --> 17:00.520 we would find a way to stop them, right? 17:00.520 --> 17:04.320 So I'm not concerned about that. 17:04.320 --> 17:08.680 And much of this has to do with the timeframe of conversation. 17:08.680 --> 17:16.120 So you can imagine a situation a hundred years from now, when the human brain is fully understood, 17:16.120 --> 17:19.880 and the next generation and next generation of brilliant MIT scientists have figured 17:19.880 --> 17:25.960 all this out, we're going to have a large number of ethics questions, right, around science 17:25.960 --> 17:29.760 and thinking and robots and computers and so forth and so on. 17:29.760 --> 17:32.360 So it depends on the question of the timeframe. 17:32.360 --> 17:37.280 In the next five to 10 years, we're not facing those questions. 17:37.280 --> 17:42.000 What we're facing in the next five to 10 years is how do we spread this disruptive technology 17:42.000 --> 17:46.520 as broadly as possible to gain the maximum benefit of it? 17:46.520 --> 17:51.880 The primary benefit should be in healthcare and in education, healthcare because it's 17:51.880 --> 17:52.880 obvious. 17:52.880 --> 17:55.840 We're all the same, even though we somehow believe we're not. 17:55.840 --> 18:00.400 As a medical matter, the fact that we have big data about our health will save lives, 18:00.400 --> 18:05.520 allow us to deal with skin cancer and other cancers, ophthalmological problems. 18:05.520 --> 18:10.080 There's people working on psychological diseases and so forth using these techniques. 18:10.080 --> 18:11.680 I go on and on. 18:11.680 --> 18:15.840 The promise of AI in medicine is extraordinary. 18:15.840 --> 18:20.360 There are many, many companies and startups and funds and solutions and we will all live 18:20.360 --> 18:22.120 much better for that. 18:22.120 --> 18:25.680 The same argument in education. 18:25.680 --> 18:31.760 Can you imagine that for each generation of child and even adult, you have a tutor educator 18:31.760 --> 18:37.320 that's AI based, that's not a human but is properly trained, that helps you get smarter, 18:37.320 --> 18:41.440 helps you address your language difficulties or your math difficulties or what have you. 18:41.440 --> 18:43.400 Why don't we focus on those two? 18:43.400 --> 18:49.240 The gains societally of making humans smarter and healthier are enormous. 18:49.240 --> 18:54.000 Those translate for decades and decades and will all benefit from them. 18:54.000 --> 18:58.560 There are people who are working on AI safety, which is the issue that you're describing. 18:58.560 --> 19:02.920 There are conversations in the community that should there be such problems, what should 19:02.920 --> 19:04.360 the rules be like? 19:04.360 --> 19:09.360 Google, for example, has announced its policies with respect to AI safety, which I certainly 19:09.360 --> 19:14.320 support and I think most everybody would support and they make sense. 19:14.320 --> 19:19.760 It helps guide the research but the killer robots are not arriving this year and they're 19:19.760 --> 19:23.840 not even being built. 19:23.840 --> 19:31.040 On that line of thinking, you said the timescale, in this topic or other topics, have you found 19:31.040 --> 19:37.920 a useful, on the business side or the intellectual side, to think beyond 5, 10 years, to think 19:37.920 --> 19:39.480 50 years out? 19:39.480 --> 19:42.000 Has it ever been useful or productive? 19:42.000 --> 19:49.040 In our industry, there are essentially no examples of 50 year predictions that have been correct. 19:49.040 --> 19:54.320 Let's review AI, which was largely invented here at MIT and a couple of other universities 19:54.320 --> 19:57.840 in 1956, 1957, 1958. 19:57.840 --> 20:01.800 The original claims were a decade or two. 20:01.800 --> 20:08.040 When I was a PhD student, I studied AI a bit and it entered during my looking at it a period 20:08.040 --> 20:13.880 which is known as AI winter, which went on for about 30 years, which is a whole generation 20:13.880 --> 20:18.800 of scientists and a whole group of people who didn't make a lot of progress because the 20:18.800 --> 20:22.160 algorithms had not improved and the computers had not improved. 20:22.160 --> 20:26.160 It took some brilliant mathematicians, starting with a fellow named Jeff Hinton at Toronto 20:26.160 --> 20:33.120 in Montreal, who basically invented this deep learning model which empowers us today. 20:33.120 --> 20:40.400 The seminal work there was 20 years ago and in the last 10 years, it's become popularized. 20:40.400 --> 20:43.520 Think about the time frames for that level of discovery. 20:43.520 --> 20:46.080 It's very hard to predict. 20:46.080 --> 20:50.240 Many people think that we'll be flying around in the equivalent of flying cars. 20:50.240 --> 20:51.240 Who knows? 20:51.240 --> 20:56.680 My own view, if I want to go out on a limb, is to say that we know a couple of things 20:56.680 --> 20:58.000 about 50 years from now. 20:58.000 --> 21:00.480 We know that there'll be more people alive. 21:00.480 --> 21:04.000 We know that we'll have to have platforms that are more sustainable because the earth 21:04.000 --> 21:09.440 is limited in the ways we all know and that the kind of platforms that are going to get 21:09.440 --> 21:13.000 billed will be consistent with the principles that I've described. 21:13.000 --> 21:17.560 They will be much more empowering of individuals, they'll be much more sensitive to the ecology 21:17.560 --> 21:20.440 because they have to be, they just have to be. 21:20.440 --> 21:24.160 I also think that humans are going to be a great deal smarter and I think they're going 21:24.160 --> 21:28.320 to be a lot smarter because of the tools that I've discussed with you and of course people 21:28.320 --> 21:29.320 will live longer. 21:29.320 --> 21:32.080 Life extension is continuing apace. 21:32.080 --> 21:36.840 A baby born today has a reasonable chance of living to 100, which is pretty exciting. 21:36.840 --> 21:40.760 It's well past the 21st century, so we better take care of them. 21:40.760 --> 21:46.160 You mentioned interesting statistic on some very large percentage, 60, 70% of people may 21:46.160 --> 21:48.360 live in cities. 21:48.360 --> 21:53.880 Today more than half the world lives in cities and one of the great stories of humanity in 21:53.880 --> 21:57.560 the last 20 years has been the rural to urban migration. 21:57.560 --> 22:02.720 This has occurred in the United States, it's occurred in Europe, it's occurring in Asia 22:02.720 --> 22:04.760 and it's occurring in Africa. 22:04.760 --> 22:09.280 When people move to cities, the cities get more crowded, but believe it or not their health 22:09.280 --> 22:15.560 gets better, their productivity gets better, their IQ and educational capabilities improve, 22:15.560 --> 22:19.840 so it's good news that people are moving to cities, but we have to make them livable 22:19.840 --> 22:22.800 and safe. 22:22.800 --> 22:28.240 So you, first of all, you are, but you've also worked with some of the greatest leaders 22:28.240 --> 22:30.180 in the history of tech. 22:30.180 --> 22:37.080 What insights do you draw from the difference in leadership styles of yourself, Steve Jobs, 22:37.080 --> 22:45.320 Elon Musk, Larry Page, now the new CEO, Sandra Pichai and others from the, I would say, calm 22:45.320 --> 22:49.600 sages to the mad geniuses? 22:49.600 --> 22:53.880 One of the things that I learned as a young executive is that there's no single formula 22:53.880 --> 22:56.200 for leadership. 22:56.200 --> 23:00.080 They try to teach one, but that's not how it really works. 23:00.080 --> 23:04.360 There are people who just understand what they need to do and they need to do it quickly. 23:04.360 --> 23:06.800 Those people are often entrepreneurs. 23:06.800 --> 23:09.080 They just know and they move fast. 23:09.080 --> 23:13.400 There are other people who are systems thinkers and planners, that's more who I am, somewhat 23:13.400 --> 23:18.760 more conservative, more thorough in execution, a little bit more risk averse. 23:18.760 --> 23:24.120 There's also people who are sort of slightly insane, right, in the sense that they are 23:24.120 --> 23:29.040 emphatic and charismatic and they feel it and they drive it and so forth. 23:29.040 --> 23:31.440 There's no single formula to success. 23:31.440 --> 23:35.320 There is one thing that unifies all of the people that you named, which is very high 23:35.320 --> 23:41.240 intelligence, at the end of the day, the thing that characterizes all of them is that they 23:41.240 --> 23:46.360 saw the world quicker, faster, they processed information faster, they didn't necessarily 23:46.360 --> 23:50.160 make the right decisions all the time, but they were on top of it. 23:50.160 --> 23:54.600 The other thing that's interesting about all those people is they all started young. 23:54.600 --> 23:58.560 Think about Steve Jobs starting Apple roughly at 18 or 19. 23:58.560 --> 24:01.840 Think about Bill Gates starting at roughly 2021. 24:01.840 --> 24:07.040 Think about by the time they were 30, Mark Zuckerberg, a more good example at 1920. 24:07.040 --> 24:13.720 By the time they were 30, they had 10 years, at 30 years old, they had 10 years of experience 24:13.720 --> 24:19.920 of dealing with people and products and shipments and the press and business and so forth. 24:19.920 --> 24:24.480 It's incredible how much experience they had compared to the rest of us who were busy getting 24:24.480 --> 24:25.480 our PhDs. 24:25.480 --> 24:26.480 Yes, exactly. 24:26.480 --> 24:32.760 We should celebrate these people because they've just had more life experience and that helps 24:32.760 --> 24:34.520 inform the judgment. 24:34.520 --> 24:41.360 At the end of the day, when you're at the top of these organizations, all the easy questions 24:41.360 --> 24:43.680 have been dealt with. 24:43.680 --> 24:45.840 How should we design the buildings? 24:45.840 --> 24:48.400 Where should we put the colors on our product? 24:48.400 --> 24:51.440 What should the box look like? 24:51.440 --> 24:55.520 The problems, that's why it's so interesting to be in these rooms, the problems that they 24:55.520 --> 25:00.200 face in terms of the way they operate, the way they deal with their employees, their 25:00.200 --> 25:04.160 customers, their innovation are profoundly challenging. 25:04.160 --> 25:09.360 Each of the companies is demonstrably different culturally. 25:09.360 --> 25:11.800 They are not, in fact, cut of the same. 25:11.800 --> 25:16.680 They behave differently based on input, their internal cultures are different, their compensation 25:16.680 --> 25:24.920 schemes are different, their values are different, so there's proof that diversity works. 25:24.920 --> 25:33.440 So when faced with a tough decision, in need of advice, it's been said that the best thing 25:33.440 --> 25:39.840 one can do is to find the best person in the world who can give that advice and find a 25:39.840 --> 25:44.880 way to be in a room with them, one on one and ask. 25:44.880 --> 25:51.920 So here we are, and let me ask in a long winded way, I wrote this down, in 1998 there were 25:51.920 --> 26:01.960 many good search engines, Lycos, Excite, Altavista, Infoseek, AskJeeves maybe, Yahoo even. 26:01.960 --> 26:07.040 So Google stepped in and disrupted everything, they disrupted the nature of search, the nature 26:07.040 --> 26:12.040 of our access to information, the way we discover new knowledge. 26:12.040 --> 26:19.120 So now it's 2018, actually 20 years later, there are many good personal AI assistants, 26:19.120 --> 26:22.360 including of course the best from Google. 26:22.360 --> 26:28.720 So you've spoken in medical and education, the impact of such an AI assistant could bring. 26:28.720 --> 26:34.920 So we arrive at this question, so it's a personal one for me, but I hope my situation represents 26:34.920 --> 26:41.200 that of many other, as we said, dreamers and the crazy engineers. 26:41.200 --> 26:46.680 So my whole life, I've dreamed of creating such an AI assistant. 26:46.680 --> 26:50.800 Every step I've taken has been towards that goal, now I'm a research scientist in Human 26:50.800 --> 26:58.920 Senate AI here at MIT, so the next step for me as I sit here, facing my passion, is to 26:58.920 --> 27:04.880 do what Larry and Sergey did in 1998, this simple start up. 27:04.880 --> 27:10.640 And so here's my simple question, given the low odds of success, the timing and luck required, 27:10.640 --> 27:14.280 the countless other factors that can't be controlled or predicted, which is all the 27:14.280 --> 27:16.560 things that Larry and Sergey faced. 27:16.560 --> 27:23.080 Is there some calculation, some strategy to follow in this step, or do you simply follow 27:23.080 --> 27:26.560 the passion just because there's no other choice? 27:26.560 --> 27:32.880 I think the people who are in universities are always trying to study the extraordinarily 27:32.880 --> 27:37.360 chaotic nature of innovation and entrepreneurship. 27:37.360 --> 27:42.880 My answer is that they didn't have that conversation, they just did it. 27:42.880 --> 27:48.840 They sensed a moment when, in the case of Google, there was all of this data that needed 27:48.840 --> 27:53.940 to be organized and they had a better algorithm, they had invented a better way. 27:53.940 --> 28:01.040 So today with Human Senate AI, which is your area of research, there must be new approaches. 28:01.040 --> 28:07.320 It's such a big field, there must be new approaches, different from what we and others are doing. 28:07.320 --> 28:12.320 There must be startups to fund, there must be research projects to try, there must be 28:12.320 --> 28:15.200 graduate students to work on new approaches. 28:15.200 --> 28:19.120 Here at MIT, there are people who are looking at learning from the standpoint of looking 28:19.120 --> 28:23.840 at child learning, how do children learn starting at each one? 28:23.840 --> 28:25.560 And the work is fantastic. 28:25.560 --> 28:30.120 Those approaches are different from the approach that most people are taking. 28:30.120 --> 28:33.980 Perhaps that's a bet that you should make, or perhaps there's another one. 28:33.980 --> 28:40.200 But at the end of the day, the successful entrepreneurs are not as crazy as they sound. 28:40.200 --> 28:43.200 They see an opportunity based on what's happened. 28:43.200 --> 28:45.400 Let's use Uber as an example. 28:45.400 --> 28:49.840 As Travis sells the story, he and his cofounder were sitting in Paris and they had this idea 28:49.840 --> 28:52.160 because they couldn't get a cab. 28:52.160 --> 28:56.800 And they said, we have smartphones and the rest is history. 28:56.800 --> 29:04.040 So what's the equivalent of that Travis Eiffel Tower, where is a cab moment that you could, 29:04.040 --> 29:08.800 as an entrepreneur, take advantage of, whether it's in Human Senate AI or something else? 29:08.800 --> 29:11.480 That's the next great start up. 29:11.480 --> 29:13.760 And the psychology of that moment. 29:13.760 --> 29:20.120 So when Sergey and Larry talk about, and listen to a few interviews, it's very nonchalant. 29:20.120 --> 29:25.280 Well, here's the very fascinating web data. 29:25.280 --> 29:29.080 And here's an algorithm we have for, you know, we just kind of want to play around with that 29:29.080 --> 29:30.080 data. 29:30.080 --> 29:32.520 And it seems like that's a really nice way to organize this data. 29:32.520 --> 29:38.000 Well, I should say what happened, remember, is that they were graduate students at Stanford 29:38.000 --> 29:41.320 and they thought this is interesting, so they built a search engine and they kept it in 29:41.320 --> 29:43.400 their room. 29:43.400 --> 29:47.520 And they had to get power from the room next door because they were using too much power 29:47.520 --> 29:48.520 in the room. 29:48.520 --> 29:51.640 So they ran an extension cord over. 29:51.640 --> 29:55.360 And then they went and they found a house and they had Google World headquarters of 29:55.360 --> 29:57.600 five people to start the company. 29:57.600 --> 30:02.560 And they raised $100,000 from Andy Bechtelstein, who was the sun founder to do this, and Dave 30:02.560 --> 30:04.520 Chariton and a few others. 30:04.520 --> 30:11.960 The point is their beginnings were very simple, but they were based on a powerful insight. 30:11.960 --> 30:14.320 That is a replicable model for any startup. 30:14.320 --> 30:16.520 It has to be a powerful insight. 30:16.520 --> 30:17.680 The beginnings are simple. 30:17.680 --> 30:19.960 And there has to be an innovation. 30:19.960 --> 30:24.280 In Larry and Sergey's case, it was PageRank, which was a brilliant idea, one of the most 30:24.280 --> 30:26.880 cited papers in the world today. 30:26.880 --> 30:29.880 What's the next one? 30:29.880 --> 30:37.280 So you're one of, if I may say, richest people in the world, and yet it seems that money 30:37.280 --> 30:43.200 is simply a side effect of your passions and not an inherent goal. 30:43.200 --> 30:48.360 But it's a, you're a fascinating person to ask. 30:48.360 --> 30:55.080 So much of our society at the individual level and at the company level and its nations is 30:55.080 --> 30:58.920 driven by the desire for wealth. 30:58.920 --> 31:01.280 What do you think about this drive? 31:01.280 --> 31:07.000 And what have you learned about, if I may romanticize the notion, the meaning of life, 31:07.000 --> 31:10.520 having achieved success on so many dimensions? 31:10.520 --> 31:16.960 There have been many studies of human happiness and above some threshold, which is typically 31:16.960 --> 31:23.600 relatively low for this conversation, there's no difference in happiness about money. 31:23.600 --> 31:30.120 The happiness is correlated with meaning and purpose, a sense of family, a sense of impact. 31:30.120 --> 31:34.440 So if you organize your life, assuming you have enough to get around and have a nice 31:34.440 --> 31:40.400 home and so forth, you'll be far happier if you figure out what you care about and work 31:40.400 --> 31:41.800 on that. 31:41.800 --> 31:44.120 It's often being in service to others. 31:44.120 --> 31:47.840 It's a great deal of evidence that people are happiest when they're serving others 31:47.840 --> 31:49.640 and not themselves. 31:49.640 --> 31:57.480 This goes directly against the sort of press induced excitement about powerful and wealthy 31:57.480 --> 32:01.840 leaders of one kind and indeed, these are consequential people. 32:01.840 --> 32:06.720 But if you are in a situation where you've been very fortunate as I have, you also have 32:06.720 --> 32:12.160 to take that as a responsibility and you have to basically work both to educate others and 32:12.160 --> 32:16.760 give them that opportunity, but also use that wealth to advance human society. 32:16.760 --> 32:20.440 In my case, I'm particularly interested in using the tools of artificial intelligence 32:20.440 --> 32:22.800 and machine learning to make society better. 32:22.800 --> 32:24.000 I've mentioned education. 32:24.000 --> 32:29.040 I've mentioned inequality and middle class and things like this, all of which are a passion 32:29.040 --> 32:30.160 of mine. 32:30.160 --> 32:31.920 It doesn't matter what you do. 32:31.920 --> 32:36.560 It matters that you believe in it, that it's important to you and that your life will be 32:36.560 --> 32:40.480 far more satisfying if you spend your life doing that. 32:40.480 --> 32:45.320 I think there's no better place to end than a discussion of the meaning of life. 32:45.320 --> 32:46.320 Eric, thank you so much. 32:46.320 --> 32:47.320 Thank you very much. 32:47.320 --> 33:16.320 Thank you.