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Chris Anderson: You were something
of a mathematical phenom.

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You had already taught at Harvard
and MIT at a young age.

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And then the NSA came calling.

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What was that about?

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Jim Simons: Well the NSA --
that's the National Security Agency --

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they didn't exactly come calling.

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They had an operation at Princeton,
where they hired mathematicians

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to attack secret codes
and stuff like that.

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And I knew that existed.

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And they had a very good policy,

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because you could do half your time
at your own mathematics,

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and at least half your time
working on their stuff.

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And they paid a lot.

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So that was an irresistible pull.

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So, I went there.

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CA: You were a code-cracker.

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JS: I was.

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CA: Until you got fired.

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JS: Well, I did get fired. Yes.

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CA: How come?

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JS: Well, how come?

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I got fired because,
well, the Vietnam War was on,

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and the boss of bosses in my organization
was a big fan of the war

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and wrote a New York Times article,
a magazine section cover story,

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about how we would win in Vietnam.

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And I didn't like that war,
I thought it was stupid.

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And I wrote a letter to the Times,
which they published,

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saying not everyone
who works for Maxwell Taylor,

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if anyone remembers that name,
agrees with his views.

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And I gave my own views ...

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CA: Oh, OK. I can see that would --

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JS: ... which were different
from General Taylor's.

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But in the end, nobody said anything.

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But then, I was 29 years old at this time,
and some kid came around

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and said he was a stringer
from Newsweek magazine

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and he wanted to interview me
and ask what I was doing about my views.

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And I told him, "I'm doing
mostly mathematics now,

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and when the war is over,
then I'll do mostly their stuff."

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Then I did the only
intelligent thing I'd done that day --

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I told my local boss
that I gave that interview.

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And he said, "What'd you say?"

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And I told him what I said.

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And then he said,
"I've got to call Taylor."

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He called Taylor; that took 10 minutes.

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I was fired five minutes after that.

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CA: OK.

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JS: But it wasn't bad.

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CA: It wasn't bad,
because you went on to Stony Brook

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and stepped up your mathematical career.

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You started working with this man here.

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Who is this?

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JS: Oh, [Shiing-Shen] Chern.

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Chern was one of the great
mathematicians of the century.

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I had known him when
I was a graduate student at Berkeley.

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And I had some ideas,

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and I brought them to him
and he liked them.

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Together, we did this work
which you can easily see up there.

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There it is.

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CA: It led to you publishing
a famous paper together.

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Can you explain at all what that work was?

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JS: No.

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(Laughter)

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JS: I mean, I could
explain it to somebody.

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(Laughter)

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CA: How about explaining this?

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JS: But not many. Not many people.

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CA: I think you told me
it had something to do with spheres,

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so let's start here.

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JS: Well, it did,
but I'll say about that work --

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it did have something to do with that,
but before we get to that --

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that work was good mathematics.

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I was very happy with it; so was Chern.

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It even started a little sub-field
that's now flourishing.

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But, more interestingly,
it happened to apply to physics,

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something we knew nothing about --
at least I knew nothing about physics,

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and I don't think Chern
knew a heck of a lot.

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And about 10 years
after the paper came out,

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a guy named Ed Witten in Princeton
started applying it to string theory

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and people in Russia started applying it
to what's called "condensed matter."

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Today, those things in there
called Chern-Simons invariants

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have spread through a lot of physics.

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And it was amazing.

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We didn't know any physics.

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It never occurred to me
that it would be applied to physics.

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But that's the thing about mathematics --
you never know where it's going to go.

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CA: This is so incredible.

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So, we've been talking about
how evolution shapes human minds

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that may or may not perceive the truth.

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Somehow, you come up
with a mathematical theory,

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not knowing any physics,

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discover two decades later
that it's being applied

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to profoundly describe
the actual physical world.

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How can that happen?

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JS: God knows.

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(Laughter)

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But there's a famous physicist
named [Eugene] Wigner,

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and he wrote an essay on the unreasonable
effectiveness of mathematics.

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Somehow, this mathematics,
which is rooted in the real world

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in some sense -- we learn to count,
measure, everyone would do that --

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and then it flourishes on its own.

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But so often it comes
back to save the day.

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General relativity is an example.

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[Hermann] Minkowski had this geometry,
and Einstein realized,

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"Hey! It's the very thing
in which I can cast general relativity."

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So, you never know. It is a mystery.

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It is a mystery.

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CA: So, here's a mathematical
piece of ingenuity.

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Tell us about this.

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JS: Well, that's a ball -- it's a sphere,
and it has a lattice around it --

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you know, those squares.

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What I'm going to show here was
originally observed by [Leonhard] Euler,

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the great mathematician, in the 1700s.

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And it gradually grew to be
a very important field in mathematics:

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algebraic topology, geometry.

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That paper up there had its roots in this.

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So, here's this thing:

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it has eight vertices,
12 edges, six faces.

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And if you look at the difference --
vertices minus edges plus faces --

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you get two.

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OK, well, two. That's a good number.

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Here's a different way of doing it --
these are triangles covering --

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this has 12 vertices and 30 edges

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and 20 faces, 20 tiles.

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And vertices minus edges
plus faces still equals two.

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And in fact, you could do this
any which way --

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cover this thing with all kinds
of polygons and triangles

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and mix them up.

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And you take vertices minus edges
plus faces -- you'll get two.

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Here's a different shape.

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This is a torus, or the surface
of a doughnut: 16 vertices

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covered by these rectangles,
32 edges, 16 faces.

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Vertices minus edges comes out to be zero.

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It'll always come out to zero.

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Every time you cover a torus
with squares or triangles

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or anything like that,
you're going to get zero.

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So, this is called
the Euler characteristic.

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And it's what's called
a topological invariant.

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It's pretty amazing.

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No matter how you do it,
you're always get the same answer.

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So that was the first sort of thrust,
from the mid-1700s,

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into a subject which is now called
algebraic topology.

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CA: And your own work
took an idea like this and moved it

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into higher-dimensional theory,

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higher-dimensional objects,
and found new invariances?

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JS: Yes. Well, there were already
higher-dimensional invariants:

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Pontryagin classes --
actually, there were Chern classes.

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There were a bunch
of these types of invariants.

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I was struggling to work on one of them

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and model it sort of combinatorially,

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instead of the way it was typically done,

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and that led to this work
and we uncovered some new things.

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But if it wasn't for Mr. Euler --

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who wrote almost 70 volumes of mathematics

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and had 13 children,

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who he apparently would dandle on his knee
while he was writing --

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if it wasn't for Mr. Euler, there wouldn't
perhaps be these invariants.

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CA: OK, so that's at least given us
a flavor of that amazing mind in there.

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Let's talk about Renaissance.

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Because you took that amazing mind
and having been a code-cracker at the NSA,

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you started to become a code-cracker
in the financial industry.

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I think you probably didn't buy
efficient market theory.

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Somehow you found a way of creating
astonishing returns over two decades.

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The way it's been explained to me,

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what's remarkable about what you did
wasn't just the size of the returns,

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it's that you took them
with surprisingly low volatility and risk,

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compared with other hedge funds.

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So how on earth did you do this, Jim?

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JS: I did it by assembling
a wonderful group of people.

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When I started doing trading, I had
gotten a little tired of mathematics.

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I was in my late 30s,
I had a little money.

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I started trading and it went very well.

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I made quite a lot of money
with pure luck.

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I mean, I think it was pure luck.

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It certainly wasn't mathematical modeling.

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But in looking at the data,
after a while I realized:

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it looks like there's some structure here.

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And I hired a few mathematicians,
and we started making some models --

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just the kind of thing we did back
at IDA [Institute for Defense Analyses].

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You design an algorithm,
you test it out on a computer.

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Does it work? Doesn't it work? And so on.

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CA: Can we take a look at this?

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Because here's a typical graph
of some commodity.

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I look at that, and I say,
"That's just a random, up-and-down walk --

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maybe a slight upward trend
over that whole period of time."

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How on earth could you trade
looking at that,

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and see something that wasn't just random?

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JS: In the old days -- this is
kind of a graph from the old days,

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commodities or currencies
had a tendency to trend.

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Not necessarily the very light trend
you see here, but trending in periods.

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And if you decided, OK,
I'm going to predict today,

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by the average move in the past 20 days --

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maybe that would be a good prediction,
and I'd make some money.

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And in fact, years ago,
such a system would work --

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not beautifully, but it would work.

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You'd make money, you'd lose
money, you'd make money.

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But this is a year's worth of days,

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and you'd make a little money
during that period.

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It's a very vestigial system.

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CA: So you would test
a bunch of lengths of trends in time

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and see whether, for example,

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a 10-day trend or a 15-day trend
was predictive of what happened next.

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JS: Sure, you would try all those things
and see what worked best.

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Trend-following would
have been great in the '60s,

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and it was sort of OK in the '70s.

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By the '80s, it wasn't.

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CA: Because everyone could see that.

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So, how did you stay ahead of the pack?

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JS: We stayed ahead of the pack
by finding other approaches --

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shorter-term approaches to some extent.

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The real thing was to gather
a tremendous amount of data --

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and we had to get it by hand
in the early days.

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We went down to the Federal Reserve
and copied interest rate histories

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and stuff like that,
because it didn't exist on computers.

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We got a lot of data.

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And very smart people -- that was the key.

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I didn't really know how to hire
people to do fundamental trading.

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I had hired a few -- some made money,
some didn't make money.

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I couldn't make a business out of that.

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But I did know how to hire scientists,

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because I have some taste
in that department.

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So, that's what we did.

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And gradually these models
got better and better,

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and better and better.

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CA: You're credited with doing
something remarkable at Renaissance,

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which is building this culture,
this group of people,

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who weren't just hired guns
who could be lured away by money.

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Their motivation was doing
exciting mathematics and science.

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JS: Well, I'd hoped that might be true.

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But some of it was money.

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CA: They made a lot of money.

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JS: I can't say that no one came
because of the money.

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I think a lot of them
came because of the money.

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But they also came
because it would be fun.

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CA: What role did machine learning
play in all this?

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JS: In a certain sense,
what we did was machine learning.

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You look at a lot of data, and you try
to simulate different predictive schemes,

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until you get better and better at it.

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It doesn't necessarily feed back on itself
the way we did things.

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But it worked.

240
00:12:56,150 --> 00:13:00,209
CA: So these different predictive schemes
can be really quite wild and unexpected.

241
00:13:00,233 --> 00:13:02,147
I mean, you looked at everything, right?

242
00:13:02,171 --> 00:13:05,488
You looked at the weather,
length of dresses, political opinion.

243
00:13:05,512 --> 00:13:08,349
JS: Yes, length of dresses we didn't try.

244
00:13:08,373 --> 00:13:10,430
CA: What sort of things?

245
00:13:10,454 --> 00:13:11,612
JS: Well, everything.

246
00:13:11,636 --> 00:13:14,900
Everything is grist for the mill --
except hem lengths.

247
00:13:16,852 --> 00:13:19,152
Weather, annual reports,

248
00:13:19,176 --> 00:13:23,908
quarterly reports, historic data itself,
volumes, you name it.

249
00:13:23,932 --> 00:13:25,083
Whatever there is.

250
00:13:25,107 --> 00:13:27,728
We take in terabytes of data a day.

251
00:13:27,752 --> 00:13:31,876
And store it away and massage it
and get it ready for analysis.

252
00:13:33,446 --> 00:13:34,828
You're looking for anomalies.

253
00:13:34,852 --> 00:13:37,805
You're looking for -- like you said,

254
00:13:37,829 --> 00:13:40,281
the efficient market
hypothesis is not correct.

255
00:13:40,305 --> 00:13:43,772
CA: But any one anomaly
might be just a random thing.

256
00:13:43,796 --> 00:13:47,454
So, is the secret here to just look
at multiple strange anomalies,

257
00:13:47,478 --> 00:13:48,806
and see when they align?

258
00:13:49,238 --> 00:13:52,451
JS: Any one anomaly
might be a random thing;

259
00:13:52,475 --> 00:13:55,514
however, if you have enough data
you can tell that it's not.

260
00:13:55,538 --> 00:14:00,488
You can see an anomaly that's persistent
for a sufficiently long time --

261
00:14:00,512 --> 00:14:05,487
the probability of it being
random is not high.

262
00:14:05,511 --> 00:14:10,369
But these things fade after a while;
anomalies can get washed out.

263
00:14:10,393 --> 00:14:12,813
So you have to keep on top
of the business.

264
00:14:12,837 --> 00:14:15,509
CA: A lot of people look
at the hedge fund industry now

265
00:14:15,533 --> 00:14:19,931
and are sort of ... shocked by it,

266
00:14:19,955 --> 00:14:22,127
by how much wealth is created there,

267
00:14:22,151 --> 00:14:24,396
and how much talent is going into it.

268
00:14:25,523 --> 00:14:29,529
Do you have any worries
about that industry,

269
00:14:29,553 --> 00:14:31,967
and perhaps the financial
industry in general?

270
00:14:31,991 --> 00:14:34,695
Kind of being on a runaway train that's --

271
00:14:34,719 --> 00:14:38,749
I don't know --
helping increase inequality?

272
00:14:38,773 --> 00:14:42,604
How would you champion what's happening
in the hedge fund industry?

273
00:14:42,628 --> 00:14:45,236
JS: I think in the last
three of four years,

274
00:14:45,260 --> 00:14:47,363
hedge funds have not done especially well.

275
00:14:47,387 --> 00:14:48,787
We've done dandy,

276
00:14:48,811 --> 00:14:52,812
but the hedge fund industry as a whole
has not done so wonderfully.

277
00:14:52,836 --> 00:14:57,738
The stock market has been on a roll,
going up as everybody knows,

278
00:14:57,762 --> 00:15:01,207
and price-earnings ratios have grown.

279
00:15:01,231 --> 00:15:04,294
So an awful lot of the wealth
that's been created in the last --

280
00:15:04,318 --> 00:15:07,668
let's say, five or six years --
has not been created by hedge funds.

281
00:15:08,458 --> 00:15:11,679
People would ask me,
"What's a hedge fund?"

282
00:15:11,703 --> 00:15:13,963
And I'd say, "One and 20."

283
00:15:13,987 --> 00:15:17,553
Which means -- now it's two and 20 --

284
00:15:17,577 --> 00:15:20,930
it's two percent fixed fee
and 20 percent of profits.

285
00:15:20,954 --> 00:15:23,306
Hedge funds are all
different kinds of creatures.

286
00:15:23,330 --> 00:15:26,569
CA: Rumor has it you charge
slightly higher fees than that.

287
00:15:27,339 --> 00:15:30,420
JS: We charged the highest fees
in the world at one time.

288
00:15:30,444 --> 00:15:33,670
Five and 44, that's what we charge.

289
00:15:33,694 --> 00:15:35,092
CA: Five and 44.

290
00:15:35,116 --> 00:15:38,350
So five percent flat,
44 percent of upside.

291
00:15:38,374 --> 00:15:41,157
You still made your investors
spectacular amounts of money.

292
00:15:41,181 --> 00:15:42,633
JS: We made good returns, yes.

293
00:15:42,657 --> 00:15:45,657
People got very mad:
"How can you charge such high fees?"

294
00:15:45,681 --> 00:15:47,308
I said, "OK, you can withdraw."

295
00:15:47,332 --> 00:15:50,150
But "How can I get more?"
was what people were --

296
00:15:50,174 --> 00:15:51,678
(Laughter)

297
00:15:51,702 --> 00:15:54,142
But at a certain point,
as I think I told you,

298
00:15:54,166 --> 00:15:59,341
we bought out all the investors
because there's a capacity to the fund.

299
00:15:59,365 --> 00:16:02,069
CA: But should we worry
about the hedge fund industry

300
00:16:02,093 --> 00:16:07,531
attracting too much of the world's
great mathematical and other talent

301
00:16:07,555 --> 00:16:10,793
to work on that, as opposed
to the many other problems in the world?

302
00:16:10,817 --> 00:16:12,746
JS: Well, it's not just mathematical.

303
00:16:12,770 --> 00:16:15,449
We hire astronomers and physicists
and things like that.

304
00:16:15,833 --> 00:16:18,264
I don't think we should worry
about it too much.

305
00:16:18,288 --> 00:16:21,430
It's still a pretty small industry.

306
00:16:21,454 --> 00:16:27,451
And in fact, bringing science
into the investing world

307
00:16:27,475 --> 00:16:29,634
has improved that world.

308
00:16:29,658 --> 00:16:33,728
It's reduced volatility.
It's increased liquidity.

309
00:16:33,752 --> 00:16:36,941
Spreads are narrower because
people are trading that kind of stuff.

310
00:16:36,965 --> 00:16:42,041
So I'm not too worried about Einstein
going off and starting a hedge fund.

311
00:16:42,478 --> 00:16:46,642
CA: You're at a phase in your life now
where you're actually investing, though,

312
00:16:46,666 --> 00:16:50,400
at the other end of the supply chain --

313
00:16:50,424 --> 00:16:54,528
you're actually boosting
mathematics across America.

314
00:16:54,552 --> 00:16:56,417
This is your wife, Marilyn.

315
00:16:56,441 --> 00:17:01,197
You're working on
philanthropic issues together.

316
00:17:01,221 --> 00:17:02,384
Tell me about that.

317
00:17:02,408 --> 00:17:06,057
JS: Well, Marilyn started --

318
00:17:06,081 --> 00:17:09,528
there she is up there,
my beautiful wife --

319
00:17:09,552 --> 00:17:12,524
she started the foundation
about 20 years ago.

320
00:17:12,548 --> 00:17:13,699
I think '94.

321
00:17:13,723 --> 00:17:15,818
I claim it was '93, she says it was '94,

322
00:17:15,842 --> 00:17:18,413
but it was one of those two years.

323
00:17:18,437 --> 00:17:20,572
(Laughter)

324
00:17:20,596 --> 00:17:27,315
We started the foundation,
just as a convenient way to give charity.

325
00:17:28,346 --> 00:17:30,853
She kept the books, and so on.

326
00:17:30,877 --> 00:17:37,591
We did not have a vision at that time,
but gradually a vision emerged --

327
00:17:37,615 --> 00:17:43,119
which was to focus on math and science,
to focus on basic research.

328
00:17:43,569 --> 00:17:46,341
And that's what we've done.

329
00:17:46,365 --> 00:17:52,720
Six years ago or so, I left Renaissance
and went to work at the foundation.

330
00:17:52,744 --> 00:17:54,315
So that's what we do.

331
00:17:54,339 --> 00:17:57,248
CA: And so Math for America
is basically investing

332
00:17:57,272 --> 00:17:59,910
in math teachers around the country,

333
00:17:59,934 --> 00:18:03,736
giving them some extra income,
giving them support and coaching.

334
00:18:03,760 --> 00:18:06,811
And really trying
to make that more effective

335
00:18:06,835 --> 00:18:09,436
and make that a calling
to which teachers can aspire.

336
00:18:09,460 --> 00:18:14,250
JS: Yeah -- instead of beating up
the bad teachers,

337
00:18:14,274 --> 00:18:19,127
which has created morale problems
all through the educational community,

338
00:18:19,151 --> 00:18:21,592
in particular in math and science,

339
00:18:21,616 --> 00:18:27,746
we focus on celebrating the good ones
and giving them status.

340
00:18:27,770 --> 00:18:30,701
Yeah, we give them extra money,
15,000 dollars a year.

341
00:18:30,725 --> 00:18:35,192
We have 800 math and science teachers
in New York City in public schools today,

342
00:18:35,216 --> 00:18:37,030
as part of a core.

343
00:18:37,054 --> 00:18:40,740
There's a great morale among them.

344
00:18:40,764 --> 00:18:43,270
They're staying in the field.

345
00:18:43,294 --> 00:18:46,189
Next year, it'll be 1,000
and that'll be 10 percent

346
00:18:46,213 --> 00:18:49,757
of the math and science teachers
in New York [City] public schools.

347
00:18:49,781 --> 00:18:55,686
(Applause)

348
00:18:55,710 --> 00:18:59,120
CA: Jim, here's another project
that you've supported philanthropically:

349
00:18:59,144 --> 00:19:01,541
Research into origins of life, I guess.

350
00:19:01,565 --> 00:19:03,012
What are we looking at here?

351
00:19:03,536 --> 00:19:05,418
Well, I'll save that for a second.

352
00:19:05,442 --> 00:19:07,604
And then I'll tell you
what you're looking at.

353
00:19:07,628 --> 00:19:10,684
Origins of life is a fascinating question.

354
00:19:10,708 --> 00:19:12,241
How did we get here?

355
00:19:13,170 --> 00:19:14,941
Well, there are two questions:

356
00:19:14,965 --> 00:19:20,833
One is, what is the route
from geology to biology --

357
00:19:20,857 --> 00:19:22,238
how did we get here?

358
00:19:22,262 --> 00:19:24,626
And the other question is,
what did we start with?

359
00:19:24,650 --> 00:19:27,752
What material, if any,
did we have to work with on this route?

360
00:19:27,776 --> 00:19:30,837
Those are two very,
very interesting questions.

361
00:19:31,773 --> 00:19:37,607
The first question is a tortuous path
from geology up to RNA

362
00:19:37,631 --> 00:19:39,889
or something like that --
how did that all work?

363
00:19:39,913 --> 00:19:42,301
And the other,
what do we have to work with?

364
00:19:42,325 --> 00:19:44,096
Well, more than we think.

365
00:19:44,120 --> 00:19:48,963
So what's pictured there
is a star in formation.

366
00:19:49,836 --> 00:19:53,261
Now, every year in our Milky Way,
which has 100 billion stars,

367
00:19:53,285 --> 00:19:55,780
about two new stars are created.

368
00:19:55,804 --> 00:19:58,274
Don't ask me how, but they're created.

369
00:19:58,298 --> 00:20:01,378
And it takes them about a million
years to settle out.

370
00:20:02,132 --> 00:20:04,308
So, in steady state,

371
00:20:04,332 --> 00:20:08,180
there are about two million stars
in formation at any time.

372
00:20:08,204 --> 00:20:11,662
That one is somewhere
along this settling-down period.

373
00:20:12,067 --> 00:20:15,003
And there's all this crap
sort of circling around it,

374
00:20:15,027 --> 00:20:16,525
dust and stuff.

375
00:20:17,479 --> 00:20:20,502
And it'll form probably a solar system,
or whatever it forms.

376
00:20:20,526 --> 00:20:22,702
But here's the thing --

377
00:20:22,726 --> 00:20:29,074
in this dust that surrounds a forming star

378
00:20:29,098 --> 00:20:35,133
have been found, now,
significant organic molecules.

379
00:20:35,958 --> 00:20:42,097
Molecules not just like methane,
but formaldehyde and cyanide --

380
00:20:42,121 --> 00:20:48,638
things that are the building blocks --
the seeds, if you will -- of life.

381
00:20:49,136 --> 00:20:51,828
So, that may be typical.

382
00:20:52,395 --> 00:20:59,329
And it may be typical
that planets around the universe

383
00:20:59,353 --> 00:21:02,965
start off with some of these
basic building blocks.

384
00:21:03,830 --> 00:21:06,545
Now does that mean
there's going to be life all around?

385
00:21:06,569 --> 00:21:07,933
Maybe.

386
00:21:07,957 --> 00:21:12,084
But it's a question
of how tortuous this path is

387
00:21:12,108 --> 00:21:16,502
from those frail beginnings,
those seeds, all the way to life.

388
00:21:16,526 --> 00:21:21,718
And most of those seeds
will fall on fallow planets.

389
00:21:21,742 --> 00:21:23,151
CA: So for you, personally,

390
00:21:23,175 --> 00:21:25,897
finding an answer to this question
of where we came from,

391
00:21:25,921 --> 00:21:29,579
of how did this thing happen,
that is something you would love to see.

392
00:21:29,603 --> 00:21:31,389
JS: Would love to see.

393
00:21:31,413 --> 00:21:32,903
And like to know --

394
00:21:32,927 --> 00:21:38,097
if that path is tortuous enough,
and so improbable,

395
00:21:38,121 --> 00:21:42,875
that no matter what you start with,
we could be a singularity.

396
00:21:43,336 --> 00:21:44,488
But on the other hand,

397
00:21:44,512 --> 00:21:47,990
given all this organic dust
that's floating around,

398
00:21:48,014 --> 00:21:51,805
we could have lots of friends out there.

399
00:21:52,947 --> 00:21:54,108
It'd be great to know.

400
00:21:54,132 --> 00:21:57,612
CA: Jim, a couple of years ago,
I got the chance to speak with Elon Musk,

401
00:21:57,636 --> 00:22:00,473
and I asked him the secret of his success,

402
00:22:00,497 --> 00:22:04,188
and he said taking
physics seriously was it.

403
00:22:04,696 --> 00:22:08,699
Listening to you, what I hear you saying
is taking math seriously,

404
00:22:08,723 --> 00:22:11,726
that has infused your whole life.

405
00:22:12,123 --> 00:22:16,686
It's made you an absolute fortune,
and now it's allowing you to invest

406
00:22:16,710 --> 00:22:21,206
in the futures of thousands and thousands
of kids across America and elsewhere.

407
00:22:21,567 --> 00:22:24,425
Could it be that science actually works?

408
00:22:24,449 --> 00:22:27,221
That math actually works?

409
00:22:27,245 --> 00:22:31,617
JS: Well, math certainly works.
Math certainly works.

410
00:22:31,641 --> 00:22:32,839
But this has been fun.

411
00:22:32,863 --> 00:22:37,809
Working with Marilyn and giving it away
has been very enjoyable.

412
00:22:37,833 --> 00:22:40,769
CA: I just find it --
it's an inspirational thought to me,

413
00:22:40,793 --> 00:22:44,800
that by taking knowledge seriously,
so much more can come from it.

414
00:22:44,824 --> 00:22:47,842
So thank you for your amazing life,
and for coming here to TED.

415
00:22:47,866 --> 00:22:48,617
Thank you.

416
00:22:48,651 --> 00:22:49,752
Jim Simons!

417
00:22:49,806 --> 00:22:54,186
(Applause)