0:00:00.817,0:00:03.651
Chris Anderson: You were something[br]of a mathematical phenom.

0:00:03.675,0:00:06.739
You had already taught at Harvard[br]and MIT at a young age.

0:00:06.763,0:00:08.953
And then the NSA came calling.

0:00:09.464,0:00:10.668
What was that about?

0:00:11.207,0:00:15.130
Jim Simons: Well the NSA --[br]that's the National Security Agency --

0:00:15.154,0:00:17.123
they didn't exactly come calling.

0:00:17.465,0:00:21.939
They had an operation at Princeton,[br]where they hired mathematicians

0:00:21.963,0:00:24.905
to attack secret codes[br]and stuff like that.

0:00:25.294,0:00:26.966
And I knew that existed.

0:00:27.315,0:00:29.495
And they had a very good policy,

0:00:29.519,0:00:33.369
because you could do half your time[br]at your own mathematics,

0:00:33.393,0:00:36.877
and at least half your time[br]working on their stuff.

0:00:37.559,0:00:39.033
And they paid a lot.

0:00:39.057,0:00:42.108
So that was an irresistible pull.

0:00:42.132,0:00:44.044
So, I went there.

0:00:44.068,0:00:45.406
CA: You were a code-cracker.

0:00:45.430,0:00:46.596
JS: I was.

0:00:46.620,0:00:47.777
CA: Until you got fired.

0:00:47.801,0:00:49.384
JS: Well, I did get fired. Yes.

0:00:49.408,0:00:50.653
CA: How come?

0:00:51.280,0:00:52.613
JS: Well, how come?

0:00:53.611,0:00:58.567
I got fired because,[br]well, the Vietnam War was on,

0:00:58.591,0:01:04.329
and the boss of bosses in my organization[br]was a big fan of the war

0:01:04.353,0:01:08.748
and wrote a New York Times article,[br]a magazine section cover story,

0:01:08.772,0:01:10.542
about how we would win in Vietnam.

0:01:10.566,0:01:13.695
And I didn't like that war,[br]I thought it was stupid.

0:01:13.719,0:01:16.384
And I wrote a letter to the Times,[br]which they published,

0:01:16.408,0:01:20.422
saying not everyone[br]who works for Maxwell Taylor,

0:01:20.446,0:01:25.132
if anyone remembers that name,[br]agrees with his views.

0:01:25.553,0:01:27.211
And I gave my own views ...

0:01:27.235,0:01:29.399
CA: Oh, OK. I can see that would --

0:01:29.423,0:01:31.978
JS: ... which were different[br]from General Taylor's.

0:01:32.002,0:01:33.908
But in the end, nobody said anything.

0:01:33.932,0:01:37.633
But then, I was 29 years old at this time,[br]and some kid came around

0:01:37.657,0:01:40.745
and said he was a stringer[br]from Newsweek magazine

0:01:40.769,0:01:46.136
and he wanted to interview me[br]and ask what I was doing about my views.

0:01:46.160,0:01:50.059
And I told him, "I'm doing[br]mostly mathematics now,

0:01:50.083,0:01:53.456
and when the war is over,[br]then I'll do mostly their stuff."

0:01:54.123,0:01:56.948
Then I did the only[br]intelligent thing I'd done that day --

0:01:56.972,0:02:01.129
I told my local boss[br]that I gave that interview.

0:02:01.153,0:02:02.612
And he said, "What'd you say?"

0:02:02.636,0:02:04.102
And I told him what I said.

0:02:04.126,0:02:06.441
And then he said,[br]"I've got to call Taylor."

0:02:06.465,0:02:08.842
He called Taylor; that took 10 minutes.

0:02:08.866,0:02:11.128
I was fired five minutes after that.

0:02:11.590,0:02:12.812
CA: OK.

0:02:12.836,0:02:13.987
JS: But it wasn't bad.

0:02:14.011,0:02:16.504
CA: It wasn't bad,[br]because you went on to Stony Brook

0:02:16.528,0:02:19.661
and stepped up your mathematical career.

0:02:19.685,0:02:22.137
You started working with this man here.

0:02:22.161,0:02:23.325
Who is this?

0:02:24.352,0:02:25.764
JS: Oh, [Shiing-Shen] Chern.

0:02:25.788,0:02:28.892
Chern was one of the great[br]mathematicians of the century.

0:02:28.916,0:02:34.149
I had known him when[br]I was a graduate student at Berkeley.

0:02:34.173,0:02:36.044
And I had some ideas,

0:02:36.068,0:02:38.515
and I brought them to him[br]and he liked them.

0:02:38.539,0:02:45.165
Together, we did this work[br]which you can easily see up there.

0:02:45.189,0:02:46.339
There it is.

0:02:47.198,0:02:50.804
CA: It led to you publishing[br]a famous paper together.

0:02:50.828,0:02:54.066
Can you explain at all what that work was?

0:02:55.028,0:02:56.186
JS: No.

0:02:56.210,0:02:58.484
(Laughter)

0:02:58.966,0:03:01.030
JS: I mean, I could[br]explain it to somebody.

0:03:01.054,0:03:03.129
(Laughter)

0:03:03.153,0:03:05.017
CA: How about explaining this?

0:03:05.041,0:03:07.770
JS: But not many. Not many people.

0:03:09.144,0:03:11.958
CA: I think you told me[br]it had something to do with spheres,

0:03:11.982,0:03:13.844
so let's start here.

0:03:13.868,0:03:17.468
JS: Well, it did,[br]but I'll say about that work --

0:03:17.492,0:03:20.692
it did have something to do with that,[br]but before we get to that --

0:03:20.716,0:03:24.256
that work was good mathematics.

0:03:24.280,0:03:26.772
I was very happy with it; so was Chern.

0:03:27.910,0:03:32.086
It even started a little sub-field[br]that's now flourishing.

0:03:32.638,0:03:37.932
But, more interestingly,[br]it happened to apply to physics,

0:03:37.956,0:03:42.251
something we knew nothing about --[br]at least I knew nothing about physics,

0:03:42.275,0:03:44.557
and I don't think Chern[br]knew a heck of a lot.

0:03:44.581,0:03:48.544
And about 10 years[br]after the paper came out,

0:03:48.568,0:03:53.048
a guy named Ed Witten in Princeton[br]started applying it to string theory

0:03:53.072,0:03:57.924
and people in Russia started applying it[br]to what's called "condensed matter."

0:03:57.948,0:04:02.841
Today, those things in there[br]called Chern-Simons invariants

0:04:02.865,0:04:04.730
have spread through a lot of physics.

0:04:04.754,0:04:05.928
And it was amazing.

0:04:05.952,0:04:07.317
We didn't know any physics.

0:04:07.714,0:04:10.568
It never occurred to me[br]that it would be applied to physics.

0:04:10.592,0:04:14.380
But that's the thing about mathematics --[br]you never know where it's going to go.

0:04:14.404,0:04:15.896
CA: This is so incredible.

0:04:15.920,0:04:20.284
So, we've been talking about[br]how evolution shapes human minds

0:04:20.308,0:04:22.816
that may or may not perceive the truth.

0:04:22.840,0:04:26.153
Somehow, you come up[br]with a mathematical theory,

0:04:26.177,0:04:28.025
not knowing any physics,

0:04:28.049,0:04:30.547
discover two decades later[br]that it's being applied

0:04:30.571,0:04:33.602
to profoundly describe[br]the actual physical world.

0:04:33.626,0:04:34.779
How can that happen?

0:04:34.803,0:04:35.960
JS: God knows.

0:04:35.984,0:04:38.094
(Laughter)

0:04:38.849,0:04:41.999
But there's a famous physicist[br]named [Eugene] Wigner,

0:04:42.023,0:04:47.611
and he wrote an essay on the unreasonable[br]effectiveness of mathematics.

0:04:47.635,0:04:51.587
Somehow, this mathematics,[br]which is rooted in the real world

0:04:51.611,0:04:56.606
in some sense -- we learn to count,[br]measure, everyone would do that --

0:04:56.630,0:04:58.460
and then it flourishes on its own.

0:04:58.976,0:05:01.817
But so often it comes[br]back to save the day.

0:05:02.293,0:05:04.471
General relativity is an example.

0:05:04.495,0:05:07.612
[Hermann] Minkowski had this geometry,[br]and Einstein realized,

0:05:07.636,0:05:11.483
"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.

0:05:15.056,0:05:16.273
It is a mystery.

0:05:16.297,0:05:19.593
CA: So, here's a mathematical[br]piece of ingenuity.

0:05:19.617,0:05:20.959
Tell us about this.

0:05:20.983,0:05:26.907
JS: Well, that's a ball -- it's a sphere,[br]and it has a lattice around it --

0:05:26.931,0:05:28.504
you know, those squares.

0:05:30.697,0:05:35.603
What I'm going to show here was[br]originally observed by [Leonhard] Euler,

0:05:35.627,0:05:37.881
the great mathematician, in the 1700s.

0:05:38.223,0:05:43.404
And it gradually grew to be[br]a very important field in mathematics:

0:05:43.428,0:05:45.762
algebraic topology, geometry.

0:05:47.039,0:05:51.403
That paper up there had its roots in this.

0:05:51.427,0:05:53.261
So, here's this thing:

0:05:53.285,0:05:57.737
it has eight vertices,[br]12 edges, six faces.

0:05:57.761,0:06:01.591
And if you look at the difference --[br]vertices minus edges plus faces --

0:06:01.615,0:06:02.767
you get two.

0:06:02.791,0:06:05.010
OK, well, two. That's a good number.

0:06:05.034,0:06:09.282
Here's a different way of doing it --[br]these are triangles covering --

0:06:09.306,0:06:13.883
this has 12 vertices and 30 edges

0:06:13.907,0:06:18.102
and 20 faces, 20 tiles.

0:06:18.576,0:06:23.167
And vertices minus edges[br]plus faces still equals two.

0:06:23.191,0:06:26.038
And in fact, you could do this[br]any which way --

0:06:26.062,0:06:29.460
cover this thing with all kinds[br]of polygons and triangles

0:06:29.484,0:06:30.804
and mix them up.

0:06:30.828,0:06:34.107
And you take vertices minus edges[br]plus faces -- you'll get two.

0:06:34.131,0:06:35.742
Here's a different shape.

0:06:36.480,0:06:41.730
This is a torus, or the surface[br]of a doughnut: 16 vertices

0:06:41.754,0:06:45.998
covered by these rectangles,[br]32 edges, 16 faces.

0:06:46.530,0:06:49.214
Vertices minus edges comes out to be zero.

0:06:49.238,0:06:50.713
It'll always come out to zero.

0:06:50.737,0:06:55.047
Every time you cover a torus[br]with squares or triangles

0:06:55.071,0:06:59.006
or anything like that,[br]you're going to get zero.

0:07:00.514,0:07:02.904
So, this is called[br]the Euler characteristic.

0:07:02.928,0:07:06.377
And it's what's called[br]a topological invariant.

0:07:06.849,0:07:08.005
It's pretty amazing.

0:07:08.029,0:07:10.820
No matter how you do it,[br]you're always get the same answer.

0:07:10.844,0:07:17.143
So that was the first sort of thrust,[br]from the mid-1700s,

0:07:17.167,0:07:20.936
into a subject which is now called[br]algebraic topology.

0:07:20.960,0:07:23.943
CA: And your own work[br]took an idea like this and moved it

0:07:23.967,0:07:26.416
into higher-dimensional theory,

0:07:26.440,0:07:29.528
higher-dimensional objects,[br]and found new invariances?

0:07:29.552,0:07:34.195
JS: Yes. Well, there were already[br]higher-dimensional invariants:

0:07:34.219,0:07:38.676
Pontryagin classes --[br]actually, there were Chern classes.

0:07:38.700,0:07:42.248
There were a bunch[br]of these types of invariants.

0:07:42.272,0:07:46.407
I was struggling to work on one of them

0:07:46.431,0:07:50.634
and model it sort of combinatorially,

0:07:50.658,0:07:53.680
instead of the way it was typically done,

0:07:53.704,0:07:58.063
and that led to this work[br]and we uncovered some new things.

0:07:58.087,0:08:01.588
But if it wasn't for Mr. Euler --

0:08:01.612,0:08:05.593
who wrote almost 70 volumes of mathematics

0:08:05.617,0:08:07.348
and had 13 children,

0:08:07.372,0:08:13.814
who he apparently would dandle on his knee[br]while he was writing --

0:08:13.838,0:08:19.612
if it wasn't for Mr. Euler, there wouldn't[br]perhaps be these invariants.

0:08:20.157,0:08:24.254
CA: OK, so that's at least given us[br]a flavor of that amazing mind in there.

0:08:24.804,0:08:26.347
Let's talk about Renaissance.

0:08:26.371,0:08:32.227
Because you took that amazing mind[br]and having been a code-cracker at the NSA,

0:08:32.251,0:08:35.480
you started to become a code-cracker[br]in the financial industry.

0:08:35.504,0:08:38.194
I think you probably didn't buy[br]efficient market theory.

0:08:38.218,0:08:44.605
Somehow you found a way of creating[br]astonishing returns over two decades.

0:08:44.629,0:08:46.300
The way it's been explained to me,

0:08:46.324,0:08:49.823
what's remarkable about what you did[br]wasn't just the size of the returns,

0:08:49.847,0:08:53.730
it's that you took them[br]with surprisingly low volatility and risk,

0:08:53.754,0:08:55.578
compared with other hedge funds.

0:08:55.602,0:08:57.531
So how on earth did you do this, Jim?

0:08:58.071,0:09:02.182
JS: I did it by assembling[br]a wonderful group of people.

0:09:02.206,0:09:06.162
When I started doing trading, I had[br]gotten a little tired of mathematics.

0:09:06.186,0:09:10.109
I was in my late 30s,[br]I had a little money.

0:09:10.133,0:09:12.642
I started trading and it went very well.

0:09:13.063,0:09:15.811
I made quite a lot of money[br]with pure luck.

0:09:15.835,0:09:17.501
I mean, I think it was pure luck.

0:09:17.525,0:09:19.634
It certainly wasn't mathematical modeling.

0:09:19.658,0:09:23.489
But in looking at the data,[br]after a while I realized:

0:09:23.513,0:09:26.066
it looks like there's some structure here.

0:09:26.090,0:09:29.787
And I hired a few mathematicians,[br]and we started making some models --

0:09:29.811,0:09:34.076
just the kind of thing we did back[br]at IDA [Institute for Defense Analyses].

0:09:34.100,0:09:36.933
You design an algorithm,[br]you test it out on a computer.

0:09:36.957,0:09:39.123
Does it work? Doesn't it work? And so on.

0:09:39.443,0:09:40.922
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.

0:09:46.487,0:09:50.528
I look at that, and I say,[br]"That's just a random, up-and-down walk --

0:09:50.552,0:09:53.414
maybe a slight upward trend[br]over that whole period of time."

0:09:53.438,0:09:55.551
How on earth could you trade[br]looking at that,

0:09:55.575,0:09:57.901
and see something that wasn't just random?

0:09:57.925,0:10:01.172
JS: In the old days -- this is[br]kind of a graph from the old days,

0:10:01.196,0:10:05.480
commodities or currencies[br]had a tendency to trend.

0:10:05.504,0:10:11.559
Not necessarily the very light trend[br]you see here, but trending in periods.

0:10:11.583,0:10:15.639
And if you decided, OK,[br]I'm going to predict today,

0:10:15.663,0:10:20.631
by the average move in the past 20 days --

0:10:20.655,0:10:23.762
maybe that would be a good prediction,[br]and I'd make some money.

0:10:23.786,0:10:29.394
And in fact, years ago,[br]such a system would work --

0:10:29.418,0:10:31.809
not beautifully, but it would work.

0:10:31.833,0:10:34.342
You'd make money, you'd lose[br]money, you'd make money.

0:10:34.366,0:10:36.564
But this is a year's worth of days,

0:10:36.588,0:10:40.829
and you'd make a little money[br]during that period.

0:10:41.884,0:10:43.842
It's a very vestigial system.

0:10:44.525,0:10:48.054
CA: So you would test[br]a bunch of lengths of trends in time

0:10:48.078,0:10:50.514
and see whether, for example,

0:10:50.538,0:10:54.019
a 10-day trend or a 15-day trend[br]was predictive of what happened next.

0:10:54.043,0:11:00.805
JS: Sure, you would try all those things[br]and see what worked best.

0:11:01.515,0:11:04.865
Trend-following would[br]have been great in the '60s,

0:11:04.889,0:11:07.021
and it was sort of OK in the '70s.

0:11:07.045,0:11:08.918
By the '80s, it wasn't.

0:11:08.942,0:11:11.759
CA: Because everyone could see that.

0:11:11.783,0:11:14.565
So, how did you stay ahead of the pack?

0:11:15.046,0:11:21.178
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.

0:11:25.107,0:11:28.454
The real thing was to gather[br]a tremendous amount of data --

0:11:28.478,0:11:32.056
and we had to get it by hand[br]in the early days.

0:11:32.080,0:11:35.546
We went down to the Federal Reserve[br]and copied interest rate histories

0:11:35.570,0:11:38.835
and stuff like that,[br]because it didn't exist on computers.

0:11:38.859,0:11:40.502
We got a lot of data.

0:11:40.526,0:11:44.686
And very smart people -- that was the key.

0:11:45.463,0:11:49.239
I didn't really know how to hire[br]people to do fundamental trading.

0:11:49.749,0:11:52.698
I had hired a few -- some made money,[br]some didn't make money.

0:11:52.722,0:11:54.602
I couldn't make a business out of that.

0:11:54.626,0:11:56.668
But I did know how to hire scientists,

0:11:56.692,0:12:00.081
because I have some taste[br]in that department.

0:12:00.105,0:12:01.943
So, that's what we did.

0:12:01.967,0:12:05.198
And gradually these models[br]got better and better,

0:12:05.222,0:12:06.557
and better and better.

0:12:06.581,0:12:09.795
CA: You're credited with doing[br]something remarkable at Renaissance,

0:12:09.819,0:12:12.420
which is building this culture,[br]this group of people,

0:12:12.444,0:12:15.586
who weren't just hired guns[br]who could be lured away by money.

0:12:15.610,0:12:19.522
Their motivation was doing[br]exciting mathematics and science.

0:12:19.860,0:12:22.259
JS: Well, I'd hoped that might be true.

0:12:22.283,0:12:25.863
But some of it was money.

0:12:25.887,0:12:27.280
CA: They made a lot of money.

0:12:27.304,0:12:29.841
JS: I can't say that no one came[br]because of the money.

0:12:29.865,0:12:32.118
I think a lot of them[br]came because of the money.

0:12:32.142,0:12:34.163
But they also came[br]because it would be fun.

0:12:34.187,0:12:36.675
CA: What role did machine learning[br]play in all this?

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

0:12:40.879,0:12:47.170
You look at a lot of data, and you try[br]to simulate different predictive schemes,

0:12:47.194,0:12:49.376
until you get better and better at it.

0:12:49.400,0:12:53.167
It doesn't necessarily feed back on itself[br]the way we did things.

0:12:53.191,0:12:55.500
But it worked.

0:12:56.150,0:13:00.209
CA: So these different predictive schemes[br]can be really quite wild and unexpected.

0:13:00.233,0:13:02.147
I mean, you looked at everything, right?

0:13:02.171,0:13:05.488
You looked at the weather,[br]length of dresses, political opinion.

0:13:05.512,0:13:08.349
JS: Yes, length of dresses we didn't try.

0:13:08.373,0:13:10.430
CA: What sort of things?

0:13:10.454,0:13:11.612
JS: Well, everything.

0:13:11.636,0:13:14.900
Everything is grist for the mill --[br]except hem lengths.

0:13:16.852,0:13:19.152
Weather, annual reports,

0:13:19.176,0:13:23.908
quarterly reports, historic data itself,[br]volumes, you name it.

0:13:23.932,0:13:25.083
Whatever there is.

0:13:25.107,0:13:27.728
We take in terabytes of data a day.

0:13:27.752,0:13:31.876
And store it away and massage it[br]and get it ready for analysis.

0:13:33.446,0:13:34.828
You're looking for anomalies.

0:13:34.852,0:13:37.805
You're looking for -- like you said,

0:13:37.829,0:13:40.281
the efficient market[br]hypothesis is not correct.

0:13:40.305,0:13:43.772
CA: But any one anomaly[br]might be just a random thing.

0:13:43.796,0:13:47.454
So, is the secret here to just look[br]at multiple strange anomalies,

0:13:47.478,0:13:48.806
and see when they align?

0:13:49.238,0:13:52.451
JS: Any one anomaly[br]might be a random thing;

0:13:52.475,0:13:55.514
however, if you have enough data[br]you can tell that it's not.

0:13:55.538,0:14:00.488
You can see an anomaly that's persistent[br]for a sufficiently long time --

0:14:00.512,0:14:05.487
the probability of it being[br]random is not high.

0:14:05.511,0:14:10.369
But these things fade after a while;[br]anomalies can get washed out.

0:14:10.393,0:14:12.813
So you have to keep on top[br]of the business.

0:14:12.837,0:14:15.509
CA: A lot of people look[br]at the hedge fund industry now

0:14:15.533,0:14:19.931
and are sort of ... shocked by it,

0:14:19.955,0:14:22.127
by how much wealth is created there,

0:14:22.151,0:14:24.396
and how much talent is going into it.

0:14:25.523,0:14:29.529
Do you have any worries[br]about that industry,

0:14:29.553,0:14:31.967
and perhaps the financial[br]industry in general?

0:14:31.991,0:14:34.695
Kind of being on a runaway train that's --

0:14:34.719,0:14:38.749
I don't know --[br]helping increase inequality?

0:14:38.773,0:14:42.604
How would you champion what's happening[br]in the hedge fund industry?

0:14:42.628,0:14:45.236
JS: I think in the last[br]three of four years,

0:14:45.260,0:14:47.363
hedge funds have not done especially well.

0:14:47.387,0:14:48.787
We've done dandy,

0:14:48.811,0:14:52.812
but the hedge fund industry as a whole[br]has not done so wonderfully.

0:14:52.836,0:14:57.738
The stock market has been on a roll,[br]going up as everybody knows,

0:14:57.762,0:15:01.207
and price-earnings ratios have grown.

0:15:01.231,0:15:04.294
So an awful lot of the wealth[br]that's been created in the last --

0:15:04.318,0:15:07.668
let's say, five or six years --[br]has not been created by hedge funds.

0:15:08.458,0:15:11.679
People would ask me,[br]"What's a hedge fund?"

0:15:11.703,0:15:13.963
And I'd say, "One and 20."

0:15:13.987,0:15:17.553
Which means -- now it's two and 20 --

0:15:17.577,0:15:20.930
it's two percent fixed fee[br]and 20 percent of profits.

0:15:20.954,0:15:23.306
Hedge funds are all[br]different kinds of creatures.

0:15:23.330,0:15:26.569
CA: Rumor has it you charge[br]slightly higher fees than that.

0:15:27.339,0:15:30.420
JS: We charged the highest fees[br]in the world at one time.

0:15:30.444,0:15:33.670
Five and 44, that's what we charge.

0:15:33.694,0:15:35.092
CA: Five and 44.

0:15:35.116,0:15:38.350
So five percent flat,[br]44 percent of upside.

0:15:38.374,0:15:41.157
You still made your investors[br]spectacular amounts of money.

0:15:41.181,0:15:42.633
JS: We made good returns, yes.

0:15:42.657,0:15:45.657
People got very mad:[br]"How can you charge such high fees?"

0:15:45.681,0:15:47.308
I said, "OK, you can withdraw."

0:15:47.332,0:15:50.150
But "How can I get more?"[br]was what people were --

0:15:50.174,0:15:51.678
(Laughter)

0:15:51.702,0:15:54.142
But at a certain point,[br]as I think I told you,

0:15:54.166,0:15:59.341
we bought out all the investors[br]because there's a capacity to the fund.

0:15:59.365,0:16:02.069
CA: But should we worry[br]about the hedge fund industry

0:16:02.093,0:16:07.531
attracting too much of the world's[br]great mathematical and other talent

0:16:07.555,0:16:10.793
to work on that, as opposed[br]to the many other problems in the world?

0:16:10.817,0:16:12.746
JS: Well, it's not just mathematical.

0:16:12.770,0:16:15.449
We hire astronomers and physicists[br]and things like that.

0:16:15.833,0:16:18.264
I don't think we should worry[br]about it too much.

0:16:18.288,0:16:21.430
It's still a pretty small industry.

0:16:21.454,0:16:27.451
And in fact, bringing science[br]into the investing world

0:16:27.475,0:16:29.634
has improved that world.

0:16:29.658,0:16:33.728
It's reduced volatility.[br]It's increased liquidity.

0:16:33.752,0:16:36.941
Spreads are narrower because[br]people are trading that kind of stuff.

0:16:36.965,0:16:42.041
So I'm not too worried about Einstein[br]going off and starting a hedge fund.

0:16:42.478,0:16:46.642
CA: You're at a phase in your life now[br]where you're actually investing, though,

0:16:46.666,0:16:50.400
at the other end of the supply chain --

0:16:50.424,0:16:54.528
you're actually boosting[br]mathematics across America.

0:16:54.552,0:16:56.417
This is your wife, Marilyn.

0:16:56.441,0:17:01.197
You're working on[br]philanthropic issues together.

0:17:01.221,0:17:02.384
Tell me about that.

0:17:02.408,0:17:06.057
JS: Well, Marilyn started --

0:17:06.081,0:17:09.528
there she is up there,[br]my beautiful wife --

0:17:09.552,0:17:12.524
she started the foundation[br]about 20 years ago.

0:17:12.548,0:17:13.699
I think '94.

0:17:13.723,0:17:15.818
I claim it was '93, she says it was '94,

0:17:15.842,0:17:18.413
but it was one of those two years.

0:17:18.437,0:17:20.572
(Laughter)

0:17:20.596,0:17:27.315
We started the foundation,[br]just as a convenient way to give charity.

0:17:28.346,0:17:30.853
She kept the books, and so on.

0:17:30.877,0:17:37.591
We did not have a vision at that time,[br]but gradually a vision emerged --

0:17:37.615,0:17:43.119
which was to focus on math and science,[br]to focus on basic research.

0:17:43.569,0:17:46.341
And that's what we've done.

0:17:46.365,0:17:52.720
Six years ago or so, I left Renaissance[br]and went to work at the foundation.

0:17:52.744,0:17:54.315
So that's what we do.

0:17:54.339,0:17:57.248
CA: And so Math for America[br]is basically investing

0:17:57.272,0:17:59.910
in math teachers around the country,

0:17:59.934,0:18:03.736
giving them some extra income,[br]giving them support and coaching.

0:18:03.760,0:18:06.811
And really trying[br]to make that more effective

0:18:06.835,0:18:09.436
and make that a calling[br]to which teachers can aspire.

0:18:09.460,0:18:14.250
JS: Yeah -- instead of beating up[br]the bad teachers,

0:18:14.274,0:18:19.127
which has created morale problems[br]all through the educational community,

0:18:19.151,0:18:21.592
in particular in math and science,

0:18:21.616,0:18:27.746
we focus on celebrating the good ones[br]and giving them status.

0:18:27.770,0:18:30.701
Yeah, we give them extra money,[br]15,000 dollars a year.

0:18:30.725,0:18:35.192
We have 800 math and science teachers[br]in New York City in public schools today,

0:18:35.216,0:18:37.030
as part of a core.

0:18:37.054,0:18:40.740
There's a great morale among them.

0:18:40.764,0:18:43.270
They're staying in the field.

0:18:43.294,0:18:46.189
Next year, it'll be 1,000[br]and that'll be 10 percent

0:18:46.213,0:18:49.757
of the math and science teachers[br]in New York [City] public schools.

0:18:49.781,0:18:55.686
(Applause)

0:18:55.710,0:18:59.120
CA: Jim, here's another project[br]that you've supported philanthropically:

0:18:59.144,0:19:01.541
Research into origins of life, I guess.

0:19:01.565,0:19:03.012
What are we looking at here?

0:19:03.536,0:19:05.418
Well, I'll save that for a second.

0:19:05.442,0:19:07.604
And then I'll tell you[br]what you're looking at.

0:19:07.628,0:19:10.684
Origins of life is a fascinating question.

0:19:10.708,0:19:12.241
How did we get here?

0:19:13.170,0:19:14.941
Well, there are two questions:

0:19:14.965,0:19:20.833
One is, what is the route[br]from geology to biology --

0:19:20.857,0:19:22.238
how did we get here?

0:19:22.262,0:19:24.626
And the other question is,[br]what did we start with?

0:19:24.650,0:19:27.752
What material, if any,[br]did we have to work with on this route?

0:19:27.776,0:19:30.837
Those are two very,[br]very interesting questions.

0:19:31.773,0:19:37.607
The first question is a tortuous path[br]from geology up to RNA

0:19:37.631,0:19:39.889
or something like that --[br]how did that all work?

0:19:39.913,0:19:42.301
And the other,[br]what do we have to work with?

0:19:42.325,0:19:44.096
Well, more than we think.

0:19:44.120,0:19:48.963
So what's pictured there[br]is a star in formation.

0:19:49.836,0:19:53.261
Now, every year in our Milky Way,[br]which has 100 billion stars,

0:19:53.285,0:19:55.780
about two new stars are created.

0:19:55.804,0:19:58.274
Don't ask me how, but they're created.

0:19:58.298,0:20:01.378
And it takes them about a million[br]years to settle out.

0:20:02.132,0:20:04.308
So, in steady state,

0:20:04.332,0:20:08.180
there are about two million stars[br]in formation at any time.

0:20:08.204,0:20:11.662
That one is somewhere[br]along this settling-down period.

0:20:12.067,0:20:15.003
And there's all this crap[br]sort of circling around it,

0:20:15.027,0:20:16.525
dust and stuff.

0:20:17.479,0:20:20.502
And it'll form probably a solar system,[br]or whatever it forms.

0:20:20.526,0:20:22.702
But here's the thing --

0:20:22.726,0:20:29.074
in this dust that surrounds a forming star

0:20:29.098,0:20:35.133
have been found, now,[br]significant organic molecules.

0:20:35.958,0:20:42.097
Molecules not just like methane,[br]but formaldehyde and cyanide --

0:20:42.121,0:20:48.638
things that are the building blocks --[br]the seeds, if you will -- of life.

0:20:49.136,0:20:51.828
So, that may be typical.

0:20:52.395,0:20:59.329
And it may be typical[br]that planets around the universe

0:20:59.353,0:21:02.965
start off with some of these[br]basic building blocks.

0:21:03.830,0:21:06.545
Now does that mean[br]there's going to be life all around?

0:21:06.569,0:21:07.933
Maybe.

0:21:07.957,0:21:12.084
But it's a question[br]of how tortuous this path is

0:21:12.108,0:21:16.502
from those frail beginnings,[br]those seeds, all the way to life.

0:21:16.526,0:21:21.718
And most of those seeds[br]will fall on fallow planets.

0:21:21.742,0:21:23.151
CA: So for you, personally,

0:21:23.175,0:21:25.897
finding an answer to this question[br]of where we came from,

0:21:25.921,0:21:29.579
of how did this thing happen,[br]that is something you would love to see.

0:21:29.603,0:21:31.389
JS: Would love to see.

0:21:31.413,0:21:32.903
And like to know --

0:21:32.927,0:21:38.097
if that path is tortuous enough,[br]and so improbable,

0:21:38.121,0:21:42.875
that no matter what you start with,[br]we could be a singularity.

0:21:43.336,0:21:44.488
But on the other hand,

0:21:44.512,0:21:47.990
given all this organic dust[br]that's floating around,

0:21:48.014,0:21:51.805
we could have lots of friends out there.

0:21:52.947,0:21:54.108
It'd be great to know.

0:21:54.132,0:21:57.612
CA: Jim, a couple of years ago,[br]I got the chance to speak with Elon Musk,

0:21:57.636,0:22:00.473
and I asked him the secret of his success,

0:22:00.497,0:22:04.188
and he said taking[br]physics seriously was it.

0:22:04.696,0:22:08.699
Listening to you, what I hear you saying[br]is taking math seriously,

0:22:08.723,0:22:11.726
that has infused your whole life.

0:22:12.123,0:22:16.686
It's made you an absolute fortune,[br]and now it's allowing you to invest

0:22:16.710,0:22:21.206
in the futures of thousands and thousands[br]of kids across America and elsewhere.

0:22:21.567,0:22:24.425
Could it be that science actually works?

0:22:24.449,0:22:27.221
That math actually works?

0:22:27.245,0:22:31.617
JS: Well, math certainly works.[br]Math certainly works.

0:22:31.641,0:22:32.839
But this has been fun.

0:22:32.863,0:22:37.809
Working with Marilyn and giving it away[br]has been very enjoyable.

0:22:37.833,0:22:40.769
CA: I just find it --[br]it's an inspirational thought to me,

0:22:40.793,0:22:44.800
that by taking knowledge seriously,[br]so much more can come from it.

0:22:44.824,0:22:47.842
So thank you for your amazing life,[br]and for coming here to TED.

0:22:47.866,0:22:48.617
Thank you.

0:22:48.651,0:22:49.752
Jim Simons!

0:22:49.806,0:22:54.186
(Applause)