9:59:59.000,9:59:59.000 Chris Anderson: You were something of[br]a mathematical phenom. 9:59:59.000,9:59:59.000 You had already taught[br]at Harvard and MIT at a young age. 9:59:59.000,9:59:59.000 And then the NSA came calling. 9:59:59.000,9:59:59.000 What was that about? 9:59:59.000,9:59:59.000 Jim Simons: Well the NSA --[br]that's the National Security Agency -- 9:59:59.000,9:59:59.000 they didn't exactly come calling. 9:59:59.000,9:59:59.000 They had an operation at Princeton[br]where they hired mathematicians 9:59:59.000,9:59:59.000 to attack secret codes[br]and stuff like that. 9:59:59.000,9:59:59.000 And I knew that existed.[br]And they had a very good policy 9:59:59.000,9:59:59.000 And they had a very good policy[br]because you could do half your time 9:59:59.000,9:59:59.000 at your own mathematics 9:59:59.000,9:59:59.000 and at least half your time[br]working on their stuff. 9:59:59.000,9:59:59.000 And they paid a lot.[br]So that was an irresistible pull. 9:59:59.000,9:59:59.000 So, I went there. 9:59:59.000,9:59:59.000 CA: So you were a code-cracker. 9:59:59.000,9:59:59.000 JS: I was. 9:59:59.000,9:59:59.000 CA: Until you got fired. 9:59:59.000,9:59:59.000 JS: Well, I did get fired. Yes. 9:59:59.000,9:59:59.000 CA: How come? 9:59:59.000,9:59:59.000 JS: Well, how come? 9:59:59.000,9:59:59.000 I got fired because,[br]well the Vietnam War was on, 9:59:59.000,9:59:59.000 and the boss of bosses in my organization[br]was a big fan of the war 9:59:59.000,9:59:59.000 and wrote a New York Times article,[br]a magazine section cover story, 9:59:59.000,9:59:59.000 about how we're going[br]to win in Vietnam and so on. 9:59:59.000,9:59:59.000 And I didn't like that war,[br]I thought it was stupid 9:59:59.000,9:59:59.000 and I wrote a letter to the Times,[br]which they published, saying 9:59:59.000,9:59:59.000 not everyone who works for Maxwell Taylor,[br]if anyone remembers that name, 9:59:59.000,9:59:59.000 agrees with his views. 9:59:59.000,9:59:59.000 And I gave my own views. 9:59:59.000,9:59:59.000 CA: Oh, OK. I can see that would -- 9:59:59.000,9:59:59.000 JS: Which were different from General Taylor's. 9:59:59.000,9:59:59.000 But in the end nobody said anything. 9:59:59.000,9:59:59.000 But then, I was 29 years old at this time[br]and some kid came around 9:59:59.000,9:59:59.000 and said he was a stringer [br]from Newsweek magazine 9:59:59.000,9:59:59.000 and he wanted to interview me[br]and ask what I was doing about my views. 9:59:59.000,9:59:59.000 And I told him, I said, [br]"I'm doing mostly mathematics now, 9:59:59.000,9:59:59.000 and when the war is over[br]then I'll do mostly their stuff." 9:59:59.000,9:59:59.000 Then I did the only [br]intelligent thing I'd done that day -- 9:59:59.000,9:59:59.000 I told my local boss[br]that I gave that interview. 9:59:59.000,9:59:59.000 And he said, "What'd you say?" 9:59:59.000,9:59:59.000 And I told him what I said. 9:59:59.000,9:59:59.000 And then he said, "I've got to call Taylor." 9:59:59.000,9:59:59.000 He calls Taylor; that took 10 minutes. 9:59:59.000,9:59:59.000 I was fired five minutes after that. 9:59:59.000,9:59:59.000 But it wasn't bad. 9:59:59.000,9:59:59.000 CA: It wasn't bad, because[br]you went on to Stony Brook 9:59:59.000,9:59:59.000 and stepped up your mathematical career. 9:59:59.000,9:59:59.000 You started working[br]with this man here. Who is this? 9:59:59.000,9:59:59.000 JS: Oh, [Shiing-Shen] Chern. 9:59:59.000,9:59:59.000 Chern was one of the great [br]mathematicians of the century. 9:59:59.000,9:59:59.000 I had known him when[br]I was a graduate student at Berkeley. 9:59:59.000,9:59:59.000 And I had some ideas, 9:59:59.000,9:59:59.000 and I brought them to him[br]and he liked them. 9:59:59.000,9:59:59.000 Together, we did this work [br]which you can easily see up there. 9:59:59.000,9:59:59.000 There it is. 9:59:59.000,9:59:59.000 CA: It led to you publishing[br]a famous paper together. 9:59:59.000,9:59:59.000 Can you explain at all what that work was? 9:59:59.000,9:59:59.000 JS: No. 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 JS: I mean, I could [br]explain it to somebody. 9:59:59.000,9:59:59.000 CA: How about explaining this? 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 JS: But not many.[br]Not many people. 9:59:59.000,9:59:59.000 CA: I think you told me[br]it had something to do with spheres, 9:59:59.000,9:59:59.000 so let's start here. 9:59:59.000,9:59:59.000 JS: Well, it did. But I'll say about that work -- 9:59:59.000,9:59:59.000 it did have something to do with that,[br]but before we get to that -- 9:59:59.000,9:59:59.000 that work was good mathematics. 9:59:59.000,9:59:59.000 I was very happy with it; so was Chern. 9:59:59.000,9:59:59.000 It even started a little subfield[br]that's now flourishing. 9:59:59.000,9:59:59.000 But, more interestingly,[br]it happened to apply to physics, 9:59:59.000,9:59:59.000 something we knew nothing about --[br]at least I knew nothing about physics, 9:59:59.000,9:59:59.000 and I don't think Chern[br]knew a heck of a lot. 9:59:59.000,9:59:59.000 And about 10 years[br]after the paper came out, 9:59:59.000,9:59:59.000 a guy named Ed Witten in Princeton[br]started applying it to string theory 9:59:59.000,9:59:59.000 and people in Russia started applying it[br]to what's called "condensed matter." 9:59:59.000,9:59:59.000 Today, those things in there[br]called Chern-Simons invariants 9:59:59.000,9:59:59.000 have spread through a lot of physics. 9:59:59.000,9:59:59.000 And it was amazing. 9:59:59.000,9:59:59.000 We didn't know any physics. 9:59:59.000,9:59:59.000 It never occurred to me[br]that it would be applied to physics. 9:59:59.000,9:59:59.000 But that's the thing about mathematics --[br]you never know where it's going to go. 9:59:59.000,9:59:59.000 CA: This is so incredible. 9:59:59.000,9:59:59.000 So, we've been talking about[br]how evolution shapes human minds 9:59:59.000,9:59:59.000 that may or may not perceive the truth. 9:59:59.000,9:59:59.000 Somehow, you come up[br]with a mathematical theory, 9:59:59.000,9:59:59.000 not knowing any physics, 9:59:59.000,9:59:59.000 discover two decades later[br]that it's being applied 9:59:59.000,9:59:59.000 to profoundly describe[br]he actual physical world. 9:59:59.000,9:59:59.000 How can that happen? 9:59:59.000,9:59:59.000 JS: God knows. 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 But there's a famous physicist[br]named [Eugene] Wigner, 9:59:59.000,9:59:59.000 and he wrote an essay on the[br]unreasonable effectiveness of mathematics. 9:59:59.000,9:59:59.000 Somehow, this mathematics, 9:59:59.000,9:59:59.000 which is rooted in the real world[br]in some sense -- we learn to count, 9:59:59.000,9:59:59.000 measure, everyone would do that --[br]and then it flourishes on its own. 9:59:59.000,9:59:59.000 But so often it comes back[br]to save the day. 9:59:59.000,9:59:59.000 General relativity is an example. 9:59:59.000,9:59:59.000 [Hermann] Minkowski had this geometry,[br]and Einstein realized, 9:59:59.000,9:59:59.000 "Hey, it's the very thing [br]in which I can cast General Relativity." 9:59:59.000,9:59:59.000 So, you never know. It is a mystery. 9:59:59.000,9:59:59.000 It is a mystery. 9:59:59.000,9:59:59.000 CA: So, here's a mathematical[br]piece of ingenuity. 9:59:59.000,9:59:59.000 Tell us about this. 9:59:59.000,9:59:59.000 JS: Well, that's a ball -- it's a sphere,[br]and it has a lattice around it -- 9:59:59.000,9:59:59.000 you know, those squares. 9:59:59.000,9:59:59.000 What I'm going to show here was [br]originally observed by [Leonhard] Euler, 9:59:59.000,9:59:59.000 the great mathematician, in the 1700's. 9:59:59.000,9:59:59.000 And it gradually grew to be [br]a very important field in mathematics: 9:59:59.000,9:59:59.000 algebraic topology, geometry. 9:59:59.000,9:59:59.000 That paper up there had its roots in this. 9:59:59.000,9:59:59.000 So, here's this thing: 9:59:59.000,9:59:59.000 it has eight vertices,[br]12 edges, six faces. 9:59:59.000,9:59:59.000 And if you look at the difference -- 9:59:59.000,9:59:59.000 vertices minus edges plus faces --[br]you get two. 9:59:59.000,9:59:59.000 OK, well, two? That's a good number. 9:59:59.000,9:59:59.000 Here's a different way of doing it --[br]these are triangles covering -- 9:59:59.000,9:59:59.000 this has 12 vertices and 30 edges[br]and 20 faces, 20 tiles. 9:59:59.000,9:59:59.000 And vertices minus edges[br]plus faces still equals two. 9:59:59.000,9:59:59.000 And in fact you could[br]do this any which way, 9:59:59.000,9:59:59.000 cover this thing with all kinds[br]of polygons and triangles 9:59:59.000,9:59:59.000 and mix them up. 9:59:59.000,9:59:59.000 And you take vertices minus edges[br]plus faces -- you'll get two. 9:59:59.000,9:59:59.000 Here's a different shape.[br]This is a torus, the surface of a donut, 9:59:59.000,9:59:59.000 16 vertices covered by these rectangles, 9:59:59.000,9:59:59.000 32 edges, 16 faces,[br]vertices minus edges comes out 0. 9:59:59.000,9:59:59.000 It'll always come out 0. 9:59:59.000,9:59:59.000 Every time you cover a torus[br]with squares or triangles 9:59:59.000,9:59:59.000 or anything like that,[br]you're going to get 0. 9:59:59.000,9:59:59.000 So, this is called[br]the Euler characteristic. 9:59:59.000,9:59:59.000 And it's what's called[br]a topological invariant. 9:59:59.000,9:59:59.000 It's pretty amazing, 9:59:59.000,9:59:59.000 no matter how you do it[br]you'll always get the same answer. 9:59:59.000,9:59:59.000 So that was the first sort of thrust, [br]from the mid-1700s, 9:59:59.000,9:59:59.000 into a subject which is now[br]called algebraic topology. 9:59:59.000,9:59:59.000 CA: And your own work[br]took an idea like this 9:59:59.000,9:59:59.000 and moved it into[br]higher-dimensional theory, 9:59:59.000,9:59:59.000 higher-dimensional objects,[br]and found new invariants? 9:59:59.000,9:59:59.000 JS: Yes. Well, there were already [br]higher-dimensional invariants: 9:59:59.000,9:59:59.000 Pontryagin classes --[br]actually, there were Chern classes. 9:59:59.000,9:59:59.000 There were a bunch[br]of these types of invariants. 9:59:59.000,9:59:59.000 I was struggling to work on one of them[br]and model it sort of combinatorially 9:59:59.000,9:59:59.000 instead of the way it was typically done, 9:59:59.000,9:59:59.000 and that led to this work[br]and we uncovered some new things. 9:59:59.000,9:59:59.000 But if it wasn't for Mr. Euler --[br]who wrote almost 70 volumes of mathematics 9:59:59.000,9:59:59.000 and had 13 children 9:59:59.000,9:59:59.000 who he apparently would dandle on his knee[br]as he was writing -- 9:59:59.000,9:59:59.000 if it wasn't for Mr. Euler, there wouldn't[br]perhaps be these invariants. 9:59:59.000,9:59:59.000 CA: OK, so that's at least given us[br]a flavor of that amazing mind in there. 9:59:59.000,9:59:59.000 Let's talk about Renaissance. 9:59:59.000,9:59:59.000 Because you took that amazing mind[br]and having been a code-cracker at the NSA, 9:59:59.000,9:59:59.000 you started to become a code-cracker[br]in the financial industry. 9:59:59.000,9:59:59.000 I think you probably didn't buy [br]efficient market theory. 9:59:59.000,9:59:59.000 Somehow you found a way of creating[br]astonishing returns over two decades. 9:59:59.000,9:59:59.000 The way it's been explained to me, 9:59:59.000,9:59:59.000 what's remarkable about what you did[br]wasn't just the size of the returns, 9:59:59.000,9:59:59.000 it's that you took them[br]with surprisingly low volatility and risk 9:59:59.000,9:59:59.000 compared with other hedge funds.[br]So how on earth did you do this, Jim? 9:59:59.000,9:59:59.000 JS: I did it by assembling[br]a wonderful group of people. 9:59:59.000,9:59:59.000 When I started doing trading, I had [br]gotten a little tired of mathematics. 9:59:59.000,9:59:59.000 I was in my late 30s. [br]I had a little money. 9:59:59.000,9:59:59.000 I started trading and it went very well. 9:59:59.000,9:59:59.000 I made quite a lot of money[br]with pure luck. 9:59:59.000,9:59:59.000 I mean, I think it was pure luck. 9:59:59.000,9:59:59.000 It certainly wasn't mathematical modeling. 9:59:59.000,9:59:59.000 But in looking at the data,[br]after a while I realized: 9:59:59.000,9:59:59.000 it looks like there's some structure here. 9:59:59.000,9:59:59.000 And I hired a few mathematicians,[br]and we started making some models -- 9:59:59.000,9:59:59.000 just the kind of thing we did back [br]at IDA [Institute for Defense Analyses]. 9:59:59.000,9:59:59.000 You design an algorithm,[br]you test it out on a computer. 9:59:59.000,9:59:59.000 Does it work? Doesn't it work? And so on. 9:59:59.000,9:59:59.000 CA: Can we take a look at this? 9:59:59.000,9:59:59.000 Because here's a typical graph[br]of some commodity. 9:59:59.000,9:59:59.000 I look at that, and I say, [br]"That's just a random, up-and-down walk -- 9:59:59.000,9:59:59.000 maybe a slight upward trend [br]over that whole period of time." 9:59:59.000,9:59:59.000 How on earth could you trade, 9:59:59.000,9:59:59.000 looking at that and see something[br]that wasn't just random? 9:59:59.000,9:59:59.000 JS: In the old days -- this is [br]kind of a graph from the old days, 9:59:59.000,9:59:59.000 commodities or currencies[br]had a tendency to trend. 9:59:59.000,9:59:59.000 Not necessarily the very light trend[br]you see here, but trending in periods. 9:59:59.000,9:59:59.000 And if you decided, "OK, I'm going [br]to predict today, by the average move 9:59:59.000,9:59:59.000 in the past 20 days -- there's 20 days -- 9:59:59.000,9:59:59.000 maybe that would be a good prediction,[br]and I'd make some money. 9:59:59.000,9:59:59.000 And in fact, years ago[br]such a system would work -- 9:59:59.000,9:59:59.000 not beautifully, but it would work. 9:59:59.000,9:59:59.000 You'd make money, [br]you'd lose money, 9:59:59.000,9:59:59.000 you'd make money. 9:59:59.000,9:59:59.000 But this is a year's worth of days, 9:59:59.000,9:59:59.000 and you'd make a little money[br]during that period. 9:59:59.000,9:59:59.000 It's a very vestigial system. 9:59:59.000,9:59:59.000 CA: So you would test[br]a bunch of lengths of trends in time 9:59:59.000,9:59:59.000 and see whether, for example, 9:59:59.000,9:59:59.000 a 10-day trend or a 15-day trend[br]was predictive of what happens next. 9:59:59.000,9:59:59.000 JS: Sure, you would try all those things[br]and see what worked best. 9:59:59.000,9:59:59.000 Trend-following would've [br]been great in the '60s, 9:59:59.000,9:59:59.000 and it was sort of OK in the '70s.[br]By the '80s, it wasn't. 9:59:59.000,9:59:59.000 CA: Because everyone could see that. 9:59:59.000,9:59:59.000 So, how did you stay ahead of the pack? 9:59:59.000,9:59:59.000 JS: We stayed ahead of the pack[br]by finding other approaches -- 9:59:59.000,9:59:59.000 shorter-term approaches to some extent. 9:59:59.000,9:59:59.000 The real thing was to gather[br]a tremendous amount of data, 9:59:59.000,9:59:59.000 and we had to get it by hand[br]in the early days. 9:59:59.000,9:59:59.000 We went down to the Federal Reserve[br]and copied interest rate histories 9:59:59.000,9:59:59.000 and stuff like that. 9:59:59.000,9:59:59.000 Because it didn't exist on computers. 9:59:59.000,9:59:59.000 We got a lot of data. 9:59:59.000,9:59:59.000 And very smart people -- that was the key. 9:59:59.000,9:59:59.000 I didn't really know how[br]to hire people to do fundamental trading. 9:59:59.000,9:59:59.000 I had hired a few -- some made money,[br]some didn't make money. 9:59:59.000,9:59:59.000 I couldn't make a business out of that. 9:59:59.000,9:59:59.000 But I did know how to hire scientists, 9:59:59.000,9:59:59.000 because I have some taste[br]in that department. 9:59:59.000,9:59:59.000 So, that's what we did. 9:59:59.000,9:59:59.000 And gradually these models[br]got better and better, 9:59:59.000,9:59:59.000 and better and better. 9:59:59.000,9:59:59.000 CA: I think your credited with[br]doing something remarkable at Renaissance, 9:59:59.000,9:59:59.000 which is building this culture,[br]this group of people, 9:59:59.000,9:59:59.000 who weren't just hired guns[br]who could be lured away by money. 9:59:59.000,9:59:59.000 Their motivation was [br]doing exciting mathematics and science. 9:59:59.000,9:59:59.000 JS: Well I'd hoped that might be true. 9:59:59.000,9:59:59.000 But some of it was money. 9:59:59.000,9:59:59.000 CA: They made a lot of money. 9:59:59.000,9:59:59.000 JS: I can't say that [br]no one came because of the money. 9:59:59.000,9:59:59.000 I think a lot of them[br]came because of the money. 9:59:59.000,9:59:59.000 But they also came[br]because it would be fun. 9:59:59.000,9:59:59.000 CA: What role did machine learning[br]play in all of this? 9:59:59.000,9:59:59.000 JS: In a certain sense,[br]what we did was machine learning. 9:59:59.000,9:59:59.000 You look at a lot of data, and you try[br]to simulate different predictive schemes 9:59:59.000,9:59:59.000 until you get better and better at it. 9:59:59.000,9:59:59.000 It doesn't necessarily feed back on itself,[br]the way we did things. 9:59:59.000,9:59:59.000 But it worked. 9:59:59.000,9:59:59.000 CA: So these different predictive schemes [br]can be really quite wild, quite unexpected. 9:59:59.000,9:59:59.000 I mean, you look at everything, right? 9:59:59.000,9:59:59.000 You look at the weather, [br]length of dresses, political opinion. 9:59:59.000,9:59:59.000 JS: Yes, length of dresses we didn't try. 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 CA: What sort of things? 9:59:59.000,9:59:59.000 JS: Well, everything. [br]Everything is grist for the mill -- 9:59:59.000,9:59:59.000 except hem lengths. 9:59:59.000,9:59:59.000 Weather, annual reports, 9:59:59.000,9:59:59.000 quarterly reports, historic data itself,[br]volumes, you know it. Whatever there is. 9:59:59.000,9:59:59.000 We take in terabytes of data a day. [br]And store it away, massage it, 9:59:59.000,9:59:59.000 get it ready for analysis. 9:59:59.000,9:59:59.000 You're looking for anomalies. 9:59:59.000,9:59:59.000 You're looking for, like you said, 9:59:59.000,9:59:59.000 the efficient market [br]hypothesis is not correct. 9:59:59.000,9:59:59.000 CA: But any one anomaly[br]might be just a random thing, 9:59:59.000,9:59:59.000 so is the secret here[br]to just look at multiple strange anomalies 9:59:59.000,9:59:59.000 and see when they align? 9:59:59.000,9:59:59.000 JS: Any one anomaly[br]might be a random thing. 9:59:59.000,9:59:59.000 However, if you have enough data[br]you can tell that it's not. 9:59:59.000,9:59:59.000 You can see an anomaly that's persistent[br]for a sufficiently long time -- 9:59:59.000,9:59:59.000 the probability[br]of it being random is not high. 9:59:59.000,9:59:59.000 But these things fade after a while. 9:59:59.000,9:59:59.000 Anomalies can get washed out;[br]you have to keep on top of the business. 9:59:59.000,9:59:59.000 CA: A lot of people [br]look at the hedge fund industry now 9:59:59.000,9:59:59.000 and are sort of shocked by it -- 9:59:59.000,9:59:59.000 by how much wealth is created there[br]and how much talent is going into it. 9:59:59.000,9:59:59.000 Do you have any worries [br]about that industry 9:59:59.000,9:59:59.000 and perhaps the financial[br]industry in general? 9:59:59.000,9:59:59.000 Kind of being on a runaway train that's,[br]I don't know, helping increase inequality? 9:59:59.000,9:59:59.000 How would you champion what's happening[br]in the hedge fund industry? 9:59:59.000,9:59:59.000 JS: Actually, I think[br]that in the last three of four years, 9:59:59.000,9:59:59.000 hedge funds have not done especially well. 9:59:59.000,9:59:59.000 We've done dandy, 9:59:59.000,9:59:59.000 but the hedge fund industry as a whole[br]has not done so wonderfully. 9:59:59.000,9:59:59.000 The stock market has been on a roll,[br]going up as everybody knows, 9:59:59.000,9:59:59.000 and price-earnings rations have grown. 9:59:59.000,9:59:59.000 So an awful lot [br]of the wealth that's been created 9:59:59.000,9:59:59.000 in the last, let's say, five or six years[br]has not been created by hedge funds. 9:59:59.000,9:59:59.000 People would ask me,[br]"What's a hedge fund?" 9:59:59.000,9:59:59.000 And I'd say, "One in 20." 9:59:59.000,9:59:59.000 Which means -- now it's two in 20 -- 9:59:59.000,9:59:59.000 it's two percent fixed fee[br]on 20 percent of profits. 9:59:59.000,9:59:59.000 Hedge funds are all different[br]kinds of creatures. 9:59:59.000,9:59:59.000 CA: Rumor has it you charge[br]slightly higher fees than that. 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 JS: We charged the highest [br]fees in the world at one time. 9:59:59.000,9:59:59.000 Five and 44, that's what we change. 9:59:59.000,9:59:59.000 CA: Five and 44. So 5 percent flat, [br]44 percent of upside. 9:59:59.000,9:59:59.000 You still made your investors[br]spectacular amounts of money. 9:59:59.000,9:59:59.000 JS: We made good returns, yes. 9:59:59.000,9:59:59.000 People got very mad at my investors:[br]"How could you charge such high fees?" 9:59:59.000,9:59:59.000 I said, "OK, you can withdraw." 9:59:59.000,9:59:59.000 "But how can I get more?" 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 But at a certain point, as I told you, 9:59:59.000,9:59:59.000 we bought out all the investors [br]because they's a capacity to the fund. 9:59:59.000,9:59:59.000 CA: But should we worry 9:59:59.000,9:59:59.000 about the hedge fund industry attracting[br]too much of the world's great mathematical 9:59:59.000,9:59:59.000 and other talent to work on that 9:59:59.000,9:59:59.000 as opposed to the many[br]other problems in the world? 9:59:59.000,9:59:59.000 JS: Well it's not just mathematical. 9:59:59.000,9:59:59.000 We hire astronomers and physicists[br]and things like that. 9:59:59.000,9:59:59.000 I don't think we should worry too much.[br]It's still a pretty small industry. 9:59:59.000,9:59:59.000 And in fact, bringing science [br]into the investing world 9:59:59.000,9:59:59.000 has improved that world. 9:59:59.000,9:59:59.000 It's reduced volatility.[br]It's increased liquidity. 9:59:59.000,9:59:59.000 Spreads are narrower because[br]people are trading that kind of stuff. 9:59:59.000,9:59:59.000 So I'm not too worried about Einstein[br]going off and starting a hedge fund. 9:59:59.000,9:59:59.000 CA: You're at a phase in your life now[br]where you're actually investing, though, 9:59:59.000,9:59:59.000 at the other end of the supply chain --[br]in boosting mathematics across America. 9:59:59.000,9:59:59.000 This is your wife, Marilyn. 9:59:59.000,9:59:59.000 You're working on [br]philanthropic issues together. 9:59:59.000,9:59:59.000 Tell me about that. 9:59:59.000,9:59:59.000 JS: Well, Marily started -- 9:59:59.000,9:59:59.000 there she is up there,[br]my beautiful wife -- 9:59:59.000,9:59:59.000 she started the foundation[br]about 20 years ago. 9:59:59.000,9:59:59.000 I think '94. 9:59:59.000,9:59:59.000 I claim it was '93, [br]she says it was '94, 9:59:59.000,9:59:59.000 but it was one of those two years. 9:59:59.000,9:59:59.000 (Laughter) 9:59:59.000,9:59:59.000 We started the foundation[br]just as a convenient way to give charity. 9:59:59.000,9:59:59.000 She kept the books, and so on. 9:59:59.000,9:59:59.000 We did not have a vision at that time,[br]but gradually a vision emerged -- 9:59:59.000,9:59:59.000 which was to focus on math and science,[br]to focus on basic research. 9:59:59.000,9:59:59.000 And that's what we've done. 9:59:59.000,9:59:59.000 Six years ago or so, I left Renaissance[br]and went to work at the foundation. 9:59:59.000,9:59:59.000 So that's what we do. 9:59:59.000,9:59:59.000 CA: And so Math for America here 9:59:59.000,9:59:59.000 is basically investing in math teachers[br]around the country, 9:59:59.000,9:59:59.000 giving them some extra income,[br]giving them support and coaching. 9:59:59.000,9:59:59.000 And really trying[br]to make that more effective 9:59:59.000,9:59:59.000 and make that a calling[br]to which teachers can aspire. 9:59:59.000,9:59:59.000 JS: Yeah. Instead of [br]beating up the bad teachers -- 9:59:59.000,9:59:59.000 which has created morale problems[br]all through the educational community, 9:59:59.000,9:59:59.000 in particular in math and science -- 9:59:59.000,9:59:59.000 we focus on celebrating the good ones [br]and giving them status. 9:59:59.000,9:59:59.000 Yeah, we give them extra money,[br]15,000 dollars a year. 9:59:59.000,9:59:59.000 We have 800 math and science teachers[br]in New York City in public schools today 9:59:59.000,9:59:59.000 as part of a core. 9:59:59.000,9:59:59.000 There's a great morale among them.[br]They're staying in the field. 9:59:59.000,9:59:59.000 Next year, it'll be 1,000 [teachers],[br]and that'll be 10 percent 9:59:59.000,9:59:59.000 of the math and science teachers[br]in New York public schools. 9:59:59.000,9:59:59.000 (Applause) 9:59:59.000,9:59:59.000 CA: Jim, here's another project[br]that you've supported philanthropically: 9:59:59.000,9:59:59.000 Research into origins of life, I guess.[br]What are we looking at here? 9:59:59.000,9:59:59.000 Well, I'll save that for a second. 9:59:59.000,9:59:59.000 And then I'll tell you[br]what you're looking at. 9:59:59.000,9:59:59.000 Origins of life is a fascinating question.[br]How did we get here? 9:59:59.000,9:59:59.000 Well, there's two questions. 9:59:59.000,9:59:59.000 One is, "What is the root[br]from geology to biology? 9:59:59.000,9:59:59.000 How did we get here?" 9:59:59.000,9:59:59.000 And the other question is,[br]"What did we start with? 9:59:59.000,9:59:59.000 What material, if any,[br]did we have to work with on this route?" 9:59:59.000,9:59:59.000 Those are two very,[br]very interesting questions. 9:59:59.000,9:59:59.000 The first question is a tortuous path[br]from geology up to RNA 9:59:59.000,9:59:59.000 or something like that --[br]how did that all work? 9:59:59.000,9:59:59.000 And the other,[br]"What do we have to work with?" 9:59:59.000,9:59:59.000 Well, more than we think. 9:59:59.000,9:59:59.000 So that picture there[br]is a star in formation. 9:59:59.000,9:59:59.000 Now every year in our Milky Way,[br]which has 100 billion stars, 9:59:59.000,9:59:59.000 about two near stars are created. 9:59:59.000,9:59:59.000 Don't ask me how, but they're created. 9:59:59.000,9:59:59.000 And it takes them[br]about a million years to settle out. 9:59:59.000,9:59:59.000 So, in steady state, 9:59:59.000,9:59:59.000 there's about 2 million stars[br]in formation at any time. 9:59:59.000,9:59:59.000 That one is somewhere along[br]this settling down period. 9:59:59.000,9:59:59.000 And there's all this crap[br]sort of circling around it. 9:59:59.000,9:59:59.000 Dust and stuff. 9:59:59.000,9:59:59.000 And it'll form probably a solar system,[br]or whatever it forms. 9:59:59.000,9:59:59.000 But here's the thing -- in this dust[br]that surrounds a forming star, 9:59:59.000,9:59:59.000 have been found, now,[br]significant organic molecules. 9:59:59.000,9:59:59.000 Molecules not just like methane[br]but formaldehyde and cyanide -- 9:59:59.000,9:59:59.000 things that are the building blocks,[br]the seeds, if you will, of life. 9:59:59.000,9:59:59.000 So, that may be typical. 9:59:59.000,9:59:59.000 And it may be typical 9:59:59.000,9:59:59.000 that planets around the universe start off[br]with some of these basic building blocks. 9:59:59.000,9:59:59.000 Now, does that mean [br]there's going to be life all around? 9:59:59.000,9:59:59.000 Maybe. 9:59:59.000,9:59:59.000 But it's a question[br]of how tortuous this path is 9:59:59.000,9:59:59.000 from those frail beginnings,[br]those seeds, all the way to life. 9:59:59.000,9:59:59.000 And most of those seeds[br]will fall on fallow planets. 9:59:59.000,9:59:59.000 CA: So for you, personally, 9:59:59.000,9:59:59.000 finding an answer to this question[br]of where we came from, 9:59:59.000,9:59:59.000 of how did this thing happen,[br]that is something you would love to see. 9:59:59.000,9:59:59.000 JS: Would love to see. And like to know. 9:59:59.000,9:59:59.000 If that path is tortuous enough, [br]and so improbable 9:59:59.000,9:59:59.000 that no matter what you start with,[br]we could be a singularity. 9:59:59.000,9:59:59.000 But, on the other hand, 9:59:59.000,9:59:59.000 given all this organic dust[br]that's floating around, 9:59:59.000,9:59:59.000 we could have lots of friends out there. 9:59:59.000,9:59:59.000 It'd be great to know. 9:59:59.000,9:59:59.000 CA: Jim, a couple of years ago, 9:59:59.000,9:59:59.000 I got the chance to speak with Elon Musk,[br]and I asked him the secret of his success. 9:59:59.000,9:59:59.000 He said taking physics seriously was it. 9:59:59.000,9:59:59.000 Listening to you, what I hear you saying[br]is taking math seriously, 9:59:59.000,9:59:59.000 that has infused your whole life. 9:59:59.000,9:59:59.000 It's made you an absolute fortune,[br]and now it's allowing you to invest 9:59:59.000,9:59:59.000 in the futures of thousands and thousands[br]of kids across America and elsewhere. 9:59:59.000,9:59:59.000 Could it be that science actually works?[br]That math actually works? 9:59:59.000,9:59:59.000 JS: Well, math certainly works.[br]Math certainly works. 9:59:59.000,9:59:59.000 But this has been fun. 9:59:59.000,9:59:59.000 Working with Marilyn and giving it away[br]has been very enjoyable. 9:59:59.000,9:59:59.000 CA: I just find it --[br]it's an inspirational though to me, 9:59:59.000,9:59:59.000 that by taking knowledge seriously,[br]so much more can come from it. 9:59:59.000,9:59:59.000 So thank you for your amazing life[br]and for coming here to TED. 9:59:59.000,9:59:59.000 Truly. Thank you. 9:59:59.000,9:59:59.000 Jim Simons. 9:59:59.000,9:59:59.000 (Applause)