Brady: Should we start at the beginning? - James Harris Simons: Okay.
- As a child were you good at mathematics?
Like was mathematics a natural thing for
you which-? - Yeah it was very natural. I
always liked it. I like counting, I like
continually multiplying things by two-
although by the time I got to a 1024
or whatever it is I was- had enough of
it. But I liked- I liked math. I discovered
as a very young kid, maybe 4, something
called Zeno's paradox - d'you ever hear of
Zeno's paradox? My father told me that
the car could run out of gas, and I was
disturbed by that notion, it never
occurred to me, but then I thought well,
it shouldn't run out; you could always use
half of what it has and then it could
use half of that and then half of that
and then you go on forever and so it would
never run out. So, now, it didn't occur to
me, 'yes but it wouldn't get very far
either' but but the idea that in
principle you didn't have to run out of
gas was a kind of a profound thought for
for a very little boy. - Did it feel like
this was where your career was going to
take you or were you like another boy who
dreamed of being a baseball or or
something-? - Oh no no no the only thing I
thought about was I would be a
mathematician. Whatever exactly that
meant. I didn't know quite what it meant
except that mathematics was the only
subject I really liked.
They wanted me to go to Berkeley and get
away from MIT, meet some new faculty
because I was quite close to the MIT
faculty and they thought- I think they
didn't want to get rid of me so much as
they thought I was probably pretty good
so I should get exposed to a certain guy
named Chern who was just coming to
Berkeley that year. And I got this very
nice fellowship and I went there, I was
very eager to work with Chern; meet Chern
except he wasn't there. He celebrated his
first year at Berkeley, he had just come
to Berkeley and he celebrated that first
year by taking a sabbatical so he wasn't
there. So I work with another guy which
which was fine and by the time Chern
came in the second year I was already
pretty far along with the, with the
thesis project. I was giving a seminar at
the beginning of my second year at
Berkeley and in walked this tall Chinese
guy, and I said to guy next me 'who's that?'
He said 'that's Chern'. That's Chern? I didn't
know he was Chinese;
I thought Chern was probably short for
Chernovski or something, or he was
probably some Polish guy who who had
shortened his name to Chern. If it had
been Chen or Chan I would have known it
was Chinese, but Chern with the R I
didn't know but anyway. So I met Chern
then and we became friends; of course I was
much younger than he but we became
friends, and later collaborators.
So we worked together and we came up with
these results, this whole structure. In
fact there's- that's the slides of the
presentation that Chern made at the
International Congress in the early 70s.
It was very nice geometry. I pushed on
with it and we defined some things
called differential characters which was
another chapter, I with working with a
guy named Cheeger, but the Chern-Simons
invariants, about ten years later, the
physicists got a hold of it. And it
seemed to be very good for what ailed
them, whatever might have ailed them.
And so- and it wasn't just string theory,
as I subsequently developed, it was kind
of all areas of physics including
condensed matter theory, even some
astronomers seemed to want to look at
those terms. That's really what's great
about basic science, in this case
mathematics. I mean I didn't know any
physics; it didn't occur to me that this
material that Chern and I had developed
would find use somewhere else altogether.
This happens in basic science all the
time, that one guy's discovery leads to
someone else's invention and leads to
another guy's machine.
Actually in the middle of my mathematics
career, which ended when I was about 37
or 38, was that I spent four years at a
place called the Institute for Defense
Analyses down in Princeton which was a
super-secret government based, national
security agency based, place for
code-cracking. But I also learned about
computers and algorithms. And I did one
thing there that was quite good - of course
I cannot tell you what it was it's all
classified. So I had a good career there,
both doing mathematics and learning
about the fun of computer modelling.
My father had made a little bit of money
and I had the opportunity to try
investing it, and that was interesting.
And I thought, you know I'm going to try
another career altogether. And so I went
into the money management business, so to
speak. - So you started with some of your
dad's money and that got you a taste for- an
interest in it? - Yeah some family money. And
then some other people put up some money.
And I did that- no models. No models for
the first two years. - So what were you
doing then? You were just using cunning
and you know just- you know like normal
people do? - Like normal people do. And I
brought in a couple of people to work
with me and we were extremely successful.
I think it was just plain good luck
but nonetheless we were very successful.
But I could see that this was a very gut
wrenching business. You know you come in
one morning, you think you're a genius,
the markets are for you; we were trading
currencies and commodities and financial
instruments and so on, not stocks but
those kinds of things. And the next morning
you come and you feel like a jerk, the
market's against you - it was very gut
wrenching. And in looking at the patterns
of prices I could see that there was
something we could study here, that there
were maybe some ways to predict prices
mathematically or statistically. And I
started working on that and then brought
in some other people, and gradually we built
models and the models got better and
better and finally the models replaced
the fundamental stuff, so it took a while.
- I would have thought, with your
background - a mathematician - this would
have almost occurred to you immediately,
like you would have straightaway seen
this. What, what was the two-year delay?
- Well two things. I saw it pretty early
but- and I brought in a guy who was a
wonderful guy, also from the
code-cracking place, and he was I thought
together we'll we'll start building
models. That was fairly early but it
wasn't right away. But he got more interested in the
fundamental stuff and he says, the models
aren't going to be very strong and so on
and so forth so we didn't get very far.
But I knew there were models to be made.
Then I brought in another mathematician
and a couple more, and a better computer
guy, and then we started making models
which really worked. But you know, the the
general- there's something called the
efficient market theory which says that
there's nothing in the data, let's say
price data, which will indicate anything
about the future because the price is sort
of always right, the price is always
right in some sense. But that's just not
true. So there are anomalies in the data,
even in the price history data. For one
thing, commodities especially used to
trend; not dramatically trend but trend
so if you could get the trend right
you'd bet on the trend and you'd make
money more often than you wouldn't,
whether it was going down or going up.
That was an anomaly in the data, but
gradually we found more and more and
more and more anomalies. None of them is
so overwhelming that you're going to
clean up on a particular anomaly; because
if they were other people would have
seen them, so they have to be subtle
things. And you put together a collection
of these subtle anomalies and you begin
to get something that will predict
pretty well.
It's it's what's called machine learning.
So you find things that are predictive.
You might guess 'Oh, such and such should
be predictive, might be predictive' and
you test it out in the computer and
maybe it is maybe it isn't. - What
discipline of mathematics, or disciplines-
is that multidisciplinary or are we talking-?
- It's mostly statistics. It's mostly statistics
and some some probability theory and- but
I can't get into, you know, what things we
do use and what things we don't use;
we we reach for different things that
might come that might be effective.
- I would imagine lots of people want to
be financially successful, most pe-
most people want that of course. - I suppose.
- And lots of people are good at
mathematics and know a lot about
computers like, you know, at your level I
would imagine. Why did you do it? Why
didn't someone else do it?
- I don't know. Well, first of all some
other people have done it. I think that
we're- our firm is better, but nonetheless -
I'm pretty sure of that - but nonetheless
other people have done some very good
modelling and so we're not alone. But it's
not easy to do and there's a big barrier
to entry. For example, huge data sets that
we've collected over the years; programs
that we've written to make it really
easy to test hypotheses and so on. The
infrastructure is exceptionally good so
everything is tuned right; it took years
to learn how to do that. - I know you guys
made the model, so you do have the
ownership of it and feel proud of it; but
it's hard to follow the model
religiously? Like is it hard for your ego
to think all the success is because of
the computer? Like and I just I just sat
there and watched? - No the computer's just a
tool that we use to be it's- a good
cabinet maker doesn't say it's all
because of my wonderful chisel. You know,
you may have great film equipment but
that's not why you're a success at doing
what you're doing, you're working
with good equipment but another guy
would make a mess of it with the same
equipment so no we no we don't feel
'oh the computer is doing everything'. The
computer does what you tell it to do.
- Given that you will put some of it down
to luck, what are you more proud of? All of
this and the business or the- or the
mathematics from that first half of your
career? - Oh...to the extent that I'm
proud- I think I'm proud of both. I think,
yeah I've done some mathematics and
some of it's had a positive effect and I'm,
guess I'm proud of that. And I've built a
nice business and I'm proud of that; I
don't say I'm prouder of one than the
other. And now for the last number of
years I've been working with my wife on
this foundation, which she started
actually in '94 with my money but
nonetheless she started the foundation
and then I joined, I got more and more
involved with the foundation as time
went on and now that's my main thing. And you
know I'm pretty proud of the foundation.
- Let me focus your- focus you more on which-
the mathematics versus the business then.
Would you, would you trade any of your-
any or all of your business success for
for being the man who cracked the
Riemann hypothesis or something like
that? - Now that's a good question.
Huh, would I trade that for- well I'd
probably trade some of it. I mean for the
Riemann hypothesis it would certainly I
guess be a thrill to to solve the
Riemann hypothesis. I'm pleased,
mostly, with what with the way my career
has gone so would I trade part of it for
something - I don't know. It's- I've never
looked back and said, I wish - at least in
business - I wish I hadn't done that or I
wish I had done this and not that
whatever it is, I've never looked back that way.
The foundation is focused on support of scientific
research. Primarily basic science but not
completely, because we have a large
autism project which involves a lot of
basic science but the- but treatment's our
a goal. But the rest is support of
mathematics, physics, computer science,
biology of all sorts, neuroscience,
genetics - we support basic science. And
that is what we like to do. There's a
certain amount of outreach, we have a
Math for America pro- we we spend maybe
10 to 15% on outreach; outreach and
education. But 80-85% is support of
basic science. - You put a lot of
money into mathematics, so you're- you've
got some right to comment on it - how are
you feeling about it? - Oh I think
mathematics is is really going
quite well worldwide, the research end of
it. A lot of new ideas are coming up, new
fields or sort of are flourishing. It
feels like a pretty healthy enterprise
to me. What's not healthy is the state of
mathematics education in our country,
that's very unhealthy for young people;
that's why we have- we started this thing
called Math for America and so on. But we
don't have enough teachers in
mathematics who know it and who know the
subject, and even of science, and that's
for a simple reason:
30-40 years ago if you knew some
mathematics, enough to teach let's say in
high school there weren't a million
other things you could do with that
knowledge. Oh yeah maybe you could become
a professor but let's suppose you- you're
not quite at that level but you you were
good at math and so on. Being a math
teacher was a nice job. But today if you
know that much mathematics you could get
a job at Google, you could get a job at IBM,
you could get a job at Goldman Sachs; I
mean there's plenty of opportunities
that are going to pay more than being a
high school teacher, right? There weren't
so many when I was going to high school,
such thing. So the quality
of high school teachers in math has
declined simply because if you know
enough to teach in high school you know
enough to work for Google. And well
they're not going to pay that much in
high school so how do you redress that?
How do we redress that as a country? Well
you have- so we work, a person works for a
combination of financial reward and
respect, right? So a guy becomes a Supreme
Court justice; he's not doing it because
he's going to make a fortune, he'll be
well-paid I suppose but you know Supreme
Court justice everyone says, huh that's a big
deal, you have a lot of respect and you
respect yourself presumably. So there are
many- so you can't pay let's say high
school teachers of math as much as they
would get at Google, but you can give
them a bump, pay them more - we give people
$15,000 a year more than they would make at
their regular salary. But we also create
a community of math and science teachers
which they love and it makes them feel
important, and they are important. - Do you
give your money to basic research
because you feel somehow indebted to it
for your own success? Or do you do it just
out of like a belief? Or do you feel
that you're giving something back to,
to what gave something to you? - That's
an interesting question. I do it because
it feels good. I like science, I like to
see it flourish. I like to be around
scientists, I like to learn new
things - and my wife feels the same way, she
loves science. So we're very happy to to
do this. Do I feel I'm giving back? Not
especially. You know, I could give back in
a lot of ways, there's a lot of things I
could do besides support science. - Do you
have a favorite number? - Seven. Next question.
- Do you have a favorite mathematician?
- Well Archimedes and Euler are my current
favorites, but maybe you meant somebody-
ah I'm very impressed with those two guys.
Thank you so much for so much of your time.
All right, well this was kind of fun.