Eric Schmidt: Google | MIT Artificial Intelligence (AI) Podcast
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Eric Schmidt: Google | MIT Artificial Intelligence (AI) Podcast

– The following is a
conversation with Eric Schmidt. He was the CEO of Google for 10 years and a chairman for six more, guiding the company through
an incredible period of growth and a series of
world-changing innovations. He is one of the most impactful leaders in the era of the internet and the powerful voice for
the promise of technology in our society. It was truly an honor to speak with him as part of the MIT course on
artificial general intelligence and the Artificial Intelligence podcast. And now, here’s my
conversation with Eric Schmidt. What was the first moment when you fell in love with technology? – I grew up in 1960’s as a boy where every boy wanted to be an astronaut and part of the space program. So like everyone else of my age, we would go out to the cow
pasture behind my house, which was literally a cow pasture, and we would shoot model rockets off, and that I think is the beginning. And of course generationally today, it would be video games and
all of the amazing things that you can do online with computers. – [Lex] There’s a
transformative inspiring aspect of science and math that maybe rockets would instill in individuals. You mentioned yesterday
that eighth grade math is where the journey through
mathematical universe diverges for many people. It’s this fork in the roadway. There’s a professor of math
at Berkeley, Edward Franco. I’m not sure if you’re familiar with him. – I am. – [Lex] He has written this amazing book I recommend to everybody
called Love and Math. Two of my favorite words. (laughs) He says that if painting
was taught like math, then students would be
asked to paint a fence. It’s just his analogy of
essentially how math is taught. So you never get a chance to discover the beauty of the art of painting or the beauty of the art of math. So how, when, and where did
you discover that beauty? – I think what happens
with people like myself is that you’re math-enabled pretty early, and all of the sudden you discover that you can use that to
discover new insights. The great scientists
will all tell a story. The men and women who are fantastic today, it’s somewhere when they were
in high school or in college they discovered that they could discover something themselves. And that sense of building something, of having an impact that you own drives knowledge acquisition and learning. In my case, it was programming and the notion that I could build things that had not existed, that I had built that had my name of it. And this was before open-source, but you could think of it as
open-source contributions. So today if I were a 16
or a 17-year-old boy, I’m sure that I would aspire
as a computer scientist to make a contribution
like the open-source heroes of the world today. That would be what would be driving me, and I would be trying and learning, and making mistakes and so
forth in the ways that it works. The repository that GitHub represents and that open-source libraries represent is an enormous bank of knowledge of all of the people who are doing that. And one of the lessons
that I learned at Google was that the world is a very big place, and there’s an awful lot of smart people. And an awful lot of
them are underutilized. So here’s an opportunity, for example, building parts or programs,
building new ideas, to contribute to the greater of society. – [Lex] So in that moment in the 70’s, the inspiring moment
where there was nothing and then you cerated
something through programming, that magical moment. So in 1975, I think, you
created a program called Lex, which I especially like
because my name is Lex. So thank you, thank you
for creating a brand that established a reputation
that’s long-lasting, reliable, and has a big impact on the
world and is still used today. So thank you for that. But more seriously, in that time, in the 70’s as an engineer personal computers were being born. Did you think you would be able to predict the 80’s, 90’s and the noughts
of where computers would go? – I’m sure I could not and
would not have gotten it right. I was the beneficiary of the great work of many many people who
saw it clearer than I did. With Lex, I worked with a
fellow named Michael Lesk who was my supervisor, and he essentially helped me architect and deliver a system
that’s still in use today. After that, I worked at Xerox
Palo Alto Research Center where the Alto was invented, and the Alto is the predecessor of the modern personal computer,
or Macintosh and so forth. And the Altos were very rare, and I had to drive an hour
from Berkeley to go use them, but I made a point of skipping classes and doing whatever it took to have access to this
extraordinary achievement. I knew that they were consequential. What I did not understand was scaling. I did not understand what would happen when you had 100 million
as opposed to 100. And so since then, and I have
learned the benefit of scale, I always look for things which are going to scale to platforms, so mobile phones, Android,
all of those things. The world is a numerous, there are many many people in the world. People really have needs. They really will use these platforms, and you can build big
businesses on top of them. – [Lex] So it’s interesting, so when you see a piece of technology, now you think what will
this technology look like when it’s in the hands
of a billion people. – That’s right. So an example would be that the
market is so competitive now that if you can’t figure out a way for something to have a million
users or a billion users, it probably is not going to be successful because something else will
become the general platform and your idea will become a lost idea or a specialized service
with relatively few users. So it’s a path to generality. It’s a path to general platform use. It’s a path to broad applicability. Now there are plenty of good
businesses that are tiny, so luxury goods for example, but if you want to have
an impact at scale, you have to look for things
which are of common value, common pricing, common distribution, and solve common problems. They’re problems that everyone has. And by the way, people
have lots of problems. Information, medicine, health,
education, and so forth, work on those problems. – [Lex] Like you said, you’re a big fan of the middle class– – ‘Cause there’s so many of them. – [Lex] There’s so many of them. – By definition. – [Lex] So any product, any
thing that has a huge impact and improves their lives is
a great business decision, and it’s just good for society. – And there’s nothing
wrong with starting off in the high-end as long as you have a plan to get to the middle class. There’s nothing wrong with starting with a specialized market in order to learn and to build and to fund things. So you start luxury market to build a general purpose market. But if you define yourself
as only a narrow market, someone else can come along
with a general purpose market that can push you to the corner, can restrict the scale of operation, can force you to be a lesser
impact than you might be. So it’s very important to think in terms of broad businesses and broad impact, even if you start in a
little corner somewhere. – [Lex] So as you look to the 70’s but also in the decades to
come and you saw computers, did you see them as tools, or was there a little
element of another entity? I remember a quote saying AI began with our dream to create the gods. Is there a feeling when
you wrote that program that you were creating another entity, giving life to something? – I wish I could say otherwise, but I simply found the
technology platforms so exciting. That’s what I was focused on. I think the majority of the
people that I’ve worked with, and there are a few exceptions,
Steve Jobs being an example, really saw this a great
technological play. I think relatively few of the
technical people understood the scale of its impact. So I used MCP which is
a predecessor to TCP/IP. It just made sense to connect things. We didn’t think of it
in terms of the internet and then companies and then Facebook and then Twitter and then
politics and so forth. We never did that build. We didn’t have that vision. And I think most people, it’s a rare person who can
see compounding at scale. Most people can see, if you ask people to predict the future, they’ll give you an answer of six to nine months or 12 months because that’s about as
far as people can imagine. But there’s an old saying, which actually was attributed to a professor at MIT a long time ago, that we overestimate what
can be done in one year. We underestimate was
can be done in a decade. And there’s a great deal of evidence that these core platforms of hardware and software take a decade. So think about self-driving cars. Self-driving cars were
thought about in the 90’s. There were projects around them. The first DARPA Grand
Challenge was roughly 2004. So that’s roughly 15 years ago. And today we have
self-driving cars operating at a city in Arizona, so 15 years. And we still have a ways to go before they’re more generally available. – [Lex] So you’ve spoken
about the importance, you just talked about
predicting into the future. You’ve spoken about the importance of thinking five years ahead and having a plan for those five years. – The way to say it is that almost everybody has a one-year plan. Almost no one has a proper five-year plan. And the key thing to have
on the five-year plan is having a model for
what’s going to happen under the underlying platforms. So here’s an example. Moore’s law as we know it, the thing that powered improvement in CPUs has largely halted in its traditional shrinking mechanisms because the costs have just gotten so high and it’s getting harder and harder. But there’s plenty of
algorithmic improvements and specialized hardware improvements. So you need to understand the
nature of those improvements and where they’ll go
in order to understand how it will change the platform. In the area of network conductivity, what are the gains that are
to be possible in wireless? It looks like there’s
an enormous expansion of wireless conductivity
at many different bands and that we will primarily, historical I’ve always thought that we were primarily
going to be using fiber, but now it looks like
we’re going to be using fiber plus very powerful high bandwidth sort of short distance conductivity
to bridge the last mile. That’s an amazing achievement. If you know that, then you’re going to build
your systems differently. By the way, those networks have
different latency properties because they’re more symmetric. The algorithms feel
faster for that reason. – [Lex] And so when you think about, whether it’s fiber or just
technologies in general, so there’s this Barbara
Wootton poem or quote that I really like. It’s from the champions of the impossible, rather than the slaves of the possible, that evolution draws its creative force. So in predicting the next five years, I’d like to talk about the
impossible and the possible. – Well, and again, one of the
great things about humanity is that we produce dreamers. We literally have people who
have a vision and a dream. They are, if you will,
disagreeable in the sense that they disagree with the, they disagree with what
the sort of zeitgeist is. They say there is another way. They have a belief. They have a vision. If you look at science, science is always marked by such people who went against some conventional wisdom, collected the knowledge at the time, and assembled it in a way that
produced a powerful platform. – [Lex] And you’ve been
amazingly honest about, in an inspiring way, about things you’ve been
wrong about predicting, and you’ve obviously been
right about a lot of things. But in this kind of tension, how do you balance 500 Internal Server Error

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