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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
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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.
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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 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.
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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: 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,
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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
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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.
-
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Do you have any worries
about that industry
-
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and perhaps the financial
industry in general?
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Kind of being on a runaway train that's,
I don't know, helping increase inequality?
-
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How would you champion what's happening
in the hedge fund industry?
-
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JS: Actually, I think
that in the last three of 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.
-
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The stock market has been on a roll,
going up as everybody knows,
-
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and price-earnings rations have grown.
-
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So an awful lot
of the wealth that's been created
-
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in the last, let's say, five or six years
has not been created by hedge funds.
-
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People would ask me,
"What's a hedge fund?"
-
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And I'd say, "One in 20."
-
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Which means -- now it's two in 20 --
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it's two percent fixed fee
on 20 percent of profits.
-
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Hedge funds are all different
kinds of creatures.
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CA: Rumor has it you charge
slightly higher fees than that.
-
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(Laughter)
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JS: We charged the highest
fees in the world at one time.
-
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Five and 44, that's what we change.
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CA: Five and 44. So 5 percent flat,
44 percent of upside.
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You still made your investors
spectacular amounts of money.
-
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JS: We made good returns, yes.
-
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People got very mad at my investors:
"How could 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?"
-
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(Laughter)
-
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But at a certain point, as I told you,
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we bought out all the investors
because they's a capacity to the fund.
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CA: But should we worry
-
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about the hedge fund industry attracting
too much of the world's great mathematical
-
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and other talent to work on that
-
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as opposed 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.
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I don't think we should worry too much.
It's still a pretty small industry.
-
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And in fact, bringing science
into the investing world
-
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has improved that world.
-
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It's reduced volatility.
It's increased liquidity.
-
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Spreads are narrower because
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.
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CA: You're at a phase in your life now
where you're actually investing, though,
-
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at the other end of the supply chain --
in boosting mathematics across America.
-
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This is your wife, Marilyn.
-
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You're working on
philanthropic issues together.
-
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Tell me about that.
-
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JS: Well, Marily started --
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there she is up there,
my beautiful wife --
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she started the foundation
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.
-
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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 --
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which was to focus on math and science,
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.
-
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So that's what we do.
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CA: And so Math for America here
-
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is basically investing in math teachers
around the country,
-
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giving them some extra income,
giving them support and coaching.
-
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And really trying
to make that more effective
-
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and make that a calling
to which teachers can aspire.
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JS: Yeah. Instead of
beating up the bad teachers --
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which has created morale problems
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.
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Yeah, we give them extra money,
15,000 dollars a year.
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We have 800 math and science teachers
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.
They're staying in the field.
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Next year, it'll be 1,000 [teachers],
and that'll be 10 percent
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of the math and science teachers
in New York public schools.
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(Applause)
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CA: Jim, here's another project
that you've supported philanthropically:
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Research into origins of life, I guess.
What are we looking at here?
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Well, I'll save that for a second.
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And then I'll tell you
what you're looking at.
-
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Origins of life is a fascinating question.
How did we get here?
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Well, there's two questions.
-
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One is, "What is the root
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?
-
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What material, if any,
did we have to work with on this route?"
-
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Those are two very,
very interesting questions.
-
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The first question is a tortuous path
from geology up to RNA
-
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or something like that --
how did that all work?
-
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And the other,
"What do we have to work with?"
-
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Well, more than we think.
-
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So that picture there
is a star in formation.
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Now every year in our Milky Way,
which has 100 billion stars,
-
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about two near 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.
-
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So, in steady state,
-
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there's about 2 million stars
in formation at any time.
-
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That one is somewhere along
this settling down period.
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And there's all this crap
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.
-
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But here's the thing -- in this dust
that surrounds a forming star,
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have been found, now,
significant organic molecules.
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Molecules not just like methane
but formaldehyde and cyanide --
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things that are the building blocks,
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
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that planets around the universe start off
with some of these basic building blocks.
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Now, does that mean
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
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from those frail beginnings,
those seeds, all the way to life.
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And most of those seeds
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,
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of how did this thing happen,
that is something you would love to see.
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JS: Would love to see. And like to know.
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If that path is tortuous enough,
and so improbable
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that no matter what you start with,
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,
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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,
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I got the chance to speak with Elon Musk,
and I asked him the secret of his success.
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He said taking physics seriously was it.
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Listening to you, what I hear you saying
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
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in the futures of thousands and thousands
of kids across America and elsewhere.
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Could it be that science actually works?
That math actually works?
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JS: Well, 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.
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CA: I just find it --
it's an inspirational though to me,
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that by taking knowledge seriously,
so much more can come from it.
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So thank you for your amazing life
and for coming here to TED.
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Truly. Thank you.
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Jim Simons.
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(Applause)
Yasushi Aoki
Well, I'll save that for a second.
->
JS: Well, I'll save that for a second.
JS: I think in the last
three of four years,
->
JS: I think in the last
three or four years,
Camille Martínez
Thank you, Yasush! The corrections have been made.
Camille Martínez
*Please note the following updates to the English subtitles as of 9/13/15:
14:43 - 14:45
JS: I think in the last
three OR four years,
19:04 - 19:05
JS: Well, I'll save that for a second. (speaker's initials were previously missing)
Margarida Ferreira
Please note error on line 6:47, which must be the following:
Vertices minus edges PLUS FACES come out to zero - (16-32+16=0)
Jim Simons speaks too fast...
Claudia Sander
6:46:53
Vertices minus edges comes out to be zero. -> Vertices minus edges plus faces comes out to be zero.
You can see it in the presentation and also calculating it.