WEBVTT 00:00:00.420 --> 00:00:03.225 Chris Anderson: You were something of a mathematical phenom. 00:00:03.225 --> 00:00:06.453 You had already taught at Harvard and MIT at a young age. 00:00:06.453 --> 00:00:09.464 And then the NSA came calling. 00:00:09.464 --> 00:00:11.128 What was that about? 00:00:11.128 --> 00:00:15.296 Jim Simons: Well the NSA -- that's the National Security Agency -- 00:00:15.296 --> 00:00:17.575 they didn't exactly come calling. 00:00:17.575 --> 00:00:21.963 They had an operation at Princeton, where they hired mathematicians 00:00:21.963 --> 00:00:25.052 to attack secret codes and stuff like that. 00:00:25.052 --> 00:00:27.658 And I knew that existed. 00:00:27.658 --> 00:00:29.519 And they had a very good policy 00:00:29.519 --> 00:00:33.225 because you could do half your time at your own mathematics 00:00:33.225 --> 00:00:36.708 and at least half your time working on their stuff. 00:00:37.768 --> 00:00:39.057 And they paid a lot. 00:00:39.057 --> 00:00:41.857 So that was an irresistible pull. 00:00:41.857 --> 00:00:43.795 So, I went there. 00:00:44.025 --> 00:00:45.500 CA: So you were a code-cracker. 00:00:45.500 --> 00:00:46.306 JS: I was. 00:00:46.306 --> 00:00:47.550 CA: Until you got fired. 00:00:47.550 --> 00:00:49.033 JS: Well, I did get fired. Yes. 00:00:49.043 --> 00:00:50.778 CA: How come? 00:00:50.778 --> 00:00:53.161 JS: Well, how come? 00:00:53.611 --> 00:00:58.487 I got fired because, well the Vietnam War was on, 00:00:58.487 --> 00:01:03.943 and the boss of bosses in my organization was a big fan of the war 00:01:03.943 --> 00:01:08.842 and wrote a New York Times article, a magazine section cover story, 00:01:08.842 --> 00:01:10.699 about how we're going to win in Vietnam. 00:01:10.699 --> 00:01:13.810 And I didn't like that war, I thought it was stupid. 00:01:13.810 --> 00:01:16.273 And I wrote a letter to the Times, which they published, 00:01:16.273 --> 00:01:20.446 saying not everyone who works for Maxwell Taylor, 00:01:20.446 --> 00:01:25.006 if anyone remembers that name, agrees with his views. 00:01:25.006 --> 00:01:27.210 And I gave my own views. 00:01:27.210 --> 00:01:29.206 CA: Oh, OK. I can see that would -- 00:01:29.206 --> 00:01:31.574 JS: Which were different from General Taylor's. 00:01:31.574 --> 00:01:34.035 But in the end nobody said anything. 00:01:34.035 --> 00:01:37.657 But then, I was 29 years old at this time and some kid came around 00:01:37.657 --> 00:01:40.769 and said he was a stringer from Newsweek magazine 00:01:40.769 --> 00:01:46.318 and he wanted to interview me and ask what I was doing about my views. 00:01:46.318 --> 00:01:49.731 And I told him, I said, "I'm doing mostly mathematics now, 00:01:49.731 --> 00:01:54.004 and when the war is over then I'll do mostly their stuff." 00:01:54.004 --> 00:01:56.859 Then I did the only intelligent thing I'd done that day -- 00:01:56.859 --> 00:02:01.247 I told my local boss that I gave that interview. 00:02:01.247 --> 00:02:02.686 And he said, "What'd you say?" 00:02:02.686 --> 00:02:04.126 And I told him what I said. 00:02:04.126 --> 00:02:06.465 And then he said, "I've got to call Taylor." 00:02:06.465 --> 00:02:08.725 He called Taylor; that took 10 minutes. 00:02:08.725 --> 00:02:11.655 I was fired five minutes after that. 00:02:11.655 --> 00:02:12.230 CA: OK. 00:02:12.750 --> 00:02:13.709 JS: But it wasn't bad. 00:02:13.709 --> 00:02:16.369 CA: It wasn't bad, because you went on to Stony Brook 00:02:16.369 --> 00:02:19.684 and stepped up your mathematical career. 00:02:19.684 --> 00:02:23.301 You started working with this man here. Who is this? 00:02:24.021 --> 00:02:25.757 JS: Oh, [Shiing-Shen] Chern. 00:02:25.757 --> 00:02:28.916 Chern was one of the great mathematicians of the century. 00:02:28.916 --> 00:02:34.173 I had known him when I was a graduate student at Berkeley. 00:02:34.173 --> 00:02:36.007 And I had some ideas, 00:02:36.007 --> 00:02:38.120 and I brought them to him and he liked them. 00:02:38.120 --> 00:02:44.738 Together, we did this work which you can easily see up there. 00:02:44.738 --> 00:02:46.490 There it is. 00:02:47.480 --> 00:02:50.681 CA: It led to you publishing a famous paper together. 00:02:50.681 --> 00:02:54.484 Can you explain at all what that work was? 00:02:55.284 --> 00:02:56.289 JS: No. 00:02:56.439 --> 00:02:58.484 (Laughter) 00:02:58.774 --> 00:03:00.654 JS: I mean, I could explain it to somebody. 00:03:00.654 --> 00:03:02.473 (Laughter) 00:03:03.153 --> 00:03:04.868 CA: How about explaining this? 00:03:05.257 --> 00:03:07.770 JS: But not many. Not many people. 00:03:08.950 --> 00:03:11.440 CA: I think you told me it had something to do with spheres, 00:03:11.440 --> 00:03:13.460 so let's start here. 00:03:13.460 --> 00:03:17.064 JS: Well, it did, but I'll say about that work -- 00:03:17.064 --> 00:03:20.880 it did have something to do with that, but before we get to that -- 00:03:20.880 --> 00:03:24.280 that work was good mathematics. 00:03:24.280 --> 00:03:27.520 I was very happy with it; so was Chern. 00:03:27.910 --> 00:03:32.638 It even started a little subfield that's now flourishing. 00:03:32.638 --> 00:03:37.956 But, more interestingly, it happened to apply to physics, 00:03:37.956 --> 00:03:42.275 something we knew nothing about -- at least I knew nothing about physics, 00:03:42.275 --> 00:03:44.179 and I don't think Chern knew a heck of a lot. 00:03:44.419 --> 00:03:47.963 And about 10 years after the paper came out, 00:03:47.963 --> 00:03:53.072 a guy named Ed Witten in Princeton started applying it to string theory 00:03:53.072 --> 00:03:57.948 and people in Russia started applying it to what's called "condensed matter." 00:03:57.948 --> 00:04:02.681 Today, those things in there called Chern-Simons invariants 00:04:02.681 --> 00:04:04.473 have spread through a lot of physics. 00:04:04.473 --> 00:04:05.724 And it was amazing. 00:04:05.724 --> 00:04:07.435 We didn't know any physics. 00:04:07.435 --> 00:04:10.556 It never occurred to me that it would be applied to physics. 00:04:10.556 --> 00:04:14.271 But that's the thing about mathematics -- you never know where it's going to go. 00:04:14.271 --> 00:04:15.920 CA: This is so incredible. 00:04:15.920 --> 00:04:20.308 So, we've been talking about how evolution shapes human minds 00:04:20.308 --> 00:04:22.630 that may or may not perceive the truth. 00:04:22.630 --> 00:04:25.804 Somehow, you come up with a mathematical theory, 00:04:25.804 --> 00:04:28.049 not knowing any physics, 00:04:28.049 --> 00:04:30.571 discover two decades later that it's being applied 00:04:30.571 --> 00:04:33.696 to profoundly describe the actual physical world. 00:04:33.799 --> 00:04:34.803 How can that happen? 00:04:34.803 --> 00:04:35.997 JS: God knows. 00:04:36.097 --> 00:04:38.094 (Laughter) 00:04:38.754 --> 00:04:41.926 But there's a famous physicist named [Eugene] Wigner, 00:04:41.926 --> 00:04:47.390 and he wrote an essay on the unreasonable effectiveness of mathematics. 00:04:47.560 --> 00:04:49.356 Somehow, this mathematics, 00:04:49.356 --> 00:04:52.420 which is rooted in the real world in some sense -- 00:04:52.420 --> 00:04:56.363 we learn to count, measure, everyone would do that -- 00:04:56.363 --> 00:04:58.713 and then it flourishes on its own. 00:04:58.713 --> 00:05:01.987 But so often it comes back to save the day. 00:05:01.987 --> 00:05:04.495 General relativity is an example. 00:05:04.495 --> 00:05:07.699 [Hermann] Minkowski had this geometry, and Einstein realized, 00:05:07.699 --> 00:05:11.507 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. 00:05:14.899 --> 00:05:16.089 It is a mystery. 00:05:16.297 --> 00:05:19.965 CA: So, here's a mathematical piece of ingenuity. 00:05:19.965 --> 00:05:20.983 Tell us about this. 00:05:20.983 --> 00:05:26.931 JS: Well, that's a ball -- it's a sphere, and it has a lattice around it -- 00:05:26.931 --> 00:05:29.562 you know, those squares. 00:05:30.582 --> 00:05:35.336 What I'm going to show here was originally observed by [Leonhard] Euler, 00:05:35.336 --> 00:05:38.306 the great mathematician, in the 1700's. 00:05:38.346 --> 00:05:43.045 And it gradually grew to be a very important field in mathematics: 00:05:43.045 --> 00:05:46.879 algebraic topology, geometry. 00:05:47.109 --> 00:05:51.427 That paper up there had its roots in this. 00:05:51.427 --> 00:05:53.285 So, here's this thing: 00:05:53.285 --> 00:05:58.114 it has eight vertices, 12 edges, six faces. 00:05:58.114 --> 00:06:01.441 And if you look at the difference -- vertices minus edges plus faces -- 00:06:01.441 --> 00:06:02.946 you get two. 00:06:02.946 --> 00:06:05.034 OK, well, two? That's a good number. 00:06:05.034 --> 00:06:09.306 Here's a different way of doing it -- these are triangles covering -- 00:06:09.306 --> 00:06:18.361 this has 12 vertices and 30 edges and 20 faces, 20 tiles. 00:06:18.361 --> 00:06:23.191 And vertices minus edges plus faces still equals two. 00:06:23.191 --> 00:06:26.279 And in fact you could do this any which way, 00:06:26.279 --> 00:06:29.484 cover this thing with all kinds of polygons and triangles 00:06:29.484 --> 00:06:31.039 and mix them up. 00:06:31.039 --> 00:06:34.336 And you take vertices minus edges plus faces -- you'll get two. 00:06:34.336 --> 00:06:36.377 Here's a different shape. 00:06:36.377 --> 00:06:39.527 This is a torus, the surface of a donut, 00:06:39.527 --> 00:06:43.315 16 vertices covered by these rectangles, 00:06:43.315 --> 00:06:46.385 32 edges, 16 faces. 00:06:46.385 --> 00:06:49.475 Vertices minus edges comes out 0. 00:06:49.475 --> 00:06:51.003 It'll always come out 0. 00:06:51.003 --> 00:06:55.071 Every time you cover a torus with squares or triangles 00:06:55.071 --> 00:06:59.881 or anything like that, you're going to get 0. 00:07:00.091 --> 00:07:03.151 So, this is called the Euler characteristic. 00:07:03.151 --> 00:07:06.726 And it's what's called a topological invariant. 00:07:06.726 --> 00:07:07.935 It's pretty amazing. 00:07:07.935 --> 00:07:10.466 No matter how you do it, you're always get the same answer. 00:07:10.466 --> 00:07:16.399 So that was the first sort of thrust, from the mid-1700s, 00:07:16.399 --> 00:07:20.960 into a subject which is now called algebraic topology. 00:07:20.960 --> 00:07:23.422 CA: And your own work took an idea like this 00:07:23.422 --> 00:07:26.440 and moved it into higher-dimensional theory, 00:07:26.440 --> 00:07:29.552 higher-dimensional objects, and found new invariants? 00:07:29.552 --> 00:07:34.219 JS: Yes. Well, there were already higher-dimensional invariants: 00:07:34.219 --> 00:07:38.700 Pontryagin classes -- actually, there were Chern classes. 00:07:38.700 --> 00:07:41.556 There were a bunch of these types of invariants. 00:07:41.556 --> 00:07:45.658 I was struggling to work on one of them 00:07:45.658 --> 00:07:50.658 and model it sort of combinatorially 00:07:50.658 --> 00:07:53.491 instead of the way it was typically done, 00:07:53.491 --> 00:07:58.251 and that led to this work and we uncovered some new things. 00:07:58.251 --> 00:08:05.118 But if it wasn't for Mr. Euler -- who wrote almost 70 volumes of mathematics 00:08:05.118 --> 00:08:07.446 and had 13 children 00:08:07.446 --> 00:08:13.252 who he apparently would dandle on his knee while he was writing -- 00:08:13.252 --> 00:08:19.612 if it wasn't for Mr. Euler, there wouldn't perhaps be these invariants. 00:08:20.366 --> 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 --> 00:08:26.541 Let's talk about Renaissance. 00:08:26.541 --> 00:08:31.394 Because you took that amazing mind and having been a code-cracker at the NSA, 00:08:31.394 --> 00:08:35.504 you started to become a code-cracker in the financial industry. 00:08:35.504 --> 00:08:38.596 I think you probably didn't buy efficient market theory. 00:08:38.596 --> 00:08:44.629 Somehow you found a way of creating astonishing returns over two decades. 00:08:44.629 --> 00:08:46.324 The way it's been explained to me, 00:08:46.324 --> 00:08:49.754 what's remarkable about what you did wasn't just the size of the returns, 00:08:49.754 --> 00:08:53.754 it's that you took them with surprisingly low volatility and risk 00:08:53.754 --> 00:08:55.524 compared with other hedge funds. 00:08:55.602 --> 00:08:57.766 So how on earth did you do this, Jim? 00:08:57.806 --> 00:09:02.035 JS: I did it by assembling a wonderful group of people. 00:09:02.035 --> 00:09:05.773 When I started doing trading, I had gotten a little tired of mathematics. 00:09:05.773 --> 00:09:09.279 I was in my late 30s. I had a little money. 00:09:09.279 --> 00:09:13.180 I started trading and it went very well. 00:09:13.180 --> 00:09:16.012 I made quite a lot of money with pure luck. 00:09:16.012 --> 00:09:17.591 I mean, I think it was pure luck. 00:09:17.591 --> 00:09:19.658 It certainly wasn't mathematical modeling. 00:09:19.658 --> 00:09:23.513 But in looking at the data, after a while I realized: 00:09:23.513 --> 00:09:26.090 it looks like there's some structure here. 00:09:26.090 --> 00:09:29.666 And I hired a few mathematicians, and we started making some models -- 00:09:29.666 --> 00:09:34.100 just the kind of thing we did back at IDA [Institute for Defense Analyses]. 00:09:34.100 --> 00:09:36.957 You design an algorithm, you test it out on a computer. 00:09:36.957 --> 00:09:39.418 Does it work? Doesn't it work? And so on. 00:09:39.418 --> 00:09:40.946 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. 00:09:45.617 --> 00:09:50.580 I look at that, and I say, That's just a random, up-and-down walk -- 00:09:50.580 --> 00:09:53.343 maybe a slight upward trend over that whole period of time. 00:09:53.343 --> 00:09:54.832 How on earth could you trade, 00:09:54.832 --> 00:09:57.543 looking at that and see something that wasn't just random? 00:09:57.543 --> 00:10:01.196 JS: In the old days -- this is kind of a graph from the old days, 00:10:01.196 --> 00:10:05.679 commodities or currencies had a tendency to trend. 00:10:05.679 --> 00:10:11.343 Not necessarily the very light trend you see here, but trending in periods. 00:10:11.343 --> 00:10:16.893 And if you decided, OK, I'm going to predict today, by the average move 00:10:16.893 --> 00:10:20.655 in the past 20 days -- there's 20 days -- 00:10:20.655 --> 00:10:23.418 maybe that would be a good prediction, and I'd make some money. 00:10:23.418 --> 00:10:29.146 And in fact, years ago such a system would work -- 00:10:29.156 --> 00:10:31.596 not beautifully, but it would work. 00:10:31.919 --> 00:10:33.400 You'd make money, you'd lose money, 00:10:33.400 --> 00:10:34.449 you'd make money. 00:10:34.449 --> 00:10:37.013 But this is a year's worth of days, 00:10:37.013 --> 00:10:41.182 and you'd make a little money during that period. 00:10:41.182 --> 00:10:44.155 It's a very vestigial system. 00:10:44.525 --> 00:10:48.078 CA: So you would test a bunch of lengths of trends in time 00:10:48.078 --> 00:10:50.795 and see whether, for example, 00:10:50.795 --> 00:10:54.674 a 10-day trend or a 15-day trend was predictive of what happens next. 00:10:54.674 --> 00:11:00.805 JS: Sure, you would try all those things and see what worked best. 00:11:01.515 --> 00:11:04.889 Trend-following would've been great in the '60s, 00:11:04.889 --> 00:11:09.512 and it was sort of OK in the '70s. By the '80s, it wasn't. 00:11:09.512 --> 00:11:11.783 CA: Because everyone could see that. 00:11:11.783 --> 00:11:15.046 So, how did you stay ahead of the pack? 00:11:15.046 --> 00:11:21.202 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. 00:11:24.423 --> 00:11:28.317 The real thing was to gather a tremendous amount of data, 00:11:28.317 --> 00:11:32.080 and we had to get it by hand in the early days. 00:11:32.080 --> 00:11:35.644 We went down to the Federal Reserve and copied interest rate histories 00:11:35.644 --> 00:11:36.653 and stuff like that. 00:11:36.653 --> 00:11:38.581 Because it didn't exist on computers. 00:11:38.581 --> 00:11:40.810 We got a lot of data. 00:11:40.810 --> 00:11:44.686 And very smart people -- that was the key. 00:11:44.986 --> 00:11:49.239 I didn't really know how to hire people to do fundamental trading. 00:11:49.399 --> 00:11:52.722 I had hired a few -- some made money, some didn't make money. 00:11:52.722 --> 00:11:54.626 I couldn't make a business out of that. 00:11:54.626 --> 00:11:56.692 But I did know how to hire scientists, 00:11:56.692 --> 00:12:00.105 because I have some taste in that department. 00:12:00.105 --> 00:12:02.148 So, that's what we did. 00:12:02.148 --> 00:12:05.167 And gradually these models got better and better, 00:12:05.167 --> 00:12:06.932 and better and better. 00:12:06.932 --> 00:12:10.122 CA: You're credited with doing something remarkable at Renaissance, 00:12:10.122 --> 00:12:12.608 which is building this culture, this group of people, 00:12:12.608 --> 00:12:15.731 who weren't just hired guns who could be lured away by money. 00:12:15.731 --> 00:12:19.819 Their motivation was doing exciting mathematics and science. 00:12:19.819 --> 00:12:22.442 JS: Well I'd hoped that might be true. 00:12:22.442 --> 00:12:25.496 But some of it was money. 00:12:25.496 --> 00:12:27.112 CA: They made a lot of money. 00:12:27.112 --> 00:12:29.681 JS: I can't say that no one came because of the money. 00:12:29.681 --> 00:12:31.966 I think a lot of them came because of the money. 00:12:31.966 --> 00:12:33.970 But they also came because it would be fun. 00:12:33.970 --> 00:12:36.699 CA: What role did machine learning play in all of this? 00:12:36.699 --> 00:12:39.763 JS: In a certain sense, what we did was machine learning. 00:12:39.763 --> 00:12:47.194 You look at a lot of data, and you try to simulate different predictive schemes 00:12:47.194 --> 00:12:49.400 until you get better and better at it. 00:12:49.400 --> 00:12:53.394 It doesn't necessarily feed back on itself the way we did things. 00:12:53.394 --> 00:12:56.318 But it worked. 00:12:56.318 --> 00:13:00.233 CA: So these different predictive schemes can be really quite wild and unexpected. 00:13:00.233 --> 00:13:02.171 I mean, you look at everything, right? 00:13:02.171 --> 00:13:05.513 You look at the weather, length of dresses, political opinion. 00:13:05.513 --> 00:13:07.417 JS: Yes, length of dresses we didn't try. 00:13:07.417 --> 00:13:08.881 (Laughter) 00:13:08.881 --> 00:13:10.112 CA: What sort of things? 00:13:10.112 --> 00:13:12.032 JS: Well, everything. 00:13:12.032 --> 00:13:16.547 Everything is grist for the mill -- except hem lengths. 00:13:16.997 --> 00:13:19.176 Weather, annual reports, 00:13:19.176 --> 00:13:24.008 quarterly reports, historic data itself, volumes, you name it. 00:13:24.008 --> 00:13:25.267 Whatever there is. 00:13:25.267 --> 00:13:27.752 We take in terabytes of data a day. 00:13:27.752 --> 00:13:33.186 And store it away, massage it, get it ready for analysis. 00:13:33.306 --> 00:13:34.963 You're looking for anomalies. 00:13:34.963 --> 00:13:36.615 You're looking for, like you said, 00:13:36.615 --> 00:13:40.305 the efficient market hypothesis is not correct. 00:13:40.305 --> 00:13:43.483 CA: But any one anomaly might be just a random thing, 00:13:43.483 --> 00:13:47.478 so is the secret here to just look at multiple strange anomalies 00:13:47.478 --> 00:13:49.607 and see when they align? 00:13:49.607 --> 00:13:52.633 JS: Any one anomaly might be a random thing. 00:13:52.633 --> 00:13:56.328 However, if you have enough data you can tell that it's not. 00:13:56.328 --> 00:14:00.156 You can see an anomaly that's persistent for a sufficiently long time -- 00:14:00.156 --> 00:14:05.218 the probability of it being random is not high. 00:14:05.218 --> 00:14:08.654 But these things fade after a while. 00:14:08.654 --> 00:14:13.041 Anomalies can get washed out; you have to keep on top of the business. 00:14:13.041 --> 00:14:15.514 CA: A lot of people look at the hedge fund industry now 00:14:15.514 --> 00:14:19.107 and are sort of -- 00:14:19.107 --> 00:14:20.177 shocked by it, 00:14:20.177 --> 00:14:22.236 by how much wealth is created there 00:14:22.236 --> 00:14:24.396 and how much talent is going into it. 00:14:24.396 --> 00:14:29.900 Do you have any worries about that industry 00:14:29.900 --> 00:14:31.966 and perhaps the financial industry in general? 00:14:31.966 --> 00:14:34.584 Kind of being on a runaway train that's -- 00:14:34.584 --> 00:14:38.884 I don't know, helping increase inequality? 00:14:38.884 --> 00:14:43.788 How would you champion what's happening in the hedge fund industry? 00:14:43.788 --> 00:14:45.849 JS: I think in the last three of four years, 00:14:45.849 --> 00:14:47.847 hedge funds have not done especially well. 00:14:47.847 --> 00:14:48.705 We've done dandy, 00:14:48.705 --> 00:14:53.119 but the hedge fund industry as a whole has not done so wonderfully. 00:14:53.119 --> 00:14:58.562 The stock market has been on a roll, going up as everybody knows, 00:14:58.562 --> 00:15:01.339 and price-earnings ratios have grown. 00:15:01.339 --> 00:15:04.570 So an awful lot of the wealth that's been created in the last, 00:15:04.570 --> 00:15:07.300 let's say, five or six years has not been created by hedge funds. 00:15:08.430 --> 00:15:11.830 People would ask me, "What's a hedge fund?" 00:15:11.830 --> 00:15:14.361 And I'd say, "One and 20." 00:15:14.361 --> 00:15:17.124 Which means -- now it's two and 20 -- 00:15:17.124 --> 00:15:21.165 it's two percent fixed fee and 20 percent of profits. 00:15:21.165 --> 00:15:23.468 Hedge funds are all different kinds of creatures. 00:15:23.468 --> 00:15:26.229 CA: Rumor has it you charge slightly higher fees than that. 00:15:26.229 --> 00:15:27.925 (Laughter) 00:15:27.925 --> 00:15:30.169 JS: We charged the highest fees in the world at one time. 00:15:30.169 --> 00:15:33.835 Five and 44, that's what we charge. 00:15:33.835 --> 00:15:35.091 CA: Five and 44. 00:15:35.091 --> 00:15:38.641 So 5 percent flat, 44 percent of upside. 00:15:38.641 --> 00:15:41.400 You still made your investors spectacular amounts of money. 00:15:41.400 --> 00:15:42.826 JS: We made good returns, yes. 00:15:42.826 --> 00:15:43.826 People got very mad at my investors. 00:15:43.826 --> 00:15:45.681 "How could you charge such high fees?" 00:15:45.681 --> 00:15:47.516 I said, "OK, you can withdraw." 00:15:47.516 --> 00:15:50.174 "But how can I get more?" 00:15:50.174 --> 00:15:51.967 (Laughter) 00:15:51.967 --> 00:15:53.692 But at a certain point, as I told you, 00:15:53.692 --> 00:15:59.351 we bought out all the investors because there's a capacity to the fund. 00:15:59.365 --> 00:16:02.315 CA: But should we worry about the hedge fund industry 00:16:02.315 --> 00:16:05.978 attracting too much of the world's great mathematical 00:16:05.978 --> 00:16:08.439 and other talent to work on that 00:16:08.439 --> 00:16:10.817 as opposed to the many other problems in the world? 00:16:10.817 --> 00:16:12.540 JS: Well it's not just mathematical. 00:16:12.540 --> 00:16:15.426 We hire astronomers and physicists and things like that. 00:16:15.676 --> 00:16:21.681 I don't think we should worry too much. It's still a pretty small industry. 00:16:21.681 --> 00:16:27.742 And in fact, bringing science into the investing world 00:16:27.742 --> 00:16:29.878 has improved that world. 00:16:29.878 --> 00:16:33.826 It's reduced volatility. It's increased liquidity. 00:16:33.826 --> 00:16:36.937 Spreads are narrower because people are trading that kind of stuff. 00:16:36.937 --> 00:16:42.394 So I'm not too worried about Einstein going off and starting a hedge fund. 00:16:42.614 --> 00:16:46.666 CA: You're at a phase in your life now where you're actually investing, though, 00:16:46.666 --> 00:16:50.182 at the other end of the supply chain -- 00:16:50.182 --> 00:16:54.072 in boosting mathematics across America. 00:16:54.072 --> 00:16:56.441 This is your wife, Marilyn. 00:16:56.441 --> 00:17:00.743 You're working on philanthropic issues together. 00:17:00.743 --> 00:17:02.408 Tell me about that. 00:17:02.408 --> 00:17:06.081 JS: Well, Marilyn started -- 00:17:06.081 --> 00:17:09.683 there she is up there, my beautiful wife -- 00:17:09.683 --> 00:17:12.678 she started the foundation about 20 years ago. 00:17:12.678 --> 00:17:13.955 I think '94. 00:17:13.955 --> 00:17:16.067 I claim it was '93, she says it was '94, 00:17:16.067 --> 00:17:18.437 but it was one of those two years. 00:17:18.437 --> 00:17:20.596 (Laughter) 00:17:20.596 --> 00:17:23.178 We started the foundation 00:17:23.178 --> 00:17:27.958 just as a convenient way to give charity. 00:17:27.958 --> 00:17:30.877 She kept the books, and so on. 00:17:30.877 --> 00:17:37.244 We did not have a vision at that time, but gradually a vision emerged -- 00:17:37.244 --> 00:17:43.119 which was to focus on math and science, to focus on basic research. 00:17:43.119 --> 00:17:46.136 And that's what we've done. 00:17:46.136 --> 00:17:52.941 Six years ago or so, I left Renaissance and went to work at the foundation. 00:17:52.941 --> 00:17:54.473 So that's what we do. 00:17:54.473 --> 00:17:56.260 CA: And so Math for America here 00:17:56.260 --> 00:17:59.442 is basically investing in math teachers around the country, 00:17:59.442 --> 00:18:03.760 giving them some extra income, giving them support and coaching. 00:18:03.760 --> 00:18:06.895 And really trying to make that more effective 00:18:06.895 --> 00:18:09.340 and make that a calling to which teachers can aspire. 00:18:09.340 --> 00:18:11.242 JS: Yeah, yeah. 00:18:11.242 --> 00:18:14.442 Instead of beating up the bad teachers -- 00:18:14.442 --> 00:18:18.691 which has created morale problems all through the educational community, 00:18:18.691 --> 00:18:21.616 in particular in math and science -- 00:18:21.616 --> 00:18:27.770 we focus on celebrating the good ones and giving them status. 00:18:27.770 --> 00:18:31.345 Yeah, we give them extra money, 15,000 dollars a year. 00:18:31.345 --> 00:18:35.478 We have 800 math and science teachers in New York City in public schools today 00:18:35.478 --> 00:18:37.266 as part of a core. 00:18:37.266 --> 00:18:43.259 There's a great morale among them. They're staying in the field. 00:18:43.259 --> 00:18:46.213 Next year, it'll be 1,000 [teachers], and that'll be 10 percent 00:18:46.213 --> 00:18:49.781 of the math and science teachers in New York public schools. 00:18:49.781 --> 00:18:54.217 (Applause) 00:18:55.227 --> 00:18:58.674 CA: Jim, here's another project that you've supported philanthropically: 00:18:58.674 --> 00:19:02.781 Research into origins of life, I guess. What are we looking at here? 00:19:03.401 --> 00:19:05.779 Well, I'll save that for a second. 00:19:05.779 --> 00:19:07.474 And then I'll tell you what you're looking at. 00:19:07.474 --> 00:19:13.486 Origins of life is a fascinating question. How did we get here? 00:19:13.486 --> 00:19:15.122 Well, there's two questions. 00:19:15.122 --> 00:19:20.905 One is, What is the root from geology to biology? 00:19:20.905 --> 00:19:22.694 How did we get here? 00:19:22.694 --> 00:19:25.024 And the other question is, What did we start with? 00:19:25.024 --> 00:19:27.955 What material, if any, did we have to work with on this route? 00:19:27.955 --> 00:19:31.583 Those are two very, very interesting questions. 00:19:31.583 --> 00:19:37.726 The first question is a tortuous path from geology up to RNA 00:19:37.726 --> 00:19:39.966 or something like that -- how did that all work? 00:19:39.966 --> 00:19:42.325 And the other, What do we have to work with? 00:19:42.325 --> 00:19:44.120 Well, more than we think. 00:19:44.120 --> 00:19:49.778 So what's pictured there is a star in formation. 00:19:49.778 --> 00:19:53.285 Now every year in our Milky Way, which has 100 billion stars, 00:19:53.285 --> 00:19:56.141 about two near stars are created. 00:19:56.141 --> 00:19:58.254 Don't ask me how, but they're created. 00:19:58.254 --> 00:20:02.132 And it takes them about a million years to settle out. 00:20:02.132 --> 00:20:04.522 So, in steady state, 00:20:04.522 --> 00:20:08.423 there's about 2 million stars in formation at any time. 00:20:08.423 --> 00:20:11.837 That one is somewhere along this settling down period. 00:20:11.837 --> 00:20:15.390 And there's all this crap sort of circling around it. 00:20:15.390 --> 00:20:17.479 Dust and stuff. 00:20:17.479 --> 00:20:20.916 And it'll form probably a solar system, or whatever it forms. 00:20:20.916 --> 00:20:23.834 But here's the thing -- 00:20:23.834 --> 00:20:28.924 in this dust that surrounds a forming star 00:20:28.924 --> 00:20:35.591 have been found, now, significant organic molecules. 00:20:35.591 --> 00:20:42.121 Molecules not just like methane but formaldehyde and cyanide -- 00:20:42.121 --> 00:20:48.816 things that are the building blocks, the seeds, if you will, of life. 00:20:49.136 --> 00:20:52.215 So, that may be typical. 00:20:52.215 --> 00:20:58.974 And it may be typical that planets around the universe 00:20:58.974 --> 00:21:03.639 start off with some of these basic building blocks. 00:21:03.639 --> 00:21:06.542 Now, does that mean there's going to be life all around? 00:21:06.542 --> 00:21:07.957 Maybe. 00:21:07.957 --> 00:21:12.276 But it's a question of how tortuous this path is 00:21:12.276 --> 00:21:16.734 from those frail beginnings, those seeds, all the way to life. 00:21:16.734 --> 00:21:21.931 And most of those seeds will fall on fallow planets. 00:21:21.931 --> 00:21:23.423 CA: So for you, personally, 00:21:23.423 --> 00:21:26.090 finding an answer to this question of where we came from, 00:21:26.090 --> 00:21:29.783 of how did this thing happen, that is something you would love to see. 00:21:29.783 --> 00:21:32.199 JS: Would love to see. And like to know. 00:21:32.199 --> 00:21:38.444 If that path is tortuous enough, and so improbable 00:21:38.444 --> 00:21:43.251 that no matter what you start with, we could be a singularity. 00:21:43.251 --> 00:21:44.691 But, on the other hand, 00:21:44.691 --> 00:21:47.710 given all this organic dust that's floating around, 00:21:47.710 --> 00:21:52.237 we could have lots of friends out there. 00:21:52.237 --> 00:21:54.304 It'd be great to know. 00:21:54.304 --> 00:21:56.440 CA: Jim, a couple of years ago, 00:21:56.440 --> 00:22:00.712 I got the chance to speak with Elon Musk, and I asked him the secret of his success. 00:22:00.712 --> 00:22:04.798 He said taking physics seriously was it. 00:22:04.798 --> 00:22:08.723 Listening to you, what I hear you saying is taking math seriously, 00:22:08.723 --> 00:22:11.903 that has infused your whole life. 00:22:11.903 --> 00:22:16.710 It's made you an absolute fortune, and now it's allowing you to invest 00:22:16.710 --> 00:22:21.098 in the futures of thousands and thousands of kids across America and elsewhere. 00:22:21.098 --> 00:22:26.648 Could it be that science actually works? That math actually works? 00:22:26.648 --> 00:22:30.920 JS: Well, math certainly works. Math certainly works. 00:22:30.920 --> 00:22:33.033 But this has been fun. 00:22:33.033 --> 00:22:37.956 Working with Marilyn and giving it away has been very enjoyable. 00:22:37.956 --> 00:22:41.230 CA: I just find it -- it's an inspirational thought to me, 00:22:41.230 --> 00:22:44.968 that by taking knowledge seriously, so much more can come from it. 00:22:44.968 --> 00:22:47.615 So thank you for your amazing life and for coming here to TED. 00:22:47.615 --> 00:22:48.614 Truly. Thank you. 00:22:48.614 --> 00:22:49.960 Jim Simons. 00:22:49.960 --> 00:22:54.186 (Applause)