The video interview features Jim Simons, discussing his impressive journey as a mathematician, codebreaker, and financial industry leader. Starting with a promising academic career, he was drawn to solve secret codes at the NSA, but his outspoken views on the Vietnam War led to his dismissal. This, however, did not deter him, as he pursued an illustrious mathematical career that led to impactful collaborations and significant contributions that underscored the unpredictable applicability of mathematics in physics and beyond.

Jim Simons recounts his transition from a mathematician to a successful trader and founder of the hedge fund Renaissance Technologies. His innovative approach involved assembling a team of scientists and employing data-driven, mathematical models to achieve extraordinary returns with low risk. Simons highlights the unpredictability in markets and how leveraging comprehensive data with advanced mathematical techniques can offer substantial insights and returns, outpacing traditional financial methods.

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Key Vocabularies and Common Phrases:

1. Phenome [fəˈnɒm] - (n.) - An individual with extraordinary talents or abilities.

You were something of a mathematical Phenome.

2. Invariants [ɪnˈveriənts] - (n.) - Quantities or properties that remain unchanged under certain conditions.

Today, those things in there called Chern Simons invariants spread through a lot of physics.

3. Lattice [ˈlætɪs] - (n.) - A regular grid structure used to delineate geometric arrangements.

Here's a mathematical piece of ingenuity here. Tell us about this. Yeah. Yeah. Well, that's a ball, it's a sphere and it has a lattice around it.

4. Topological [ˌtɒpəˈlɒdʒɪkəl] - (adj.) - Relating to the study of geometrical properties and spatial relations unaffected by the continuous change of shape or size.

Those things in there called Chern Simons invariants spread through a lot of physics.

5. Algorithm [ˈælɡəˌrɪðəm] - (n.) - A defined set of instructions for solving specific problems or performing a task.

You design an Algorithm, you test it out on a computer...

6. Philanthropic [ˌfɪlənˈθrɒpɪk] - (adj.) - Relating to the desire to promote the welfare of others, typically through the donation of money.

Tell me about that. Yeah, we are. Marilyn started...

7. Volatility [ˌvɒləˈtɪlɪti] - (n.) - The likelihood of rapid, unpredictable changes, especially in financial contexts.

Offers returns with surprisingly low Volatility and risk compared with other hedge funds.

8. Hedging [ˈhedʒɪŋ] - (v.) - Making investments or strategies intended to offset potential losses.

How on earth could you trade, look at that and see something that wasn't just random...

9. Liquidity [lɪˈkwɪdəti] - (n.) - The availability of liquid assets to a market or company.

It's reduced Volatility, it's increased liquidity.

10. Anomalies [əˈnɒməliz] - (n.) - Deviations or departures from the norm or what is expected.

You're looking for anomalies.

Decoding Secrets to Success in Science, Finance & Philanthropy

You were something of a mathematical Phenome. You had already taught at Harvard and MIT at a young age. And then the NSA came calling. What was that about? Well, the NSA didn't exactly come calling. 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.

So you were a code cracker until you got fired? Well, I did get fired, yes. How come? 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 we're 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. Okay, I can see that. 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 to said he was a stringer from News Week magazine and he wanted to interview me and ask my. What I was doing about my views. And I told him, 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, and 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 ten minutes. I was fired five minutes after that. Okay, so. But it wasn't bad.

It wasn't bad because you went on to Stony Brook and I went on to stepped up your mathematical career and you started working with this man here. Who is this? 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. It led to you publishing a famous paper together.

Can you explain at all what that work was? No. I'll tell you. What can you explain to me? I could explain it to somebody, but how about explaining this? But not many, not many people, I think you told me it had something to do with spheres, so let's start here. 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. 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.

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. 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, we learn to count and measure. That 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. And it is a mystery. It is a mystery. So here's a mathematical piece of ingenuity here. Tell us about this. Yeah. Yeah. 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 17 hundreds. And it gradually grew to be a very important field in mathematics, algebraic topology and geometry. And that paper up there had its roots in this. So, okay, so here's this thing. It has eight vertices and twelve 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 twelve 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 doughnut, 16 vertices covered by these rectangles, 32 edges, 16 faces. Hey, this comes out zero, vertices minus edge, 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 17 hundreds into a subject which is now called algebraic topology. 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, well, there were already higher dimensional invariants, Pontryagan 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. If it wasn't for Mister Euler, who wrote almost 70 volumes of mathematics, had 13 children, whom he apparently would dandel on his knee while he was writing and so on, if it wasn't for Mister Euler, well, there wouldn't perhaps be these invariants. Ok, 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 code cracker at the NSA, you started to become a code 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 that 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? 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 thirties. I had a little money, I started trading, and it went very well. I made quite a lot of money with 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. 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.

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 of. And if you decided, okay, I'm going to predict today by the average move in the past 20 days. There's 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 have, you'd make a little money during that period, but so you would, it was a very vestigial system.

So you would test a bunch of different sort of lengths of trends and time and see whether, for example, a ten day trend or a 15 day trend was predictive of what happened next. Sure, you would try all those things and see what worked best, but the trend following would have been great in the sixties, and it was sort of okay in the seventies. By the eighties, it wasn't such a good thing. Everyone could, other people say, so how did you stay ahead of the pack then? 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. Computers, we got a lot of data and very smart people, and that was the, 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 didn't 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. I mean, I think you're credited with doing something remarkable at Renaissance, which is building this culture on 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. Well, I hoped 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.

What role did machine learning play in all this? 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. It worked. So these different predictive schemes can be really quite, quite wild, quite unexpected. I mean, you look at everything, right? You look at weather, length of dresses. Yes, length of dresses we didn't try.

But what sort of things? 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, you know, the efficient market hypothesis.

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? Well, any one Anomaly might be a random thing. However, if you have enough data, you can tell that it's nothing. So you can see an Anomaly that's persisted for a sufficiently long time, so 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. 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. How would you champion what's.

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 on. 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. People would ask me, what's a hedge fund? And I say, one in 20, which means now it's two and 20, but I bet, you know, it's 2% fixed fee and 20% of profits.

Hedge funds are all different kind of creatures. Rumor has it you charge slightly higher fees than we charge the highest fees in the world at one time. Five and 44. That's what we charge. Five and 44, 5% fixed fee, 45% flat, 44% of upside. You still made your investors spectacular good returns. Yes. People got very mad at my investors. How can you charge such high fees? I said, okay, you can withdraw, but how can I get more? Was what? 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 deploy. But should we worry about the hedge fund industry attracting too much of the world's great mathematical and other talent to work on that, as opposed to many other problems in the world? Well, it's not just mathematics. 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. I'm not too worried about Einstein going off and starting a hedge fund 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. Here's your wife, Marilyn. And you're working on philanthropic issues together. Tell me about that. Yeah, we are. Marilyn started. There she is up there. Beautiful wife. She started the foundation about 20 years ago, I think 94 I claim it was 93. She says 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. And six years ago or so, I left Renaissance and went to work at the foundation. So that's what we do. So math for America here is basically investing in the math teachers around the country, giving them some extra income and giving them support and coaching and really trying to make. Make that more effective and make that a calling to which teachers can aspire. 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 1000, and that'll be 10% of the math and science teachers in New York public schools. And that. Jim, here's another project that you've supported philanthropically. Research into origins of life, I guess. What are we looking at here?

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 did 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? Any 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 it 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 2 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 going to 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 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 and 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. Lots of friends out there. Be great to know.

Jim, a couple, couple years ago, I got a chance to speak with Elon Musk and asked him the secret of his success, really, of. 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. 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? Math actually works? Well, math certainly works. Math certainly works.

But this has been fun. Working with Marilyn and giving it away has been very enjoyable. I just find 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. Thank you. Jim Simon.

Mathematics, Innovation, Technology, Renaissance Technologies, Jim Simons, Philanthropy