back to indexEric Schmidt: Google | Lex Fridman Podcast #8
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The following is a conversation with Eric Schmidt.
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He was the CEO of Google for 10 years
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and a chairman for six more,
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guiding the company through an incredible period of growth
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and a series of world changing innovations.
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He is one of the most impactful leaders
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in the era of the internet and the powerful voice
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for the promise of technology in our society.
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It was truly an honor to speak with him
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as part of the MIT course
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on artificial general intelligence
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and the artificial intelligence podcast.
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And now here's my conversation with Eric Schmidt.
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What was the first moment
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when you fell in love with technology?
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I grew up in the 1960s as a boy
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where every boy wanted to be an astronaut
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and part of the space program.
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So like everyone else of my age,
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we would go out to the cow pasture behind my house,
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which was literally a cow pasture
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and we would shoot model rockets off.
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And that I think is the beginning.
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And of course, generationally today,
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it would be video games and all the amazing things
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that you can do online with computers.
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There's a transformative, inspiring aspect of science
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and math that maybe rockets would bring
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would instill in individuals.
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You've mentioned yesterday that eighth grade math
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is where the journey through mathematical universe
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diverges from many people.
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It's this fork in the roadway.
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There's a professor of math at Berkeley, Edward Frankel.
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He, I'm not sure if you're familiar with him.
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He has written this amazing book
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I recommend to everybody called Love and Math.
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Two of my favorite words.
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He says that if painting was taught like math,
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then the students would be asked to paint a fence,
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which is his analogy of essentially how math is taught.
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And so you never get a chance to discover the beauty
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of the art of painting or the beauty of the art of math.
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So how, when, and where did you discover that beauty?
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I think what happens with people like myself
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is that your math enabled pretty early
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and all of a sudden you discover that you can use that
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to discover new insights.
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The great scientists will all tell a story,
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the men and women who are fantastic today,
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that somewhere when they were in high school or in college,
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they discovered that they could discover
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something themselves.
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And that sense of building something,
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of having an impact that you own,
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drives knowledge acquisition and learning.
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In my case, it was programming.
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And the notion that I could build things
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that had not existed that I had built,
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that it had my name on it.
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And this was before open source,
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but you could think of it as open source contributions.
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So today, if I were a 16 or 17 year old boy,
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I'm sure that I would aspire as a computer scientist
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to make a contribution like the open source heroes
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of the world today.
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That would be what would be driving me.
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And I'd be trying and learning and making mistakes
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and so forth in the ways that it works.
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The repository that GitHub represents
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and that open source libraries represent
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is an enormous bank of knowledge
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of all of the people who are doing that.
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And one of the lessons that I learned at Google
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was that the world is a very big place
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and there's an awful lot of smart people.
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And an awful lot of them are underutilized.
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So here's an opportunity, for example,
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building parts of programs, building new ideas
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to contribute to the greater of society.
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So in that moment in the 70s,
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the inspiring moment where there was nothing
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and then you created something through programming,
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that magical moment.
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So in 1975, I think you've created a program called Lex,
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which I especially like because my name is Lex.
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So thank you, thank you for creating a brand
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that established a reputation that's long lasting, reliable
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and has a big impact on the world and still used today.
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So thank you for that.
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But more seriously, in that time, in the 70s,
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as an engineer, personal computers were being born.
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Do you think you'd be able to predict the 80s, 90s
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and the aughts of where computers would go?
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I'm sure I could not and would not have gotten it right.
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I was the beneficiary of the great work
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of many, many people who saw it clearer than I did.
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With Lex, I worked with a fellow named Michael Lesk,
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who was my supervisor.
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And he essentially helped me architect
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and deliver a system that's still in use today.
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After that, I worked at Xerox Palo Alto Research Center,
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where the Alto was invented.
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And the Alto is the predecessor
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of the modern personal computer or Macintosh and so forth.
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And the Altos were very rare.
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And I had to drive an hour from Berkeley to go use them.
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But I made a point of skipping classes
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and doing whatever it took to have access
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to this extraordinary achievement.
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I knew that they were consequential.
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What I did not understand was scaling.
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I did not understand what would happen
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when you had 100 million as opposed to 100.
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And so the, since then,
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and I have learned the benefit of scale,
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I always look for things
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which are going to scale to platforms, right?
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So mobile phones, Android, all those things.
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There are, the world is in numerous,
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there are many, many people in the world,
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people really have needs.
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They really will use these platforms
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and you can build big businesses on top of them.
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So it's interesting.
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So when you see a piece of technology,
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now you think, what will this technology look like
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when it's in the hands of a billion people?
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So an example would be that the market is so competitive now
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that if you can't figure out a way
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for something to have a million users or a billion users,
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it probably is not going to be successful
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because something else will become the general platform
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and your idea will become a lost idea
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or a specialized service with relatively few users.
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So it's a path to generality.
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It's a path to general platform use.
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It's a path to broad applicability.
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Now there are plenty of good businesses that are tiny.
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So luxury goods, for example.
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But if you want to have an impact at scale,
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you have to look for things which are of common value,
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common pricing, common distribution
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and solve common problems.
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They're problems that everyone has.
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And by the way, people have lots of problems.
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Information, medicine, health, education and so forth.
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Work on those problems.
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Like you said, you're a big fan of the middle class.
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Because there's so many of them.
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There's so many of them.
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So any product, any thing that has a huge impact
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and improves their lives is a great business decision
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and it's just good for society.
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And there's nothing wrong with starting off in the high end
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as long as you have a plan to get to the middle class.
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There's nothing wrong with starting with a specialized
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market in order to learn and to build and to fund things.
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So you start with a luxury market
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to build a general purpose market.
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But if you define yourself as only a narrow market,
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someone else can come along with a general purpose market
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that can push you to the corner,
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can restrict the scale of operation,
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can force you to be a lesser impact than you might be.
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So it's very important to think in terms of broad businesses
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Even if you start in a little corner somewhere.
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So as you look to the 70s but also in the decades to come
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and you saw computers, did you see them as tools
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or was there a little element of another entity?
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I remember a quote saying AI began with our dream
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to create the gods.
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Is there a feeling when you wrote that program
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that you were creating another entity,
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giving life to something?
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I wish I could say otherwise,
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but I simply found the technology platforms so exciting.
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That's what I was focused on.
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I think the majority of the people that I've worked with,
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and there are a few exceptions, Steve Jobs being an example,
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really saw this as a great technological play.
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I think relatively few of the technical people understood
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the scale of its impact.
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So I used NCP, which is a predecessor to TCPIP.
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It just made sense to connect things.
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We didn't think of it in terms of the internet
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and then companies and then Facebook and then Twitter
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and then politics and so forth.
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We never did that build.
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We didn't have that vision.
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And I think most people, it's a rare person
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who can see compounding at scale.
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Most people can see,
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if you ask people to predict the future,
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they'll give you an answer of six to nine months
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because that's about as far as people can imagine.
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But there's an old saying,
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which actually was attributed to a professor at MIT
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that we overestimate what can be done in one year
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and we underestimate what can be done in a decade.
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And there's a great deal of evidence
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that these core platforms at hardware and software
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take a decade, right?
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So think about self driving cars.
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Self driving cars were thought about in the 90s.
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There were projects around them.
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The first DARPA Grand Challenge was roughly 2004.
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So that's roughly 15 years ago.
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And today we have self driving cars operating
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in a city in Arizona, right?
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It's 15 years and we still have a ways to go
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before they're more generally available.
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So you've spoken about the importance,
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you just talked about predicting into the future.
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You've spoken about the importance of thinking
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five years ahead and having a plan for those five years.
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The way to say it is that almost everybody
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has a one year plan.
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Almost no one has a proper five year plan.
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And the key thing to having a five year plan
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is to having a model for what's going to happen
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under the underlying platforms.
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So here's an example.
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Moore's Law as we know it,
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the thing that powered improvements in CPUs
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has largely halted in its traditional shrinking mechanism
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because the costs have just gotten so high.
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It's getting harder and harder.
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But there's plenty of algorithmic improvements
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and specialized hardware improvements.
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So you need to understand the nature of those improvements
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and where they'll go in order to understand
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how it will change the platform.
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In the area of network connectivity,
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what are the gains that are gonna be possible in wireless?
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It looks like there's an enormous expansion
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of wireless connectivity at many different bands.
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And that we will primarily,
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historically I've always thought
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that we were primarily gonna be using fiber,
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but now it looks like we're gonna be using fiber
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plus very powerful high bandwidth
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sort of short distance connectivity
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to bridge the last mile.
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That's an amazing achievement.
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then you're gonna build your systems differently.
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By the way, those networks
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have different latency properties, right?
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Because they're more symmetric,
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the algorithms feel faster for that reason.
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And so when you think about whether it's a fiber
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or just technologies in general,
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so there's this barber wooden poem or quote
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that I really like.
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It's from the champions of the impossible
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rather than the slaves of the possible
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that evolution draws its creative force.
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So in predicting the next five years,
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I'd like to talk about the impossible and the possible.
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Well, and again, one of the great things about humanity
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is that we produce dreamers, right?
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We literally have people who have a vision and a dream.
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They are, if you will, disagreeable
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in the sense that they disagree with the,
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they disagree with what the sort of zeitgeist is.
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They say there is another way.
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They have a belief, they have a vision.
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If you look at science, science is always marked
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by such people who went against some conventional wisdom,
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collected the knowledge at the time
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and assembled it in a way that produced a powerful platform.
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And you've been amazingly honest about,
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in an inspiring way, about things you've been wrong
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about predicting and you've obviously been right
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about a lot of things, but in this kind of tension,
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how do you balance, as a company,
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in predicting the next five years,
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the impossible, planning for the impossible,
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so listening to those crazy dreamers, letting them do,
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letting them run away and make the impossible real,
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make it happen, and slow, you know,
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that's how programmers often think,
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and slowing things down and saying,
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well, this is the rational, this is the possible,
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the pragmatic, the dreamer versus the pragmatist,
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so it's helpful to have a model
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which encourages a predictable revenue stream
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as well as the ability to do new things.
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So in Google's case, we're big enough
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and well enough managed and so forth
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that we have a pretty good sense of what our revenue will be
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for the next year or two, at least for a while.
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And so we have enough cash generation
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that we can make bets, and indeed,
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Google has become alphabet,
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so the corporation is organized around these bets,
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and these bets are in areas of fundamental importance
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to the world, whether it's artificial intelligence,
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medical technology, self driving cars,
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connectivity through balloons, on and on and on.
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And there's more coming and more coming.
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So one way you could express this
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is that the current business is successful enough
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that we have the luxury of making bets.
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And another one that you could say
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is that we have the wisdom of being able to see
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that a corporate structure needs to be created
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to enhance the likelihood of the success of those bets.
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So we essentially turned ourselves into a conglomerate
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of bets and then this underlying corporation, Google,
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which is itself innovative.
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So in order to pull this off,
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you have to have a bunch of belief systems,
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and one of them is that you have to have
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bottoms up and tops down.
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The bottoms up we call 20% time,
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and the idea is that people can spend 20% of the time
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whatever they want, and the top down
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is that our founders in particular
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have a keen eye on technology
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and they're reviewing things constantly.
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So an example would be they'll hear about an idea
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or I'll hear about something and it sounds interesting,
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let's go visit them.
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And then let's begin to assemble the pieces
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to see if that's possible.
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And if you do this long enough,
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you get pretty good at predicting what's likely to work.
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So that's a beautiful balance that struck.
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Is this something that applies at all scale?
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It seems to be that Sergey, again, 15 years ago,
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came up with a concept called 10% of the budget
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should be on things that are unrelated.
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It was called 70, 20, 10.
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70% of our time on core business,
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20% on adjacent business, and 10% on other.
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And he proved mathematically,
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of course he's a brilliant mathematician,
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that you needed that 10% to make the sum
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of the growth work.
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And it turns out he was right.
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So getting into the world of artificial intelligence,
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you've talked quite extensively and effectively
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to the impact in the near term,
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the positive impact of artificial intelligence,
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whether it's especially machine learning
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in medical applications and education,
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and just making information more accessible, right?
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In the AI community, there is a kind of debate.
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There's this shroud of uncertainty
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as we face this new world
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with artificial intelligence in it.
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And there's some people, like Elon Musk,
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you've disagreed, at least on the degree of emphasis
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he places on the existential threat of AI.
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So I've spoken with Stuart Russell,
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Max Tegmark, who share Elon Musk's view,
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and Yoshua Bengio, Steven Pinker, who do not.
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And so there's a lot of very smart people
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who are thinking about this stuff, disagreeing,
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which is really healthy, of course.
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So what do you think is the healthiest way
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for the AI community to,
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and really for the general public,
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to think about AI and the concern
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of the technology being mismanaged in some kind of way?
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So the source of education for the general public
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has been robot killer movies.
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And Terminator, et cetera.
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And the one thing I can assure you we're not building
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are those kinds of solutions.
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Furthermore, if they were to show up,
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someone would notice and unplug them, right?
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So as exciting as those movies are,
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and they're great movies,
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were the killer robots to start,
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we would find a way to stop them, right?
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So I'm not concerned about that.
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And much of this has to do
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with the timeframe of conversation.
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So you can imagine a situation 100 years from now
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when the human brain is fully understood
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and the next generation and next generation
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of brilliant MIT scientists have figured all this out,
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we're gonna have a large number of ethics questions, right?
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Around science and thinking and robots and computers
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and so forth and so on.
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So it depends on the question of the timeframe.
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In the next five to 10 years,
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we're not facing those questions.
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What we're facing in the next five to 10 years
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is how do we spread this disruptive technology
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as broadly as possible to gain the maximum benefit of it?
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The primary benefits should be in healthcare
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Healthcare because it's obvious.
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We're all the same even though we somehow believe we're not.
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As a medical matter,
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the fact that we have big data about our health
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will save lives, allow us to deal with skin cancer
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and other cancers, ophthalmological problems.
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There's people working on psychological diseases
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and so forth using these techniques.
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I can go on and on.
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The promise of AI in medicine is extraordinary.
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There are many, many companies and startups
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and funds and solutions
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and we will all live much better for that.
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The same argument in education.
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Can you imagine that for each generation of child
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and even adult, you have a tutor educator that's AI based,
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that's not a human but is properly trained,
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that helps you get smarter,
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helps you address your language difficulties
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or your math difficulties or what have you.
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Why don't we focus on those two?
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The gains societally of making humans smarter and healthier
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are enormous and those translate for decades and decades
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and we'll all benefit from them.
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There are people who are working on AI safety,
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which is the issue that you're describing
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and there are conversations in the community
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that should there be such problems,
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what should the rules be like?
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Google, for example, has announced its policies
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with respect to AI safety, which I certainly support
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and I think most everybody would support
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and they make sense, right?
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So it helps guide the research
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but the killer robots are not arriving this year
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and they're not even being built.
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And on that line of thinking, you said the time scale.
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In this topic or other topics,
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have you found it useful on the business side
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or the intellectual side to think beyond five, 10 years,
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to think 50 years out?
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Has it ever been useful or productive?
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In our industry, there are essentially no examples
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of 50 year predictions that have been correct.
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Let's review AI, right?
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AI, which was largely invented here at MIT
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and a couple of other universities in the 1956, 1957,
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1958, the original claims were a decade or two.
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And when I was a PhD student, I studied AI a bit
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and it entered during my looking at it,
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a period which is known as AI winter,
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which went on for about 30 years,
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which is a whole generation of science,
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scientists and a whole group of people
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who didn't make a lot of progress
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because the algorithms had not improved
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and the computers had not approved.
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It took some brilliant mathematicians
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starting with a fellow named Jeff Hinton
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at Toronto and Montreal who basically invented
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this deep learning model which empowers us today.
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The seminal work there was 20 years ago
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and in the last 10 years, it's become popularized.
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So think about the timeframes for that level of discovery.
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It's very hard to predict.
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Many people think that we'll be flying around
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in the equivalent of flying cars, who knows?
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My own view, if I wanna go out on a limb,
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is to say that we know a couple of things
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about 50 years from now.
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We know that there'll be more people alive.
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We know that we'll have to have platforms
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that are more sustainable because the earth is limited
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in the ways we all know and that the kind of platforms
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that are gonna get built will be consistent
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with the principles that I've described.
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They will be much more empowering of individuals.
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They'll be much more sensitive to the ecology
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because they have to be, they just have to be.
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I also think that humans are gonna be a great deal smarter
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and I think they're gonna be a lot smarter
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because of the tools that I've discussed with you
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and of course, people will live longer.
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Life extension is continuing apace.
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A baby born today has a reasonable chance
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of living to 100, which is pretty exciting.
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It's well past the 21st century,
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so we better take care of them.
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And you mentioned an interesting statistic
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on some very large percentage, 60, 70% of people
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may live in cities.
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Today, more than half the world lives in cities
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and one of the great stories of humanity
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in the last 20 years has been the rural to urban migration.
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This has occurred in the United States,
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it's occurred in Europe, it's occurring in Asia
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and it's occurring in Africa.
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When people move to cities, the cities get more crowded,
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but believe it or not, their health gets better,
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their productivity gets better,
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their IQ and educational capabilities improve.
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So it's good news that people are moving to cities,
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but we have to make them livable and safe.
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So you, first of all, you are,
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but you've also worked with some of the greatest leaders
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in the history of tech.
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What insights do you draw from the difference
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in leadership styles of yourself,
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Steve Jobs, Elon Musk, Larry Page,
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now the new CEO, Sandra Pichai and others?
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From the, I would say, calm sages to the mad geniuses.
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One of the things that I learned as a young executive
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is that there's no single formula for leadership.
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They try to teach one, but that's not how it really works.
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There are people who just understand what they need to do
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and they need to do it quickly.
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Those people are often entrepreneurs.
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They just know and they move fast.
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There are other people who are systems thinkers
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and planners, that's more who I am,
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somewhat more conservative, more thorough in execution,
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a little bit more risk of risk.
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A little bit more risk averse.
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There's also people who are sort of slightly insane,
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in the sense that they are emphatic and charismatic
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and they feel it and they drive it and so forth.
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There's no single formula to success.
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There is one thing that unifies all of the people
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that you named, which is very high intelligence.
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At the end of the day, the thing that characterizes
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all of them is that they saw the world quicker, faster,
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they processed information faster.
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They didn't necessarily make the right decisions
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all the time, but they were on top of it.
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And the other thing that's interesting
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about all those people is they all started young.
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So think about Steve Jobs starting Apple
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roughly at 18 or 19.
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Think about Bill Gates starting at roughly 20, 21.
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Think about by the time they were 30,
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Mark Zuckerberg, a good example, at 19, 20.
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By the time they were 30, they had 10 years.
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At 30 years old, they had 10 years of experience
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of dealing with people and products and shipments
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and the press and business and so forth.
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It's incredible how much experience they had
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compared to the rest of us who were busy getting our PhDs.
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So we should celebrate these people
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because they've just had more life experience, right?
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And that helps inform the judgment.
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At the end of the day, when you're at the top
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of these organizations, all the easy questions
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have been dealt with, right?
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How should we design the buildings?
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Where should we put the colors on our product?
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What should the box look like, right?
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The problems, that's why it's so interesting
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to be in these rooms, the problems that they face, right,
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in terms of the way they operate,
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the way they deal with their employees,
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their customers, their innovation,
link |
are profoundly challenging.
link |
Each of the companies is demonstrably different culturally.
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They are not, in fact, cut of the same.
link |
They behave differently based on input.
link |
Their internal cultures are different.
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Their compensation schemes are different.
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Their values are different.
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So there's proof that diversity works.
link |
So, so when faced with a tough decision,
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in need of advice, it's been said that the best thing
link |
one can do is to find the best person in the world
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who can give that advice and find a way to be
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in a room with them, one on one and ask.
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So here we are, and let me ask in a long winded way,
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I wrote this down.
link |
In 1998, there were many good search engines,
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Lycos, Excite, AltaVista, Infoseek, Ask Jeeves maybe,
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So Google stepped in and disrupted everything.
link |
They disrupted the nature of search,
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the nature of our access to information,
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the way we discover new knowledge.
link |
So now it's 2018, actually 20 years later.
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There are many good personal AI assistants,
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including, of course, the best from Google.
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So you've spoken in medical and education,
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the impact of such an AI assistant could bring.
link |
So we arrive at this question.
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So it's a personal one for me,
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but I hope my situation represents that of many other,
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as we said, dreamers and the crazy engineers.
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So my whole life, I've dreamed of creating
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such an AI assistant.
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Every step I've taken has been towards that goal.
link |
Now I'm a research scientist in human centered AI
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So the next step for me as I sit here,
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so facing my passion is to do what Larry and Sergey did
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in 98, this simple startup.
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And so here's my simple question.
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Given the low odds of success, the timing and luck required,
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the countless other factors that can't be controlled
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or predicted, which is all the things
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that Larry and Sergey faced,
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is there some calculation, some strategy
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to follow in this step?
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Or do you simply follow the passion
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just because there's no other choice?
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I think the people who are in universities
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are always trying to study
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the extraordinarily chaotic nature of innovation
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and entrepreneurship.
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My answer is that they didn't have that conversation.
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They sensed a moment when in the case of Google,
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there was all of this data that needed to be organized
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and they had a better algorithm.
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They had invented a better way.
link |
So today with human centered AI,
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which is your area of research,
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there must be new approaches.
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It's such a big field.
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There must be new approaches,
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different from what we and others are doing.
link |
There must be startups to fund.
link |
There must be research projects to try.
link |
There must be graduate students to work on new approaches.
link |
Here at MIT, there are people who are looking at learning
link |
from the standpoint of looking at child learning.
link |
How do children learn starting at age one and two?
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And the work is fantastic.
link |
Those approaches are different from the approach
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that most people are taking.
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Perhaps that's a bet that you should make
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or perhaps there's another one.
link |
But at the end of the day,
link |
the successful entrepreneurs are not as crazy as they sound.
link |
They see an opportunity based on what's happened.
link |
Let's use Uber as an example.
link |
As Travis sells the story,
link |
he and his co founder were sitting in Paris
link |
and they had this idea because they couldn't get a cab.
link |
And they said, we have smartphones and the rest is history.
link |
So what's the equivalent of that Travis Eiffel Tower,
link |
where is a cab moment that you could,
link |
as an entrepreneur, take advantage of?
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Whether it's in human centered AI or something else.
link |
That's the next great startup.
link |
And the psychology of that moment.
link |
So when Sergey and Larry talk about,
link |
and listen to a few interviews, it's very nonchalant.
link |
Well, here's the very fascinating web data
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and here's an algorithm we have for,
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we just kind of want to play around with that data.
link |
And it seems like that's a really nice way
link |
to organize this data.
link |
I should say what happened to remember
link |
is that they were graduate students at Stanford
link |
and they thought this was interesting.
link |
So they built a search engine
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and they kept it in their room.
link |
And they had to get power from the room next door
link |
because they were using too much power in the room.
link |
So they ran an extension cord over, right?
link |
And then they went and they found a house
link |
and they had Google world headquarters of five people,
link |
right, to start the company.
link |
And they raised $100,000 from Andy Bechtolsheim,
link |
who was the Sun founder to do this
link |
and Dave Cheriton and a few others.
link |
The point is their beginnings were very simple
link |
but they were based on a powerful insight.
link |
That is a replicable model for any startup.
link |
It has to be a powerful insight.
link |
The beginnings are simple.
link |
And there has to be an innovation.
link |
In Larry and Sergey's case, it was PageRank,
link |
which was a brilliant idea,
link |
one of the most cited papers in the world today.
link |
What's the next one?
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So you're one of, if I may say,
link |
richest people in the world.
link |
And yet it seems that money is simply a side effect
link |
of your passions and not an inherent goal.
link |
But you're a fascinating person to ask.
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So much of our society at the individual level
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and at the company level and as nations
link |
is driven by the desire for wealth.
link |
What do you think about this drive?
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And what have you learned about,
link |
if I may romanticize the notion,
link |
the meaning of life,
link |
having achieved success on so many dimensions?
link |
There have been many studies of human happiness
link |
and above some threshold,
link |
which is typically relatively low for this conversation,
link |
there's no difference in happiness about money.
link |
The happiness is correlated with meaning and purpose,
link |
a sense of family, a sense of impact.
link |
So if you organize your life,
link |
assuming you have enough to get around
link |
and have a nice home and so forth,
link |
you'll be far happier if you figure out
link |
what you care about and work on that.
link |
It's often being in service to others.
link |
There's a great deal of evidence that people are happiest
link |
when they're serving others and not themselves.
link |
This goes directly against the sort of
link |
press induced excitement about
link |
powerful and wealthy leaders of one kind.
link |
And indeed these are consequential people.
link |
But if you are in a situation
link |
where you've been very fortunate as I have,
link |
you also have to take that as a responsibility
link |
and you have to basically work both to educate others
link |
and give them that opportunity,
link |
but also use that wealth to advance human society.
link |
In my case, I'm particularly interested in
link |
using the tools of artificial intelligence
link |
and machine learning to make society better.
link |
I've mentioned education, I've mentioned inequality
link |
and middle class and things like this,
link |
all of which are a passion of mine.
link |
It doesn't matter what you do,
link |
it matters that you believe in it,
link |
that it's important to you,
link |
and that your life will be far more satisfying
link |
if you spend your life doing that.
link |
I think there's no better place to end
link |
than a discussion of the meaning of life.
link |
Eric, thank you so much.