Chris Anderson - You were something of a mathematical phenom you had  already taught at Harvard and MIT at a young age. And then the NSA came  calling. What was that about? 

Jim Simons - Well, the NSA didn't exactly, that's the National Security Agency, it  didn't exactly come calling. They had an operation in Princeton where they hired mathematicians to attack secret codes and stuff like that. And I knew that  existed. 

And it had a very good policy because you could do half your time at your own  mathematics and at least half your time working on their stuff. And they paid a  lot. So that was an irresistible pull. So I went there.  

Chris Anderson - So you were a code cracker until you got fired. Jim Simon - Well, I did get fired, yes. 

Chris Anderson - How come?  

Jim Simon - Well, how come I got fired? Because, well, the Vietnam War was on and the boss of bosses in my organization was a big fan of the war and wrote a  New York Times article, magazine section, cover story about how it was going to win in Vietnam. 

And I didn't like that war. I thought it was stupid. And I wrote a letter to the  Times, which they published, saying not everyone who works for, it was Maxwell Taylor, if anyone remembers that name, works for General Taylor, agrees with  his views. 

And I gave my own views, which were different from General Taylor's. But on the other hand, nobody said anything. But then I was 29 years old at this time and  some kid came around and said he was a stringer from Newsweek magazine  and he wanted to interview me and ask my, what I was doing about my views. And I told him, I said, I'm doing mostly mathematics now and when the war is  over, then I'll do mostly their stuff. Then I did the only intelligent thing I'd done  that day. I told my local boss that I gave that interview. 

He said, what did you say? And I told him what I said. And then he said, I got to  call Taylor. He called Taylor. That took 10 minutes. I was fired five minutes after  that. But it wasn't bad. It wasn't bad. 

Chris Anderson - Because you went on to Stony Brook and stepped up your 

mathematical career. You started working with this man here. Who is this? 

Jim Simons - Oh, Chern. Yeah, Chern was one of the great mathematicians of  the century. I had known him when I was a graduate student, actually, at  Berkeley. And I had some ideas and I brought them to him and he liked  them. And together we did this work, which you can easily see up there. There it is. 

Chris Anderson - It led to you publishing a famous paper together. Can you  explain at all what that work was? 

Jim Simons - No. I'll tell you what. 

Chris Anderson - Can you explain this? 

Jim Simons - I can explain it to somebody. Well, how about explaining this? 

Chris Anderson - But not many, not many people. I think you told me it had  something to do with spheres. So let's start here. 

Jim Simons - Well, it did. But I'll say about that work, it did have something to do with that. But before we get to that, that work was good mathematics. I was very happy with it. So was Chern. It even started a little subfield that's now  flourishing. 

But more interestingly, it happened to apply to physics, something we knew  nothing about. At least I knew nothing about physics and I don't think Chern  knew a heck of a lot. And about ten years after the paper came out, a guy  named Ed Witten in Princeton started applying it to string theory and people in  Russia started applying it to what's called condensed matter. 

And today, those things in there called Chern-Simons invariants spread through  a lot of physics. And it was amazing. We didn't know any physics. It never  occurred to me that it would be applied to physics. But that's the thing about  mathematics. You never know where it's going to go. 

Chris Anderson - But this is so incredible. So we've been talking about how  evolution shapes human minds that may or may not perceive the truth.  Somehow, you come up with a mathematical theory not knowing any physics,  discover two decades later that it's being applied to profoundly describe the  actual physical world. How can that happen? God knows.

Jim Simons - But there was a famous physicist named Wigner and he wrote an  essay on the unreasonable effectiveness of mathematics. So somehow this  mathematics, which is rooted in the real world in some sense, you know, we  learn to count and measure, that's kind of, everyone would do that, and then it  flourishes on its own. But so often it comes back to save the day. 

General relativity is an example. Minkowski had this geometry and Einstein  realized, hey, it's the very thing in which I can cast general relativity. So you  never know. It is a mystery. It is a mystery. 

Chris Anderson - So here's a mathematical piece of ingenuity here. Tell us  about this. 

Jim Simons - Yeah, yeah. Well, this, well, that's a ball. It's a sphere and it has a  lattice around it, you know, those squares, sort of things. 

And what I'm going to show here was originally observed by Euler, the great  mathematician in the 1700s. And it gradually grew to be a very important field in  mathematics, algebraic topology, geometry. And that paper up there was, had its roots in this. 

So okay, so here's this thing. It has eight vertices and 12 edges and six  faces. And if you look at the difference, vertices minus edges plus faces, you get two. 

Okay, well, two, that's a good number. Oh, here's a different way of doing  it. These are triangles covering, oh, this has 12 vertices and 30 edges and 20  faces, 20 tiles. 

And vertices minus edges plus faces still equals two. And in fact, you could do  this any which way. Cover this thing with all kinds of polygons and triangles and  mix them up. 

And you take vertices minus edges plus faces, you'll get two. Now here's a  different shape. This is a torus or the surface of a donut. 16 vertices covered by  these rectangles, 32 edges, 16 faces. Hey, this comes out zero, vertices minus  edges. It'll always come out zero. 

Every time you cover a torus with squares or triangles or anything like that,  you're going to get zero when you take that thing. So this is called the Euler  characteristic. And it's what's called a topological invariant.

That's pretty amazing. No matter how you do it, you're always going to get the  same answer. So that was the first sort of thrust from the mid-1700s into a  subject which is now called algebraic topology. 

Chris Anderson - And your own work took an idea like this and moved it into  higher dimensional theory. You looked at higher dimensional objects and found  new invariances? Yes. 

Jim Simons - Well, there were already higher dimensional invariants, Pontryagin classes. Actually, there were Chern classes. There were a bunch of these types  of invariants. 

But I was struggling to work on one of them and model it sort of combinatorially  instead of the way it was typically done. And that led to this work. And we  uncovered some new things. 

But if it wasn't for Mr. Euler, who wrote almost 70 volumes of mathematics, had  13 children whom he apparently would dandle on his knee while he was  writing. If it wasn't for Mr. Euler, well, there wouldn't perhaps be these invariants. 

Chris Anderson - So that's at least given us a flavor of that amazing mind in  there. Let's talk about Renaissance, because you took that amazing mind and  having been a co-cracker at the NSA, you started to become a co-cracker of the financial industry. I think you probably didn't buy efficient market theory. 

And somehow, you found a way of creating these astonishing returns over two  decades. I think the way it's been explained to me, what's remarkable about  what you did was it wasn't just the size of the returns. It was that you took them  with surprisingly low volatility and risk compared with other hedge funds. So how on earth did you do this, Jim? 

Jim Simons - Well, I did it by assembling a wonderful group of people. When I  started doing trading, I had gotten a little tired of mathematics. I was in my late  30s. I had a little money. I started trading, and it went very well. I made quite a  lot of money. 

I was pure luck. I mean, I think it was pure luck. It certainly wasn't mathematical  modeling. But in looking at the data, after a while, I realized, hey, it looks like  there's some structure here. And I hired a few mathematicians, and we started  trying to make some models. Just the kind of thing we did back at IDA. 

You design an algorithm. You test it out on a computer. Does it work? Doesn't it 

work? And so on. 

Chris Anderson - So let's, can we take a look at this? Because, I mean, here's a  typical, like, graph of some commodity or whatever. I mean, I look at that, and I  say, that's just a random up and down walk. Maybe a slight upward trend over  that whole period of time. How on earth could you trade, look at that, and see  something that wasn't just random? 

Jim Simons - Well, it turns out, in the old days, and this is kind of a graph from  the old days, commodities, or currencies, had a tendency to trend. Not  necessarily the very light trend you see here, but trending in periods. And if you  decided, okay, I'm going to predict today, by the average move in the past 20  days, it is 20 days, maybe that would be a good prediction, and I'd make some  money. 

And, in fact, years ago, such a system would work. Not beautifully, but it would  work. So you'd make money. You'd lose money. You'd make money. But this is a year's worth of days. And you'd, you know, you'd make a little money during that  period. But it's a very vestigial system. 

Chris Anderson - So you would test a bunch of different lengths of trends and  time, and see whether, for example, a 10-day trend or a 15-day trend was  predictive of what happened next. 

Jim Simons - Sure, sure. You would, you know, try all those things and see what worked best. But the trend following would have been great in the 60s, and it  was sort of okay in the 70s. By the 80s, it wasn't such a wonderful  thing. Because everyone could see that. 

Chris Anderson - Yeah, a lot of people did. So what happened? So how did you  stay ahead of the pack? 

Jim Simons - Well, we stayed ahead of the pack by finding other approaches,  and shorter-term approaches to some extent. But the real thing was to gather a  tremendous amount of data. And we had to get it by hand in the early days. 

We went down to the Federal Reserve and copied interest rate histories and  stuff like that, because it didn't exist on computers. We got a lot of data, and  very smart people. And that was the key. 

I didn't really know how to hire people to do fundamental trading. I had hired a  few. Some made money, some didn't make money. I couldn't make a business 

out of that. But I did know how to hire scientists, because I have some taste in  that department. And so that's what we did. And gradually, these models got  better and better and better and better. 

Chris Anderson - I mean, I think you're credited with doing something  remarkable at Renaissance, which is building this culture of this group of people  who weren't just hired guns who could be lured away by money. Their motivation was doing exciting mathematics and science. 

Jim Simons - Well, I hope that might be true. But some of it was money. They  made a lot of money. I can't say that no one came because of the money. I think  a lot of them came because of the money. But they also came because it would  be fun. 

Chris Anderson - What role did machine learning play in all this?  

Jim Simon - Well, in a certain sense, what we did was machine learning. You  look at a lot of data, and you try to simulate different predictive schemes until  you get better and better at it. It doesn't necessarily feed back on itself the way  we did things. But it worked. 

Chris Anderson - So these different predictive schemes can be really quite wild,  quite unexpected. I mean, you look at everything, right? You look at weather,  length of dresses, political opinion. 

Jim Simons - Length of dresses, we didn't try. 

Chris Andersdon - What sort of things?  

Jim Simon - Well, everything. I mean, everything is grist for the mill except hem  lengths, I have to say. Weather, annual reports, monthly, quarterly reports, the  historic data itself, volumes, you name it, whatever there is. We take in terabytes of data a day and store it away and massage it and get it ready for analysis. And you're looking for anomalies. You're looking for, like you said, the efficient  market hypothesis. 

Chris Anderson - But any one anomaly might be just a random thing. So is the  secret here to just look at multiple strange anomalies and see when they align? 

Jim Simons - Well, any one anomaly might be a random thing. However, if you  have enough data, you can tell that it's not. So you can see an anomaly that's  persisted for a sufficiently long time. The probability of it being random is not 

high. But these things fade after a while. Anomalies can get washed out. So you have to keep on top of the business. 

Chris Anderson - A lot of people look at the hedge fund industry now and are  sort of shocked by it, by how much wealth is created there and how much talent  is going into it. Do you have any worries about that industry, and perhaps the  financial industry in general, kind of being on a runaway train that's, I don't know, helping increase inequality? Or how would you champion what's happening in  the hedge fund industry? 

Jim Simons - Well, actually, I think in the last three or four years, hedge funds  have not done especially well. We've done dandy. But the hedge fund industry  as a whole has not done so well. 

The stock market has been on a roll going up, as everybody knows. And price  earnings ratios have grown. So an awful lot of the wealth that's been created in  the last, let's say, five or six years has not been created by hedge funds. 

So it's just another, you know, people would ask me, what's a hedge fund? And I say, one in 20, which means now it's two in 20. You know, it's 2% fixed fee and  20% of profits. Hedge funds are all different kind of creatures. 

Chris Anderson - Rumor has it you charge slightly higher fees than that. 

Jim Simons - We charge higher fees, yeah. We charge the highest fees in the  world at one time. Five and 44, that's what we charge. 

Chris Anderson - Five and 44. 

Jim Simons - 5% fixed fee. 

Chris Anderson - 5% flat, 44% of upside. 

Jim Simons - But you still made your investors spectacular amounts of  money. We made good returns, yes. People got very mad at my investors. How  can you charge such high fees? I said, okay, you can withdraw. But no, how can  I get more was what they... How can I get more? But at a certain point, as I think I told you, we bought out all the investors because there's a capacity. There's a  capacity to the fund. 

Chris Anderson - But should we worry about the hedge fund industry attracting  too much of the world's great mathematical and others' talent to work on that as 

opposed to the many other problems in the world? 

Jim Simons - It's not just mathematical. We hired astronomers and physicists  and things like that. I don't think we should worry about it too much. It's still a  pretty small industry. And in fact, bringing in science into the investing world has  improved that world. It's reduced volatility. It's increased liquidity. Spreads are  narrower because people are trading that kind of stuff. So I'm not too worried  about Einstein going off and starting a hedge fund. 

Chris Anderson - Now, you're at a phase in your life now where you're actually  investing, though, at the other end of the supply chain in actually boosting  mathematics across America. This is your wife, Marilyn. And you're working on  philanthropic issues together. Tell me about that. 

Jim Simons - Well, yeah, we are. Marilyn started, there she is up there, a  beautiful wife. She started the foundation about 20 years ago, I think, 94. I claim  it was 93. She says it was 94. But it was one of those two years we started the  foundation. 

And just as a convenient way to give charity, she kept the books and so on. And  we did not have a vision at that time. But gradually, a vision emerged, which was to focus on math and science, to focus on basic research. And that's what we've done. Six years ago or so, I left Renaissance and went to work at the  

foundation. So that's what we do. 

Chris Anderson - And so Math for America here is basically investing in math  teachers around the country, giving them some extra income, and giving them  support and coaching, and really trying to make that more effective and make  that a calling to which teachers can aspire. 

Jim Simons - Yeah, yeah. Instead of beating up the bad teachers, which has  created morale problems all through the educational community, in particular in  math and science, we focus on celebrating the good ones and giving them  status. Yeah, we give them extra money, $15,000 a year. 

We have 800 math and science teachers in New York City in the public schools  today as part of a core. There's a great morale among them. They're staying in  the field. Next year, it'll be 1,000, and that'll be 10% of the math and science  teachers in New York public schools. And that's... 

Chris Anderson - And, Jim, here's another project that you've supported  philanthropically, research into origins of life, I guess. What are we looking at 

here? 

Jim Simons - Well, I'll save that for a second, and then I'll tell you what you're  looking at. But so origins of life is a fascinating question. How do we get here? What's a root? Well, there's two questions. One is, what is the root from geology to biology? How did we get here? And the other question is, what did we start  with? What material, if any, did we have to work with on this route? 

Those are two very, very interesting questions. The first question is a tortuous  path from geology up to RNA or something like that. How did that all work? And the other, what do we have to work with? Well, more than we think. So  what's pictured there is a star in formation. 

Now, every year in our Milky Way, which has 100 billion stars, about two new  stars are created. Don't ask me how, but they're created. And it takes them  about a million years to settle out. 

So in steady state, there's about two million stars in formation at any time. That  one is somewhere along this settling down period. And there's all this crap sort  of circling around it, dust and stuff. 

And it'll form probably a solar system or whatever it forms. But here's the  thing. In this dust that surrounds a forming star have been found now significant  organic molecules. 

Molecules not just like methane, but formaldehyde and cyanide, things that are  the building blocks, the seeds, if you will, of life. So that may be typical. And it  may be typical that planets around the universe start off with some of these  basic building blocks. 

Now, does that mean, oh, well, there's gonna be life all around? Maybe. But it's  a question of how tortuous this path is from those frail beginnings, those seeds,  all the way to life. And most of those seeds will fall on fallow planets. But maybe  some will. 

Chris Anderson - So for you personally, finding an answer to this question of  where we came from, how did this thing happen? That is something you would  love to see. I'd love to see. 

Jim Simons - I'd love to see. And I'd like to know, you know, if that path is  tortuous enough and so improbable that no matter what you start with, we could  be a singularity. But on the other hand, given all this organic dust that's floating 

around, we could have lots of friends out there. 

Chris Anderson - Be great to know. Jim, a couple of years ago, I got a chance to speak with Elon Musk and I asked him the secret of his success, really. And he  said taking physics seriously was it. 

Listening to you, what I hear you saying is, you know, taking math seriously, that that has infused your whole life. It's made you an absolute fortune. And now it's  allowing you to invest in the futures of thousands and thousands of kids across  

America and elsewhere. Could it be that science actually works? That math  actually works? 

Jim Simons - I mean, it's a- Well, math certainly works. Math certainly  works. But this has been fun, working with Marilyn and giving it away has been  very enjoyable. 

Chris Anderson - I just find it, it's an inspirational thought to me that by taking  knowledge seriously, so much more can come from it. So thank you for your  amazing life and for coming here to TED. Okay.Thank you. Jim Simon. Thank  you, it's really interesting.



آخر تعديل: الاثنين، 27 يوليو 2026، 9:40 AM