0:00:00.420,0:00:03.225
Chris Anderson: You were something of[br]a mathematical phenom.

0:00:03.225,0:00:06.453
You had already taught[br]at Harvard and MIT at a young age.

0:00:06.453,0:00:09.464
And then the NSA came calling.

0:00:09.464,0:00:11.128
What was that about?

0:00:11.128,0:00:15.296
Jim Simons: Well the NSA --[br]that's the National Security Agency --

0:00:15.296,0:00:17.575
they didn't exactly come calling.

0:00:17.575,0:00:21.963
They had an operation at Princeton,[br]where they hired mathematicians

0:00:21.963,0:00:25.052
to attack secret codes[br]and stuff like that.

0:00:25.052,0:00:27.658
And I knew that existed.

0:00:27.658,0:00:29.519
And they had a very good policy

0:00:29.519,0:00:33.225
because you could do half your time[br]at your own mathematics

0:00:33.225,0:00:36.708
and at least half your time[br]working on their stuff.

0:00:37.768,0:00:39.057
And they paid a lot.

0:00:39.057,0:00:41.857
So that was an irresistible pull.

0:00:41.857,0:00:43.795
So, I went there.

0:00:44.025,0:00:45.500
CA: So you were a code-cracker.

0:00:45.500,0:00:46.306
JS: I was.

0:00:46.306,0:00:47.550
CA: Until you got fired.

0:00:47.550,0:00:49.033
JS: Well, I did get fired. Yes.

0:00:49.043,0:00:50.778
CA: How come?

0:00:50.778,0:00:53.161
JS: Well, how come?

0:00:53.611,0:00:58.487
I got fired because,[br]well the Vietnam War was on,

0:00:58.487,0:01:03.943
and the boss of bosses in my organization[br]was a big fan of the war

0:01:03.943,0:01:08.842
and wrote a New York Times article,[br]a magazine section cover story,

0:01:08.842,0:01:10.699
about how we're going[br]to win in Vietnam.

0:01:10.699,0:01:13.750
And I didn't like that war,[br]I thought it was stupid.

0:01:13.750,0:01:16.373
And I wrote a letter to the Times,[br]which they published,

0:01:16.373,0:01:20.446
saying not everyone [br]who works for Maxwell Taylor,

0:01:20.446,0:01:25.006
if anyone remembers that name,[br]agrees with his views.

0:01:25.006,0:01:27.210
And I gave my own views.

0:01:27.210,0:01:29.206
CA: Oh, OK. I can see that would --

0:01:29.206,0:01:31.574
JS: Which were different[br]from General Taylor's.

0:01:31.574,0:01:34.035
But in the end nobody said anything.

0:01:34.035,0:01:37.657
But then, I was 29 years old at this time[br]and some kid came around

0:01:37.657,0:01:40.769
and said he was a stringer [br]from Newsweek magazine

0:01:40.769,0:01:46.318
and he wanted to interview me[br]and ask what I was doing about my views.

0:01:46.318,0:01:49.731
And I told him, I said, [br]"I'm doing mostly mathematics now,

0:01:49.731,0:01:54.004
and when the war is over[br]then I'll do mostly their stuff."

0:01:54.004,0:01:56.859
Then I did the only [br]intelligent thing I'd done that day --

0:01:56.859,0:02:01.247
I told my local boss[br]that I gave that interview.

0:02:01.247,0:02:02.686
And he said, "What'd you say?"

0:02:02.686,0:02:04.126
And I told him what I said.

0:02:04.126,0:02:06.465
And then he said,[br]"I've got to call Taylor."

0:02:06.465,0:02:08.725
He called Taylor; that took 10 minutes.

0:02:08.725,0:02:11.655
I was fired five minutes after that.

0:02:11.655,0:02:12.660
CA: OK.

0:02:12.660,0:02:13.709
JS: But it wasn't bad.

0:02:13.709,0:02:16.369
CA: It wasn't bad, because[br]you went on to Stony Brook

0:02:16.369,0:02:19.684
and stepped up your mathematical career.

0:02:19.684,0:02:23.301
You started working[br]with this man here. Who is this?

0:02:24.021,0:02:25.757
JS: Oh, [Shiing-Shen] Chern.

0:02:25.757,0:02:28.916
Chern was one of the great [br]mathematicians of the century.

0:02:28.916,0:02:34.173
I had known him when[br]I was a graduate student at Berkeley.

0:02:34.173,0:02:36.007
And I had some ideas,

0:02:36.007,0:02:38.120
and I brought them to him[br]and he liked them.

0:02:38.120,0:02:44.738
Together, we did this work [br]which you can easily see up there.

0:02:44.738,0:02:46.490
There it is.

0:02:47.480,0:02:50.681
CA: It led to you publishing[br]a famous paper together.

0:02:50.681,0:02:54.484
Can you explain at all what that work was?

0:02:55.284,0:02:56.289
JS: No.

0:02:56.439,0:02:58.484
(Laughter)

0:02:58.684,0:03:00.774
JS: I mean, I could [br]explain it to somebody.

0:03:00.774,0:03:02.473
(Laughter)

0:03:03.153,0:03:04.868
CA: How about explaining this?

0:03:05.257,0:03:07.770
JS: But not many.[br]Not many people.

0:03:08.620,0:03:11.440
CA: I think you told me[br]it had something to do with spheres,

0:03:11.440,0:03:13.460
so let's start here.

0:03:13.460,0:03:17.064
JS: Well, it did,[br]but I'll say about that work --

0:03:17.064,0:03:20.880
it did have something to do with that,[br]but before we get to that --

0:03:20.880,0:03:24.280
that work was good mathematics.

0:03:24.280,0:03:27.520
I was very happy with it; so was Chern.

0:03:27.910,0:03:32.638
It even started a little subfield[br]that's now flourishing.

0:03:32.638,0:03:37.956
But, more interestingly,[br]it happened to apply to physics,

0:03:37.956,0:03:42.275
something we knew nothing about --[br]at least I knew nothing about physics,

0:03:42.275,0:03:44.389
and I don't think Chern[br]knew a heck of a lot.

0:03:44.419,0:03:47.963
And about 10 years[br]after the paper came out,

0:03:47.963,0:03:53.072
a guy named Ed Witten in Princeton[br]started applying it to string theory

0:03:53.072,0:03:57.948
and people in Russia started applying it[br]to what's called "condensed matter."

0:03:57.948,0:04:02.681
Today, those things in there[br]called Chern-Simons invariants

0:04:02.681,0:04:04.473
have spread through a lot of physics.

0:04:04.473,0:04:05.724
And it was amazing.

0:04:05.724,0:04:07.435
We didn't know any physics.

0:04:07.435,0:04:10.506
It never occurred to me[br]that it would be applied to physics.

0:04:10.506,0:04:14.271
But that's the thing about mathematics --[br]you never know where it's going to go.

0:04:14.271,0:04:15.920
CA: This is so incredible.

0:04:15.920,0:04:20.308
So, we've been talking about[br]how evolution shapes human minds

0:04:20.308,0:04:22.630
that may or may not perceive the truth.

0:04:22.630,0:04:25.804
Somehow, you come up[br]with a mathematical theory,

0:04:25.804,0:04:28.049
not knowing any physics,

0:04:28.049,0:04:30.571
discover two decades later[br]that it's being applied

0:04:30.571,0:04:33.696
to profoundly describe[br]the actual physical world.

0:04:33.799,0:04:34.803
How can that happen?

0:04:34.803,0:04:35.997
JS: God knows.

0:04:36.097,0:04:38.094
(Laughter)

0:04:38.754,0:04:41.926
But there's a famous physicist[br]named [Eugene] Wigner,

0:04:41.926,0:04:47.390
and he wrote an essay on the[br]unreasonable effectiveness of mathematics.

0:04:47.560,0:04:49.356
Somehow, this mathematics,

0:04:49.356,0:04:52.420
which is rooted in the real world[br]in some sense --

0:04:52.420,0:04:56.363
we learn to count, measure, [br]everyone would do that --

0:04:56.363,0:04:58.713
and then it flourishes on its own.

0:04:58.713,0:05:01.987
But so often it comes back[br]to save the day.

0:05:01.987,0:05:04.495
General relativity is an example.

0:05:04.495,0:05:07.699
[Hermann] Minkowski had this geometry,[br]and Einstein realized,

0:05:07.699,0:05:11.507
hey, it's the very thing [br]in which I can cast General Relativity.

0:05:11.507,0:05:14.619
So, you never know. It is a mystery.

0:05:14.899,0:05:16.089
It is a mystery.

0:05:16.297,0:05:19.965
CA: So, here's a mathematical[br]piece of ingenuity.

0:05:19.965,0:05:20.983
Tell us about this.

0:05:20.983,0:05:26.931
JS: Well, that's a ball -- it's a sphere,[br]and it has a lattice around it --

0:05:26.931,0:05:29.562
you know, those squares.

0:05:30.582,0:05:35.336
What I'm going to show here was [br]originally observed by [Leonhard] Euler,

0:05:35.336,0:05:38.306
the great mathematician, in the 1700's.

0:05:38.346,0:05:43.045
And it gradually grew to be [br]a very important field in mathematics:

0:05:43.045,0:05:46.879
algebraic topology, geometry.

0:05:47.109,0:05:51.427
That paper up there had its roots in this.

0:05:51.427,0:05:53.285
So, here's this thing:

0:05:53.285,0:05:58.114
it has eight vertices,[br]12 edges, six faces.

0:05:58.114,0:06:01.441
And if you look at the difference --[br]vertices minus edges plus faces --

0:06:01.441,0:06:02.946
you get two.

0:06:02.946,0:06:05.034
OK, well, two? That's a good number.

0:06:05.034,0:06:09.306
Here's a different way of doing it --[br]these are triangles covering --

0:06:09.306,0:06:18.361
this has 12 vertices and 30 edges[br]and 20 faces, 20 tiles.

0:06:18.361,0:06:23.191
And vertices minus edges[br]plus faces still equals two.

0:06:23.191,0:06:26.279
And in fact you could[br]do this any which way,

0:06:26.279,0:06:29.484
cover this thing with all kinds[br]of polygons and triangles

0:06:29.484,0:06:31.039
and mix them up.

0:06:31.039,0:06:34.336
And you take vertices minus edges[br]plus faces -- you'll get two.

0:06:34.336,0:06:36.377
Here's a different shape.

0:06:36.377,0:06:39.527
This is a torus, the surface of a donut,

0:06:39.527,0:06:43.315
16 vertices covered by these rectangles,

0:06:43.315,0:06:46.385
32 edges, 16 faces.

0:06:46.385,0:06:49.475
Vertices minus edges comes out 0.

0:06:49.475,0:06:51.003
It'll always come out 0.

0:06:51.003,0:06:55.071
Every time you cover a torus[br]with squares or triangles

0:06:55.071,0:06:59.881
or anything like that,[br]you're going to get 0.

0:07:00.091,0:07:03.151
So, this is called[br]the Euler characteristic.

0:07:03.151,0:07:06.726
And it's what's called[br]a topological invariant.

0:07:06.726,0:07:07.935
It's pretty amazing.

0:07:07.935,0:07:10.726
No matter how you do it,[br]you're always get the same answer.

0:07:10.726,0:07:16.399
So that was the first sort of thrust, [br]from the mid-1700s,

0:07:16.399,0:07:20.960
into a subject which is now[br]called algebraic topology.

0:07:20.960,0:07:23.422
CA: And your own work[br]took an idea like this

0:07:23.422,0:07:26.440
and moved it into[br]higher-dimensional theory,

0:07:26.440,0:07:29.552
higher-dimensional objects,[br]and found new invariants?

0:07:29.552,0:07:34.219
JS: Yes. Well, there were already [br]higher-dimensional invariants:

0:07:34.219,0:07:38.700
Pontryagin classes --[br]actually, there were Chern classes.

0:07:38.700,0:07:41.556
There were a bunch[br]of these types of invariants.

0:07:41.556,0:07:45.658
I was struggling to work on one of them

0:07:45.658,0:07:50.658
and model it sort of combinatorially

0:07:50.658,0:07:53.491
instead of the way it was typically done,

0:07:53.491,0:07:58.251
and that led to this work[br]and we uncovered some new things.

0:07:58.251,0:08:05.118
But if it wasn't for Mr. Euler --[br]who wrote almost 70 volumes of mathematics

0:08:05.118,0:08:07.446
and had 13 children

0:08:07.446,0:08:13.252
who he apparently would dandle on his knee[br]while he was writing --

0:08:13.252,0:08:19.612
if it wasn't for Mr. Euler, there wouldn't[br]perhaps be these invariants.

0:08:20.366,0:08:24.254
CA: OK, so that's at least given us[br]a flavor of that amazing mind in there.

0:08:24.804,0:08:26.541
Let's talk about Renaissance.

0:08:26.541,0:08:31.394
Because you took that amazing mind[br]and having been a code-cracker at the NSA,

0:08:31.394,0:08:35.504
you started to become a code-cracker[br]in the financial industry.

0:08:35.504,0:08:38.596
I think you probably didn't buy [br]efficient market theory.

0:08:38.596,0:08:44.629
Somehow you found a way of creating[br]astonishing returns over two decades.

0:08:44.629,0:08:46.324
The way it's been explained to me,

0:08:46.324,0:08:49.754
what's remarkable about what you did[br]wasn't just the size of the returns,

0:08:49.754,0:08:53.754
it's that you took them[br]with surprisingly low volatility and risk

0:08:53.754,0:08:55.524
compared with other hedge funds.

0:08:55.602,0:08:57.766
So how on earth did you do this, Jim?

0:08:57.806,0:09:02.035
JS: I did it by assembling[br]a wonderful group of people.

0:09:02.035,0:09:05.773
When I started doing trading, I had [br]gotten a little tired of mathematics.

0:09:05.773,0:09:09.279
I was in my late 30s. [br]I had a little money.

0:09:09.279,0:09:13.180
I started trading and it went very well.

0:09:13.180,0:09:16.012
I made quite a lot of money[br]with pure luck.

0:09:16.012,0:09:17.591
I mean, I think it was pure luck.

0:09:17.591,0:09:19.658
It certainly wasn't mathematical modeling.

0:09:19.658,0:09:23.513
But in looking at the data,[br]after a while I realized:

0:09:23.513,0:09:26.090
it looks like there's some structure here.

0:09:26.090,0:09:29.666
And I hired a few mathematicians,[br]and we started making some models --

0:09:29.666,0:09:34.100
just the kind of thing we did back [br]at IDA [Institute for Defense Analyses].

0:09:34.100,0:09:36.957
You design an algorithm,[br]you test it out on a computer.

0:09:36.957,0:09:39.418
Does it work? Doesn't it work? And so on.

0:09:39.418,0:09:40.946
CA: Can we take a look at this?

0:09:40.946,0:09:45.487
Because here's a typical graph[br]of some commodity.

0:09:45.617,0:09:50.580
I look at that, and I say, [br]That's just a random, up-and-down walk --

0:09:50.580,0:09:53.343
maybe a slight upward trend[br]over that whole period of time.

0:09:53.343,0:09:54.832
How on earth could you trade,

0:09:54.832,0:09:57.543
looking at that and see something[br]that wasn't just random?

0:09:57.543,0:10:01.196
JS: In the old days -- this is [br]kind of a graph from the old days,

0:10:01.196,0:10:05.679
commodities or currencies[br]had a tendency to trend.

0:10:05.679,0:10:11.343
Not necessarily the very light trend[br]you see here, but trending in periods.

0:10:11.343,0:10:16.893
And if you decided, OK, I'm going [br]to predict today, by the average move

0:10:16.893,0:10:20.655
in the past 20 days -- there's 20 days --

0:10:20.655,0:10:23.608
maybe that would be a good prediction,[br]and I'd make some money.

0:10:23.608,0:10:29.146
And in fact, years ago[br]such a system would work --

0:10:29.156,0:10:31.596
not beautifully, but it would work.

0:10:31.729,0:10:33.400
You'd make money, [br]you'd lose money,

0:10:33.400,0:10:34.449
you'd make money.

0:10:34.449,0:10:37.013
But this is a year's worth of days,

0:10:37.013,0:10:41.182
and you'd make a little money[br]during that period.

0:10:41.182,0:10:44.155
It's a very vestigial system.

0:10:44.525,0:10:48.078
CA: So you would test[br]a bunch of lengths of trends in time

0:10:48.078,0:10:50.795
and see whether, for example,

0:10:50.795,0:10:54.674
a 10-day trend or a 15-day trend[br]was predictive of what happens next.

0:10:54.674,0:11:00.805
JS: Sure, you would try all those things[br]and see what worked best.

0:11:01.515,0:11:04.889
Trend-following would've [br]been great in the '60s,

0:11:04.889,0:11:09.512
and it was sort of OK in the '70s.[br]By the '80s, it wasn't.

0:11:09.512,0:11:11.783
CA: Because everyone could see that.

0:11:11.783,0:11:15.046
So, how did you stay ahead of the pack?

0:11:15.046,0:11:21.202
JS: We stayed ahead of the pack[br]by finding other approaches --

0:11:21.202,0:11:23.943
shorter-term approaches to some extent.

0:11:24.423,0:11:28.317
The real thing was to gather[br]a tremendous amount of data,

0:11:28.317,0:11:32.080
and we had to get it by hand[br]in the early days.

0:11:32.080,0:11:35.644
We went down to the Federal Reserve[br]and copied interest rate histories

0:11:35.644,0:11:36.653
and stuff like that.

0:11:36.653,0:11:38.581
Because it didn't exist on computers.

0:11:38.581,0:11:40.810
We got a lot of data.

0:11:40.810,0:11:44.686
And very smart people -- that was the key.

0:11:44.986,0:11:49.239
I didn't really know how[br]to hire people to do fundamental trading.

0:11:49.399,0:11:52.722
I had hired a few -- some made money,[br]some didn't make money.

0:11:52.722,0:11:54.626
I couldn't make a business out of that.

0:11:54.626,0:11:56.692
But I did know how to hire scientists,

0:11:56.692,0:12:00.105
because I have some taste[br]in that department.

0:12:00.105,0:12:02.148
So, that's what we did.

0:12:02.148,0:12:05.167
And gradually these models[br]got better and better,

0:12:05.167,0:12:06.932
and better and better.

0:12:06.932,0:12:10.122
CA: You're credited with doing [br]something remarkable at Renaissance,

0:12:10.122,0:12:12.608
which is building this culture,[br]this group of people,

0:12:12.608,0:12:15.731
who weren't just hired guns[br]who could be lured away by money.

0:12:15.731,0:12:19.819
Their motivation was [br]doing exciting mathematics and science.

0:12:19.819,0:12:22.442
JS: Well I'd hoped that might be true.

0:12:22.442,0:12:25.496
But some of it was money.

0:12:25.496,0:12:27.112
CA: They made a lot of money.

0:12:27.112,0:12:29.681
JS: I can't say that [br]no one came because of the money.

0:12:29.681,0:12:31.966
I think a lot of them[br]came because of the money.

0:12:31.966,0:12:33.970
But they also came[br]because it would be fun.

0:12:33.970,0:12:36.699
CA: What role did machine learning[br]play in all of this?

0:12:36.699,0:12:39.763
JS: In a certain sense,[br]what we did was machine learning.

0:12:39.763,0:12:47.194
You look at a lot of data, and you try[br]to simulate different predictive schemes

0:12:47.194,0:12:49.400
until you get better and better at it.

0:12:49.400,0:12:53.394
It doesn't necessarily feed back on itself[br]the way we did things.

0:12:53.394,0:12:56.318
But it worked.

0:12:56.318,0:13:00.233
CA: So these different predictive schemes [br]can be really quite wild and unexpected.

0:13:00.233,0:13:02.171
I mean, you look at everything, right?

0:13:02.171,0:13:05.513
You look at the weather, [br]length of dresses, political opinion.

0:13:05.513,0:13:07.497
JS: Yes, length of dresses we didn't try.

0:13:07.497,0:13:08.881
(Laughter)

0:13:08.881,0:13:10.112
CA: What sort of things?

0:13:10.112,0:13:12.032
JS: Well, everything.

0:13:12.032,0:13:16.547
Everything is grist for the mill --[br]except hem lengths.

0:13:16.997,0:13:19.176
Weather, annual reports,

0:13:19.176,0:13:24.008
quarterly reports, historic data itself,[br]volumes, you name it.

0:13:24.008,0:13:25.267
Whatever there is.

0:13:25.267,0:13:27.752
We take in terabytes of data a day.

0:13:27.752,0:13:33.186
And store it away, massage it,[br]get it ready for analysis.

0:13:33.306,0:13:34.963
You're looking for anomalies.

0:13:34.963,0:13:36.615
You're looking for, like you said,

0:13:36.615,0:13:40.305
the efficient market [br]hypothesis is not correct.

0:13:40.305,0:13:43.483
CA: But any one anomaly[br]might be just a random thing,

0:13:43.483,0:13:47.478
so is the secret here[br]to just look at multiple strange anomalies

0:13:47.478,0:13:49.607
and see when they align?

0:13:49.607,0:13:52.633
JS: Any one anomaly[br]might be a random thing.

0:13:52.633,0:13:56.328
However, if you have enough data[br]you can tell that it's not.

0:13:56.328,0:14:00.156
You can see an anomaly that's persistent[br]for a sufficiently long time --

0:14:00.156,0:14:05.218
the probability[br]of it being random is not high.

0:14:05.218,0:14:08.654
But these things fade after a while.

0:14:08.654,0:14:12.961
Anomalies can get washed out;[br]you have to keep on top of the business.

0:14:12.961,0:14:15.644
CA: A lot of people look [br]at the hedge fund industry now

0:14:15.644,0:14:19.107
and are sort of --

0:14:19.107,0:14:20.177
shocked by it,

0:14:20.177,0:14:22.236
by how much wealth is created there

0:14:22.236,0:14:24.396
and how much talent is going into it.

0:14:24.396,0:14:29.820
Do you have any worries [br]about that industry

0:14:29.820,0:14:31.966
and perhaps the financial[br]industry in general?

0:14:31.966,0:14:34.584
Kind of being on a runaway train that's --

0:14:34.584,0:14:38.884
I don't know, helping increase inequality?

0:14:38.884,0:14:43.788
How would you champion what's happening[br]in the hedge fund industry?

0:14:43.788,0:14:45.849
JS: I think in the last[br]three of four years,

0:14:45.849,0:14:47.847
hedge funds have not done especially well.

0:14:47.847,0:14:48.705
We've done dandy,

0:14:48.705,0:14:53.119
but the hedge fund industry as a whole[br]has not done so wonderfully.

0:14:53.119,0:14:58.562
The stock market has been on a roll,[br]going up as everybody knows,

0:14:58.562,0:15:01.339
and price-earnings ratios have grown.

0:15:01.339,0:15:04.570
So an awful lot of the wealth [br]that's been created in the last,

0:15:04.570,0:15:07.660
let's say, five or six years[br]has not been created by hedge funds.

0:15:08.430,0:15:11.830
People would ask me,[br]"What's a hedge fund?"

0:15:11.830,0:15:14.361
And I'd say, "One and 20."

0:15:14.361,0:15:17.124
Which means -- now it's two and 20 --

0:15:17.124,0:15:21.165
it's two percent fixed fee[br]and 20 percent of profits.

0:15:21.165,0:15:23.468
Hedge funds are all different[br]kinds of creatures.

0:15:23.468,0:15:26.229
CA: Rumor has it you charge[br]slightly higher fees than that.

0:15:26.229,0:15:27.895
(Laughter)

0:15:27.895,0:15:30.609
JS: We charged the highest [br]fees in the world at one time.

0:15:30.609,0:15:33.835
Five and 44, that's what we charge.

0:15:33.835,0:15:35.091
CA: Five and 44.

0:15:35.091,0:15:38.641
So 5 percent flat,[br]44 percent of upside.

0:15:38.641,0:15:41.400
You still made your investors[br]spectacular amounts of money.

0:15:41.400,0:15:42.826
JS: We made good returns, yes.

0:15:42.826,0:15:43.826
People got very mad at my investors.

0:15:43.826,0:15:45.681
"How could you charge such high fees?"

0:15:45.681,0:15:47.516
I said, "OK, you can withdraw."

0:15:47.516,0:15:50.174
"But how can I get more?"

0:15:50.174,0:15:51.887
(Laughter)

0:15:51.887,0:15:53.762
But at a certain point, as I told you,

0:15:53.762,0:15:59.351
we bought out all the investors [br]because there's a capacity to the fund.

0:15:59.365,0:16:02.315
CA: But should we worry[br]about the hedge fund industry

0:16:02.315,0:16:05.978
attracting too much[br]of the world's great mathematical

0:16:05.978,0:16:08.439
and other talent to work on that

0:16:08.439,0:16:10.817
as opposed to the many[br]other problems in the world?

0:16:10.817,0:16:12.540
JS: Well it's not just mathematical.

0:16:12.540,0:16:15.426
We hire astronomers and physicists[br]and things like that.

0:16:15.676,0:16:21.681
I don't think we should worry too much.[br]It's still a pretty small industry.

0:16:21.681,0:16:27.742
And in fact, bringing science [br]into the investing world

0:16:27.742,0:16:29.878
has improved that world.

0:16:29.878,0:16:33.746
It's reduced volatility.[br]It's increased liquidity.

0:16:33.746,0:16:36.937
Spreads are narrower because[br]people are trading that kind of stuff.

0:16:36.937,0:16:42.394
So I'm not too worried about Einstein[br]going off and starting a hedge fund.

0:16:42.614,0:16:46.666
CA: You're at a phase in your life now[br]where you're actually investing, though,

0:16:46.666,0:16:50.182
at the other end of the supply chain --

0:16:50.182,0:16:54.072
in boosting mathematics across America.

0:16:54.072,0:16:56.441
This is your wife, Marilyn.

0:16:56.441,0:17:00.743
You're working on [br]philanthropic issues together.

0:17:00.743,0:17:02.408
Tell me about that.

0:17:02.408,0:17:06.081
JS: Well, Marilyn started --

0:17:06.081,0:17:09.683
there she is up there,[br]my beautiful wife --

0:17:09.683,0:17:12.678
she started the foundation[br]about 20 years ago.

0:17:12.678,0:17:13.955
I think '94.

0:17:13.955,0:17:16.067
I claim it was '93, [br]she says it was '94,

0:17:16.067,0:17:18.437
but it was one of those two years.

0:17:18.437,0:17:20.596
(Laughter)

0:17:20.596,0:17:23.178
We started the foundation

0:17:23.178,0:17:27.958
just as a convenient way to give charity.

0:17:27.958,0:17:30.877
She kept the books, and so on.

0:17:30.877,0:17:37.244
We did not have a vision at that time,[br]but gradually a vision emerged --

0:17:37.244,0:17:43.119
which was to focus on math and science,[br]to focus on basic research.

0:17:43.119,0:17:46.136
And that's what we've done.

0:17:46.136,0:17:52.941
Six years ago or so, I left Renaissance[br]and went to work at the foundation.

0:17:52.941,0:17:54.473
So that's what we do.

0:17:54.473,0:17:56.260
CA: And so Math for America here

0:17:56.260,0:17:59.442
is basically investing in math teachers[br]around the country,

0:17:59.442,0:18:03.760
giving them some extra income,[br]giving them support and coaching.

0:18:03.760,0:18:06.835
And really trying[br]to make that more effective

0:18:06.835,0:18:09.340
and make that a calling[br]to which teachers can aspire.

0:18:09.340,0:18:11.242
JS: Yeah, yeah.

0:18:11.242,0:18:14.442
Instead of beating up the bad teachers --

0:18:14.442,0:18:18.691
which has created morale problems[br]all through the educational community,

0:18:18.691,0:18:21.616
in particular in math and science --

0:18:21.616,0:18:27.770
we focus on celebrating the good ones [br]and giving them status.

0:18:27.770,0:18:31.345
Yeah, we give them extra money,[br]15,000 dollars a year.

0:18:31.345,0:18:35.478
We have 800 math and science teachers[br]in New York City in public schools today

0:18:35.478,0:18:37.266
as part of a core.

0:18:37.266,0:18:43.259
There's a great morale among them.[br]They're staying in the field.

0:18:43.259,0:18:46.213
Next year, it'll be 1,000 [teachers],[br]and that'll be 10 percent

0:18:46.213,0:18:49.781
of the math and science teachers[br]in New York public schools.

0:18:49.781,0:18:54.217
(Applause)

0:18:55.227,0:18:58.674
CA: Jim, here's another project[br]that you've supported philanthropically:

0:18:58.674,0:19:02.781
Research into origins of life, I guess.[br]What are we looking at here?

0:19:03.401,0:19:05.779
Well, I'll save that for a second.

0:19:05.779,0:19:07.934
And then I'll tell you[br]what you're looking at.

0:19:07.934,0:19:13.486
Origins of life is a fascinating question.[br]How did we get here?

0:19:13.486,0:19:15.122
Well, there's two questions.

0:19:15.122,0:19:20.905
One is, What is the root[br]from geology to biology?

0:19:20.905,0:19:22.694
How did we get here?

0:19:22.694,0:19:25.024
And the other question is,[br]What did we start with?

0:19:25.024,0:19:27.955
What material, if any,[br]did we have to work with on this route?

0:19:27.955,0:19:31.583
Those are two very,[br]very interesting questions.

0:19:31.583,0:19:37.726
The first question is a tortuous path[br]from geology up to RNA

0:19:37.726,0:19:39.966
or something like that --[br]how did that all work?

0:19:39.966,0:19:42.325
And the other,[br]What do we have to work with?

0:19:42.325,0:19:44.120
Well, more than we think.

0:19:44.120,0:19:49.778
So what's pictured there[br]is a star in formation.

0:19:49.778,0:19:53.285
Now every year in our Milky Way,[br]which has 100 billion stars,

0:19:53.285,0:19:56.141
about two near stars are created.

0:19:56.141,0:19:58.254
Don't ask me how, but they're created.

0:19:58.254,0:20:02.132
And it takes them[br]about a million years to settle out.

0:20:02.132,0:20:04.522
So, in steady state,

0:20:04.522,0:20:08.423
there's about 2 million stars[br]in formation at any time.

0:20:08.423,0:20:11.837
That one is somewhere along[br]this settling down period.

0:20:11.837,0:20:15.390
And there's all this crap[br]sort of circling around it.

0:20:15.390,0:20:17.479
Dust and stuff.

0:20:17.479,0:20:20.916
And it'll form probably a solar system,[br]or whatever it forms.

0:20:20.916,0:20:23.834
But here's the thing --

0:20:23.834,0:20:28.924
in this dust that surrounds a forming star

0:20:28.924,0:20:35.591
have been found, now,[br]significant organic molecules.

0:20:35.591,0:20:42.121
Molecules not just like methane[br]but formaldehyde and cyanide --

0:20:42.121,0:20:48.816
things that are the building blocks,[br]the seeds, if you will, of life.

0:20:49.136,0:20:52.215
So, that may be typical.

0:20:52.215,0:20:58.974
And it may be typical[br]that planets around the universe

0:20:58.974,0:21:03.639
start off with some of these[br]basic building blocks.

0:21:03.639,0:21:06.542
Now, does that mean [br]there's going to be life all around?

0:21:06.542,0:21:07.957
Maybe.

0:21:07.957,0:21:12.276
But it's a question[br]of how tortuous this path is

0:21:12.276,0:21:16.734
from those frail beginnings,[br]those seeds, all the way to life.

0:21:16.734,0:21:21.931
And most of those seeds[br]will fall on fallow planets.

0:21:21.931,0:21:23.423
CA: So for you, personally,

0:21:23.423,0:21:26.090
finding an answer to this question[br]of where we came from,

0:21:26.090,0:21:29.783
of how did this thing happen,[br]that is something you would love to see.

0:21:29.783,0:21:32.499
JS: Would love to see. And like to know.

0:21:32.759,0:21:38.444
If that path is tortuous enough, [br]and so improbable

0:21:38.444,0:21:43.251
that no matter what you start with,[br]we could be a singularity.

0:21:43.251,0:21:44.691
But, on the other hand,

0:21:44.691,0:21:47.710
given all this organic dust[br]that's floating around,

0:21:47.710,0:21:52.947
we could have lots of friends out there.

0:21:52.947,0:21:54.304
It'd be great to know.

0:21:54.304,0:21:56.280
CA: Jim, a couple of years ago,

0:21:56.280,0:22:00.712
I got the chance to speak with Elon Musk,[br]and I asked him the secret of his success.

0:22:00.712,0:22:04.798
He said taking physics seriously was it.

0:22:04.798,0:22:08.723
Listening to you, what I hear you saying[br]is taking math seriously,

0:22:08.723,0:22:11.903
that has infused your whole life.

0:22:11.903,0:22:16.710
It's made you an absolute fortune,[br]and now it's allowing you to invest

0:22:16.710,0:22:21.098
in the futures of thousands and thousands[br]of kids across America and elsewhere.

0:22:21.098,0:22:27.418
Could it be that science actually works?[br]That math actually works?

0:22:27.418,0:22:31.680
JS: Well, math certainly works.[br]Math certainly works.

0:22:31.680,0:22:33.033
But this has been fun.

0:22:33.033,0:22:37.956
Working with Marilyn and giving it away[br]has been very enjoyable.

0:22:37.956,0:22:41.060
CA: I just find it --[br]it's an inspirational thought to me,

0:22:41.060,0:22:44.968
that by taking knowledge seriously,[br]so much more can come from it.

0:22:44.968,0:22:47.935
So thank you for your amazing life,[br]and for coming here to TED.

0:22:47.935,0:22:48.664
Thank you.

0:22:48.664,0:22:49.960
Jim Simons.

0:22:49.970,0:22:54.186
(Applause)