TED2015
Jim Simons: The mathematician who cracked Wall Street
詹姆斯·西蒙斯: 与横扫华尔街数学家的珍贵对话
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詹姆斯·西蒙斯曾是一位数学家与密码破译者。他意识到：过去曾用来破译密码的复杂数学，能够帮助他解读世界经济模式。赚了几十亿之后，他正致力于支持下一代的数学教师和学者。TED总监克里斯·安德森与西蒙斯对话，谈谈他沉浸在数字中的别样人生。
Jim Simons - Philanthropist, mathematician
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
Double-click the English transcript below to play the video.
00:12
Chris Anderson: You were something
of a mathematical phenom.
of a mathematical phenom.
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您可以说是数学界出类拔萃的人物了
00:15
You had already taught at Harvard
and MIT at a young age.
and MIT at a young age.
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年轻时就已经在哈佛和麻省理工授课了
00:18
And then the NSA came calling.
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后来NSA主动找上门来
00:21
What was that about?
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那是怎么回事呢？
00:23
Jim Simons: Well the NSA --
that's the National Security Agency --
that's the National Security Agency --
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NSA就是国家安全局
00:27
they didn't exactly come calling.
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确切来说 也不是他们找上我的
00:29
They had an operation at Princeton,
where they hired mathematicians
where they hired mathematicians
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他们在普林斯顿专设有一个机构
00:33
to attack secret codes
and stuff like that.
and stuff like that.
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专门雇佣数学家 用于破解密码之类的
00:37
And I knew that existed.
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我本来就知道这个机构的存在
00:39
And they had a very good policy,
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他们的政策非常诱人
00:41
because you could do half your time
at your own mathematics,
at your own mathematics,
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因为你可以把半数时间花在你自己的数学研究上
00:45
and at least half your time
working on their stuff.
working on their stuff.
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还有至少一半的时间要为他们解决事务
00:49
And they paid a lot.
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而且他们给的报酬很丰厚
00:51
So that was an irresistible pull.
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这有着无法抵抗的诱惑力
00:54
So, I went there.
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所以我就去那儿了
00:56
CA: You were a code-cracker.
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所以你曾是个密码破译者？
00:57
JS: I was.
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对
00:58
CA: Until you got fired.
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直到你被炒了？
00:59
JS: Well, I did get fired. Yes.
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嗯我确实被炒了，对
01:01
CA: How come?
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为什么呢？
01:03
JS: Well, how come?
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啊 为什么呢
01:05
I got fired because,
well, the Vietnam War was on,
well, the Vietnam War was on,
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我之所以被解雇是因为
当时正值越南战争之际 我组织内的最高领导是个好战分子
01:10
and the boss of bosses in my organization
was a big fan of the war
was a big fan of the war
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01:16
and wrote a New York Times article,
a magazine section cover story,
a magazine section cover story,
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他给《纽约时报》杂志版块的封面故事
01:20
about how we would win in Vietnam.
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写了一篇关于 我们如何在越南获得胜利的文章
01:22
And I didn't like that war,
I thought it was stupid.
I thought it was stupid.
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我不喜欢那场战争 我觉得那很蠢
01:25
And I wrote a letter to the Times,
which they published,
which they published,
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我给《纽约时报》写了封信 他们后来刊登了出来
01:28
saying not everyone
who works for Maxwell Taylor,
who works for Maxwell Taylor,
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那封信写了 如果还有人记得Maxwell Taylor（就是他最高领导）的话
01:32
if anyone remembers that name,
agrees with his views.
agrees with his views.
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不是每个在他手下工作的人 都同意他的观点
01:37
And I gave my own views ...
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我给出了我自己的观点
01:39
CA: Oh, OK. I can see that would --
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好吧 我可以想见那将……
01:41
JS: ... which were different
from General Taylor's.
from General Taylor's.
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（我的观点）是和Taylor将军不一样的
01:44
But in the end, nobody said anything.
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但最后 也没人说什么
01:45
But then, I was 29 years old at this time,
and some kid came around
and some kid came around
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后来，我当时是29岁，有个孩子来采访我
01:49
and said he was a stringer
from Newsweek magazine
from Newsweek magazine
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说他是《新闻周刊》的特约记者
01:52
and he wanted to interview me
and ask what I was doing about my views.
and ask what I was doing about my views.
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他想要与我面谈 问我是如何实践我的观点的
01:58
And I told him, "I'm doing
mostly mathematics now,
mostly mathematics now,
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我告诉他 我现在（战争期间）主要是做数学研究
02:02
and when the war is over,
then I'll do mostly their stuff."
then I'll do mostly their stuff."
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战争结束后 我才会主要给他们做事
02:06
Then I did the only
intelligent thing I'd done that day --
intelligent thing I'd done that day --
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接着我做了那天最明智的一件事
02:08
I told my local boss
that I gave that interview.
that I gave that interview.
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我告诉我当地的上司 我接受了那个访问
02:13
And he said, "What'd you say?"
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他问我 你怎么说的？
02:14
And I told him what I said.
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我就把我说的告诉他了
02:16
And then he said,
"I've got to call Taylor."
"I've got to call Taylor."
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然后他说：“我必须要给Taylor打个电话”
02:18
He called Taylor; that took 10 minutes.
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他打给了Taylor 花了十分钟
02:20
I was fired five minutes after that.
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又过了五分钟 我就被解雇了
02:23
CA: OK.
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OK
02:24
JS: But it wasn't bad.
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但那并不是一件坏事
02:26
CA: It wasn't bad,
because you went on to Stony Brook
because you went on to Stony Brook
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那并不糟 因为你接下来去了纽约石溪大学
02:28
and stepped up your mathematical career.
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使你的数学生涯更上一层楼
02:31
You started working with this man here.
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你开始和这个人一起共事
02:34
Who is this?
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这是谁呢
02:36
JS: Oh, [Shiing-Shen] Chern.
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噢 陈（陈省身）
02:37
Chern was one of the great
mathematicians of the century.
mathematicians of the century.
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陈是本世纪最伟大的数学家之一
02:40
I had known him when
I was a graduate student at Berkeley.
I was a graduate student at Berkeley.
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我在伯克利当研究生的时候 就已经知道他了
02:46
And I had some ideas,
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我带着一些想法去找他
02:48
and I brought them to him
and he liked them.
and he liked them.
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他很喜欢这些想法
02:50
Together, we did this work
which you can easily see up there.
which you can easily see up there.
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我们一起开展这项理论研究 你可以在这里看到
02:57
There it is.
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就是这个
02:59
CA: It led to you publishing
a famous paper together.
a famous paper together.
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基于这项研究 你们一起发表了一篇著名的文章
03:02
Can you explain at all what that work was?
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你可以给大家解释一下这项研究吗？
03:07
JS: No.
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不
03:08
(Laughter)
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（笑）
03:10
JS: I mean, I could
explain it to somebody.
explain it to somebody.
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我的意思是 我可以向某些人解释
03:13
(Laughter)
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（笑）
03:15
CA: How about explaining this?
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要不讲下这个？
03:17
JS: But not many. Not many people.
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但不是很多人
03:21
CA: I think you told me
it had something to do with spheres,
it had something to do with spheres,
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我记得你告诉我 它和球体有关
03:23
so let's start here.
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我们从这里说起吧
03:25
JS: Well, it did,
but I'll say about that work --
but I'll say about that work --
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确实 但我要讲一讲那项研究
03:29
it did have something to do with that,
but before we get to that --
but before we get to that --
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它确实和这球体有关 但在此之前我要说
03:32
that work was good mathematics.
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这是一个非常棒的数学理论
03:36
I was very happy with it; so was Chern.
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我非常喜欢研究它的过程，陈也一样
03:39
It even started a little sub-field
that's now flourishing.
that's now flourishing.
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它甚至开创了一个现在很繁荣的副领域
03:44
But, more interestingly,
it happened to apply to physics,
it happened to apply to physics,
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但更有趣的是 它正巧可以应用于物理
03:49
something we knew nothing about --
at least I knew nothing about physics,
at least I knew nothing about physics,
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一个我们完全不了解的东西 至少我是完全不了解的
03:54
and I don't think Chern
knew a heck of a lot.
knew a heck of a lot.
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我觉得陈也不会了解太多
03:56
And about 10 years
after the paper came out,
after the paper came out,
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在文章发表大约十年后
04:00
a guy named Ed Witten in Princeton
started applying it to string theory
started applying it to string theory
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普林斯顿一个叫Ed Witten的人 开始把它应用于弦理论
04:05
and people in Russia started applying it
to what's called "condensed matter."
to what's called "condensed matter."
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俄罗斯人开始把它应用在 被称作“凝聚体”的物理学中
04:09
Today, those things in there
called Chern-Simons invariants
called Chern-Simons invariants
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如今 这些被称为“陈-西蒙斯不变量”的东西
04:14
have spread through a lot of physics.
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衍伸进了很多物理学理论中
04:16
And it was amazing.
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这非常不可思议
04:17
We didn't know any physics.
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我们根本不懂物理
04:19
It never occurred to me
that it would be applied to physics.
that it would be applied to physics.
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我从没想到 它可以被应用于物理学
04:22
But that's the thing about mathematics --
you never know where it's going to go.
you never know where it's going to go.
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但这就是数学的迷人之处 你永远不知道它将去往何处
04:26
CA: This is so incredible.
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这太奇妙了
04:27
So, we've been talking about
how evolution shapes human minds
how evolution shapes human minds
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我们谈到 人类的思想 无论是否触及到真理
04:32
that may or may not perceive the truth.
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是如何被进步的理论所改变的
04:34
Somehow, you come up
with a mathematical theory,
with a mathematical theory,
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无意间 在不了解任何物理学的情况下
04:38
not knowing any physics,
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你提出了一个数学理论
04:40
discover two decades later
that it's being applied
that it's being applied
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发现数十年之后 它已经被深度应用于
04:42
to profoundly describe
the actual physical world.
the actual physical world.
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描述真实的物理世界了
04:45
How can that happen?
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那是怎样发生的呢？
04:46
JS: God knows.
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天知道
04:47
(Laughter)
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（笑）
04:50
But there's a famous physicist
named [Eugene] Wigner,
named [Eugene] Wigner,
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有个著名的物理学家 Wigner
04:54
and he wrote an essay on the unreasonable
effectiveness of mathematics.
effectiveness of mathematics.
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他写过一篇名为《数学在自然科学中不可思议的有效性》的文章
04:59
Somehow, this mathematics,
which is rooted in the real world
which is rooted in the real world
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某种程度上 数学植根于真实世界
05:03
in some sense -- we learn to count,
measure, everyone would do that --
measure, everyone would do that --
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某种意义上 我们学着计算 测量 每个人都会这样
05:08
and then it flourishes on its own.
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接着它就自己繁荣了起来
05:10
But so often it comes
back to save the day.
back to save the day.
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却又常常回过头来挽救大局
05:14
General relativity is an example.
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广义相对论就是一个例子
05:16
[Hermann] Minkowski had this geometry,
and Einstein realized,
and Einstein realized,
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闵可夫斯基给出了他的四维空间理论 而爱因斯坦意识到
05:19
"Hey! It's the very thing
in which I can cast general relativity."
in which I can cast general relativity."
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嘿！就是这玩意儿 可以用来表达我的广义相对论
05:23
So, you never know. It is a mystery.
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你永远也想不到 就是这么神奇
05:27
It is a mystery.
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对 很神奇
05:28
CA: So, here's a mathematical
piece of ingenuity.
piece of ingenuity.
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这是一个精巧的数学模型
05:31
Tell us about this.
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给我们讲讲吧
05:32
JS: Well, that's a ball -- it's a sphere,
and it has a lattice around it --
and it has a lattice around it --
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噢 这是一个球 球体 外面有格子状的框架
05:38
you know, those squares.
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你知道 这些正方形
05:42
What I'm going to show here was
originally observed by [Leonhard] Euler,
originally observed by [Leonhard] Euler,
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我接下来要展示的 最初是由十八世纪伟大的数学家
05:47
the great mathematician, in the 1700s.
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欧拉发现的
05:50
And it gradually grew to be
a very important field in mathematics:
a very important field in mathematics:
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后来逐步发展成为 数学中非常重要的一个领域
05:55
algebraic topology, geometry.
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代数拓扑 几何学
05:59
That paper up there had its roots in this.
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上面的那篇文章是基于这个理论基础的
06:03
So, here's this thing:
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是这样子的
06:05
it has eight vertices,
12 edges, six faces.
12 edges, six faces.
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它有8个顶点 12条边 6个面
06:09
And if you look at the difference --
vertices minus edges plus faces --
vertices minus edges plus faces --
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如果你算一下 定点数 - 边的个数 + 面的个数（8-12+6）
06:13
you get two.
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会得到2
06:14
OK, well, two. That's a good number.
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好 2 是个好数字
06:17
Here's a different way of doing it --
these are triangles covering --
these are triangles covering --
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我们还可以这样算 表面覆盖了三角形
06:21
this has 12 vertices and 30 edges
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这样的话 有12个顶点 30条边
06:25
and 20 faces, 20 tiles.
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和20个面 铺了20片
06:30
And vertices minus edges
plus faces still equals two.
plus faces still equals two.
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顶点数 - 边的个数 + 面的个数（12-30+20）还是等于2
06:35
And in fact, you could do this
any which way --
any which way --
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事实上 你随便怎么算
06:38
cover this thing with all kinds
of polygons and triangles
of polygons and triangles
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用各种多边形和三角来覆盖表面
06:41
and mix them up.
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混在一起
06:42
And you take vertices minus edges
plus faces -- you'll get two.
plus faces -- you'll get two.
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再计算 顶点数 - 边的个数 + 面的个数 总是会等于2
06:46
Here's a different shape.
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这儿有另外一个形状
06:48
This is a torus, or the surface
of a doughnut: 16 vertices
of a doughnut: 16 vertices
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它有一个环面 或者说轮状表面
表面附有长方形 形成的16个顶点 32条边 16个面
06:53
covered by these rectangles,
32 edges, 16 faces.
32 edges, 16 faces.
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06:58
Vertices minus edges comes out to be zero.
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点-边+面（16-32+16）结果是0
07:01
It'll always come out to zero.
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并且总是0
07:02
Every time you cover a torus
with squares or triangles
with squares or triangles
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每次你用正方形或三角形或类似的形状
07:07
or anything like that,
you're going to get zero.
you're going to get zero.
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覆盖一个环形 你总会得到0
07:12
So, this is called
the Euler characteristic.
the Euler characteristic.
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这就是欧拉示性数
07:14
And it's what's called
a topological invariant.
a topological invariant.
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也是一种拓扑不变量
07:18
It's pretty amazing.
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相当神奇
07:20
No matter how you do it,
you're always get the same answer.
you're always get the same answer.
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无论你怎么做 总会得到相同的答案
07:22
So that was the first sort of thrust,
from the mid-1700s,
from the mid-1700s,
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这是自十八世纪中叶以来 首次 算是进入了一个
07:29
into a subject which is now called
algebraic topology.
algebraic topology.
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如今被称作 代数拓扑的学科
07:32
CA: And your own work
took an idea like this and moved it
took an idea like this and moved it
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您自己的研究 是把像这样的一个概念
07:35
into higher-dimensional theory,
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推进到了高维空间理论
07:38
higher-dimensional objects,
and found new invariances?
and found new invariances?
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高维空间物体 并发现了新的不变量？
07:41
JS: Yes. Well, there were already
higher-dimensional invariants:
higher-dimensional invariants:
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对 之前已经有高维空间不变量了
07:46
Pontryagin classes --
actually, there were Chern classes.
actually, there were Chern classes.
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庞特里亚金类（Pontryagin classes）
事实上 还有陈类（Chern classes）
事实上 还有陈类（Chern classes）
07:50
There were a bunch
of these types of invariants.
of these types of invariants.
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这些类型的不变量有很多
07:54
I was struggling to work on one of them
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我努力研究其中一个
07:58
and model it sort of combinatorially,
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用组合数学的方法 而非传统方法
08:02
instead of the way it was typically done,
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给他们建模
08:05
and that led to this work
and we uncovered some new things.
and we uncovered some new things.
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从而得出了这个成果 我们揭示了一些新的东西
08:10
But if it wasn't for Mr. Euler --
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但如果没有欧拉先生
08:13
who wrote almost 70 volumes of mathematics
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写下了近70卷数学著作
08:17
and had 13 children,
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还有13个子女
08:19
who he apparently would dandle on his knee
while he was writing --
while he was writing --
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显然在他写作时 承欢膝下
08:25
if it wasn't for Mr. Euler, there wouldn't
perhaps be these invariants.
perhaps be these invariants.
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如果没有欧拉先生 可能就不会有这些不变量了
08:32
CA: OK, so that's at least given us
a flavor of that amazing mind in there.
a flavor of that amazing mind in there.
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所以这至少 给这个精彩的思想 增加了一丝风味
让我们谈谈文艺复兴（Simons所创立的科技公司）
08:36
Let's talk about Renaissance.
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08:38
Because you took that amazing mind
and having been a code-cracker at the NSA,
and having been a code-cracker at the NSA,
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因为你带着那个精彩的想法 曾在国安局做着一名密码破译者
08:44
you started to become a code-cracker
in the financial industry.
in the financial industry.
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你开始在金融业做密码破译者
08:47
I think you probably didn't buy
efficient market theory.
efficient market theory.
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我觉得你应该没买有效市场理论(有效市场假说认为市场价格波动是随机的，交易者不可能持续从市场中获利。)
08:50
Somehow you found a way of creating
astonishing returns over two decades.
astonishing returns over two decades.
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二十年后 你突然找到一种创造惊人收益的方法
08:56
The way it's been explained to me,
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你解释给我的方法
08:58
what's remarkable about what you did
wasn't just the size of the returns,
wasn't just the size of the returns,
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你所做之事的卓越之处 并不只是收益的规模
09:01
it's that you took them
with surprisingly low volatility and risk,
with surprisingly low volatility and risk,
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更是因为 相比其他对冲基金
09:05
compared with other hedge funds.
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你的方法有着出奇低的波动性和风险
09:07
So how on earth did you do this, Jim?
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你究竟是怎么做到的呢 Jim
09:10
JS: I did it by assembling
a wonderful group of people.
a wonderful group of people.
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我能做到是因为 我聚集了一个非常优秀的团队
09:14
When I started doing trading, I had
gotten a little tired of mathematics.
gotten a little tired of mathematics.
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我开始经商的时候 已经有点厌倦数学了
09:18
I was in my late 30s,
I had a little money.
I had a little money.
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人近四十 有些小钱
09:22
I started trading and it went very well.
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我开始经商 而且进行得很顺利
09:25
I made quite a lot of money
with pure luck.
with pure luck.
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光凭运气赚了相当多的钱
09:27
I mean, I think it was pure luck.
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我的意思是 我觉得那完全是运气
09:29
It certainly wasn't mathematical modeling.
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这当然不是数学建模
09:31
But in looking at the data,
after a while I realized:
after a while I realized:
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但过一阵子 当我看着那些数据 我意识到
09:35
it looks like there's some structure here.
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那里面好像存在着某种结构
09:38
And I hired a few mathematicians,
and we started making some models --
and we started making some models --
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我招募了一些数学家 我们开始建立一些模型
09:41
just the kind of thing we did back
at IDA [Institute for Defense Analyses].
at IDA [Institute for Defense Analyses].
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和我们当初在IDA（国防分析研究所）做的事情差不多
09:46
You design an algorithm,
you test it out on a computer.
you test it out on a computer.
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你设计一个算法 在电脑上测试
09:48
Does it work? Doesn't it work? And so on.
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管用？不管用？之类的
09:51
CA: Can we take a look at this?
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我们可以看一下这个吗
09:52
Because here's a typical graph
of some commodity.
of some commodity.
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这儿有一份 某个商品的典型图表
09:58
I look at that, and I say,
"That's just a random, up-and-down walk --
"That's just a random, up-and-down walk --
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我看着它 只能说 这只是一条随机的 上上下下的走势图
10:02
maybe a slight upward trend
over that whole period of time."
over that whole period of time."
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大概整体上有轻微上升的趋势
你究竟就怎么看着这样的东西 来做交易的呢
10:05
How on earth could you trade
looking at that,
looking at that,
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10:07
and see something that wasn't just random?
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还能看出点不随机的东西呢
10:09
JS: In the old days -- this is
kind of a graph from the old days,
kind of a graph from the old days,
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在过去 这是过时的一种图表
10:13
commodities or currencies
had a tendency to trend.
had a tendency to trend.
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可以通过趋势来追踪商品或货币
10:17
Not necessarily the very light trend
you see here, but trending in periods.
you see here, but trending in periods.
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并不必然是你这儿看到的轻微的趋势 可能是周期性的趋势
10:23
And if you decided, OK,
I'm going to predict today,
I'm going to predict today,
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如果你决定 好 我今天打算要做预测
10:27
by the average move in the past 20 days --
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通过前20天的平均变化
10:32
maybe that would be a good prediction,
and I'd make some money.
and I'd make some money.
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可能会有一个好的预测 还赚了点钱
10:35
And in fact, years ago,
such a system would work --
such a system would work --
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事实上 几年前 这样子的系统是有用的
10:41
not beautifully, but it would work.
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并不完美 但确实有用
10:43
You'd make money, you'd lose
money, you'd make money.
money, you'd make money.
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你赚点钱 亏点钱 再赚点钱
10:46
But this is a year's worth of days,
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但这是一年中的黄金几天
10:48
and you'd make a little money
during that period.
during that period.
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你在那个阶段可以赚到点钱
10:53
It's a very vestigial system.
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这是一个非常不健全的系统
10:56
CA: So you would test
a bunch of lengths of trends in time
a bunch of lengths of trends in time
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所以你会及时地测试大量的趋势区间
11:00
and see whether, for example,
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看是否 举个例子
11:02
a 10-day trend or a 15-day trend
was predictive of what happened next.
was predictive of what happened next.
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是否10天或15天的走向 可以对下一步做出较准确的预判
11:06
JS: Sure, you would try all those things
and see what worked best.
and see what worked best.
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当然 你要测试各种类型 来判断哪个最有效
11:13
Trend-following would
have been great in the '60s,
have been great in the '60s,
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跟踪趋势在60年代是很好的策略
11:16
and it was sort of OK in the '70s.
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在70年代就一般了
11:19
By the '80s, it wasn't.
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80年代 就没用了
11:20
CA: Because everyone could see that.
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因为每个人都能看到
11:23
So, how did you stay ahead of the pack?
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所以 你是如何保持领先地位呢
11:27
JS: We stayed ahead of the pack
by finding other approaches --
by finding other approaches --
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我们保持领先是通过 寻找其他方法
11:33
shorter-term approaches to some extent.
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某种程度上来说 更短期的方法
11:37
The real thing was to gather
a tremendous amount of data --
a tremendous amount of data --
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具体来说是收集大量数据
11:40
and we had to get it by hand
in the early days.
in the early days.
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早期 我们不得不手动来收集数据
11:44
We went down to the Federal Reserve
and copied interest rate histories
and copied interest rate histories
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我们到美联储 拷贝历史利率之类的数据
11:47
and stuff like that,
because it didn't exist on computers.
because it didn't exist on computers.
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因为电脑上根本没有
11:50
We got a lot of data.
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我们得到了很多数据
11:52
And very smart people -- that was the key.
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和非常聪明的人——这是关键
11:57
I didn't really know how to hire
people to do fundamental trading.
people to do fundamental trading.
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我不太知道要怎么去雇佣做基本贸易的人
12:01
I had hired a few -- some made money,
some didn't make money.
some didn't make money.
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我请了些 有的赚钱了 有的没有
12:04
I couldn't make a business out of that.
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因为这样 我没有成功地打开局面
12:06
But I did know how to hire scientists,
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但我知道怎么请科学家
12:08
because I have some taste
in that department.
in that department.
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因为在那个领域 我还是有点眼光的
12:12
So, that's what we did.
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所以我们这么做了
12:13
And gradually these models
got better and better,
got better and better,
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渐渐地 这些模型越来越好
12:17
and better and better.
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越来越好
12:18
CA: You're credited with doing
something remarkable at Renaissance,
something remarkable at Renaissance,
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您在文艺复兴科技公司所做的最为人称道的事
12:21
which is building this culture,
this group of people,
this group of people,
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就是建立起了这样的文化 组建这样的团队
12:24
who weren't just hired guns
who could be lured away by money.
who could be lured away by money.
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他们不是会被简单地 被金钱诱惑的雇佣兵
12:27
Their motivation was doing
exciting mathematics and science.
exciting mathematics and science.
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他们的动力在于令人激动的数学和科学
12:31
JS: Well, I'd hoped that might be true.
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噢我挺希望这是真的
12:34
But some of it was money.
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但有些原因也是钱
12:37
CA: They made a lot of money.
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他们金钵满盈
12:39
JS: I can't say that no one came
because of the money.
because of the money.
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我不能断言 没有人是冲着钱来的
12:41
I think a lot of them
came because of the money.
came because of the money.
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我觉得他们中大多数都是为了钱
12:44
But they also came
because it would be fun.
because it would be fun.
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但也是因为 这会很好玩
12:46
CA: What role did machine learning
play in all this?
play in all this?
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机器学习在这里扮演了怎样一个角色？
12:48
JS: In a certain sense,
what we did was machine learning.
what we did was machine learning.
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某种意义上 我们做的就是机器学习
12:52
You look at a lot of data, and you try
to simulate different predictive schemes,
to simulate different predictive schemes,
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你观察一大堆数据 模拟不同的预测方案
12:59
until you get better and better at it.
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直到你越来越擅长于此
13:01
It doesn't necessarily feed back on itself
the way we did things.
the way we did things.
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我们所做之事 不见得一定有自我反馈
13:05
But it worked.
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但确实有效
13:08
CA: So these different predictive schemes
can be really quite wild and unexpected.
can be really quite wild and unexpected.
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所以这些不同的预测方案 很有可能相当不受控制 且无法预料
13:12
I mean, you looked at everything, right?
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我的意思是 你着眼于万事万物 不是吗
13:14
You looked at the weather,
length of dresses, political opinion.
length of dresses, political opinion.
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你要看天气 裙长 政见
13:17
JS: Yes, length of dresses we didn't try.
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嗯 我们可没试过裙长
13:20
CA: What sort of things?
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那是什么样的事物呢？
13:22
JS: Well, everything.
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嗯 各种东西
13:23
Everything is grist for the mill --
except hem lengths.
except hem lengths.
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各种对工作有价值的东西 衣服下摆长度不算在内
13:28
Weather, annual reports,
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天气 年报
13:31
quarterly reports, historic data itself,
volumes, you name it.
volumes, you name it.
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季报 历史数据 成交量
13:35
Whatever there is.
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应有尽有
13:37
We take in terabytes of data a day.
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我们一天内接收兆兆字节的数据
13:39
And store it away and massage it
and get it ready for analysis.
and get it ready for analysis.
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储存 处理 准备用于分析
13:45
You're looking for anomalies.
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你寻找的是异常现象
13:46
You're looking for -- like you said,
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你找的是 就像你说的
13:49
the efficient market
hypothesis is not correct.
hypothesis is not correct.
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有效市场假说（Efficient Markets Hypothesis，EMH。有效市场假说认为市场价格波动是随机的，交易者不可能持续从市场中获利。）是不正确的
13:52
CA: But any one anomaly
might be just a random thing.
might be just a random thing.
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但任何一个异常现象 都有可能只是一个随机事件
13:55
So, is the secret here to just look
at multiple strange anomalies,
at multiple strange anomalies,
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所以 这儿的秘诀是 只看那些 重复出现的奇特异常现象
13:59
and see when they align?
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并观察他们是否一致
14:01
JS: Any one anomaly
might be a random thing;
might be a random thing;
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任何一个异常现象可能是随机事件
14:04
however, if you have enough data
you can tell that it's not.
you can tell that it's not.
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然而 只要你有足够的数据 可以看出来它其实不是
14:07
You can see an anomaly that's persistent
for a sufficiently long time --
for a sufficiently long time --
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你可以在足够长的时间段里 看到这些异常现象是长期存在的
14:12
the probability of it being
random is not high.
random is not high.
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它是随机事件的可能性不高
14:17
But these things fade after a while;
anomalies can get washed out.
anomalies can get washed out.
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但有一些异常现象不久后就消逝了 会淡出市场
14:22
So you have to keep on top
of the business.
of the business.
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所以你必须在商业上保持优势
14:24
CA: A lot of people look
at the hedge fund industry now
at the hedge fund industry now
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如今很多人关注对冲基金产业
14:27
and are sort of ... shocked by it,
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有点被它
产生了那么多的财富
14:31
by how much wealth is created there,
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14:34
and how much talent is going into it.
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那么多的天才投身其中 所惊吓到
14:37
Do you have any worries
about that industry,
about that industry,
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你对这个产业有什么担忧吗
14:41
and perhaps the financial
industry in general?
industry in general?
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可能宽泛来说 整个金融产业？
14:43
Kind of being on a runaway train that's --
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有点像在一辆 停不下来的火车上
14:46
I don't know --
helping increase inequality?
helping increase inequality?
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它助长了不平等
14:50
How would you champion what's happening
in the hedge fund industry?
in the hedge fund industry?
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你会怎样在目前的对冲基金产业获胜呢？
14:54
JS: I think in the last
three or four years,
three or four years,
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我认为 在过去三四年里
14:57
hedge funds have not done especially well.
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对冲基金没有表现得特别好
14:59
We've done dandy,
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我们做的看似繁荣
15:00
but the hedge fund industry as a whole
has not done so wonderfully.
has not done so wonderfully.
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但对冲基金产业整体上 没有表现太过如意
15:04
The stock market has been on a roll,
going up as everybody knows,
going up as everybody knows,
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4902
众所周知 证券市场一路顺风地向上发展
15:09
and price-earnings ratios have grown.
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3445
市价盈利率增长了
15:13
So an awful lot of the wealth
that's been created in the last --
that's been created in the last --
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3063
过去五到六年创造了大量财富，
15:16
let's say, five or six years --
has not been created by hedge funds.
has not been created by hedge funds.
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而不是对冲基金创造了极大量财富
15:20
People would ask me,
"What's a hedge fund?"
"What's a hedge fund?"
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人们会问我 “什么是对冲基金”
15:23
And I'd say, "One and 20."
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我会说 “一和二十”
15:25
Which means -- now it's two and 20 --
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现在是二和二十了 意思是
15:29
it's two percent fixed fee
and 20 percent of profits.
and 20 percent of profits.
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2%的管理费 和20%的收益
15:32
Hedge funds are all
different kinds of creatures.
different kinds of creatures.
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对冲基金有各种各样的
15:35
CA: Rumor has it you charge
slightly higher fees than that.
slightly higher fees than that.
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有传言说您（公司）比那个收费稍微高一点？
15:39
JS: We charged the highest fees
in the world at one time.
in the world at one time.
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我们一度是全世界收费最高的
15:42
Five and 44, that's what we charge.
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5和44 我们是这么收的
15:45
CA: Five and 44.
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5和44
15:47
So five percent flat,
44 percent of upside.
44 percent of upside.
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所以抽取了固定5% 收益部分44% (抽取5％的资产管理费和44％的投资收益分成)
15:50
You still made your investors
spectacular amounts of money.
spectacular amounts of money.
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你仍然让你的投资者们获得了可观的收益
15:53
JS: We made good returns, yes.
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我们有很好的回报率 没错
15:54
People got very mad:
"How can you charge such high fees?"
"How can you charge such high fees?"
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人们都要疯了 “你怎么能收这么高呢”
15:57
I said, "OK, you can withdraw."
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我说 “好啊 你可以撤资嘛”
15:59
But "How can I get more?"
was what people were --
was what people were --
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但 “我怎么赚更多” 是人们所（关注的）
16:02
(Laughter)
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（笑）
16:03
But at a certain point,
as I think I told you,
as I think I told you,
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但某种程度上 正如我说过的
16:06
we bought out all the investors
because there's a capacity to the fund.
because there's a capacity to the fund.
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我们买下了所有的投资者 因为对于基金 我们有能力
16:11
CA: But should we worry
about the hedge fund industry
about the hedge fund industry
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但我们应该担心对冲基金产业
16:14
attracting too much of the world's
great mathematical and other talent
great mathematical and other talent
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吸引了太多世界上厉害的数学家和其他天才
16:19
to work on that, as opposed
to the many other problems in the world?
to the many other problems in the world?
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而对世界上很多其他问题视而不见吗
16:22
JS: Well, it's not just mathematical.
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嗯 不只是数学
16:24
We hire astronomers and physicists
and things like that.
and things like that.
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我们还雇了天文学家和物理学家 之类的
16:27
I don't think we should worry
about it too much.
about it too much.
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我不觉得我们应该对此 太过担忧
16:30
It's still a pretty small industry.
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这仍然是相当小的一个产业
16:33
And in fact, bringing science
into the investing world
into the investing world
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事实上 将科学引进投资世界
16:39
has improved that world.
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令它得到了改善
16:41
It's reduced volatility.
It's increased liquidity.
It's increased liquidity.
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减少了波动性 增加了流动性
16:45
Spreads are narrower because
people are trading that kind of stuff.
people are trading that kind of stuff.
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因为人们在交易这样子的东西 传播变得有限
16:48
So I'm not too worried about Einstein
going off and starting a hedge fund.
going off and starting a hedge fund.
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所以我不太担心爱因斯坦会跑去开始玩对冲基金
16:54
CA: You're at a phase in your life now
where you're actually investing, though,
where you're actually investing, though,
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您现在的人生阶段 尽管实际上
16:58
at the other end of the supply chain --
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你在投资另外一个产业链
17:02
you're actually boosting
mathematics across America.
mathematics across America.
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但实际推动了整个美国的数学
17:06
This is your wife, Marilyn.
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这是您妻子 Marilyn
17:08
You're working on
philanthropic issues together.
philanthropic issues together.
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你们一起致力于慈善事业
17:13
Tell me about that.
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和我说说这个吧
17:14
JS: Well, Marilyn started --
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好 Marilyn开创了
17:18
there she is up there,
my beautiful wife --
my beautiful wife --
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这就是她 我美丽的老婆
17:21
she started the foundation
about 20 years ago.
about 20 years ago.
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她在大约20年前创建了一个基金会
17:24
I think '94.
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我想是1994年
17:25
I claim it was '93, she says it was '94,
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我觉得是1993年 但她说是1994年
17:27
but it was one of those two years.
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反正是这两年当中一个
17:30
(Laughter)
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（笑）
17:32
We started the foundation,
just as a convenient way to give charity.
just as a convenient way to give charity.
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我们创建这个基金 作为更方便做慈善的一个途径
17:40
She kept the books, and so on.
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她管账 处理相关事务
17:42
We did not have a vision at that time,
but gradually a vision emerged --
but gradually a vision emerged --
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那时我们没什么愿景 但渐渐地浮现出一个想法
17:49
which was to focus on math and science,
to focus on basic research.
to focus on basic research.
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就是致力于数学和科学 致力于基础研究
17:55
And that's what we've done.
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这就是我们所做的
17:58
Six years ago or so, I left Renaissance
and went to work at the foundation.
and went to work at the foundation.
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大概六年前 我离开文艺复兴科技公司 开始在基金会做事
18:04
So that's what we do.
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所以这就是我们做的
18:06
CA: And so Math for America
is basically investing
is basically investing
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所以美国数学协会（Math for America）主要投资
18:09
in math teachers around the country,
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全国范围的数学教师
18:11
giving them some extra income,
giving them support and coaching.
giving them support and coaching.
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提供他们额外收入 给予他们支持和辅导
18:15
And really trying
to make that more effective
to make that more effective
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而且确实努力地变得更有效率
18:18
and make that a calling
to which teachers can aspire.
to which teachers can aspire.
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使它成为老师们可以立志追求的渴望
18:21
JS: Yeah -- instead of beating up
the bad teachers,
the bad teachers,
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是啊 不去管打击了教育界士气的
18:26
which has created morale problems
all through the educational community,
all through the educational community,
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那些坏老师
18:31
in particular in math and science,
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特别是数学和科学方面的
18:33
we focus on celebrating the good ones
and giving them status.
and giving them status.
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我们致力于赞美好的老师 给予他们重要的地位
18:39
Yeah, we give them extra money,
15,000 dollars a year.
15,000 dollars a year.
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对了 我们每年提供给他们15000美元的额外资金
18:42
We have 800 math and science teachers
in New York City in public schools today,
in New York City in public schools today,
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如今我们在纽约的公立学校里有800位数学和科学老师
18:47
as part of a core.
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作为核心部分
18:49
There's a great morale among them.
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他们都很有斗志
18:52
They're staying in the field.
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坚守于他们的领域
18:55
Next year, it'll be 1,000
and that'll be 10 percent
and that'll be 10 percent
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明年将会有1000个
18:58
of the math and science teachers
in New York [City] public schools.
in New York [City] public schools.
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会有10%纽约公立学校的数学、科学教师
19:01
(Applause)
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（鼓掌）
19:07
CA: Jim, here's another project
that you've supported philanthropically:
that you've supported philanthropically:
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Jim 这是你所慈善事业的另外一个项目
19:11
Research into origins of life, I guess.
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我猜是 探究生命起源
19:13
What are we looking at here?
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我们看到的这是什么？
19:15
JS: Well, I'll save that for a second.
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这个我一会儿来讲
19:17
And then I'll tell you
what you're looking at.
what you're looking at.
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我会告诉你看到的什么
19:19
Origins of life is a fascinating question.
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生命的起源是一个迷人的问题
19:22
How did we get here?
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我们来自何处
19:25
Well, there are two questions:
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有两个问题
19:26
One is, what is the route
from geology to biology --
from geology to biology --
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一个是 从地质学到生物学 发展路线是什么
19:32
how did we get here?
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我们是怎样发展到现在的
19:34
And the other question is,
what did we start with?
what did we start with?
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另一个问题是 我们是怎么开始的
19:36
What material, if any,
did we have to work with on this route?
did we have to work with on this route?
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什么物质 如果有的话 是这条线路上必须参与的
19:39
Those are two very,
very interesting questions.
very interesting questions.
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这是两个非常非常有趣的问题
19:43
The first question is a tortuous path
from geology up to RNA
from geology up to RNA
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第一个是从地质学发展到RNA 其间曲折的道路
19:49
or something like that --
how did that all work?
how did that all work?
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或者类似的 那是怎么发展的
19:51
And the other,
what do we have to work with?
what do we have to work with?
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另外一个 是什么东西是我们必不可少的
19:54
Well, more than we think.
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超乎我们的想象
19:56
So what's pictured there
is a star in formation.
is a star in formation.
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所以那张图是形成中的一颗恒星
20:01
Now, every year in our Milky Way,
which has 100 billion stars,
which has 100 billion stars,
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现在 每年 在我们拥有一千亿恒星的银河系中
20:05
about two new stars are created.
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大约有两个正在形成的恒星
20:07
Don't ask me how, but they're created.
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不要问我怎么做到的 但它们正在形成中
20:10
And it takes them about a million
years to settle out.
years to settle out.
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它们耗去了一百万年慢慢沉积
20:14
So, in steady state,
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进入稳定状态
20:16
there are about two million stars
in formation at any time.
in formation at any time.
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随时随刻 都有两百万的恒星处于生成状态
20:20
That one is somewhere
along this settling-down period.
along this settling-down period.
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那一个是处于稳定状态的某处
20:24
And there's all this crap
sort of circling around it,
sort of circling around it,
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这些宇宙垃圾围绕着它转动
20:27
dust and stuff.
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灰尘 和其他东西
20:29
And it'll form probably a solar system,
or whatever it forms.
or whatever it forms.
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它可能会形成一个太阳系 或随便什么
20:32
But here's the thing --
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重点是
20:34
in this dust that surrounds a forming star
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6348
在围绕着这个形成恒星的尘埃中
20:41
have been found, now,
significant organic molecules.
significant organic molecules.
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6035
现在被发现存在 有着重大意义的 有机分子
20:47
Molecules not just like methane,
but formaldehyde and cyanide --
but formaldehyde and cyanide --
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不只是像甲烷那样的分子 还有甲醛和氰化物
20:54
things that are the building blocks --
the seeds, if you will -- of life.
the seeds, if you will -- of life.
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像是生命结构基础（building blocks） 生命的种子的物质
21:01
So, that may be typical.
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所以那可能很有典型意义
21:04
And it may be typical
that planets around the universe
that planets around the universe
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6934
可能宇宙中的行星 起源于这些基础的结构基石
21:11
start off with some of these
basic building blocks.
basic building blocks.
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是具有典型意义的
21:15
Now does that mean
there's going to be life all around?
there's going to be life all around?
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这是否意味着周围会产生生命体呢
21:18
Maybe.
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1364
有可能
21:19
But it's a question
of how tortuous this path is
of how tortuous this path is
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但问题是 从那些脆弱的开端 那些种子
21:24
from those frail beginnings,
those seeds, all the way to life.
those seeds, all the way to life.
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一路演变为生命的道路 是如何曲折
21:28
And most of those seeds
will fall on fallow planets.
will fall on fallow planets.
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那些种子大部分会掉落到荒芜的行星
21:33
CA: So for you, personally,
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对您个人而言
21:35
finding an answer to this question
of where we came from,
of where we came from,
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找到这些问题的答案
21:37
of how did this thing happen,
that is something you would love to see.
that is something you would love to see.
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我们从哪里来 又是怎么发生的
是您乐于看到的
是您乐于看到的
21:41
JS: Would love to see.
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乐于看到
21:43
And like to know --
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而且想要知道
21:44
if that path is tortuous enough,
and so improbable,
and so improbable,
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如果那条道路足够曲折 难以实现
21:50
that no matter what you start with,
we could be a singularity.
we could be a singularity.
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4754
无论起源是什么 我们可能是个特例
21:55
But on the other hand,
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另一方面
21:56
given all this organic dust
that's floating around,
that's floating around,
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考虑到所有这些漂浮在周围的有机灰尘
22:00
we could have lots of friends out there.
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宇宙中我们可能有很多朋友
22:04
It'd be great to know.
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很高兴知道
22:06
CA: Jim, a couple of years ago,
I got the chance to speak with Elon Musk,
I got the chance to speak with Elon Musk,
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几年前 我有机会和伊隆·马斯克（南非企业家）谈话
22:09
and I asked him the secret of his success,
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我问到他成功的秘诀
22:12
and he said taking
physics seriously was it.
physics seriously was it.
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他说 秘诀就是 严肃地对待物理
22:16
Listening to you, what I hear you saying
is taking math seriously,
is taking math seriously,
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听了你的言论 我听到你说的就是 严肃地对待数学
22:20
that has infused your whole life.
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这个理念贯彻了你整个生命
22:24
It's made you an absolute fortune,
and now it's allowing you to invest
and now it's allowing you to invest
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4563
它使你拥有了可观的财富 如今又引领你
22:28
in the futures of thousands and thousands
of kids across America and elsewhere.
of kids across America and elsewhere.
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4496
投资美国和其他地方成千上万孩子们的未来
22:33
Could it be that science actually works?
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2858
有没有可能 科学确实起作用了
22:36
That math actually works?
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2772
数学确实起作用了呢
22:39
JS: Well, math certainly works.
Math certainly works.
Math certainly works.
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4372
数学当然起作用了
22:43
But this has been fun.
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这很有趣
22:44
Working with Marilyn and giving it away
has been very enjoyable.
has been very enjoyable.
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和Marilyn在一起工作 施予别人 让我感到非常愉快
22:49
CA: I just find it --
it's an inspirational thought to me,
it's an inspirational thought to me,
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我刚发现 有个想法让我醍醐灌顶
22:52
that by taking knowledge seriously,
so much more can come from it.
so much more can come from it.
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4007
就是严肃地对待知识 你可以从中得到很多很多
22:56
So thank you for your amazing life,
and for coming here to TED.
and for coming here to TED.
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感谢您精彩的人生 感谢您来到TED
22:59
Thank you.
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谢谢
23:00
Jim Simons!
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詹姆斯 西蒙斯！
23:01
(Applause)
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ABOUT THE SPEAKER
Jim Simons - Philanthropist, mathematicianAfter astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy.
Why you should listen
As a mathematician who cracked codes for the National Security Agency on the side, Jim Simons had already revolutionized geometry -- and incidentally laid the foundation for string theory -- when he began to get restless. Along with a few hand-picked colleagues he started the investment firm that went on to become Renaissance, a hedge fund working with hitherto untapped algorithms, and became a billionaire in the process.
Now retired as Renaissance’s CEO, Simons devotes his time to mathematics and philanthropy. The Simons Foundation has committed more than a billion dollars to math and science education and to autism research.
Jim Simons | Speaker | TED.com