WEBVTT

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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.
And they had a very good policy

99:59:59.999 --> 99:59:59.999
And they had a very good policy
because you could do half your time

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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.
So that was an irresistible pull.

99:59:59.999 --> 99:59:59.999
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 and so on.

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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, saying

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not everyone who works for Maxwell Taylor,
if anyone remembers that name,

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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 calls Taylor; that took 10 minutes.

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I was fired five minutes after that.

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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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CA: How about explaining this?

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(Laughter)

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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
he 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 -- we learn to count,

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measure, everyone would do that --
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 --

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vertices minus edges plus faces --
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.
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,
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'll 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
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
as 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.
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.

99:59:59.999 --> 99:59:59.999
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: I think your 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.

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CA: So these different predictive schemes 
can be really quite wild, quite unexpected.

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I mean, you look at everything, right?

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You look at the weather, 
length of dresses, political opinion.

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JS: Yes, length of dresses we didn't try.

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(Laughter)

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CA: What sort of things?

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JS: Well, everything. 
Everything is grist for the mill --

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except hem lengths.

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Weather, annual reports,

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quarterly reports, historic data itself,
volumes, you know it. Whatever there is.

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We take in terabytes of data a day. 
And store it away, massage it,

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get it ready for analysis.

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You're looking for anomalies.

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You're looking for, like you said,

99:59:59.999 --> 99:59:59.999
the efficient market 
hypothesis is not correct.

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CA: But any one anomaly
might be just a random thing,

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so is the secret here
to just look at multiple strange anomalies

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and see when they align?

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JS: Any one anomaly
might be a random thing.

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However, if you have enough data
you can tell that it's not.

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You can see an anomaly that's persistent
for a sufficiently long time --

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the probability
of it being random is not high.

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But these things fade after a while.

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Anomalies can get washed out;
you have to keep on top of the business.

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CA: A lot of people 
look at the hedge fund industry now

99:59:59.999 --> 99:59:59.999
and are sort of shocked by it --

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by how much wealth is created there
and how much talent is going into it.

99:59:59.999 --> 99:59:59.999
Do you have any worries 
about that industry

99:59:59.999 --> 99:59:59.999
and perhaps the financial
industry in general?

99:59:59.999 --> 99:59:59.999
Kind of being on a runaway train that's,
I don't know, helping increase inequality?

99:59:59.999 --> 99:59:59.999
How would you champion what's happening
in the hedge fund industry?

99:59:59.999 --> 99:59:59.999
JS: Actually, I think
that in the last three of four years,

99:59:59.999 --> 99:59:59.999
hedge funds have not done especially well.

99:59:59.999 --> 99:59:59.999
We've done dandy,

99:59:59.999 --> 99:59:59.999
but the hedge fund industry as a whole
has not done so wonderfully.

99:59:59.999 --> 99:59:59.999
The stock market has been on a roll,
going up as everybody knows,

99:59:59.999 --> 99:59:59.999
and price-earnings rations have grown.

99:59:59.999 --> 99:59:59.999
So an awful lot 
of the wealth that's been created

99:59:59.999 --> 99:59:59.999
in the last, let's say, five or six years
has not been created by hedge funds.

99:59:59.999 --> 99:59:59.999
People would ask me,
"What's a hedge fund?"

99:59:59.999 --> 99:59:59.999
And I'd say, "One in 20."

99:59:59.999 --> 99:59:59.999
Which means -- now it's two in 20 --

99:59:59.999 --> 99:59:59.999
it's two percent fixed fee
on 20 percent of profits.

99:59:59.999 --> 99:59:59.999
Hedge funds are all different
kinds of creatures.

99:59:59.999 --> 99:59:59.999
CA: Rumor has it you charge
slightly higher fees than that.

99:59:59.999 --> 99:59:59.999
(Laughter)

99:59:59.999 --> 99:59:59.999
JS: We charged the highest 
fees in the world at one time.

99:59:59.999 --> 99:59:59.999
Five and 44, that's what we change.

99:59:59.999 --> 99:59:59.999
CA: Five and 44. So 5 percent flat, 
44 percent of upside.

99:59:59.999 --> 99:59:59.999
You still made your investors
spectacular amounts of money.

99:59:59.999 --> 99:59:59.999
JS: We made good returns, yes.

99:59:59.999 --> 99:59:59.999
People got very mad at my investors:
"How could you charge such high fees?"

99:59:59.999 --> 99:59:59.999
I said, "OK, you can withdraw."

99:59:59.999 --> 99:59:59.999
"But how can I get more?"

99:59:59.999 --> 99:59:59.999
(Laughter)

99:59:59.999 --> 99:59:59.999
But at a certain point, as I told you,

99:59:59.999 --> 99:59:59.999
we bought out all the investors 
because they's a capacity to the fund.

99:59:59.999 --> 99:59:59.999
CA: But should we worry

99:59:59.999 --> 99:59:59.999
about the hedge fund industry attracting
too much of the world's great mathematical

99:59:59.999 --> 99:59:59.999
and other talent to work on that

99:59:59.999 --> 99:59:59.999
as opposed to the many
other problems in the world?

99:59:59.999 --> 99:59:59.999
JS: Well it's not just mathematical.

99:59:59.999 --> 99:59:59.999
We hire astronomers and physicists
and things like that.

99:59:59.999 --> 99:59:59.999
I don't think we should worry too much.
It's still a pretty small industry.

99:59:59.999 --> 99:59:59.999
And in fact, bringing science 
into the investing world

99:59:59.999 --> 99:59:59.999
has improved that world.

99:59:59.999 --> 99:59:59.999
It's reduced volatility.
It's increased liquidity.

99:59:59.999 --> 99:59:59.999
Spreads are narrower because
people are trading that kind of stuff.

99:59:59.999 --> 99:59:59.999
So I'm not too worried about Einstein
going off and starting a hedge fund.

99:59:59.999 --> 99:59:59.999
CA: You're at a phase in your life now
where you're actually investing, though,

99:59:59.999 --> 99:59:59.999
at the other end of the supply chain --
in boosting mathematics across America.

99:59:59.999 --> 99:59:59.999
This is your wife, Marilyn.

99:59:59.999 --> 99:59:59.999
You're working on 
philanthropic issues together.

99:59:59.999 --> 99:59:59.999
Tell me about that.

99:59:59.999 --> 99:59:59.999
JS: Well, Marily started --

99:59:59.999 --> 99:59:59.999
there she is up there,
my beautiful wife --

99:59:59.999 --> 99:59:59.999
she started the foundation
about 20 years ago.

99:59:59.999 --> 99:59:59.999
I think '94.

99:59:59.999 --> 99:59:59.999
I claim it was '93, 
she says it was '94,

99:59:59.999 --> 99:59:59.999
but it was one of those two years.

99:59:59.999 --> 99:59:59.999
(Laughter)

99:59:59.999 --> 99:59:59.999
We started the foundation
just as a convenient way to give charity.

99:59:59.999 --> 99:59:59.999
She kept the books, and so on.

99:59:59.999 --> 99:59:59.999
We did not have a vision at that time,
but gradually a vision emerged --

99:59:59.999 --> 99:59:59.999
which was to focus on math and science,
to focus on basic research.

99:59:59.999 --> 99:59:59.999
And that's what we've done.

99:59:59.999 --> 99:59:59.999
Six years ago or so, I left Renaissance
and went to work at the foundation.

99:59:59.999 --> 99:59:59.999
So that's what we do.

99:59:59.999 --> 99:59:59.999
CA: And so Math for America here

99:59:59.999 --> 99:59:59.999
is basically investing in math teachers
around the country,

99:59:59.999 --> 99:59:59.999
giving them some extra income,
giving them support and coaching.

99:59:59.999 --> 99:59:59.999
And really trying
to make that more effective

99:59:59.999 --> 99:59:59.999
and make that a calling
to which teachers can aspire.

99:59:59.999 --> 99:59:59.999
JS: Yeah. Instead of 
beating up the bad teachers --

99:59:59.999 --> 99:59:59.999
which has created morale problems
all through the educational community,

99:59:59.999 --> 99:59:59.999
in particular in math and science --

99:59:59.999 --> 99:59:59.999
we focus on celebrating the good ones 
and giving them status.

99:59:59.999 --> 99:59:59.999
Yeah, we give them extra money,
15,000 dollars a year.

99:59:59.999 --> 99:59:59.999
We have 800 math and science teachers
in New York City in public schools today

99:59:59.999 --> 99:59:59.999
as part of a core.

99:59:59.999 --> 99:59:59.999
There's a great morale among them.
They're staying in the field.

99:59:59.999 --> 99:59:59.999
Next year, it'll be 1,000 [teachers],
and that'll be 10 percent

99:59:59.999 --> 99:59:59.999
of the math and science teachers
in New York public schools.

99:59:59.999 --> 99:59:59.999
(Applause)

99:59:59.999 --> 99:59:59.999
CA: Jim, here's another project
that you've supported philanthropically:

99:59:59.999 --> 99:59:59.999
Research into origins of life, I guess.
What are we looking at here?

99:59:59.999 --> 99:59:59.999
Well, I'll save that for a second.

99:59:59.999 --> 99:59:59.999
And then I'll tell you
what you're looking at.

99:59:59.999 --> 99:59:59.999
Origins of life is a fascinating question.
How did we get here?

99:59:59.999 --> 99:59:59.999
Well, there's two questions.

99:59:59.999 --> 99:59:59.999
One is, "What is the root
from geology to biology?

99:59:59.999 --> 99:59:59.999
How did we get here?"

99:59:59.999 --> 99:59:59.999
And the other question is,
"What did we start with?

99:59:59.999 --> 99:59:59.999
What material, if any,
did we have to work with on this route?"

99:59:59.999 --> 99:59:59.999
Those are two very,
very interesting questions.

99:59:59.999 --> 99:59:59.999
The first question is a tortuous path
from geology up to RNA

99:59:59.999 --> 99:59:59.999
or something like that --
how did that all work?

99:59:59.999 --> 99:59:59.999
And the other,
"What do we have to work with?"

99:59:59.999 --> 99:59:59.999
Well, more than we think.

99:59:59.999 --> 99:59:59.999
So that picture there
is a star in formation.

99:59:59.999 --> 99:59:59.999
Now every year in our Milky Way,
which has 100 billion stars,

99:59:59.999 --> 99:59:59.999
about two near stars are created.

99:59:59.999 --> 99:59:59.999
Don't ask me how, but they're created.

99:59:59.999 --> 99:59:59.999
And it takes them
about a million years to settle out.

99:59:59.999 --> 99:59:59.999
So, in steady state,

99:59:59.999 --> 99:59:59.999
there's about 2 million stars
in formation at any time.

99:59:59.999 --> 99:59:59.999
That one is somewhere along
this settling down period.

99:59:59.999 --> 99:59:59.999
And there's all this crap
sort of circling around it.

99:59:59.999 --> 99:59:59.999
Dust and stuff.

99:59:59.999 --> 99:59:59.999
And it'll form probably a solar system,
or whatever it forms.

99:59:59.999 --> 99:59:59.999
But here's the thing -- in this dust
that surrounds a forming star,

99:59:59.999 --> 99:59:59.999
have been found, now,
significant organic molecules.

99:59:59.999 --> 99:59:59.999
Molecules not just like methane
but formaldehyde and cyanide --

99:59:59.999 --> 99:59:59.999
things that are the building blocks,
the seeds, if you will, of life.

99:59:59.999 --> 99:59:59.999
So, that may be typical.

99:59:59.999 --> 99:59:59.999
And it may be typical

99:59:59.999 --> 99:59:59.999
that planets around the universe start off
with some of these basic building blocks.

99:59:59.999 --> 99:59:59.999
Now, does that mean 
there's going to be life all around?

99:59:59.999 --> 99:59:59.999
Maybe.

99:59:59.999 --> 99:59:59.999
But it's a question
of how tortuous this path is

99:59:59.999 --> 99:59:59.999
from those frail beginnings,
those seeds, all the way to life.

99:59:59.999 --> 99:59:59.999
And most of those seeds
will fall on fallow planets.

99:59:59.999 --> 99:59:59.999
CA: So for you, personally,

99:59:59.999 --> 99:59:59.999
finding an answer to this question
of where we came from,

99:59:59.999 --> 99:59:59.999
of how did this thing happen,
that is something you would love to see.

99:59:59.999 --> 99:59:59.999
JS: Would love to see. And like to know.

99:59:59.999 --> 99:59:59.999
If that path is tortuous enough, 
and so improbable

99:59:59.999 --> 99:59:59.999
that no matter what you start with,
we could be a singularity.

99:59:59.999 --> 99:59:59.999
But, on the other hand,

99:59:59.999 --> 99:59:59.999
given all this organic dust
that's floating around,

99:59:59.999 --> 99:59:59.999
we could have lots of friends out there.

99:59:59.999 --> 99:59:59.999
It'd be great to know.

99:59:59.999 --> 99:59:59.999
CA: Jim, a couple of years ago,

99:59:59.999 --> 99:59:59.999
I got the chance to speak with Elon Musk,
and I asked him the secret of his success.

99:59:59.999 --> 99:59:59.999
He said taking physics seriously was it.

99:59:59.999 --> 99:59:59.999
Listening to you, what I hear you saying
is taking math seriously,

99:59:59.999 --> 99:59:59.999
that has infused your whole life.

99:59:59.999 --> 99:59:59.999
It's made you an absolute fortune,
and now it's allowing you to invest

99:59:59.999 --> 99:59:59.999
in the futures of thousands and thousands
of kids across America and elsewhere.

99:59:59.999 --> 99:59:59.999
Could it be that science actually works?
That math actually works?

99:59:59.999 --> 99:59:59.999
JS: Well, math certainly works.
Math certainly works.

99:59:59.999 --> 99:59:59.999
But this has been fun.

99:59:59.999 --> 99:59:59.999
Working with Marilyn and giving it away
has been very enjoyable.

99:59:59.999 --> 99:59:59.999
CA: I just find it --
it's an inspirational though to me,

99:59:59.999 --> 99:59:59.999
that by taking knowledge seriously,
so much more can come from it.

99:59:59.999 --> 99:59:59.999
So thank you for your amazing life
and for coming here to TED.

99:59:59.999 --> 99:59:59.999
Truly. Thank you.

99:59:59.999 --> 99:59:59.999
Jim Simons.

99:59:59.999 --> 99:59:59.999
(Applause)