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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: So 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're going
to 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 said, 
"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. 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 subfield
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,

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which is rooted in the real world
in some sense --

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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 1700's.

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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
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, the surface of a donut,

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16 vertices covered by these rectangles,

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32 edges, 16 faces.

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Vertices minus edges comes out 0.

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It'll always come out 0.

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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 0.

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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

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and moved it into
higher-dimensional theory,

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higher-dimensional objects,
and found new invariants?

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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 --
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,

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looking at that 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, by the average move

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in the past 20 days -- there's 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,

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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 happens 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've 
been great in the '60s,

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

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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 of 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
00:12:56,318 --> 00:13:00,233
CA: So these different predictive schemes 
can be really quite wild and unexpected.

241
00:13:00,233 --> 00:13:02,171
I mean, you look at everything, right?

242
00:13:02,171 --> 00:13:05,513
You look at the weather, 
length of dresses, political opinion.

243
00:13:05,513 --> 00:13:07,417
JS: Yes, length of dresses we didn't try.

244
00:13:07,417 --> 00:13:08,881
(Laughter)

245
00:13:08,881 --> 00:13:10,112
CA: What sort of things?

246
00:13:10,112 --> 00:13:12,032
JS: Well, everything.

247
00:13:12,032 --> 00:13:16,547
Everything is grist for the mill --
except hem lengths.

248
00:13:16,997 --> 00:13:19,176
Weather, annual reports,

249
00:13:19,176 --> 00:13:24,008
quarterly reports, historic data itself,
volumes, you name it.

250
00:13:24,008 --> 00:13:25,267
Whatever there is.

251
00:13:25,267 --> 00:13:27,752
We take in terabytes of data a day.

252
00:13:27,752 --> 00:13:33,186
And store it away, massage it,
get it ready for analysis.

253
00:13:33,306 --> 00:13:34,963
You're looking for anomalies.

254
00:13:34,963 --> 00:13:36,615
You're looking for, like you said,

255
00:13:36,615 --> 00:13:40,305
the efficient market 
hypothesis is not correct.

256
00:13:40,305 --> 00:13:43,483
CA: But any one anomaly
might be just a random thing,

257
00:13:43,483 --> 00:13:47,478
so is the secret here
to just look at multiple strange anomalies

258
00:13:47,478 --> 00:13:49,607
and see when they align?

259
00:13:49,607 --> 00:13:52,633
JS: Any one anomaly
might be a random thing.

260
00:13:52,633 --> 00:13:56,328
However, if you have enough data
you can tell that it's not.

261
00:13:56,328 --> 00:14:00,156
You can see an anomaly that's persistent
for a sufficiently long time --

262
00:14:00,156 --> 00:14:05,218
the probability
of it being random is not high.

263
00:14:05,218 --> 00:14:08,654
But these things fade after a while.

264
00:14:08,654 --> 00:14:13,041
Anomalies can get washed out;
you have to keep on top of the business.

265
00:14:13,041 --> 00:14:15,514
CA: A lot of people look 
at the hedge fund industry now

266
00:14:15,514 --> 00:14:19,107
and are sort of --

267
00:14:19,107 --> 00:14:20,177
shocked by it,

268
00:14:20,177 --> 00:14:22,236
by how much wealth is created there

269
00:14:22,236 --> 00:14:24,396
and how much talent is going into it.

270
00:14:24,396 --> 00:14:29,900
Do you have any worries 
about that industry

271
00:14:29,900 --> 00:14:31,966
and perhaps the financial
industry in general?

272
00:14:31,966 --> 00:14:34,584
Kind of being on a runaway train that's --

273
00:14:34,584 --> 00:14:38,884
I don't know, helping increase inequality?

274
00:14:38,884 --> 00:14:43,788
How would you champion what's happening
in the hedge fund industry?

275
00:14:43,788 --> 00:14:45,849
JS: I think in the last
three of four years,

276
00:14:45,849 --> 00:14:47,847
hedge funds have not done especially well.

277
00:14:47,847 --> 00:14:48,705
We've done dandy,

278
00:14:48,705 --> 00:14:53,119
but the hedge fund industry as a whole
has not done so wonderfully.

279
00:14:53,119 --> 00:14:58,562
The stock market has been on a roll,
going up as everybody knows,

280
00:14:58,562 --> 00:15:01,339
and price-earnings ratios have grown.

281
00:15:01,339 --> 00:15:04,570
So an awful lot of the wealth 
that's been created in the last,

282
00:15:04,570 --> 00:15:07,300
let's say, five or six years
has not been created by hedge funds.

283
00:15:08,430 --> 00:15:11,830
People would ask me,
"What's a hedge fund?"

284
00:15:11,830 --> 00:15:14,361
And I'd say, "One and 20."

285
00:15:14,361 --> 00:15:17,124
Which means -- now it's two and 20 --

286
00:15:17,124 --> 00:15:21,165
it's two percent fixed fee
and 20 percent of profits.

287
00:15:21,165 --> 00:15:23,468
Hedge funds are all different
kinds of creatures.

288
00:15:23,468 --> 00:15:26,229
CA: Rumor has it you charge
slightly higher fees than that.

289
00:15:26,229 --> 00:15:27,925
(Laughter)

290
00:15:27,925 --> 00:15:30,169
JS: We charged the highest 
fees in the world at one time.

291
00:15:30,169 --> 00:15:33,835
Five and 44, that's what we charge.

292
00:15:33,835 --> 00:15:35,091
CA: Five and 44.

293
00:15:35,091 --> 00:15:38,641
So 5 percent flat,
44 percent of upside.

294
00:15:38,641 --> 00:15:41,400
You still made your investors
spectacular amounts of money.

295
00:15:41,400 --> 00:15:42,826
JS: We made good returns, yes.

296
00:15:42,826 --> 00:15:43,826
People got very mad at my investors.

297
00:15:43,826 --> 00:15:45,681
"How could you charge such high fees?"

298
00:15:45,681 --> 00:15:47,516
I said, "OK, you can withdraw."

299
00:15:47,516 --> 00:15:50,174
"But how can I get more?"

300
00:15:50,174 --> 00:15:51,967
(Laughter)

301
00:15:51,967 --> 00:15:53,692
But at a certain point, as I told you,

302
00:15:53,692 --> 00:15:59,351
we bought out all the investors 
because there's a capacity to the fund.

303
00:15:59,365 --> 00:16:02,315
CA: But should we worry
about the hedge fund industry

304
00:16:02,315 --> 00:16:05,978
attracting too much
of the world's great mathematical

305
00:16:05,978 --> 00:16:08,439
and other talent to work on that

306
00:16:08,439 --> 00:16:10,817
as opposed to the many
other problems in the world?

307
00:16:10,817 --> 00:16:12,540
JS: Well it's not just mathematical.

308
00:16:12,540 --> 00:16:15,426
We hire astronomers and physicists
and things like that.

309
00:16:15,676 --> 00:16:21,681
I don't think we should worry too much.
It's still a pretty small industry.

310
00:16:21,681 --> 00:16:27,742
And in fact, bringing science 
into the investing world

311
00:16:27,742 --> 00:16:29,878
has improved that world.

312
00:16:29,878 --> 00:16:33,826
It's reduced volatility.
It's increased liquidity.

313
00:16:33,826 --> 00:16:36,937
Spreads are narrower because
people are trading that kind of stuff.

314
00:16:36,937 --> 00:16:42,394
So I'm not too worried about Einstein
going off and starting a hedge fund.

315
00:16:42,614 --> 00:16:46,666
CA: You're at a phase in your life now
where you're actually investing, though,

316
00:16:46,666 --> 00:16:50,182
at the other end of the supply chain --

317
00:16:50,182 --> 00:16:54,072
in boosting mathematics across America.

318
00:16:54,072 --> 00:16:56,441
This is your wife, Marilyn.

319
00:16:56,441 --> 00:17:00,743
You're working on 
philanthropic issues together.

320
00:17:00,743 --> 00:17:02,408
Tell me about that.

321
00:17:02,408 --> 00:17:06,081
JS: Well, Marilyn started --

322
00:17:06,081 --> 00:17:09,683
there she is up there,
my beautiful wife --

323
00:17:09,683 --> 00:17:12,678
she started the foundation
about 20 years ago.

324
00:17:12,678 --> 00:17:13,955
I think '94.

325
00:17:13,955 --> 00:17:16,067
I claim it was '93, 
she says it was '94,

326
00:17:16,067 --> 00:17:18,437
but it was one of those two years.

327
00:17:18,437 --> 00:17:20,596
(Laughter)

328
00:17:20,596 --> 00:17:23,178
We started the foundation

329
00:17:23,178 --> 00:17:27,958
just as a convenient way to give charity.

330
00:17:27,958 --> 00:17:30,877
She kept the books, and so on.

331
00:17:30,877 --> 00:17:37,244
We did not have a vision at that time,
but gradually a vision emerged --

332
00:17:37,244 --> 00:17:43,119
which was to focus on math and science,
to focus on basic research.

333
00:17:43,119 --> 00:17:46,136
And that's what we've done.

334
00:17:46,136 --> 00:17:52,941
Six years ago or so, I left Renaissance
and went to work at the foundation.

335
00:17:52,941 --> 00:17:54,473
So that's what we do.

336
00:17:54,473 --> 00:17:56,260
CA: And so Math for America here

337
00:17:56,260 --> 00:17:59,442
is basically investing in math teachers
around the country,

338
00:17:59,442 --> 00:18:03,760
giving them some extra income,
giving them support and coaching.

339
00:18:03,760 --> 00:18:06,895
And really trying
to make that more effective

340
00:18:06,895 --> 00:18:09,340
and make that a calling
to which teachers can aspire.

341
00:18:09,340 --> 00:18:11,242
JS: Yeah, yeah.

342
00:18:11,242 --> 00:18:14,442
Instead of beating up the bad teachers --

343
00:18:14,442 --> 00:18:18,691
which has created morale problems
all through the educational community,

344
00:18:18,691 --> 00:18:21,616
in particular in math and science --

345
00:18:21,616 --> 00:18:27,770
we focus on celebrating the good ones 
and giving them status.

346
00:18:27,770 --> 00:18:31,345
Yeah, we give them extra money,
15,000 dollars a year.

347
00:18:31,345 --> 00:18:35,478
We have 800 math and science teachers
in New York City in public schools today

348
00:18:35,478 --> 00:18:37,266
as part of a core.

349
00:18:37,266 --> 00:18:43,259
There's a great morale among them.
They're staying in the field.

350
00:18:43,259 --> 00:18:46,213
Next year, it'll be 1,000 [teachers],
and that'll be 10 percent

351
00:18:46,213 --> 00:18:49,781
of the math and science teachers
in New York public schools.

352
00:18:49,781 --> 00:18:54,217
(Applause)

353
00:18:55,227 --> 00:18:58,674
CA: Jim, here's another project
that you've supported philanthropically:

354
00:18:58,674 --> 00:19:02,781
Research into origins of life, I guess.
What are we looking at here?

355
00:19:03,401 --> 00:19:05,779
Well, I'll save that for a second.

356
00:19:05,779 --> 00:19:07,474
And then I'll tell you
what you're looking at.

357
00:19:07,474 --> 00:19:13,486
Origins of life is a fascinating question.
How did we get here?

358
00:19:13,486 --> 00:19:15,122
Well, there's two questions.

359
00:19:15,122 --> 00:19:20,905
One is, What is the root
from geology to biology?

360
00:19:20,905 --> 00:19:22,694
How did we get here?

361
00:19:22,694 --> 00:19:25,024
And the other question is,
What did we start with?

362
00:19:25,024 --> 00:19:27,955
What material, if any,
did we have to work with on this route?

363
00:19:27,955 --> 00:19:31,583
Those are two very,
very interesting questions.

364
00:19:31,583 --> 00:19:37,726
The first question is a tortuous path
from geology up to RNA

365
00:19:37,726 --> 00:19:39,966
or something like that --
how did that all work?

366
00:19:39,966 --> 00:19:42,325
And the other,
What do we have to work with?

367
00:19:42,325 --> 00:19:44,120
Well, more than we think.

368
00:19:44,120 --> 00:19:49,778
So what's pictured there
is a star in formation.

369
00:19:49,778 --> 00:19:53,285
Now every year in our Milky Way,
which has 100 billion stars,

370
00:19:53,285 --> 00:19:56,141
about two near stars are created.

371
00:19:56,141 --> 00:19:58,254
Don't ask me how, but they're created.

372
00:19:58,254 --> 00:20:02,132
And it takes them
about a million years to settle out.

373
00:20:02,132 --> 00:20:04,522
So, in steady state,

374
00:20:04,522 --> 00:20:08,423
there's about 2 million stars
in formation at any time.

375
00:20:08,423 --> 00:20:11,837
That one is somewhere along
this settling down period.

376
00:20:11,837 --> 00:20:15,390
And there's all this crap
sort of circling around it.

377
00:20:15,390 --> 00:20:17,479
Dust and stuff.

378
00:20:17,479 --> 00:20:20,916
And it'll form probably a solar system,
or whatever it forms.

379
00:20:20,916 --> 00:20:23,834
But here's the thing --

380
00:20:23,834 --> 00:20:28,924
in this dust that surrounds a forming star

381
00:20:28,924 --> 00:20:35,591
have been found, now,
significant organic molecules.

382
00:20:35,591 --> 00:20:42,121
Molecules not just like methane
but formaldehyde and cyanide --

383
00:20:42,121 --> 00:20:48,816
things that are the building blocks,
the seeds, if you will, of life.

384
00:20:49,136 --> 00:20:52,215
So, that may be typical.

385
00:20:52,215 --> 00:20:58,974
And it may be typical
that planets around the universe

386
00:20:58,974 --> 00:21:03,639
start off with some of these
basic building blocks.

387
00:21:03,639 --> 00:21:06,542
Now, does that mean 
there's going to be life all around?

388
00:21:06,542 --> 00:21:07,957
Maybe.

389
00:21:07,957 --> 00:21:12,276
But it's a question
of how tortuous this path is

390
00:21:12,276 --> 00:21:16,734
from those frail beginnings,
those seeds, all the way to life.

391
00:21:16,734 --> 00:21:21,931
And most of those seeds
will fall on fallow planets.

392
00:21:21,931 --> 00:21:23,423
CA: So for you, personally,

393
00:21:23,423 --> 00:21:26,090
finding an answer to this question
of where we came from,

394
00:21:26,090 --> 00:21:29,783
of how did this thing happen,
that is something you would love to see.

395
00:21:29,783 --> 00:21:32,199
JS: Would love to see. And like to know.

396
00:21:32,199 --> 00:21:38,444
If that path is tortuous enough, 
and so improbable

397
00:21:38,444 --> 00:21:43,251
that no matter what you start with,
we could be a singularity.

398
00:21:43,251 --> 00:21:44,691
But, on the other hand,

399
00:21:44,691 --> 00:21:47,710
given all this organic dust
that's floating around,

400
00:21:47,710 --> 00:21:52,237
we could have lots of friends out there.

401
00:21:52,237 --> 00:21:54,304
It'd be great to know.

402
00:21:54,304 --> 00:21:56,440
CA: Jim, a couple of years ago,

403
00:21:56,440 --> 00:22:00,712
I got the chance to speak with Elon Musk,
and I asked him the secret of his success.

404
00:22:00,712 --> 00:22:04,798
He said taking physics seriously was it.

405
00:22:04,798 --> 00:22:08,723
Listening to you, what I hear you saying
is taking math seriously,

406
00:22:08,723 --> 00:22:11,903
that has infused your whole life.

407
00:22:11,903 --> 00:22:16,710
It's made you an absolute fortune,
and now it's allowing you to invest

408
00:22:16,710 --> 00:22:21,098
in the futures of thousands and thousands
of kids across America and elsewhere.

409
00:22:21,098 --> 00:22:26,648
Could it be that science actually works?
That math actually works?

410
00:22:26,648 --> 00:22:30,920
JS: Well, math certainly works.
Math certainly works.

411
00:22:30,920 --> 00:22:33,033
But this has been fun.

412
00:22:33,033 --> 00:22:37,956
Working with Marilyn and giving it away
has been very enjoyable.

413
00:22:37,956 --> 00:22:41,230
CA: I just find it --
it's an inspirational thought to me,

414
00:22:41,230 --> 00:22:44,968
that by taking knowledge seriously,
so much more can come from it.

415
00:22:44,968 --> 00:22:47,615
So thank you for your amazing life
and for coming here to TED.

416
00:22:47,615 --> 00:22:48,614
Truly. Thank you.

417
00:22:48,614 --> 00:22:49,960
Jim Simons.

418
00:22:49,960 --> 00:22:54,186
(Applause)