you was something of a mathematical
phenom you had already taught at Harvard
and MIT at a young age and then the NSA
came in calling what was that about 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 so you
were a code crack huh I was ill 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 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
Howard and a win in Vietnam and I didn't
like that war thought it was stupid and
I wrote a letter to The Times which they
published they're 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 so then I can
see that would which would different
from general Taylor's but on the other
hand nobody said anything but then you
know I was 29 years old at this time at
some kid came around to said he was a
stringer from Newsweek magazine and he
wanted to interview me and asked my but
I was doing about my views and I told
him that 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
into
he said what did you say I told him what
I said and then he said I got a call
tailored he called tailor that took ten
minutes I was fired five minutes after
that okay so but it wasn't bad it wasn't
bad cuz you went you went on to Stony
Brook and I went on stepped up your
mathematical career and you started
working with with this man here who is
this Oh
churn yeah churn was one of the great
mathematicians of the of the century I
had known him when I was a graduate
student actually and 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 and it led to you publishing a
famous paper together
can you explain at all what that work
was can you explain how you explain it
to somebody how about explaining this
but not many I think you told me it had
something to do with spheres so let's
let's start let's start 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 was good
mathematics I was very happy with it
so was churn 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 turn to a heck of a
lot and about 10 years after the paper
came out guy named ed Witten and in
Princeton started applying it to string
theory and people in Russia started
applying it to what's called condensed
matter and today that those things in
there called chern-simons invariants
spread through a lot of physics and 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 yeah
how can I happen God knows it better but
yeah but there's a 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 some sense you know we learned 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 a live 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 so it is a mystery it is a
mystery so here's a mathematical piece
of ingenuity here don't tell us about
this yeah yeah well this well that's a
ball it's a 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 twelve edges and six faces
and if you look at the difference
vertices minus edges plus faces you get
- okay well - that's a good number
oh here's a different way of doing it
these are triangles covering over has 12
vertices and 30 edges and 20 faces it's
20 tiles and vertices minus edges plus
faces still equals 2 and in fact you
could do this any which way cover this
thing with all kinds of
yawns and triangles or 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 60 and faces hey
that this comes out zero vertices minus
8 it'll always come out to 0
every time you cover a torus with
squares or triangles or anything like
that you're gonna get a zero when you
take that thing so this is called the
Euler characteristic and it's what's
called a topological invariant it's
pretty amazing no matter how you do it
you're always going to get the same
answer so that was the first shot of
thrust from the mid-1700s into a subject
which is now called algebraic topology
and your own work took an idea like this
and moved it into high dimensional
theory well
objects and found new invariances yes
well they were already higher
dimensional invariants pontryagin
classes actually there were Chern
classes or a bunch of these types of it
variants but I 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 some new
things but if it wasn't for mr. Euler
who wrote almost 70 volumes of
mathematics had 13 children whom he had
apparently would dandelo on his knee
while he was writing that's all if it
wasn't for mr. Euler well it wouldn't
perhaps be these in various okay so
that's the least giving us a flavor of
that amazing mind in there let's talk
about Renaissance because you took that
amazing mind and you know having been a
coke cracker the NSA you start to become
a coke rock or 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 weights 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 the other average
funds so how on earth did you do this
Jim why did it by assembling a wonderful
group of people right what 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 how it with
pure luck I mean I think it was pure
luck simply wasn't mathematical modeling
but in looking at the data after a while
I realized hey this 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 eye-dea
you design an algorithm you test it out
on a computer does it work doesn't it
work
and so on can you 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 random up and down and walk maybe a
slight upward trend over that whole
period of time how on earth could you
trade look at that and see something the
world 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 that very like trend you see
here but but trending in in periods and
if you decided okay I'm going to predict
today by the average move in the past 20
days today 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 and make money but this is a
year's worth of days and you you know
you'd make a little money during that
period but so you would think it's a
very best digial system 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 sharks
sure
you would uh you you know try all those
things to 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 it wasn't
it didn't because everyone could so what
happened so how did you stay ahead of
the pack then we stayed ahead of the
pack by finding by finding other other
approaches and shorter term approaches
to some extent but the the real thing
was to gather a tremendous amount of
data and and we had to get it by hand in
the early days and 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 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 marvels got better and
better and better and better I mean I
think your credit should be doing second
remarkable 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
was doing exciting mathematics and
science well I hope that might be true
but some of it was money I think made a
lot him I can't say that 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 you 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 doesn't necessarily feed back on
itself the way we did things but it
worked these different predictive skins
can be really quite quite wild quite
unexpected I mean you look at everything
I look at weather lengths addresses yes
yes full opinion waste of dressers we
didn't try
but uh what sort of things well
everything I mean everything is grist
for the mill except hem links have to
say whether annual reports monthly
record early 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 there any one anomaly
might be just a random thing so is is
the secret here to just look at multiple
strange anomalies and see when they're
lying 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 so it the
probability of it being random is 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 as 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 that's I don't know helping
increase inequality or or how would you
how would you champion what's 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 halt has not
done so on if we 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 fund so it's just
another you know people would ask me
what's a hedge fund and I say one in 20
I mean if they would which means now
it's two or twenty you know it's two
percent fixed fee and twenty percent of
profits hedge funds are all different
kind of creature rumor has it you charge
slightly higher fees we had tried we had
to judge the highest fees in the world
at one time five and forty four five and
forty four five percent fixed fee forty
five percent flat forty four percent of
upside it still made your investors many
good returns yes people got very mad at
my investor so how can you charge such
high fees I said okay you can withdraw
but no 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 as
a capacity to this 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 there are any
other problems it's not just
mathematically higher there on immers
and physicists and things like that
I I don't think we should worry about it
too much it's still it's still a pretty
small industry and in fact bringing in
science into the investing world has has
improved that world it's it's reduced
volatility it's increased liquidity
spreads are narrower because people are
trading that kind of stuff so I don't
I'm not too worried about Einstein going
off and starting a hedge fund not your
defense in your life now where you're
actually investing though at the other
end of the supply chain in actually
boosting mathematics across America is
your wife Marilyn and you know your your
you're working on philanthropic issues
together yeah tell me about that well
you know we are Marilyn started there
she is up there beautiful wife she
started the foundation about twenty
years ago I think 94 I claim it was 93
she says was 94 but it was a row one of
those two years
we started the foundation and just as a
convenient way to give charity but 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 that's what we've
done in six years ago or so I left
Renaissance and went to work at the
foundation so that's what we do it's a
month for America here is basically
investing in math teachers around the
country giving them some extra income
and giving them support and coaching and
and really trying to make yeah make that
more effective and make that a calling
to which teachers can aspire yeah yeah
it's a instead of beating up the bad
teachers which has created morale
problems all through the educational
community and in particular in math and
science we focus on celebrating the good
ones and 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 they're there
there's a great morale among them
they're staying in the field next year
it'll be a thousand and that'll be ten
percent of the math and science teachers
in New York Public Schools and and
that's
and Jim here's another project 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 out of how do we
get here what's what's a well there's
two questions so one is what is the
route from geology to biology how did
how did we get here and the other
questions what did we start with what
material if any that we have to work
with on this route those are two very
very interesting questions the first
question is tortuous path from from
geology up to RNA or something like that
how do I tall 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 a hundred 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 and
anytime that wood is somewhere along
this settling down period and as always
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 all
others 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 compete 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 get well after seeing
life like to know you know if that path
is tortuous enough and loop and so
improbable that no matter what you start
with we're we could be a singularity but
on the other hand given all this organic
dust that's floating around we could
have what's it ones friends out there be
great you know Jim a couple couple years
ago I got a chance to speak with Elon
Musk and I asked him the secret of his
success rate of and and he said taking
physics seriously what was it listening
to you what what I hear you saying is
you know taking math seriously that 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 and
math actually works well math certainly
works math certainly works and but this
has been fun working with Marilyn and
giving it away has been very enjoyable
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 turn 30
thank you Jim Simons
really
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