WEBVTT 00:00:00.817 --> 00:00:03.651 Chris Anderson: You were something of a mathematical phenom. 00:00:03.675 --> 00:00:06.739 You had already taught at Harvard and MIT at a young age. 00:00:06.763 --> 00:00:08.953 And then the NSA came calling. 00:00:09.464 --> 00:00:10.668 What was that about? NOTE Paragraph 00:00:11.207 --> 00:00:15.130 Jim Simons: Well the NSA -- that's the National Security Agency -- 00:00:15.154 --> 00:00:17.123 they didn't exactly come calling. 00:00:17.465 --> 00:00:21.939 They had an operation at Princeton, where they hired mathematicians 00:00:21.963 --> 00:00:24.905 to attack secret codes and stuff like that. 00:00:25.294 --> 00:00:26.966 And I knew that existed. 00:00:27.315 --> 00:00:29.495 And they had a very good policy, 00:00:29.519 --> 00:00:33.369 because you could do half your time at your own mathematics, 00:00:33.393 --> 00:00:36.877 and at least half your time working on their stuff. 00:00:37.559 --> 00:00:39.033 And they paid a lot. 00:00:39.057 --> 00:00:42.108 So that was an irresistible pull. 00:00:42.132 --> 00:00:44.044 So, I went there. NOTE Paragraph 00:00:44.068 --> 00:00:45.406 CA: You were a code-cracker. NOTE Paragraph 00:00:45.430 --> 00:00:46.596 JS: I was. NOTE Paragraph 00:00:46.620 --> 00:00:47.777 CA: Until you got fired. NOTE Paragraph 00:00:47.801 --> 00:00:49.384 JS: Well, I did get fired. Yes. NOTE Paragraph 00:00:49.408 --> 00:00:50.653 CA: How come? NOTE Paragraph 00:00:51.280 --> 00:00:52.613 JS: Well, how come? 00:00:53.611 --> 00:00:58.567 I got fired because, well, the Vietnam War was on, 00:00:58.591 --> 00:01:04.329 and the boss of bosses in my organization was a big fan of the war 00:01:04.353 --> 00:01:08.748 and wrote a New York Times article, a magazine section cover story, 00:01:08.772 --> 00:01:10.542 about how we would win in Vietnam. 00:01:10.566 --> 00:01:13.695 And I didn't like that war, I thought it was stupid. 00:01:13.719 --> 00:01:16.384 And I wrote a letter to the Times, which they published, 00:01:16.408 --> 00:01:20.422 saying not everyone who works for Maxwell Taylor, 00:01:20.446 --> 00:01:25.132 if anyone remembers that name, agrees with his views. 00:01:25.553 --> 00:01:27.211 And I gave my own views ... NOTE Paragraph 00:01:27.235 --> 00:01:29.399 CA: Oh, OK. I can see that would -- NOTE Paragraph 00:01:29.423 --> 00:01:31.978 JS: ... which were different from General Taylor's. 00:01:32.002 --> 00:01:33.908 But in the end, nobody said anything. 00:01:33.932 --> 00:01:37.633 But then, I was 29 years old at this time, and some kid came around 00:01:37.657 --> 00:01:40.745 and said he was a stringer from Newsweek magazine 00:01:40.769 --> 00:01:46.136 and he wanted to interview me and ask what I was doing about my views. 00:01:46.160 --> 00:01:50.059 And I told him, "I'm doing mostly mathematics now, 00:01:50.083 --> 00:01:53.456 and when the war is over, then I'll do mostly their stuff." 00:01:54.123 --> 00:01:56.948 Then I did the only intelligent thing I'd done that day -- 00:01:56.972 --> 00:02:01.129 I told my local boss that I gave that interview. 00:02:01.153 --> 00:02:02.612 And he said, "What'd you say?" 00:02:02.636 --> 00:02:04.102 And I told him what I said. 00:02:04.126 --> 00:02:06.441 And then he said, "I've got to call Taylor." 00:02:06.465 --> 00:02:08.842 He called Taylor; that took 10 minutes. 00:02:08.866 --> 00:02:11.128 I was fired five minutes after that. NOTE Paragraph 00:02:11.590 --> 00:02:12.812 CA: OK. NOTE Paragraph 00:02:12.836 --> 00:02:13.987 JS: But it wasn't bad. NOTE Paragraph 00:02:14.011 --> 00:02:16.504 CA: It wasn't bad, because you went on to Stony Brook 00:02:16.528 --> 00:02:19.661 and stepped up your mathematical career. 00:02:19.685 --> 00:02:22.137 You started working with this man here. 00:02:22.161 --> 00:02:23.325 Who is this? NOTE Paragraph 00:02:24.352 --> 00:02:25.764 JS: Oh, [Shiing-Shen] Chern. 00:02:25.788 --> 00:02:28.892 Chern was one of the great mathematicians of the century. 00:02:28.916 --> 00:02:34.149 I had known him when I was a graduate student at Berkeley. 00:02:34.173 --> 00:02:36.044 And I had some ideas, 00:02:36.068 --> 00:02:38.515 and I brought them to him and he liked them. 00:02:38.539 --> 00:02:45.165 Together, we did this work which you can easily see up there. 00:02:45.189 --> 00:02:46.339 There it is. NOTE Paragraph 00:02:47.198 --> 00:02:50.804 CA: It led to you publishing a famous paper together. 00:02:50.828 --> 00:02:54.066 Can you explain at all what that work was? NOTE Paragraph 00:02:55.028 --> 00:02:56.186 JS: No. NOTE Paragraph 00:02:56.210 --> 00:02:58.484 (Laughter) NOTE Paragraph 00:02:58.966 --> 00:03:01.030 JS: I mean, I could explain it to somebody. NOTE Paragraph 00:03:01.054 --> 00:03:03.129 (Laughter) NOTE Paragraph 00:03:03.153 --> 00:03:05.017 CA: How about explaining this? NOTE Paragraph 00:03:05.041 --> 00:03:07.770 JS: But not many. Not many people. NOTE Paragraph 00:03:09.144 --> 00:03:11.958 CA: I think you told me it had something to do with spheres, 00:03:11.982 --> 00:03:13.844 so let's start here. NOTE Paragraph 00:03:13.868 --> 00:03:17.468 JS: Well, it did, but I'll say about that work -- 00:03:17.492 --> 00:03:20.692 it did have something to do with that, but before we get to that -- 00:03:20.716 --> 00:03:24.256 that work was good mathematics. 00:03:24.280 --> 00:03:26.772 I was very happy with it; so was Chern. 00:03:27.910 --> 00:03:32.086 It even started a little sub-field that's now flourishing. 00:03:32.638 --> 00:03:37.932 But, more interestingly, it happened to apply to physics, 00:03:37.956 --> 00:03:42.251 something we knew nothing about -- at least I knew nothing about physics, 00:03:42.275 --> 00:03:44.557 and I don't think Chern knew a heck of a lot. 00:03:44.581 --> 00:03:48.544 And about 10 years after the paper came out, 00:03:48.568 --> 00:03:53.048 a guy named Ed Witten in Princeton started applying it to string theory 00:03:53.072 --> 00:03:57.924 and people in Russia started applying it to what's called "condensed matter." 00:03:57.948 --> 00:04:02.841 Today, those things in there called Chern-Simons invariants 00:04:02.865 --> 00:04:04.730 have spread through a lot of physics. 00:04:04.754 --> 00:04:05.928 And it was amazing. 00:04:05.952 --> 00:04:07.317 We didn't know any physics. 00:04:07.714 --> 00:04:10.568 It never occurred to me that it would be applied to physics. 00:04:10.592 --> 00:04:14.380 But that's the thing about mathematics -- you never know where it's going to go. NOTE Paragraph 00:04:14.404 --> 00:04:15.896 CA: This is so incredible. 00:04:15.920 --> 00:04:20.284 So, we've been talking about how evolution shapes human minds 00:04:20.308 --> 00:04:22.816 that may or may not perceive the truth. 00:04:22.840 --> 00:04:26.153 Somehow, you come up with a mathematical theory, 00:04:26.177 --> 00:04:28.025 not knowing any physics, 00:04:28.049 --> 00:04:30.547 discover two decades later that it's being applied 00:04:30.571 --> 00:04:33.602 to profoundly describe the actual physical world. 00:04:33.626 --> 00:04:34.779 How can that happen? NOTE Paragraph 00:04:34.803 --> 00:04:35.960 JS: God knows. NOTE Paragraph 00:04:35.984 --> 00:04:38.094 (Laughter) NOTE Paragraph 00:04:38.849 --> 00:04:41.999 But there's a famous physicist named [Eugene] Wigner, 00:04:42.023 --> 00:04:47.611 and he wrote an essay on the unreasonable effectiveness of mathematics. 00:04:47.635 --> 00:04:51.587 Somehow, this mathematics, which is rooted in the real world 00:04:51.611 --> 00:04:56.606 in some sense -- we learn to count, measure, everyone would do that -- 00:04:56.630 --> 00:04:58.460 and then it flourishes on its own. 00:04:58.976 --> 00:05:01.817 But so often it comes back to save the day. 00:05:02.293 --> 00:05:04.471 General relativity is an example. 00:05:04.495 --> 00:05:07.612 [Hermann] Minkowski had this geometry, and Einstein realized, 00:05:07.636 --> 00:05:11.483 "Hey! It's the very thing in which I can cast general relativity." 00:05:11.507 --> 00:05:14.619 So, you never know. It is a mystery. 00:05:15.056 --> 00:05:16.273 It is a mystery. NOTE Paragraph 00:05:16.297 --> 00:05:19.593 CA: So, here's a mathematical piece of ingenuity. 00:05:19.617 --> 00:05:20.959 Tell us about this. NOTE Paragraph 00:05:20.983 --> 00:05:26.907 JS: Well, that's a ball -- it's a sphere, and it has a lattice around it -- 00:05:26.931 --> 00:05:28.504 you know, those squares. 00:05:30.697 --> 00:05:35.603 What I'm going to show here was originally observed by [Leonhard] Euler, 00:05:35.627 --> 00:05:37.881 the great mathematician, in the 1700s. 00:05:38.223 --> 00:05:43.404 And it gradually grew to be a very important field in mathematics: 00:05:43.428 --> 00:05:45.762 algebraic topology, geometry. 00:05:47.039 --> 00:05:51.403 That paper up there had its roots in this. 00:05:51.427 --> 00:05:53.261 So, here's this thing: 00:05:53.285 --> 00:05:57.737 it has eight vertices, 12 edges, six faces. 00:05:57.761 --> 00:06:01.591 And if you look at the difference -- vertices minus edges plus faces -- 00:06:01.615 --> 00:06:02.767 you get two. 00:06:02.791 --> 00:06:05.010 OK, well, two. That's a good number. 00:06:05.034 --> 00:06:09.282 Here's a different way of doing it -- these are triangles covering -- 00:06:09.306 --> 00:06:13.883 this has 12 vertices and 30 edges 00:06:13.907 --> 00:06:18.102 and 20 faces, 20 tiles. 00:06:18.576 --> 00:06:23.167 And vertices minus edges plus faces still equals two. 00:06:23.191 --> 00:06:26.038 And in fact, you could do this any which way -- 00:06:26.062 --> 00:06:29.460 cover this thing with all kinds of polygons and triangles 00:06:29.484 --> 00:06:30.804 and mix them up. 00:06:30.828 --> 00:06:34.107 And you take vertices minus edges plus faces -- you'll get two. 00:06:34.131 --> 00:06:35.742 Here's a different shape. 00:06:36.480 --> 00:06:41.730 This is a torus, or the surface of a doughnut: 16 vertices 00:06:41.754 --> 00:06:45.998 covered by these rectangles, 32 edges, 16 faces. 00:06:46.530 --> 00:06:49.214 Vertices minus edges comes out to be zero. 00:06:49.238 --> 00:06:50.713 It'll always come out to zero. 00:06:50.737 --> 00:06:55.047 Every time you cover a torus with squares or triangles 00:06:55.071 --> 00:06:59.006 or anything like that, you're going to get zero. 00:07:00.514 --> 00:07:02.904 So, this is called the Euler characteristic. 00:07:02.928 --> 00:07:06.377 And it's what's called a topological invariant. 00:07:06.849 --> 00:07:08.005 It's pretty amazing. 00:07:08.029 --> 00:07:10.820 No matter how you do it, you're always get the same answer. 00:07:10.844 --> 00:07:17.143 So that was the first sort of thrust, from the mid-1700s, 00:07:17.167 --> 00:07:20.936 into a subject which is now called algebraic topology. NOTE Paragraph 00:07:20.960 --> 00:07:23.943 CA: And your own work took an idea like this and moved it 00:07:23.967 --> 00:07:26.416 into higher-dimensional theory, 00:07:26.440 --> 00:07:29.528 higher-dimensional objects, and found new invariances? NOTE Paragraph 00:07:29.552 --> 00:07:34.195 JS: Yes. Well, there were already higher-dimensional invariants: 00:07:34.219 --> 00:07:38.676 Pontryagin classes -- actually, there were Chern classes. 00:07:38.700 --> 00:07:42.248 There were a bunch of these types of invariants. 00:07:42.272 --> 00:07:46.407 I was struggling to work on one of them 00:07:46.431 --> 00:07:50.634 and model it sort of combinatorially, 00:07:50.658 --> 00:07:53.680 instead of the way it was typically done, 00:07:53.704 --> 00:07:58.063 and that led to this work and we uncovered some new things. 00:07:58.087 --> 00:08:01.588 But if it wasn't for Mr. Euler -- 00:08:01.612 --> 00:08:05.593 who wrote almost 70 volumes of mathematics 00:08:05.617 --> 00:08:07.348 and had 13 children, 00:08:07.372 --> 00:08:13.814 who he apparently would dandle on his knee while he was writing -- 00:08:13.838 --> 00:08:19.612 if it wasn't for Mr. Euler, there wouldn't perhaps be these invariants. NOTE Paragraph 00:08:20.157 --> 00:08:24.254 CA: OK, so that's at least given us a flavor of that amazing mind in there. 00:08:24.804 --> 00:08:26.347 Let's talk about Renaissance. 00:08:26.371 --> 00:08:32.227 Because you took that amazing mind and having been a code-cracker at the NSA, 00:08:32.251 --> 00:08:35.480 you started to become a code-cracker in the financial industry. 00:08:35.504 --> 00:08:38.194 I think you probably didn't buy efficient market theory. 00:08:38.218 --> 00:08:44.605 Somehow you found a way of creating astonishing returns over two decades. 00:08:44.629 --> 00:08:46.300 The way it's been explained to me, 00:08:46.324 --> 00:08:49.823 what's remarkable about what you did wasn't just the size of the returns, 00:08:49.847 --> 00:08:53.730 it's that you took them with surprisingly low volatility and risk, 00:08:53.754 --> 00:08:55.578 compared with other hedge funds. 00:08:55.602 --> 00:08:57.531 So how on earth did you do this, Jim? NOTE Paragraph 00:08:58.071 --> 00:09:02.182 JS: I did it by assembling a wonderful group of people. 00:09:02.206 --> 00:09:06.162 When I started doing trading, I had gotten a little tired of mathematics. 00:09:06.186 --> 00:09:10.109 I was in my late 30s, I had a little money. 00:09:10.133 --> 00:09:12.642 I started trading and it went very well. 00:09:13.063 --> 00:09:15.811 I made quite a lot of money with pure luck. 00:09:15.835 --> 00:09:17.501 I mean, I think it was pure luck. 00:09:17.525 --> 00:09:19.634 It certainly wasn't mathematical modeling. 00:09:19.658 --> 00:09:23.489 But in looking at the data, after a while I realized: 00:09:23.513 --> 00:09:26.066 it looks like there's some structure here. 00:09:26.090 --> 00:09:29.787 And I hired a few mathematicians, and we started making some models -- 00:09:29.811 --> 00:09:34.076 just the kind of thing we did back at IDA [Institute for Defense Analyses]. 00:09:34.100 --> 00:09:36.933 You design an algorithm, you test it out on a computer. 00:09:36.957 --> 00:09:39.123 Does it work? Doesn't it work? And so on. NOTE Paragraph 00:09:39.443 --> 00:09:40.922 CA: Can we take a look at this? 00:09:40.946 --> 00:09:45.487 Because here's a typical graph of some commodity. 00:09:46.487 --> 00:09:50.528 I look at that, and I say, "That's just a random, up-and-down walk -- 00:09:50.552 --> 00:09:53.414 maybe a slight upward trend over that whole period of time." 00:09:53.438 --> 00:09:55.551 How on earth could you trade looking at that, 00:09:55.575 --> 00:09:57.901 and see something that wasn't just random? NOTE Paragraph 00:09:57.925 --> 00:10:01.172 JS: In the old days -- this is kind of a graph from the old days, 00:10:01.196 --> 00:10:05.480 commodities or currencies had a tendency to trend. 00:10:05.504 --> 00:10:11.559 Not necessarily the very light trend you see here, but trending in periods. 00:10:11.583 --> 00:10:15.639 And if you decided, OK, I'm going to predict today, 00:10:15.663 --> 00:10:20.631 by the average move in the past 20 days -- 00:10:20.655 --> 00:10:23.762 maybe that would be a good prediction, and I'd make some money. 00:10:23.786 --> 00:10:29.394 And in fact, years ago, such a system would work -- 00:10:29.418 --> 00:10:31.809 not beautifully, but it would work. 00:10:31.833 --> 00:10:34.342 You'd make money, you'd lose money, you'd make money. 00:10:34.366 --> 00:10:36.564 But this is a year's worth of days, 00:10:36.588 --> 00:10:40.829 and you'd make a little money during that period. 00:10:41.884 --> 00:10:43.842 It's a very vestigial system. NOTE Paragraph 00:10:44.525 --> 00:10:48.054 CA: So you would test a bunch of lengths of trends in time 00:10:48.078 --> 00:10:50.514 and see whether, for example, 00:10:50.538 --> 00:10:54.019 a 10-day trend or a 15-day trend was predictive of what happened next. NOTE Paragraph 00:10:54.043 --> 00:11:00.805 JS: Sure, you would try all those things and see what worked best. 00:11:01.515 --> 00:11:04.865 Trend-following would have been great in the '60s, 00:11:04.889 --> 00:11:07.021 and it was sort of OK in the '70s. 00:11:07.045 --> 00:11:08.918 By the '80s, it wasn't. NOTE Paragraph 00:11:08.942 --> 00:11:11.759 CA: Because everyone could see that. 00:11:11.783 --> 00:11:14.565 So, how did you stay ahead of the pack? NOTE Paragraph 00:11:15.046 --> 00:11:21.178 JS: We stayed ahead of the pack by finding other approaches -- 00:11:21.202 --> 00:11:23.943 shorter-term approaches to some extent. 00:11:25.107 --> 00:11:28.454 The real thing was to gather a tremendous amount of data -- 00:11:28.478 --> 00:11:32.056 and we had to get it by hand in the early days. 00:11:32.080 --> 00:11:35.546 We went down to the Federal Reserve and copied interest rate histories 00:11:35.570 --> 00:11:38.835 and stuff like that, because it didn't exist on computers. 00:11:38.859 --> 00:11:40.502 We got a lot of data. 00:11:40.526 --> 00:11:44.686 And very smart people -- that was the key. 00:11:45.463 --> 00:11:49.239 I didn't really know how to hire people to do fundamental trading. 00:11:49.749 --> 00:11:52.698 I had hired a few -- some made money, some didn't make money. 00:11:52.722 --> 00:11:54.602 I couldn't make a business out of that. 00:11:54.626 --> 00:11:56.668 But I did know how to hire scientists, 00:11:56.692 --> 00:12:00.081 because I have some taste in that department. 00:12:00.105 --> 00:12:01.943 So, that's what we did. 00:12:01.967 --> 00:12:05.198 And gradually these models got better and better, 00:12:05.222 --> 00:12:06.557 and better and better. NOTE Paragraph 00:12:06.581 --> 00:12:09.795 CA: You're credited with doing something remarkable at Renaissance, 00:12:09.819 --> 00:12:12.420 which is building this culture, this group of people, 00:12:12.444 --> 00:12:15.586 who weren't just hired guns who could be lured away by money. 00:12:15.610 --> 00:12:19.522 Their motivation was doing exciting mathematics and science. NOTE Paragraph 00:12:19.860 --> 00:12:22.259 JS: Well, I'd hoped that might be true. 00:12:22.283 --> 00:12:25.863 But some of it was money. NOTE Paragraph 00:12:25.887 --> 00:12:27.280 CA: They made a lot of money. NOTE Paragraph 00:12:27.304 --> 00:12:29.841 JS: I can't say that no one came because of the money. 00:12:29.865 --> 00:12:32.118 I think a lot of them came because of the money. 00:12:32.142 --> 00:12:34.163 But they also came because it would be fun. NOTE Paragraph 00:12:34.187 --> 00:12:36.675 CA: What role did machine learning play in all this? NOTE Paragraph 00:12:36.699 --> 00:12:39.763 JS: In a certain sense, what we did was machine learning. 00:12:40.879 --> 00:12:47.170 You look at a lot of data, and you try to simulate different predictive schemes, 00:12:47.194 --> 00:12:49.376 until you get better and better at it. 00:12:49.400 --> 00:12:53.167 It doesn't necessarily feed back on itself the way we did things. 00:12:53.191 --> 00:12:55.500 But it worked. NOTE Paragraph 00:12:56.150 --> 00:13:00.209 CA: So these different predictive schemes can be really quite wild and unexpected. 00:13:00.233 --> 00:13:02.147 I mean, you looked at everything, right? 00:13:02.171 --> 00:13:05.488 You looked at the weather, length of dresses, political opinion. NOTE Paragraph 00:13:05.512 --> 00:13:08.349 JS: Yes, length of dresses we didn't try. NOTE Paragraph 00:13:08.373 --> 00:13:10.430 CA: What sort of things? NOTE Paragraph 00:13:10.454 --> 00:13:11.612 JS: Well, everything. 00:13:11.636 --> 00:13:14.900 Everything is grist for the mill -- except hem lengths. 00:13:16.852 --> 00:13:19.152 Weather, annual reports, 00:13:19.176 --> 00:13:23.908 quarterly reports, historic data itself, volumes, you name it. 00:13:23.932 --> 00:13:25.083 Whatever there is. 00:13:25.107 --> 00:13:27.728 We take in terabytes of data a day. 00:13:27.752 --> 00:13:31.876 And store it away and massage it and get it ready for analysis. 00:13:33.446 --> 00:13:34.828 You're looking for anomalies. 00:13:34.852 --> 00:13:37.805 You're looking for -- like you said, 00:13:37.829 --> 00:13:40.281 the efficient market hypothesis is not correct. NOTE Paragraph 00:13:40.305 --> 00:13:43.772 CA: But any one anomaly might be just a random thing. 00:13:43.796 --> 00:13:47.454 So, is the secret here to just look at multiple strange anomalies, 00:13:47.478 --> 00:13:48.806 and see when they align? NOTE Paragraph 00:13:49.238 --> 00:13:52.451 JS: Any one anomaly might be a random thing; 00:13:52.475 --> 00:13:55.514 however, if you have enough data you can tell that it's not. 00:13:55.538 --> 00:14:00.488 You can see an anomaly that's persistent for a sufficiently long time -- 00:14:00.512 --> 00:14:05.487 the probability of it being random is not high. 00:14:05.511 --> 00:14:10.369 But these things fade after a while; anomalies can get washed out. 00:14:10.393 --> 00:14:12.813 So you have to keep on top of the business. NOTE Paragraph 00:14:12.837 --> 00:14:15.509 CA: A lot of people look at the hedge fund industry now 00:14:15.533 --> 00:14:19.931 and are sort of ... shocked by it, 00:14:19.955 --> 00:14:22.127 by how much wealth is created there, 00:14:22.151 --> 00:14:24.396 and how much talent is going into it. 00:14:25.523 --> 00:14:29.529 Do you have any worries about that industry, 00:14:29.553 --> 00:14:31.967 and perhaps the financial industry in general? 00:14:31.991 --> 00:14:34.695 Kind of being on a runaway train that's -- 00:14:34.719 --> 00:14:38.749 I don't know -- helping increase inequality? 00:14:38.773 --> 00:14:42.604 How would you champion what's happening in the hedge fund industry? NOTE Paragraph 00:14:42.628 --> 00:14:45.236 JS: I think in the last three or four years, 00:14:45.260 --> 00:14:47.363 hedge funds have not done especially well. 00:14:47.387 --> 00:14:48.787 We've done dandy, 00:14:48.811 --> 00:14:52.812 but the hedge fund industry as a whole has not done so wonderfully. 00:14:52.836 --> 00:14:57.738 The stock market has been on a roll, going up as everybody knows, 00:14:57.762 --> 00:15:01.207 and price-earnings ratios have grown. 00:15:01.231 --> 00:15:04.294 So an awful lot of the wealth that's been created in the last -- 00:15:04.318 --> 00:15:07.668 let's say, five or six years -- has not been created by hedge funds. 00:15:08.458 --> 00:15:11.679 People would ask me, "What's a hedge fund?" 00:15:11.703 --> 00:15:13.963 And I'd say, "One and 20." 00:15:13.987 --> 00:15:17.553 Which means -- now it's two and 20 -- 00:15:17.577 --> 00:15:20.930 it's two percent fixed fee and 20 percent of profits. 00:15:20.954 --> 00:15:23.306 Hedge funds are all different kinds of creatures. NOTE Paragraph 00:15:23.330 --> 00:15:26.569 CA: Rumor has it you charge slightly higher fees than that. NOTE Paragraph 00:15:27.339 --> 00:15:30.420 JS: We charged the highest fees in the world at one time. 00:15:30.444 --> 00:15:33.670 Five and 44, that's what we charge. NOTE Paragraph 00:15:33.694 --> 00:15:35.092 CA: Five and 44. 00:15:35.116 --> 00:15:38.350 So five percent flat, 44 percent of upside. 00:15:38.374 --> 00:15:41.157 You still made your investors spectacular amounts of money. NOTE Paragraph 00:15:41.181 --> 00:15:42.633 JS: We made good returns, yes. 00:15:42.657 --> 00:15:45.657 People got very mad: "How can you charge such high fees?" 00:15:45.681 --> 00:15:47.308 I said, "OK, you can withdraw." 00:15:47.332 --> 00:15:50.150 But "How can I get more?" was what people were -- NOTE Paragraph 00:15:50.174 --> 00:15:51.678 (Laughter) NOTE Paragraph 00:15:51.702 --> 00:15:54.142 But at a certain point, as I think I told you, 00:15:54.166 --> 00:15:59.341 we bought out all the investors because there's a capacity to the fund. NOTE Paragraph 00:15:59.365 --> 00:16:02.069 CA: But should we worry about the hedge fund industry 00:16:02.093 --> 00:16:07.531 attracting too much of the world's great mathematical and other talent 00:16:07.555 --> 00:16:10.793 to work on that, as opposed to the many other problems in the world? NOTE Paragraph 00:16:10.817 --> 00:16:12.746 JS: Well, it's not just mathematical. 00:16:12.770 --> 00:16:15.449 We hire astronomers and physicists and things like that. 00:16:15.833 --> 00:16:18.264 I don't think we should worry about it too much. 00:16:18.288 --> 00:16:21.430 It's still a pretty small industry. 00:16:21.454 --> 00:16:27.451 And in fact, bringing science into the investing world 00:16:27.475 --> 00:16:29.634 has improved that world. 00:16:29.658 --> 00:16:33.728 It's reduced volatility. It's increased liquidity. 00:16:33.752 --> 00:16:36.941 Spreads are narrower because people are trading that kind of stuff. 00:16:36.965 --> 00:16:42.041 So I'm not too worried about Einstein going off and starting a hedge fund. NOTE Paragraph 00:16:42.478 --> 00:16:46.642 CA: You're at a phase in your life now where you're actually investing, though, 00:16:46.666 --> 00:16:50.400 at the other end of the supply chain -- 00:16:50.424 --> 00:16:54.528 you're actually boosting mathematics across America. 00:16:54.552 --> 00:16:56.417 This is your wife, Marilyn. 00:16:56.441 --> 00:17:01.197 You're working on philanthropic issues together. 00:17:01.221 --> 00:17:02.384 Tell me about that. NOTE Paragraph 00:17:02.408 --> 00:17:06.057 JS: Well, Marilyn started -- 00:17:06.081 --> 00:17:09.528 there she is up there, my beautiful wife -- 00:17:09.552 --> 00:17:12.524 she started the foundation about 20 years ago. 00:17:12.548 --> 00:17:13.699 I think '94. 00:17:13.723 --> 00:17:15.818 I claim it was '93, she says it was '94, 00:17:15.842 --> 00:17:18.413 but it was one of those two years. NOTE Paragraph 00:17:18.437 --> 00:17:20.572 (Laughter) NOTE Paragraph 00:17:20.596 --> 00:17:27.315 We started the foundation, just as a convenient way to give charity. 00:17:28.346 --> 00:17:30.853 She kept the books, and so on. 00:17:30.877 --> 00:17:37.591 We did not have a vision at that time, but gradually a vision emerged -- 00:17:37.615 --> 00:17:43.119 which was to focus on math and science, to focus on basic research. 00:17:43.569 --> 00:17:46.341 And that's what we've done. 00:17:46.365 --> 00:17:52.720 Six years ago or so, I left Renaissance and went to work at the foundation. 00:17:52.744 --> 00:17:54.315 So that's what we do. NOTE Paragraph 00:17:54.339 --> 00:17:57.248 CA: And so Math for America is basically investing 00:17:57.272 --> 00:17:59.910 in math teachers around the country, 00:17:59.934 --> 00:18:03.736 giving them some extra income, giving them support and coaching. 00:18:03.760 --> 00:18:06.811 And really trying to make that more effective 00:18:06.835 --> 00:18:09.436 and make that a calling to which teachers can aspire. NOTE Paragraph 00:18:09.460 --> 00:18:14.250 JS: Yeah -- instead of beating up the bad teachers, 00:18:14.274 --> 00:18:19.127 which has created morale problems all through the educational community, 00:18:19.151 --> 00:18:21.592 in particular in math and science, 00:18:21.616 --> 00:18:27.746 we focus on celebrating the good ones and giving them status. 00:18:27.770 --> 00:18:30.701 Yeah, we give them extra money, 15,000 dollars a year. 00:18:30.725 --> 00:18:35.192 We have 800 math and science teachers in New York City in public schools today, 00:18:35.216 --> 00:18:37.030 as part of a core. 00:18:37.054 --> 00:18:40.740 There's a great morale among them. 00:18:40.764 --> 00:18:43.270 They're staying in the field. 00:18:43.294 --> 00:18:46.189 Next year, it'll be 1,000 and that'll be 10 percent 00:18:46.213 --> 00:18:49.757 of the math and science teachers in New York [City] public schools. NOTE Paragraph 00:18:49.781 --> 00:18:55.686 (Applause) NOTE Paragraph 00:18:55.710 --> 00:18:59.120 CA: Jim, here's another project that you've supported philanthropically: 00:18:59.144 --> 00:19:01.541 Research into origins of life, I guess. 00:19:01.565 --> 00:19:03.012 What are we looking at here? 00:19:03.536 --> 00:19:05.418 JS: Well, I'll save that for a second. 00:19:05.442 --> 00:19:07.604 And then I'll tell you what you're looking at. 00:19:07.628 --> 00:19:10.684 Origins of life is a fascinating question. 00:19:10.708 --> 00:19:12.241 How did we get here? 00:19:13.170 --> 00:19:14.941 Well, there are two questions: 00:19:14.965 --> 00:19:20.833 One is, what is the route from geology to biology -- 00:19:20.857 --> 00:19:22.238 how did we get here? 00:19:22.262 --> 00:19:24.626 And the other question is, what did we start with? 00:19:24.650 --> 00:19:27.752 What material, if any, did we have to work with on this route? 00:19:27.776 --> 00:19:30.837 Those are two very, very interesting questions. 00:19:31.773 --> 00:19:37.607 The first question is a tortuous path from geology up to RNA 00:19:37.631 --> 00:19:39.889 or something like that -- how did that all work? 00:19:39.913 --> 00:19:42.301 And the other, what do we have to work with? 00:19:42.325 --> 00:19:44.096 Well, more than we think. 00:19:44.120 --> 00:19:48.963 So what's pictured there is a star in formation. 00:19:49.836 --> 00:19:53.261 Now, every year in our Milky Way, which has 100 billion stars, 00:19:53.285 --> 00:19:55.780 about two new stars are created. 00:19:55.804 --> 00:19:58.274 Don't ask me how, but they're created. 00:19:58.298 --> 00:20:01.378 And it takes them about a million years to settle out. 00:20:02.132 --> 00:20:04.308 So, in steady state, 00:20:04.332 --> 00:20:08.180 there are about two million stars in formation at any time. 00:20:08.204 --> 00:20:11.662 That one is somewhere along this settling-down period. 00:20:12.067 --> 00:20:15.003 And there's all this crap sort of circling around it, 00:20:15.027 --> 00:20:16.525 dust and stuff. 00:20:17.479 --> 00:20:20.502 And it'll form probably a solar system, or whatever it forms. 00:20:20.526 --> 00:20:22.702 But here's the thing -- 00:20:22.726 --> 00:20:29.074 in this dust that surrounds a forming star 00:20:29.098 --> 00:20:35.133 have been found, now, significant organic molecules. 00:20:35.958 --> 00:20:42.097 Molecules not just like methane, but formaldehyde and cyanide -- 00:20:42.121 --> 00:20:48.638 things that are the building blocks -- the seeds, if you will -- of life. 00:20:49.136 --> 00:20:51.828 So, that may be typical. 00:20:52.395 --> 00:20:59.329 And it may be typical that planets around the universe 00:20:59.353 --> 00:21:02.965 start off with some of these basic building blocks. 00:21:03.830 --> 00:21:06.545 Now does that mean there's going to be life all around? 00:21:06.569 --> 00:21:07.933 Maybe. 00:21:07.957 --> 00:21:12.084 But it's a question of how tortuous this path is 00:21:12.108 --> 00:21:16.502 from those frail beginnings, those seeds, all the way to life. 00:21:16.526 --> 00:21:21.718 And most of those seeds will fall on fallow planets. NOTE Paragraph 00:21:21.742 --> 00:21:23.151 CA: So for you, personally, 00:21:23.175 --> 00:21:25.897 finding an answer to this question of where we came from, 00:21:25.921 --> 00:21:29.579 of how did this thing happen, that is something you would love to see. NOTE Paragraph 00:21:29.603 --> 00:21:31.389 JS: Would love to see. 00:21:31.413 --> 00:21:32.903 And like to know -- 00:21:32.927 --> 00:21:38.097 if that path is tortuous enough, and so improbable, 00:21:38.121 --> 00:21:42.875 that no matter what you start with, we could be a singularity. 00:21:43.336 --> 00:21:44.488 But on the other hand, 00:21:44.512 --> 00:21:47.990 given all this organic dust that's floating around, 00:21:48.014 --> 00:21:51.805 we could have lots of friends out there. 00:21:52.947 --> 00:21:54.108 It'd be great to know. NOTE Paragraph 00:21:54.132 --> 00:21:57.612 CA: Jim, a couple of years ago, I got the chance to speak with Elon Musk, 00:21:57.636 --> 00:22:00.473 and I asked him the secret of his success, 00:22:00.497 --> 00:22:04.188 and he said taking physics seriously was it. 00:22:04.696 --> 00:22:08.699 Listening to you, what I hear you saying is taking math seriously, 00:22:08.723 --> 00:22:11.726 that has infused your whole life. 00:22:12.123 --> 00:22:16.686 It's made you an absolute fortune, and now it's allowing you to invest 00:22:16.710 --> 00:22:21.206 in the futures of thousands and thousands of kids across America and elsewhere. 00:22:21.567 --> 00:22:24.425 Could it be that science actually works? 00:22:24.449 --> 00:22:27.221 That math actually works? NOTE Paragraph 00:22:27.245 --> 00:22:31.617 JS: Well, math certainly works. Math certainly works. 00:22:31.641 --> 00:22:32.839 But this has been fun. 00:22:32.863 --> 00:22:37.809 Working with Marilyn and giving it away has been very enjoyable. NOTE Paragraph 00:22:37.833 --> 00:22:40.769 CA: I just find it -- it's an inspirational thought to me, 00:22:40.793 --> 00:22:44.800 that by taking knowledge seriously, so much more can come from it. 00:22:44.824 --> 00:22:47.842 So thank you for your amazing life, and for coming here to TED. NOTE Paragraph 00:22:47.866 --> 00:22:48.617 Thank you. NOTE Paragraph 00:22:48.651 --> 00:22:49.752 Jim Simons! NOTE Paragraph 00:22:49.806 --> 00:22:54.186 (Applause)