TED2015
Jim Simons: The mathematician who cracked Wall Street
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Jim Simons was a mathematician and cryptographer who realized: the complex math he used to break codes could help explain patterns in the world of finance. Billions later, he's working to support the next generation of math teachers and scholars. TED's Chris Anderson sits down with Simons to talk about his extraordinary life in numbers.
Jim Simons - Philanthropist, mathematician
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
Double-click the English transcript below to play the video.
00:12
Chris Anderson: You were something
of a mathematical phenom.
of a mathematical phenom.
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You had already taught at Harvard
and MIT at a young age.
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 --
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
where they hired mathematicians
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to attack secret codes
and stuff like that.
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,
at your own mathematics,
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and at least half your time
working on their stuff.
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,
well, the Vietnam War was on,
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01:10
and the boss of bosses in my organization
was a big fan of the war
was a big fan of the war
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and wrote a New York Times article,
a magazine section cover story,
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.
I thought it was stupid.
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And I wrote a letter to the Times,
which they published,
which they published,
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saying not everyone
who works for Maxwell Taylor,
who works for Maxwell Taylor,
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if anyone remembers that name,
agrees with his views.
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.
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
and some kid came around
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and said he was a stringer
from Newsweek magazine
from Newsweek magazine
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and he wanted to interview me
and ask what I was doing about my views.
and ask what I was doing about my views.
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And I told him, "I'm doing
mostly mathematics now,
mostly mathematics now,
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and when the war is over,
then I'll do mostly their stuff."
then I'll do mostly their stuff."
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Then I did the only
intelligent thing I'd done that day --
intelligent thing I'd done that day --
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I told my local boss
that I gave that interview.
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."
"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
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.
mathematicians of the century.
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I had known him when
I was a graduate student at Berkeley.
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.
and he liked them.
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Together, we did this work
which you can easily see up there.
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.
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.
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,
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 --
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 --
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.
that's now flourishing.
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But, more interestingly,
it happened to apply to physics,
it happened to apply to physics,
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something we knew nothing about --
at least I knew nothing about physics,
at least I knew nothing about physics,
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and I don't think Chern
knew a heck of a lot.
knew a heck of a lot.
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And about 10 years
after the paper came out,
after the paper came out,
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a guy named Ed Witten in Princeton
started applying it to string theory
started applying it to string theory
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and people in Russia started applying it
to what's called "condensed matter."
to what's called "condensed matter."
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Today, those things in there
called Chern-Simons invariants
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.
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.
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
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,
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
that it's being applied
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to profoundly describe
the actual physical world.
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,
named [Eugene] Wigner,
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and he wrote an essay on the unreasonable
effectiveness of mathematics.
effectiveness of mathematics.
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Somehow, this mathematics,
which is rooted in the real world
which is rooted in the real world
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in some sense -- we learn to count,
measure, everyone would do that --
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.
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,
and Einstein realized,
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"Hey! It's the very thing
in which I can cast general relativity."
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.
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 --
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,
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:
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.
12 edges, six faces.
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And if you look at the difference --
vertices minus edges plus faces --
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 --
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.
plus faces still equals two.
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And in fact, you could do this
any which way --
any which way --
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cover this thing with all kinds
of polygons and triangles
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.
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
of a doughnut: 16 vertices
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covered by these rectangles,
32 edges, 16 faces.
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
with squares or triangles
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or anything like that,
you're going to get zero.
you're going to get zero.
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So, this is called
the Euler characteristic.
the Euler characteristic.
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And it's what's called
a topological invariant.
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.
you're always get the same answer.
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So that was the first sort of thrust,
from the mid-1700s,
from the mid-1700s,
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into a subject which is now called
algebraic topology.
algebraic topology.
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CA: And your own work
took an idea like this and moved it
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?
and found new invariances?
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JS: Yes. Well, there were already
higher-dimensional invariants:
higher-dimensional invariants:
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Pontryagin classes --
actually, there were Chern classes.
actually, there were Chern classes.
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There were a bunch
of these types of invariants.
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.
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 --
while he was writing --
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if it wasn't for Mr. Euler, there wouldn't
perhaps be these invariants.
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.
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,
and having been a code-cracker at the NSA,
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you started to become a code-cracker
in the financial industry.
in the financial industry.
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I think you probably didn't buy
efficient market theory.
efficient market theory.
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Somehow you found a way of creating
astonishing returns over two decades.
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,
wasn't just the size of the returns,
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it's that you took them
with surprisingly low volatility and risk,
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.
a wonderful group of people.
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09:14
When I started doing trading, I had
gotten a little tired of mathematics.
gotten a little tired of mathematics.
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I was in my late 30s,
I had a little money.
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.
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:
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 --
and we started making some models --
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just the kind of thing we did back
at IDA [Institute for Defense Analyses].
at IDA [Institute for Defense Analyses].
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You design an algorithm,
you test it out on a computer.
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.
of some commodity.
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I look at that, and I say,
"That's just a random, up-and-down walk --
"That's just a random, up-and-down walk --
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maybe a slight upward trend
over that whole period of time."
over that whole period of time."
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How on earth could you trade
looking at that,
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,
kind of a graph from the old days,
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commodities or currencies
had a tendency to trend.
had a tendency to trend.
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Not necessarily the very light trend
you see here, but trending in periods.
you see here, but trending in periods.
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And if you decided, OK,
I'm going to predict today,
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.
and I'd make some money.
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And in fact, years ago,
such a system would work --
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.
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.
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
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.
was predictive of what happened next.
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JS: Sure, you would try all those things
and see what worked best.
and see what worked best.
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Trend-following would
have been great in the '60s,
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 --
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 --
a tremendous amount of data --
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and we had to get it by hand
in the early days.
in the early days.
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We went down to the Federal Reserve
and copied interest rate histories
and copied interest rate histories
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and stuff like that,
because it didn't exist on computers.
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.
people to do fundamental trading.
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I had hired a few -- some made money,
some didn't make 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.
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,
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,
something remarkable at Renaissance,
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which is building this culture,
this group of people,
this group of people,
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who weren't just hired guns
who could be lured away by money.
who could be lured away by money.
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Their motivation was doing
exciting mathematics and science.
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.
because of the money.
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I think a lot of them
came because of the money.
came because of the money.
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But they also came
because it would be fun.
because it would be fun.
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CA: What role did machine learning
play in all this?
play in all this?
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JS: In a certain sense,
what we did was machine learning.
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,
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.
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 and unexpected.
can be really quite wild and unexpected.
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I mean, you looked at everything, right?
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You looked at the weather,
length of dresses, political opinion.
length of dresses, political opinion.
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JS: Yes, length of dresses we didn't try.
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CA: What sort of things?
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JS: Well, everything.
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Everything is grist for the mill --
except hem lengths.
except hem lengths.
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Weather, annual reports,
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quarterly reports, historic data itself,
volumes, you name it.
volumes, you name it.
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Whatever there is.
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We take in terabytes of data a day.
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And store it away and massage it
and get it ready for analysis.
and 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,
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the efficient market
hypothesis is not correct.
hypothesis is not correct.
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CA: But any one anomaly
might be just a random thing.
might be just a random thing.
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So, is the secret here to just look
at multiple strange anomalies,
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;
might be a random thing;
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however, if you have enough data
you can tell that it's not.
you can tell that it's not.
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You can see an anomaly that's persistent
for a sufficiently long time --
for a sufficiently long time --
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the probability of it being
random is not high.
random is not high.
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But these things fade after a while;
anomalies can get washed out.
anomalies can get washed out.
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So you have to keep on top
of the business.
of the business.
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CA: A lot of people look
at the hedge fund industry now
at the hedge fund industry now
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and are sort of ... shocked by it,
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by how much wealth is created there,
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and how much talent is going into it.
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Do you have any worries
about that industry,
about that industry,
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and perhaps the financial
industry in general?
industry in general?
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Kind of being on a runaway train that's --
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I don't know --
helping increase inequality?
helping increase inequality?
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How would you champion what's happening
in the hedge fund industry?
in the hedge fund industry?
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JS: I think in the last
three or four years,
three or four years,
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hedge funds have not done especially well.
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We've done dandy,
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but the hedge fund industry as a whole
has not done so wonderfully.
has not done so wonderfully.
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The stock market has been on a roll,
going up as everybody knows,
going up as everybody knows,
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and price-earnings ratios have grown.
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So an awful lot of the wealth
that's been created in the last --
that's been created in the last --
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let's say, five or six years --
has not been created by hedge funds.
has not been created by hedge funds.
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People would ask me,
"What's a hedge fund?"
"What's a hedge fund?"
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And I'd say, "One and 20."
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Which means -- now it's two and 20 --
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it's two percent fixed fee
and 20 percent of profits.
and 20 percent of profits.
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Hedge funds are all
different kinds of creatures.
different kinds of creatures.
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CA: Rumor has it you charge
slightly higher fees than that.
slightly higher fees than that.
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JS: We charged the highest fees
in the world at one time.
in the world at one time.
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Five and 44, that's what we charge.
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CA: Five and 44.
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So five percent flat,
44 percent of upside.
44 percent of upside.
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You still made your investors
spectacular amounts of money.
spectacular amounts of money.
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JS: We made good returns, yes.
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People got very mad:
"How can you charge such high fees?"
"How can you charge such high fees?"
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I said, "OK, you can withdraw."
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But "How can I get more?"
was what people were --
was what people were --
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(Laughter)
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But at a certain point,
as I think I told you,
as I think I told you,
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we bought out all the investors
because there's a capacity to the fund.
because there's a capacity to the fund.
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CA: But should we worry
about the hedge fund industry
about the hedge fund industry
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attracting too much of the world's
great mathematical and other talent
great mathematical and other talent
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to work on that, as opposed
to the many other problems in the world?
to the many other problems in the world?
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JS: Well, it's not just mathematical.
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We hire astronomers and physicists
and things like that.
and things like that.
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I don't think we should worry
about it too much.
about it too much.
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It's still a pretty small industry.
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And in fact, bringing science
into the investing world
into the investing world
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has improved that world.
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It's reduced volatility.
It's increased liquidity.
It's increased liquidity.
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Spreads are narrower because
people are trading that kind of stuff.
people are trading that kind of stuff.
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So I'm not too worried about Einstein
going off and starting a hedge fund.
going off and starting a hedge fund.
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CA: You're at a phase in your life now
where you're actually investing, though,
where you're actually investing, though,
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at the other end of the supply chain --
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you're actually boosting
mathematics across America.
mathematics across America.
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This is your wife, Marilyn.
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You're working on
philanthropic issues together.
philanthropic issues together.
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Tell me about that.
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JS: Well, Marilyn started --
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there she is up there,
my beautiful wife --
my beautiful wife --
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she started the foundation
about 20 years ago.
about 20 years ago.
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I think '94.
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I claim it was '93, she says it was '94,
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but it was one of those two years.
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(Laughter)
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We started the foundation,
just as a convenient way to give charity.
just as a convenient way to give charity.
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She kept the books, and so on.
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We did not have a vision at that time,
but gradually a vision emerged --
but gradually a vision emerged --
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which was to focus on math and science,
to focus on basic research.
to focus on basic research.
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And that's what we've done.
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Six years ago or so, I left Renaissance
and went to work at the foundation.
and went to work at the foundation.
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So that's what we do.
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CA: And so Math for America
is basically investing
is basically investing
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in math teachers around the country,
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giving them some extra income,
giving them support and coaching.
giving them support and coaching.
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And really trying
to make that more effective
to make that more effective
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and make that a calling
to which teachers can aspire.
to which teachers can aspire.
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JS: Yeah -- instead of beating up
the bad teachers,
the bad teachers,
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which has created morale problems
all through the educational community,
all through the educational community,
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in particular in math and science,
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we focus on celebrating the good ones
and giving them status.
and giving them status.
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Yeah, we give them extra money,
15,000 dollars a year.
15,000 dollars a year.
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We have 800 math and science teachers
in New York City in public schools today,
in New York City in public schools today,
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as part of a core.
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There's a great morale among them.
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They're staying in the field.
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Next year, it'll be 1,000
and that'll be 10 percent
and that'll be 10 percent
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of the math and science teachers
in New York [City] public schools.
in New York [City] public schools.
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(Applause)
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CA: Jim, here's another project
that you've supported philanthropically:
that you've supported philanthropically:
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Research into origins of life, I guess.
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What are we looking at here?
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JS: Well, I'll save that for a second.
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And then I'll tell you
what you're looking at.
what you're looking at.
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Origins of life is a fascinating question.
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How did we get here?
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Well, there are two questions:
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One is, what is the route
from geology to biology --
from geology to biology --
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how did we get here?
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And the other question is,
what did we start with?
what did we start with?
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What material, if any,
did we have to work with on this route?
did we have to work with on this route?
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Those are two very,
very interesting questions.
very interesting questions.
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The first question is a tortuous path
from geology up to RNA
from geology up to RNA
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or something like that --
how did that all work?
how did that all work?
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And the other,
what do we have to work with?
what do we have to work with?
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Well, more than we think.
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So what's pictured there
is a star in formation.
is a star in formation.
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Now, every year in our Milky Way,
which has 100 billion stars,
which has 100 billion stars,
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about two new stars are created.
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Don't ask me how, but they're created.
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And it takes them about a million
years to settle out.
years to settle out.
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So, in steady state,
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there are about two million stars
in formation at any time.
in formation at any time.
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That one is somewhere
along this settling-down period.
along this settling-down period.
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And there's all this crap
sort of circling around it,
sort of circling around it,
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dust and stuff.
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And it'll form probably a solar system,
or whatever it forms.
or whatever it forms.
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But here's the thing --
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in this dust that surrounds a forming star
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have been found, now,
significant organic molecules.
significant organic molecules.
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Molecules not just like methane,
but formaldehyde and cyanide --
but formaldehyde and cyanide --
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things that are the building blocks --
the seeds, if you will -- of life.
the seeds, if you will -- of life.
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So, that may be typical.
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And it may be typical
that planets around the universe
that planets around the universe
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start off with some of these
basic building blocks.
basic building blocks.
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Now does that mean
there's going to be life all around?
there's going to be life all around?
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Maybe.
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But it's a question
of how tortuous this path is
of how tortuous this path is
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from those frail beginnings,
those seeds, all the way to life.
those seeds, all the way to life.
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And most of those seeds
will fall on fallow planets.
will fall on fallow planets.
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CA: So for you, personally,
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finding an answer to this question
of where we came from,
of where we came from,
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of how did this thing happen,
that is something you would love to see.
that is something you would love to see.
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JS: Would love to see.
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And like to know --
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if that path is tortuous enough,
and so improbable,
and so improbable,
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that no matter what you start with,
we could be a singularity.
we could be a singularity.
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But on the other hand,
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given all this organic dust
that's floating around,
that's floating around,
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we could have lots of friends out there.
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It'd be great to know.
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CA: Jim, a couple of years ago,
I got the chance to speak with Elon Musk,
I got the chance to speak with Elon Musk,
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and I asked him the secret of his success,
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and he said taking
physics seriously was it.
physics seriously was it.
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Listening to you, what I hear you saying
is taking math seriously,
is taking math seriously,
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that has infused your whole life.
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It's made you an absolute fortune,
and now it's allowing you to invest
and now it's allowing you to invest
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in the futures of thousands and thousands
of kids across America and elsewhere.
of kids across America and elsewhere.
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Could it be that science actually works?
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That math actually works?
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JS: Well, math certainly works.
Math certainly works.
Math certainly works.
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But this has been fun.
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Working with Marilyn and giving it away
has been very enjoyable.
has been very enjoyable.
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CA: I just find it --
it's an inspirational thought to me,
it's an inspirational thought to me,
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that by taking knowledge seriously,
so much more can come from it.
so much more can come from it.
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So thank you for your amazing life,
and for coming here to TED.
and for coming here to TED.
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Thank you.
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23:00
Jim Simons!
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(Applause)
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ABOUT THE SPEAKER
Jim Simons - Philanthropist, mathematicianAfter astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy.
Why you should listen
As a mathematician who cracked codes for the National Security Agency on the side, Jim Simons had already revolutionized geometry -- and incidentally laid the foundation for string theory -- when he began to get restless. Along with a few hand-picked colleagues he started the investment firm that went on to become Renaissance, a hedge fund working with hitherto untapped algorithms, and became a billionaire in the process.
Now retired as Renaissance’s CEO, Simons devotes his time to mathematics and philanthropy. The Simons Foundation has committed more than a billion dollars to math and science education and to autism research.
Jim Simons | Speaker | TED.com